| 1. |
- Engström, Emma, PhD, 1982-, et al.
(författare)
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Comparing and modeling the use of online recommender systems
- 2024
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Ingår i: Computers in Human Behavior Reports. - : Elsevier BV. - 2451-9588. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- This study explores a new way to model the adoption of AI, specifically online recommender systems. It aims to find factors that can explain the variation in usage in terms of differences between individuals and differences over technologies. We analyzed survey data from users of online platforms in the U.S. using a two-level structural equation model (SEM) (N = 1007). In this model, the dependent variable was the usage rate, which was defined as the share of time a person used a particular recommender system (e.g., “People You May Know”) when they use the platform (e.g., Facebook). The individual responses (within-systems level) were clustered in the 26 recommender systems (between-systems level). We hypothesized that three technology-specific factors, adapted from the Diffusion of Innovations (DOI) theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), could explain the variations in usage at both levels: perceived performance expectancy (PE), perceived effort expectancy (EE), and perceived hedonic motivation (HM). Our estimated model showed that usage was associated with PE and HM at the within-system level and only with PE at the between-system level. A considerable part of the variation in usage across the 26 systems could be explained by PE only (R2 = 0.30). The most important contribution to practitioners is that this study provides evidence for the idea that there are inherent, measurable differences across recommender technologies that affect their usage rates, and specifically it finds usefulness to be a key factor. This is potentially valuable for app developers and marketeers who look to promote the adoption of novel recommender systems. The main contribution to the literature is that it presents a proof-of-concept of a two-level model for AI adoption, conceptualizing it as an effect of both variations over users and variations over applications. This finding is potentially valuable for policymakers, as better predictive models might enable improved assessments of AI's social implications. In future studies, the two-level approach presented here could be applied to other forms of AI, such as voice assistants, chatbots, or Internet of Things (IoT).
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| 2. |
- Engström, Emma, PhD, 1982-, et al.
(författare)
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Projecting environmental impacts with varying population, affluence and technology using IPAT – Climate change and land use scenarios
- 2024
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Ingår i: Vienna Yearbook of Population Research. - : Osterreichische Akademie der Wissenschaften, Verlag. - 1728-4414 .- 1728-5305. ; 22, s. 1-29
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Tidskriftsartikel (refereegranskat)abstract
- We theoretically explore the interrelations between population (P), affluence (A) and technology (T) for various environmental impacts (I ) using IPAT-type modelling. To illustrate the differences across environmental dimensions, climate and land use impacts are modelled. We use middle-of-the-road projections for population and per capita income and different forecasting methods for technology, including extrapolations of historical trends, models based on stochastic IPAT (STIRPAT) and predictions in the literature. The different approaches are compared within the IPAT framework. We also explore the consequences of alternative trajectories for P, A and T, and we discuss the implications of these trajectories for reaching global goals based on our modelling. The findings are analysed in light of three theories in environmental sociology, each of which places a different emphasis on the different components of IPAT. We argue that the large amount of technological mitigation assumed in many forecasts makes affluence and population relatively irrelevant for climate change. However, we also consider it likely that both factors will be determinants of land use impact in the 21st century.
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| 3. |
- Söderlund, Kasia, et al.
(författare)
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Regulating high-reach AI : On transparencydirections in the Digital Services Act
- 2024
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Ingår i: Internet Policy Review. - Berlin : Internet Policy Review, Alexander von Humboldt Institute for Internet and Society. - 2197-6775. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- By introducing the concept of high-reach AI, this paper focuses on AI systems whose widespread use may generate significant risks for both individuals and societies. While some of those risks have been recognised under the AI Act, we analyse the rules laid down by the Digital Services Act (DSA) for recommender systems used by dominant social media platforms as a prominent example of high-reach AI. Specifically, we examine transparency provisions aimed at addressing adverse effects of these AI technologies employed by social media very large online platforms (VLOPs). Drawing from AI transparency literature, we analyse DSA transparency measures through the conceptual lens of horizontal and vertical transparency. Our analysis indicates that while the DSA incorporates transparency provisions in both dimensions, the most progressive amendments emerge within the vertical transparency, for instance, by the introduction of the systemic risk assessment mechanism. However, we argue that the true impact of the new transparency provisions extends beyond their mere existence, emphasising the critical role of oversight entities in implementation and application of the DSA. Overall, this study highlights the paramount importance of vertical transparency in providing a comprehensive understanding of the aggregated risks associated with high-reach AI technologies, exemplified by social media recommender systems.
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