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There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]

This paper explores the effect of chatbot empathy and identity disclosure on willingness to donate (WTD) to a fundraising project and the psychological mechanism in the relationship. In a 3 (information-only vs. cognitive empathy vs. affective empathy) × 2 (chatbot vs. human identity) experiment, 496 US adults had a conversation about a fundraising event with one of six artificial intelligence-powered chatbots. The results revealed that neither chatbot empathy nor identity disclosure has a significant effect on WTD, rejecting the hypotheses. However, the data supported a significant interaction effect between chatbot empathy and identity disclosure on WTD. Moreover, the results suggested that the interaction effect between chatbot empathy and identity disclosure on WTD would be mediated through human likeness and social presence. This study suggests how empathy and human identity of chatbots should be applied with care to avoid the uncanny valley effect. [ABSTRACT FROM AUTHOR]

This paper presents a study about communicability of conversational interfaces (namely chatbots) under a semiotic perspective. A chatbot is a software system that allows you to simulate real conversations between devices and users by means of a conversational interface (CI). After introducing the chatbot concept, focusing on its advantages and issues, we will present two domains of use in which chatbot interfaces can be effective: healthcare and smart home. For carrying out simple tasks such as finding information or triggering operations, users need an easy-to-use and to an easy-to-learn system to communicate with. To face this, conversational interfaces represent the latest trend in the field of digital design. For studying the communicability aspects of a CI, we carried out a user test to compare traditional and chatbot interfaces. This paper aims at evaluating the benefits at the communicability level of a chatbot in comparison to traditional GUI for incrementing the effectiveness and efficacy of communication between users and the system specifically for users with poor attitude in using technologies. In details, we evaluated the communicability of two prototypes that can be used to solve simple tasks in order to favour user inclusion, including everyone with very little exposure to technologies. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) became a part of daily life, it has become important to determine user competence in using AI technology. Here, we propose the concept of AI literacy and develop a quantitative scale for obtaining accurate data regarding the AI literacy of ordinary users. We first identified the primary core constructs of AI literacy, including awareness, use, evaluation, and ethics. Next, we generated 65 items to capture these four constructs; only 31 items were retained after a three-step content validation process. Then, we conducted a survey, and collected two samples of data. By reducing the number of items using the first sample and performing reliability and validity tests on the second sample, we obtained a 12-item instrument for the quantitative measurement of AI literacy. The results confirmed that the proposed four-construct model is an adequate representation of AI literacy. Further, AI literacy is significantly related to digital literacy, attitude towards robots, and users' daily usage of AI. This study will not only aid researchers in understanding how user competence in using AI technology affects human–AI interactions but will also help designers develop AI applications that are aligned with the AI literacy levels of the target users. [ABSTRACT FROM AUTHOR]

Interventions to improve health and well-being abound. Whether they are designed for prevention, maintenance or improvement, a key challenge is the motivation of the user to change their current behaviours, such as persisting or taking new actions. To encourage someone to change their behaviour requires persuading them to change their goals and/or their beliefs about the behaviour or their ability to perform it. Our embodied conversational agent (ECA) uses explanations based on the goals and beliefs of the user to promote a sense of personalisation and engagement with the treatment plan which could form a bond as the dyad develop shared goals and tasks together. To keep our message minimal and understand whether belief-based or goal-based explanations are more efficacious in changing behaviour intention, we collected data in the context of a scenario where the ECA seeks to change four behaviours recommended to help students manage their study stress. Our findings suggest that when the behaviour requires a change in desire, we need goal-based explanation, when adoption of the behaviour requires addressing a barrier we need belief-based explanation and warrant future investigation. Further, the stratified analysis suggested that more tailoring to the student's context could provide more motivation to change. [ABSTRACT FROM AUTHOR]

Individuals tend to hold back their ideas because they feel concerned about being evaluated. This leads to the untapped creative potential for organisations that depend on the creative abilities and ideas of their employees, as idea evaluation is essential for further developing and assessing creative ideas that inhibit the potential to turn into innovative products or services. In my research, I propose the use of AI-based computer systems for idea evaluation to address evaluation apprehension. With the help of an experiment (n = 228), I test whether individuals feel concerned about evaluation when a computer evaluates their idea. The results show that people do not feel evaluation apprehension when they present their idea to an AI-based system, but in contrast, feel concerned when they present their idea to a human. These findings contribute to the theory of evaluation apprehension but also to theories of human-computer collaboration and hold potential for companies to increase their creative outcome. [ABSTRACT FROM AUTHOR]

In order to make AI tools for spam review detection to be more trustworthy, two types of explanation were designed, the Text-AI tool and the Behaviour-AI tool. The Text-AI tool establishes detection criteria based on the textual features of reviews, while the Behaviour-AI tool establishes detection criteria based on the behavioural features of reviewers. The trust of younger (20–26 years) and older (50–78 years) adults in AI tools and the changes in credibility judgments for reviews and overall attitude toward the product made by them based on AI tools' detection results were measured. We mainly found that: (i) Almost all the older participants reported that they trust AI tools' prediction, but 48.7% of them would abandon AI when AI's prediction was different from their own judgment. (ii) Younger adults showed a higher trust in the Behaviour-AI tool than in the Text-AI tool, especially when the AI tool detected more spam reviews than themselves. (iii) Regardless of age and types of explanation, participants perceived AI tools as more competent and benevolent when AI tools outperformed participants by detecting unexpected spam reviews. [ABSTRACT FROM AUTHOR]

The aim of this study is to provide insights concerning the effects of the ubiquitous digital environment on the way people think and the subsequent need to equip young individuals with the necessary skills. Several studies focus on defining the so-called digital skills, also providing indications that higher cognitive skills are required. However, they do not examine how young individuals could perform better to adapt to the continuously evolving digital environment. To address this gap, the study introduces and analyses the construct of digital intelligence, representing the new way of thinking and behaviour in the digital environment. For the purposes of the study, a set of tests was given to students at Greek high schools, targeting 15–16 years old students and employing original tests to assess digital intelligence. According to the findings, digital intelligence is composed of: (1) logical reasoning, algorithms, and evaluation, (2) abstraction, decomposition, and patterns and generalisation, (3) digital emotional intelligence and communication, (4) digital safety and security, and (5) digital identity, use, literacy, and rights. Assessing digital intelligence, as proposed in this study, could be used to assist vocational guidance, employee selection and evaluation, and development of revised school curricula. [ABSTRACT FROM AUTHOR]

Using data from 28 European countries, this study examines factors influencing public attitude towards the use of AI/Robot. Its multilevel SEM analysis finds that several factors at the individual level including Perceived threat of job loss and Digital technology efficacy predict public Acceptance of AI/Robot. Although country-level factors such as economic development, government effectiveness and innovation do not directly influence public acceptance of AI/Robot, they do have significant effects on Perceived threat of general job loss due to AI/Robot, and Digital technology efficacy. Findings indicate that these nationally macro variables influence people's perceptions of AI and robotics technologies and their confidence in their digital skills. This research enriches the application of the Technology Acceptance Model by using predictive variables at two levels: individual and country. Furthermore, at the individual level, this study uses two variables (e.g. Perceived threat of job loss and Digital technology efficacy) that are unconventional to TAM, thus contributing to this theoretical model. [ABSTRACT FROM AUTHOR]

Contrary to the traditional concept of artificial intelligence, explainable artificial intelligence (XAI) aims to provide explanations for the prediction results and make users perceive the system as being reliable. However, despite its importance, only a few studies have investigated how the explanations of an XAI system should be designed. This study investigates how people attribute the perceived ability of XAI systems based on perceived attributional qualities and how the power status of the XAI and anthropomorphism affect the attribution process. In a laboratory experiment, participants (N = 500) read a scenarios of using an XAI system with either lower or higher power status and reported their perceptions of the system. Results indicated that an XAI system with a higher power status caused users to perceive the outputs of the XAI system to be more controllable by intention, and higher perceived stability and uncontrollability resulted in greater confidence in the system's ability. The effect of perceived controllability on perceived ability was moderated by the extent to which participants anthropomorphised the system. Several design implications for XAI systems are suggested based on our findings. [ABSTRACT FROM AUTHOR]

As collaborative robots and artificial intelligence (AI) systems are being deployed in ever-increasing contexts, we are more and more called upon to make judgements on their moral behaviour. Understanding the factors, affecting our ethical judgements involving these types of agents, would thus seem of the uttermost importance to allow for safer and well-regulated interactions between humans and machines. So far, however, this topic has been rarely investigated. We compared the perception of the seriousness of an action committed by either a person or a robot, causing harm to either some persons or some robots, and the attribution of the appropriated punishment for that action. The results showed a significant effect of the type of victim: the action was considered more a serious offence, and deemed worthy of more severe punishment, if the victims were humans than if they were robots. A significant agent-by-victim interaction was also found in the punishment judgements: for human victims, a human agent was punished more severely than a robot, while for robot victims, a robot agent was attributed a more severe punishment than a human one. The results are discussed in the light of the theories linking moral judgements to mind perception. [ABSTRACT FROM AUTHOR]

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyse high-volume and high-velocity data streams, dealing with information overload, among others. To eliminate such limitations, in this work, we first show that textual and imagery content on social media provide complementary information useful to improve situational awareness. We then explore ways in which various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields can exploit such complementary information generated during disaster events. Finally, we propose a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts. We conduct extensive experiments using textual and imagery content from millions of tweets posted during the three major disaster events in the 2017 Atlantic Hurricane season. Our study reveals that the distributions of various types of useful information can inform crisis managers and responders and facilitate the development of future automated systems for disaster management. [ABSTRACT FROM AUTHOR]