@article{DaschnerObermaier2024, author = {Daschner, Stefan and Obermaier, Robert}, title = {Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage}, series = {Journal of Behavioral Decision Making (ISSN: 1099-0771)}, volume = {37}, journal = {Journal of Behavioral Decision Making (ISSN: 1099-0771)}, number = {5}, publisher = {Wiley}, issn = {0894-3257}, doi = {10.1002/bdm.70001}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-17615}, pages = {18 Seiten}, year = {2024}, abstract = {Algorithms are capable of advising human decision-makers in an increasing number of management accounting tasks such as business forecasts. Due to expected potential of these (intelligent) algorithms, there are growing research efforts to explore ways how to boost algorithmic advice usage in forecasting tasks. However, algorithmic advice can also be erroneous. Yet, the risk of using relatively bad advice is largely ignored in this research stream. Therefore, we conduct two online experiments to examine this risk of using relatively bad advice in a forecasting task. In Experiment 1, we examine the influence of performance feedback (revealing previous relative advice quality) and source of advice on advice usage in business forecasts. The results indicate that the provision of performance feedback increases subsequent advice usage but also the usage of subsequent relatively bad advice. In Experiment 2, we investigate whether advice representation, that is, displaying forecast intervals instead of a point estimate, helps to calibrate advice usage towards relative advice quality. The results suggest that advice representation might be a potential countermeasure to the usage of relatively bad advice. However, the effect of this antidote weakens when forecast intervals become less informative.}, language = {en} } @article{SchweiklObermaier2022, author = {Schweikl, Stefan and Obermaier, Robert}, title = {Lost in translation: IT business value research and resource complementarity : an integrative framework, shortcomings and future research directions}, series = {Management Review Quarterly}, volume = {2022}, journal = {Management Review Quarterly}, number = {73}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s11301-022-00284-7}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2022093021520340946532}, pages = {1713 -- 1749}, year = {2022}, abstract = {Despite longstanding research efforts, there is still ambiguity surrounding the business value created by IT. To approach this conundrum, research focus has progressed from an isolated investigation of IT to the assessment of complementarity between IT and different non-IT resources such as work practices or decision structures. However, incoherence around the characteristics and scope of these complementary non-IT resources has created a fragmented body of research, preventing a sustainable knowledge creation. Thus, in this paper we synthesize the dispersed research efforts, identify shortcomings in the extant literature, and derive opportunities for future research. Specifically, we present a converging definition of complementary non-IT resources and specify their role in the value creation process from IT by viewing it through three distinct lenses: microeconomic theory, resource-based view, and contingency theory. We structure current research efforts by organizing complementary non-IT resources into distinct categories, namely strategy, structure, practices, processes, and culture (organizational resources), top management support, internal relations, and external relations (relational resources), worker skill (non-IT human resources), non-IT physical resources, as well as internal funds and external funds (financial resources). Finally, we highlight five important shortcomings in the current literature, such as the predominant use of reductionist approaches or monolithic IT measures, and make actionable recommendations to resolve them.}, language = {en} } @article{DaschnerObermaier2022, author = {Daschner, Stefan and Obermaier, Robert}, title = {Algorithm aversion? On the influence of advice accuracy on trust in algorithmic advice}, series = {Journal of Decision Systems}, volume = {31 (2022)}, journal = {Journal of Decision Systems}, number = {S1}, publisher = {Taylor \& Francis}, address = {London}, doi = {10.1080/12460125.2022.2070951}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-19120}, pages = {77 -- 97}, year = {2022}, abstract = {There is empirical evidence that decision makers show negative behaviours towards algorithmic advice compared to human advice, termed as algorithm aversion. Taking a trust theoretical perspective, this study broadens the quite monolithic view on behaviour to its cognitive antecedent: cognitive trust, i.e. trusting beliefs and trusting intentions. We examine initial trust (cognitive trust and behaviour) as well as its development after performance feedback by conducting an online experiment that asked participants to forecast the expected demand for a product. Advice accuracy was manipulated by ± 5 \% relative to the participant's initial forecasting accuracy determined in a pre-test. Results show that initial behaviour towards algorithmic advice is not influenced by cognitive trust. Furthermore, the decision maker's initial forecasting accuracy indicates a threshold between near-perfect and bad advice. When advice accuracy is at this threshold, we observe behavioural algorithm appreciation, particularly due to higher trusting integrity beliefs in algorithmic advice.}, language = {en} }