The Impact of Predisposed Traits and Organizational Factors on the U.S. Federal Employee Perception of Whistleblowing

Abstract

Whistleblowers play a critical role in revealing organizational wrongdoing. Even after the passage of the 1989 Whistleblower Protection Act and the 2012 Whistleblower Protection Enhancement Act, numerous studies find that public employees are still reluctant to report wrongdoing due to various forms of retaliation. Drawing on insights from a framework of predisposition and environmental perspectives, this study examines which type of factors—predisposed characteristics or organizational/environmental factors—are more influential and consistent in increasing the favorable perception of public employees about whistleblowing. To test the model, this study uses multiple waves of data including the 2013, 2014, 2015, and 2016 Federal Employee Viewpoint Surveys to perform agency-level analyses. The findings suggest that organizational/environmental factors increase favorable federal employee perception of whistleblowing over time while predisposed characteristics show inconsistent influence.

Keywords

public whistleblowing whistleblowing intention predisposed traits nurture factors information sharing federal government

Introduction

One important research question regarding government whistleblowing is why public employees blow the whistle in spite of the known risk of retaliation (Caillier & Sa, 2017; Chang et al., 2017; Cho & Song, 2015; Jos et al., 1989; Lavena, 2016; Lee, 2020; Near & Miceli, 2008; Rothwell & Baldwin, 2006). Whistleblowing is a bottom-up oversight for ensuring accountability in government that gives public employees the opportunity to report wrongdoing in the organization (Klingner et al., 2010; Near & Miceli, 2008). Yet, according to the Merit Principles Surveys (MPS) and Federal Employee Viewpoint Surveys (FEVS), a large number of federal employees are reluctant to disclose wrongdoing in their organization due to possible retaliation, and many whistleblowers experienced retaliation or a threat of reprisal (Caillier & Sa, 2017; Lavena, 2016), such as revealing the whistleblower’s identity, blacklisting, transferring, demoting, or terminating (Bowman, 1980; Lee, 2020; Martin, 1996; Miethe, 1999; Schwellenbach, 2019).

Given the risk of retaliation, numerous studies have examined what factors affect individual perception and behavior regarding whistleblowing. A critical review of the literature reveals that most studies on whistleblowing have focused on predisposed characteristics (e.g., Cassematis & Wortley, 2013; Jos et al., 1989) or organizational and environmental factors (e.g., Bashir et al., 2011; Caillier & Sa, 2017; Chang et al., 2017; Jeon, 2017; Lavena, 2016; Lee, 2020; Near & Miceli, 2008; Rothwell & Baldwin, 2006).

Whereas previous whistleblowing studies attempted to identify the determinants of whistleblowing behavior or intention, including predisposed characteristics and organizational and environmental factors, few research studies have been done to examine which type of factors —that is, predisposed characteristics or organizational/environmental factors—is more influential in affecting public-sector whistleblowing. This article aims to further the study of whistleblowing as an oversight system by examining “which type of factors—predisposed characteristics or organizational/environmental factors—are more influential and consistent in increasing the favorable perception of public employees about whistleblowing?” To address our research question, this article develops and tests hypotheses by drawing on insights from the integrative frameworks of previous whistleblowing literature, and heredity and environment approaches—nature versus nurture factors (e.g., Bandura, 1977 Galton, 1892; Hager & Brudney, 2011).

Furthermore, most whistleblowing research has used individual-level measures from single cross-sectional data to assess the effects of individual characteristics and/or organizational/environmental factors on whistleblowing. This study seeks to bridge this gap in the literature. To test the model, this study uses multiple years of survey data and performs multiple-wave analyses where the independent variables precede the dependent variable in time, which is significantly different from previous research. By so doing, this study overcomes the limitations of cross-sectional analysis concerning causality of relationships and endeavors to validate, clarify, and extend previous research to better understand the relative impacts of predisposed characteristics and organizational/environmental factors on the favorable perception of federal employees about whistleblowing.

The findings from this study make important practical contributions to the literature by informing public personnel managers of a structured, detailed guide for enhancing the effectiveness of government whistleblowing. If whistleblowing stems from organizational/environmental factors, public personnel managers can benefit from identifying which factors nurture whistleblowers and help enhance the effectiveness of public-sector whistleblowing. In contrast, if whistleblowing lies in predisposed characteristics of employees, public personnel managers may better understand how they can improve the organization by identifying which personality traits are closely related to whistleblowing. The following sections will provide an overview of the definition and roles of whistleblowing, review the whistleblowing literature, develop a framework for research hypotheses, report empirical findings, and conclude with a discussion of the implications for management practices and further research.

Whistleblowing in the Workplace

Whistleblowing is defined as “disclosure by organization members (former or current) of illegal, immoral, and illegitimate practices under the control of their employers, to persons or organizations that may be able to effect action” (Near & Miceli, 1985, p. 4). It is a form of prosocial and extra-role behavior, which means when employees choose to report the wrongdoing, they are motivated by a sense of obligation to help and hold the organization more accountable (Miceli et al., 2008; Morrison, 2011). Previous studies have shown that public employees’ whistleblowing is a proactive and bottom-up oversight, which can benefit both the public and the whistleblower’s organization (Caillier, 2015; Cho & Song, 2015; Henik, 2008; Heumann et al., 2013; Hirschman, 1970; Jeon, 2017; Kaplan et al., 2010; Near & Miceli, 1985, 2008).

Nonetheless, whistleblowing literature presents a mixed picture about the effects and perception of whistleblowing in the government workplace. On one hand, as a bottom-up oversight, whistleblowing has the potential to increase the safety and well-being of organizational members, increase support for codes of ethics, prevent escalation of wrongdoings, and enhance government accountability (Apaza & Chang, 2017; Kaplan et al., 2010; Klingner et al., 2010; Miceli & Near, 2013; Miethe & Rothschild, 1994; Rothschild, 2008). On the other hand, whistleblowing can cause negative consequences in the workplace. It may be seen as a challenge to authority and represents a disruption to organizational viability (Cassematis & Wortley, 2013; Miceli & Near, 1992) and may have a negative impact on the organization’s reputation and incur high administrative and legal costs to the organization (Lee, 2020; Vadera et al., 2009). In this vein, whistleblowing can lead to various forms of retaliation, such as bureaucratic isolation, reduction in job responsibilities or salary, harassment, transfer, blacklisting, demotion, or even losing jobs (Cassematis & Wortley, 2013; Chang et al., 2017; Jos et al., 1989; Near & Miceli, 2008).

As aforementioned, retaliation (or threats of retaliation) against whistleblowers hinders public employees from reporting the wrongdoing. For example, a well-known whistleblower—Vince Cefalu—in the Bureau of Alcohol, Tobacco, Firearms and Explosives was punished by his agency after he blew the whistle on the failed anti-gunrunning operation that let thousands of guns slip across the U.S.–Mexico border.¹ His interview with an independent news organization reveals the many ways in which his agency retaliates against him, including multiple suspensions, reprimands, transfers, and being “idled in a cage, basically being left in an empty office with nothing to do, for two years, terminated twice.”² Despite the Whistleblower Protection Enhancement Act of 2012, retaliation against whistleblowers is still prevalent, and as a result, a large number of public employees are reluctant to report wrongdoing in the public workplace.

It is not a big surprise that the 2010 MPS finds that a considerable number of federal employees are unwilling to report wrongdoing. Less than one third of respondents in federal agencies reported that they are very likely to blow a whistle on their coworker (32.6%), supervisor (29.9%), and higher level of supervisor (31.8%). When a (suspected) wrongdoer is either a federal employee outside their work group or a contractor or vendor, only 39.9% and 54% of the respondents, respectively, answered that they are very likely to report it. Without more effective ways to reduce negative consequences against whistleblowing, public employees are likely to remain silent instead of reporting wrongdoing in their organizations.

Juxtaposing Determinants of Public Employee Perception of Whistleblowing: Predisposition versus Organizational Perspectives

Recent whistleblowing research has examined which factors influence public-sector whistleblowing behavior or intention. Cho and Song (2015) examined individual perspectives (personal costs and public-service motivation) and organizational supports (protection and education) as determinants of public-sector whistleblowing. Lavena (2016) used the 2005 MPS data to study whistleblowing behavior in the federal government by examining individual (norm-based and affective work motive) and organizational characteristics (organizational culture with respect and openness, cooperativeness and flexibility, fair treatment, and trust in the supervisor). Chang et al. (2017) also contributed to the whistleblowing literature by examining the impact of both individual factors (i.e., attitudes toward whistleblowing, knowledge of whistleblowing process) and organizational factors (i.e., colleague and organizational support for whistleblowing). Caillier and Sa (2017) and Lee (2020) expanded the research on public-sector whistleblowing by examining the role of individual characteristics, leadership support, organizational structure and responsiveness, and political environment by using the FEVS data (Caillier & Sa, 2017) and the MPS data (Lee, 2020).

Beyond the recent whistleblowing studies that focused primarily on examining which factors influence whistleblowing, this study seeks to examine which factors—individual traits or organizational/environmental characteristics—are more influential on public employee perception of whistleblowing. Drawing insights from the disposition and environment literature (e.g., Bandura & Walters, 1977; Galton, 1892; Hager & Brudney, 2011; Jensen, 2002), we argue that the determinants of individual perception of whistleblowing can be divided into two categories of nature and nurture, and the nurture factors shape public employee perception more than the nature factors in the workplace because, as it is explained in more detail later, whistleblowing literature has failed to present consistent findings about the impact of nature factors (e.g., gender and ethnicity) on whistleblowing behavior and/or intention. Nature factors are defined as individual traits, such as gender and ethnicity—innate and inherent (Galton, 1892; Hager & Brudney, 2011, p.138). In contrast, nurture factors are the decision-making process, training, leadership, institutional arrangements, and environments that are cultivated in the workplace to influence employee perceptions and behaviors (Bandura & Walters, 1977; Hager & Brudney, 2011).

In addition, although there is a “distance” between whistleblowing perception and actual whistleblowing, we treat whistleblowing perception as a measure of prospective whistleblowing and use multiple waves of data to measure the dependent variable to overcome the common method bias (Meier & O’Toole, 2013). It is not common to carry out investigations into actual whistleblowing in public organizations because of the difficulty in identifying actual whistleblowers and the potential bias of whistleblowing data based on self-reporting of past behaviors. Given the difficulty to study actual whistleblowing, it is important to examine employee perception of whistleblowing as an individual’s favorable perception, which can be a good gauge of the judgment affecting the intention to report the wrongdoing (Mesmer-Magnus & Viswesvaran, 2005). By developing and testing hypotheses for predisposed and organizational/environmental factors and comparing their relative impacts, this article contributes to public personnel management literature and field by informing practicing managers and leaders of what types of factors increase favorable employee perception of whistleblowing.

Predisposed Factors

Whistleblowing intention varies depending on individual traits. In their study of whistleblowers, Jos et al. (1989) identified unique individual traits of whistleblowers and concluded that some whistleblowers seem to be born, not made (p. 557). Literature on the predisposition perspective argues that human perceptions and behaviors are largely predisposed by individual traits (Galton, 1892; Jensen, 2002), and the innate variation in these traits leads to differences in individual perception of whistleblowing. For instance, other things being equal, a certain gender may be more inclined to report wrongdoing than another gender. Some studies show that the whistleblowing decision does not come from calculating its costs and benefits but personal characteristics of whistleblowers, which influence the decision between expressing dissent and acquiescing (Fritzsche & Becker, 1984; Jos et al., 1989). Whereas several nature factors—gender and ethnicity—were found in some studies to be determinants of whistleblowing intention, however, more recent studies presented mixed findings about the impact of nature factors.

Some studies find that women are more willing to report wrongdoing than their male counterparts (Keil et al., 2010; Mesmer-Magnus & Viswesvaran, 2005). Yet, other studies found that men are more willing to blow the whistle than women (Bashir et al., 2011; Chang et al., 2017; Miceli & Near, 1988; Sims & Keenan, 1999). As mentioned previously, whistleblowers may be seen as brave individuals who are willing to risk their careers to make the organization a better place and protect the public interest, however, the reaction from the higher authority and the public may create a perception that some whistleblowers are just disgruntled employees or troublemakers. The concern for the negative response to whistleblowing and direct (and threat of) retaliation by the employer likely weigh on employees’ decision about whether to blow the whistle. Given the continued concern about gender and racial equality in the civil service (U.S. Office of Personnel Management, 2016b), women and men may have different responses when they witness misconducts. For instance, some studies show that when women decide whether to report the wrongdoing, they are concerned more about not only the cost of retaliation, but also the psychological and social consequences from the negative reaction of their family, friends, and the community (Bishara et al., 2013; Lobel, 2012). Therefore, women are more likely than men to weigh the social norms and reactions from other employees in the organization and their family and friends in their decisions to blow the whistle. Other researchers have argued that women’s tenure and relationship to their organizations are also relevant to their decision about reporting the wrongdoing. Women’s career patterns tend to be interrupted by maternity and family responsibilities, and the shorter tenure may lead more women to feel as “outsiders” to their institution and less loyal to the organization and, thus, more likely to blow the whistle (Sellers, 2014).

In their study of whistleblowers, Jos et al. (1989) find that among men, Whites are more likely to blow the whistle than non-Whites in the government. The variation in whistleblowing between Whites and non-Whites is also reported in the research by Rothschild and Miethe (1999). Their study indicates that African Americans are nearly twice as likely as Whites to suffer retaliation by their employers. These findings suggest that racial minorities worry about retaliation more than Whites, and it may deter them from blowing the whistle in the face of wrongdoing. However, racial equality remains an important issue, especially for racial minorities in the public workplace (Broadnax, 2018). It may be argued that racial minorities’ experience in the workplace and the community make them more sensitive to injustice in the face of wrongdoing, which may weigh on their decision about whether to blow the whistle.

Instead of treating gender and ethnicity as control variables, we develop two hypotheses from the predisposition perspective to examine the possible impact of gender and race on public employee perception regarding whistleblowing. However, for each possible explanation, we can find compelling counterarguments, and due to the mixed findings about these factors, our hypotheses will be tested with no prediction about the direction of the relationship (a two-tailed hypothesis).

Hypothesis 1 (H1): A federal organization’s gender ratio influences the organization’s overall level of employee perception of whistleblowing.

Hypothesis 2 (H2): A federal organization’s minority ratio influences the organization’s overall level of employee perception of whistleblowing.

Organizational and Environmental Factors

Willingness to blow the whistle is inherently dependent on organizational characteristics and environment. Lee (2020) comments that “organizational structure, political environment, and internal system responsiveness play meaningful roles in explaining whistleblowing in U.S. federal agencies” (p. 171). Unlike the predisposition perspective, literature on the nurture argument posits that human perception and behaviors can change through nurturing and learning (Bandura & Walters, 1977). In this research, we propose that nurture factors are also expected to influence individual perception of whistleblowing. First, previous studies examined the impact of political control on public-sector whistleblowing and illustrated mixed findings about the impact. Chang et al. (2017) find that political appointees are more likely to blow the whistle than other public employees because of their powerful positions in the hierarchy. In contrast, Lee finds that political appointees influence whistleblowing differently based on whether or not they have a fixed term that insulates them from presidential power. For instance, political appointees with a fixed term are less likely to promote whistleblowing by exerting more hierarchical controls over whistleblowing, which can be considered as a challenge to their authority.

However, little research has focused on the impact of partisan political environment on public-sector whistleblowing. We argue that partisan political environment influences public employee whistleblowing intention. Bowman (1980) illustrates that whistleblowing is a political phenomenon that occurs in organizations that are not supposed to be partisan political systems (p. 20). It is expected that a high level of partisan politics in the organizational environment hinders public employees from whistleblowing because it can be regarded as a challenge to political systems and cause severe political backlash against whistleblowers. In this respect, public employees who perceive a high level of partisan politics in the organizational environment are less willing to blow the whistle.

Second, access to inside information may lead to favorable perception of whistleblowing. Organizations have a huge amount of inside information, but not everyone is able to access such information. Organizational members have the advantage of accessing inside knowledge about wrongdoings in the organization as opposed to outsiders (Burke & Cooper, 2013; Chang et al., 2017; Cho & Song, 2015; Miethe & Rothschild, 1994). Nevertheless, it is often the case that higher ranking officers, such as those in managerial positions, are likely to blow the whistle because they have more access to decision-making and inside information (Jos et al., 1989). In particular, previous studies find that higher level employees with more access to inside information about the organization are in a better position to blow the whistle (Keenan, 1990; Lee, 2020; Mesmer-Magnus & Viswesvaran, 2005). We argue that whether organizational members have more access to inside information depends on what extent the management is willing to share inside information with them. Thus, the level of access that organizational members have to inside information is critical in influencing their likelihood to witness wrongdoing and decision to blow the whistle.

We argue that important nurture factors absent from previous studies but worth examining are the extent of partisan political environment and information sharing in the workplace. As mentioned earlier, nonpartisan political environment can promote whistleblowing (Bowman, 1980; Weinstein, 1979). Also, organizational members play a key role in whistleblowing because outsiders who do not have access to wrongdoing cannot blow the whistle (Miethe, 1999; Near & Miceli, 2008, p. 276). Other things being equal, government employees would be more likely to identify wrongdoing and blow the whistle when they have less partisan political environment and/or more access to inside information. Thus, the following hypotheses are presented:

Hypothesis 3 (H3): The higher a federal organization’s level of employee perception of partisan political environment, the lower the organization’s overall level of favorable employee perception of whistleblowing.

Hypothesis 4 (H4): A federal organization’s level of employee satisfaction with information sharing has a positive impact on the organization’s overall level of favorable employee perception of whistleblowing.

The following organizational/environmental factors were also chosen based on previous whistleblowing literature, and we develop a hypothesis for each factor. Previous studies have examined other nurture factors of employee perception of whistleblowing. One such factor is employee training to increase employee awareness about whistleblowing, such as reporting mechanism in the organization. In general, training and educating employees with essential information for job performance and workplace culture play an important role for overcoming various managerial issues, such as violations of rules and laws and unethical practices (Kroll & Moynihan, 2015). In relation to whistleblowing, providing whistleblowing training for employees can be a proactive workplace approach for increasing effectiveness of whistleblowing (Bjørkelo, 2013). Cho and Song (2015) find that whistleblowing education increases employee willingness to blow the whistle in the federal government. These findings indicate that public agencies need to provide whistleblowing training and education. More importantly, such training and education must be well designed and effective to achieve the intended goals.

Perception of fairness is a critical nurture factor related to individual perception of whistleblowing. Individual employee perception of their workplace environment can influence their attitudes and behaviors (Lewis & Gilman, 2012; Mulki et al., 2008; Schwepker, 2001). For example, the likelihood of employees to act on a particular issue is based on the extent to which their workplace environment tolerates the issue. Previous research on whistleblowing finds that a workplace environment that emphasizes procedural justice and pursuing fairness in personnel practices affects employee attitudes and behaviors toward reporting wrongdoing, thereby increasing employee willingness to express their voice about wrongdoing in the workplace (Chang et al., 2017; Rothwell & Baldwin, 2006). Thus, we argue that government employees are more likely to express their voice against wrongdoing when they perceive their organization pursues fairness in personnel practices.

Transformational leaders inspire people in organizations to do things they may never have done. Leaders shape employee perceptions and actions (Simon, 1997). Transformational leaders serve as an ideal role model for their employees to emulate their attitudes and behaviors (Bass, 1996). According to the whistleblowing research, two rationales explain the relationship between transformational leadership and employee perception of whistleblowing. First, the care and support of transformational leaders increase the level of employee comfort with voicing dissent in the face of wrongdoing (Caillier & Sa, 2017; Zacher et al., 2014). Second, transformational leaders strengthen employee morality and encourage them to respond to wrongdoing properly by blowing the whistle (Caillier & Sa, 2017; Near & Miceli, 2008; Zhu et al., 2011). Caillier and Sa (2017) find that transformational leadership is positively related to federal employee willingness to disclose wrongdoing. As such, employees are likely to have favorable perception of whistleblowing by emulating their transformational leaders who maintain high standards of honesty and integrity.

Based on the above literature, we propose the following hypotheses for training, perception of fairness, and transformational leadership:

Hypothesis 5 (H5): A federal organization’s level of training effectiveness has a positive impact on the organization’s overall level of favorable employee perception of whistleblowing.

Hypothesis 6 (H6): A federal organization’s level of fairness in personnel practices has a positive impact on the organization’s overall level of favorable employee perception of whistleblowing.

Hypothesis 7 (H7): A federal organization’s level of transformational leadership has a positive impact on the organization’s overall level of favorable employee perception of whistleblowing.

Control Variables

Previous research finds that supervisory status, tenure, and age affect employee perception of whistleblowing (Caillier & Sa, 2017; Chang et al., 2017; Hacker, 1978; Jos et al., 1989; Keil et al., 2010; Zhang et al., 2009). These factors are included as control variables. This study uses a spectrum of factors—nature, nurture, and control variables—which provide an integrative model to understand the determinants of employee perception of whistleblowing. Figure 1 presents the definitions of our variables.

Figure 1.

Nature, nurture, and control factors: Definitions and examples.

Data and Method

Data Source

This study tests the hypothesized relationships at the organizational level (measured at the sub-agency level) by using multiple years of FEVS data. One strength of our study that comes from using multiple waves of data is that our study measures the independent and dependent variables from different years to overcome potential common method bias, which is often a serious concern in individual-level cross-sectional studies. In our multiple-wave analyses, the independent variables precede the dependent variable, which is significantly different from previous research (e.g., Caillier & Sa, 2017). The multi-wave analyses allow us to establish more definitive relationships between the independent and dependent variables.

This study conducted the analysis at the organizational level, and at the sub-agency level in particular. We chose federal sub-agency as our unit of analysis for the following reasons. First, because the FEVS do not include individual identifiers for respondents, it was not possible to conduct panel data analysis at the individual level. Even if the FEVS were able to conduct individual-level panel data analysis, the excessively large sample size of the FEVS causes concern for yielding misleading results. An analysis of an extremely large sample could lead to statistically significant results even when there is no meaningful impact of a variable—that is, an overestimation of the significance of test results. Next, instead of using agency as the unit of analysis, we conducted our analysis at the sub-agency level. Sub-agencies, even if they are under the same umbrella agency, are likely to have their own unique organizational characteristics and culture different from other sub-agencies. For instance, the Department of Justice is composed of multiple sub-agencies, such as Federal Bureau of Investigation, U.S. Marshals Service, Office of the U.S. Attorneys, Drug Enforcement Administration, and so on. These organizations or sub-agencies are likely to have different cultures and characteristics. Previous research on the federal government has also used sub-agency as their unit of analysis (e.g., Fernandez et al., 2010; Moldogaziev & Silvia, 2015).

Multi-wave data

For the organizational-level analyses (again, measured at the sub-agency level), we aggregate the 2013, 2014, 2015, and 2016 FEVS data by sub-agency to create three pairs of data. We combined the 2013 and 2014 data to create the first pair (99 federal sub-agencies), the 2014 and 2015 data for the second pair (97 federal sub-agencies), and the 2015 and 2016 data for the third pair (89 federal sub-agencies). And, then, we combined these three pairs to create the final dataset, which include data from 2013 to 2016 (285 federal sub-agencies in total). Previous research has also used multi-wave FEVS data to create panel data (Moon, 2016). For the first pair, independent variables were measured using the 2013 data, and the dependent variable was measured using the 2014 data. For the second pair, independent variables came from the 2014 data, and the dependent variable came from the 2015 data. For the third pair, independent variables were measured using the 2015 data, and the dependent variable was measured using the 2016 data. We measured independent and dependent variables from different years so that the independent variables precede the dependent variable. These three pairs were integrated to construct the final dataset from 2013 to 2016.

Dependent Variable

The dependent variable measures a federal sub-agency’s overall level of favorable perception of whistleblowing. One question in the FEVS 2014, 2015, and 2016, “I can disclose a suspected violation of any law, rule or regulation without fear of reprisal,” was used to assess employee perception of whistleblowing. Responses for the question range from 5 = “Strongly Agree” to 1 = “Strongly Disagree.” To acquire sub-agency level estimate of the dependent variable, we aggregated and computed the mean value of individual-level responses to the question for the years 2014, 2015, and 2016 (1 year succeeding the independent variables in time). Table 1 presents the percent of respondents for each category in the 2013, 2014, 2015, and 2016 FEVS. The descriptive findings echo the findings from the 2010 MPS that a considerable number of federal employees are reluctant to blow the whistle in the face of wrongdoing. The ordinary least squares (OLS) regression was used to explain the relationship between the dependent and independent variables, which are all continuous variables.

Table 1.

Federal Employee Whistleblowing Intention (Question “I Can Disclose a Suspected Violation of Any Law, Rule or Regulation Without Fear of Reprisal”).

Federal Employee Whistleblowing Intention	2013 FEVS (n = 357,689)	2014 FEVS (n = 373,094)	2015 FEVS (n = 400,156)	2016 FEVS (n = 386,924)
Strongly Disagree	34,363 (9.6%)	37,813 (10.1%)	40,376 (10.1%)	37,317 (9.6%)
Disagree	32,752 (9.2%)	35,013 (9.4%)	36,222 (9.1%)	33,668 (8.7%)
Neither	67,694 (18.9%)	70,255 (18.8%)	73,055 (18.3%)	67,466 (17.4%)
Agree	134,858 (37.7%)	137,454 (36.8%)	145,777 (36.4%)	140,846 (36.4%)
Strongly Agree	88,022 (24.6%)	92,559 (24.8%)	104,726 (26.2%)	107,627 (27.8%)

Note. FEVS = Federal Employee Viewpoint Surveys.

Independent Variables

Gender and minority status are included as the predisposed factors in the model. For gender, male was coded as 0, and female was coded as 1. Minority status was coded as 0 for respondents who identified themselves as non-minority and 1 for minority. For the organizational-level analysis, these variables were calculated as an average ratio of male and minority for each sub-agency.

The nurture factors—partisan political environment, information sharing, training effectiveness, fairness in personnel practices, and transformational leadership—are included in the model. Partisan political environment was measured by the following survey question: “Arbitrary action, personal favoritism and coercion for partisan political purposes are not tolerated.” The question was measured by a 5-point Likert-type scale with 1 as strongly disagree and 5 as strongly agree. Information sharing was measured by the following question: “How satisfied are you with the information you receive from management on what’s going on in your organization?” The question was measured by a 5-point Likert-type scale with 1 as very dissatisfied and 5 as very satisfied. Training effectiveness was measured with two survey questions: “My training needs are assessed” and “How satisfied are you with the training you receive for your present job?” The questions were measured by a 5-point Likert-type scale with 1 as strongly disagree or very dissatisfied and 5 as strongly agree or very satisfied. Training effectiveness was measured by computing the mean value of responses to these questions. Cronbach’s alpha for the scale was .82. Because the FEVS did not ask any specific question concerning whistleblowing training, we were not able to measure directly employee perception of whistleblowing training, but instead, we used these two questions as a proxy measure. According to the Notification and Federal Employee Antidiscrimination and Retaliation Act of 2002 (also known as No Fear Act), all federal agencies are required to provide employee training on the rights and remedies regarding whistleblower protection laws and retaliation for engaging in protected activity. Because all federal employees are required to take such training provided by their agency, we argue that their perceptions on the two questions relating to overall training effectiveness is associated with their perceptions of whistleblowing training effectiveness, that is, the higher the perceived satisfaction on these questions, the higher the perceived satisfaction on whistleblowing training effectiveness.

Fairness in personnel practices was measured with the following survey question: “Prohibited personnel practices (e.g., illegally discriminating for or against any employee/applicant, obstructing a person’s right to compete for employment, knowingly violating veterans’ preference requirements) are not tolerated.” Transformational leadership was measured with four survey questions, which were used by previous research (Caillier & Sa, 2017; Oberfield, 2014): “In my organization, leaders generate high levels of motivation and commitment in the workforce,” “Employees have a feeling of personal empowerment and ownership of work processes,” “My organization’s leaders maintain high standards of honesty and integrity,” and “I feel encouraged to come up with new and better ways of doing things.” Caillier and Sa (2017) also used these four questions to measure transformational leadership and suggested that each of these survey items measures different dimensions of transformational leadership: the dimensions of inspirational motivation, individualized consideration, idealized influence, and intellectual stimulation, respectively. The questions were measured by a 5-point Likert-type scale with 1 as strongly disagree and 5 as strongly agree. The transformational leadership variable was measured by computing the mean value of responses to these questions. Cronbach’s alpha for the scale was .88. Because the FEVS are individual-level surveys, for the organizational-level analyses, these nurture variables—that is, partisan political environment, information sharing, training effectiveness, fairness in personnel practices, and transformational leadership—were constructed by averaging the computed mean values of individual respondents in each sub-agency.

As control variables, supervisory status, tenure, and age were also included in the model. Supervisory status was measured by the following question: “What is your supervisory status?” FEVS response option was dichotomous with identifying oneself as either “non-supervisor/team leader” or “supervisor/manager/executive (senior leader).” The former indicates a non-supervisory status and was coded as 0. The latter indicates a supervisory status and was coded as 1. Tenure was measured by the following question: “How long have you been with the Federal Government?” Response options in the FEVS are “5 or fewer years,” “6-14 years,” and “15 or more years.” Respondents who answered “5 or fewer years” were coded as 1, “6-14 years” as 2, and “more years” as 3. Finally, age was measured using the following question, “What is your age group?” Responses of “under 40” were coded as 1, “40-49” as 2, “50-59” as 3, and “60 or older” as 4. For the organizational-level analyses, the responses for each of the control variables are aggregated and calculated as an average ratio for each federal agency. Table 2 shows the descriptive statistics of the study variables used in this research.

Table 2.

Descriptive Statistics for Study Variables (Analysis Unit: Federal Sub-Agencies).

Variables	N	M	SD	Minimum	Maximum
Whistleblowing intention	285	3.64	0.22	1	5
Partisan political environment	285	3.39	0.24	1	5
Information sharing	285	3.28	0.22	1	5
Training effectiveness	285	3.30	0.30	1	5
Fairness in personnel practices	285	3.76	0.20	1	5
Transformational leadership	285	3.32	0.30	1	5
Gender	285	0.42	0.13	0	1
Minority	285	0.31	0.12	0	1
Age	285	2.47	0.20	1	4
Supervisory status	285	0.21	0.06	0	1
Tenure	285	2.21	0.21	1	3

Results

Because the dependent variable is continuous at the organizational-level analysis, we ran an OLS regression using four waves of FEVS data from 2013, 2014, 2015, and 2016. In total, 99 federal sub-agencies were included in the 2013 and 2014 pair, 97 sub-agencies were included in the 2014 and 2015 pair, and 89 sub-agencies were included in the 2015-2016 pair, yielding a total of 285 sub-agencies in our dataset. The time-series nature of the data raises a concern for auto-correlation. To detect auto-correlation, this study conducted the Durbin-Watson d test. The test yielded the Durbin-Watson statistic of 1.494, which falls below the lower bound and suggests that there is a positive auto-correlation. To avoid any (potential) errors related to auto-correlation, we separated our data file into three pairs: the 2013-2014 pair, the 2014-2015 pair, and the 2015-2016 pair and ran our regression for each pair of data separately. The OLS regression results for FEVS 2013-2014, for FEVS 2014-2015, and for FEVS 2015-2016 are presented in Tables 3, 4, and 5, respectively.

Table 3.

OLS Regression Results for FEVS 2013-2014 Pair.

	Model 1			Model 2			Model 3			Model 4			Model 5
Variables	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β
Nurture Factors
Partisan political environment	.41***	.08	.44
Information sharing				.80***	.05	.82
Training effectiveness							.63***	.07	.67
Fairness in personnel practices										.42***	.10	.38
Transformational leadership													.80***	.05	.82
Nature Factors
Gender	−.17	.15	−.11	−.21*	.09	−.13	−.25*	.12	−.15	−.20	.15	−.12	−.16	.08	−.10
Minority status	−.62***	.17	−.34	−.23*	.10	−.13	−.24	.14	−.13	−.60**	.18	−.33	−.20*	.10	−.11
Controls
Age	.04	.09	.04	.10	.05	.10	.13	.07	.12	.05	.09	.04	.04	.05	.04
Supervisory status	−.73*	.31	−.20	−.20	.18	−.06	−.52*	.24	−.15	−.72*	.33	−.20	−.43*	.16	−.12
Tenure	−.12	.10	−.11	−.14*	.06	−.12	−.06	.08	−.05	−.13	.11	−.11	−.06	.06	−.05
Constant	2.81***	.39		1.24***	.26		1.64***	.35		2.63***	.46		1.20***	.24
F statistic	10.20***			57.83***			24.81***			8.37***			68.53***
Adjusted R²	.37			.78			.59			.32			.81
N	99			99			99			99			99

Note. OLS = ordinary least squares; FEVS = Federal Employee Viewpoint Surveys.

p < .05. **p < .01. ***p < .001.

Table 4.

OLS Regression Results for FEVS 2014-2015 Pair.

	Model 1			Model 2			Model 3			Model 4			Model 5
Variables	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β
Nurture Factors
Partisan political environment	.79***	.05	.87
Information sharing				.86***	.05	.85
Training effectiveness							.71***	.07	.73
Fairness in personnel practices										.98***	.06	.89
Transformational leadership													.77***	.05	.83
Nature Factors
Gender	−.09	.10	−.05	−.11	.09	−.06	−.24	.12	−.13	−.13	.10	−.07	−.10	.09	−.05
Minority status	.04	.11	.02	−.25*	.10	−.13	−.23	.13	−.12	.20	.12	.11	−.23*	.10	−.12
Controls
Age	.03	.06	.03	.18**	.05	.16	.23**	.07	.20	.15**	.06	.13	.07	.05	.06
Supervisory status	−.32	.19	−.09	.07	.19	.02	−.25	.25	−.07	−.44*	.19	−.12	−.24	.18	−.06
Tenure	−.12	.06	−.10	−.14*	.06	−.11	−.10	.08	−.08	−.14*	.06	−.11	−.05	.06	−.04
Constant	1.26***	.27		.81**	.28		1.18**	.37		−.04	.33		1.21***	.25
F statistic	56.85***			63.23***			30.01***			56.55***			69.30***
Adjusted R²	.78			.80			.65			.78			.81
N	97			97			97			97			97

Note. OLS = ordinary least squares; FEVS = Federal Employee Viewpoint Surveys.

p < .05. **p < .01. ***p < .001.

Table 5.

OLS Regression Results for FEVS 2015-2016 Pair.

	Model 1			Model 2			Model 3			Model 4			Model 5
Variables	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β	Coefficient	SE	β
Nurture Factors
Partisan political environment	.75***	.04	.89
Information sharing				.79***	.06	.81
Training effectiveness							.20***	.05	.37
Fairness in personnel practices										.92***	.06	.90
Transformational leadership													.27***	.04	.49
Nature Factors
Gender	−.08	.08	−.05	−.11	.10	−.06	−.05	.17	−.03	−.08	.09	−.05	−.16	.16	−.09
Minority status	.08	.10	.04	−.19	.11	−.10	−.43*	.19	−.22	.28*	.12	.14	−.44*	.17	−.23
Controls
Age	−.01	.05	−.01	.10	.05	.10	.20*	.09	.19	.11*	.05	.11	.13	.08	.12
Supervisory status	−.46**	.17	−.13	−.23	.21	−.06	−1.19**	.33	−.32	−.62*	.19	−.17	−1.06**	.30	−.29
Tenure	.04	.04	.04	.02	.04	.03	.05	.07	.06	.01	.04	.01	.04	.07	.05
Constant	1.17***	.20		.93**	.26		2.81***	.36		−.03	.29		2.81***	.31
F statistic	79.13***			52.25***			8.94***			61.41***			13.58***
Adjusted R²	.84			.78			.35			.81			.46
N	89			89			89			89			89

Note. OLS = ordinary least squares; FEVS = Federal Employee Viewpoint Surveys.

p < .05. **p < .01. ***p < .001.

There are five different models in Tables 3, 4, and 5. Each model tests the effect of each of the five nurture factors. We examined each nurture factor individually to mitigate the multicollinearity issue, as the variance inflation factor (VIF) test detected multicollinearity. Thus, instead of examining the impact of nurture variables in a full model, we test the effect of nurture variables in individual models.

H1 and H2 posit nondirectional predictions on the impact of a sub-agency’s gender and minority ratios on the organization’s overall level of employee perception of whistleblowing. The results in Tables 3, 4, and 5 suggest that none of the nature factors, which either an employee or an organization has no to little control over, has any consistent significant impact on a sub-agency’s overall level of employee perception of whistleblowing in the federal government. In some models, they are statistically significant, but in other models, they are not. These findings are consistent with previous studies, which suggest mixed findings about the influence of these nature factors.

H3 predicted that a federal organization’s level of employee perception of partisan political environment has a negative impact on the organization’s overall level of favorable perception of whistleblowing. In the tables, the positive coefficients for partisan political environment mean that partisan political environment and favorable perception of whistleblowing are negatively associated because partisan political environment was measured by the survey question asking to what extent such partisan political environment is “not tolerated.” As such, H3 is supported by findings in all of Tables 3, 4, and 5. As a sub-agency’s level of employee perception of partisan political environment becomes more negative, the sub-agency’s overall level of favorable perception of whistleblowing decreases. H4 proposes a positive impact of information sharing on favorable employee perception of whistleblowing. Tables 3, 4, and 5 show that this hypothesis is supported. As a sub-agency’s level of employee satisfaction with information sharing increases, so does the overall favorable employee perception of reporting wrongdoing in the sub-agency. H5 posits that a sub-agency’s level of training effectiveness positively influences the sub-agency’s overall level of favorable employee perception of whistleblowing. The results in Tables 3, 4, and 5 show that as the perceived level of training effectiveness in a sub-agency increases, the sub-agency presents a higher level of favorable perception of whistleblowing from its employees. Thus, H5 is supported. H6 and H7 predict that a federal organization’s level of fairness in personnel practices and level of transformational leadership, respectively, have a positive impact on the organization’s level of favorable employee perception of whistleblowing. The results in Tables 3, 4, and 5 show that a sub-agency’s level of fairness in personnel practices and level of transformational leadership increase the sub-agency’s overall level of favorable employee perception of whistleblowing. Therefore, the findings support both H6 and H7.

In regard to the control variables, the results presented in Tables 3, 4, and 5 suggest that supervisory status and tenure tend to be negatively associated with a sub-agency’s level of favorable employee perception of whistleblowing, although they fail to show a consistently significant relationship with an organization’s level of favorable perception of whistleblowing over time and across different models. In addition, the results suggest that age tends to be positively associated with a federal organization’s level of favorable employee perception of whistleblowing. However, age also fails to have a consistently significant impact on the organization’s level of favorable employee perception of whistleblowing over time and across different models.

To compare the relative size of the effects of independent variables, this study also looked at the standardized coefficients (beta in the tables). In every model from Model 1 through Model 5 in Tables 3, 4, and 5, nurture factors—partisan political environment, information sharing, training effectiveness, fairness in personnel practices, and transformational leadership—have the largest beta value. This means that nurture factors have greater impact on a federal organization’s level of favorable employee perception of whistleblowing than the nature factors and other variables in each model. These findings provide support for the nurture perspective, suggesting that the federal government can promote the likelihood of employees to blow the whistle by providing them with supportive environment through protection of employees from partisan political environment, information sharing, effective training, fairness in personnel practices, and transformational leadership.

Discussion

With legal protections for whistleblowing, public employees are encouraged to blow the whistle to report and reduce organizational wrongdoing. Public employee whistleblowing has been seen as a proactive and bottom-up oversight system that provides various benefits for public organizations and the society, such as watching over public health and safety, prohibiting escalation of wrongdoing, and reducing government mismanagement. However, because public-sector whistleblowing often leads to a variety of unexpected negative consequences—for instance, political backlash, bureaucratic isolation, blacklisting, demotion, and dismissal—a substantial number of public employees are still reluctant to blow the whistle on wrongdoing. Drawing on the insight from the predisposition and environmental perspectives, this study explores how nature and nurture factors increase favorable public employee perception of whistleblowing.

Findings from this research have several practical and theoretical implications. For successful implementation of the bottom-up oversight system, public employees should be insulated from bipartisan politics. Rosenbloom (2008) argues that while politics and administration might be inseparable during the policy-making process, partisan politics should not influence public personnel practices. As findings confirm, a partisan political environment is likely to reduce federal employee willingness to report wrongdoing. It suggests that federal agencies should establish better procedures and/or policies to insulate public whistleblowers from partisan backlash against them. In particular, anonymity and confidentiality for public whistleblowers need to be reinforced and warranted amid presidential and/or congressional abusive authority.

Second, public employees should be provided with more access to information about what happens in the organization. As shown in the results, information sharing is an important nurture factor in enhancing favorable perception of whistleblowing. When management shares more information with organizational members, it will increase the likelihood of those members being able to identify and report wrongdoing (Miethe, 1999; Near & Miceli, 2008). This also calls for more efforts from federal agencies to develop various channels to engage organizational members in decision-making processes, which help them gain more inside information so that the employees would become prospective whistleblowers for detecting and reporting the wrongdoing.

Third, public organizations should provide, through effective training programs, legal and policy guidelines regarding whistleblowing to increase the knowledge and comfort level of organizational members with whistleblowing. Previous studies find that lack of knowledge regarding appropriate channels and procedures of whistleblowing can hinder organizational members from blowing the whistle (Chang et al., 2017; Cho & Song, 2015; Near & Miceli, 1985). This study suggests that effective whistleblowing training with clear objectives, informative curriculum, successful delivery, and desirable evaluation should be developed for organizational members so that they can learn important and valuable information about whistleblowing processes and whistleblower protections.

Fourth, leadership and work environment should be supportive of whistleblowing. These factors are recurring themes in public-sector whistleblowing studies and critical to the success of enhancing employee willingness to blow the whistle (Caillier & Sa, 2017; Chang et al., 2017; Cho & Song, 2015; Lavena, 2016). Previous studies show that public employees are hesitant to blow the whistle due to lack of leadership support and organizational support. One major obstacle to whistleblowing is potential retaliations against the whistleblower. This study suggests that transformational leaders can inspire employees by supporting employee morality and being mindful of employee concerns about wrongdoing. In addition, it is important to pursue fair personnel practices that can help employees feel more comfortable about reporting wrongdoing. Such leadership support and procedural justice help organizational members reduce their fear of negative consequences of whistleblowing.

Finally, this study contributes to the literature on whistleblowing by finding the greater influence of nurture factors than nature factors. The perspectives of heredity and environment enable us to distinguish two types of explanatory factors as determinants of public employee perception of whistleblowing and compare which type of factors are more influential for whistleblowing attitudes. This research is also one of very few studies that analyze the determinants of public employee perception of whistleblowing by using multi-wave data. This framework may be used to further our understanding of whistleblowing perception and behaviors at all levels of government. Future research should consider gathering and testing longitudinal data on how a favorable perception of whistleblowing may lead to a whistleblowing action. By so doing, future research can shed more light on better understanding the effects of various nature and nurture factors on favorable perception and behavior of whistleblowing. Finally, future research should also expand on this and other studies to examine how public organizations may implement whistleblowing policies to help prospective whistleblowers overcome the fear of potential retaliations against whistleblowing.

Limitations

This study is not without limitations. The first limitation of the present research relates to the measure of whistleblowing training effectiveness. As aforementioned, the effectiveness of whistleblowing training was measured by a proxy indicator. The FEVS did not have a question specifically relating to how satisfied federal employees are with whistleblowing training, or how federal employees perceive whistleblowing training effectiveness. This study assumed that if overall training programs are perceived as effective by federal employees, a whistleblowing training would be, to some extent, perceived as effective, because whistleblowing training is required by all federal agencies as part of employee training. However, due to the limitation of using proxy measures, the impact of training effectiveness found in this study should be interpreted with caution.

The second limitation of our study may come from aggregating and analyzing the data at the organizational level. Many public-sector studies that used the FEVS as a single source of data have mentioned the potential for common method bias (CMB) as a limitation of their research. Unlike these studies, the present research analyzed the FEVS at the organizational level. We used multiple years of the FEVS from 2013 through 2016 and conducted multiple-wave analyses, where the independent variables precede the dependent variable in time. By so doing, the present research significantly reduced, if not avoided, the potential for CMB. In addition, by analyzing the FEVS at the organizational level, this study avoided reporting potentially misleading results that may come from analyzing an excessively large sample size. The FEVS are very large in size, approximately 400,000 or more federal employees having responded to the survey each year from 2013 through 2016, for instance. As aforementioned, an analysis of a too large sample could lead to statistically significant results even when there is no meaningful impact of a variable—that is, an overestimation of the significance of test results. By analyzing the FEVS at the organizational level, this study avoided such an overestimation.

However, by aggregating and analyzing the FEVS at the organizational level, this study may have lost some information, which is useful in understanding individual-level associations, in particular. Because the FEVS do not include individual identifiers for respondents, it was not possible to conduct panel data analysis at the individual level. However, as Jeon and Kukla-Acevedo (2019) mentioned, “an organizational-level analysis does not capture the individual-level impacts of [explanatory variables]” (p. 479). Thus, the results of this study should be interpreted with caution, as the impact of the nature and nurture factors may vary depending on individual employees.

Finally, this study did not include all of its identified nurture factors in a single model because multicollinearity was detected among some of the nurture factors. Thus, we were not able to compare which nurture factor is more influential than others in increasing a federal organization’s overall level of favorable public-employee perception of whistleblowing. We suggest that future research extend the understanding of favorable public-employee perception of whistleblowing by testing the relative size of impact of various nurture factors.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Myungjung Kwon

So Hee Jeon

Notes

Author Biographies

Myungjung Kwon PhD, is a professor of Public Administration and Policy in the Division of Politics, Administration and Justice at California State University Fullerton. His research focuses on public management, human resource management, sustainability policy, policy analysis, and strategic management. He has published his research in Review of Public Personnel Administration, Public Personnel Management, Journal of Public Administration Research and Theory, Public Administration Review, The American Review of Public Administration, and Public Administration Quarterly.

So Hee Jeon PhD, is an associate professor and the director of the undergraduate public and nonprofit administration program in the Department of Political Science and Public Administration at Central Michigan University. Her research focuses on public and nonprofit management, with an emphasis on human resource management, administrative ethics, whistleblowing, and interorganizational networks.

Yuan Ting is a professor of public administration at California State University Fullerton. His scholarly interests include administrative behavior, HRM, performance management, and policy analysis. He has published his research in Administrative Science Quarterly, American Review of Public Administration, Industrial and Labor Relations Review, Public Administration Review, Public Personnel Management, and Review of Policy Research.

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