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Behavioral Integrity: Examining the Effects of Trust Velocity and Psychological Contract Breach

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Abstract

Leader behavioral integrity (BI) is central to perceived credibility and thus to leaders’ effectiveness at fostering ethical and other climates. Our research broadens the theoretical foundation for BI research by integrating the cognitive–attributional role of trust in the formation and maintenance of leader BI perceptions. Guided by recent research on trust primacy and prior theories of fairness used to examine ethical behavior, we examine how perceptions of leader BI can be either diminished or maintained through trust velocity following a psychological contract breach. Using a field and an experimental study, we explore the manner in which followers perceive leader’s actions when conflicting interests lead to unfulfilled promises. We found that trust velocity mediates the relationship between a psychological contract breach and leader BI (study 1), and that informational justice moderates this relationship (study 2), suggesting that leaders can attenuate the impact of broken promises on ascribed BI. Our findings offer a pathway for leaders operating in dynamic contexts to preserve BI and also help address concerns that have been raised about the behavioral integrity construct regarding its conceptual overlap with related constructs such as trust, psychological contracts, and informational justice.

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Notes

  1. Results of a repeated-measures ANOVA confirm significant within-subject variability in trust in leader across the four waves of the survey (F(1,106) = 4.51, p < .05), supporting the notion of trust velocity as a meaningful variable within the context of our study. Although we did not formally hypothesize effects of BI velocity (i.e., direction and rate of change in leader BI perceptions over time), we also generated a measure of this construct in an effort to rule out concerns of reverse causality. The non-significant within-person variability in leader BI (F(1,106) = 1.28, n.s.) is not surprising, and in line with our theorizing suggests that trust may be more malleable and efficacious in explaining BI perceptions than vice versa.

  2. We ultimately dropped the reverse scored item, “If people knew more about this individual and his/her background, they would be more concerned and monitor his/her performance more closely” as this item demonstrated poor factor loadings (λ = .06 pre, .12 post) and item-total correlations (r = .05, .12, respectively). Reliability for the scale increased (pre α = .69 to .81; post α = .76 to .84) when omitting this item.

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The authors declare that they have complied with all ethical standards, and did not receive any external funding for this work.

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Appendix

Appendix

Measurement Invariance (Study 1)

The hypothesized two-factor model was tested for longitudinal measurement invariance following the recommendations outlined by Vandenberg and Lance (2000) to determine whether trust and BI perceptions were measured similarly across time. We conducted the four measurement invariance tests described by Hausknecht et al. (2011) to establish conditions for subsequently testing changes in trust. The first, baseline, model examined configural invariance (equivalent factor structure). Results (see M1 in Table

Table 4 MCFA measurement invariance model summaries and comparisons

4 below), in combination with the series of single versus two-factor model tests above, supported this aspect of measurement invariance.

Next, we tested metric invariance (invariant factor loadings), by freeing these parameters at different time periods and comparing this model (M2) to the model with fixed factor loadings (M1). Whereas these models were not significantly different, we retained the more parsimonious invariant model (M1). Third, we tested for invariant uniqueness (test of equal error variances) by freeing these parameters at different time periods (M3). Results of this model suggested that error variances differed across time periods, prompting further investigation. It is noteworthy that while perfect measurement invariance is ideal, models often fail to support this for all items (see Milfont, Duckitt and Cameron 2006; Vandenberg and Lance 2000), and that configural invariance and (at least partial) metric invariance need to be established before testing any further partial invariance models. Indeed, Vandenberg and Lance (2000) argue that partial invariance is permissible if the parameters relaxed to vary across groups (in our case, time) are a minority of indicators.

Having met the first two conditions of configural and metric invariance, we set out to determine the source and extent of non-equivalent error variance. After testing individual items across BI and trust constructs, we isolated the source to a single BI item (see M3.1), and to a single time point of measurement (M3.2). Relaxing this one item’s uniqueness for a specific time point provided an improved fit. It is noteworthy, that this parameter represented 1.5% (i.e., minority) of the total number of parameters compared in the measurement invariance tests, and the respective parameter changed from θδ = 0.18 to θδ = 0.12 (standardized). This made no detectable difference in the overall model fit indices (i.e., SRMR, CIR, NNFI).

In our fourth and final test of measurement invariance, we examined invariant factor variances by comparing this refined baseline model (M3.2) to a model that freely estimated latent construct variances and covariances (M4). Results suggested that people did not use a narrower range of the construct continuum, and that latent constructs remained equally correlated over time. In total, results supported measurement invariance, helping to rule out measurement artifacts as an alternative explanation for study findings and hypothesis tests described next.

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Vogelgesang, G.R., Crossley, C., Simons, T. et al. Behavioral Integrity: Examining the Effects of Trust Velocity and Psychological Contract Breach. J Bus Ethics 172, 175–190 (2021). https://doi.org/10.1007/s10551-020-04497-2

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