Although Decision Support Systems DSSs have been in use since the early seventies, there is as yet no strong theoretical base for predicting how a DSS will influence decision making. Furthermore, t...
This paper explores the tradeoffs and information issues in model specification, i.e., choosing the appropriate level of detail and precision of decision support models. For many real-time and distributed decision problems, decision-makers face a wide spectrum of information gathering choices that vary in the amount and quality of information as well as their associated costs and delays. Modeling these problems raises several operational and strategic questions including: • how much and what type of information to acquire before making a decision. • what protocol to use for exchanging information among multiple decision-makers. • what organization and information structure (e.g., centralized, coordinated, distributed) to use for effective information sharing and decision-making. We highlight the modeling issues and tradeoffs using examples from production planning, distributed processing, and network routing, and draw upon research in diverse fields, including information economics and game theory, knowledge logic in computer science, and distributed computation, to obtain model specification insights. Our discussions emphasize and illustrate two main model specification themes. First, acquiring additional information often has diminishing returns (in terms of the quality of decisions suggested by the model); therefore, an imprecise model based on partial information might be more appropriate than using a detailed and accurate model that identifies ‘optimal’ decisions. Concepts from information economics and team theory provide a framework for analyzing this tradeoff. We also briefly discuss some heuristic methods to identify effective information gathering strategies. Our second theme applies to systems consisting of several decision-makers who make interdependent decisions. In this context, the decision model for each agent must be based on an understanding of what information to exchange, how frequently, and what protocol to use for exchanging information. We illustrate how complete information sharing among distributed decision-makers might even be impossible because of a possible dead-lock in decision-making. This game-theoretic phenomenon has implications for designing the organization structure, information systems, and communication protocols to support multi-agent decision-making.
As with many complex design problems, physical database design is difficult, ill-structured, and highly human intensive. In order to effectively construct support systems or improve the practice of database design, it is important to understand how human designers reason about the task. We report an empirical study of physical database design problem solving. Thirteen subjects each solved two physical database design problems: five subjects were experienced designers; eight were graduate students with little or no actual design experience, though they were exposed to the principles of design through course-work. For each problem, subjects were presented with a list of available problem information (hardware, content, and activity data) and were directed to generate a physical design (record structures and access paths) that would minimize retrieval time and storage space. All sessions were audiotaped. Three types of data were incorporated for the analysis: information acquisition patterns, solution generation patterns, and verbal protocol. It was hypothesized that database design reasoning embodies forms of deliberation to reduce problem-solving complexity and that these forms resemble those found in other design problem-solving studies—commonality of task environmental demands will result in commonality in problem-solving methods in response to those demands. In particular, we expected to find specific control strategies, the use of hierarchical abstraction, the use of problem-specific heuristics, and the use of qualtitative reasoning with mental models of dynamic components of the task. Our results indicate that these forms are indeed present and of significant value in physical database design problem solving. Experience played a significant role in determining both the form and substance of reasoning used in physical database design. Both experienced and inexperienced database designers exhibited at least some of these forms of reasoning. Experienced designers, however, effectively applied these forms, demonstrating a substance of reasoning, although their methods of application varied considerably. The least experienced designers did not effectively apply these forms and, lost in the detail of the design problems, were unable to generate reasonable designs. It is concluded that recognition of appropriate reasoning forms and the effective application of these forms are critical to developing efficient physical database designs. The implications of the findings are discussed.
This research examined the impact of Computerized Performance Monitoring and Control Systems (CPMCSs) on service workers and their perceptions of work. Drawing on a thermostat model of control systems, the work built a causal model of CPMCS impact. The model demonstrated how four monitor design dimensions (tasks measured, frequency of measurement, object of monitoring, and recipient of the monitor data) affected the importance employees placed on production and service. Other constructs in the model included employees' acceptance of quantitative measures, computer appropriateness, computer accuracy, and employer's production and service messages. Using a holdback sample, an initial and revised model were tested on responses from 1,498 workers in 51 Canadian service sector organizations. Both versions of the model exhibited good explanatory power. The research led to three important conclusions. First, monitoring may not increase production. Even if it does, it need not reduce the importance of service. Second, studying monitors as multidimensional systems demonstrates that various monitor features can be altered to change the impact. Third, the credibility of the computer is a factor in the monitor's impact.
An exploratory study was conducted (a) to examine whether experienced software managers could generate accurate estimates of effort required for proposed software projects and (b) to document the strategies they bring to bear in their estimations. Five experienced software project managers served as expert subjects for the study. Each manager was first asked to sort a set of 37 commonly-used estimation parameters according to the importance of their effect on effort estimation. Once this task was completed, the manager was then presented with data from ten actual software projects, one at a time, and asked to estimate the effort (in worker-months) required to complete the projects. The project sizes ranged from 39,000 to 450,000 lines of code and varied from 23 to 1,107 worker-months to complete. All managers were tested individually. The results were compared to those of two popular analytical models-Function Points and COCOMO. Results show that the managers made more accurate estimates than the uncalibrated analytical models. Additionally, a process-tracing analysis revealed that the managers used two dissimilar types of strategies to solve the estimation problems—algorithmic and analogical. Four managers invoked algorithmic strategies, which relied on the selection of a base productivity rate as an anchor that was further adjusted to compensate for productivity factors impacting the project. The fifth manager invoked analogical strategies, which did not rely on a base productivity rate as an anchor, but centered around the analysis of the Function Point data to assist in retrieving information regarding a similar, previously-managed project. The manager using the latter, analogical reasoning approach produced the most accurate estimates.
Determining user requirements and generating alternative system solutions to meet these requirements are two critical steps in the requirement analysis phase of the system development life cycle. Much of the MIS research in the requirement analysis phase has been devoted to the topic of requirement determination and its verification. Alternative generation and evaluation is left, to a significant degree, to the judgment and expertise of an analyst. This paper proposes a multiple criteria decision making (MCDM) approach for generating and evaluating alternatives when the user requirements are expressed in terms of certain operational criteria such as time, cost, risk, etc. These alternatives form the basis for the user to make the necessary trade-offs.
Although Decision Support Systems (DSSs) have been in use since the early seventies, there is as yet no strong theoretical base for predicting how a DSS will influence decision making. Furthermore, the findings of various empirical studies on the outcomes of DSS use are often contradictory. Consequently, there is a need in the Decision Support Systems field for theories or explanatory models to formulate hypotheses, to conduct research in a directed, parsimonious manner and to interpret findings in a coherent way. This will assist both academics and practitioners interested in the use of information systems to support managerial workers. This paper proposes the use of a cognitive effort model of decision making to explain decision maker behavior when assisted by a DSS. The central proposition is that specific features can be incorporated within a DSS that will alter the effort required to implement a particular strategy, and thus influence strategy selection by the decision maker. This was investigated in a series of three experimental studies which examined the influence of computer based decision aids on decision making strategies. In the three experiments, subjects were given different degrees of support to deal with various components of cognitive effort (processing effort, memory effort and information tracking effort) associated with the strategies applicable to preferential choice problems. The results show that decision makers tend to adapt their strategy selection to the type of decision aids available in such a way as to reduce effort. These results suggest that the assumption that decision makers use a DSS exclusively to maximize decision quality is open to question. DSS studies which consider the joint effects of effort and quality, or control one while manipulating the other, are more likely to provide consistent and interpretable results.
It is our intention to challenge the commonly held assumption that information systems development (ISD) can be conceived of as a normative process reflecting conventional economic rationality. We ask: is systems development the rational process so eloquently described in the “classic” works of DeMarco (1978), Gane and Sarson (1979), Weinberg (1980), Yourdon (1982), Jackson (1983), and Martin (1985)? Or does this orthodox view fail to explain the actual practice of systems development? It is our view that even the basic assumptions about the rationality of the actors and the social processes they engage in need to be critically appraised. We suggest that if the assumptions about economic rationality are closely analyzed, it can be seen that they do not reflect the reality of systems development. ISD tends to defy rational explanations. As an alternative to basing our understanding of systems development on economic rationality, we contend that symbolism holds more promise. Instead of focusing on data flow diagrams, structured walkthroughs, requirements specifications, and the like, we concentrate on the role of myth, metaphor, and magic. These concepts offer considerable scope in interpreting the social actions that are embodied within ISD. We feel they facilitate a much richer understanding of systems development.
Recent work in social theory departs from prior traditions in proposing that social phenomena can be understood as comprising both subjective and objective elements. We apply this premise of duality to understanding the relationship between information technology and organizations. We construct a theoretical framework in which the development and deployment of information technology in organizations is a social phenomenon, and in which the organizational consequences of technology are products of both material and social dimensions. The framework is based on Giddens' theory of structuration, and it allows us to progress beyond several of the false dichotomies (subjective vs objective, socially constructed vs material, macro vs micro, and qualitative vs quantitative) that persist in investigations of the interaction between organizations and information technology. The framework can be used to guide studies in two main areas of information systems research—systems development and the organizational consequences of using information technology.
Information systems (IS) cannot be effective unless they are used. However, people sometimes do not use systems that could potentially increase their performance. This study compares two models that predict an individual's intention to use an IS: the technology acceptance model (TAM) and the theory of planned behavior (TPB). The comparison was designed to be as fair as possible, not favoring one model over the other. Both TAM and TPB predicted intention to use an IS quite well, with TAM having a slight empirical advantage. TAM is easier to apply, but only supplies very general information on users' opinions about a system. TPB provides more specific information that can better guide development.