In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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The decision-making process in boardrooms has a significant impact on organizational performance. In the last two decades, scientific research on the decision-making process in boardrooms has increased. This resulted in a substantial body of knowledge about boardroom factors and their relation to organizational performance. However, the effectiveness of the decision-making process in boardrooms is still mainly a black box. Amongst other things, scientific findings seem to contradict each other, which could mean additional insights are still missing. This research aims to contribute to a better understanding of this black box.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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As migrant populations age, the care system is confronted with the question how to respond to care needs of an increasingly diverse population of older adults. We used qualitative intersectional analysis to examine differential preferences and experiences with care at the end of life of twenty-five patients and their relatives from Suriname, Morocco and Turkey living in The Netherlands. Our analysis focused on the question how–in light of impairment–ethnicity, religion and gender intersect to create differences in social position that shape preferences and experiences related to three main themes: place of care at the end of life; discussing prognosis, advance care, and end-of-life care; and, end-of-life decision-making. Our findings show that belonging to an ethnic or religious minority brings forth concerns about responsive care. In the nursing home, patients’ minority position and the interplay thereof with gender make it difficult for female patients to request and receive responsive care. Patients with a strong religious affiliation prefer to discuss diagnosis but not prognosis. These preferences are at interplay with factors related to socioeconomic status. The oversight of this variance hampers responsive care for patients and relatives. Preferences for discussion of medical aspects of care are subject to functional impairment and faith. Personal values and goals often remain unexpressed. Lastly, preferences regarding medical end-of-life decisions are foremost subject to religious affiliation and associated moral values. Respondents’ impairment and limited Dutch language proficiency requires their children to be involved in decision-making. Intersecting gendered care roles determine that mostly daughters are involved. Considering the interplay of aspects of social identity and their effect on social positioning, and pro-active enquiry into values, goals and preferences for end-of-life care of patients and their relatives are paramount to achieve person centred and family-oriented care responsive to the needs of diverse communities.
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The hospitality industry, comprising diverse Small and Medium Enterprises (SMEs) such as restaurants, hotels, and catering facilities plays an important role in local and regional communities by providing employment opportunities, facilitating the organization of community events, and supporting local social activities and sports teams (Panteia, 2023). The hospitality industry thereby represents a major source of income in Europe, but also a commensurate burden on the environment because of its relatively high usage of water and energy consumption, and food waste, leading to the formulation of several initiatives to increase the sustainability of hotels, restaurants, and resorts, such as farm to fork and towel reuse (Bux & Amicarelli, 2023). Another avenue for hospitality organizations to make progress towards sustainability goals is through circular economy strategies (Bux & Amicarelli, 2023) based on the creation of small regenerative loops that require the involvement of multiple stakeholders (Tomassini & Cavagnaro, 2022). Nevertheless, hospitality operators need to track their progress towards sustainability goals while keep sight of their financial goals (Bux & Amicarelli, 2023), requiring a data-driven decision-making approach to sustainability and circularity. Big data analytics have therefore been identified as an enabler of the circular economy paradigm by reducing uncertainty and allowing organizations to predict results (Awan et al., 2021; Gupta et al., 2019). Hospitality organizations however remain behind in leveraging data analytics for decisionmaking (Mariani & Baggio, 2022). The purpose of the study is therefore to examine how hospitality organizations can leverage data analytics to make data-driven decisions regarding circularity. Using a multiple case study approach of three Dutch hospitality SMEs, enablers and inhibitors of data analytics for datadriven decisions regarding circularity are examined. This addresses the call by Tomassini and Cavagnaro (2022) for more exploration of the circularity paradigm in hospitality. Despite the ongoing interest in increasing the sustainability of the hospitality industry (European Commission, 2013), relatively little attention has been paid to the development of circularity strategies and what is needed to implement them.
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The role of expert knowledge of the environment in decision-making about urban development has been intensively debated, largely in terms of a so-called ‘science-policy gap’. Most contributions to this debate have studied the use of knowledge in the decision-making process from the knowledge providers’ point of view. In this paper, we reverse the perspective and try to unearth how decision-makers use scientific knowledge in decision-making about an urban plan. We confronted municipal administrators, responsible for local urban development, with conceptions of the use of knowledge that were derived from the literature on this issue. From the reactions obtained, we conclude that, in the context of urban redevelopment, local administrators hardly perceive a barrier between themselves as decision-makers and experts – both environmental scientists and urban designers. They do, however, acknowledge that experts and decision-makers have distinct roles: unlike experts, local administrators have to balance all interests relevant to an urban plan. It is argued, therefore, that experts should engage in providing better decision frameworks rather than more or better knowledge.
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Background and objective Public involvement in palliative care is challenging and difficult, because people in need of palliative care are often not capable of speaking up for themselves. Patient representatives advocate for their common interests. The aim of our study was to examine in depth the current practice of public involvement in palliative care. Setting and sample The study was conducted in the province of Limburg in the Netherlands, with six palliative care networks. Study participants were 16 patient representatives and 12 professionals. Method This study had a descriptive design using qualitative methods: 18 in-depth interviews and three focus groups were conducted. The critical incident technique was used. The data were analysed using an analytical framework based on Arnstein’s involvement classification and the process of decision making. Impact categories as well as facilitators and barriers were analysed using content analysis. Findings and conclusion The perceived impact of public involvement in palliative care in terms of citizen control and partnership is greatest with regard to quality of care, information development and dissemination, and in terms of policymaking with regard to the preparation and implementation phases of decision making. The main difference in perceived impact between patient representatives and professionals relates to the tension between operational and strategic involvement. Patient representatives experienced more impact regarding short-term solutions to practical problems, while professionals perceived great benefits in long-term, strategic processes. Improving public involvement in palliative care requires positive attitudes, open communication, sufficient resources and long-term support, to build a solid basis for pursuing meaningful involvement in the entire decision-making process.
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