Effects of voice assistant recommendations on consumer behavior

The present study compares the influence of text-based recommendations; traditionally known as online consumer reviews, and the influence of voice-based recommendations provided by voice-driven virtual assistants on consumer behaviors. Based on media richness theory, the research model investigates how voice versus text modality influences consumers' perceptions of credibility and usefulness, as well as their behavioral intentions and actual behaviors. In addition, the study analyses if these relationships vary based on the type of product and compares the influence of masculine and feminine voices. Two studies were conducted using between-subjects experimental designs, partial least squares-structural equation modeling, and logistic regression. The core finding is that voice-based recommendations are more effective than online consumer reviews in altering consumer behaviors. In addition, the first study showed that the influence of recommendations on behavioral intentions is mediated by consumers' perceptions of their credibility and usefulness. The second study confirmed, in a realistic setting, that voice-based recommendations affect consumer choices to a greater extent. Recommendations for search products and provided by males are also found to be more effective. These results contribute to the voice assistant and e-WOM literature by highlighting the effectiveness of voice-based recommendations in predicting consumer behaviors, confirming that credibility and usefulness are key factors that determine the influence of recommendations, and showing that recommendations are more effective when they focus on search products.     

国外人工智能开源生态运营模式剖析

开源正成为新一代人工智能发展的全球趋势。本文从开源政策、开源组织、开源基础设施三个方面,基于典型实践案例,总结了国外人工智能开源生态的运营模式和主要特征。研究发现,欧美等发达国家的人工智能开源生态已进入到一个元素基本完整、运营基本顺畅的发展阶段,并通过所有权中立、社区治理专业化、顶尖企业引领、商业模式创新等方式实现开源生态的可持续运营。建议围绕打造良好的人工智能开源政策环境、推动开源组织有序分工合作、夯实开源基础设施等方面,进一步完善我国人工智能开源生态。     

英国人工智能路线图及对我国的启示

人工智能是新一轮科技革命和产业变革的必争领域之一。英国高度重视人工智能发展,成立了专门的人工智能办公室,并于2021年1月发布人工智能路线图。本文介绍了英国人工智能发展基本情况和路线图的主要内容,并对中国的人工智能发展提出了建议。     

印度情报侦察装备的现状与未来发展

印度建立的情报侦察体系具有较强的实战能力,具备全天候的战略战术侦察能力.基于印度情报侦察发展的借鉴作用,介绍了印度天基、空基、陆基、海基的情报侦察装备的现状,并预测了印度情报侦察装备系统未来的发展趋势,最后总结了印度情报侦察的发展特点和经验.     

Interpretable selective learning in credit risk

Forecasting credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution because of its accuracy and interpretability. Although complex machine learning models may improve accuracy over simple logistic regressions, their interpretability has prevented their use in credit risk assessment. We introduce a neural network with a selective option to increase interpretability by distinguishing whether linear models can explain the dataset. Our methods are tested on two datasets: 25,000 samples from the Taiwan payment system collected in October 2005 and 250,000 samples from the 2011 Kaggle competition. We find that, for most of samples, logistic regression will be sufficient, with reasonable accuracy; meanwhile, for some specific data portions, a shallow neural network model leads to much better accuracy without significantly sacrificing interpretability.     

The influence of chatbot humour on consumer evaluations of services

Technological advances have enabled firms to automate customer service by employing artificial intelligence (AI) chatbots. Despite their many potential benefits, interactions with chatbots may still feel machine-like and cold. The current study proposes the use of humour by chatbots as a gateway to humanizing them and thereby enhancing the customer experience. Across three experimental studies, the results reveal that (i) the use of humour enhances service satisfaction when it is used by a chatbot but not when it is used by a human agent, (ii) this chatbot humour effect is serially mediated by enhanced perceptions of anthropomorphism and interestingness of the interactions with the chatbot, and (iii) whilst both positively and negatively valenced chatbot humour may enhance the interestingness of the interactions, socially appropriate (i.e., affiliative) humour as opposed to inappropriate (i.e., aggressive) humour leads to enhanced service satisfaction. This study extends the understanding of the humanization processes of chatbots and provides guidelines for how firms should use chatbot humour to positively influence consumers' service satisfaction.     

Controllers’ role in managerial sensemaking and information trust building in a business intelligence environment

This study explores how an organization’s controllers (management accountants) give sense to the information provided by its business intelligence (BI) system, and thus shape the construction of information trust. A qualitative case study was conducted within a Finnish food manufacturing company, building on the notion of trust related to management accounting information and sensemaking theory. The study was informed through open-ended interviews and an examination of internal accounting and management reports. The authors found that the company used an integrated BI system that enabled the production of information in a timely and perceivably standardized manner. Controllers managed this accounting information and gave sense to it, helping deliver a shared understanding of the daily business situation. The findings show that controllers play a pivotal role in building information trust by giving sense to the information provided by the BI system.     

Human–machine trans roles conflict in the organization: How sensitive are customers to intelligent robots replacing the human workforce?

In organizations facing digital transformation, intelligent technologies are starting to replace the human workforce. At present, managers delegate tasks to an artificial agent and rarely consider the customer reception of such decisions. This arouses tensions between the main stakeholders of the organization. This paper shows that the rash adaptation of the digital workforce may be perceived as an irresponsible innovation that brings negative consequences for companies. If a task is regarded by customers as dedicated to humans, and managers delegate it to machines, a new type of conflict – human-machine trans roles conflict (HMTRC), appears. This paper intends to show that customers are sensitive to HMTRC. This research uses quantitative methods and consists of three stages. First, people were asked to indicate which tasks in an organization should be performed by (a) humans and (b) machines. According to these results, two leaflets for customers were designed (low vs. high HMTRC). In the second stage, standard procedures were used to construct a scale measuring customer reactions to HMTRC on three dimensions: cognitive, emotional and behavioural. Ultimately, the scale and two leaflets were used to check how customers react to different intensities of HMTRC. The research results show that customers are aware when HMTRC occurs and perceive it negatively (cognitive response). Moreover, it evokes negative emotions (emotional response) and prompts customers to take action against the company in which this conflict takes place (behavioural response). The practical contribution of this research is the three-dimensional scale. It may predict customers' reactions to task delegation with different intensities of HMTRC and help build a technologically sustainable organization.     

A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.