THE IMPLEMENTATION OF A DECISION SUPPORT SYSTEM FOR FAMILY-RELATED LEGAL PROCEDURES

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Starting in the late 1970s, numerous vendors, practitioners, and academics actively promoted the creation of computer-based Decision Support Systems (DSS). These efforts generated significant expectations for DSS and fostered considerable optimism regarding their potential to enhance decision-making processes (Vaishnavi and Kuechler, 2015). However, despite the initial enthusiasm and promotional buildup, the overall success rate of decision support applications has remained disappointing. While the computing industry has revolutionized the processing of business transactions and data, families have often been let down by efforts to apply computers and information technology in support of decision making (Stefano et al., 2014). More recently, advancements in technology have led families to show greater enthusiasm for adopting innovative decision support initiatives. This shift in attitude represents a positive step forward; nevertheless, both families and Management Information Systems (MIS) practitioners should engage in thorough discussions and re-evaluate their expectations concerning Decision Support Systems prior to launching new projects.

As defined by 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). In this work, Decision Support Systems are broadly understood as interactive computer-based systems that assist individuals in utilizing computer communications, data, documents, knowledge, and models to address problems and reach decisions (Vaishnavi and Kuechler, 2015). DSS function as supplementary or auxiliary systems and are not designed to substitute for experienced decision makers.

The adoption of Decision Support Systems is warranted when two key assumptions hold: first, that high-quality information is likely to enhance decision making; and second, that families both require and desire computerized decision support (Atkinson et al., 2015). Anecdotal evidence and empirical research indicate that certain computer-based DSS can equip families with analytical tools and information that lead to better decisions.

In the pursuit of improved decision making, a wide variety of computerized DSS have been developed to assist both decision-making teams and individual decision makers (Bertot et al., 2012). Some systems deliver structured information straight to families. Others enable families and specialized staff to examine situations through diverse modeling approaches. Certain DSS store and provide access to knowledge for families, while others facilitate decision making within small or large groups.

Designing policy options constitutes a complex decision-making and planning activity. The impacts of different policy alternatives are frequently delayed over time, and their final effects are influenced by numerous factors operating within rapidly evolving environments (Burt, 2011). Policy interventions take place inside a system of interest with the aim of enhancing overall outcomes for that system. Given the dynamic and intricate nature of problems in family legal procedures, a multidisciplinary and systemic approach is essential (Danielson and Ekenberg, 2015). A systemic viewpoint effectively captures the requirements of a family legal procedure decision maker who must anticipate the consequences of a policy, primarily because any policy influences multiple subsystems within society including humans, critical infrastructure, financial systems, communities, cultures, and others (Stefano et al., 2014). Issues in core policy domains such as finance, energy, transportation, innovation, and economic growth are generally systemic, meaning they involve complex systems. An appropriate policy response to such problems must adopt a holistic view of the entire system (Danielson and Ekenberg, 2015). This perspective may be unfamiliar to many policymakers and can result in overly simplistic and ineffective policies. Gaining insight into complex systems necessitates the application of formal models and simulations to validate mental models and strengthen intuition about system behavior. This demands proficiency with concepts including feedback loops, stocks and flows, time delays, and nonlinearity (Sterman, 2002).

Computational methods can provide valuable support for decision making in such contexts. Decision analysis offers a structured method for integrating evidence into policy and management decisions (Burt, 2011). It not only identifies the relevant evidence but also evaluates the likely impacts of choosing among various options. Simulation modeling is commonly used to deepen and illustrate understanding of the target system. Regrettably, model outputs often fall short as a standalone basis for policymaker decisions because interpreting them typically requires scientific or technical expertise and knowledge of the modeling assumptions. However, when a model can be used with minimal additional interpretation, it may serve directly as an analytical tool for decision making or as a valuable complement to existing analytical methods (Janssen and Wimmer, 2015).

Recent developments in information and communications technologies (ICT), along with advanced techniques for data collection, visualization, and analysis, have significantly increased our capacity to comprehend, present, and share complex, time-sensitive, and spatially distributed information with varied audiences (Pagano et al., 2014). Concurrently, improvements in computational capabilities have broadened the range of instruments and tools available for examining dynamic systems and their interconnections. Technology infusion is transforming policy processes at both individual and collective levels. Notable innovations opening pathways for creative policymaking include social media for public engagement (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 modeling techniques (Parrott, 2011). Consequently, this study aimed to investigate the implementation of a decision support system for family-related legal procedures.

1.2 Statement of the Problem

Vaishnavi and Kuechler (2015) describe the decision-making process as consisting of three main phases: intelligence, design, and choice. During the intelligence phase, participants share pertinent information (Taylor et al., 2015). The design phase focuses on generating potential options or decision alternatives, while the choice phase entails selecting the most suitable option (Danielson and Ekenberg, 2015). Pagano et al. (2014) differentiate between two forms of rationality in decision making: substantive rationality (concerned with what to choose) and procedural rationality (concerned with how to choose). In theory, any policy planning process should yield a satisfactory solution at best (March, 1988) one that is acceptable to most involved parties rather than strictly optimal. From a practical standpoint, support for policy planning primarily addresses procedural rationality rather than substantive rationality. The applied side of prescriptive decision theory (guiding how decisions should be made) seeks to develop methodologies, tools, and software that enable better decision making (Zagonari and Rossi, 2013). The most methodical and complete software tools emerging from this effort are known as decision support systems (DSS).

Policy specialists often lack the necessary resources and methodologies to gather, integrate, and analyze relevant information and up-to-date data from multiple sources in support of well-informed policy decisions (Zaraté, 2013). Due to the complexity and uncertainty surrounding policy outcomes, policy researchers, analysts, and decision makers must construct policy models, gather validation data, and run simulations to assess the consequences of different assumptions and alternative courses of action. There remains a shortage of decision support tools capable of delivering practical, operational, or formal methods to aid the cognitive tasks involved in structuring family legal procedure decision situations, generating policy options or alternative actions, and assessing those alternatives to arrive at final policy choices (Atkinson et al., 2015).

Although powerful computer modeling and simulation tools exist and are utilized by domain experts, operations research specialists, and management scientists, these tools tend to be time-intensive, difficult for decision makers to use directly, and often produce models that reflect the modeler’s understanding of the problem more closely than the decision maker’s perspective (Zaraté, 2013). There is therefore a clear need for the development of robust, user-friendly decision support tools that create an environment conducive to model-based collaborative governance. In light of these considerations, the present study evaluates the implementation of a decision support system for family-related legal procedures.

1.3 Objectives of the Study

The primary objective of this study is to implement a decision support system for family-related legal procedures. The specific objectives are as follows:

1. To facilitate the implementation of family legal policy option design and assessment through the use of ICT tools.

2. To evaluate multi-criteria policy options according to impact assessment outcomes.

3. To examine the fitness for use of these decision support tools in the analysis of problems associated with family policy formulation.

1.4 Research Questions

1. What is the procedure for implementing family legal policy option design and assessment using ICT tools?

2. What is the evaluation of multi-criteria policy options based on impact assessment results?

3. 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

Drawing on the research problem, questions, and challenges outlined earlier, this section explains the rationale for the current study and summarizes its anticipated contributions to both academic research and practical application.

Contributions to Research

The existing policymaking literature reveals a gap in research on family legal procedures that incorporates quantitative and empirical model testing (Atkinson et al., 2015). A systems thinking approach directly tackles this central challenge in empirical investigation. Applying a systems perspective to policy analysis enables more effective operationalization of research evidence, supports decision making for complex issues, and strengthens connections among policymakers, stakeholders, and researchers. The proposed systems methodology for policy analysis is intended to help policy actors across various governmental bodies organize comprehensive system-wide intelligence on specific policy problems and determine optimal strategies for achieving defined policy objectives.

This research offers a theoretical foundation for model-based decision support in policy analysis. The suggested approach accommodates both qualitative and quantitative data related to the policy issue, thereby streamlining the modeling process. It also adds to current knowledge on the application of problem structuring methods (PSMs) for modeling intricate strategic decisions by employing causal maps for knowledge representation and systems analysis, while integrating these with contemporary decision evaluation techniques.

Utilizing this decision support approach in family legal procedure analysis is expected to advance policy modeling and simulation, generate a substantial knowledge base through structured problem definition, and lead to the creation of participatory, transparent, and forward-looking decision support tools for family legal procedure decision making.

Contributions to Practice

The proposed DSS represents a sophisticated sociological construct that captures human cognition and knowledge regarding public problems by clarifying, testing, and revising assumptions about the network of cause-and-effect relationships that define the situation. Once established, the policy model serves as the explicit basis for defining the problem and appraising policy options. Causal mapping abstracts problems as gaps between current conditions and desired goals or standards; these gaps arise from changes that spread through causal linkages. A policy intervention is viewed as a deliberate, goal-directed action intended to correct these deviations and attain policy aims. Furthermore, analyzing how changes propagate through a policy system enables analytical examination of the system and supports assessment of the effectiveness and efficiency of various change scenarios in meeting objectives. A policy model can be combined with simplified decision models such as optimization models and decision trees thereby enhancing scenario generation and policy option design by explicitly considering costs, benefits, resource limitations, and risks across natural, socio-economic, and technological systems.

The main target users include policy specialists involved in policy planning, implementation, and evaluation (for example, mayors, executive officers, heads of administration, elected representatives, civil servants, policy advisors, analysts, and researchers). Beyond those working in government departments and parliaments, engagement with the research community can create multiplier effects on policymaking across all institutional levels.

Systems modeling and simulation tools enable groups of users to construct and exchange mental models, examine assumptions, and experiment with alternative scenarios. These tools can therefore establish a formal mechanism for continuous communication and knowledge translation between researchers and policymakers via policy models. Converting the implicit mental models of policy actors into explicit models provides valuable input for specialized scientific models and allows iterative refinement as new or evolving evidence emerges.

A secondary, wider target audience encompasses all stakeholders who stand to gain from enhanced decision making, facilitated dialogue and interaction with policymakers, and greater alignment of policymaking with their needs.

1.6 Scope of the Study

This research is restricted in terms of geographical scope, temporal coverage, and the specific assessment models employed. The proposed decision support approach focuses on selected activities within the policy formulation stage of the overall policymaking process.

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