Organizational CQ: Cultural intelligence for 21st-century organizations

We live in an age of massive global disruption. Technological advancements threaten century-old business models, globalization is reordering supply chains, and people need to work with colleagues and customers who have vastly different backgrounds. On top of that, we have been in the midst of a global pandemic, and customers, employers, and investors are demanding more than just a Black Lives Matter social media post from organizations that purport to take social justice seriously. Organizations with high cultural intelligence (CQ) are able to navigate this volatility and complexity effectively. Over the last two decades, scholars from across the world have published hundreds of articles on CQ, the capability to relate and work effectively in complex, culturally diverse situations. Most of the work has examined CQ at the individual level. But what about organizations? Can organizations be culturally intelligent? The emerging research on CQ at the organizational level offers leaders and organizations critical insights for navigating today’s diverse, digital world. Organizational CQ is a firm’s capability to function effectively in a complex and unpredictable multicultural world. This article stresses the importance of the culturally intelligent organization and explains how to develop organizational CQ.     

Coopetition strategies for competitive intelligence practices-evidence from full-service hotels

Our study investigates how coopetition strategies can influence hotels’ competitive intelligence (CI) practices to achieve a stronger competitive advantage. In-depth interviews were conducted with 39 hoteliers from 22 hotel groups in Hong Kong. Participating hotels employed different kinds of CI activities, though they were unaware of this concept. In particular, internal customer intelligence was added to integrated intelligence to better describe CI in the service sector. Still, investing in CI can be expensive and time-consuming since it requires hoteliers to align all insights from their respective intelligence pools toward building a holistic understanding of the results. We propose coopetition as a strategic approach allowing hotels to construct collective actions around CI without losing individual competitiveness. Actual coopetition in CI was only found between sister properties. Hence, we propose a coopetition model in which hotels can collaborate and compete in CI at an inter-organizational level via focusing on sharing open-source information and knowledge.     

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.     

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.     

Understanding precedents for frontline employee turnover in luxury hotels: Emotional intelligence as a unifying factor

Current turnover research fails to serve the needs of an industry that is long plagued by employee turnover. Existing literature focuses more on evaluating bundles of human resource practices and fail to provide precise and clear guidance for practitioners. This study proposes that emotional intelligence (EI) unifies sufficient individual factors and organizational factors that affect employee turnover and serves as a single significant precedent for turnover. Data were collected from frontline employees at eight luxury hotels. The direct, indirect, and total impacts of employee EI on employee turnover were tested by structural equation modeling and bootstrap tests. The results suggest that EI has significant indirect impacts through the mediation of perceived organizational support, pay satisfaction and job burnout, and significant total impacts on turnover. Implication suggestions include integrating EI into the recruiting process for new employees and providing training opportunities for current employees to improve their EI.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Application of collective knowledge diffusion in a social network environment

ABSTRACT

We present a group dynamics model that shows knowledge integration as a process occurring over time. As each individual in the group contact others, his own knowledge changes, and over time the collective knowledge is obtained. This allows modeling knowledge diffusion in a social network and while the models presented in this paper are not competitive in that area, they approach the problem from previously unconsidered direction. We test the behavior of the model in a multi-agent simulation and we test a simple advertisement campaign in a social network. We provide discussion of elements needed for making model more competitive.     

The Impact of Students’ Cultural Intelligence on Their Psychological Safety in Global Virtual Project Teams

This study addresses a call for the design and implementation of course curricula that prepare students to develop their CQ and gain experience working with peers on global virtual project teams. We explored how US-based and Peru-based students’ cultural intelligence (CQ) impacted their sense of psychological safety (PS) during a month-long global, virtual team project. We also examined the students’ people-focused (PF) and task-focused (TF) behaviors as mediators of the CQ-PS relationship. The results of mediation analyses provide support for our hypothesis that the relationship between cultural intelligence and psychological safety will be mediated by people-focused behaviors. Finally, we provide a model and suggestions for virtually bringing together students from different countries to collaborate on a global virtual project, and avenues for future research. Here we encourage a focus on a curriculum that educates students about their cultural intelligence and ways to develop psychologically safe learning environments. We also highlight the potential learning for faculty teaching such courses, and note how our experience collaborating with our counterpart in Peru constituted a fractal of what our students were experiencing on their global projects.     

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.