Developing a Situational Model of Information System Project Success

Scholars have not found a common understanding of information system (IS) project success nor does a common measurement approach exist in empirical research. A plausible cause for the lack of such an agreed on definition is that a generally accepted definition of IS project success practically cannot be derived due to stakeholders’ different perceptions of success. Therefore, we develop a situational model for the group of IS projects’ contractors. We gathered data from 52 IS projects managers and apply PLS path modeling to determine the relevant success dimensions from their subjective perspectives. Our results show customer satisfaction, process efficiency, and functional requirements as the three most important dimensions for contractors. Scholars and practitioners should shift from measuring IS project success in terms of adherence to planning to an approach adapted to the specific terms of the project under consideration and the stakeholder group addressed

PRO MENTE OBERÖSTERREICH – INCLUSION INSTEAD OF EXCLUSION

pro mente OÖ is one of the biggest organisations offering psycho-social care and treatment. It is a crucial part of the differentiated system of social psychiatric services in Austria. First, the article describes the organization pro mente OÖ, its history and services. Then, a selection of framework conditions of social psychiatric supply is presented. In this respect some existing strengths and weaknesses of the Austrian psycho-social supply system are discussed

In-situ comparison of the NOy instruments flown in MOZAIC and SPURT

Two aircraft instruments for the measurement of total odd nitrogen (NOy) were compared side by side aboard a Learjet A35 in April 2003 during a campaign of the AFO2000 project SPURT (Spurengastransport in der Tropopausenregion). The instruments albeit employing the same measurement principle (gold converter and chemiluminescence) had different inlet configurations. The ECO-Physics instrument operated by ETH-Zürich in SPURT had the gold converter mounted outside the aircraft, whereas the instrument operated by FZ-Jülich in the European project MOZAIC III (Measurements of ozone, water vapour, carbon monoxide and nitrogen oxides aboard Airbus A340 in-service aircraft) employed a Rosemount probe with 80 cm of FEP-tubing connecting the inlet to the gold converter. The NOy concentrations during the flight ranged between 0.3 and 3 ppb. The two data sets were compared in a blind fashion and each team followed its normal operating procedures. On average, the measurements agreed within 7%, i.e. within the combined uncertainty of the two instruments. This puts an upper limit on potential losses of HNO3 in the Rosemount inlet of the MOZAIC instrument. Larger transient deviations were observed during periods after calibrations and when the aircraft entered the stratosphere. The time lag of the MOZAIC instrument observed in these instances is in accordance with the time constant of the MOZAIC inlet line determined in the laboratory for HNO3

Zur Methodologie der technologischen Forschung in der Betriebswirtschaftslehre

In diesem Beitrag zeigen wir, dass theoretische und technologische Aussagensysteme zwei eigenständige Forschungszweige darstellen und damit die Notwendigkeit erwächst, eine eigene Methodologie technologischer Aussagensysteme zu entwerfen. Wir stellen erste Ansatzpunkte vor, wie eine solche Methodologie technologischer Aussagensysteme aussehen kann. Dabei gehen wir auch der Frage nach der Wissenschaftlichkeit technologischer Forschung nach

Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield

Autonomous driving perception techniques are typically based on supervised machine learning models that are trained on real-world street data. A typical training process involves capturing images with a single car model and windshield configuration. However, deploying these trained models on different car types can lead to a domain shift, which can potentially hurt the neural networks performance and violate working ADAS requirements. To address this issue, this paper investigates the domain shift problem further by evaluating the sensitivity of two perception models to different windshield configurations. This is done by evaluating the dependencies between neural network benchmark metrics and optical merit functions by applying a Fourier optics based threat model. Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient

Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient

Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way, which for modern perception stacks implies meaningful for artificial intelligence (AI) algorithms. The measured optical quality needs to be linked to the performance of these algorithms, such that performance limits - and thus production tolerance limits - can be defined. In this article we demonstrate that the main metric established in the industry - refractive power - is fundamentally not capable of capturing relevant optical properties of windscreens. Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system. Hence, the required goal of a qualification system that is installed at the windscreen supplier and independently measures the optical quality cannot be achieved using MTF. We propose a novel concept to determine the optical quality of windscreens and to use simulation to link this optical quality to the performance of AI algorithms, which can hopefully lead to novel inspection systems