Bruno Jäger

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
Locally adaptive modeling of unconditional heteroskedasticity
(2025-06-11)
Matthias Reginald Fengler
;
Okhrin, Ostap
;
Bruno Jäger
Type:conference paper
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124566
Some of the metrics are blocked by your
consent settings
Customer Education in the Digital Age: Intended and Unintended Effects
(2023)
Benjamin von Walter
;
Bruno Jäger
;
Christian Heumann
;
Dietmar Kremmel
Many companies implement customer education to improve their customers’ abilities to interact with digital service technologies. In this research, we investigate how customers respond to different forms of customer education. A study of customer data of a financial service provider and an experiment show that customer education has a positive effect on acceptance of service technologies. However, we also demonstrate that customers may not always respond positively to customer education. Whereas customers who receive education adaptive to their individual abilities react favorably, customers who receive non-adaptive education feel negatively about the service technology that has been the subject of customer education. In sum, these findings emphasize the potential of customer education to increase technology acceptance and point to the importance of making customer education more adaptive.
Type:conference paper
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118475
Some of the metrics are blocked by your
consent settings
The impact of lay beliefs about AI on adoption of algorithmic advice
(2021)
von Walter, Benjamin
;
Kremmel, Dietmar
;
Bruno Jäger
Type:forthcoming
Journal:Marketing Letters
URL:https://link.springer.com/article/10.1007/s11002-021-09589-1
DOI:10.1007/s11002-021-09589-1
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/111451
Scopus© Citations 56
Some of the metrics are blocked by your
consent settings
Locally Adaptive Modeling of Unconditional Heteroskedasticity
(Elsevier BV, 2025-06-20)
Fengler, Matthias
;
Bruno Jäger
;
Okhrin, Ostap
We study local change-point detection in variance using generalized likelihood ratio tests. Building on Suvorikova & Spokoiny (2017), we utilize the multiplier bootstrap to approximate the unknown, non-asymptotic distribution of the test statistic and introduce a multiplicative bias correction that improves upon the existing additive version. This proposed correction offers a clearer interpretation of the bootstrap estimators while significantly reducing computational costs. Simulation results demonstrate that our method performs comparably to the original approach. We apply it to the growth rates of U.S. inflation, industrial production, and Bitcoin returns.
Type:working paper
URL:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5323772
DOI:10.2139/ssrn.5323772
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123122

Bruno Jäger

Filters

Settings

Sort By

Results per page