Concerning the common method bias, we adopt both procedural remedy and statistical remedy to eliminate its effect (Cote & Buckley, 1987). We applied two procedural controls: First, we separated the questionnaire into two parts and asked each respondent to complete these two parts at different times, endeavoring to separate predictors and dependent variables. At Time 1, we measured basic information and social bond elements. At Time 2, we measured PL, ISP, compliance intentions, and self-reported compliance behavior. The time lag ranged from the length of a lecture to several days. As a result, the respondents were less likely to retain the same mood condition or refer to their short-term memory for consistent answers, which helped to reduce some sources of CMB (P. M. Podsakoff et al., 2003). Second, we asked respondents to give their names in order to match them with their supervisors. We informed them in advance that their data would be confidential and not shared with anybody. This was intended to help respondents provide honest answers and not manipulate their answers due to social desirability or leniency concerns (P. M. Podsakoff et al., 2003).
We also applied statistical remedies to supplement the procedural control. We identified social desirability (SD) as a primary source of method bias. According to P. M. Podsakoff et al. (2003), SD concerns the individual needs of social approval and acceptance, and this need is met by behaving in culturally acceptable and appropriate ways. SD drives people to present themselves in culturally favorable ways regardless of the nature of their true selves. Many behavioral studies have examined the existence of SD (Crowne & Marlowe, 1964). The SD bias may exist in our research because complying with ISPs is considered to be an acceptable and appropriate behavior, and respondents may falsely report high compliance even if they do not comply. Similarly, employees may overreport the existence of a social bond because employees are expected to attach strongly to leaders, demonstrate career commitment, frequently involve themselves in organizational activities, and believe in the norms within their organization. To control for this bias, we included SD in the research model by linking it to social bond, ISP compliance intention and actual compliance behavior, as recommended by Crowne & Marlowe, 1964; Reynolds, 1982; Turel, Serenko, & Giles, 2011. We compared the model results with and without SD. As Table E1 and Table E2 show, the model controlling for SD also fits the data well in terms of the fit indexes including CFI, GFI, TLI, RMSEA, and SRMR. We found SD to have significant relationships with ISP compliance intention, compliance behavior, and social bond, suggesting that much variance in the data is attributable to SD. The path coefficients among major constructs differed between the two models in both value and significance. This suggests that social desirability, if not controlled, can both inflate and deflate the path coefficients, leading to biased results. According to P. M. Podsakoff et al. (2003), the model estimates after controlling for method bias are more truthful. Therefore, our formal hypothesis testing results are based on the SD controlled model.
Since it is not easy to ask respondents to honestly and precisely report their actual compliance behavior to ISP, we collected data for this construct from both employees and their direct supervisors. In all, we collected 314 pairs of data on compliance behavior. According to P. M. Podsakoff et al. (2003), self-reported data (Mean = 1.4; SD = 1.074) may suffer from rating bias, which includes both social desirability and the unintentional bias caused by holding different judgement standards for compliance. Supervisor-reported compliance behavior (Mean = 1.31; SD = 0.629) may be affected by the knowledge bias because supervisors rated their subordinates based on their own observations rather than their actual compliance behavior. In our research, testing the model with the behavior data reported by different source may lead to different results (using self-reported behavior data, CI → CB, β = 0.366, P < 0.001; using leader- reported data, CI → CB, β = -0.001, NS). Since both self-reported and supervisor-reported data are subject to potential bias, relying on single source data may be problematic (P. M. Podsakoff et al., 2003). On the one hand, self-reported data could be exaggerated or deflated by social desirability concerns, because employees may not want to honestly disclose their deviant behaviors. On the other hand, leader-reported data could be inaccurate due to knowledge bias because the leaders may not be fully aware of the employees’ actual ISP compliance and deviant behaviors. According to Burton-Jones (2009), the average score of self-reported data and leaders-reported data is expected to be able to more accurately measure the true score. Therefore, we use the average score of employees’ self-reported and leader-rated compliance behavior in our research, which weakens the potential method bias caused by single source data.
Table E1. Model Fit Comparison
Model CMIN/DF CFI GFI TLI RMSEA SRMR
SD controlled 1.73 0.942 0.839 0.936 0.048 0.0853
No SD 1.719 0.948 0.852 0.943 0.048 0.0706
Table E2. Path Coefficients Comparison
Path
SD controlled No SD controlled
Path weight Path weight
AL → CI 0.168*** 0.05 BL → CI 0.125* 0.079 ML → CI 0.132* 0.157* Social bond → CI 0.378*** 0.472*** AL → Social bond 0.023 -0.064 BL → Social bond 0.217** 0.191** ML → Social bond 0.219** 0.253*** COMP → CB -0.309*** -0.556*** Age → CI 0.029 0.041 Edu → CI 0.05 -0.002 Gender → CI 0.084* 0.127** Org size → CI -0.087* -0.122** Age → CB 0.012 0.006 Edu → CB -0.01 0.057 Gender → CB -0.091* -0.14** Org size → CB -0.043 -0.028