The influence of DSS types, decision style, and environment on individual decision making

Cognitive style, measured by Myers-Briggs Type Indicator, was used to categorize decision makers. Information source in the form of different DSS types was provided to help the decision makers make more effective decisions. The research attempted to investigate systematically the effects of cognitive style and DSS usage on the decision maker\u27s perception of risk in the context of capital expansion projects. The research encompassed analysis of behavior under conditions of uncertainty for two values of the cognitive dimension, sensing-intuition (S-N), and use of two types of information sources, data-bases DSS (DBDSS) and model based DSS (MBDSS). The behavior was studied within the boundaries of four decision scenarios (2 information sources x 2 cognitive styles). The research attempted to establish the interaction of decision support systems and cognitive style on perceived risk, in a decision-making situation under uncertainty. The decision maker\u27s choice in a risky situation is influenced by the risk perceived by the decision maker. The perception of risk is a result of an interaction between a decision maker\u27s personal characteristics and the environment in which he/she faces the problem. Each type of individual needs the kind of information to which he/she is psychologically attuned in order to use it most effectively. The information needed by the decision maker can come from different types of DSS. DSS supports the decision-making activity and enhances the decision maker\u27s effectiveness. From the literature review, previous researchers have indicated that considering the human variable of cognitive style is very necessary for the successful design of decision support systems. The objective of this research was to study the level of risk perceived by people of different cognitive styles, using different types of decision support systems, when they face problems under uncertainty/. The following research hypothesis was supported in Experiment 2, when decision environment was introduced as a control variable. Perceived risk will be influenced by the compatibility of the information source and the cognitive style of the decision maker

The impact of resources on decision making

Decision making is a significant activity within industry and although much attention has been paid to the manner in which goals impact on how decision making is executed, there has been less focus on the impact decision making resources can have. This article describes an experiment that sought to provide greater insight into the impact that resources can have on how decision making is executed. Investigated variables included the experience levels of decision makers and the quality and availability of information resources. The experiment provided insights into the variety of impacts that resources can have upon decision making, manifested through the evolution of the approaches, methods, and processes used within it. The findings illustrated that there could be an impact on the decision-making process but not on the method or approach, the method and process but not the approach, or the approach, method, and process. In addition, resources were observed to have multiple impacts, which can emerge in different timescales. Given these findings, research is suggested into the development of resource-impact models that would describe the relationships existing between the decision-making activity and resources, together with the development of techniques for reasoning using these models. This would enhance the development of systems that could offer improved levels of decision support through managing the impact of resources on decision making

How and Why Decision Models Influence Marketing Resource Allocations

We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be "good?"; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes.Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes.marketing models;resource allocation;DSS;decision process;decision quality

Cognition Matters: Enduring Questions in Cognitive IS Research

We explore the history of cognitive research in information systems (IS) across three major research streams in which cognitive processes are of paramount importance: developing software, decision support, and human-computer interaction. Through our historical analysis, we identify “enduring questions” in each area. The enduring questions motivated long-standing areas of inquiry within a particular research stream. These questions, while perhaps unapparent to the authors cited, become evident when one adopts an historical perspective. While research in all three areas was influenced by changes in technologies, research techniques, and the contexts of use, these enduring questions remain fundamental to our understanding of how to develop, reason with, and interact with IS. In synthesizing common themes across the three streams, we draw out four cognitive qualities of information technology: interactivity, fit, cooperativity, and affordances. Together these cognitive qualities reflect IT’s ability to influence cognitive processes and ultimately task performance. Extrapolating from our historical analysis and looking at the operation of these cognitive qualities in concert, we envisage a bright future for cognitive research in IS: a future in which the study of cognition in IS extends beyond the individual to consider cognition distributed across teams, communities and systems, and a future involving the study of rich and dynamic social and organizational contexts in which the interplay between cognition, emotion, and attitudes provides a deeper explanation of behavior with IS

Support for the Inclusion of Personal Value Preferences in Decision Support Systems

We consider the important issue of including personal value preferences in decision support systems (DSS). Various personal differences have been shown to affect the acceptance, use, and effectiveness of DSS. Decision-making models offer a theoretical basis for the inclusion of various personal differences (including personal value preferences) in decision-making. Research in the field of psychology has long recognized the importance of values in both motivation and choice behavior. Other research has also found personal values to be relevant in decision-making. We posit that since personal values are important in the decision-making process, they should also be important in the support of decision-making and thus in decision support systems

IMPACT OF EXPLAINABLE AI ON COGNITIVE LOAD: INSIGHTS FROM AN EMPIRICAL STUDY

While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice XAI research targets developers rather than actual end-users. Unsurprisingly, end-users are unwilling to use XAI-based decision support systems. Similarly, there is scarce interdisciplinary research on end-users’ behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users’ cognitive load, task performance, and task time. Based on these findings, we classified the explanation types in a mental efficiency matrix, ranking local XAI explanation types as best, and thereby providing recommendations for future applications and implications for sociotechnical XAI research

Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study

While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research.Comment: Thirty-first European Conference on Information Systems (ECIS 2023

CONTRIBUTIONS OF THE MANAGEMENT SCIENCES TO THE EVOLUTION OF MANAGEMENT INFORMATION SYSTEMS

The management sciences concern disciplines that . identify, extend, or unify scientific knowledge pertaining to the process and substance of management. The field of management science is often closely allied with the area called operations researcK through common analytical methods and models. The application and implementation of management science recognizes well the behavioral and economic realities of management practice in organizations. During the past twenty-five years, the management sciences and management\u27s use of information systems technology have evolved together· In this survey we highlight three aspects of this mutual evolution: first, as a basis for enunciating and understanding issues involved in theory and practice; second, as providing tools and techniques to solve managerial (and technical) problems related to MIS design and development; and third, as a component of MIS Technology available for application and use

DECISION SUPPORT SYSTEMS FOR AGRICULTURE AND RURAL FUTURES

Farm Management,