Objective: Self-management is a core theme within chronic care and several evidence-based interventions (EBIs) exist to promote self-management ability. However, these interventions cannot be adapted in a mere copy-paste manner. The current study describes and demonstrates a planned approach in adapting EBI’s in order to promote self-management in community-dwelling people with chronic conditions. Methods: We used Intervention Mapping (IM) to increase the intervention’s fit with a new context. IM helps researchers to take decisions about whether and what to adapt, while maintaining the working ingredients of existing EBI’s. Results: We present a case study in which we used IM to adapt EBI’s to the Flemish primary care context to promote self-management in people with one or more chronic disease. We present the reader with a contextual analysis, intervention aims, and content, sequence and scope of the resulting intervention. Conclusion: IM provides an excellent framework in providing detailed guidance on intervention adaption to a new context, while preserving the essential working ingredients of EBI’s. Practice Implications: The case study is exemplary for public health researchers and practitioners as a planned approach to seek and find EBI’s, and to make adaptations.
Although reengineering is strategically advantageous fororganisations in order to keep functional and sustainable, safety must remain apriority and respective efforts need to be maintained. This paper suggeststhe combination of soft system methodology (SSM) and Pareto analysison the scope of safety management performance evaluation, and presents theresults of a survey, which was conducted in order to assess the effectiveness,efficacy and ethicality of the individual components of an organisation’s safetyprogram. The research employed quantitative and qualitative data and ensureda broad representation of functional managers and safety professionals, whocollectively hold the responsibility for planning, implementing and monitoringsafety practices. The results showed that SSM can support the assessment ofsafety management performance by revealing weaknesses of safety initiatives,and Pareto analysis can underwrite the prioritisation of the remedies required.The specific methodology might be adapted by any organisation that requires adeep evaluation of its safety management performance, seeks to uncover themechanisms that affect such performance, and, under limited resources, needsto focus on the most influential deficiencies.
In 2017, I introduced a new theoretical framework in Archival Science, that of the ‘Archive–as–Is’. This framework proposes a theoretical foundation for Enterprise Information Management (EIM) in World 2.0, the virtual, interactive, and hyper connected platform that is developing around us. This framework should allow EIM to end the existing ‘information chaos’, to computerize information management, to improve the organizational ability to reach business objectives, and to define business strategies. The concepts of records and archives are crucial for those endeavours. The framework of the ‘Archive–as–Is’ is an organization–oriented archival theory, consisting of five components, namely: [1] four dimensions of information, [2] two archival principles, [3] five requirements of information accessibility, [4] the information value chain; and [5] organizational behaviour. In this paper, the subject of research is component 5 of the framework: organizational behaviour. Behaviour of employees (including archivists) is one of the most complicated aspects within organizations when creating, processing, managing, and preserving information, records, and archives. There is an almost universal ‘sound of silence’ in scholarly literature from archival and information studies although this subject and its effects on information management are studied extensively in many other disciplines, like psychology, sociology, anthropology, and organization science. In this paper, I want to study how and why employees behave as they do when they are working with records and archives and how EIM is influenced by this behaviour.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Despite Dutch Hospitality industry’s significant economic value, employers struggle to attract and retain early career professionals at a time when tourism is forecasted to grow exponentially (Ruël, 2018). Universally, hospitality management graduates are shunning hospitality careers preferring other career paths; stimulating the Dutch Hospitality to find innovative ways of attracting and retaining early career professionals. Following calls from the Human Resource Management (HRM) community (Ehnert, 2009), we attribute this trend to personnel being depicted as rentable resources, driving profit’’ often at personal expense. For example, hotels primarily employ immigrants and students for a minimum wage suppressing salaries of local talent (Kusluvan, et al 2010, O’Relly and Pfeffer, 2010). Similarly, flattening organizational structures have eliminated management positions, placing responsibility on inexperienced shoulders, with vacancies commonly filled by pressured employees accepting unpaid overtime jeopardizing their work life balance (Davidson, et al 2010,). These HRM practices fuel attrition by exposing early career professionals to burnout (Baum et al, 2016, Goh et al, 2015, Deery and Jog, 2009). Collectively this has eroded the industry’s employer brand, now characterized by unsocial working hours, poor compensation, limited career opportunities, low professional standing, high turnover and substance abuse (Mooney et al, 2016, Gehrels and de Looij, 2011). In contrast, Sustainable HRM “enables an organizational goal achievement while simultaneously reproducing the human resource base over a long-lasting calendar time (Ehnert, 2009, p. 74).” Hence, to overcome this barrier we suggest embracing the ROC framework (Prins et al, 2014), which (R)espects internal stakeholders, embraces an (O)pen HRM approach while ensuring (C)ontinuity of economic and societal sustainability which could overcome this barrier. Accordingly, we will employ field research, narrative discourse, survey analysis and quarterly workshops with industry partners, employees, union representatives, hotel school students to develop sustainable HRM practices attracting and retaining career professionals to pursue Dutch hospitality careers.
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.
