@inproceedings{62885,
  author       = {{Osnabrügge, Malin and Tenberge, Claudia and Fechner, Sabine}},
  keywords     = {{Artificial intelligence, primary education, science and technology education}},
  location     = {{Norrköping, Sweden}},
  title        = {{{Artificial Intelligence in primary science and technology education with a focus on implementation of AI in learning context – Results of a Scoping Review}}},
  year         = {{2026}},
}

@inbook{65032,
  abstract     = {{Despite its status as a key enabling technology, the adoption of 5G in
industry remains limited, mainly due to a lack of expertise, which can be addressed
by incorporating 5G training into vocational schools. Since Augmented Reality
(AR) faces similar challenges, it is used as the 5G application. This research aims
to determine the extent to which a learning unit using 5G and AR for collaborative
work can increase the intention to use these technologies in the vocational setting
and gathers the factors that make up 5G acceptance. It is based on the Technology Acceptance Model 2 (TAM2). The sample includes 23 industrial mechanics
students who participated in the developed learning unit. All items are scored on
seven-tiered Likert scales (0 “totally disagree”; 6 “totally agree”).
The results showed a non-significant change in behavioural intention to use
5G, with a mean of 4.95 (SD: 1.18) for the pre-test and 4.43 (SD: 1.68) for the posttest. The change in intention to use AR did not change significantly either, from
3.12 (SD: 1.38) at pre-test to 3.37 (SD: 1.16) at post-test. The factors Perceived
Usefulness, Image, and Relevance had the lowest mean scores, indicating areas for
targeted improvement. The significant change in Output Quality ratings is likely to
reflect initial overestimation by students. The difference in Behavioural Intentions
between 5G and AR suggests that AR may not be an effective technology for
increasing 5G adoption in educational contexts. One recommendation is to address
5G in the learning unit not only indirectly.}},
  author       = {{Schäfers, Johannes and Temmen, Katrin}},
  booktitle    = {{Proceedings of the 27th International Conference on Interactive Collaborative Learning (ICL2024), Volume 4}},
  editor       = {{Auer, Michael E. and Rüütmann, Tiia}},
  isbn         = {{9783031835193}},
  issn         = {{2367-3370}},
  keywords     = {{5G, Key Technology, TAM · Augmented Reality, VET}},
  location     = {{Tallinn}},
  pages        = {{84 – 91}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{The Influence of an Immersive-Collaborative Learning Unit on the Technology Acceptance of 5G and AR of Industrial Mechanic Students}}},
  doi          = {{10.1007/978-3-031-83520-9_8}},
  volume       = {{1281}},
  year         = {{2025}},
}

@article{52686,
  author       = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}},
  issn         = {{2509-3428}},
  journal      = {{Journal of Hardware and Systems Security}},
  keywords     = {{General Engineering, Energy Engineering and Power Technology}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Post-configuration Activation of Hardware Trojans in FPGAs}}},
  doi          = {{10.1007/s41635-024-00147-5}},
  year         = {{2024}},
}

@article{53213,
  author       = {{Amiri, Arman and Tavana, Madjid and Arman, Hosein}},
  issn         = {{2542-6605}},
  journal      = {{Internet of Things}},
  keywords     = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction}}},
  doi          = {{10.1016/j.iot.2023.101027}},
  volume       = {{25}},
  year         = {{2024}},
}

@article{53309,
  author       = {{Hölsch, Lukas and Brosch, Anian and Steckel, Richard and Braun, Tristan and Wendel, Sebastian and Böcker, Joachim and Wallscheid, Oliver}},
  issn         = {{0885-8969}},
  journal      = {{IEEE Transactions on Energy Conversion}},
  keywords     = {{Electrical and Electronic Engineering, Energy Engineering and Power Technology}},
  pages        = {{1--12}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Insights and Challenges of Co-Simulation-Based Optimal Pulse Pattern Evaluation for Electric Drives}}},
  doi          = {{10.1109/tec.2024.3374962}},
  year         = {{2024}},
}

@techreport{56494,
  abstract     = {{Many industrialized countries have recognized the need to mitigate energy cost increases faced by low-income households by fostering the adoption of energy-efficient technologies. How to meet this need is an open question, but “behavioral insights” are likely components of future policy designs. Applying well-established behavioral insights to low-income house- holds raises questions of transportability as they are typically underrepresented in the existing evidence base. We illustrate this problem by conducting a randomized field experiment on scalable, low-cost design elements to improve program take-up in one of the world’s largest en- ergy efficiency assistance programs. Observing investment decisions of over 1,800 low-income households in Germany’s “Refrigerator Replacement Program”, we find that the transportabil- ity problem is real and consequential: First, the most effective policy design would not have been chosen based on existing behavioral insights. Second, design elements favored by these insights either prove ineffective or even backfire, violating ‘do no harm’ principles of policy advice. Systematic testing remains crucial for addressing the transportability problem, partic- ularly for policies targeting vulnerable groups.
}},
  author       = {{Kesternich, Martin and Chlond , Bettina and Goeschl, Timo  and Werthschulte, Madeline}},
  keywords     = {{Transportability, low-income households, field experiment, randomized controlled trial, governmental welfare programs, energy efficiency, technology adoption}},
  publisher    = {{ AWI Discussion Paper Series No. 755}},
  title        = {{{Transporting behavioral insights to low-income households: A field experiment on energy efficiency investments}}},
  year         = {{2024}},
}

@article{45826,
  author       = {{Niemann, Valerie A. and Huck, Marten and Steinrück, Hans-Georg and Toney, Michael F. and Tarpeh, William A. and Bone, Sharon E.}},
  issn         = {{2690-0637}},
  journal      = {{ACS ES&T Water}},
  keywords     = {{Water Science and Technology, Environmental Chemistry, Chemistry (miscellaneous), Chemical Engineering (miscellaneous)}},
  pages        = {{2627--2637}},
  publisher    = {{American Chemical Society (ACS)}},
  title        = {{{X-ray Absorption Spectroscopy Reveals Mechanisms of Calcium and Silicon Fouling on Reverse Osmosis Membranes Used in Wastewater Reclamation}}},
  doi          = {{10.1021/acsestwater.3c00144}},
  volume       = {{3}},
  year         = {{2023}},
}

@article{45866,
  author       = {{Knorr, Lukas and Schlosser, Florian and Meschede, Henning}},
  issn         = {{1848-9257}},
  journal      = {{Journal of Sustainable Development of Energy, Water and Environment Systems}},
  keywords     = {{Energy Engineering and Power Technology, Water Science and Technology, Environmental Science (miscellaneous), Renewable Energy, Sustainability and the Environment}},
  number       = {{3}},
  pages        = {{0--0}},
  publisher    = {{SDEWES Centre}},
  title        = {{{Assessment of Energy Efficiency and Flexibility Measures in Electrified Process Heat Generation Based on Simulations in the Animal Feed Industry}}},
  doi          = {{10.13044/j.sdewes.d11.0444}},
  volume       = {{ 11}},
  year         = {{2023}},
}

@article{48517,
  author       = {{Hubner-Benz, Sylvia and Baum, Matthias}},
  issn         = {{1742-5360}},
  journal      = {{International Journal of Entrepreneurial Venturing}},
  keywords     = {{Management of Technology and Innovation, Strategy and Management, Business and International Management}},
  number       = {{1}},
  publisher    = {{Inderscience Publishers}},
  title        = {{{What predicts effectuation preferences Disentangling individual and environmental factors and illuminating decision criteria}}},
  doi          = {{10.1504/ijev.2023.129283}},
  volume       = {{15}},
  year         = {{2023}},
}

@article{48900,
  author       = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}},
  issn         = {{0958-5192}},
  journal      = {{The International Journal of Human Resource Management}},
  keywords     = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}},
  pages        = {{1--29}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}},
  doi          = {{10.1080/09585192.2023.2273331}},
  year         = {{2023}},
}

@article{49446,
  author       = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}},
  issn         = {{0958-5192}},
  journal      = {{The International Journal of Human Resource Management}},
  keywords     = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}},
  pages        = {{1--29}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}},
  doi          = {{10.1080/09585192.2023.2273331}},
  year         = {{2023}},
}

@article{49565,
  author       = {{Ebersold, Felix and Hechelmann, Ron-Hendrik and Holzapfel, Peter and Meschede, Henning}},
  issn         = {{2590-1745}},
  journal      = {{Energy Conversion and Management: X}},
  keywords     = {{Energy Engineering and Power Technology, Fuel Technology, Nuclear Energy and Engineering, Renewable Energy, Sustainability and the Environment}},
  publisher    = {{Elsevier BV}},
  title        = {{{Carbon insetting as a measure to raise supply chain energy efficiency potentials: Opportunities and challenges}}},
  doi          = {{10.1016/j.ecmx.2023.100504}},
  volume       = {{20}},
  year         = {{2023}},
}

@inproceedings{45270,
  abstract     = {{Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.}},
  author       = {{Halimeh, Haya and Caron, Matthew and Müller, Oliver}},
  booktitle    = {{Hawaii International Conference on System Sciences}},
  keywords     = {{Social Media and Healthcare Technology, early depression detection, liwc, mental health, transfer learning, transformer architectures}},
  title        = {{{Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features}}},
  year         = {{2023}},
}

@article{53074,
  author       = {{Kasper, Tina and Hansen, Nils}},
  issn         = {{0010-2180}},
  journal      = {{Combustion and Flame}},
  keywords     = {{General Physics and Astronomy, Energy Engineering and Power Technology, Fuel Technology, General Chemical Engineering, General Chemistry}},
  publisher    = {{Elsevier BV}},
  title        = {{{Resonance enhanced multiphoton ionization detection of aromatics formation in fuel-rich flames}}},
  doi          = {{10.1016/j.combustflame.2023.112820}},
  volume       = {{257}},
  year         = {{2023}},
}

@article{53220,
  author       = {{Tavana, Madjid and Khalili Nasr, Arash and Ahmadabadi, Alireza Barati and Amiri, Alireza Shamekhi and Mina, Hassan}},
  issn         = {{2542-6605}},
  journal      = {{Internet of Things}},
  keywords     = {{Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software}},
  publisher    = {{Elsevier BV}},
  title        = {{{An interval multi-criteria decision-making model for evaluating blockchain-IoT technology in supply chain networks}}},
  doi          = {{10.1016/j.iot.2023.100786}},
  volume       = {{22}},
  year         = {{2023}},
}

@article{53226,
  author       = {{Marín, Raquel and Santos-Arteaga, Francisco J. and Tavana, Madjid and Di Caprio, Debora}},
  issn         = {{2444-569X}},
  journal      = {{Journal of Innovation & Knowledge}},
  keywords     = {{Management of Technology and Innovation, Marketing, Economics and Econometrics, Business and International Management}},
  number       = {{4}},
  publisher    = {{Elsevier BV}},
  title        = {{{Value Chain digitalization and technological development as innovation catalysts in small and medium-sized enterprises}}},
  doi          = {{10.1016/j.jik.2023.100454}},
  volume       = {{8}},
  year         = {{2023}},
}

@article{53224,
  author       = {{Santos-Arteaga, Francisco J. and Di Caprio, Debora and Tavana, Madjid}},
  issn         = {{0040-1625}},
  journal      = {{Technological Forecasting and Social Change}},
  keywords     = {{Management of Technology and Innovation, Applied Psychology, Business and International Management}},
  publisher    = {{Elsevier BV}},
  title        = {{{A combinatorial data envelopment analysis with uncertain interval data with application to ICT evaluation}}},
  doi          = {{10.1016/j.techfore.2023.122510}},
  volume       = {{191}},
  year         = {{2023}},
}

@inproceedings{49785,
  abstract     = {{Reputation is indispensable for online business since it supports customers in their buying decisions and
allows sellers to justify premium prices. While IS research has investigated reputation systems mainly
as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions
have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to
propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a
transaction is performed, customers commit to pay a bonus that is granted if a service provider has
performed a service properly. As opposed to rival reputation systems that build on cumulated ratings
or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online
transactions. We expect this system class to provide more trustworthy ratings, which might reduce
agency costs and serve quality providers to establish a reputation towards new customers, building on
second-order trust.}},
  author       = {{Hemmrich, Simon}},
  booktitle    = {{Proceedings of 31st European Conference on Information Systems (ECIS 2023)}},
  keywords     = {{Trust, Risk, Reputation System, Blockchain Technology, Business Reputation System.}},
  location     = {{Kristiansand}},
  title        = {{{Business Reputation Systems based on Blockchain Technology—A Risky Advance}}},
  year         = {{2023}},
}

@article{39976,
  abstract     = {{The context of the study is the increasing digitalisation of the living environment of primary school students, which is to be introduced into primary schools according to theoretical and educational policy guidelines. In this regard, further teacher
training on digital media in classrooms are particularly relevant, on the one hand to promote teachers’ digital-related pedagogical knowledge and content knowledge (DPaCK). On the other hand, studies also reveal positive correlations among teacher training, teaching activities, and students’ learning outcomes. In-service teacher training courses with adaptive support by a trainer in particular have
proven to be effective. Against the background of various research studies on professional development of teachers, a corresponding model of tripartite learning outcomes has been established and serves as a broad theoretical framework. However, the specific relationship between in-service teacher training with adaptive support, DPaCK, and computational thinking of primary school students in the context of the German primary school subject Sachunterricht has not been sufficiently studied. Therefore, the following research questions can be derived: (1) To what extent does training with adaptive support on the topic of learning robots contribute to the development of teachers’ DPaCK? (2) Which effects can be ascertained on the students’ computational thinking in technology-related Sachunterricht? To investigate this relationship, an intervention study in a pre-post design with an experimental group, a control group, and a baseline is appropriate. As results are not yet available at this point, the present paper focuses on the presentation of the theoretical background and empirical approaches.}},
  author       = {{Janicki, Nicole and Tenberge, Claudia}},
  journal      = {{Australasian Journal of Technology Education}},
  keywords     = {{technology education, teacher professionalisation, Computational Thinking, digitalization, learning robots}},
  title        = {{{Technology education in elementary school using the example of 'learning robots' – development and evaluation of an in-service teacher training concept}}},
  doi          = {{https://doi.org/10.15663/ajte.v9.i0.103}},
  volume       = {{9}},
  year         = {{2023}},
}

@article{36834,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Increasing average temperatures and heat waves are having devasting impacts on human health and well-being but studies of heat impacts and how people adapt are rare and often confined to specific locations. In this study, we explore how analysis of conversations on social media can be used to understand how people feel about heat waves and how they respond. We collected global Twitter data over four months (from January to April 2022) using predefined hashtags about heat waves. Topic modelling identified five topics. The largest (one-third of all tweets) was related to sports events. The remaining two-thirds could be allocated to four topics connected to communication about climate-related heat or heat waves. Two of these were on the impacts of heat and heat waves (health impacts 20%; social impacts 16%), one was on extreme weather and climate change attribution (17%) and the last one was on perceptions and warning (13%). The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia, the USA and South Asia (India and Pakistan), indicating that people posting tweets were aware of the threat from heat and its impacts on the society. Among the words frequently used within the topic ‘Social impacts’ were ‘air-conditioning’ and ‘electricity’, suggesting links between coping strategies and financial pressure. Apart from analysing the content of tweets, new insights were also obtained from analysing how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were then shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence that misinformation about climate change-related heat is widespread.
</jats:p>}},
  author       = {{Zander, Kerstin K. and Rieskamp, Jonas and Mirbabaie, Milad and Alazab, Mamoun and Nguyen, Duy}},
  issn         = {{0921-030X}},
  journal      = {{Natural Hazards}},
  keywords     = {{Earth and Planetary Sciences (miscellaneous), Atmospheric Science, Water Science and Technology}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Responses to heat waves: what can Twitter data tell us?}}},
  doi          = {{10.1007/s11069-023-05824-2}},
  year         = {{2023}},
}