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Linking formal controls to motor carrier performance: Curvilinear and interaction effects

发布时间：2014-4-1413:37:2来源：作者：John P. Saldanha, Jason W. Miller, C. Shane Hunt, John E. Mello

John P. Saldanha^{a}

Jason W. Miller^{b}, ^{1}

C. Shane Hunt^{c},^{ 2}

John E. Mello^{c}, ^{3}

**Highlights**

•We conducted a national survey of motor carriers’ formal management controls.

•We found complex relationships between formal controls and carriers’ performance.

•Activity control exhibits a negative quadratic effect on market performance.

•Activity and output control exhibit a positive interaction on market performance.

Keywords

Formal control; Path–goal theory; Truck driver; Motor carrier performance; Nonlinear structural equation model; Moderation

Abstract

We examine the relationships between formal management controls (FMCs)—activity controls and output controls—and motor carriers’ operational and market performance. Using nonlinear structural equation modeling, we identify curvilinear and interaction effects of FMCs on carrier performance. We contribute to theory by providing evidence that the relationships between FMCs and performance are more complex than previously theorized. Namely, excessive activity control will adversely affect market performance, especially when output control is low. However, when output control is high, increasing activity control improves market performance—up to a point. Accordingly, we provide managerial guidance on appropriate levels of FMC use.

Article Outline

1. Introduction

2. Literature review

3. Hypotheses

3.1. Effect of activity control on performance

3.2. Interaction effect of activity control and output control on performance

3.3. Effect of operational performance on market performance

4. Data and methodology

4.1. Scale development

4.2. Pilot test

4.3. Main survey

4.4. Response bias

4.5. Covariates

5. Results

5.1. Main analysis

5.2. Robustness tests

6. Discussion

7. Limitations

8. Conclusion

Acknowledgments

Appendix A. Scale items

Appendix B. Variable correlation matrix and descriptive statistics

Appendix C. Derivation of the standard error of the conditional effect of activity control and output control on market performance

References

Figures

Fig. 1.

Theoretical model providing the theoretical rationale for the hypothesized curvilinear and interaction effects.

Fig. 2.

Diagram of the estimated nonlinear structural equation model (NSEM). The quadratic and interaction terms do not have observed indicators, as these effects are directly modeled by the LMS algorithm.

Fig. 3.

Plot of the predicted value of market performance from +1 standard deviation of activity control at low (1 standard deviation below the mean), average (0), and high (1 standard deviation above the mean) of output control.

Fig. 4.

Johnson–Neyman plot of the simple slope of activity control on market performance given a low level of output control (1 standard deviation below the mean) from +1 standard deviation of activity control.

Fig. 5.

Johnson–Neyman plot of the simple slope of activity control on market performance given a high level of output control (1 standard deviation above the mean) from +1 standard deviation of activity control.

Fig. 6.

Johnson–Neyman plot of the simple slope of output control on market performance from +1 standard deviation of activity control.

Tables

Table 1. Non-response bias test using geographic, revenue, and fleet-size characteristic comparisons.

Table 2. Titles of respondents.

Table 3. Descriptive statistics for the covariates before transformations.

Table 4. Standardized factor loadings, average variance extracted (AVE), and composite reliabilities (CR) for multi-item reflective latent variables.

Table 5. Correlations between the reflective latent variables and covariates from the full CFA model. The square root of the average variance extracted is on the diagonal for the multi-item reflective latent variables.

Table 6. Unstandardized results for the nonlinear structural equation model (NSEM) where operational performance (Panel A) and market performance (Panel B) are the dependent variables.

Table 7. Results from the robustness analysis to determine whether the parameter estimates of the structural coefficients differ between private fleets and for-hire carriers.a

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