Information Technology based Decision-Making Support Systems

(DSS) in order to categorize them concerning their application in the decision- making process. In addition, to learn about their limitations and understand their relation to social media serving as DSS. Power (2008) distinguishes different types of DSS such as the data and information based. They primarily serve the decision maker with organizational data sets, context information and statistical reports. Another type, he mentions, are knowledge-driven, which belong to rule- and case-based systems also called expert systems. They guide the decision maker with suggestions about solutions through the decision-making process. The last category are systems supporting group and collective decision-making, which Power (2008) defines as communication- driven systems. This category of decision-making support systems shows similarities to social media applications. However, a comprehensive understanding of the different types, categories and limits is required to draw parallels to capabilities about social media and decision-making.

The primary purpose of DSS is to provide the decision-maker at different stages of the decision-making process with relevant information about a decision problem (Power, 2008). In the 1950s, early DSS provided mainly operational data, statistics and reports as a basis of managerial business decisions. Since then various types of DSS followed, such as rule- and case- based systems. According to Simon (1997b) the structure of a decision problem determines the design of a DSS. Therefore, he distinguishes between structured, unstructured and semi-structured decision problems. From a mathematical and algorithm perspective, he explained that structured routine and repeatable decision processes are programmable. This means structured decision-making problems can be described as mathematical model utilizing for instance decision trees or other mathematical algorithms from decision theory to solve for instance routine problems. Simon (1997b) emphasizes that unstructured decision problems are rather difficult to solve with computer programs, because of their complexity, the number of options and parameters that influence the decision. Semi-structured decision problems are in scope of DSS, which means that DSS support the decision process, but judgement and the final choice stays with the decision-maker.

Besides behavioural research and the limitations about rationality Simon (1997b) created different models and concepts of decision-making and problem solving in organizational, political and economic contexts. The outcomes of his research are various contributions including a four phases

decision-making process and concepts about technological decision-making support systems (Pomerol & Adam, 2004). This four-phase decision-making process starts with the intelligence phase, which is about gathering information to identify a decision problem. This follows the design phase that creates options available for the choice. The third phase performs the choice and the last phase reviews the implementation. If in one of these phases, for instance the problem solution is not satisfactory or in the intelligence phase not enough information available these phases are iterated. This is of relevance of DSS that support the different stages of the decision-making process, as they should allow changing parameters and repeating the process of information gathering if needed.

Information technology based DSS can be further divided into different categories such as information providing, mathematical modelling and simulation, expert systems, and mediating systems to distinguish how they support the decision-making process itself. The objective of this categorization is to compare social media with these categories to derive possibilities of social media based decision-making support systems and applications. The first categories of “classical” decision-making support systems are enterprise data processing systems. They provide the decision maker with different kinds of reports such as financial data, production input and output, sales, profits, market data, product quality and measurements and statistics. One of the earliest data processing systems introduced in business were management information systems (MIS) (Meixner & Haas, 2010). They provide managers primarily with reports of operational data to allow them to monitor and control the efficiency of the organization from a production, sales and financial perspective (Meixner & Haas, 2010). Evolutions of these systems were executive information systems (EIS). According to Meixner and Haas (2010) these data based DSS built cross relations of data sets, presenting them in spreadsheets and diagrams and allowing multidimensional data analysis. The data itself was collected from different organizational sources to predict for instance future trends about market demand or to derive counter measures if costs increased, sales dropped or margin diminished (March & Hevner, 2007). Other management and DSS followed, such as data warehousing (DW), business intelligence (BI), online analytical processing systems (OLAP), enterprise resource planning systems (ERP) and enterprise information management systems (EIM) to provide the decision-maker with information needed to make and justify decisions based on the reported data (Shim et al., 2002).

All these systems collect organizational and market data to generate specific information for management decision problems (Meixner & Haas, 2010). They belong to the information-providing category of decision-making support systems. This means, they provide only the basis but do not present the

solution of the problem or advise what alternative fits best. The technologies discussed above are widely disseminated in organizations and managers utilize them regularly to justify the course of action. However, the final choice falls back the decision maker. Hence, subjective interpretations and judgement, personal beliefs and opinions about the presented data influence the final choice. The second category are advanced DSS that use mathematical models, for instance models from rational decision theory, solving complex decision problems by using simulations, integrating significant amounts of data, and evaluating numerous alternatives (Sauter, 2011). Rule- and case-based DSS belong to the category of expert systems. They guide the decision process by applying rules and cases gained from experience and heuristics of experts to present the decision maker with a problem solution applied to similar cases (Sauter, 2011). The last category are group and collective decision-making systems, which support the decision process that involves more than one decision maker (Massey, 2008). They mediate the decision process and aim to avoid problems of collective decision processes such as groupthink.

A decision-making group consists of more than one individual identifying a problem and searching for a solution of a problem in a deliberative, participative and collaborative way. The members of a decision-making group perceive themselves as member of the group even if they do not have to be at the same location but responsible to jointly make a decision (Desanctis & Gallupe, 1987). Interpersonal communication among the members of the group, the exchange of opinions, ideas, and the contribution of knowledge based on the members experience are the foundations of group decisions and are a source of bias as well (Silver, 2013). Therefore in the 1980s group decision support systems (GDSS) were introduced with the aim to improve group collaboration, and supporting the collective decision-making process using computer and communication technology (Eisenführ et al., 2010). The main focus, GDSS follow, is the improvement of communication within a decision-making group in the process of converging to an accepted choice (Desanctis & Gallupe, 1987). GDSS direct the problem and decision analysis performed by groups and improve the exchange of information during the decision-making process (Nunamaker & Deokar, 2008). These systems are based on different technologies using computers to communicate and interact with the decision support software. They provide decision modelling methods such as risk analysis, decision trees and forecasting and integrate Delphi and nominal group methods to enhance the decision process in group meetings (Desanctis & Gallupe, 1987).

The impact of these systems depends on how they intervene in the decision process for instance to what extent they determine the rules the discussion has to follow. In other words, GDSS change the way in which individuals

participate in a group decision-making process. Desanctis and Gallupe (1987) identified three levels of group decision-making support systems. The first level serves as medium to avoid communication barriers among the group members by providing large screens to share ideas, including messaging features or anonymous voting functions. The second level provides features about risk analysis, structuring, and analysis methods and social judgement formation. The third level mediates the group decision-process utilizing controlling rules and expert advice.

As Eisenführ et al. (2010) explains, GDSS could reduce negative effects of group behaviour by allowing participants to act to some extent anonymously in the process, and provide their opinions more freely. For instance, a first level GDSS consists of computers connected to a projector. Each participant enters comments, feedback, options and suggestions about a decision problem into their computer and the results appear on the screen in front of the group. A common problem of group decision-making support systems is the generalization of group behaviour (Eisenführ et al., 2010). This means that a generic system design that fits most requirements across all group settings is too complex (Eisenführ et al., 2010). Groups exchange information in different ways and approach the different steps of the decision process following their specific requirements. For instance, decision-making rules may not fit to each problem and therefore the group has to decide which rules fits best. If a system offers many different rules and methods, the initial setup could become time consuming and even demotivating for the participants. Hence, following the levels defined by Desanctis and Gallupe (1987) a system of level one and two that enhances communication, structures the problem, and provides analytical decision tools is more likely applicable in practice than systems that prescribe the entire decision-making process.

How social media supports individuals and groups making decisions is comparable to the principle of GDSS systems. However, it differs in a higher number of participants, communication possibilities and applied aggregation mechanisms. Social networks, communities, blogs, social bookmarking and wikis support the information flow through different communication channels and invite individuals to participate and contribute. In other words, social media offers a platform to facilitate deliberation and collaboration in the decision- making process, and capturing and aggregating thoughts, comments, feedback and opinions. Hence, social media could be the bridge that connects knowledge and experience of individuals to support collective decision- making, but on a larger scale without geographical boundaries. In addition, social media could mitigate negative group behaviour such as groupthink, social pressures, conformity, polarization, leadership effects, hidden profiles and amplifying errors similar to the GDSS approach. This means, social media users participating in an online deliberation or discussion during a decision-

making process are not directly confronted with other participants. However, comment and feedback mechanisms of social media allow immediately reacting to the contributions and therefore influencing the participants and their contributions that may follow. In other words, social pressures, conformity, polarization, leadership effects, hidden profiles and amplifying errors could appear in collective decision-making processes mediated by social media as well. In essence, the findings of GDSS relate to social media and introduce a new focus of organizational social media application on the area of collective decision-making.

In order to understand and to identify the areas of application in organizational decision-making processes the following sections review theories about social media, characteristics, implementations and findings. The aim is to derive capabilities and the types of social media a collective decision-making process could benefit.

2.5 Social Media: Theoretical Framework and