Usage Of Decision Support Systems Research Articles

Contribution of institutional DSS to organizational performance: evidence from a longitudinal study

Most studies have reported decision support systems (DSS) impact on individual performance under simulated conditions. There is need to examine the impact on performance of widely used institutional DSS. Utilizing longitudinal data from multiple healthcare organizations, this study measures the contribution of an institutional contract modeling DSS to user organizations' performance. Least squares regression analysis results indicate that actual usage of DSS is significantly and positively related to the reimbursement rate for services. We find confirmation of lag effects in realizing revenues following DSS usage.

Model Composition Using Filter Spaces

Decision support systems (DSS) typically contain data and models to facilitate decision making. DSS users, in response to a particular decision-making situation, often execute a sequence of models, in which inputs to a model in the sequence are obtained from outputs of other models upstream in the sequence and from database retrievals. The problem of generating a sequence of models from the set of available models is known as the model composition problem. In this paper, we propose a new construct called filter spaces to support model composition. We show how filter spaces can significantly facilitate automation of model composition and execution process, and provide effective means to integrate partial solutions from multiple composite models and databases.

Decision Support for What-If Analysis and the Confirmation Bias

The effects of the confirmation bias on users of a Decision Support System (DSS) for what-if analysis were explored through a laboratory experiment to determine how those users' decision processes were affected when they were predisposed toward a particular solution. A theoretical model was developed based on the hypotheses that users' predispositions affect their decision processes and their decisions and that users' decision processes affect their decisions. This model was tested using structural equation modeling techniques. Findings indicated that DSS users' predispositions did not directly affect the conclusions of the analyses they performed, but only their expectations of what those results would indicate to be the best solution. An additional finding was that DSS users' decisions are based directly on their recollections of the analyses they perform as opposed to the actual conclusions of the analyses. A modified model which should be of interest to researchers and practitioners is presented.

Ironic uses of group communication technology: Evidence from meeting transcripts and interviews with group decision support system users

This research examined unexpected uses of group decision support systems (GDSSs) related to three key communication characteristics of that technology: anonymity, equality of participation, and equality of influence. Using Adaptive Structuration Theory (AST) as a heuristic framework for exploring ironic appropriations of technology, this study included both interview data from users and transcripts from actual group meetings. The results suggest several ironic uses related to anonymous communication (including overt efforts to determine another's identity both during and after meetings, signing of comments, sensory evidence used to determine a comments’ source, and actual attributions of a comment's source—as well as extreme efforts to preserve anonymity that also go against the overall spirit of the technology). Several ironic uses relevant to influence equality were also noted; however, the data revealed very little evidence of ironic use related to participation equality (only social loafing was detected). Based on these findings, an expanded framework for ironic uses of communication technology is proposed.

DSS and EIS applications in Taiwan

In the past several decades, Taiwanese companies have been successful in the world market. However, knowledge about how these firms use information technology is very limited. Investigates the application of decision support systems (DSS) and executive information systems (EIS) in Taiwan. Two mail surveys were conducted to explore who used the system, where the systems were applied, what decision models were used and reasons for not using these systems. The results indicate that: more than 20 per cent of the firms already used DSS or EIS and an additional 37 per cent had plans to adopt them shortly; 87 per cent of the firms considered DSS and EIS to be important to their competitiveness; middle‐ and lower‐level managers were major DSS and EIS users; half of them used the system every day; the most troublesome problem in developing DSS/EIS was the difficulty in determining information requirements; and information aggregation and what‐if analysis were major functions for decision support. Furthermore, quantitative models were employed only in large and old companies. The most popular models included linear programming, PERT/CPM, and regression analysis, whereas the most popular domains for application were finance and production management.

The Decision Support System of PSI, and how it Meets the Needs of the Users

Four categories of users of a decision support system (DSS) are characterised particularly with respect to their know-how and needs: scientists, technicians, radiation safety officers and decision makers. The capabilities of a modern DSS of high performance lie in reliability, flexibility and user-friendliness. These features are put into perspective of the potential user needs, which are not necessarily compatible. Missing know-how has to be compensated either by expert judgement or by more complex monitoring and modelling. The DSS structure at PSI allows capabilities of different performance to be grouped in basically two levels of problem complexity with two operational levels (interactive or automatic) each to four modes, which are compatible with different users' needs. The system is designed to simulate the atmospheric transport of radioactive effluents in flat and complex terrain in real time. The automatic operation of a DSS with all its limitations is an important step to a better overall performance of the system even for difficult problems such as atmospheric transport in complex terrain: particularly, in connection with data assimilation for unknown releases. So far, the challenge of meeting the needs of most DSS users is achievable.

Aiding DSS users in the use of complex OR models

The under-utilization of decision support systems (DSS) based on complex OR models is a reality faced by many OR/IS system designers. The research finding that DSS users trade off decision making effort versus decision accuracy has been utilized as a ready-made explanation for this phenomenon. That is, it has been argued that complex DSSs may never get to be used at all due to the increased effort required in using them. We argue, in contrast, that DSSs have been found to elicit a zero-sum effort versus accuracy tradeoff precisely because they are architecturally designed to do so. A modification of DSS architecture designed to deal with this problem is proposed, and a research prototype based upon this architecture is described. Two preliminary laboratory experiments designed to examine the utility of this prototype have indicated that DSS design can significantly impact the level of the effort versus accuracy tradeoff. We conclude, therefore, that the "complexity ceiling" which has heretofore limited the amount of complexity an OR/IS designer can usefully incorporate into DSSs is not inviolate.

A web-based decision support system for waste disposal and recycling

Insufficient recycling of household and industrial waste in the U.S.A. has led to increased fullness and losses in landfills. Decision support systems (DSS) can encourage recycling, by giving consumers better access to information and decision models that maximize their economic incentives for recycling. This paper presents such a model (for optimizing delivery of waste to transfer stations) and describes its implementation in a DSS that is installed on a World Wide Web server and stores detailed data and the decision models. The model determines the transfer stations that are to be visited, items that are to be dropped at each station visited, and the travel route between stations, and makes the best tradeoff between the monetary payoff and travel times. DSS users access the system via standard Web browsers, and can query the database or use the decision models. Providers of recycling services can update, also using a Web browser, data about their site via hypertext mark-up language (HTML) forms.

An examination of organizational strategies for supporting DSS

Decision support systems (DSS) are widely used by managers to provide problem solving assistance. Despite their widespread use, little is known about how organizations support DSS users. This paper discusses support practices that are thought to affect DSS groups' ability to provide support services, and reports on a field study that sought to identify the support characteristics of a sample of groups. This study also investigated differences among groups that were placed in different organizational locations. Managers of twenty-three DSS groups were interviewed to provide information on group organizational location, staff-to-user ratio, staff backgrounds, formalization, and other support characteristics. The results suggest that DSS groups generally follow some of the recommendations found in the literature with respect to their management while disregarding others. The support characteristics also tend to vary when comparing groups across organizational locations, suggesting that certain strengths and limitations may be associated with organizational support strategies.

Model Integration Using Metagraphs

The availability of a large and diverse collection of stored modules such as data relations and decision models is a desirable feature in a decision support system (DSS). However, it is usually infeasible to design a DSS in which every problem instance can be solved using a single module. Instead, it may be necessary to combine several stored modules into an integrated model that is sufficient to solve the given problem. We show that modules such as data files and decision models in a DSS can be usefully represented by a metagraph, a graph-theoretic construct that captures relationships between pairs of sets of elements. In addition to the visualization benefits that graphical representation offers, we show that many useful questions faced by the designers and users of DSS can be addressed by exploiting analytical properties of metagraphs. In particular, we show that the process of model integration can be significantly facilitated by exploiting certain connectivity properties in metagraphs.

Empirically assessing factors related to DSS benefits

This paper reports the results of a field study surveying 201 US business managers to test the relationship between decision support systems (DSS) benefits and a list of factors expected to affect them. DSS benefits considered are: decision quality, competitive edge, improved communication, cost reduction, increased productivity, time savings, overall satisfaction, overall cost effectiveness and total benefits. The determinants of DSS benefits fall into four broad categories encompassing attributes of the industry (strategic position and degree of competition), the organization (size of organization, task structuredness, frequency of use, quality of training, organization support and vendor support), the DSS (timeliness of output, completeness of output, accuracy of output, relevance of output, flexibility, range of alternatives and user-friendliness) and of the DSS user (age of user, experience with DSS, experience on job, education level, attitude of user and expectations of user). Correlation coefficients and incremental R square measures (stepwise regression) show that much of the variance (52-84%) in each of the benefits can be explained by the factors included in the study. Based on the results, managerial recommendations on how to proceed to increase DSS benefits are presented.

An empirical investigation into decision support systems capabilities: A proposed taxonomy

The capabilities provided by decision support systems (DSS) can have a significant impact on the usefulness and satisfaction provided by a DSS. A questionnaire was sent to nonacademic members of the Institute of Management Sciences in an effort to identify characteristics and capabilities common to DSS. Analysis indicated that five different categories of DSS could be identified. Each of these categories provided the DSS user with a unique set of capabilities ranging from simple data retrieval to a fully integrated DSS. Analysis also indicated that the five DSS categories identified in this study mapped easily onto Alter's DSS taxonomy. By utilizing Alter's taxonomy and the results obtained in this study, the DSS builder is provided a starting point for the development of future DSS.

Empirically testing the outcomes of user involvement in DSS development

The widespread use of decision support systems (DSS) for decisions critical to company survival and growth, and the sizeable investments most companies are increasingly making in the related technologies, motivate a better understanding of the factors contributing to DSS success. User involvement in the DSS development process has been proposed by many as an important determinant of success. This paper reports an empirical test of existing theory and the results from previous studies regarding the relationships between user involvement and three of the more widely used measures of system success: user overall satisfaction with the DSS, user perceived system benefits, and DSS usage. Two previously proposed intervening variables, systems friendliness and user satisfaction with DSS specific characteristics, are also included in the proposed path analytic model. A sample of 185 business managers and professionals provided the basis for statistical analysis using the partial least squares (PLS) method which tests both the measurement model and the structural model simultaneously. The results strongly confirm the importance of user involvement and DSS friendliness as determinants to the different measures of DSS success.

The effectiveness of Decision Support Systems: the implications of task complexity and DSS sophistication

All Decision Support Systems (DSS) are, by their nature, designed to improve decision making effectiveness, yet a review of the experimental literature reveals that achievement of this objective is mixed. We propose that this is because DSS effectiveness is contingent upon a number of factors related to the task and DSS under investigation. This paper reports a longitudinal experiment designed to evaluate the relationship between DSS effectiveness and two such factors: DSS sophistication and task complexity. In comparison to unaided human judgement, two levels of DSS were evaluated: a deterministic spreadsheet model and a probabilistic model with a graphical risk analysis aid. Our subjects made decisions in a business simulation providing two successive phases of increasing task complexity. Initially, when task complexity was low, we found that neither DSS affected subjects' performance. In the more complex phase, both types of DSS users performed significantly better than unaided subjects. However, risk analysis users performed no better than model-only users. Interestingly, DSS users performed less homogeneously than unaided subjects in the complex phase. DSS users had greater confidence and considered more alternatives than their unaided counterparts. Risk analysis users took longer making decisions in the early stages, while model-only users became the most efficient in the later stages.

The Effectiveness of Decision Support Systems: The Implications of Task Complexity and Dss Sophistication

All Decision Support Systems (DSS) are, by their nature, designed to improve decision making effectiveness, yet a review of the experimental literature reveals that achievement of this objective is mixed. We propose that this is because DSS effectiveness is contingent upon a number of factors related to the task and DSS under investigation. This paper reports a longitudinal experiment designed to evaluate the relationship between DSS effectiveness and two such factors: DSS sophistication and task complexity. In comparison to unaided human judgement, two levels of DSS were evaluated: a deterministic spreadsheet model and a probabilistic model with a graphical risk analysis aid. Our subjects made decisions in a business simulation providing two successive phases of increasing task complexity. Initially, when task complexity was low, we found that neither DSS affected subjects’ performance. In the more complex phase, both types of DSS users performed significantly better than unaided subjects. However, risk analysis users performed no better than model-only users. Interestingly, DSS users performed less homogeneously than unaided subjects in the complex phase. DSS users had greater confidence and considered more alternatives than their unaided counterparts. Risk analysis users took longer making decisions in the early stages, while model-only users became the most efficient in the later stages.

The effects of DSS group characteristics on client satisfaction

Despite the widespread use of decision-support systems (DSSs) by managers for problem-solving, little is known about how organizations support DSS users. The authors report on a field study of DSS groups to identify support characteristics that are related to client satisfaction with DSS services. The findings indicate that the proportion of DSS staff with functional data background is positively associated with client satisfaction with DSS group services. The findings also suggest that DSS group location, formalization, use of an evolutionary methodology, and top management support have no effect on client satisfaction. Suggestions for practice and future research are presented. >

Decision Support Systems (DSS) development using a model of user inquiry types: Methodological proposals and a case study

A study of the DSS implementation area reveals an increased emphasis on methodologies for DSS development. As the DSS field matures, a larger number of methodological options are becoming available to the DSS developer. Existing methodologies have adopted various methods for performing the important task of DSS requirements analysis and specification. In envisaging a hierarchy of requirements analysis methods, a method for improved requirements analysis is proposed to remedy observed deficiencies within it. This method is based on a view of generic types of inquiries or solicitations made by the DSS user during decision-making. This formalism constitutes the basis of a proposed DSS development methodology that offers several benefits to DSS development. The intended benefits include a sharper and more focused requirements analysis to improve the DSS development process. This paper describes the model and methodology, alongside details of a real-world example of their use in developing a Marketing Decision Support System (MKDSS) for the support of marketing decision-making.

A comparative analysis of DSS user-friendliness and effectiveness

Both researchers and practitioners consider usability of decision support systems (DSS) to be the most important feature that an effective DSS should possess. The buzzword ‘user-friendly’ is so overused by vendors and system designers that it has almost lost its meaning. In this era when DSSs are deployed by organizations towards strategic ends, it makes sense to determine at what price userfriendliness should be obtained. The main purpose of this study was to determine empirically whether user-friendliness is indeed associated with DSS effectiveness. Results from a mail survey of 201 DSS users indicated that user-friendliness has a positive association with decision quality and overall satisfaction, but has no significant relationship with time savings and cost effectiveness. The results also indicate that user-friendliness may hinder productivity. This finding has some implications for DSS designers/developers and researchers. Overall, the findings suggest that while user-friendliness is highly desirable of a DSS, it does not contribute to DSS effectiveness in terms of some of the major measures — time savings, productivity, and overall cost effectiveness.

The Determinants of DSS Success: An Integrated Model*

ABSTRACTThe results of a field study investigating the determinants of decision support systems (DSS) success are presented. A multivariate model was developed and tested using multiple regression hierarchical analysis on responses from 118 DSS users. Several specific hypotheses are also proposed and tested. Data analyses indicate that DSS success, as measured by DSS satisfaction and perceived benefits, depends on several factors: previous user experience with DSS, user involvement, user training, top management support, information sources, the level of managerial activity, and task structure, difficulty and interdependence. Based on the research results, several suggestions are made to improve the likelihood of successful DSS implementation.

Report on the ChI'90 workshop on computer-human interaction in aerospace systems

The purpose of the CHI'90 workshop was to explore issues facing designers and users of decision support systems in real-time, high-risk aerospace systems, such as aviation, satellite control and shuttle support. The workshop focused on the following issues:• how do we model the user/operator in complex time-critical environments• how do we design the human-computer interaction (displays, controls, and aids to ensure that the user is integrated into the decision process• what comprises an intelligent computer interface for a supervisory controller• how do we provide timely context-sensitive information in real time without overloading or distracting the human operator• how do we design operator aids/tutors using knowledge-based technology that enhance the human-computer interaction and overall system effectiveness rather than replacing the human decision maker.