COMPLETE SCHOOL PROJECT TOPICS & MATERIALS :
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CHAPTER ONE
INTRODUCTION
1.1 Background of the study
Beginning in the late 1970s, many vendors, practitioners, and academics promoted the development of computer-based Decision Support Systems (DSS). Their actions created high expectations for DSS and generated much optimism about the prospects for improving decision making (Vaishnavi and Kuechler, 2015). Despite the buildup and excitement, the success rate of decision support applications has been unsatisfactory. Although the computing industry has transformed how business transactions and data are processed, families have frequently been disappointed by attempts to use computers and information technology to support decision making (Stefano et al., 2014). Recently, because of technological developments, families have become more enthusiastic about implementing innovative decision support projects. This attitude change is a positive development, but both families and Management Information Systems (MIS) practitioners need to discuss and review their expectations about Decision Support Systems before beginning new projects.
According to Stewart and Shamdasani (2014), “DSS comprise a class of information system that draws on transaction processing systems and interacts with the other parts of the overall information system to support the decision-making activities of families and other knowledge workers in organizations” (p. 9). Decision Support Systems are defined broadly in this book as interactive computer-based systems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions (Vaishnavi and Kuechler, 2015). DSS are ancillary or auxiliary systems; they are not intended to replace skilled decision makers.
Decision Support Systems should be considered when two assumptions seem reasonable: first, good information is likely to improve decision making; and second, families need and want computerized decision support (Atkinson et al., 2015). Anecdotes and research show that some computer-based DSS can provide families with analytical capabilities and information that improves decision making.
In pursuing the goal of improving decision making, many different types of computerized DSS have been built to help decision teams and individual decision makers (Bertot et al., 2012). Some systems provide structured information directly to families. Other systems can help families and staff specialists analyze situations using various types of models. Some DSS store knowledge and make it available to families. Some systems support decision making by small and large groups.
The design of policy options is a complex decision-making and planning process, in which the effects of policy alternatives are often delayed in time and the ultimate impact is affected by a multitude of factors within rapidly changing environments (Burt, 2011). Policy interventions are carried out within a system of interest with the intention of improving the overall outcomes of that system. A multidisciplinary and systemic approach is needed due to the dynamic and complex contexts of family legal procedure problems (Danielson and Ekenberg, 2015). A systemic perspective effectively represents the needs of a family legal procedure maker facing the expected impacts of a policy; this is mainly due to the fact that a policy always affects several sub-systems of a society (humans, critical infrastructure, financial systems, communities, cultures, etc.) (Stefano et al., 2014). Problems in central policy areas, such as finance, energy and transportation systems and policy for innovation and growth, are typically systemic; that is, they deal with complex systems. A suitable policy for dealing with these problems needs to be based on a view of the system as a whole (Danielson and Ekenberg, 2015). This may be an unfamiliar concept for many policymakers, and may lead to simplified and ineffective policies. An understanding of complex systems requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. This requires a mastery of concepts such as feedback, stocks and flows, time delays and nonlinearity (Sterman, 2002).
The use of computational methods can support decision making in these situations. Decision analysis represents a systematic approach to incorporating evidence into policy and management decision-making processes, (Burt, 2011), since it does not only describe the evidence that must be taken into account but also estimates the impact of taking any of the various options. Simulation modelling is frequently employed to improve and demonstrate an understanding of the system of interest. Unfortunately, the results of these models usually form an insufficient basis for decision making by policymakers, since the interpretation of the results often requires a scientific and technical background and an understanding of the choices that were made when developing a particular model; however, if the model does not require substantial interpretation for decision making, it can be presented as an analytical methodology for the purposes of decision making, or as a complement to one of the existing analytical methodologies for interpreting the results of the model (Janssen and Wimmer, 2015).
New information and communications technologies (ICT) and sophisticated modern techniques of data gathering, visualization and analysis have expanded our ability to understand, display and disseminate complex, temporal and spatial information to diverse audiences (Pagano et al., 2014). At the same time, enhancements in terms of computational power have expanded the repertoire of the instruments and tools that are available for studying dynamic systems and their interdependencies. There has been an infusion of technology that is changing policy processes at both the individual and group level. Some of these developments that are opening new avenues for innovative policymaking are social media as a means to interact with the public (Bertot et al., 2012), blogs (Taylor et al., 2015), open data (Janssen et al., 2012; Zuiderwijk and Janssen, 2013), freedom of information (Burt, 2011), the wisdom of crowds (Mennis, 2006), open collaboration and transparency in policy simulation (Wimmer et al., 2012) and hybrid modelling techniques (Parrott, 2011). This study therefore sought to examine the implementation of decision support system for family related legal procedure.
1.2 Statement of the problem
The decision-making process was defined by Vaishnavi and Kuechler (2015) as comprising three steps: intelligence, design, and choice. At the intelligence stage, group members exchange relevant information (Taylor et al., 2015). The design stage involves the development of potential options or decision alternatives, while choice involves the process of selecting the option that is identified as being the best (Danielson and Ekenberg, 2015). Pagano et al. (2014) distinguishes between two types of rationality in decision making: substantive rationality (what to choose) and procedural rationality (how to choose). Any type of policy planning will, in theory, lead to a satisfactory solution in the best case (March, 1988); this is a solution path that is acceptable to rather than optimal for almost all of the parties concerned. From an application perspective, support for policy planning lies in the area of procedural rather than substantive rationality. The practical application of prescriptive decision theory (how people ought to make decisions) aims to find methodologies, tools and software to help people make better decisions (Zagonari and Rossi, 2013). The most systematic and comprehensive software tools developed in this way are called decision support systems (DSS).
Policy specialists lack the resources and the methodology to elicit, integrate and process relevant information and the most current data from various sources in order to support informed policy decisions (Zaraté, 2013). In view of the complexity and uncertainty of policy outcomes, policy researchers, analysts and decision makers need to develop policy models, collect data to validate these models and simulate scenarios to explore the implications of the various possible assumptions and alternative actions. There is a lack of decision support tools that can provide operational approaches and/or formal methods of facilitating the cognitive activity of structuring family legal procedure decision situations, designing policy options or alternative actions to be taken, and evaluating alternatives leading to a policy decision (Atkinson et al., 2015).
Despite the availability of powerful computer modelling and simulation tools used by domain specialists, operations research and management science researchers, these tools are time-consuming, not user-friendly for decision makers and frequently result in models that fit the modeller’s conception of the problem rather than the decision maker’s (Zaraté, 2013). There is a need to develop robust, intuitive decision support tools that provide an environment for model-based collaborative governance. Against the backdrop of this research, the study assesses the implementation of decision support system for family related legal procedure.
1.3 Objectives of the study
The main objective of the study is to carry out an implementation of decision support system for family related legal procedure. The following are the specific objectives of the study:
- To support the implementation of family legal design of policy options and assessment using ICT tools.
- To evaluate the multi-criteria policy options based on impact assessment results.
- To assess the fitness for use of these decision support tools in problem analysis related to family policy formulations.
1.4 Research questions
- What is the procedure for the implementation of family legal design of policy options and assessment using ICT tools?
- What is the evaluation of multi-criteria policy options based on impact assessment results?
- What is the fitness for use of these decision support tools in problem analysis related to family policy formulations?
1.5 Significance of the study
Based on an understanding of the research problem, questions and challenges described in the previous section, this section justifies the need for the current research and provides an overview of its expected contributions to both research and practice.