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.     

The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types

While artificial intelligence products are widely used in the market, their anthropomorphic appearance design is becoming a frontier issue in product strategy and consumer behavior research. The aim of this study was to investigate the influence of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. It was conducted in China, a new but rapidly-growing country in the field of Internet, AI technology and AI product consumption. This study conducted four situational experiments with a 2 (anthropomorphic design: anthropomorphic vs. non-anthropomorphic) × 2 (product type: hedonic vs. utilitarian) between subjects’ experimental design. Data was collected from 1172 Chinese “Digital Natives” by using a structured questionnaire. The findings revealed that for hedonic AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived entertainment, and intelligence level significantly moderates the mediating effect of perceived entertainment; while for practical AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived usefulness, and intelligence level does not significantly moderate the mediating effect of perceived usefulness. There is no significant moderating effect of intelligence level on perceived usefulness. The study contributes to development and validation of a more comprehensive understanding and theoretical foundation of anthropomorphism, and furthermore explores the impact of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. This study also provides insights for companies to apply anthropomorphic strategies.     

动态能力视阈下我国汽车制造企业智能化转型升级路径研究

基于3家实施智能化转型升级的国内汽车制造企业，进行了跨案例研究，构建了我国汽车制造企业智能化转型升级的理论模型。该理论模型表明，我国汽车制造企业智能化转型升级可以分为自动化、信息化以及智能化3个阶段。企业基于动态能力不断调整其在不同阶段所拥有的核心能力，在自动化阶段，主要是通过实施以提高制造能力为核心的战略实现转型升级；在信息化阶段，主要是通过实施以标准化、信息化集成为核心的战略实现转型升级；在智能化阶段，主要是通过实施以企业柔性化建设为核心的战略实现转型升级。     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations

The hospitality and tourism industry faces serious challenges during public health emergencies such as COVID-19. Managers are concerned not only about how to maintain business and provide humanized services but also about social distancing. This study presented artificial intelligence (AI) technology-based service encounters as a possible solution and examined the antecedents and consequences of the encounter triad including customers, employees, and AI. Based on a systematic literature review, the study identified 4 modes of AI technology-based service encounters: AI-supplemented, AI-generated, AI-mediated, and AI-facilitated encounters. In addition, the study developed an integrated model to specify the factors that influence AI technology-infused service encounters in general and the customer service outcomes that result from the encounters. The findings contribute to service management and AI application theoretically and practically.     

Optimal capacity and operation design of a robot logistics system for the hotel industry

This study introduces an optimal capacity and operation design of a robot logistics system that can reduce human workload in the hotel industry. In hotels, there are several duties that can be replaced by robots such as accompanying guests to the certain areas, delivering specific items to the guest rooms, and transporting some items that guests want to be removed from the guest rooms. To apply the robot logistics system in the hotel industry, the capacity and operation design should be prepared adequately. For that, a mathematical model-based optimization technique is used to decide the number of robots with the concept of minimizing total investment cost and to derive the optimal job assigning with the purpose of maximizing total covered jobs. In addition, a numerical example is performed to validate the proposed mathematical model and the sensitivity tests are examined to reflect the random features.     

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.     

Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis

The main goal of this research is to provide a comprehensive understanding of the actual progresses in artificial intelligence, with emphasis on chatbots as emerging forms of customer assistance in online retailing. Drawing upon an analysis of the chatbot patents in the past 20 years, our findings show the increasing technology push towards the adoption of new conversational agents based on natural language. Findings also highlight the extent to which the research and development efforts are attempting to improve artificial intelligence systems that characterize chatbots. To this end, technology advancements are mainly focusing on: (i) improving chatbot ability to automatically draw inferences on users starting from multiple data sources, and (ii) using consumers’ knowledge adaptively to provide more customized solutions. Finally, results show the tight relationship between the digital assistants’ analytical skills and their ability to automatically interact with the users.     

Engaging and retaining customers with AI and employee service

Artificial intelligence (AI) permeates in service organisations as a tool to enhance operational efficiency and improve customer experience. Reports show that most consumers prefer human interactions with service employees. Drawing on this observation, the current study examines how customers' service experiences with employees and AI influence customer engagement and loyalty. Customers’ emotional intelligence is proposed as a moderator between service experience and customer engagement. The study was conducted with hotel customers in Australia. The results show that whilst both service experience with employees and AI are significantly related to customer engagement and loyalty, only certain dimensions make significant unique variances in the outcome variables. The findings indicate that customers prefer employee service. These service experiences also have significant partial mediation effects on customer loyalty. Emotional intelligence has a significant moderation effect on customer engagement. Discussion of these findings and implications derived from this study concludes this paper.     

How artificial intelligence will affect the future of retailing

Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.