When decision support systems fail: insights for strategic information systems from Formula

Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual - and organization - level strategic decisions . Yet, when organizations excessively rely on their potential the outcome may be decision - making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context , identifies three interrelated sources of strategic failure that are worth investigation for decision - makers using DSS and big data: (1) t he situated nature and affordances of decision - making ; (2) t he distributed nature of cognition in decision - making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research

Bioenergy decision support systems:worth the effort?

The purpose of this research is to explore the disparity between the existing model-orientated bioenergy decision support system (DSS) functions and what is desired by practitioners, in particular bioenergy project developers. This research has compiled the published bioenergy project development models, to highlight the characteristics emphasised by academics. When contrasted against a UK practitioner’s perspective through the administration of a Likert style questionnaire, it is clear that the general DSS issues still persist. Finally, the research suggests how this ’theory-practice’ divide could be addressed. The research contribut

Towards a Design Methodology for Decision Support Systems

The authors propose the use of process models for DSS design. The kind of process models suggested are task structures and decision structures with simple graphical syntax and semantics. The process models form the basis for a coherent DSS design methodology, based upon the bounded rationality paradigm. The history of DSS and DSS design is discussed to form a theoretical position. The resulting methodology has been tested and evaluated in a laboratory experiment. The results of this evaluation will be used for continuous improvement of the methodolog

‘Engage the World’: examining conflicts of engagement in public museums

Public engagement has become a central theme in the mission statements of many cultural institutions, and in scholarly research into museums and heritage. Engagement has emerged as the go-to-it-word for generating, improving or repairing relations between museums and society at large. But engagement is frequently an unexamined term that might embed assumptions and ignore power relationships. This article describes and examines the implications of conflicting and misleading uses of ‘engagement’ in relation to institutional dealings with contested questions about culture and heritage. It considers the development of an exhibition on the Dead Sea Scrolls by the Royal Ontario Museum, Toronto in 2009 within the new institutional goal to ‘Engage the World’. The chapter analyses the motivations, processes and decisions deployed by management and staff to ‘Engage the World’, and the degree to which the museum was able to re-think its strategies of public engagement, especially in relation to subjects,issues and publics that were more controversial in nature

Evidence based healthcare planning in developing countries: An Informatics perspective

Most of the national Health Information Systems (HIS) in resource limited developing countries do not serve the purpose of management support and thus the service is adversely affected. While emphasising the importance of timely and accurate health information in decision making in healthcare planning, this paper explains that Health Management Information System Failure is commonly seen in developing countries as well as the developed countries. It is suggested that the possibility of applying principles of Health Informatics and the technology of Decision Support Systems should be seriously considered to improve the situation. A brief scientific explanation of the evolution of these two disciplines is included

Usability and Psychosocial Impact of Decision Support to Increase Sexual Health Education in American Indian and Alaska Native Communities

Despite sexual and reproductive health disparities, few evidence-based sexual health education programs exist for American Indian/Alaska Native (AI/AN) youth, with even fewer tools available to assist AI/AN communities in adopting, implementing, and maintaining such programs. iCHAMPSS (Choosing And Maintaining effective Programs for Sex education in Schools) is a theory- and web-based decision-support-system designed to address dissemination barriers and increase the reach and fidelity of evidence-based programs (EBPs), specifically sexual health education programs. To investigate the potential of iCHAMPSS in AI/AN communities, we pilot-tested iCHAMPSS with adult stakeholders (N = 36) from agencies across the country that serve AI/AN communities. Stakeholders were recruited to review selected iCHAMPSS tools over two weeks in spring 2016. Pre- and post-surveys were administered to assess usability constructs, short-term psychosocial outcomes, and perceived feasibility. Data were analyzed using descriptive and non-parametric statistics. iCHAMPSS was perceived as acceptable, easy to use, credible, appealing, more helpful than current resources, and impactful of EBP adoption, implementation, and maintenance. Conversely, using iCHAMPSS significantly increased participants’ perceived barriers to adopting an EBP (p = 0.01). Overall, AI/AN stakeholders responded positively to iCHAMPSS, indicating the potential for adaptation to support the dissemination and implementation of evidence-based sexual health education in AI/AN communities

Aspects of automation of selective cleaning

Cleaning (pre-commercial thinning) is a silvicultural operation, primarily used to improve growing conditions of remaining trees in young stands (ca. 3 - 5 m of height). Cleaning costs are considered high in Sweden and the work is laborious. Selective cleaning with autonomous artificial agents (robots) may rationalise the work, but requires new knowledge. This thesis aims to analyse key issues regarding automation of cleaning; suggesting general solutions and focusing on automatic selection of main-stems. The essential requests put on cleaning robots are to render acceptable results and to be cost competitive. They must be safe and be able to operate independently and unattended for several hours in a dynamic and non-deterministic environment. Machine vision, radar, and laser scanners are promising techniques for obstacle avoidance, tree identification, and tool control. Horizontal laser scannings were made, demonstrating the possibility to find stems and make estimations regarding their height and diameter. Knowledge regarding stem selections was retrieved through qualitative interviews with persons performing cleaning. They consider similar attributes of trees, and these findings and current cleaning manuals were used in combination with a field inventory in the development of a decision support system (DSS). The DSS selects stems by the attributes species, position, diameter, and damage. It was used to run computer-based simulations in a variety of young forests. A general follow-up showed that the DSS produced acceptable results. The DSS was further evaluated by comparing its selections with those made by experienced cleaners, and by a test in which laymen performed cleanings following the system. The DSS seems to be useful and flexible, since it can be adjusted in accordance with the cleaners’ results. The laymen’s results implied that the DSS is robust and that it could be used as a training tool. Using the DSS in automatic, or semi-automatic, cleaning operations should be possible if and when selected attributes can be automatically perceived. A suitable base-machine and thorough research, regarding e.g. safety, obstacle avoidance, and target identification, is needed to develop competitive robots. However, using the DSS as a training-tool for inexperienced cleaners could be an interesting option as of today

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF