بررسی ادراک اجزای اطلاعات در سیستم های پشتیبانی از تصمیم گیری های مالی

Estimating the contribution of DSS to financial consulting decision-making is attracting considerable interest in the fast-growing field of banking DSS. This study evaluated the perceived role of banking DSS in the decision-making of investment counselors. A questionnaire was submitted to 40 investment counselors to determine the comparative importance of DSS information components. Data were analyzed using two complementary methods (analytical hierarchy processing and Neumann–Segev). The most important information components were customer's and investment risk classification, and customer goals and nature of investment. The results differed across administrative ranks and as a function of the user's experience level.

In the past few years, information systems have entered the field of investment counseling, where they are used to support the work of bank employees in charge of customer portfolios. These bankers assist customers in making investments in a way that suits their lifestyles and financial goals. Traditionally, bankers simply remembered their customers and their customers' profiles and tried to design investment portfolios adapted to each. This approach may have been appropriate in the past, but is less so in an age in which there are so many financial instruments that befit a far wider classification of customer types and investment types. Vahidov and Kersten [37] described the major changes in the business environment in the past thirty years and in particular, the globalization of economy and the growing complexity of economic relationships. These in turn contribute to the complexity of the decision problems faced by decision makers [11]. Additionally, important financial decisions require reasonably accurate forecasts based on various probabilistic information components such as forecasting future medical needs and costs [39], household financial planning in years to come [35], etc. It seems virtually impossible for an investor to utilize all the available information in the short time needed to respond to market trends. Therefore, a control mechanism is needed to help bankers equilibrate deliberation and timely decision-making [36]. All the above clearly point to the increasing need for sophisticated decision support tools. Although a DSS should improve decision quality, research has shown that at times the opposite is true or that implementation of a system has no effect at all [20]. This is not entirely unexpected since multiple variables, issues and contexts are involved. Nonetheless, DSSs have a vast potential for supplying complete, uniform, exact, up to date, accessible and reliable banking information, which can improve decision quality and reduce risks and uncertainties that stem from lack of information [5]. Specifically, information about customers, such as their goals and the nature of their investments, the investment horizon, the customer's risk classification, investment history, etc. can enhance the decision making processes of bankers who deal with investment counseling. Assessing the value of such information as part of the decision making processes is one of the most important topics in several research fields that deal with organizational information, and has been studied extensively over a wide range of disciplines that deal with information systems in organizations [2]. Components of the system must be examined using variables that relate to the characteristics of the information to best maximize utility. According to Ahituv [1] there are three main characteristics: time variables, for instance system response time, frequency of receipt of data, etc., content variables, for instance relevancy, precision, suitability of data to what the user wants, aggregation of data, etc., and format variables such as visual presentation, interactive visual analytic tools [31], interactive media, arranging data in tables versus graphs, graphic design, etc. Ideally, each variable in the utility function should be clearly defined and possible to measure, and the relationship between the variables in the utility function and the cost variables should be known and mathematically defined. In such a situation, optimization tools help choose the most appropriate system. In reality, however, the maximizing utility method has several shortcomings related to problems of measurement [27]. The main purpose of this study was to assess the perceived value of information obtained from a DSS designed to assist the decision-making processes of bankers who deal with customer investment counseling. As officially stated by the bank, the DSS characterizes the customer in terms of purpose of investment and risk classification, takes into account current macroeconomic parameters, and then constructs and presents a documented recommendation for investment. This paper is organized as follows. The next section reviews the literature regarding user expectations and evaluations of DSSs and a perceived value of information approach, and details the objectives of the present study. Section 3 presents the analysis tools used to assess the perceived value of information; namely, the AHP (analytical hierarchy processing) method [29] and the Neumann–Segev method of analyzing correlations [26]. The results are presented in Section 4, and discussed in Section 5. Section 6 covers limitations and future research directions, and Section 7 presents practical conclusions and recommendations for improvement in the specific DSS surveyed here.

There is some evidence that DSSs lead to bias reduction [10]; this paper makes some heuristics visible. DSS designers should take these data into consideration when working toward additional bias reduction, by eliminating some of the complexity of the decisions. Understanding the cognitive as well as emotional processes of decision-makers is an issue of great importance to the information system field, and these concepts are invaluable to system designers [41]. DSS designers should also consider the dynamics of the situation and environment, in which many sets of portfolio alternatives exist and decisions are made at the end of the whole counseling process [12]. This study provides a unique inside view of the informational behavior of specific bank counselors. For a DSS to be useful and trusted, an extremely large knowledge base is required, and this content must be kept up to date. In the absence of effective governance practices, implementation of a DSS may fail, despite the purchase or development of a sophisticated system [40]. Second, the impact of information components should be objectively monitored. Appropriate assessments could relate IT usage and performance, and identify which applications contribute most to individual performance [33]. The breadth, depth and speed of access of the information may also need to be adapted to different types of users. The level of accuracy and updating of information may be much more important when the information components involved have been ranked as essential for carrying out the counselors' tasks; therefore budget allocations may be reconsidered. Finally, DSS usage should include initial as well as ongoing training, as it makes the system more usable and trustworthy [20].