The findings from our study extend the literature by demonstrating empirical support for the relationships between specific driver control strategies and carrier performance. Our study provides a theoretical rationale for how the combinations of formal and informal controls in our study influence motor carrier performance. In addition, the current study informs practitioners of the impact that management actions in support of formal and informal driver control can have on carrier performance across diverse motor carrier settings. Although we examined the phenomenon of management control in the trucking industry, our results may also be applicable to other logistics contexts, such as managing autonomous vehicle operators of other transportation modes and managing remote autonomous employees and business units.
This study represents the first effort to integrate complementary theoretical perspectives to explain the antecedent effect of formal controls on informal controls and ultimately firm performance. Research from marketing and logistics suggests that formal controls influence informal controls (Jaworski et al. 1993; Mello and Hunt 2009). In addition, the organizational literature discusses the benefits of using more formal and informal controls (Cravens et al. 2004; Baldauf et al. 2005). However, the signaling effect of formal controls on informal controls and subsequently performance has not been investigated before. The results of our analyses provide the first empirical evidence that formal control (output control) affects informal controls (POS and professional control). On the other hand, despite theoretical support, our results do not indicate that there is a direct effect of activity control on informal controls.
The results of our analyses present a new process whereby formal and informal controls affect market performance. To explain how formal and informal controls affect market performance, we integrated logistics knowledge about the operational performance effects on market performance into our model (Stank et al. 1999, 2003; Inman et al. 2011). The results of our study also support the logistics literature, wherein it is held that operational performance affects market performance even across a large and diverse sample of motor carriers.
Previous research examining activity control does not show evidence of a link between activity control and market performance (Jaworski et al. 1993; Oliver and Anderson 1994; Challagalla and Shervani 1996). Our research, then, is the first to provide any empirical support for the idea that formal controls affect market performance by influencing operational performance. Our results show weak support for the direct effect of activity control on operational performance; however, we show strong support for the indirect effect of activity control on market performance through operational performance. From the results of the mediation analyses, using the mediation framework provided by Zhao et al. (2010) we can see that in the current theoretical framework operational performance is the lone mediator of the effect of activity control on market performance.
This study constitutes the first endeavor to empirically test the effects of informal control on operational performance. It is surprising that POS does not affect operational performance; however, we did find that professional control has a significant effect on operational performance. Professional control effects on organizational performance are well understood in the marketing and sales literature (Agarwal and Ramaswami 1993; Lembke and Wilson 1998). However, our study extends the literature, as the first to show that professional control has a significant effect on market performance through operational performance.
In addition, our study examines the mediating effects of informal controls, such as how professional control mediates the effect of output control on firm performance. Studies from marketing and sales have resulted in inconsistent findings with regard to the effect of formal controls on market performance (Baldauf et al. 2005). The results of our study provide empirical support for the mediating role of an informal control, that is, professional control, on the relationship between output control and operational performance. Neither we did not find that POS acted as a mediator nor did we find the effect of activity control on operational performance to be mediated by the informal controls included in our study. Furthermore, new methodological advances allowed for enhanced mediation analyses that enabled us to test indirect effects that had previously been unavailable. And, although we did not directly hypothesize about the indirect effects of output control on market performance, we can infer this effect from the serial mediation effects. Hence, the results of our bootstrapping mediation analyses provide empirical support for the position that the effect of output control on market performance is mediated through professional control and operational performance. As a result, our study provides the first evidence of a process by which output control influences market performance. We hope that this provides a foundation for future research to build on in efforts to develop a theory about the effects of formal controls on performance.
Our initial model, that is, the model without informal controls (Figure 2), follows the existing theoretical control framework. It reveals that both the direct path from output control to market performance and the indirect path through operational performance are positive and significant. Consequently, the mediation framework proposed by Zhao et al. (2010) suggests that such a result indicates an incomplete theoretical framework wherein important mediators are excluded. Following the theoretical direction of Mello and Hunt (2009), our proposed theoretical paradigm includes two informal controls, professional control and POS, as mediators to explain the link between formal controls and performance. Our results indicate that professional control is likely to be the only mediator necessary to explain the link between output control, operational performance, and market performance in our theoretical framework. By demonstrating the mediation effects of informal controls on the effects of formal controls on performance, we are the first to empirically validate the theoretical framework for driver management as set out by Mello and Hunt (2009).
We used two structural models to illustrate the incomplete conclusions that could be drawn should mediators not be included in the theoretical framework. We thereby demonstrated the need for a strong theoretical framework supporting mediation coupled with a rigorous mediation analysis to understand how informal controls influence performance. We consider this demonstration of the use of bias-corrected bootstrap methods to be a major contribution to the field. These bootstrap methods were first advocated by Shrout and Bolger (2002), subsequently implemented by Preacher and Hayes (2008) and Preacher and Kelley (2011), and strongly advocated by Zhao et al. (2010). These methods enable the calculation of indirect effects with bias-corrected confidence intervals to test the significance of complex mediation effects, which offer tremendous potential for investigating the complex relationships inhering in supply chain management.
Managers may not perceive their actions as having any influence on whether and to what extent positive peer pressure develops among drivers or how drivers perceive organizational support. However, we show that when firms measure and incentivize employees' efforts, they can have a direct impact on employees in two ways: by fostering positive peer pressure among employees and by fostering the perception on the part of employees that the firm is providing them with a high level of organizational support. Likewise, managers may not perceive any benefits from exercising intangible informal controls, such as professional control. However, our results suggest that tangible benefits accrue from informal controls, such as professional control, which are influenced by management actions through implementing formal controls, such as output control.
Logistics managers could use the results of this research to select appropriate policies and procedures in an effort to motivate drivers to behave in ways that support the firm's objectives. Our results demonstrate that management strategies influence firms' operational goals and thus affect the bottom line. Our results suggest that when drivers are encouraged to interact with each other that the resulting positive peer pressure facilitates the full benefit of detailed driver feedback and driving-performance-based incentives on firm performance. Hence, firms that encourage their drivers to interact, cooperate, and discuss their work with each other reinforce the feedback drivers receive and the incentives designed to influence them. Our results suggest that driving-performance-based incentives could lead drivers to discuss feedback they receive from the firm. This could result in improved delivery reliability and responsiveness to customers, which could in turn lead to the firm realizing greater sales growth and market share. For example, a manager could post certain individual driver performance metrics in a prominent location in a terminal. This action could motivate drivers to influence each other through peer pressure, resulting in overall higher performance for all drivers and thus positively driving the bottom-line performance of the firm.
Logistics managers could use the results of this study to gain a better understanding of the importance of scheduling their drivers' work activities, determining work procedures, and regularly monitoring those activities. Our results demonstrate the resulting expected improvements in the reliability of delivery and responsiveness to customer needs for managers wishing to undertake these activities. By taking such steps, managers will position their firms to grow sales and gain a larger market share.
Limitations and future research directions
The results of this study are most applicable to firms with fleet sizes of 50 trucks or more because we excluded smaller fleets after consulting with industry experts. As with all empirical data, our data contains inherently random and nuisance variation. Perceptual data instead of actual performance data were collected to measure firm performance. Although the study could have benefited from drawing on firms' actual performance data such data is hard to come by due to its confidential nature. Perceptual data could cause different data sets to generate different results. However, we minimized this risk by adapting existing validated scale items from the literature. In addition, we specifically chose our methodology to minimize the effects of any spurious variation. Another possible limitation of this research is our use of a single key informant to collect data. Using a single informant from each firm allowed us to manage the cost of data collection; however, each informant was an expert with regard both to management controls and firm-level performance. Using structural equation modeling, we accounted for possible errors in measuring the different constructs and in validating our model's efficacy with regard to uncovering the underlying process. The use of a single respondent may also raise concerns with regard to common method variance, which can result in common method bias (CMB) (e.g. Podsakoff et al. 2003). However, Harman's single-factor test of our data provided evidence that the risk of CMB is minimal. Furthermore, recent simulation studies argue that single-respondent surveys do not exhibit bias (Richardson et al. 2009; Lance et al. 2010). Another possible limitation is our use of two response formats, self-administered and interviewer-administered. However, rigorous tests for multigroup configural and metric invariance showed the risk of bias from using the two response formats to be minimal. There could also be concern that changing the wording of the output control measures between the pilot and the main data collection might have compromised the results. The changes in wording, however, did not alter the meaning of the measures or the results, as evidenced in the results of the full model, for the main sample only (n = 573) (Appendix C).
Given the theoretical support, we were surprised to see no significant effects of POS on either operational or market performance. As our budget constraints dictated, we used the driver manager as the single key informant; however, it is likely that by measuring POS from the manager's perspective, we did not capture the construct effectively. It would be advisable, therefore, for future research to consider a research design and budget that would allow constructs to be measured based on interviews with multiple relevant key informants, such as driver managers and drivers. In general, the direct effects of formal and informal controls on performance were insignificant. Future research could take advantage of methodological advances in moderated mediation structural equation modeling (Preacher et al. 2007) to revisit the moderation effects of environmental factors and task characteristics theorized by Jaworski (1988) and Jaworski and MacInnis (1989). In addition, the field would benefit were future qualitative and quantitative research to be expanded to investigate driver control in a way that included the driver's perspective. For example, integrating the theoretical paradigm suggested in this study with that of Williams et al. (2011) would be useful in efforts to understand the effects that management driver control strategies have on drivers' intentions with regard to whether to stay in the employ of a motor carrier. Furthermore, future research could look at the effects of driver control on motor carrier safety and drivers' turnover intentions. In doing so, we could triangulate the data and compare performance with data from the Federal Motor Carrier Safety Administration safety database.
The model evaluated in this study includes a subset of formal and informal controls that are relevant to our theoretical framework of signaling theory, that is, SET and SIT. Future research could build on the current framework by investigating other formal or informal control strategies associated with other relevant theoretical paradigms. For example, research could investigate the combination of controls implemented by a motor carrier and their influence on how drivers exercise self-control in going about their jobs. Self-control is another type of informal control that could be beneficial in efforts to understand the management control process, especially when measured from the driver's perspective. Researchers could extend this framework to other logistics settings that employ autonomous employees, such as vehicle operators, in other transportation modes or autonomous teams that operate remotely and/or largely unsupervised.
Finally, Mello and Hunt (2009) suggest the use of technology as a complementary control to the formal controls used herein. In this study, our theories precluded consideration of theories from the information technology literature. Consequently, we did not consider the control aspects of recent innovations in QUALCOMM systems, electronic log-books, hand-held devices, or electronic on-board recorders. Future research could build on the results of this study to inform theory regarding and practices associated with the effects of using technology for driver-control purposes.