1,720,962 research outputs found
In AI we Trust: Determinants of continuous trust in the user/system interaction
No full textThe main objective of this study is to examine which factors influence users’ continuous trust in automated systems – and, more specifically, AI-based systems – and, subsequently, to develop an empirical model representing those factors. Influenced by the fourth wave of industrialisation, society and business undergo significant changes (Hancock, 2017; Brynjolfsson & McAfee, 2017). This wave comes with a vast array of new digital technologies (Schwab, 2016a; Schwab, 2016b) – consider artificial intelligence (Hansen & Bogh, 2020) – that are positioned as assets to leverage digital transformation efforts (Besson & Rowe, 2012). People and digital technology (Kane, 2015) interact in this context (Christ-Brendemühl & Schaarschmidt, 2019; Glikson & Woolley, 2020), and automation frequently acts on behalf of humans (Russel & Norvig, 2009; Xu, Mak, and Brintrup, 2021). However, less-rational factors (such as fear) are more at play, even more so often higher levels of automation appear (Sarter, Woods, and Billings, 1997). Organisations risk employees developing technology perceptions that breed resistance (Venkatesh, 2006), reluctance (Kane et al., 2019), and disappointment. These, in turn, impact people’s interactions with technology (Bardakei & Ünver, 2019) which leads to users neglecting beneficial decision aids (Davis & Kotteman, 1995) and discounting advice from algorithms (Prahl & Van Swol, 2017). Faulty interactions like these ould cause a decrease in trust and subsequent disuse or sabotage of technology (Parasuraman & Riley, 1997). Various theories emerged in the technology and acceptance literature (e.g., Technology Acceptance Model (TAM3; Vankatesh & Bala, 2008)), where trust, considered the cornerstone of social interaction (Blau, 1964), was also found to mediate human-technology relationships (Taddeo, 2017) and is seen as the degree to which a user can rely on the technology to achieve their goals under conditions of uncertainty and vulnerability (Lee & See, 2004). Especially when the system becomes too complex to be understood completely, will trust navigate complexity and enable reliance (Gsenger & Strle, 2021). Focusing on AI, we found that many concerns relating to AI usage link back to trust (e.g., the perception of AI as a black box; Logg et al., 2019; Lockey et al., 2021). The concept of trust, however, has received very little attention in AI literature thus far (Emaminejad et al., 2015). When trust is researched, the primary focus is on aspects of the technology itself rather than also including aspects of the individual and the environment (Toreini et al., 2019). We did not find any model in the literature that explains trust in systems deploying AI. Nor could we find any survey that allows for measuring trust (Böckle et al., 2021). As it stands, the majority of the current state of knowledge of human-machine interaction and the trust relationship draws on research in the context of automation. Further explorative research in the realm of AI is warranted.Given that explorative research is required, we opt for qualitative research through Grounded Theory (Strauss & Corbin, 1994) and collect our data through semi-structured interviews with practitioners that use AI-based systems in their work context. We will use an interview guide with open-ended questions and transcribe the nterviews verbatim. After every inte incorporate our reflections into the interview guide before continuing a new round (Charmaz, 2006). This cycle (from recruiting data to coding and comparing excerpts in Nviv12) will be repeated until theoretical saturation is reached. This will allow us to build the first empirical model of the antecedents of trust in AI-based systems. 593Results have not yet been obtained, but we are confident to have a final empirical model before the EAWOP conference. As this study is primarily explorative, the emerging antecedents and model will require testing to analyse their reliability and utility. While conclusions cannot yet be drawn, we aim to increase the understanding of both academics and practitioners on users’ continuous trust in AI-based systems. This research also lays the fundament for a follow-up study to build and validate a survey to measure trust in AI-based systems.Studying continuous trust in AI systems connects to the changing world of work, especially given that the amount of human-technology interactions has increased over recent decades and is expected to continue increasing. This topic links to EAWOP’s topic 16 (i.e., technology) and, specifically, subitems Artificial Intelligence and Human-Machine-System
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Trust in AI: Building Human-Tech Collaboration That Works
No full textArtificial Intelligence (AI) is reshaping how organisations make decisions, streamline operations and innovate. But to fully realise its value, trust between people and intelligent systems is essential. In a recent Vlerick Business School webinar, Professors Karlien Vanderheyden and researcher Ignace Decroix shared insights from their research on trust, AI and the human factor in digital transformation. The session explored how AI is already part of many decision-making processes, and how organisations can foster collaboration between people and technology to achieve better outcomes
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