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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Multi-objective optimization decision-making of quality dependent product recovery for sustainability
TL;DR: A quality-dependent multi-objective optimization model was developed and validated to identify the optimal or near optimal product recovery solution that best balances the economic, environmental and societal performances of product recovery for sustainability.
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The importance of strategic readiness in an emerging e‐government environment
TL;DR: Examination of how information technology, strategic planning processes, and people interact in an emerging e‐government environment finds government agencies must evaluate how strategic e‐ government plans are developed, communicated, and integrated into the work environment.
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Factors that determine residents’ acceptance of smart city technologies
TL;DR: This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services, and reveals perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of Trust in government.
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The moderating effect of occupation on the perception of information services quality and success
TL;DR: In this article, the authors proposed a modified IS success model that includes service quality as one of the success factors and examined occupation as a moderator of that model to evaluate information system service quality from the perspective of occupation.
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An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products
TL;DR: In this article, an integrated method of multi-target reverse-recursion and partial topological sorting is used to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling.