Business decisions and business logic are important organizational assets. As transparency is becoming an increasingly important aspect for organizations, business decisions and underlying business logic, i.e., their business rules, must be implemented, in information systems, in such a way that transparency is guaranteed as much as possible. Based on previous research, in this study, we aim to identify how current design principles for business rules management add value in terms of transparency. To do so, a recently published transparency framework is decomposed into criteria, which are evaluated against the current business rules management principles. This evaluation revealed that eight out of twenty-two design principles do not add value to transparency, which should be taken into account when the goal of an organization is to increase transparency. Future research should focus on how to implement the design principles that add to transparency.
Research, advisory companies, consultants and system integrators all predict that a lot of money will be earned with decision management (business rules, algorithms and analytics). But how can you actually make money with decision management or in other words: Which business models are exactly available? In this article, we present seven business models for decision management.
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Decisions and business rules are essential Components of an organization. Combined, these components form a basis for securing the implementation of new laws, regulations and internal policies into processes, work instructions and information systems. To ensure proper implementation, business rule types must be taken into account, as the functions per type may be different. The current body of knowledge on decision and business rule management offers some insights into different types of business rules, however, these types are often presented as a secondary focus of a contribution or set in stone without proper evidence supporting these claims. This study therefore aims to explore the different business rule types utilized in the body of knowledge as well as practice. This will form a basis to determine possible overlap and inconsistencies and aid in establishing the functional differences between the defined business rule types. By applying a literature review, semi-structured interviews and secondary data analysis, we observed that the current body of knowledge shows serious diffusion with regards to business rule types, the same holds for practice. Therefore, future research should focus to research these differences in detail with the aim to harmonize the proliferation of business rule types.
The focus of this project is on improving the resilience of hospitality Small and Medium Enterprises (SMEs) by enabling them to take advantage of digitalization tools and data analytics in particular. Hospitality SMEs play an important role in their local community but are vulnerable to shifts in demand. Due to a lack of resources (time, finance, and sometimes knowledge), they do not have sufficient access to data analytics tools that are typically available to larger organizations. The purpose of this project is therefore to develop a prototype infrastructure or ecosystem showcasing how Dutch hospitality SMEs can develop their data analytic capability in such a way that they increase their resilience to shifts in demand. The one year exploration period will be used to assess the feasibility of such an infrastructure and will address technological aspects (e.g. kind of technological platform), process aspects (e.g. prerequisites for collaboration such as confidentiality and safety of data), knowledge aspects (e.g. what knowledge of data analytics do SMEs need and through what medium), and organizational aspects (what kind of cooperation form is necessary and how should it be financed).Societal issueIn the Netherlands, hospitality SMEs such as hotels play an important role in local communities, providing employment opportunities, supporting financially or otherwise local social activities and sports teams (Panteia, 2023). Nevertheless, due to their high fixed cost / low variable business model, hospitality SMEs are vulnerable to shifts in consumer demand (Kokkinou, Mitas, et al., 2023; Koninklijke Horeca Nederland, 2023). This risk could be partially mitigated by using data analytics, to gain visibility over demand, and make data-driven decisions regarding allocation of marketing resources, pricing, procurement, etc…. However, this requires investments in technology, processes, and training that are oftentimes (financially) inaccessible to these small SMEs.Benefit for societyThe proposed study touches upon several key enabling technologies First, key enabling technology participation and co-creation lies at the center of this proposal. The premise is that regional hospitality SMEs can achieve more by combining their knowledge and resources. The proposed project therefore aims to give diverse stakeholders the means and opportunity to collaborate, learn from each other, and work together on a prototype collaboration. The proposed study thereby also contributes to developing knowledge with and for entrepreneurs and to digitalization of the tourism and hospitality sector.Collaborative partnersHZ University of Applied Sciences, Hotel Hulst, Hotel/Restaurant de Belgische Loodsensociëteit, Hotel Zilt, DM Hotels, Hotel Charley's, Juyo Analytics, Impuls Zeeland.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Hogeschool Rotterdam wil in samenwerking met IT-Campus en Rotterdamse mkb-bedrijven onderzoeken of de dataskills die studenten in hun opleiding verwerven, aansluiten op de datageletterdheid die van hen als startende professionals wordt verlangd. Om dit te beoordelen vragen we Rotterdamse ondernemers naar de datagedreven uitdagingen en problemen die zij voor zich zien en of zij bij de instroom van startende professionals voldoende kennis en skills zien om die uitdagingen het hoofd te bieden. Met de uitkomsten kunnen kennisinstellingen een helder beeld krijgen van het concept datageletterdheid en hiermee een handvat bieden aan opleidingen om dataskills in de curricula aan te laten sluiten op de behoefte in de arbeidsmarkt van de Metropoolregio Rotterdam-Den Haag (MRDH). We werken toe naar een ontwerp Data Skills-set. Misschien is het beter om te spreken van datacompetenties, hetgeen onderdeel is van de zoektocht in dit onderzoek. Welke terminologie is het meest behulpzaam in het oplijnen van onderwijs en werkveld op het gebied van data: geletterdheid, competenties, skills of een combinatie daarvan. Is het van belang of juist contraproductief om daarin (merk)specifieke tooling een plek te geven? We vragen ons ook af of datageletterdheid als een generiek concept domeinoverstijgend bruikbaar is, bijvoorbeeld tussen het economisch en technisch domein. De verwachting is dat de bevindingen op het gebied van datageletterdheid in de regio Rotterdam te generaliseren zijn naar andere delen van Nederland. Ook die hypothese willen we verkennen in dit onderzoek. Door het beantwoorden van deze vragen willen we een start maken voor het ontwerp van een instrument voor professionele ontwikkeling in het werkveld als ook een referentiekader voor het gesprek met onderwijspartners en overheid. Daarnaast kan zo’n ontwerp DataSkills-set ervoor zorgen dat de onderwijsdomeinen in gesprek blijven met elkaar ten aanzien van nieuwe methoden en onderwijsvormen voor vaardigheden.
