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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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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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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The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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This study was presented at the Bled eConference and received the Outstanding Paper Award. The study examines how two organizational aspects, Transformational Leadership and Employee Empowerment contribute to companies harnessing their Data Analytic Capability to develop a Data Driven Culture. The findings of a cross-sectional survey design show that Transformational Leadership compounds the positive effect of Data Analytic Capability on Data Driven Culture.
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Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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Clinical Decision Support Systems (CDSS) are increasingly developed for hospital nursing practice, yet their impact on decision-making, workflow efficiency, and patient outcomes remains complex. This rapid review synthesizes findings from 21 studies, highlighting both the benefits and challenges of CDSS implementation focused on three key areas. CDSS can enhance nursing decision-making by reducing variability and improving standardization, but there are concerns about system usability and the tendency to override recommendations. While CDSS improve workflow efficiency by prioritizing tasks, issues such as alert fatigue and poor interoperability with hospital systems hinder their potential. Patient outcomes benefit from CDSS-driven medication safety and risk prevention, yet adherence to recommendations varies among nurses. These findings underscore the need for user-centered CDSS that align with nursing values. Future research should explore long-term effectiveness, implementation strategies and best practices for integrating CDSS into nursing workflows.
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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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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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