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.     

Evaluating museum visitor experiences based on user-generated travel photos

ABSTRACT

This research introduces online travel photos published on social media platforms as a complementary data resource to examine the behavior and experience of museum visitors. The practical value of online travel photos is demonstrated through a case study of popular Hong Kong museums, particularly by using the photo content and metadata available from the Flickr platform. The proposed approach is a generic method for understanding museum visitor behavior and preferences, and supports museum practitioners in developing improved products for visitors. The case study findings are particularly beneficial for tourism managers, especially those in Hong Kong, in promoting and attracting tourists to visit local museums.     

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.     

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.     

Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years

We commemorate the 50th anniversary of Ball and Brown [1968] by chronicling its impact on capital market research in accounting. We trace the evolution of various research paths that post–Ball and Brown [1968] researchers took as they sought to build on the foundation laid by Ball and Brown [1968] to create a body of research on the usefulness, timeliness, and other properties of accounting numbers. We discuss how those paths often link back to the groundwork laid and questions originally posed in Ball and Brown [1968].     

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 Effectiveness of Brand- and Customer-Centric Content Strategies at Generating Shares, ‘Likes’, and Comments

Brands are constantly competing for user attention on Facebook and are trying to encourage fans to share, click ‘like’, and comment their posts. Yet, it remains unclear what content strategies are successful at encouraging those behaviors. Noting that the effectiveness of brand content strategies still remains an uncharted research area, this study adopts a discovery-oriented approach and employs workshops, quantitative content analysis, and episodic interviews, to link desired user responses (namely clicks share, ‘like’, and comment) to brand content strategies. This study reveals that customer-centric content strategies are the most effective at encouraging user responses. Managers can use provided recommendations to engage their customers by adopting content strategies that result in desired behavioral reactions.     

谈体育教学改革发展趋势

体育教学“以人为本”观念形成的社会环境是现代化发展的必然结果。我们教育改革思路提出“健康第一”的指导思想，同样验证了“以人为本”的现代体育观念的社会环境背景。学生对体育课的要求和看法将对体育教学改革提供“主体”依据，体育教学改革要“以人为本”。     

西方现代股利政策理论及实证研究回顾

本文对西方现代公司股利政策方面的理论和实证研究进行了一个综合性的回顾和分析。其中包括主流的股利行为模型、股利的信息内涵、代理成本、追随者效应,也包括国内研究中较少涉及的债务约束、声誉学说、成长机会和交易成本问题,以期对国内相对落后的股利政策研究和随意性较强的股利政策制定带来一定的借鉴意义。     

基于适应性商务智能的虚拟企业合作伙伴选择模型

合作伙伴选择是虚拟企业运行中至关重要的问题。迅速而准确地找到合适的合作伙伴并实现协调运作，直接影响到虚拟企业合作绩效和整体目标的实现。文章将环境感知和适应性商务智能理论引入虚拟企业合作伙伴选择中，提出了基于适应性商务智能的虚拟企业合作伙伴选择模型，设计了模型的基本架构，分析了模型的构成模块，探讨了模型的应用机理。