Artificial intelligence-based predictions of movie audiences on opening Saturday

Marketing activity by distributors is a significant factor in attracting audiences to theaters before a movie is released. Importantly, audience numbers on opening weekend are highly affected by marketing activity before the release, and these numbers determine how many screens will be allocated to the movie. Therefore, distributors need to predict audience numbers on opening weekend and develop marketing strategies in order to gain a competitive advantage over other films being screened at the same time. However, as distributors make predictions based on their experiences and intuitions, it is difficult to quantify the reliability of predicted values and deliver the correct marketing strategy. In this study, we propose a model that predicts audience numbers on the opening Saturday using market research data obtained through online and offline surveys to help distributors develop better marketing strategies.     

Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy

The field of artificial intelligence (AI) is experiencing a period of intense progress due to the consolidation of several key technological enablers. AI is already deployed widely and has a high impact on work and daily life activities. The continuation of this process will likely contribute to deep economic and social changes. To realise the tremendous benefits of AI while mitigating undesirable effects will require enlightened responses by many stakeholders. Varying national institutional, economic, political, and cultural conditions will influence how AI will affect convenience, efficiency, personalisation, privacy protection, and surveillance of citizens. Many expect that the winners of the AI development race will dominate the coming decades economically and geopolitically, potentially exacerbating tensions between countries. Moreover, nations are under pressure to protect their citizens and their interests—and even their own political stability—in the face of possible malicious or biased uses of AI. On the one hand, these different stressors and emphases in AI development and deployment among nations risk a fragmentation between world regions that threatens technology evolution and collaboration. On the other hand, some level of differentiation will likely enrich the global AI ecosystem in ways that stimulate innovation and introduce competitive checks and balances through the decentralisation of AI development. International cooperation, typically orchestrated by intergovernmental and non-governmental organisations, private sector initiatives, and by academic researchers, has improved common welfare and avoided undesirable outcomes in other technology areas. Because AI will most likely have more fundamental effects on our lives than other recent technologies, stronger forms of cooperation that address broader policy and governance challenges in addition to regulatory and technological issues may be needed. At a time of great challenges among nations, international policy coordination remains a necessary instrument to tackle the ethical, cultural, economic, and political repercussions of AI. We propose to advance the emerging concept of technology diplomacy to facilitate the global alignment of AI policy and governance and create a vibrant AI innovation system. We argue that the prevention of malicious uses of AI and the enhancement of human welfare create strong common interests across jurisdictions that require sustained efforts to develop better, mutually beneficial approaches. We hope that new technology diplomacy will facilitate the dialogues necessary to help all interested parties develop a shared understanding and coordinate efforts to utilise AI for the benefit of humanity, a task whose difficulty should not be underestimated.     

Blockchain adoption: A value driver perspective

The ongoing discussion regarding blockchain technologies is focused primarily on cryptocurrencies, but blockchain features and functionalities have developed beyond financial instruments. As the technologies provide new functionalities, the associated value proposition changes as well. This study explores the relationship between blockchain technologies and their underlying value drivers. Four identified distinct blockchain stages of increased maturity are analyzed and discussed. This covers the evolutionary technology types focused on transactions, smart contracts, decentralized applications, and the introduction of artificial intelligence supporting decentralized decision making. In addition, we address management issues around appropriate blockchain adoption using a blockchain value driver-focused framework that gives decision makers actionable questions and recommendations. We provide practitioners with a method for assessing suitable blockchain adoption that addresses the specific value creation associated with a given organizational strategy. For academics, we critically identify and assess the characteristics of the blockchain stages and their strategy implications and provide a structured approach conceptualizing blockchain technology evolution.     

Machine learning for enterprises: Applications,algorithm selection,and challenges

Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.     

Online big data-driven oil consumption forecasting with Google trends

The rapid development of big data technologies and the Internet provides a rich mine of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption — an essential but uncertain factor in the oil supply chain. An online big data-driven oil consumption forecasting model is proposed that uses Google trends, which finely reflect various related factors based on a myriad of search results. This model involves two main steps, relationship investigation and prediction improvement. First, cointegration tests and a Granger causality analysis are conducted in order to statistically test the predictive power of Google trends, in terms of having a significant relationship with oil consumption. Second, the effective Google trends are introduced into popular forecasting methods for predicting both oil consumption trends and values. The experimental study of global oil consumption prediction confirms that the proposed online big-data-driven forecasting work with Google trends improves on the traditional techniques without Google trends significantly, for both directional and level predictions.     

Team challenges: Is artificial intelligence the solution?

Artificial intelligence (AI) is emerging as a potential growth area for facilitating the improvement and development of teams in the workplace. AI, as used in the team context, is currently underdeveloped and limited, thus reducing the wide-scale adoption and implementation of AI to improve team effectiveness. The use of AI to provide team diagnostics and improvements represents a significant shift in the approach organizations currently use to facilitate and strengthen effective teamwork. We describe the challenges involved in developing team effectiveness in organizations and the potential application of AI to improve teamwork. Further, we report on our experiences using AI in business school student project teams, the important advantages and disadvantages that emerged from this, and insights for future consideration when adopting and implementing AI in the workplace. Based on our use of AI and our experience training high-performing teams, we propose a multistep process for analyzing and improving teams in organizations.     

Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives

Artificial intelligence is another advance in technology for the hotel industry and its role is undetermined at this time. The overarching purpose of this treatise was to examine hotel employees’ perception of AI and its impact by identifying the critical role of job insecurity, job engagement, and turnover intention through a pragmatic approach. An explanatory sequential mixed-methods design was used by conducting a quantitative study with an empirical survey method followed by a qualitative study with a case study method. The results from the quantitative study demonstrated that perceived job insecurity significantly affected perceived job engagement and perceived job insecurity indirectly affected turnover intention through intermediary variable of perceived job engagement. There were no statistical differences between non-managerial positions and managerial positions. These results were fully supported by the qualitative study. The implications from these findings were provided to articulate the influence of AI on hotel employees.     

Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations

This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.     

基于创新失败情境的情绪智力结构关系研究

基于情绪事件理论，利用收集的298份正式调研样本进行实证分析，探究创新失败情境下情绪智力的内部结构关系。结果发现：①在创新失败情境下，情绪智力4个维度在发挥作用时存在先后次序；②自我和他人情绪评价、情绪控制对失败学习存在显著正向影响；③情绪控制在两种情绪评价与失败学习关系中起中介作用；④与自我情绪评价相比，他人情绪评价对失败学习的作用效果更佳。研究结论有助于廓清创新失败情境下情绪智力结构，明晰情绪智力4个维度间的内部关系，打破以往学者对情绪智力这一构念的固化认知，同时也为中国企业创新失败后的情绪管理实践提供参考。     

我国人工智能政策文本量化研究——政策现状与前沿趋势

以我国公布的30份人工智能政策文本为样本，基于政策工具和PMC政策评价模型，采用文本挖掘及内容分析法，对我国当前人工智能政策文本进行量化分析。结果发现，我国人工智能政策中的需求型政策工具需进一步加强，环境型政策工具结构有待调整；通过对处于不同评价等级、针对不同产业发展政策文本的PMC指数进行比较，识别出影响我国人工智能政策文本评价等级的具体变量。最后，结合我国人工智能研究前沿趋势分析，探寻未来政策制定方向，为后续人工智能政策的制定和修改提供具体、可操作性建议。