V
Victor R. Prybutok
Researcher at University of North Texas
Publications - 270
Citations - 10112
Victor R. Prybutok is an academic researcher from University of North Texas. The author has contributed to research in topics: Service quality & Quality (business). The author has an hindex of 44, co-authored 239 publications receiving 8749 citations. Previous affiliations of Victor R. Prybutok include College of Business Administration & California State University, San Bernardino.
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Cautions on the Use of the SERVQUAL Measure to Assess the Quality of Information Systems Services
TL;DR: This article provides an illustrative example utilizing data collected from 138 executive and information systems professional customers of a multibillion dollar information services provider in order to examine the validity and reliability of Kettinger and Lee's modified SERVQUAL instrument.
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An Integrated Model for Customer Online Repurchase Intention
TL;DR: An integrated model by examining how utilitarian factors, the hedonic factor, and social/psychological factors directly or indirectly influenced consumers' continuance intention in the context of online shopping provides statistically significant explanations of the variation in consumers' online repurchase intention.
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A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area.
Junsub Yi,Victor R. Prybutok +1 more
TL;DR: A neural network model for forecasting daily maximum ozone levels is developed that is superior to the regression and Box-Jenkins ARIMA models the authors tested and compared the neural network's performance with those of two traditional statistical models.
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Latent Semantic Analysis: five methodological recommendations
TL;DR: Five methodological issues that need to be addressed by the researcher who will embark on Latent Semantic Analysis are reviewed, involving the analysis of abstracts for papers published in the European Journal of Information Systems.
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A customer value, satisfaction, and loyalty perspective of mobile application recommendations
TL;DR: It is found that intention to recommend is a strong predictor of app recommendation behavior and the identification of the knowledge of alternative quality factor examines users' IT behaviors with consideration of the effects of alternatives.