The Experts below are selected from a list of 719742 Experts worldwide ranked by ideXlab platform
Taizan Chan - One of the best experts on this subject based on the ideXlab platform.
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re conceptualizing information system success the is impact Measurement Model
Journal of the Association for Information Systems, 2008Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper re-conceptualizes 'information system success' as a formative, multidimensional index. A validated and widely accepted such index would facilitate cumulative research on the impacts of IS, while at the same time providing a benchmark for organizations to track their IS performance. The proposed IS-Impact Measurement Model represents the stream of net benefits from an Information System (IS), to date and anticipated, as perceived by all key-user-groups. Model measures are formulated to be robust, economical and simple, yielding results that are comparable across diverse systems and contexts, and from multiple user perspectives. The Model includes 4 dimensions in two halves. The 'impact' half measures benefits to date, or Individual- and Organizational-Impact; the 'quality' half, uses System-Quality and Information-Quality as proxies for probable future impacts. Study findings evidence the necessity, additivity and completeness of these four dimensions. The validation study involved three separate surveys, including exploratory and confirmatory phases preceded by an identification-survey. Content analysis of 485 qualitative 'impacts' cited by 137 respondents from across 27 Australian Government Agencies that implemented SAP Financials in the late 90's, identified salient dimensions and measures. The resultant a-priori Model ('pool' of 37 measures) was operationalized in the subsequent specification-survey, yielding 310 responses across the same 27 agencies. The confirmation-survey, employing 27 validated measures from the specification-survey, was next conducted in a large university that had implemented ORACLE Financials. Confirmatory analysis of the 153 responses provides further strong evidence of Model validity.
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a factor and structural equation analysis of the enterprise systems success Measurement Model
Americas Conference on Information Systems, 2004Co-Authors: Darshana Sedera, Guy G Gable, Taizan ChanAbstract:Enterprise systems entail complex organizational interventions. Accurately gauging the impact of any complex information system requires understanding its multidimensionality, and the development of a correspondent, standardized, validated, and robust Measurement instrument. Despite the popularity and potential of enterprise systems in modern organizations, no acceptably valid and reliable enterprise system success assessment scale has heretofore been developed. The present study tests the reliability and construct validity of the enterprise system success (ESS) Measurement Model and variants against new empirical data. Results from a confirmatory factor analysis utilizing structural equation Modeling techniques confirm the existence of four distinct and individually important dimensions of ESS: individual impact, organizational impact, system quality, and information quality. Based on the analysis of results, the ESS instrument demonstrates strong reliability and validity.
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enterprise systems success a Measurement Model
International Conference on Information Systems, 2003Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper presents a validated Measurement Model and instrument for assessing enterprise systems success from multiple perspectives. The final validated study Model employs 27 measures of the four dimensions: information quality, system quality, individual impact, and organizational impact. The Model is empirically tested with survey data gathered from 27 public sector organizations that implemented SAP R/3 in the late 1990s. The study consists of an exploratory inventory survey (Model building) to identify the salient success dimensions and measures, followed by a confirmatory weights survey, for testing Model validity (Model testing). Test results demonstrate the discriminant validity of the four dimensions, as well as their convergence on a single higher-order phenomenon: enterprise systems success (ESS). Criterion validity testing further demonstrates the additivity of the four dimensions of success, and the completeness of the resultant overarching, second-order measure of ESS.
Darshana Sedera - One of the best experts on this subject based on the ideXlab platform.
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re conceptualizing information system success the is impact Measurement Model
Journal of the Association for Information Systems, 2008Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper re-conceptualizes 'information system success' as a formative, multidimensional index. A validated and widely accepted such index would facilitate cumulative research on the impacts of IS, while at the same time providing a benchmark for organizations to track their IS performance. The proposed IS-Impact Measurement Model represents the stream of net benefits from an Information System (IS), to date and anticipated, as perceived by all key-user-groups. Model measures are formulated to be robust, economical and simple, yielding results that are comparable across diverse systems and contexts, and from multiple user perspectives. The Model includes 4 dimensions in two halves. The 'impact' half measures benefits to date, or Individual- and Organizational-Impact; the 'quality' half, uses System-Quality and Information-Quality as proxies for probable future impacts. Study findings evidence the necessity, additivity and completeness of these four dimensions. The validation study involved three separate surveys, including exploratory and confirmatory phases preceded by an identification-survey. Content analysis of 485 qualitative 'impacts' cited by 137 respondents from across 27 Australian Government Agencies that implemented SAP Financials in the late 90's, identified salient dimensions and measures. The resultant a-priori Model ('pool' of 37 measures) was operationalized in the subsequent specification-survey, yielding 310 responses across the same 27 agencies. The confirmation-survey, employing 27 validated measures from the specification-survey, was next conducted in a large university that had implemented ORACLE Financials. Confirmatory analysis of the 153 responses provides further strong evidence of Model validity.
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a factor and structural equation analysis of the enterprise systems success Measurement Model
Americas Conference on Information Systems, 2004Co-Authors: Darshana Sedera, Guy G Gable, Taizan ChanAbstract:Enterprise systems entail complex organizational interventions. Accurately gauging the impact of any complex information system requires understanding its multidimensionality, and the development of a correspondent, standardized, validated, and robust Measurement instrument. Despite the popularity and potential of enterprise systems in modern organizations, no acceptably valid and reliable enterprise system success assessment scale has heretofore been developed. The present study tests the reliability and construct validity of the enterprise system success (ESS) Measurement Model and variants against new empirical data. Results from a confirmatory factor analysis utilizing structural equation Modeling techniques confirm the existence of four distinct and individually important dimensions of ESS: individual impact, organizational impact, system quality, and information quality. Based on the analysis of results, the ESS instrument demonstrates strong reliability and validity.
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enterprise systems success a Measurement Model
International Conference on Information Systems, 2003Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper presents a validated Measurement Model and instrument for assessing enterprise systems success from multiple perspectives. The final validated study Model employs 27 measures of the four dimensions: information quality, system quality, individual impact, and organizational impact. The Model is empirically tested with survey data gathered from 27 public sector organizations that implemented SAP R/3 in the late 1990s. The study consists of an exploratory inventory survey (Model building) to identify the salient success dimensions and measures, followed by a confirmatory weights survey, for testing Model validity (Model testing). Test results demonstrate the discriminant validity of the four dimensions, as well as their convergence on a single higher-order phenomenon: enterprise systems success (ESS). Criterion validity testing further demonstrates the additivity of the four dimensions of success, and the completeness of the resultant overarching, second-order measure of ESS.
Guy G Gable - One of the best experts on this subject based on the ideXlab platform.
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re conceptualizing information system success the is impact Measurement Model
Journal of the Association for Information Systems, 2008Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper re-conceptualizes 'information system success' as a formative, multidimensional index. A validated and widely accepted such index would facilitate cumulative research on the impacts of IS, while at the same time providing a benchmark for organizations to track their IS performance. The proposed IS-Impact Measurement Model represents the stream of net benefits from an Information System (IS), to date and anticipated, as perceived by all key-user-groups. Model measures are formulated to be robust, economical and simple, yielding results that are comparable across diverse systems and contexts, and from multiple user perspectives. The Model includes 4 dimensions in two halves. The 'impact' half measures benefits to date, or Individual- and Organizational-Impact; the 'quality' half, uses System-Quality and Information-Quality as proxies for probable future impacts. Study findings evidence the necessity, additivity and completeness of these four dimensions. The validation study involved three separate surveys, including exploratory and confirmatory phases preceded by an identification-survey. Content analysis of 485 qualitative 'impacts' cited by 137 respondents from across 27 Australian Government Agencies that implemented SAP Financials in the late 90's, identified salient dimensions and measures. The resultant a-priori Model ('pool' of 37 measures) was operationalized in the subsequent specification-survey, yielding 310 responses across the same 27 agencies. The confirmation-survey, employing 27 validated measures from the specification-survey, was next conducted in a large university that had implemented ORACLE Financials. Confirmatory analysis of the 153 responses provides further strong evidence of Model validity.
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a factor and structural equation analysis of the enterprise systems success Measurement Model
Americas Conference on Information Systems, 2004Co-Authors: Darshana Sedera, Guy G Gable, Taizan ChanAbstract:Enterprise systems entail complex organizational interventions. Accurately gauging the impact of any complex information system requires understanding its multidimensionality, and the development of a correspondent, standardized, validated, and robust Measurement instrument. Despite the popularity and potential of enterprise systems in modern organizations, no acceptably valid and reliable enterprise system success assessment scale has heretofore been developed. The present study tests the reliability and construct validity of the enterprise system success (ESS) Measurement Model and variants against new empirical data. Results from a confirmatory factor analysis utilizing structural equation Modeling techniques confirm the existence of four distinct and individually important dimensions of ESS: individual impact, organizational impact, system quality, and information quality. Based on the analysis of results, the ESS instrument demonstrates strong reliability and validity.
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enterprise systems success a Measurement Model
International Conference on Information Systems, 2003Co-Authors: Guy G Gable, Darshana Sedera, Taizan ChanAbstract:This paper presents a validated Measurement Model and instrument for assessing enterprise systems success from multiple perspectives. The final validated study Model employs 27 measures of the four dimensions: information quality, system quality, individual impact, and organizational impact. The Model is empirically tested with survey data gathered from 27 public sector organizations that implemented SAP R/3 in the late 1990s. The study consists of an exploratory inventory survey (Model building) to identify the salient success dimensions and measures, followed by a confirmatory weights survey, for testing Model validity (Model testing). Test results demonstrate the discriminant validity of the four dimensions, as well as their convergence on a single higher-order phenomenon: enterprise systems success (ESS). Criterion validity testing further demonstrates the additivity of the four dimensions of success, and the completeness of the resultant overarching, second-order measure of ESS.
Cheryl Burke Jarvis - One of the best experts on this subject based on the ideXlab platform.
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the problem of Measurement Model misspecification in behavioral and organizational research and some recommended solutions
Journal of Applied Psychology, 2005Co-Authors: Scott B Mackenzie, Philip M Podsakoff, Cheryl Burke JarvisAbstract:The purpose of this study was to review the distinction between formative- and reflective-indicator Measurement Models, articulate a set of criteria for deciding whether measures are formative or reflective, illustrate some commonly researched constructs that have formative indicators, empirically test the effects of Measurement Model misspecification using a Monte Carlo simulation, and recommend new scale development procedures for latent constructs with formative indicators. Results of the Monte Carlo simulation indicated that Measurement Model misspecification can inflate unstandardized structural parameter estimates by as much as 400% or deflate them by as much as 80% and lead to Type I or Type II errors of inference, depending on whether the exogenous or the endogenous latent construct is misspecified. Implications of this research are discussed.
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a critical review of construct indicators and Measurement Model misspecification in marketing and consumer research
Journal of Consumer Research, 2003Co-Authors: Cheryl Burke Jarvis, Scott B Mackenzie, Philip M PodsakoffAbstract:A review of the literature suggests that few studies use formative indicator Measurement Models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective Measurement Models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be Modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of Measurement Model misspecification in the field, (d) estimate the extent to which Measurement Model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for Modeling formative indicator constructs.
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a critical review of construct indicators and Measurement Model misspecification in marketing and consumer research
Journal of Consumer Research, 2003Co-Authors: Cheryl Burke Jarvis, Scott B Mackenzie, Philip M PodsakoffAbstract:A review of the literature suggests that few studies use formative indicator Measurement Models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective Measurement Models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be Modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of Measurement Model misspecification in the field, (d) estimate the extent to which Measurement Model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for Modeling formative indicator constructs. Copyright 2003 by the University of Chicago.
Philip M Podsakoff - One of the best experts on this subject based on the ideXlab platform.
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the problem of Measurement Model misspecification in behavioral and organizational research and some recommended solutions
Journal of Applied Psychology, 2005Co-Authors: Scott B Mackenzie, Philip M Podsakoff, Cheryl Burke JarvisAbstract:The purpose of this study was to review the distinction between formative- and reflective-indicator Measurement Models, articulate a set of criteria for deciding whether measures are formative or reflective, illustrate some commonly researched constructs that have formative indicators, empirically test the effects of Measurement Model misspecification using a Monte Carlo simulation, and recommend new scale development procedures for latent constructs with formative indicators. Results of the Monte Carlo simulation indicated that Measurement Model misspecification can inflate unstandardized structural parameter estimates by as much as 400% or deflate them by as much as 80% and lead to Type I or Type II errors of inference, depending on whether the exogenous or the endogenous latent construct is misspecified. Implications of this research are discussed.
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a critical review of construct indicators and Measurement Model misspecification in marketing and consumer research
Journal of Consumer Research, 2003Co-Authors: Cheryl Burke Jarvis, Scott B Mackenzie, Philip M PodsakoffAbstract:A review of the literature suggests that few studies use formative indicator Measurement Models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective Measurement Models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be Modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of Measurement Model misspecification in the field, (d) estimate the extent to which Measurement Model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for Modeling formative indicator constructs.
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a critical review of construct indicators and Measurement Model misspecification in marketing and consumer research
Journal of Consumer Research, 2003Co-Authors: Cheryl Burke Jarvis, Scott B Mackenzie, Philip M PodsakoffAbstract:A review of the literature suggests that few studies use formative indicator Measurement Models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective Measurement Models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be Modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of Measurement Model misspecification in the field, (d) estimate the extent to which Measurement Model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for Modeling formative indicator constructs. Copyright 2003 by the University of Chicago.
