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.     

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.     

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。