Shifts in consumer behavior towards organic products: Theory-driven data analytics

Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.     

人工智能技术变革对国际贸易的影响

人工智能技术的快速发展正催生第四次工业革命,可能引发全球价值链深度重构和世界经贸格局重大变革。世界主要经济强国将发展人工智能技术作为争夺新一轮产业竞争优势的重要战略抓手。本文基于全球价值链视角研究人工智能技术变革对国际贸易的影响,我们发现人工智能技术变革可能推动国际贸易规模扩大,提升服务贸易份额,并促进国际贸易交易模式平台化、小宗化,可为中小企业创造更多参与国际贸易的机会。然而,人工智能技术变革也可能通过降低企业劳动力需求从而对我国等发展中国家的出口拉动型增长模式造成严重的潜在威胁。为应对人工智能技术变革,我国应部署并强化对人工智能产业发展的政策支持,加快培育制造业国际竞争新优势,大力推动先进制造业与现代生产性服务业深度融合发展,全面促进"中国制造"攀升全球价值链中高端。     

人工智能在我国公共卫生建设中的应用与思考

新冠疫情的暴发以及长期防治对中国的公共卫生体系造成深远影响，给我国突发公共卫生事件应急与防控机制带来巨大的考验。在疫情防控的过程中，信息化技术为传统医疗机构带来瞩目的帮助与改变，人工智能、大数据、5G高速网络等新兴高科技的蓬勃发展在直接提升医疗水平方面表现出巨大潜力。本文就人工智能在疫情期间的医疗辅助功能展开讨论，并对未来我国公共卫生体系的建设进行了思考。     

Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney

New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems (EEs). In particular, general purpose technologies are major drivers of entrepreneurship. Thus, a nuanced understanding on technological knowledge and its spillovers among actors within an EE is warranted. Using knowledge‐spillover‐based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this article proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This article uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney's AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the article shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.     

关键核心技术攻关新型举国体制研究

面对新一轮科技革命,传统创新组织和模式已难以适应新形势的要求,美国科技界认为会聚创新模式将成为推动科技发展的重大战略举措。本文深入分析会聚创新兴起的内在机理,以人工智能领域为切入点,广泛借鉴国际创新组织不同模式经验,提出构建社会主义市场经济条件下关键核心技术攻关新型举国体制的若干建议。     

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.     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

Designing,developing, and deploying artificial intelligence systems: Lessons from and for the public sector

Artificial intelligence applications in cognitive computing systems can be found in organizations across every market, including chatbots that help customers navigate websites, predictive analytics systems used for fraud detection, and augmented decision-support systems for knowledge workers. In this article, we share reflections and insights from our experience with AI projects in the public sector that can add value to any organization. We organized our findings into four thematic domains—(1) data, (2) technology, (3) organizational, and (4) environmental—and examine them relative to the phases of AI. We conclude with best practices for capturing value with cognitive computing systems.     

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.     

节能降耗是建筑业可持续发展的关键

建筑业可持续发展,要抓住节能降耗这个关键环节,以科技创新为突破口,建立健全政府管理体制和市场激励机制,全面推广绿色与智能建筑,从根本上转变建筑业增长方式,实现人与环境和谐,确实做到科学发展。