When decision support systems fail: Insights for strategic information systems from Formula 1 (original) (raw)
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Measuring How Decision Support Systems Improve Newsvendors’ Performance: The Subjects’ Version
Sustainability, 2021
Despite the emerging contribution of machine automation, artificial intelligence and information systems, humans remain yet the most fragile ring of any organization. Decision support systems are widespread, supporting us to decide among uncertainties, such as weather conditions, suppliers’ performances and financial opportunities, but how humans take into account this information and, most of all, how they trust their own management knowledge is a controversial issue. This paper assesses, by means of a controlled experiment and ex post interviews, how individuals consider and use decision support systems in the context of the Newsvendor Problem. In accordance with prior research, the results show that individuals’ order quantities are pull-to-center biased. Moreover, ex post direct interviews suggest that (i) the individuals’ trust in decision support systems is not blind; (ii) individuals do not play the business game as a real task, (iii) they are biased by the type of incentive ...
Decision support systems: The reality that seems hard to accept?
Omega, 1986
The authors have identified 350 articles written and published on Decision Support Systems (DSS) since 1970. 77.3% of those articles were published between January 1980 and November 1983. Many critics see this sudden surge in the DSS literature as no more than a fad and the term itself as no more than a label. This paper examines the reasons behind the emergence of DSS in the light of this sudden surge in the DSS literature and seeks to determine whether DSSs are unique or merely a repackaging of Management Science/Operational Research and Management Information systems.
2020
The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data collection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse, corporate announcements, digital journalism, mobile telephony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and the new capabilities that researchers have to conduct research that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science. The new thinking related to empirical regularities analysis, experimental design, and longitudinal empirical research further suggests that these approaches can be tailored for rapid acquisition of big data sets. This will allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, and illustrate our perspective with comparisons between earlier and current research inquiry. We argue against the assertion that theory no longer matters and offer some new research directions.
A Critical Analysis of Decision Support Systems Research
Journal of Information Technology, 2005
This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal DSS, group support systems, negotiation support systems, intelligent DSS, knowledge management-based DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the majo...
2018
Business Intelligence (BI) has received wide recognition in the business world as a tool to address ‘big’ data-related problems, to help managers understand their businesses and to assist them in making effective decisions. To date, however, there have been few studies which have clearly articulated a theoretically grounded model that explains how the use of BI systems provides benefits to organisations, or explains what factors influence the actual use of BI systems. To fully achieve greater decision-making performance and effective use of BI, we contend that BI systems integration with a systems user’s work routine (dependence on the systems) is essential. Following this argument, we examine the effects of system dependent use along with effective use (infusion) on individual’s decision-making performance with BI. Additionally, we pro-pose that a fact-based decision-making culture, and data quality of source systems are constraints factors that impact on BI system dependence and i...
Can computerised market models improve strategic decision-making? An exploratory study
Journal of Socio-economics, 2003
Decision Support Systems (DSS) have been used to assist managerial decision-making for more than 30 years. Our insights whether the use of such systems improves decision-making are, however, still modest. In particular, there has been little research on the effects of DSS use on strategic decision-making. This paper focuses on experienced managers'/analysts' use of a custom-built DSS in a strategic, real-life situation. We report on an exploratory study, where a management team in a large oil company evaluated the consequences for the company of a critical market event, first without and then with the DSS. It was found that use of the DSS implied a shift in the direction of more rational decision-making, but also a focus on the system variables only.
The increasing dynamic and complexity of todays global supply chains and the growing amount and complexity of information challenge decision makers in manufacturing companies. Decision Support Systems (DSS) can be a viable solution to address these challenges and increase the overall decision efficiency and effectivity. However, a thought-through design and implementation of these systems is crucial for their efficacy. This article presents the current state-of-the-art of Decisions Support Systems and highlights their benefits and pitfalls. Also, we present an empirical study in which we compared different levels of decision support and decision automation in a simulated supply chain game environment. We identify and quantify how human factors influence the decision quality and decision performance in this supply chain scenario. We show that an adequately designed system raises the overall performance. However, insufficiently designed systems have the reverse effect and lead operators to miss severe situations, which can have fatal consequences for manufacturing companies.
Eight key issues for the decision support systems discipline
Decision Support Systems, 2008
This paper integrates a number of strands of a long-term project that is critically analysing the academic field of decision support systems (DSS). The project is based on the content analysis of 1093 DSS articles published in 14 major journals from 1990 to 2004. An examination of the findings of each part of the project yields eight key issues that the DSS field should address for it to continue to play an important part in information systems scholarship. These eight issues are: the relevance of DSS research, DSS research methods and paradigms, the judgement and decision-making theoretical foundations of DSS research, the role of the IT artifact in DSS research, the funding of DSS research, inertia and conservatism of DSS research agendas, DSS exposure in general "A" journals, and discipline coherence. The discussion of each issue is based on the data derived from the article content analysis. A number of suggestions are made for the improvement of DSS research. These relate to case study research, design science, professional relevance, industry funding, theoretical foundations, data warehousing, and business intelligence. The suggestions should help DSS researchers construct high quality research agendas that are relevant and rigorous.
Assessing Today: Determining the Decision Value of Decision Support Systems
2000
Decision support systems (DSS) have been assessed on the basis of single criteria such as the improvement in decision outcome, and using multiple criteria such as the perspectives of different stakeholders. This paper uses a systems approach to extend previous studies by linking the type of support provided to the decision maker with the specific DSS design characteristics needed to
A Look Toward the Future: Decision Support Systems Research is Alive and Well
2012
This commentary examines the historical importance of decision support to the information systems (IS) field from the viewpoint of four researchers whose work spans the several decades of decision support systems (DSS) research. Given this unique "generational" vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support.