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Mobile apps and employee behavior: An empirical investigation of the implementation of a fleet-management app
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0003-4685-379X
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0003-3565-797X
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID iD: 0000-0001-5165-5259
2019 (English)In: International Journal of Information Management, ISSN 0268-4012, E-ISSN 1873-4707, Vol. 49, p. 355-365Article in journal (Refereed) Published
Abstract [en]

Whereas implementing a mobile application (app) in support of organizational processes is quite common in contemporary organizations, only few empirical studies have investigated the impact of app implementation in an organizational context. This study explores the association between the driving behavior of employed drivers and pre-driving app use of a fleet-management app. Users can get from the app not only real-time notifications while driving but can also take advantage of a unique app capability, that more traditional driving technologies do not provide, and receive feedback about their driving before their next drive. We hypothesize that pre-driving app use is associated with reduced risky driving behavior, and that this association is mitigated by real-time notifications and enhanced by experience with the app. The supportive results of the study confirm the organizational impact of implementing a fleet-management app via better driving behavior of employees who engage in pre-driving app use.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 49, p. 355-365
Keywords [en]
Mobile app, Fleet management, Employee behavior, Pre-driving appuse, Real-time notifications
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:oru:diva-79411DOI: 10.1016/j.ijinfomgt.2019.07.006ISI: 000489702000026Scopus ID: 2-s2.0-85069049085OAI: oai:DiVA.org:oru-79411DiVA, id: diva2:1388674
Available from: 2020-01-27 Created: 2020-01-27 Last updated: 2022-02-11Bibliographically approved

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Kurtser, Polina

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