AIMQ: a methodology for information quality assessment
01 Dec 2002-Information & Management (Elsevier Science Publishers B. V.)-Vol. 40, Iss: 2, pp 133-146
TL;DR: The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures, which are applied to analyze the gap between an organization and best practices.
About: This article is published in Information & Management.The article was published on 2002-12-01. It has received 1542 citations till now. The article focuses on the topics: Information quality & Information Quality Management.
Citations
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TL;DR: Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, thetypes of information systems addressed by each methodology.
Abstract: The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
1,048 citations
Cites methods from "AIMQ: a methodology for information..."
...The AIMQ methodology is the only information quality methodology focusing on benchmarking [Lee et al. 2002], that is an objective and domainindependent technique for quality evaluation....
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...…Quality Methodology Jeusfeld et al. 1998 TIQM Total Information Quality Management English 1999 AIMQ A methodology for information quality assessment Lee et al. 2002 CIHI Canadian Institute for Health Information methodology Long and Seko 2005 DQA Data Quality Assessment Pipino et al....
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TL;DR: A model consisting of nine fundamental determinants of quality in an information technology context, four under the rubric of information quality and five that describe system quality are developed, suggesting that the determinants are indeed predictive of overall information and system quality in data warehouse environments.
Abstract: Understanding the successful adoption of information technology is largely based upon understanding the linkages among quality, satisfaction, and usage. Although the satisfaction and usage constructs have been well studied in the information systems literature, there has been only limited attention to information and system quality over the past decade. To address this shortcoming, we developed a model consisting of nine fundamental determinants of quality in an information technology context, four under the rubric of information quality (the output of an information system) and five that describe system quality (the information processing system required to produce the output). We then empirically examined the aptness of our model using a sample of 465 data warehouse users from seven different organizations that employed report-based, query-based, and analytical business intelligence tools. The results suggest that our determinants are indeed predictive of overall information and system quality in data warehouse environments, and that our model strikes a balance between comprehensiveness and parsimony. We conclude with a discussion of the implications for both theory and the development and implementation of information technology applications in practice.
878 citations
Cites background from "AIMQ: a methodology for information..."
...Format is tied to the notion of representational quality [4, 47, 53, 82]....
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...Thus, the intrinsic view reflects a measure of agreement between the data values presented by an IS and the actual values the data represents in the real world [47, 60], the degree to which data values are not inaccurate, outdated, and inconsistent [48], and the accuracy of information generated by an IS [31, 67, 82]....
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...A context-based view extends the notion of information quality, suggesting that it needs to be defined relative to the user of the information, the task being completed, and the application being employed [47, 60]....
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TL;DR: The authors used the Theory of Reasoned Action and the Technology Acceptance Model to develop the WebQual instrument for consumer evaluation of Web sites and it is a highly validated instrument that can provide both wide- and fine-grained measurements of organizational Web sites.
Abstract: Despite the critical need to know how consumers' perceptions of Web sites influence their behavior, and especially their intention to revisit or purchase, there is no extant general measure for evaluating Web sites and no consensus on what such an instrument should measure. The authors used the Theory of Reasoned Action and the Technology Acceptance Model to develop the WebQual instrument for consumer evaluation of Web sites. They refined it through a literature review and interviews with Web designers and users, and tested it using four samples of Web consumers. WebQual includes 12 dimensions (informational fit-to-task, tailored information, trust, response time, ease of understanding, intuitive operations, visual appeal, innovativeness, emotional appeal, consistent image, on-line completeness, relative advantage) and shows strong measurement validity. It is a highly validated instrument that can provide both wide- and fine-grained measurements of organizational Web sites.
754 citations
Cites background from "AIMQ: a methodology for information..."
...This further confirms the relevance of the total set of dimensions for Web site evaluation: information quality [18], functional fit-to-task [79], tailored information [83], trust [62, 66, 75, 90, 98], response time [36], ease of use [69, 88], intuitive operations [69, 88], visual appeal [45], consistent image [18, 90], relative advantage [18, 77], and customer service [83, 112]....
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TL;DR: The data quality problem in the context of supply chain management (SCM) is introduced and methods for monitoring and controlling data quality are proposed and highlighted.
652 citations
TL;DR: Perceived information quality (PIQ) is proposed as a factor of perceived risk and trusting beliefs, which will directly affect intention to use the exchange and two important system design factors---control transparency and outcome feedback---will incrementally influence PIQ.
Abstract: This study examines the role of information quality in the success of initial phase interorganizational (I-O) data exchanges. We propose perceived information quality (PIQ) as a factor of perceived risk and trusting beliefs, which will directly affect intention to use the exchange. The study also proposes that two important system design factors---control transparency and outcome feedback---will incrementally influence PIQ. An empirical test of the model demonstrated that PIQ predicts trusting beliefs and perceived risk, which mediate the effects of PIQ on intention to use the exchange. Thus, PIQ constitutes an important indirect factor influencing exchange adoption. Furthermore, control transparency had a significant influence on PIQ, while outcome feedback had no significant incremental effect over that of control transparency. The study contributes to the literature by demonstrating the important role of PIQ in I-O systems adoption and by showing that information cues available to a user during an initial exchange session can help build trusting beliefs and mitigate perceived exchange risk. For managers of I-O exchanges, the study implies that building into the system appropriate control transparency mechanisms can increase the likelihood of exchange success.
600 citations
References
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TL;DR: A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success, but the dependent variable in these studies-I/S success-has been an elusive one to define.
Abstract: A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I/S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.
10,023 citations
"AIMQ: a methodology for information..." refers background or methods in this paper
...We develop and validate the questionnaire and use it to collect data on the status of organizational IQ....
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...Keywords: Information quality; Information quality assessment; Information quality benchmarking; Information quality analysis; Information quality improvement; Total Data Quality Management (TDQM)...
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TL;DR: The development of an instrument designed to measure the various perceptions that an individual may have of adopting an information technology IT innovation, comprising eight scales which provides a useful tool for the study of the initial adoption and diffusion of innovations.
Abstract: This paper reports on the development of an instrument designed to measure the various perceptions that an individual may have of adopting an information technology IT innovation. This instrument is intended to be a tool for the study of the initial adoption and eventual diffusion of IT innovations within organizations. While the adoption of information technologies by individuals and organizations has been an area of substantial research interest since the early days of computerization, research efforts to date have led to mixed and inconclusive outcomes. The lack of a theoretical foundation for such research and inadequate definition and measurement of constructs have been identified as major causes for such outcomes. In a recent study examining the diffusion of new end-user IT, we decided to focus on measuring the potential adopters' perceptions of the technology. Measuring such perceptions has been termed a "classic issue" in the innovation diffusion literature, and a key to integrating the various findings of diffusion research. The perceptions of adopting were initially based on the five characteristics of innovations derived by Rogers 1983 from the diffusion of innovations literature, plus two developed specifically within this study. Of the existing scales for measuring these characteristics, very few had the requisite levels of validity and reliability. For this study, both newly created and existing items were placed in a common pool and subjected to four rounds of sorting by judges to establish which items should be in the various scales. The objective was to verify the convergent and discriminant validity of the scales by examining how the items were sorted into various construct categories. Analysis of inter-judge agreement about item placement identified both bad items as well as weaknesses in some of the constructs' original definitions. These were subsequently redefined. Scales for the resulting constructs were subjected to three separate field tests. Following the final test, the scales all demonstrated acceptable levels of reliability. Their validity was further checked using factor analysis, as well as conducting discriminant analysis comparing responses between adopters and nonadopters of the innovation. The result is a parsimonious, 38-item instrument comprising eight scales which provides a useful tool for the study of the initial adoption and diffusion of innovations. A short, 25 item, version of the instrument is also suggested.
8,586 citations
"AIMQ: a methodology for information..." refers methods in this paper
...The development of the IQA instrument followed standard methods for questionnaire development and testing, see for example [26,31]....
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TL;DR: Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs and this research provides a basis for future studies that measure data quality along the dimensions of this framework.
Abstract: Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
4,069 citations
"AIMQ: a methodology for information..." refers background or methods in this paper
...E-mail addresses: y.lee@neu.edu (Y.W. Lee), dstrong@wpi.edu (D.M. Strong), bkahn@acad.suffolk.edu (B.K. Kahn), rwang@mit.edu (R.Y. Wang)....
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...One provided overall coverage for the IQ construct by empirically developing the dimensions from information consumers, such as in the Wang and Strong study....
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...MITRE [25] Same as [39] Same as [39] Same as [39] Same as [39]...
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...The Jarke and Vassiliou [16] study modified the Wang–Strong dimensions in their study of data warehouse quality....
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...Wang and Strong [39] Accuracy, believability,...
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TL;DR: This paper reports on a technique for measuring and analyzing computer user satisfaction, starting with the literature and using the critical incident interview technique, and creating a questionnaire for measuring satisfaction using the semantic differential scaling technique.
Abstract: This paper reports on a technique for measuring and analyzing computer user satisfaction. Starting with the literature and using the critical incident interview technique, 39 factors affecting satisfaction were identified. Adapting the semantic differential scaling technique, a questionnaire for measuring satisfaction was then created. Finally, the instrument was pilot tested to prove its validity and reliability. The results of this effort and suggested uses of the questionnaire are reported here.
2,634 citations
"AIMQ: a methodology for information..." refers background in this paper
...Two studies, one of which is the wellknown user satisfaction study by Bailey and Pearson [3], include nine measures....
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TL;DR: In this paper, the authors provide a synthesis of the quality literature by identifying eight critical factors (areas) of quality management in a business unit and develop operational measures of these factors using data collected from 162 general managers and quality managers of 89 divisions of 20 companies.
Abstract: Much has been written about how quality should be managed in an organization. The quality literature contains many case studies of successful companies and descriptions of quality concepts and quality improvement programs. To date, however, there has been no systematic attempt to organize and synthesize the various prescriptions offered, nor have measures of organizational quality management been proposed for areas such as top management leadership, training, employee involvement, and supplier management. While many organizations collect quality data such as defect rates, error rates, rework cost, and scrap cost, these are not measures of organization-wide quality management.
This paper provides a synthesis of the quality literature by identifying eight critical factors (areas) of quality management in a business unit. Operational measures of these factors are developed using data collected from 162 general managers and quality managers of 89 divisions of 20 companies. The measures can be used individually or in concert to produce a profile of organization-wide quality practices. The measures are found to be both valid and reliable. Such measures could be used by decision makers in an organization to assess the status of quality management in order to direct improvements in the quality area. Researchers can use such measures to better understand quality management practice and to build theories and models that relate the critical factors of quality management to the organization's quality environment and quality performance.
2,094 citations