وظایفی که سیستم های پشتیبانی تصمیم گیری را در یادگیری سازمانی توانا میسازد

Organizations routinely process information, make decisions, and implement them. Recent advances in computer and communications technologies have changed the way in which organizations perform these functions. Decision support systems (DSSs) are a major category of tools that an organization utilizes to support and enhance its decision-making activities. Traditionally, organizations are considered to have a predefined and static set of goals. However, in order to stay competitive and survive in today's dynamic environment, organizations must be able to quickly respond and adapt to changes in their business settings. Such changes could be due to technological advances, growing and changing customer demands, competitive forces, changes in the labor force, environmental and political concerns, societal impacts, security concerns, and others. In recent years, the field of DSS has become more sophisticated to encompass such paradigms as expert systems (ESs), intelligent DSSs, active DSSs, and adaptive DSSs. Artificial intelligence (AI)-based techniques are being embedded in many DSS applications, thus enhancing the support capabilities of the DSS. Such paradigms have application potential in both individual and organizational learning contexts. However, the degree to which current DSSs can support organizational learning has yet to be investigated in depth. This paper examines the learning strategies employed by organizations and DSSs and provides a framework to demonstrate how a DSS can enhance organizational learning.

Drucker [17] observed that the world is entering a post-industrial era in which availability and processing of information will become critical. Hence, organizations whose structures, processes, and technologies are not well suited to deal with the increasing environmental complexity and knowledge are unlikely to survive [32]. In order to survive and thrive in these ever increasing competitive markets and complex environments, organizations must continually learn and process new skills, knowledge, and routines about products, processes, and social relations. Argyris and Schon [3] defines organizational learning as a process of detecting and correcting errors so that organizations are able to function and realize their goals and objectives. If organizations do not learn and adapt to their ever-changing environments, they face prospects of eroding their competitiveness and eventually, maybe, extinction. Exploration and controlled experimentation are essential factors of the learning process. One of the key factors that permits an organizational actor or a decision maker (DM) to take risks and seek varieties is directly related to the DM's personal preference and choice. Decision support systems (DSSs) can play a major role in enhancing the DM's decision-making abilities. In recent years, the field of DSS has become more sophisticated to encompass such paradigms as expert systems (ESs), intelligent DSSs, active DSSs, and adaptive DSSs. Such paradigms have application potential in both individual and organizational learning contexts. However, the degree and depth to which current DSSs supports organizational learning has yet to be investigated. This paper examines the learning strategies employed by DSSs and provides a framework to demonstrate how a DSS can support and enhance organizational learning. DSSs can support and enhance a DM's decision-making capabilities by processing data and allowing participants to simulate a variety of scenarios quickly and make effective decisions in an efficient manner. A DSS can also help to assess and compare the benefits and risks of exploration within the organization [32]. In spite of mutual linkages between the DSS and organizational learning, the concept of how a DSS can enhance and facilitate organizational learning has not been explored. This paper examines the learning strategies employed by DSSs and organizations and discusses different kinds of DSSs that can facilitate, promote, and enhance organizational learning. We believe this paper will be useful in providing guidance to managers, as managers in different companies are enamored by the concept of organizational learning and are looking for new ways to enhance and promote learning in their organizations. This paper also provides insights and an overview for researchers exploring the relationship between DSSs and organizational learning. The rest of the paper is organized as follows. In Section 2, we briefly discuss organizational learning and the nature of the resultant expertise. In Section 3, we present the different functions and characteristics of DSSs. In Section 4, we discuss the different DSS paradigms in terms of their underlying learning strategies to acquire and reorganize its knowledge and thus enhance the organization's performance. In this section, we also discuss the different ways in which DSSs can facilitate, support, and enhance learning in organizations. In Section 5, we highlight the key attributes of DSSs that can promote and enable organizational learning. Section 6 discusses the potential future of using DSSs to facilitate and enhance organizational learning and Section 7 contains concluding remarks.

In this paper, we have proposed ways in which DSSs can facilitate, promote, enhance, and support organizational learning. In current dynamic environments, the potential of DSSs for enhancing organizational learning can be even more important. For example, when decision-makers are faced with making quick ad-hoc decisions, a DSS can provide efficient and effective modeling capabilities. Using these GDSSs, managers can easily communicate their decisions across the hierarchies. This helps in bringing organizational members together for creating a mental schema of the problem and its solution [66] and [67]. A well-designed DSS provides managers with the options to check and evaluate different mental schemas and their outcomes. This usually results in managers selecting the best solution consistent with their organization's overall goals and mission. The validation of models through a DSS can be useful, as it will usually provide the same decision in the same contexts. If users and other organizational members can check and validate the accuracy of their decisions through a DSS, they usually become aware of the critical variables and contexts in which a particular decision is made. In essence, different DSS paradigms, ranging from a conventional DSS to adaptive DSS, can potentially influence both single-loop as well as double-loop learning in organizations. A DSS is a tool of self-expression and explanation for the DM. Self-expressions and explanations not only require flexibility in the use of the DSS, but also a sense of direct control over the DSS. The flexibility of the DSS can be managed by easy to work user interfaces and easy modeling capabilities. Confirming the conceptual models of the DMs with the DSS models can provide the control over the DSS. This compatibility between the DM and the DSS provides a direct opportunity to the DM to evaluate the operations of the DSS and integrate the information provided by the DSS with information provided by the other sources. Additional research needs to be done to examine, in detail, the issues and criteria that will help identify the appropriate DSS(s) that will promote organizational learning. In general, in complex situations, where humans are unable to analyze the effect of the several interacting variables simultaneously, a DSS can provide a better perspective of their interactions and the corresponding solution by offering its data mining, modeling, and analytical capabilities.