Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Michael T. Knierim"'
Publikováno v:
Computers in Human Behavior Reports, Vol 16, Iss , Pp 100488- (2024)
Using repeated measurements in everyday life, we assessed whether a smartphone-based just-in-time adaptive intervention prompting use of metacognitive strategies enhances flow at work. Assuming that setting clear goals and disengaging from unattainab
Externí odkaz:
https://doaj.org/article/86e47634427c4a42b21716ef01916129
Publikováno v:
Frontiers in Psychology, Vol 14 (2023)
Flow, the holistic experience of intrinsic motivation and effortless attention, is positively associated with job performance, work engagement, and well-being. As many individuals struggle to enter and maintain flow states, interventions that foster
Externí odkaz:
https://doaj.org/article/4d3c73b3a805424bbfd29dec4992ce50
Publikováno v:
HardwareX, Vol 12, Iss , Pp e00357- (2022)
We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduce
Externí odkaz:
https://doaj.org/article/700d59df492d481083919863b100c122
Autor:
Michael T. Knierim, Nico Loewe, Mario Nadj, Raphael Rissler, Alexander Maedche, Maximilian Xiling Li
Publikováno v:
IEEE Transactions on Affective Computing. 14:463-474
The focal role of flow in promoting desirable outcomes in companies, such as increased employees' well-being and performance, led scholars to study flow in the context of work. However, current measurement approaches which assess flow via self-report
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e2cd8b8ce7ebc8802f8f14cd4f47bd5
https://doi.org/10.1109/taffc.2020.3045269
https://doi.org/10.1109/taffc.2020.3045269
Publikováno v:
Brain-Computer Interfaces. 8:161-179
Observing brain activity in real life offers exciting possibilities like the support of physical health, mental well-being, and thought-controlled interaction modalities. The development of such applications is, however, strongly impeded by low acces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::938d5e69b3c74ee4b9ebbf293fd0b005
https://doi.org/10.1080/2326263x.2021.1972633
https://doi.org/10.1080/2326263x.2021.1972633
Publikováno v:
Proceedings of the ACM on Human-Computer Interaction. 4:1-30
Fueled by the pervasion of tools like Slack or Microsoft Teams, the usage of text-based communication in distributed teams has grown massively in organizations. This brings distributed teams many advantages, however, a critical shortcoming in these s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::378fd58349b740769b7f7ce79cf7d945
https://doi.org/10.1145/3415189
https://doi.org/10.1145/3415189
Publikováno v:
Information Systems and Neuroscience ISBN: 9783031130632
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f266b0422f82160eb2288a41fa001735
https://doi.org/10.1007/978-3-031-13064-9_15
https://doi.org/10.1007/978-3-031-13064-9_15
Publikováno v:
Information Systems and Neuroscience ISBN: 9783030888992
NeuroIS scholars increasingly rely on more extensive and diverse sensor data to improve the understanding of information system (IS) use and to develop adaptive IS that foster individual and organizational productivity, growth, and well-being. Collec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b8440d6d118a31f5d59b4f3b4c6fd7f
https://doi.org/10.1007/978-3-030-88900-5_6
https://doi.org/10.1007/978-3-030-88900-5_6
Publikováno v:
Market Engineering ISBN: 9783030666606
Information systems (IS) are nowadays at the core of many personal and institutional activities and influence daily life more than ever before. To understand, evaluate and envision the forms of how we interact with IS, interdisciplinary and multiface
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2783368282e72e592bceb468413d902e
https://doi.org/10.1007/978-3-030-66661-3_1
https://doi.org/10.1007/978-3-030-66661-3_1
Publikováno v:
Information Systems and Neuroscience ISBN: 9783030888992
Predicting flow intensities from unobtrusively collected sensor data is considered an important yet challenging endeavor for NeuroIS scholars aiming to understand and support flow during IS use. In this direction, a limitation has been the focus on c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a4dc78e706e7b50818340b68810614d
http://hdl.handle.net/11311/1204594
http://hdl.handle.net/11311/1204594