What Other Factors Might Impact Building Trust in Government Decisions Based on Decision Support Systems, Except for Transparency and Explainability?

Decision Support Systems (DSS) are increasingly being used to support operational decision-making using large amounts of data. One key aspect to successful adoption is that the user trusts the DSS. Large contributors to trust often mentioned in literature and practice are transparency and explainability. But what happens when a DSS is transparent and explainable by design? What other contributors to trust are relevant is the main focus of this paper, in the context of Dutch governmental subject-matter experts designing and working with DSSs. We used a Mixed-Method Sequential Explanatory Design in which a survey was conducted to gather empirical data. The findings present 20 focal points contributing toward trust in DSS. These focal points require future research, specifically on considering these for development by the design of a DSS. Ultimately, this could help in increasing the adoption of DSSs in general

Translating Business Process Models to Class Diagrams

Choreography of business processes can track messages between different services. At the time of writing, there are no guidelines t o d raw a U ML C lass D iagram f rom t he Business Process Choreography. This paper reports an experiment using a set of guidelines. Objective: Evaluate the subjects’ performance and perceptions when applying the BPc2Class-guidelines and BPc2Class-discovery process. Method: To measure the performance and user perception of both ways of mapping the processes, a comparative experiment was conducted with 38 subjects. The subjects, being master students, solved a process case in the first session and a guidelines case in the second session. A survey was filled in by the subjects to measure the user perception variables. Results: The results indicated that the guidelines showed significantly better results in five out of the six measured variables. Conclusion: Based on the findings and limitations of this research the use of guidelines looks promising, but future research is necessary to further generalize the conclusion

Future Challenges in Decision Mining at Governmental Institutions

Decisions are made in fast-changing situations. To cope with this, decision mining could be utilized to support the decision-making process. Decision mining is an emerging field which could support an organizations decision-making process. For proper utilization of decision mining, possible challenges should be identified to take into account when mining decisions. As such, two focus groups were conducted where we identified 11 main challenges that seven Dutch governmental institutions deemed important and which should be taken into consideration when mining decisions. The identified challenges are depicted further together with existing literature and the coded observations. The identified challenges could be utilized as future research directions and are discussed as such

A Classification of Modification Categories for Business Rules

Business rules play a critical role in an organization’s daily activities. With the increased use of business rules (solutions) the interest in modelling guidelines that address the manageability of business rules has increased as well. However, current research on modelling guidelines is mainly based on a theoretical view of modifications that can occur to a business rule set. Research on actual modifications that occur in practice is limited. The goal of this study is to identify modifications that can occur to a business rule set and underlying business rules. To accomplish this goal we conducted a grounded theory study on 229 rules set, as applied from March 2006 till June 2014, by the National Health Service. In total 3495 modifications have been analysed from which we defined eleven modification categories that can occur to a business rule set. The classification provides a framework for the analysis and design of business rules management architectures

An Explorative Study Into Situational Artefact Construction in Business Rules Management

The implementation of software products is a time-consuming activity and requires specific expertise to be completed successfully. This is especially the case in research fields where there is no or little tool support available, such as Business Rules Management (BRM) and Business Rules Management Solutions (BRMS). Tool support is essential to successfully guide the organizational implementation of a BRMS. Motivated by the diversity of organizational structures and their BRMS implementation contexts, we design a situational-aware framework for the organizational implementation of BRMS. The framework is based on the theory of situational artefact construction. Using situational artefact construction, we study 13 BRMS implementation cases distributed over the financial and public sectors in the Netherlands. Based on the results of the cases analysed we present a framework with three main artefacts that are a stepping-stone towards further research on situational implementation methodology in the BRM field

Business Rules Management and Decision Mining - Filling in the Gaps

Proper decision-making is one of the most important capabilities of an organization. Adequately managing these decisions is therefore of high importance. Business Rules Management (BRM) is an approach that helps in managing decisions and underlying business logic. However, questions still arise if the decisions are properly improved based on decision data. Decision Mining (DM) could complement BRM capabilities in order to improve towards effective and efficient decision-making. In this study, we propose the integration of BRM and DM through a simulation using a government and a healthcare case. During this simulation, three entry points are presented that describe how decision-related data should be utilized between BRM capabilities and DM phases to be able to integrate them. The presented results provide a basis from which more technical research on the three DM phases can be further explored

An Explorative Dive into Decision Rights and Governance of Blockchain: A Literature Review and Empirical Study

Background: Blockchain technology and accompanying programmed protocols (smart contracts) offer disruptive opportunities for businesses, public institutions, society, and its citizens. However, blockchain is a relatively young research area: the number of publications available regarding blockchain did not begin to rise significantly until 2012, and certain fields of the blockchain domain remain to be explored. A similar situation exists with research into the governance of blockchain solutions focusing on decision rights: the limited number of theoretical and empirical contributions hinders the proper adoption of governance mechanisms in practice. Method: A mixed-method approach was conducted in which 1) a structured literature review, 2) semi-structured interviews, and 3) a focus group discussion were utilized to determine the current situation regarding decision rights in the context of blockchain governance. Results: The structured literature review resulted in a total of 23 relevant contributions. Those contributions were consolidated to serve as input for a total of twelve semi-structured interviews, and for a focus group session with five participants, who were not part of the interviewee pool. Using that approach, an overview of the concepts, relationships and mechanisms pertinent to decision rights was composed. Conclusions: Considered together, the results show that decision rights are often overlooked at the start of a blockchain project, where technical considerations are dominant in the discussion with stakeholders. However, research also points out that the longer it takes to address decision rights in a blockchain consortium, the more complex and costly it becomes to introduce governance mechanisms at a later stage. Another important conclusion is that consensus is currently lacking as to what constitutes blockchain governance and what part decision rights play in governance processes, in both theoretical and practical terms

The identification of Ethical Focus Areas: A Literature Study Into Data Mining Ethical Focus Areas

Improper use of data must be avoided, as the consequences of improper use of data can be catastrophic. In the design of information systems, ethical focus areas could help combat improper use of data. Currently, more research is available on ethical focus areas in Data Mining compared to related research fields of Data Mining, such as Decision Mining and Process Mining. For this paper, a theoretical review was conducted to identify ethical focus areas of Data Mining and their possible solutions. Seven ethical focus areas were identified focussing on privacy, collection of personal information, consent, unpredictability and inaccuracy, group profiling and biased data. Future research is needed on the ethical focus areas, to validate the possible solutions related to these ethical focus areas in the context of related research fields of Data Mining

Social Robots in Elderly Healthcare: A Burden or a Gift?

The healthcare sector is currently under enormous pressure and the COVID-19 pandemic does not improve this situation. The quality of healthcare will be negatively impacted when this pressure continues in the longer term. In 2050 it is expected that a total of 2.1 billion people will be aged 60+ years old. To overcome the increasing demand for healthcare by this age group, various studies are being conducted into various technological solutions, such as social robots. In this study, the Alpha Mini social robot was used in an experiment to research which tasks a social robot could assist with, to reduce the work pressure of healthcare professionals and to help the elderly live longer at their own homes. The experiment was carried out using interviews with healthcare professionals and informal caregivers about the demonstrated Alpha Mini. In addition to the experiment and interviews a survey was sent out to 237 healthcare organizations in the Netherlands to identify the 1) work pressure, 2) daily tasks, 3) social robot experiences, and 4) the features a social robot should have to gather requirements. The experiment failed due to work pressure at the healthcare organization. The survey resulted in 181 respondents. The results suggest that tasks such as reminders, setting alarms and physiotherapy have a great potential to help the healthcare professional in reducing their work pressure and tasks, and the elderly to be able to stay living longer at their own home

Putting decision mining into context: a literature study

The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach