During recent years the world has seen rapid changes such as globalization, the Internet, and the rise of new economies. To survive these changes organizations need to be in control of their processes, and be able to continuously improve the process performance. Therefore many organizations are increasingly adopting Business Process Management (BPM). However, it is not clear if the implementation of BPM(S) is really adding value to an organization. Consequently, in this paper, we try to answer the following research question: 'Does adoption of Business Process Management lead to a higher process performance?' Based on quantitative research we show that there is dependence between the performance of processes within an organization and the BPM maturity of that organization. As a result we conclude that improvement in process performance can be attained by increasing the BPM maturity of an organization.
From the article: Abstract Since more and more business rules management solutions are utilized, organizations search for guidance to design such solutions. Principles are often applied to guide the design of information systems in general. Scientific research on principles for business rules management is limited. The purpose of this paper is to specify, classify, and validate design principles that can be applied to guide the design of a business rules management solution. We conducted a three round focus group and three round Delphi Study, which led to the identification of 22 principles. These 22 principles can be clustered into four categories: 1) deep structure principles, 2) physical structure principles, 3) surface structure principles, and 4) organizational structure principles. Our results provide a framework for the design and analysis of business rules management solutions.
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Change has become continuous, and innovation is a primary approach for hospitality, i.e., hotel companies, to become or remain economically viable and sustainable. An increasing number of management researchers are paying more attention to workplace rather than technological innovation. This study investigates workplace innovation in the Dutch hotel industry, in three- and four-star hotels in the Netherlands, by comparing them to other industries. Two samples were questioned using the Workplace Innovation survey created by the Dutch Network of Social Innovation (NSI). The first was conducted in the hospitality industry, and these data were compared with data collected in a sample of other industries. Results suggest that greater strategic orientation on workplace innovation and talent development has a positive influence on four factors of organizational performance. Greater internal rates of change, the ability to self-organize, and investment in knowledge also had positive influences on three of the factors—growth in revenue, sustainability, and absenteeism. Results also suggest that the hospitality industry has lower workplace innovation than other industries. However, no recent research has assessed to what degree the hospitality industry fosters workplace innovation, especially in the Netherlands. Next to that, only few studies have examined management in the Dutch hotel industry, how workplace innovation is used there, and whether it improves practices.
The ELSA AI lab Northern Netherlands (ELSA-NN) is committed to the promotion of healthy living, working and ageing. By investigating cultural, ethical, legal, socio-political, and psychological aspects of the use of AI in different decision-makingcontexts and integrating this knowledge into an online ELSA tool, ELSA-NN aims to contribute to knowledge about trustworthy human-centric AI and development and implementation of health technology innovations, including AI, in theNorthern region.The research in ELSA-NN will focus on developing and mapping ELSA knowledge around three general concepts of importance for the development, monitoring and implementation of trustworthy and human-centric AI: availability, use,and performance. These concepts will be explored in two lines of research: 1) use case research investigating the use of different AI applications with different types of data in different decision-making contexts at different time periods duringthe life course, and 2) an exploration among stakeholders in the Northern region of needs, knowledge, (digital) health literacy, attitudes and values concerning the use of AI in decision-making for healthy living, working and ageing. Specificfocus will be on investigating low social economic status (SES) perspectives, since health disparities between high and low SES groups are growing world-wide, including in the Northern region and existing health inequalities may increase with theintroduction and use of innovative health technologies such as AI.ELSA-NN will be integrated within the AI hub Northern-Netherlands, the Health Technology Research & Innovation Cluster (HTRIC) and the Data Science Center in Health (DASH). They offer a solid base and infrastructure for the ELSA-NNconsortium, which will be extended with additional partners, especially patient/citizens, private, governmental and researchrepresentatives, to have a quadruple-helix consortium. ELSA-NN will be set-up as a learning health system in which much attention will be paid to dialogue, communication and education.
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.
