Mobile apps and employee behavior: An empirical investigation of the implementation of a fleet-management app

Levi-Bliech, Michal; Kurtser, Polina; Pliskin, Nava; Fink, Lior

doi:10.1016/j.ijinfomgt.2019.07.006

Cited by 11 publications

(4 citation statements)

References 76 publications

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“…Current research, for instance, addresses predictive maintenance by developing machine learning algorithms (Killeen et al, 2019) or dashboards (Arulraj et al, 2019) for an existing FMS. Moreover, the driving behavior of fleets is analyzed to reduce risky behavior through app notifications (Levi-Bliech et al, 2018). Similarly, lowering fuel consumption is also studied by identifying environmentally and economically beneficial driving modes (Walnum & Simonsen, 2015).…”

Section: Related Work and Foundationsmentioning

confidence: 99%

“…Thereby, fleet managers can remotely check relevant data such as fuel consumption, missing supplies, or illuminated indicator lights (Killeen et al, 2019). Accordingly, the CFMS provides a detailed look at vehicles that became conspicuous (informed action) to initiate maintenance measures if necessary and thus avoid vehicle downtime (Levi-Bliech et al, 2018). Therefore, we propose the following design principle.…”

Section: Design Principles On Operational Fleet Managementmentioning

confidence: 99%

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Introduction to the Minitrack on Qualitative Comparative Analysis in Business and Management Research

Nikou,

Mezei

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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The connected car has recently evolved from a theoretical concept to reality.Especially in professionally managed fleets, car connectivity promises additional benefits in terms of costs, environment, and maintenance. However, many fleet managers are unaware of using connected car data and still associate telematics with retrofitting each vehicle. Thus, we aim to develop a connected fleet management system to increase fleet operations' efficiency and effectiveness by utilizing multi-brand data from car manufacturers' backend shared by data marketplaces. Thereby, we follow a design science research approach using inputs from the existing body of knowledge and the practical problem domain. Drawing on the theory of effective use, we propose meta-requirements and tentative design principles and instantiate them in a prototype artifact.

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Section: Related Work and Foundationsmentioning

confidence: 99%

Section: Design Principles On Operational Fleet Managementmentioning

confidence: 99%

Introduction to the Minitrack on Qualitative Comparative Analysis in Business and Management Research

Nikou,

Mezei

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Despite varying complexities among the apps [ 44 ], the extent of complexity deters the apps users’ trialability and intention to use [ 45 ]. As a result, these barriers associated with mHealth apps may expose users to increased health hazards such as inconsistent functionalities, delayed start of treatment, and a worse pandemic condition [ 46 ]. Based on these studies, complexity is thought to be negatively associated with the trialability of mHealth apps.…”

Section: Theories and Hypothesis Developmentmentioning

confidence: 99%

Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study

Jilani¹,

Moniruzzaman²,

Dey

et al. 2022

IJERPH

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Recent advancements in mHealth apps and services have played a vital role in strengthening healthcare services and enabling their accessibility to marginalized people. With the alarming rise in COVID-19 infection rates around the world, there appears to be an urgent call to modernize traditional medical practices to combat the pandemic. This study aims to investigate the key factors influencing the trialability of mHealth apps/services and behavioral intention to adopt mobile health applications. The study also examines the moderating effects of self-discipline motivation, knowledge, and attitude on the relationship between trialability and behavioral intention to use. The deductive reasoning approach was followed in a positivism paradigm. The study used convenience sampling and collected responses from 280 Generation Y participants in Bangladesh. Partial least square-based structural equation modeling was employed. The results revealed that relative advantage (β = 0.229, p < 0.05), compatibility (β = 0.232, p < 0.05), complexity (β = −0.411, p < 0.05), and observability (β = 0.235, p < 0.05) of mHealth apps influence the trialability of mHealth apps and services among users. Trialability compatibility (β = 0.425, p < 0.05) of mHealth was positively related to the behavioral intention to use these mobile apps. The study found no moderating effects of attitude (β = 0.043, p > 0.05) or self-discipline motivation (β = −0.007, p > 0.05) on the hypothesized relationships. The empirical findings of this study may facilitate the development, design process, and implementation of mHealth applications with improved features that can lead to high user acceptance among Generation Y during future health crises.

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“…On the other hand, smartphones have also become an important platform for mobile applications, in general, and for the transport and mobility sector, in particular. These include, for example, app-based vehicle information systems [44] or fleet management apps, which, if used appropriately, can even lead to the prevention of risky driving behaviour [45,46]. In addition, smartphone sensors and cameras are already being used to enable driver monitoring systems, and researchers are exploring new ways to improve detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Papatheocharous,

Kaiser,

Moser

et al. 2023

Sensors

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Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

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Mobile apps and employee behavior: An empirical investigation of the implementation of a fleet-management app

Cited by 11 publications

References 76 publications

Introduction to the Minitrack on Qualitative Comparative Analysis in Business and Management Research

Introduction to the Minitrack on Qualitative Comparative Analysis in Business and Management Research

Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

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