Decision-makers endeavor to obtain the decision quality which puts them in a position to reach their goals. In order to control or influence decision quality, the processes by which individuals form their beliefs must be understood. In addition, many decision makers rely on decision support technologies to help find patterns in data and make sense of the input, so these technologies must be considered in parallel with the processes.
There have been numerous studies conducted to illuminate the factors which affect decision quality, however, many of these studies focused on objective measures and factors. This approach ignores individual perception, belief, and judgment. The evaluation of decision quality as perceived by the decision maker is important because these perceptions will direct future processes, decisions, and actions of the decision-makers. Also, by considering the perspective of the decision maker, theory and practice are being brought closer together. The focus of this study is to understand which factors contribute to individual’s perceptions of decision quality by combining a priori and observation-based methods through a theoretical lens. Attribution theory is a well-established theory often applied when researching individual perception and serves as the foundation for the proposed model. The model examines the impact of the environmental attributes of task-technology fit and internal aspects of the decision-maker including intolerance for ambiguity and self-efficacy on decision quality.
This study empirically tested the proposed model using a two-phased approach. A pilot of 84 students was used to validate the instrument. The primary study of 413 business decision-makers who used decision support technology was used to validate the structural model. The model was validated using partial least squares structural equation modeling (PLS-SEM). Results show support that the perception of the fit between the decision support technology and the decision task directly affects decision-makers’ perception of the resulting decision quality, as does the decision-makers’ self-efficacy with decision making and with the decision support technology. Also supported is that task-technology fit and intolerance for ambiguity influence both self-efficacy with decision-making and with the decision support technology.
","abstract_html":"<p>Decision-makers endeavor to obtain the decision quality which puts them in a position to reach their goals. In order to control or influence decision quality, the processes by which individuals form their beliefs must be understood. In addition, many decision makers rely on decision support technologies to help find patterns in data and make sense of the input, so these technologies must be considered in parallel with the processes. </p> <p>There have been numerous studies conducted to illuminate the factors which affect decision quality, however, many of these studies focused on objective measures and factors. This approach ignores individual perception, belief, and judgment. The evaluation of decision quality as perceived by the decision maker is important because these perceptions will direct future processes, decisions, and actions of the decision-makers. Also, by considering the perspective of the decision maker, theory and practice are being brought closer together. The focus of this study is to understand which factors contribute to individual’s perceptions of decision quality by combining a priori and observation-based methods through a theoretical lens. Attribution theory is a well-established theory often applied when researching individual perception and serves as the foundation for the proposed model. The model examines the impact of the environmental attributes of task-technology fit and internal aspects of the decision-maker including intolerance for ambiguity and self-efficacy on decision quality. </p> <p>This study empirically tested the proposed model using a two-phased approach. A pilot of 84 students was used to validate the instrument. The primary study of 413 business decision-makers who used decision support technology was used to validate the structural model. The model was validated using partial least squares structural equation modeling (PLS-SEM). Results show support that the perception of the fit between the decision support technology and the decision task directly affects decision-makers’ perception of the resulting decision quality, as does the decision-makers’ self-efficacy with decision making and with the decision support technology. Also supported is that task-technology fit and intolerance for ambiguity influence both self-efficacy with decision-making and with the decision support technology.</p>","abstract_has_math":false,"creators":["Niese, Bethany"],"institution":null,"degree_name":"Information Systems","degree_level":"Dissertation","degree_discipline":"Business Administration","degree_department":null,"school":null,"contributors":["Reza Vaezi","Michael Gallivan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-17T07:00:00Z","date_published":"2019-10-17T07:00:00Z","updated_at":"2026-07-24T02:43:33Z","subjects":["Decision quality","attribution theory","decision support systems","task-technology fit","intolerance for ambiguity","self-efficacy","Business","Business Administration, Management, and Operations","Business Analytics","Management Information Systems"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/phdba_etd/12","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reza Vaezi","Michael Gallivan"]},{"key":"dc:creator","label":"Author","values":["Niese, Bethany"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2021-12-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business Administration"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Decision quality","attribution theory","decision support systems","task-technology fit","intolerance for ambiguity","self-efficacy","Business","Business Administration, Management, and Operations","Business Analytics","Management Information Systems"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/phdba_etd/12"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Decision-makers endeavor to obtain the decision quality which puts them in a position to reach their goals. In order to control or influence decision quality, the processes by which individuals form their beliefs must be understood. In addition, many decision makers rely on decision support technologies to help find patterns in data and make sense of the input, so these technologies must be considered in parallel with the processes.
There have been numerous studies conducted to illuminate the factors which affect decision quality, however, many of these studies focused on objective measures and factors. This approach ignores individual perception, belief, and judgment. The evaluation of decision quality as perceived by the decision maker is important because these perceptions will direct future processes, decisions, and actions of the decision-makers. Also, by considering the perspective of the decision maker, theory and practice are being brought closer together. The focus of this study is to understand which factors contribute to individual’s perceptions of decision quality by combining a priori and observation-based methods through a theoretical lens. Attribution theory is a well-established theory often applied when researching individual perception and serves as the foundation for the proposed model. The model examines the impact of the environmental attributes of task-technology fit and internal aspects of the decision-maker including intolerance for ambiguity and self-efficacy on decision quality.
This study empirically tested the proposed model using a two-phased approach. A pilot of 84 students was used to validate the instrument. The primary study of 413 business decision-makers who used decision support technology was used to validate the structural model. The model was validated using partial least squares structural equation modeling (PLS-SEM). Results show support that the perception of the fit between the decision support technology and the decision task directly affects decision-makers’ perception of the resulting decision quality, as does the decision-makers’ self-efficacy with decision making and with the decision support technology. Also supported is that task-technology fit and intolerance for ambiguity influence both self-efficacy with decision-making and with the decision support technology.
"]},{"key":"dc:title","label":"Title","values":["MAKING GOOD DECISIONS: AN ATTRIBUTION MODEL OF DECISION QUALITY IN DECISION TASKS"]}]}],"canonical_facts":{"dc:contributor":["Reza Vaezi","Michael Gallivan"],"dc:creator":["Niese, Bethany"],"dc:date.available":["2021-12-01T08:00:00Z"],"dc:description.abstract":["Decision-makers endeavor to obtain the decision quality which puts them in a position to reach their goals. In order to control or influence decision quality, the processes by which individuals form their beliefs must be understood. In addition, many decision makers rely on decision support technologies to help find patterns in data and make sense of the input, so these technologies must be considered in parallel with the processes.
There have been numerous studies conducted to illuminate the factors which affect decision quality, however, many of these studies focused on objective measures and factors. This approach ignores individual perception, belief, and judgment. The evaluation of decision quality as perceived by the decision maker is important because these perceptions will direct future processes, decisions, and actions of the decision-makers. Also, by considering the perspective of the decision maker, theory and practice are being brought closer together. The focus of this study is to understand which factors contribute to individual’s perceptions of decision quality by combining a priori and observation-based methods through a theoretical lens. Attribution theory is a well-established theory often applied when researching individual perception and serves as the foundation for the proposed model. The model examines the impact of the environmental attributes of task-technology fit and internal aspects of the decision-maker including intolerance for ambiguity and self-efficacy on decision quality.
This study empirically tested the proposed model using a two-phased approach. A pilot of 84 students was used to validate the instrument. The primary study of 413 business decision-makers who used decision support technology was used to validate the structural model. The model was validated using partial least squares structural equation modeling (PLS-SEM). Results show support that the perception of the fit between the decision support technology and the decision task directly affects decision-makers’ perception of the resulting decision quality, as does the decision-makers’ self-efficacy with decision making and with the decision support technology. Also supported is that task-technology fit and intolerance for ambiguity influence both self-efficacy with decision-making and with the decision support technology.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/phdba_etd/12"],"dc:subject":["Decision quality","attribution theory","decision support systems","task-technology fit","intolerance for ambiguity","self-efficacy","Business","Business Administration, Management, and Operations","Business Analytics","Management Information Systems"],"dc:title":["MAKING GOOD DECISIONS: AN ATTRIBUTION MODEL OF DECISION QUALITY IN DECISION TASKS"],"thesis:degree_discipline":["Business Administration"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Information Systems"]},"updated_at":"2026-07-24T02:43:33Z"}