Since 2016, it is mandatory for all future students at the department of Media, Information and Communication, to participate in a study choice test (SCT), prior their enrollment. However, the outcome is not binding and students are still entitled to enter the first year after receiving negative advice. With the help of a structural model, built for my PhD research, the predictive value of the SCT is tested by comparing the time it takes the students to finish all first year exams, their average grade point and attrition, against the results of the SCT. By using the structural model, various background variables are also measured, such as engagement, effort and commitment are also measured. By using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), the fit of the model is established. In addition, a comparison of the direct and indirect influence of the SCT will provide more knowledge about the correlations between the different variables, the SCT and ultimately student success.
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
PurposeThis study aims to develop an understanding of how customers of a physical retail store valuate receiving location-based mobile phone messages when they are in proximity of the store. It proposes and tests a model relating two benefits (personalization and location congruency) and two sacrifices (privacy concern and intrusiveness) to message value perceptions and store visit attitudes.Design/methodology/approachThe study uses a vignette-based survey to collect data from a sample of 1,225 customers of a fashion retailer. The postulated research model is estimated using SmartPLS 3.0 with the consistent-PLS algorithm and further validated via a post-hoc test.FindingsThe empirical testing confirms the predictive validity and robustness of the model and reveals that location congruency and intrusiveness are the location-based message characteristics with the strongest effects on message value and store visit attitude.Originality/valueThe paper adds to the underexplored field of store entry research and extends previous location-based messaging studies by integrating personalization, location congruency, privacy concern and intrusiveness into one validated model.
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The postdoc candidate, Sondos Saad, will strengthen connections between research groups Asset Management(AM), Data Science(DS) and Civil Engineering bachelor programme(CE) of HZ. The proposed research aims at deepening the knowledge about the complex multidisciplinary performance deterioration prediction of turbomachinery to optimize cleaning costs, decrease failure risk and promote the efficient use of water &energy resources. It targets the key challenges faced by industries, oil &gas refineries, utility companies in the adoption of circular maintenance. The study of AM is already part of CE curriculum, but the ambition of this postdoc is that also AM principles are applied and visible. Therefore, from the first year of the programme, the postdoc will develop an AM material science line and will facilitate applied research experiences for students, in collaboration with engineering companies, operation &maintenance contractors and governmental bodies. Consequently, a new generation of efficient sustainability sensitive civil engineers could be trained, as the labour market requires. The subject is broad and relevant for the future of our built environment being more sustainable with less CO2 footprint, with possible connections with other fields of study, such as Engineering, Economics &Chemistry. The project is also strongly contributing to the goals of the National Science Agenda(NWA), in themes of “Circulaire economie en grondstoffenefficiëntie”,”Meten en detecteren: altijd, alles en overall” &”Smart Industry”. The final products will be a framework for data-driven AM to determine and quantify key parameters of degradation in performance for predictive AM strategies, for the application as a diagnostic decision-support toolbox for optimizing cleaning &maintenance; a portfolio of applications &examples; and a new continuous learning line about AM within CE curriculum. The postdoc will be mentored and supervised by the Lector of AM research group and by the study programme coordinator(SPC). The personnel policy and job function series of HZ facilitates the development opportunity.
