Refine
H-BRS Bibliography
- yes (73)
Departments, institutes and facilities
- Fachbereich Angewandte Naturwissenschaften (21)
- Fachbereich Informatik (18)
- Fachbereich Ingenieurwissenschaften und Kommunikation (12)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (12)
- Fachbereich Wirtschaftswissenschaften (11)
- Institut für funktionale Gen-Analytik (IFGA) (9)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (6)
- Institut für Sicherheitsforschung (ISF) (4)
- Institute of Visual Computing (IVC) (4)
- Stabsstelle Kommunikation und Marketing (3)
Document Type
- Article (44)
- Report (5)
- Conference Object (4)
- Master's Thesis (4)
- Bachelor Thesis (3)
- Part of a Book (3)
- Part of Periodical (3)
- Preprint (3)
- Working Paper (3)
- Book (monograph, edited volume) (1)
Year of publication
- 2020 (73) (remove)
Has Fulltext
- yes (73) (remove)
Keywords
- Digitalisierung (2)
- Hochschule Bonn-Rhein-Sieg (2)
- Inborn error of metabolism (2)
- Ketone body (2)
- Metabolic acidosis (2)
- Organic aciduria (2)
- Usable Security (2)
- bank customers (2)
- ACAT1 (1)
- ADP release (1)
2-methylacetoacetyl-coenzyme A thiolase (beta-ketothiolase) deficiency: one disease - two pathways
(2020)
Background: 2-methylacetoacetyl-coenzyme A thiolase deficiency (MATD; deficiency of mitochondrial acetoacetyl-coenzyme A thiolase T2/ “beta-ketothiolase”) is an autosomal recessive disorder of ketone body utilization and isoleucine degradation due to mutations in ACAT1.
Methods: We performed a systematic literature search for all available clinical descriptions of patients with MATD. Two hundred forty-four patients were identified and included in this analysis. Clinical course and biochemical data are presented and discussed.
Results: For 89.6% of patients at least one acute metabolic decompensation was reported. Age at first symptoms ranged from 2 days to 8 years (median 12 months). More than 82% of patients presented in the first 2 years of life, while manifestation in the neonatal period was the exception (3.4%). 77.0% (157 of 204 patients) of patients showed normal psychomotor development without neurologic abnormalities. Conclusion: This comprehensive data analysis provides a systematic overview on all cases with MATD identified in the literature. It demonstrates that MATD is a rather benign disorder with often favourable outcome, when compared with many other organic acidurias.
Background: 3-hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is an autosomal recessive disorder of ketogenesis and leucine degradation due to mutations in HMGCL.
Method: We performed a systematic literature search to identify all published cases. Two hundred eleven patients of whom relevant clinical data were available were included in this analysis. Clinical course, biochemical findings and mutation data are highlighted and discussed. An overview on all published HMGCL variants is provided.
Results: More than 95% of patients presented with acute metabolic decompensation. Most patients manifested within the first year of life, 42.4% already neonatally. Very few individuals remained asymptomatic. The neurologic long-term outcome was favorable with 62.6% of patients showing normal development.
Conclusion: This comprehensive data analysis provides a systematic overview on all published cases with HMGCLD including a list of all known HMGCL mutations.
4GREAT is an extension of the German Receiver for Astronomy at Terahertz frequencies (GREAT) operated aboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). The spectrometer comprises four different detector bands and their associated subsystems for simultaneous and fully independent science operation. All detector beams are co-aligned on the sky. The frequency bands of 4GREAT cover 491-635, 890-1090, 1240-1525 and 2490-2590 GHz, respectively. This paper presents the design and characterization of the instrument, and its in-flight performance. 4GREAT saw first light in June 2018, and has been offered to the interested SOFIA communities starting with observing cycle 6.
The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.
A Comparative Study of Uncertainty Estimation Methods in Deep Learning Based Classification Models
(2020)
Deep learning models produce overconfident predictions even for misclassified data. This work aims to improve the safety guarantees of software-intensive systems that use deep learning based classification models for decision making by performing comparative evaluation of different uncertainty estimation methods to identify possible misclassifications.
In this work, uncertainty estimation methods applicable to deep learning models are reviewed and those which can be seamlessly integrated to existing deployed deep learning architectures are selected for evaluation. The different uncertainty estimation methods, deep ensembles, test-time data augmentation and Monte Carlo dropout with its variants, are empirically evaluated on two standard datasets (CIFAR-10 and CIFAR-100) and two custom classification datasets (optical inspection and RoboCup@Work dataset). A relative ranking between the methods is provided by evaluating the deep learning classifiers on various aspects such as uncertainty quality, classifier performance and calibration. Standard metrics like entropy, cross-entropy, mutual information, and variance, combined with a rank histogram based method to identify uncertain predictions by thresholding on these metrics, are used to evaluate uncertainty quality.
The results indicate that Monte Carlo dropout combined with test-time data augmentation outperforms all other methods by identifying more than 95% of the misclassifications and representing uncertainty in the highest number of samples in the test set. It also yields a better classifier performance and calibration in terms of higher accuracy and lower Expected Calibration Error (ECE), respectively. A python based uncertainty estimation library for training and real-time uncertainty estimation of deep learning based classification models is also developed.
Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user’s background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate Hyper-Pref, a cyclic co-creative process between human and computer, which triggers the user’s intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user’s preferences, discovering the preference hypervolume within large search spaces.
Failure prognostic builds up on constant data acquisition and processing and fault diagnosis and is an essential part of predictive maintenance of smart manufacturing systems enabling condition based maintenance, optimised use of plant equipment, improved uptime and yield and to prevent safety problems. Given known control inputs into a plant and real sensor outputs or simulated measurements, the model-based part of the proposed hybrid method provides numerical values of unknown parameter degradation functions at sampling time points by the evaluation of equations that have been derived offline from a bicausal diagnostic bond graph. These numerical values are computed concurrently to the constant monitoring of a system and are stored in a buffer of fixed length. The data-driven part of the method provides a sequence of remaining useful life estimates by repeated projection of the parameter degradation into the future based on the use of values in a sliding time window. Existing software can be used to determine the best fitting function and can account for its random parameters. The continuous parameter estimation and their projection into the future can be performed in parallel for multiple isolated simultaneous parametric faults on a multicore, multiprocessor computer.
The proposed hybrid bond graph model-based, data-driven method is verified by an offline simulation case study of a typical power electronic circuit. It can be used to implement embedded systems that enable cooperating machines in smart manufacturing to perform prognostic themselves.
AErOmAt Abschlussbericht
(2020)
Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln, um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen Optimierungsdomänen einzusparen. Die Hochschule Bonn-Rhein-Sieg (H-BRS) hat auf diesem Weg einen gesellschaftlich relevanten und gleichzeitig wirtschaftlich verwertbaren Beitrag zur Energieeffizienzforschung geleistet. Das Projekt führte außerdem zu einer schnelleren Integration der neuberufenen Antragsteller in die vorhandenen Forschungsstrukturen.
The ability to finely segment different instances of various objects in an environment forms a critical tool in the perception tool-box of any autonomous agent. Traditionally instance segmentation is treated as a multi-label pixel-wise classification problem. This formulation has resulted in networks that are capable of producing high-quality instance masks but are extremely slow for real-world usage, especially on platforms with limited computational capabilities. This thesis investigates an alternate regression-based formulation of instance segmentation to achieve a good trade-off between mask precision and run-time. Particularly the instance masks are parameterized and a CNN is trained to regress to these parameters, analogous to bounding box regression performed by an object detection network.
In this investigation, the instance segmentation masks in the Cityscape dataset are approximated using irregular octagons and an existing object detector network (i.e., SqueezeDet) is modified to regresses to the parameters of these octagonal approximations. The resulting network is referred to as SqueezeDetOcta. At the image boundaries, object instances are only partially visible. Due to the convolutional nature of most object detection networks, special handling of the boundary adhering object instances is warranted. However, the current object detection techniques seem to be unaffected by this and handle all the object instances alike. To this end, this work proposes selectively learning only partial, untainted parameters of the bounding box approximation of the boundary adhering object instances. Anchor-based object detection networks like SqueezeDet and YOLOv2 have a discrepancy between the ground-truth encoding/decoding scheme and the coordinate space used for clustering, to generate the prior anchor shapes. To resolve this disagreement, this work proposes clustering in a space defined by two coordinate axes representing the natural log transformations of the width and height of the ground-truth bounding boxes.
When both SqueezeDet and SqueezeDetOcta were trained from scratch, SqueezeDetOcta lagged behind the SqueezeDet network by a massive ≈ 6.19 mAP. Further analysis revealed that the sparsity of the annotated data was the reason for this lackluster performance of the SqueezeDetOcta network. To mitigate this issue transfer-learning was used to fine-tune the SqueezeDetOcta network starting from the trained weights of the SqueezeDet network. When all the layers of the SqueezeDetOcta were fine-tuned, it outperformed the SqueezeDet network paired with logarithmically extracted anchors by ≈ 0.77 mAP. In addition to this, the forward pass latencies of both SqueezeDet and SqueezeDetOcta are close to ≈ 19ms. Boundary adhesion considerations, during training, resulted in an improvement of ≈ 2.62 mAP of the baseline SqueezeDet network. A SqueezeDet network paired with logarithmically extracted anchors improved the performance of the baseline SqueezeDet network by ≈ 1.85 mAP.
In summary, this work demonstrates that if given sufficient fine instance annotated data, an existing object detection network can be modified to predict much finer approximations (i.e., irregular octagons) of the instance annotations, whilst having the same forward pass latency as that of the bounding box predicting network. The results justify the merits of logarithmically extracted anchors to boost the performance of any anchor-based object detection network. The results also showed that the special handling of image boundary adhering object instances produces more performant object detectors.
Are There Extended Cognitive Improvements from Different Kinds of Acute Bouts of Physical Activity?
(2020)
Acute bouts of physical activity of at least moderate intensity have shown to enhance cognition in young as well as older adults. This effect has been observed for different kinds of activities such as aerobic or strength and coordination training. However, only few studies have directly compared these activities regarding their effectiveness. Further, most previous studies have mainly focused on inhibition and have not examined other important core executive functions (i.e., updating, switching) which are essential for our behavior in daily life (e.g., staying focused, resisting temptations, thinking before acting), as well. Therefore, this study aimed to directly compare two kinds of activities, aerobic and coordinative, and examine how they might affect executive functions (i.e., inhibition, updating, and switching) in a test-retest protocol. It is interesting for practical implications, as coordinative exercises, for example, require little space and would be preferable in settings such as an office or a classroom. Furthermore, we designed our experiment in such a way that learning effects were controlled. Then, we tested the influence of acute bouts of physical activity on the executive functioning in both young and older adults (young 16–22 years, old 65–80 years). Overall, we found no differences between aerobic and coordinative activities and, in fact, benefits from physical activities occurred only in the updating tasks in young adults. Additionally, we also showed some learning effects that might influence the results. Thus, it is important to control cognitive tests for learning effects in test-retest studies as well as to analyze effects from physical activity on a construct level of executive functions.
In der heutigen Zeit nimmt die Bedeutung schlanker und effektiver Prozesse in Unternehmen vor dem Hintergrund des Wettbewerbs sowie Kostendrucks stetig zu. Um dieser Herausforderung entgegenzuwirken, fokussieren sich Unternehmen auf die Identifikation neuer innovativer Potenziale. Aufgrund der Tatsache, dass monotone und regelbasierte Prozesse durch Softwareroboter automatisiert werden können, ist das Interesse an Robotic Process Automation (RPA) in den letzten Jahren stetig gestiegen. Bevor sich Unternehmen allerdings für oder gegen den Einsatz von RPA entscheiden, ist es zunächst notwendig, dass die Entscheidungsträger ein Verständnis von RPA erlangen sowie die entsprechenden Einsatzpotenziale und Risiken einschätzen können. Dieser Artikel trägt diesem Bedürfnis Rechnung, indem es diese auf Basis einer Literaturrecherche ermittelt und bewertet. Im Ausblick wird das zukünftige Potenzial von RPA eingeschätzt.
Object detectors have improved considerably in the last years by using advanced Convolutional Neural Networks (CNNs) architectures. However, many detector hyper-parameters are not generally tuned, and they are used with values set by the detector authors. Blackbox optimization methods have gained more attention in recent years because of its ability to optimize the hyper-parameters of various machine learning algorithms and deep learning models. However, these methods are not explored in improving CNN-based object detector's hyper-parameters. In this research work, we propose the use of blackbox optimization methods such as Gaussian Process based Bayesian Optimization (BOGP), Sequential Model-based Algorithm Configuration (SMAC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to tune the hyper-parameters in Faster R-CNN and Single Shot MultiBox Detector (SSD). In Faster R-CNN, tuning the input image size, prior box anchor scales and ratios using BOGP, SMAC, and CMA-ES has increased the performance around 1.5% in terms of Mean Average Precision (mAP) on PASCAL VOC. Tuning the anchor scales of SSD has increased the mAP by 3% on PASCAL VOC and marine debris datasets. On the COCO dataset with SSD, mAP improvement is observed in the medium and large objects, but mAP decreases by 1% in small objects. The experimental results show that the blackbox optimization methods have proved to increase the mAP performance by optimizing the object detectors. Moreover, it has achieved better results than the hand-tuned configurations in most of the cases.
Background: Coniferous woods (Abies nordmanniana (Stev.) Spach, Abies procera Rehd, Picea abies (L.) H.Karst, and Picea pungens Engelm.) could contain useful secondary metabolites to produce sustainable packaging materials, e.g., by substitution of harmful petrol-based additives in plastic packaging. This study aims to characterise the antioxidant and light-absorbing properties and ingredients of different coniferous wood extracts with regard to different plant fragments and drying conditions. Furthermore, the valorisation of used Christmas trees is evaluated. Methods: Different drying and extraction techniques were applied with the extracts being characterised by determining the total phenolic content (TPC), total antioxidant capacity (TAC), and absorbance in the ultraviolet range (UV). Gas chromatography coupled with mass spectrometry (GC-MS) and an acid–butanol assay (ABA) were used to characterise the extract constituents. Results: All the extracts show a considerably high UV absorbance while interspecies differences did occur. All the fresh and some of the dried biomass extracts reached utilisable TAC and TPC values. A simplified extraction setup for industrial application is evaluated; comparable TAC results could be reached with modifications. Conclusion: Coniferous woods are a promising renewable resource for preparation of sustainable antioxidants and photostabilisers. This particularly applies to Christmas trees used for up to 12 days. After extraction, the biomass can be fully valorised by incorporation in paper packaging.
Miscanthus crops possess very attractive properties such as high photosynthesis yield and carbon fixation rate. Because of these properties, it is currently considered for use in second-generation biorefineries. Here we analyze the differences in chemical composition between M. x giganteus, a commonly studied Miscanthus genotype, and M. nagara, which is relatively understudied but has useful properties such as increased frost resistance and higher stem stability. Samples of M. x giganteus (Gig35) and M. nagara (NagG10) have been separated by plant portion (leaves and stems) in order to isolate the corresponding lignins. The organosolv process was used for biomass pulping (80% ethanol solution, 170 °C, 15 bar). Biomass composition and lignin structure analysis were performed using composition analysis, Fourier-transform infrared (FTIR), ultraviolet-visible (UV-Vis) and nuclear magnetic resonance (NMR) spectroscopy, thermogravimetric analysis (TGA), size exclusion chromatography (SEC) and pyrolysis gas-chromatography/mass spectrometry (Py-GC/MS) to determine the 3D structure of the isolated lignins, monolignol ratio and most abundant linkages depending on genotype and harvesting season. SEC data showed significant differences in the molecular weight and polydispersity indices for stem versus leaf-derived lignins. Py-GC/MS and hetero-nuclear single quantum correlation (HSQC) NMR revealed different monolignol compositions for the two genotypes (Gig35, NagG10). The monolignol ratio is slightly influenced by the time of harvest: stem-derived lignins of M. nagara showed increasing H and decreasing G unit content over the studied harvesting period (December–April).
Quantum mechanical theories are used to search and optimized the conformations of proposed small molecule candidates for treatment of SARS-CoV-2. These candidate compounds are taken from what is reported in the news and in other pre-peer-reviewed literature (e.g. ChemRxiv, bioRxiv). The goal herein is to provided predicted structures and relative conformational stabilities for selected drug and ligand candidates, in the hopes that other research groups can make use of them for developing a treatment.
Bedingt durch die fortlaufende Digitalisierung und den Big Data-Trend stehen immer mehr Daten zur Verfügung. Daraus resultieren viele Potenziale – gerade für Unternehmen. Die Fähigkeit zur Bewältigung und Auswertung dieser Daten schlägt sich in der Rolle des Data Scientist nieder, welcher aktuell einer der gefragtesten Berufe ist. Allerdings ist die Integration von Daten in Unternehmensstrategie und -kultur eine große Herausforderung. So müssen komplexe Daten und Analyseergebnisse auch nicht datenaffinen Stakeholdern kommuniziert werden. Hier kommt dem Data Storytelling eine entscheidende Rolle zu, denn um mit Daten eine Veränderung hervorrufen zu können, müssen vorerst Verständnis und Motivation für den Sachverhalt zielgruppenspezifisch geschaffen werden. Allerdings handelt es sich bei Data Storytelling noch um ein Nischenthema. Diese Arbeit leitet mithilfe einer systematischen Literaturanalyse die Erfolgsfaktoren von Data Storytelling für eine effektive und effiziente Kommunikation von Daten her, um Data Scientists in Forschung und Praxis bei der Kommunikation der Daten und Ergebnisse zu unterstützen.
Unsachgemäß entsorgte Zigarettenkippen stellen aufgrund der in ihnen enthaltenen Giftstoffe ein relevantes, ökologisches Problem dar. Diese Forschungsarbeit untersucht den Einsatz von Nudging zur Bekämpfung der Problematik. In einer quantiativen Online-Befragung wurden zunächst die Gründe für das umweltschädliche Verhalten untersucht (N = 96). Hierbei konnte die Gegenwartstendenz von Personen als statistisch signifikanter Hauptgrund ermittelt werden. Viele Personen gaben an, die langfristigen ökologischen Kosten einer unsachgemäßen Entsorgung aufgrund des kurzfristigen persönlichen Nutzens zu ignorieren. Dieser entsteht durch die Gemütlichkeit des „Wegschnipsens“ einer Zigarettenkippe. Im Anschluss wurde ein auf die Gegenwartstendenz von Personen fokussierter Nudge entwickelt und in einem Feldexperiment auf seine Wirksamkeit überprüft, indem die Relation von unsachgemäß zu sachgemäß entsorgten Zigarettenkippen vor und nach dem Einsatz des Nudges dokumentiert wurde. Ohne Einsatz des Nudges (N = 92) wurden am Erhebungsort 64.1 Prozent und mit Einsatz des Nudges (N = 142) lediglich 38.0 Prozent der Zigarettenkippen unsachgemäß entsorgt. In dem Feldexperiment konnte der Nudge effektiv zur Förderung von nachhaltigem Verhalten eingesetzt werden.
Any political phenomenon can only be properly understood in its broader con-text. Questions of international cooperation are thus necessarily framed by his-torical processes and relations of power. We therefore start our first discussion with an examination of the global ‘status quo’ and embed the topic of this pub-lication, ODA graduation, into the shifting world order, analysing current roles and settings in international relations and identifying changes in positions, sta-tus and categories. What are the overarching issues determining world politics and who are the old and the new actors driving them? What is the impact of these global shifts on international cooperation, especially development coop-eration? Of what relevance are roles, status and categories and what is the im-pact of changes in positions and relations? What challenges face multilateralism and what ways exist to maintain and renew strategic partnerships and shared values?
Die Bundesrepublik Deutschland erlebt in jüngster Vergangenheit verstärkt Dieselfahrverbote in Großstädten. Gleichzeitig erfahren Großstädte als Lebensmittelpunkt eine steigende Beliebtheit. Für Verkehrsunternehmen gilt es, der Bevölkerung nachhaltige Mobilitätslösungen zu bieten, die ein Höchstmaß an Flexibilität ermöglichen. Moderne Mobility-as-a-Service-Konzepte und Innovationen in der Mobilität stellen den klassischen, planorientierten, öffentlichen Personennahverkehr und damit auch die Existenz von Bushaltestellen infrage. Mittels qualitativer Experten-Interviews lässt sich feststellen, dass sich die Bushaltestelle in den Innenstädten vor dem Hintergrund zunehmender digitaler Vernetzung von Mobilitätsanbietern und daraus resultierender modernen Mobility-as-a-service-Konzepte verändern wird. Die Ergebnisse deuten darauf hin, dass die Bushaltestelle in den Innenstädten auch in Zukunft bestehen bleibt und um „on demand“-Verkehre ergänzt wird. Ein radikaler Wandel, wie eine flächendeckende Einführung von autonom fahrenden Bussen, könnte langfristig eine Runderneuerung der Haltestelle zur Folge haben.
Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030, Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5 % and 97.5 %, respectively.
"Diffusion - create clarity, overcome barriers, be heard" is the title of this year's annual report. It shows how the university is searching for answers to the multilayered, complex questions of our time. Whether digitalisation, climate change or social responsibility - scientists are getting through their subject areas and in the end they have to make their findings heard.
Die Internationalisierung von Hochschulen muss mit passenden Angeboten für internationale Studierende einhergehen. Ein systemisches und kultursensitives Peer-Coaching kann internationale Studierende unterstützen, sich zu orientieren, zu integrieren und berufliche Ziele zu verwirklichen. Gleichzeitig entwickeln die zu Peer-Coaches ausgebildeten Studierenden Sensibilität für Diversität. Insgesamt wird ein kultursensitives Miteinander an der Hochschule gefördert.
Do socio-economic factors impede the engagement in online banking transactions? Evidence from Ghana
(2020)
Researchers have long pondered on the online banking transaction adoption. Some of these studies focus primarily on the motivating factors that affect customers’ intention to adopt/accept these services (technologies). However, research into the constraining factors, in particular socio-economic factors, barely exist in the literature, especially in the context of sub-Saharan Africa. Against this background, the paper seeks to fill in this gap by: (1) assessing the socio-economic factors impeding the engagement of e-banking transactions among retail bank customers in Ghana, and (2) examining the moderating effect of ‘customer experience of Internet’ on the identified factors that inhibit the engagement in online banking in Ghana. The paper used a quantitative research approach to obtain data from two leading Ghanaian banks. Out of the 450 questionnaires distributed, 393 were valid for analysis. Data were analyzed with the aid of PLS-SEM (partial least squares and structural equation modeling). Findings revealed that perceived knowledge gap and the price of digital devices were directly important to the intention to disembark on e-banking transactions among Ghanaian bank customers. Whilst customer experience (frequent use of the Internet), as a moderator variable, has a significant effect on the interaction between perceived knowledge gap and the intent to disembark on e-banking transactions; and finance charges and the intent to disembark on e-banking transactions. Study implications and directions for future research are discussed in the paper.
"Durchdringen - Klarheit schaffen, Barrieren überwinden, Gehör finden" lautet diesmal das Motto des Jahresberichts. Er zeigt, wie die Hochschule nach Antworten auf die vielschichtigen, komplexen Fragen der Zeit sucht. Ob Digitalisierung, Klimawandel oder gesellschaftliche Verantwortung - Wissenschaftlerinnen und Wissenschaftler durchdringen ihre Themengebiete, und sie müssen am Ende mit ihren Erkenntnissen Gehör finden.