Context in Decision Support Systems Development

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

A study of the DSS implementation area reveals an increased emphasis on methodologies for DSS development. As the DSS field matures, a larger number of methodological options are becoming available to the DSS developer. Existing methodologies have adopted various methods for performing the important task of DSS requirements analysis and specification. In envisaging a hierarchy of requirements analysis methods, a method for improved requirements analysis is proposed to remedy observed deficiencies within it. This method is based on a view of generic types of inquiries or solicitations made by the DSS user during decision-making. This formalism constitutes the basis of a proposed DSS development methodology that offers several benefits to DSS development. The intended benefits include a sharper and more focused requirements analysis to improve the DSS development process. This paper describes the model and methodology, alongside details of a real-world example of their use in developing a Marketing Decision Support System (MKDSS) for the support of marketing decision-making.

We explore the critical role played by decision support system (DSS) developers (also called systems analysts or DSS designers) as they enact the role of the change agent. In DSS development, systems analysts actively strive to change the decision maker directly through the DSS application and its presentation. We suggest that the role of change advocate serves DSS development the best, chiefly because decision makers’ enhanced interest in emerging information technologies has made it possible for decision makers to accept new Web-based DSS technologies. We also briefly explore some of the issues in the development of decision support systems over time and the importance they will play in the future. We illustrate what a systems analyst should take into consideration in designing a DSS display today, using a dashboard as an example. We then describe new tools used for DSS development such as widgets, gadgets, and mashups that once again may change the way decision makers solve problems. We see a promising future for changing decision makers as they interact with the analyst as a change agent and can visualize decision makers evolving through their DSS interactions, thereby improving decision quality, and creating a strong, contributory role for analysts to play in DSS development.

The introduction of Industry 4.0 leads to new decision-making scenarios in the construction industry. The construction sector still hesitates to adopt emerging technologies and many construction companies require support in the field of digitalisation and automation of processes. One way to support this sector is the use of decision support systems that perform a careful assessment of the feasibility for replacing traditional systems with automated ones. However, the design and the development of decision support systems is a complex and time-consuming task. In this work we apply Axiomatic Design to define design guidelines for the development of decision support systems in construction equipment selection. The needs of the construction industry for decision support in these scenarios are derived from existing literature in the field of construction and the key characteristic of decision-theoretic expert systems. These needs are analysed and translated into functional requirements. Then, through a mapping and decomposition process, the design solutions for the development of a decision support system are deduced. Functional requirements and design solutions are transformed into design guidelines that support the systematic development and implementation of decision support systems for the construction phase of a building.

This article reviews methods to the development of a decision support system (DSS) solution for small business owners/managers. The main objective of designing the DSS artefact is to support the strategic decision-making for achieving competitive advantages in the business-to-consumer (B2C) e-commerce environment. Many researchers in the DSS domain utilised various methods to design of information systems (IS) artefact, mostly intended for large businesses. Researchers have paid much attention to the business environment as a knowledge source for DSS design and development for the small business strategic decision support needs. User-centred design (UCD) principles were adopted for DSS development. Prior a novel DSS development, multiple case studies were carried out for understanding the user needs and system requirements. Also, knowledge was sourced from the external business environment via the analysis of small business website features against their overseas competitors. The findings suggested developing a DSS solution for small business needs.

This article reviews methods to the development of a decision support system (DSS) solution for small business owners/managers. The main objective of designing the DSS artefact is to support the strategic decision-making for achieving competitive advantages in the business-to-consumer (B2C) e-commerce environment. Many researchers in the DSS domain utilised various methods to design of information systems (IS) artefact, mostly intended for large businesses. Researchers have paid much attention to the business environment as a knowledge source for DSS design and development for the small business strategic decision support needs. User-centred design (UCD) principles were adopted for DSS development. Prior a novel DSS development, multiple case studies were carried out for understanding the user needs and system requirements. Also, knowledge was sourced from the external business environment via the analysis of small business website features against their overseas competitors. The findings suggested developing a DSS solution for small business needs.

: The objectives of this study, Development of a computer Decision Support System for Women with BRCA1 or BRCA2 Mutations was to create a decision support system that provided individualized information about the expected benefits of alternative cancer risk reduction strategies for women with either a BRCA1 or BRCA2 mutation. The project was comprised of three phases: 1) development of the education booklet, 2) development of the Decision Support System (DSS), and 3) randomized controlled trial of the DSS. The primary outcome of the trial was decision satisfaction. Secondary outcomes included knowledge of the effects of alternative management options on cancer risk, anxiety and depression, and behavior and behavioral intentions. Women who were in the Decision Support System arm reported higher satisfaction with their decisions than did women in the control arm (mean Decision Satisfaction Score 31.2 vs. 26.2, p=O.O4). In addition, higher decision satisfaction at follow up was associated with cancer anxiety at baseline and having had breast cancer or a prophylactic mastectomy prior to enrollment. The results of this trial are currently being submitted for publication. In addition, we are currently developing a methodological review highlighting the ouestions raised by this trial and similar trials.

Management of water resources in the central Platte River basin of Nebraska incorporates both ground water and surface water. Development of a decision support system is being contemplated by state and federal governmental agencies and by a number of local stakeholders. Requirements and priorities for the decision support system vary with the perspectives of the different entities – with some focusing on short term operational management on a daily or hourly time step and others focusing on larger time increments and longer range priorities. Water quality and ground water – surface water interaction will also be key issues which the decision support system will need to address. A detailed evaluation of a number of river basin models was undertaken by a team of modeling specialists from the Bureau of Reclamation, the Nebraska Department of Water Resources and several prominent stakeholder groups. Results of this analysis are presented and discussed. Future work will focus on identifying necessary data collection and analysis, selecting and implementing a river basin model suitable for use on the central Platte River system and, ultimately, formulating linkages with ground water and water quality models which can meet the requirements of the participants in this study. Study Area: The Platte River drains an area of more than 85,000 square miles (220,000 km) in southeastern Wyoming, northeastern Colorado and Nebraska. Much of the region is semi arid – especially in the western part of the basin and irrigation is a frequent practice. 1 Hydraulic Engineer, Bureau of Reclamation, Technical Service Center, Lakewood, CO 80225 2 Supervisory Civil Engineer, Bureau of Reclamation, Nebraska-Kansas Area Office, Grand Island, NE 68802 3 Engineer, United States Army, Formerly Hydraulic Engineer, Bureau of Reclamation, Technical Service Center, Lakewood, CO 80225 Copyright ASCE 2006 World Environmental and Water Resources Congress 2006 2 Figure 1 – Location Map Showing Platte River Basin and Central Platte Sub-Basin (courtesy, Bureau of Reclamation) Management of the water resources requires judicious use of both surface water and ground water – and a variety of modeling tools have been used to address each. Stakeholders and water resources management agencies in the central part of the basin came to the conclusion that the development of a comprehensive decision support system might be beneficial and a review of available modeling tools was conducted to determine which tools could best address the decision support needs of the central Platte basin.

The problem domain of decision support systems (DSS) has been characterised as ill-specified, influenced by many different sources, and subject to change. The complex and changing environment in which decision support systems are developed makes specification of the final system requirements difficult. Decision support systems need to be designed using an adaptive process of learning and experimentation. The emphasis in DSS development is on supporting the decision making process by improving the decision maker’ s understanding of the decision domain. This paper proposes a conceptual and a technological framework for adaptive development of decision support systems (DSS) through knowledge discovery from historical data. In this approach the users and developers gradually learn about their decision domain by an iterative process of extracting patterns from the database and building and refining predictive models using these patterns. A hybrid rough sets/neural networks framework that can facilitate this development is proposed. Finally a case study where the approach was applied in the development of a real-world decision support system is described and discussed. The framework can be applied to a variety of industrial and business decision problems where historical data exists.Keywordsdecision support systemsknowledge discoverysoft computingneural networksrough sets

Abstract. This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.

Remote decision support system: a distributed information management system

Distributed decision support and organizational connectivity: A case study

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Maritime operations are inevitably influenced by the wind, wave, sea currents, and other perturbations at sea. Providing decision support for these operations based on historical and real-time data of ship status is thus of great concern in terms of ship safety. However, it is challenging for collecting and analysing large quantities of ship data in real operations. Moreover, the development of an onboard decision support system (DSS) will be a gradual and iterative process subject to extensive testing and simulation. Consequently, the paper presents an integrated simulation framework which provides testing and simulation environment for the DSS development. The system enables navigation data transmission from a well-designed simulator and automatic determining of the safe maneuver of a ship within the framework. The development of DSS is divided into three steps. The ship maneuvering data from simulator is collected; the data is then classified and fed into an imitation learning (IL) algorithm to learn an initial policy; the result is further applied to a reinforcement learning (RL) algorithm for safe decision making of the operation. In this way, it could speed up the learning efficiency by extracting more information from available experience. To verify the effectiveness of the proposed integrated simulation framework, in this study, we implemented the proposed DSS in ship docking operation under various environmental disturbances. It is interacted with the simulator to obtain data. By processing these data, it provides the shipmaster with the information about the consequences of the ship maneuvering decisions. The simulation results demonstrate that the proposed DSS could assist the shipmaster in deciding policies and increase the efficiency of decision making.