Currently, promising new tools are under development that will enable crime scene investigators to analyze fingerprints or DNA-traces at the crime scene. While these technologies could help to find a perpetrator early in the investigation, they may also strengthen confirmation bias when an incorrect scenario directs the investigation this early. In this study, 40 experienced Crime scene investigators (CSIs) investigated a mock crime scene to study the influence of rapid identification technologies on the investigation. This initial study shows that receiving identification information during the investigation results in more accurate scenarios. CSIs in general are not as much reconstructing the event that took place, but rather have a “who done it routine.” Their focus is on finding perpetrator traces with the risk of missing important information at the start of the investigation. Furthermore, identification information was mostly integrated in their final scenarios when the results of the analysis matched their expectations. CSIs have the tendency to look for confirmation, but the technology has no influence on this tendency. CSIs should be made aware of the risks of this strategy as important offender information could be missed or innocent people could be wrongfully accused.
From the article: Many organizations are striving for a structural and professional approach toward business information management (BIM). With help of BiSL they can shape the BIM responsibilities and processes, but they struggle with the required capacity for the BIM activities necessary for their particular situation. Therefore, research was started to develop an instrument to determine the required capacity of the BIM activities in an organization. In this paper the construction of the instrument will be described. A limited set of factors may be of importance to identify the required capacity of BIM activities that is needed: complexity of business processes, complexity of IS/IT, dynamics of the organization and its environment and the size of the organization are examples of relevant factors. However, factors that appear relevant may prove useless in practice due to the fact that organizations have no data on these indicators available. Furthermore, the relationships between the present and desired quality of information and information services are part of the instrument. The instrument was tested in practice to determine the usefulness. The results show that the instrument has the potential to determine the required capacity of BIM.
MULTIFILE
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.
Binnen dit project wordt gekeken hoe ontwikkelingsorganisaties ondersteund kunnen worden om beter om te gaan met digitale transformatie.Doel In afstemming met mantelorganisaties actief in ontwikkelingssamenwerking wil dit project een antwoord te geven op de vraag hoe maatschappelijke organisaties digitale transformatie kunnen organiseren in de context van ontwikkelingssamenwerking met als doel om diverse Sustainable Development Goals te behalen. Resultaten Het project resulteert in ontwikkelde instrumenten en trainingsmateriaal voor de ontwikkelingssamenwerkingsorganisaties voor het omgaan met digital transformatie. Binnen het project is een eerste paper geschreven: Digital Transformation of Development NGOs: the Case of Transitioning Northern-based Development NGOs Impact van het project Dit project versterkt de volgende opleidingen: master Data Driven Business, master of Informatics, master of Project Management en bachelor minor Business Information Management. Looptijd 01 september 2021 - 30 september 2023 Aanpak Dit onderzoek bouwt verder op de kennis van het lectoraat Procesinnovatie & Informatiesystemen en het project social media gebruik in ontwikkelingssamenwerking. Daarbij breidt de kennis uit op het vlak van maatschappelijke organisaties actief in ontwikkelingssamenwerking, waar de dynamiek van geografisch wijd verspreide stakeholders en verschillende niveaus van digitale ontwikkelingen van organisaties zowel vanuit de praktijk als vanuit een academisch perspectief een interessante onderzoekdimensie opleveren. De kernvraag van het onderzoek is: hoe managen maatschappelijke organisaties digitale transformatie door socio-technische systemen in de context van meervoudige waardecreatie in ontwikkelingssamenwerking?
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
