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.     

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.     

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.     

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.     

意大利发展人工智能的主要举措及启示

意大利重视人工智能及数字化发展,将其作为疫后经济复苏的优先方向和重要支柱,颁布了相关战略和计划,启动大型超级计算机建设。人工智能在筛选新冠肺炎药物方面取得重要进展。意大利对人工智能赋能未来产业发展和改善社会公共服务寄予厚望,其相关思路值得借鉴。     

人工智能发展对通胀动态的影响及政策挑战——基于动态随机一般均衡模型的分析

人工智能的发展是生产力进步的表现，但广泛使用人工智能技术的经济效应尚待深入研究。基于人工智能对劳动的替代作用，将人工智能投资引入动态随机一般均衡模型，通过比较静态分析和短期动态分析考察人工智能发展对通胀动态的影响。稳态分析表明：人工智能投资效率提高和使用范围扩大均会提升劳动生产率进而提高产出水平，但对长期或趋势通胀没有影响；在人工智能发展初级阶段人工智能的发展会引起实际工资下降，而在相对高级阶段人工智能的发展会引起实际工资上升。短期动态分析显示：人工智能发展水平的提高，无论是投资效率的提高还是使用范围的扩大，都会导致通胀及实际边际成本对偏好冲击和技术冲击等外生冲击的反应变弱。人工智能的发展会使通胀动态与实际经济活动变化间的联系减弱，其原因是人工智能对劳动的替代作用使工资调整不再是应对外生冲击的必然选择，进而改变了外生冲击通过影响工资来影响通胀的传导机制。因此，发展人工智能在促进生产率提升和产出增长的同时，还会改变宏观经济变量间的传导机制，进而对通胀等政策调控目标变量的准确预测以及政策调整和实施的有效性带来新的挑战，也为金融理论研究提出新的课题。     

Marketing via smart speakers: what should Alexa say?

ABSTRACT

A smart speaker is a wireless device with artificial intelligence that can be activated through voice command. The artificial intelligence interacts in the form of a virtual personal assistant, such as Amazon’s “Alexa”. Companies are currently creating voice applications for smart speakers that allow consumers to use the personal assistant to perform tasks, such as acquiring information and ordering products. This is the dawn of a new type of interaction between brands and consumers, a new touchpoint. Brands need to catch the vision and create content for this new technology, content that is useful in the buying process. The purpose of this study is to determine what types of marketing messages people find acceptable on smart speakers. Based on the findings, a cognitive message strategy is effective with smart speakers. The paper presents three types of executional frameworks that are best suited for the design of a cognitive message: authoritative, testimonial, and slice-of-life. The overriding requirement for marketing on smart speakers is that the message must provide value to the listener.     

Siri,Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications

ABSTRACT

Digital assistants (e.g., Apple’s Siri, Amazon’s Alexa, Google’s Google Assistant) are highly complex and advanced artificial intelligence (AI) based technologies. Individuals can use digital assistants to perform basic personal tasks as well as for more advanced capabilities. Yet, the functional and topical use of a digital assistant tends to vary by individual. This study reflects the contextual experiences of the respondents. At present, there is little empirical evidence of customer satisfaction with digital assistants. PLS-SEM was used to analyse 244 survey responses to examine this research gap. The results confirmed that expectations and confirmation of expectations have a positive and significant relationship on customer satisfaction with digital assistants. This study provides evidence that customer expectations are being satisfied through the digital assistant interaction experience. As firms integrate digital assistants into their operations, they must help customers properly define what to expect from the firm’s interactive experience.     

Far from the madding crowd: collective wisdom in prediction markets

We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.     

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.