Decision making is considered one of the most critical activities for human efficacy (Shirgaonkar et al., 2010). Unsurprisingly, decision making is central to many scientific disciplines such as engineering, psychology, operations research, artificial intelligence and many more (Vroom and Yetton, 1973; Martinez et al., 2010) and has led to the development of systems to support the process. The concepts involved in decision support systems (DSS) were first discussed in the early 70s by Scott Morton under the term ‘management decision systems’ (Sprague, 1980). The term ‘decision support system’ itself first appeared in a paper
by Scott Morton in 1971, although some scholars argue that the DSS field began in 1965 with the acceptance of Scott Morton’s PhD topic “Using a Computer to Support the Decision- Making of a Manager” by the Harvard Business School (Arnott and Pervan, 2005). In the 1980s, user organisations, information systems vendors, and researchers engaged in discussions of a new "era" in information described by Sprague (1980, p. 23) as a "DSS Movement". The DSS Movement was characterised by events and mechanisms such as systems development in organisations, hardware and software developments, publishing activities to report DSS experiences and research, and conferences to provide a forum for the exchange of DSS ideas among interested parties (Sprague, 1980). Since then, over the past 30 years, the DSS field has explored the use of every kind of technology to support DSS development and progression, including: spreadsheets; databases; networks; hypermedia; expert systems; visual programming; intelligent agents; neural networks; and many more (Beynon et al., 2002). Computer-based DSS have been increasingly developed to support decision-makers in application areas such as production and operations, marketing and logistics, and management information systems (Eom and Kim, 2006).
There are numerous definitions of DSS. Arnott and Pervan (2008, p. 657), for instance, define DSS as “the area of the information systems (IS) discipline that is focused on supporting and improving managerial decision-making”. (Uran and Janssen, 2003, p. 512) argue that “a DSS implies a computer program that:
assists individuals or groups of individuals in their decision process; supports rather than replaces judgements of individuals; and
improves the effectiveness rather than the efficiency of a decision process”.
A widely quoted definition of DSS is one proposed by Sprague and Carlson in 1982 which articulates that DSS are “computer-based systems that help decision makers confront ill- structured problems through direct interaction with data and analysis models” (Lyons and Stuth, 1992, p. 124).
Initial DSS were primarily designed to support individual decision-makers but, with development of new technologies and the advent of the Web, DSS applications expanded to supporting teams, workgroups and groups of organisations (Shim et al., 2002; Bharati and Chaudhury, 2004). Modern DSS provide their users or groups of users with a broad range of capabilities and facilitate a wide variety of decision tasks including information gathering and analysis, model building, sensitivity analysis, collaboration, alternative evaluation, and decision implementation (Bhargava et al., 2007). While DSS are often developed and used to support ad hoc analyses, increasingly DSS technologies are being integrated into business processes and information systems (Bhargava et al., 2007).
DSS is not a homogenous field. Throughout the history of the field, a number of fundamentally different approaches to DSS have had a period of popularity in both research and practice (Arnott and Pervan, 2005). Various types of DSS have been established, including, but not limited to: Personal Decision Support Systems; Group Support Systems; Negotiation Support Systems; Intelligent Decision Support Systems; Data Warehouses; Knowledge Management-Based DSS; Enterprise Reporting and Analysis Systems; and Spatial Decision Support Systems. Each of these DSS types utilise a variety of technologies, support diverse types of users, and represent different methods of support, system scales, levels of investment, and potential organisational impacts. Personal Decision Support Systems (PDSS) are usually small-scale systems that are developed to support decision tasks of one manager, or a small number of independent managers (Arnott, 2008). Group Support Systems (GSS) facilitate effective work of groups of users; this type of DSS utilises a combination of communication and DSS technologies (Fan and Shen, 2011). Negotiation Support Systems (NSS) primarily focus on supporting negotiation between opposing parties (Arnott and Pervan, 2012). Intelligent Decision Support Systems (IDSS) apply artificial intelligence techniques to decision support (Guerlain et al., 2000). Knowledge Management- Based DSS (KMDSS) are systems that support decision making by aiding knowledge storage, retrieval, transfer and application (Arnott and Pervan, 2005). Data Warehousing (DW) are systems that provide large-scale data infrastructures to empower decision-makers with information that allows them to make decisions based on solid facts (Nemati et al., 2002). Enterprise Reporting and Analysis Systems (ERASs) are enterprise-scale systems that include executive information systems (EISs), online analytical processing systems (OLAP), corporate performance management systems (CPM), business intelligence (BI), and, more recently, business analytics (BA) (Arnott and Pervan, 2012). Finally, Spatial Decision Support Systems (SDSS) are systems designed to support decision-makers in solving complex, semi-structured decision problems that have a spatial reference (Rinner, 2003).
With technological developments and the increasing availability and use of spatial data, SDSS are becoming increasingly popular in decision making processes (Uran and Janssen, 2003). Despite ongoing discussions, to date there is no general agreement on the definition of SDSS, with scholars usually listing general characteristics that apply to SDSS. This thesis will adopt the statement by Densham (1991, p. 405) that SDSS are “explicitly designed to provide the user with a decision-making environment that enables the analysis of geographical information to be carried out in a flexible manner”. SDSS include wide areas of applications such as water management, crop management, urban planning, environmental planning, recycling, and many more. Due to the nature of complex spatial problems, SDSS often provide capabilities and functions that:
support input of spatial data;
allow representation of the complex spatial data relations and structures; provide analytical techniques specific to spatial and geospatial analysis; and output the results in a variety of spatial forms such as maps (Densham, 1991).
Additionally, SDSS typically include databases that integrate a variety of spatial data which needs to be selected prior to data analysis and decision making. SDSS do not, however, offer functionality to support users in searching and selecting spatial or geospatial data. Unfortunately, common geospatial data portals and clearninghouses do not offer decision support functions.
Over two decades ago, Angehrn and Lüthi (1990, p. 27) articulated that “human-computer interaction remains a central issue in the DSS domain, and further research is needed to realise a high level of human-machine cooperation in problem solving and decision making”. To date, unfortunately, lack of practical relevance of DSS to their end-users still remains a major problem. Uran and Janssen (2003), for example, conducted a study in a search for explanations or reasons for success or failure of SDSS by systematically comparing five representative SDSS examples. The results of their study revealed that in all of the evaluated systems, contact with the decision process was lost during the SDSS development stage. Researchers found strong indications that users are not always able to adopt systems into use as intended or expected by developers. Consequently, the produced systems lack practical relevance and usefulness to their intended users. The authors argue that, to provide users with relevant and useful tools, there is a need for a closer link between developers and users during the SDSS development stage. In their critical analyses of the nature and state of DSS research, Arnott and Pervan (2005) demonstrated that almost 90% of DSS research has failed to identify the principal clients and approximately 60% failed to identify the DSS users. Furthermore, only 10% of reviewed DSS research demonstrated high or very high practical relevance, with approximately half of DSS research being regarded as having low or no practical impact. In their follow-up evaluation study, Arnott and Pervan (2008) again demonstrated extremely low relevance of DSS research to end-users. Their survey revealed that, overall, only 10.1% of DSS research is regarded as having high or very high practical relevance, while 49.2% of DSS research is regarded as either having low practical relevance or none at all. Consequently, the authors conclude that “the relative lack of exposure of academics to contemporary professional practice is a particular problem for DSS” (Arnott and Pervan, 2008, p. 661).