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Purpose — This chapter is aimed at testing the strength of three different drivers to engage in dishonest behavior at work — financial gain, response to injustice, and escape from boredom — and shedding light to the power of individual and organizational values to hold down the effect of these drivers.Design/methodology/approach — We analyze the data of 167 service employees from a large retail organization, who responded to questionnaires which manipulated drivers and organizational values.Findings — As a result we find that the financial and injustice drivers are effectively triggering several dishonest behaviors, whereas — contrary to the expectations — boredom at work does not threaten employers with employee engagement in dishonest behavior. We do find weak moderating effect of individual values in reacting to the drivers for some forms of dishonest behaviors, but the role of organizational values was marginal.Originality/value — In this chapter dishonest behavior is divided into nine specific dishonest acts involving management and customers as the stakeholders whose interests are at stake. We attempt to associate these behaviors with particular drivers. We also look at the moderators in this process: individual and organizational values. To date, espoused values of the organization is an underexplored organizational instrument compared to other situational variables, for instance, the existence of codes of ethics.

The study aims to examine social-psychological beliefs and personality traits and their linkage with driver predispositions and road safety behaviour grounded on notions derived from an integration of the health belief model (HBM) and the theory of planned behaviour (TPB) in social marketing.

The study used a cross-sectional survey to gather data from 587 licenced drivers in 3 major urban settlements in Ghana. The theoretical model was tested by using covariance-based structural equation modelling.

The study finds that the effects of perceived benefits, perceived behavioural control, social norms and cues to action on road safety behaviour are direct; the effects of perceived susceptibility, severity and barriers on road safety behaviour are fully mediated by driver attitude towards safe driving. Some of these effects were moderated by conscientiousness and neuroticism.

The findings offer empirical grounds for the development of evidence-based social marketing interventions that leverage efficacy-centred messages, social influence through community-based approaches, informational cues with consistent education and are tailored to the personality traits of drivers with the aim of inducing wilful on-road safety behaviour towards achieving sustainable road safety culture.

This study extends the integrative applicability of the HBM and TPB in understanding road safety behaviour and establishes attitude as a vital facilitator, and personality traits as moderators of the belief-preventive behaviour linkage within a developing country context. It contributes towards the use of theory-based outcomes to enhance the efficacy of social marketing road safety campaigns.

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.