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Empirical testing of a human performance model| Understanding success in federal agencies using second-order structural equation modeling

<p>Even though various models have been developed in the field of human performance technology (HPT), little research has been done to empirically test these models of human performance (HP) with large amounts of data. This insufficient evidence on whether or not HP models work in practice discourages HPT professionals and workers from applying HP models into their own contexts. This study aims to examine structural relationships among performance support systems (PSS), human behaviors (HB), and performance (PER) in order to test the proposed performance model. Using national government-wide representative data from the 2012 Federal Employee Viewpoint Survey (FEVS, N=687,687, 82 federal agencies), this study has the opportunity to empirically test a comprehensive performance model (Bichelmeyer & Horvitz, 2006) using structural equation modeling (SEM).
In measurement model I, a 1st order confirmatory factor analysis (CFA), all model fit indices were found to be adequate (?</p><p>2(93) = 90800.207; CFI = .949; TLI = .926; RMSEA = .038; SRMR = .030). All factor loadings of the observed variables were significant (p < .001), indicating that all first-order factors were well measured by the indicators. In measurement model II, a 2nd order CFA, all model fit indices were also found to be in adequate range (?</p><p>2(111) = 120515.246; CFI = .933; TLI = .918; RMSEA = .040; SRMR = .036). All factor loadings of the first-order factors used to measure the second-order factors were statistically significant (p < .001), indicating that all the second-order factors were well measured by the first-order factors. In structural model, a 2nd order SEM, all model fit indices demonstrated the proposed model is entirely adequate (?</p><p>2(111) = 120515.381; CFI = .933; TLI = .918; RMSEA = .040; SRMR = .036). In terms of structural relationships, results supported the hypothesized direct associations among PSS, HB, and PER. Four steps outlined by Baron and Kenny (1986) and Judd and Kenny (1981) were taken for mediation analysis. In addition, bootstrapping (1,000) with confidence intervals was used for a robust examination of the mediating effect of HB. The results indicated that HB partially mediated the relationship between PSS and PER (?_11* ?_21 = .35, p < .001, 95% CI [0.34 to 0.37]).
Finally, implications are discussed based on the results and findings of this study. At the first-order factor level, various sets of practices for Human Performance Technology (HPT), Human Resource Management (HRM), and Human Resource Development (HRD) are presented.

Identiferoai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3715873
Date06 August 2015
CreatorsKang, In Gu
PublisherIndiana University
Source SetsProQuest.com
LanguageEnglish
Detected LanguageEnglish
Typethesis

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