Journal of Business Ethics

, Volume 120, Issue 2, pp 149–164 | Cite as

The Silent Samaritan Syndrome: Why the Whistle Remains Unblown

Article

Abstract

Whistle blowing programs have been central to numerous government, legislative, and regulatory reform efforts in recent years. To protect investors, corporate boards have instituted numerous measures to promote whistle blowing. Despite significant whistle blowing incentives, few individuals blow the whistle when presented with the opportunity. Instead, individuals often remain fallaciously silent and, in essence, become passive fraudsters themselves. Using the fraud triangle and models of moral behavior, we model and analyze fallacious silence and identify factors that may motivate an individual to rationalize fallacious silence. We use a survey of graduate accounting students to test hypothesized factors that contribute to fallacious silence rationalizations in an academic setting. We find evidence that the ability to rationalize fallacious silence is related to community influences and personal traits such as awareness and moral competence.

Keywords

Whistleblowing Silence Fraud triangle Rationalization Moral competence Moral behavior Professional identity Awareness Incentives Community influences 

Introduction

Arguably, no single governance mechanism has greater potential to end an ongoing fraud than whistleblowing. Whistleblowing is “the disclosure by organization members (former or current) of illegal, immoral, or illegitimate practices under the control of their employers, to persons or organizations that may be able to affect action” (Near and Miceli 1985, p. 4; Miceli and Near 1984; Keenan and McClain 1992). Dyck et al. (2008) reported that almost one out of every five corporate fraud cases detected in large U.S. companies between 1996 and 2004 involved whistleblowers. In recent years, whistleblowers have been responsible for ending some of the largest frauds such as WorldCom and Enron (Bowen et al. 2010).

Recognizing whistleblowing’s usefulness, legislators have made whistleblowing laws central to several major security reforms. In 1988, the SEC instituted a bounty program as part of the Insider Trading and Securities Fraud Act of 1988. In 2002, the Sarbanes–Oxley Act required all public companies to implement whistleblower hotlines. In 2007, the Frank-Dodd Act required that the Securities Exchange Commission provide greater monetary incentives to support and encourage whistleblowers. In some ways, these measures have had limited success in encouraging whistleblowing. For example, between 1988 and September 2009, the SEC only rewarded one whistleblower under its bounty program (Feldman and Lobel 2010). Further, researchers find that nearly half of the individuals aware of wrongdoing choose to remain silent (Fredin 2012); many witnesses knowingly remain silent.

This paper seeks to better understand why individuals would choose to remain silent when they observe wrongdoing in an academic setting. Academic fraud remains a growing and widespread problem at most academic institutions. Steven Davis reported that cheating in the USA increased from about 20 % in the 1940 s to 75 % in the 1990 s (Putka 1992). Cheating is an especially large problem among business students (Klein et al. 2007) and occurs in even the most prestigious universities.1 Smyth and Davis (2004) reported that 45 % of college students admitted to cheating while 74 % had observed cheating. Many students have opportunities to report observed academic fraud, yet frequently they do not. Although they have the opportunity to act, report, and help preserve academic integrity, many remain silent Samaritans.2

We consider what factors are associated with students’ fallacious silence rationalizations. Fallacious silence refers to a state in which individuals refrain from any form of genuine expression that calls attention to illegal or immoral issues that violate personal, moral or legal standards (Knoll and Dick 2012; Pinder and Harlos 2001, p. 334). Since fallacious silence complicitly contributes to an ongoing fraud, we use the fraud triangle (Cressey 1953) along with a model of moral behavior (Rest 1984) to organize literature within a model for analyzing how opportunities and incentives can influence individuals’ fallacious silence rationalizations at different levels. We test resulting hypotheses by conducting a survey of graduate accounting students’ willingness to engage in fallacious silence in an academic setting. Each subject had successfully completed an internship in public accounting and a senior-level ethics class. We assess the subjects’ willingness to engage in fallacious silence across twelve different rule-violating actions. Consistent with expectations, we find that an individual’s willingness to remain silent depends upon their awareness that an action is wrong, community ties, moral competence, and professional standards as measured by time spent on academics.

Our work adds to and extends the whistleblowing literature in three ways (Mesmer-Magnus and Viswesvaran 2005; Miceli and Near 1992; Park et al. 2005). First, based on the notion that the decision to remain fallaciously silent is essentially a complicit decision to allow fraud, we use the fraud triangle (Cressey 1953) and a model of moral behavior (Rest 1984) to develop a model of the fallacious silence decision. Second, we provide evidence that the decisions to remain silent depend on factors that influence rationalizations at different levels of moral behavior. Third, there are significant practical education and professional development implications and future research extensions.

Whistleblowing Literature Review and Hypotheses Development

Effective whistleblowing programs provide governance and control mechanisms for communicating forms of wrongdoing so that corrective action can be taken (Taylor and Curtis 2010; Vandekerckhove and Tsahuridu 2010; Near and Miceli 1985; Miceli and Near 1984). These programs increase the likelihood of properly detecting and correcting current fraud and deterring future fraud (Vandekerckhove and Tsahuridu 2010). However, research suggests whistleblowing programs are not always effective (Dworkin 2007; Moberly 2006).

When fraud is observed, witnesses are in a Good Samaritan position with the opportunity to help others by reporting wrongdoing (Fabre 2002; Vandekerckhove and Tsahuridu 2010; Malm 2000; McCabe 1984; Scott 2000). Fallacious silence occurs when a witness chooses to remain silent. Fallacious silence represents a significant problem because it allows a fraud to continue, inhibits the detection and correction of wrongdoing (Hirschman 1970), possibly permits a fraud to escalate undetected, and impedes learning and development (e.g., Argyris and Schön 1978). In general, the shortage of research on fallacious silence is an impediment to progress in understanding why and when individuals withhold information (Knoll and Dick 2012). For example, although Morrison and Miliken (2000) investigated how organizational-level phenomenon led to employee silence from a top-down perspective, how employee rationalizations contribute to fallacious silence from a bottom-up perspective has not yet been given much research attention.

Since fallacious silence complicitly contributes to academic fraud, we use and integrate the fraud triangle (Cressey 1953) with Rest’s (1984) model of moral behavior to provide a framework to organize and structure whistleblowing and silence research, analyze elements contributing to fallacious silence decisions, and develop our hypotheses. According to the fraud triangle, fallacious activities, like fallacious silence, occur when three elements are present: opportunity, incentives, and rationalization (see Fig. 1).
Fig. 1

Fraud triangle elements: factors contributing to silent samaritan syndrome

Opportunity

Opportunities for fallacious silence exist when someone (e.g., a student) has asymmetrical knowledge of wrongdoing, a moral or legal duty to report the act, and institutional procedures exist for reporting concerns. Uncovering knowledge of wrongdoing creates an information asymmetry for potential whistleblowers. They face a moral hazard dilemma between blowing the whistle or remaining fallaciously silent since unreported wrongdoing could continue unknown to superiors. Institutional mechanisms for reporting and reconciling concerns (Vandekerckhove and Lewis 2012; Miceli et al. 2009; Tsahuridu and Vandekerckhove 2008) provide direction and avenues for reporting wrongdoing. The policies, procedures, and reporting mechanisms can affect a whistleblowing program’s opportunities (King 1999; Sims and Keenan 1998). Park et al. (2008) consider the relationship between whistleblowing policy characteristics (e.g., external vs. internal reporting; anonymous vs. identified reporting) and whistleblowing program opportunities and effectiveness.

Incentives

Incentives that influence fallacious silence decisions can take three primary forms: economic, social and moral (Levitt and Dubner 2006). Economic incentives involve self-interests, personal costs, and benefits (Gundlach et al. 2003; Near and Miceli 1985). Easy whistleblowing occurs with minimal personal costs, risks, inconveniences, and economic disincentives to the whistleblower (Malm 2000); however, generally there is no easy whistleblowing (Vandekerckhove and Tsahuridu 2010). In reality, economic disincentives can make blowing the whistle difficult. By remaining silent, individuals avoid substantial retaliation (Alford 2001); bad treatment such as nullification, isolation, or defamation (Smith and Brown 2008; Brown and Olsen 2008; Dworkin and Baucus 1998); and risks to jobs and careers (see Hassink et al. 2007, p. 30; Arnold and Ponemon 1991; Dworkin and Baucus 1998; Miceli and Near 1994; Mesmer-Magnus and Viswesvaran 2005; Near and Miceli 1986). Reports indicate that up to 38 % of whistleblowers face some form of reprisal (Miceli et al. 1999), and retaliation is more likely with systematic and significant reported misconduct (Rothschild and Miethe 1999). Even potential retaliation increases the incentives to remain silent (Miceli et al. 2009). It is possible that students face or fear similar costs for reporting academic wrongdoing.

Offsetting whistleblowing benefits can provide economic incentives to encourage individuals to break their silence. For example, regulators have implemented significant whistleblowing incentives like the “Qui tam” process under the False Claims Act (Feldman and Lobel 2010),3 IRS financial rewards for reporting tax fraud, and SEC rewards for reporting insider trading. However, regulator-mandated whistleblowing incentives do not always result in corporate infrastructure conducive to whistleblowing (Near and Dvorkin 1998). Additional benefits from whistleblowing include avoiding the costs of remaining silent (Fredin 2012) like disciplinary action if unreported wrongdoing is discovered (Dozier and Miceli 1985), claw back provisions in the Frank Dodd Act (Beck 2012), and psychological costs4 of silence (Cortina and Magley 2003; Perlow and Williams 2003).

Economic incentives can motivate several types of fallacious silence like: (1) quiescent silence: withholding relevant information in order to protect oneself from retaliatory consequences (Knoll and Dick 2012), (2) opportunistic silence: strategically withholding information to achieve an advantage for oneself while accepting harm of others (Knoll and Dick 2012), and (3) economically motivated prosocial silence: withholding information to benefit the organization or other people (Van Dyne et al. 2003, p. 1368) with an interest in maintaining social capital (Adler and Kwon 2002) and protecting social identity (Ashforth and Mael 1989) (Knoll and Dick 2012).

Social incentives involve individuals’ aversion to being seen by others as engaging in abnormal behavior regardless of whether or not the norm is acceptable (O’Fallon and Butterfield 2005). There are many types of social incentives like obedience to authority (Milgram 1963), compliance with legal standards like whistleblowing policies and procedures (Vandekerckhove and Lewis 2012; Hassink et al. 2007),5 and conformance to cultural and group norms (Asch 1958). Numerous studies have specifically considered the role of community influences on whistleblowing (Horne 2001; Greenberger et al. 1987; Gundlach et al. 2003; Park et al. 2005; Callahan and Dworkin 1994; Miceli and Near 1992; Miceli et al. 2008; Miethe 1999). Group characteristics like size (Miceli and Near 1988) and group response (Keil et al. 2010), individual characteristics like collectivism (Nayir and Herzig 2012; Park et al. 2008), and cultural influences (Brody et al. 1998; Ergeneli 2005; Gernon 1993; Thomas and Miller 2005) have been investigated. In general, members of a group are more likely to remain silent (Dozier and Miceli 1985; Near and Miceli 1985; MacNab and Worthley 2008). Taylor and Curtis (2010) found that professional accountants’ reporting intent is negatively associated with commitment to colleagues. Similarly, these social factors would seem to be applicable to students in an academic setting.

These social incentives can motivate several types of fallacious silence like: (1) acquiescent silence: passively withholding relevant information due to submission and resignation to beliefs that opinions are not wanted or valued by superiors and/or a climate promoting conformity and suppressing dissent (Knoll and Dick 2012), and (2) socially motivated prosocial silence: withholding information to benefit the organization or other people (Van Dyne et al. 2003, p. 1368) due to a high motive for affiliation (Knoll and Dick 2012).

Moral incentives involve individuals’ aversion to doing something they consider wrong and focus on duties, responsibilities and obligations. Whistleblowers possess greater moral reasoning capacities (Dozier and Miceli 1985) and may feel the “pure motives” (Oliver 2003) of a moral duty and obligation to report (Keil et al. 2010) regardless of personal costs or social consequences. However, efforts to instill a duty to report in practice have proved somewhat ineffective (Vandekerckhove and Tsahuridu 2010). A duty of loyalty can conflict with and trump the duty to report. For example, Bouville (2008) likened the whistleblowing decision to national betrayal. Deontological considerations of preferred organizational loyalty and silence (Miceli et al. 2009) and utilitarian considerations of the type and severity of wrongdoing (Near et al. 2004; Hassink et al. 2007) can influence the silence decision. Balancing whistleblowing’s conflicting moral duties can be morally challenging (see e.g., Boatright 2003; DeGeorge 2006). Morally motivated prosocial silence could result in withholding information to benefit the organization or other people based on altruism or cooperative motives (Van Dyne et al. 2003, p. 1368; Knoll and Dick 2012).

Rationalization

Rationalization involves the post hoc cognitive justification of a predetermined decision, like fallacious silence, as individuals internally respond to opportunities and incentives6 and attempt to mentally eliminate the difference between the chosen decision and action (i.e., what is done) and normative ideals (i.e., what should be done). This can occur on many levels. Rest (1984) and Rest and Narvaez (1994) modeled moral behavior as a multifaceted (Guthrie 1997) function of:
  1. 1.

    Moral sensitivity the awareness of possible alternatives and effects including interpreting the situation and identifying moral problems (Guthrie 1997).

     
  2. 2.

    Moral judgment deciding moral actions from alternatives (Narvaez and Rest 1995). Kohlberg’s (1969) stages of moral development7 advanced this component.

     
  3. 3.

    Moral motivation the priority and persistence of moral values (Guthrie 1997). For example, Taylor and Curtis (2010) examine whistleblower persistence after the initial decision.

     
  4. 4.

    Moral character skills needed to carry out the chosen action (Narvaez and Rest 1995).

     

We hypothesize that factors contributing to cognitive rationalizations can occur at any level as individuals respond to whistleblowing opportunities and incentives.

Moral Sensitivity: Recognizing Opportunity

Students may rationalize fallacious silence because they fail to recognize the inappropriateness and seriousness of an action and their opportunity and responsibility to report the inappropriate action. They may be unaware that a particular action is wrong if they would engage in the activity themselves. They would not report someone doing something they would do consistent with MacGregor and Stuebs’ (2012) finding that the willingness to engage in academic fraud is negatively associated with awareness that the action is wrong. However, unawareness of, or ignorance to, a whistleblowing opportunity does not always relieve an individual from a whistleblowing duty. Students can be held culpably ignorant and responsible for actions they should have reasonably taken, like blowing the whistle.

Hypothesis 1

Awareness hypothesis: A student’s willingness to engage in fallacious silence directly varies with the belief that the student would engage in the action.

Moral Judgment: Influence of Incentives

Individual judgment can be influenced by social incentives to comply with norms and rules at Kohlberg’s (1969) conventional levels of moral development. As mentioned in the social incentives discussion above, individuals facing greater social pressure are more likely to conform with social norms in general (O’Fallon and Butterfield 2005). In a recent related study of professional accountants, Taylor and Curtis (2010) found that commitment to colleagues is negatively associated with reporting intent and can lead to fallacious silence. We extend this work by examining if Taylor and Curtis’ (2010) result holds for accounting students—soon-to-be professionals in the accounting field. Often, inappropriate academic activities like cheating are seen as acceptable norms by students (McCabe and Trevino 1997), and these collegial student norms lead to our second hypothesis.

Hypothesis 2

Group and community influences hypothesis: A student’s willingness to engage in fallacious silence directly varies with the student’s commitment to the community.

Keil et al. (2010) find that organizational culture and perceived management response directly influence whistleblowing decisions. The expectations placed on individuals by organizational leaders (Milgram 1963) set the tone at the top which influences organizational culture and internal controls (Committee on Sponsoring Organizations 1992). Student perceptions of professor and university expectations influence perceptions of appropriateness of actions like whistleblowing. Institutional culture can be positive and supportive. For example, universities may emphasize ethics and social responsibilities above grades. However, when universities value short-run success at all costs, then aggressive means toward this end may seem more acceptable. This leads to our third hypothesis.

Hypothesis 3

Performance culture hypothesis: A student’s willingness to engage in fallacious silence directly varies with the perceived pressure to achieve institutional goals at all costs relative to pressure to act ethically.

Moral Motivation and Moral Character: Rationalizing Behavior

An individual’s willingness to report wrongdoing may depend on the standards and approach a student takes toward academic activities. A profession is partly defined in terms of responsibilities and standards. A student who recognizes, accepts and invests effort to meet high academic responsibilities and standards takes a professional approach toward academic learning activities. If an individual recognizes and sacrifices personal resources to achieve (or try to achieve) high standards of performance, then he or she is more likely to hold others accountable to those responsibilities and standards of performance. Taylor and Curtis (2010) find that professionalism is positively associated with reporting intent for a sample of professional accountants. We extend their work by examining how professional standards influence the willingness to remain silent for accounting students—new entrants into the accounting profession. This leads to our fourth hypothesis.

Hypothesis 4

Professional standards hypothesis: A student’s willingness to engage in fallacious silence is inversely related with the sacrifices the student has made to achieve professional/organizational objectives.

Willingness to meet moral responsibilities is directly linked to moral development. When an individual lacks moral development his or her ability to make a decision to report wrongdoing and uphold morality is diminished, whereas an individual with greater moral development feels greater obligations to honor his or her duties and is more likely to report wrongdoing. Therefore, we hypothesize:

Hypothesis 5

Moral competence hypothesis: A student’s willingness to engage in fallacious silence inversely varies with the student’s moral competence.

Method and Subjects

To test our hypotheses, we conduct a survey of whistleblowing attitudes among graduate accounting students attending a private, primarily residential Southwestern University in the United States.8 At the time of survey, all the students in the graduate program spoke English as their first language, had no work experience other than professional internships, and entered graduate school as part of a joint undergraduate-graduate program designed to satisfy the state’s education requirements for the Certified Public Accountant (CPA) exam. In terms of ethics training, all students completed an ethics course to satisfy CPA state board requirements, and a general business class that included an explicit review of the university’s honor code. All 90 students enrolled in the graduate program were invited to participate in the survey, and 79 elected to participate. The subjects were given a nominal gift (value less than $2) for participating in the study.

We believe this sample provides a uniquely appropriate opportunity to examine whistleblowing attitudes for three reasons. First, whistleblowing expectations were communicated to students. The university maintains and promotes a whistleblowing program requiring students to communicate academic impropriety to faculty or the honor council. Students violate the honor code if they fail to report witnessed academic misconduct. Second, the prevalence of academic fraud (e.g., McCabe et al. 2001, 2006; McCabe 2005; Davis et al. 1992) suggests that many students have likely had the opportunity and experience of witnessing academic impropriety. Third, students must resolve social and relational impacts in making a whistleblowing decision. Subjects had developed peer relationships with colleagues over 3 years together. Table 1 contains descriptive statistics for the sample.
Table 1

Descriptive statistics

 

Minimum

Maximum

Mean

Std. Deviation

Dependent variables

 DECISION

0.00

1.00

0.60

0.30

Independent variables

 RESP

0.00

0.88

0.54

0.20

 COM

0.22

1.00

0.65

0.19

 SOC

0.00

1.00

0.10

0.21

 PERF

0.00

1.00

0.60

0.18

 GRADE

0.00

1.00

0.85

0.20

 HIGH STAN

0.00

1.00

0.73

0.44

 ACAD

0.00

1.00

0.44

0.25

 MCI

0.59

0.94

0.79

0.08

 WOR

0.00

1.00

0.47

0.28

 IMAGINECHEAT

0.00

1.00

0.34

0.48

 GENDER

0.00

1.00

0.47

0.50

Variable definitions: DECISION-i the sum of student i’s reported willingness to remain silent for twelve rule-violating situations. The sum is scaled from 0 to 1 with 1 indicating a high willingness to remain silent; RESP-i the sum of student i’s twelve reported beliefs that each of the twelve rule-violating situations is prohibited. The sum is then scaled from 0 to 1 with 1 indicating that all activities should be prohibited; COMi Student i’s use of social community technology like social networks and text messages measured from 0 to 1 where 1 indicates a high level of community technology use; SOC i Student i’s time spent per week on other university-related activities (e.g., clubs, Greek organizations). Student i’s response is scaled from 0 to 1 where 1 indicates high university involvement; PERFi Student i’s belief that the priority is finding the “right” answer and academic behaviors are permissible unless explicitly prohibited. Student i’s responses are summed and scaled from 0 to 1 where 1 indicates a high performance culture; GRADEi the importance of grades to student subject i measured on a seven-point scale. Student i’s response is scaled from 0 to 1 where 1 indicates grades are very important; HIGHSTANi equals 1 if subject i believes they are held to a higher standard than other students, 0 = otherwise; ACADi Student i’s time spent per week on academic work outside of class time. Student i’s response is scaled from 0 to 1 where 1 indicates a significant amount of out-of-class academic work; MCIi Student i’s moral competency index. The sum of student i’s response to forty questions. The sum is scaled from 0 to 1 with 1 indicating high moral competency; WORi the frequency of student i’s worship attendance measured on a seven-point scale. Student i’s response is scaled from 0 to 1 where 1 indicates high religious involvement; IMAGINECHEATi equals 1 if subject i can imagine a scenario where he/she would cheat, 0 = otherwise; GENDERi Student i’s gender: 1 = male, 0 = female

Dependent Variables of Interest

Our dependent variable is the subjects’ willingness to remain silent when they observe a peer violating the rules of an assignment. We consider twelve different rule violations to minimize the likelihood that our results are being driven by a particular action. We specifically ask the subjects to assume that each action violates the rules of an assignment and to assess their willingness to remain silent measured on a 101-point scale with a high score indicating a willingness to remain silent for each violation. The specific violations we consider are:
  • Working in a group

  • Comparing answers with their classmates

  • Consulting their textbook

  • Consulting a message board

  • Purchasing a solution manual off e-bay

  • Consulting their parents

  • Consulting other professors

  • Downloading a solution manual from a free website

  • Asking a graduate student to help understand the issue

  • Asking a former student for guidance

  • Asking a professional for guidance

  • Paying a Graduate Student to help understand the issue

We calculate our measure of the willingness to remain silent as the sum of the responses to the twelve scenarios scaled from 0 to 1 so that 0 indicates no willingness to remain silent and 1 indicates the subjects would always remain silent. We find a mean of 0.60 indicating that across the twelve decisions, the subjects indicate on average a 60 percent likelihood that they would choose to remain silent when they observed a prohibited activity. This is consistent with the 50 % range of reporting documented in previous studies (Fredin 2012).

Independent Variables of Interest

We use the following independent variables to test our hypotheses.

Hypothesis 1: Awareness Hypothesis

To test our first hypothesis, we measure student awareness of an opportunity and responsibility to report wrongdoing (RESP) by asking subjects to report their beliefs regarding the appropriateness of each of the academic activities in the twelve reporting situations. Specifically, we ask subjects a question about the acceptability of using each of the twelve rule-violating resources or activities when there are no instructions from the professor. We asked a total of twelve questions, i.e., one for each rule-violating activity, and measured student responses on a 101-point scale from 0 = definitely allowed to 100 = definitely prohibited. The University Honor Code specifically prohibits any form of collaboration unless explicitly permitted and prohibits any action which may give an unfair academic advantage. We calculate RESP as the sum of these twelve questions scaled from 0 to 1 so that 0 indicates that all activities should be permitted and 1 indicates that all activities should be prohibited. As reported in Table 1, we find great variation in beliefs that an action should be prohibited with a range from 0 to 0.88 and a mean of 0.54.

Hypothesis 2: Group and Community Influences Hypothesis

We assess strength of community connections to test Hypothesis 2. First we use two questions to measure subjects’ use of social community technology. Ellison et al. (2007) found a positive association between undergraduate use of social network sites (such as Facebook) and depth of social connection. We assess use of text messages on a five-point scale.9 We assess social network usage on a six-point scale with the low point indicating the subject does not use social networks and the high point indicating the subject uses social networks as much as possible.10 We calculate COM as the sum of the responses to these questions scaled from 0 to 1 so that 0 indicates the lowest level of community technology usage and 1 indicates the highest level of community technology. We report a sample mean of 0.65 in Table 1. Second, we assess community connection by considering the time spent on university activities (SOC). Specifically, SOC measures the number of hours per week that a subject normally spends on other university-related social activities (e.g., clubs, Greek organizations) measured on a six-point scale.11 We scale SOC from 0 to 1 so that 0 indicates low university involvement, and 1 indicates high university involvement with a sample mean of 0.10. We restrict our consideration to university activities as we are attempting to assess students’ social connection to their peers.

Hypothesis 3: Performance Culture Hypothesis

To test Hypothesis 3, we use two measures of subjects’ perceptions of a performance culture. First, we measure subject agreement (0 = disagree; 100 = agree) with two statements: 1) the professor’s priority is for the student to find the ‘right’ answer; 2) if the professor does not prohibit a behavior than it is permitted. We calculate PERF as the sum of a subject’s responses to these questions scaled from 0 to 1 so that 0 indicates a low performance culture and a 1 indicates a high performance culture. PERF’s sample mean is 0.60 indicating a relatively high sample perception of a performance culture. Second, GRADE measures the importance of grades to the subject measured on a seven-point scale.12 We scale responses from 0 to 1 with 0 indicating grades are not important and 1 indicating grades are very important. The sample mean of 0.85 indicates that, on average, grades are very important to our student subjects. Schwieren and Weichselbaumer (2010) find that student competitiveness is directly related to engaging in unethical behavior.

Hypothesis 4: Professional Standards Hypothesis

We assess subjects’ perceptions of academic standards and time spent on academics to test Hypothesis 4. HIGHSTAN measures subjects’ response to a binary question about whether or not their school holds them to higher standards (1 = held to higher standards; 0 = otherwise).13 HIGHSTAN’s sample mean of 0.73 indicates an overall sample perception of relatively high standards. We use time spent on academic work outside of class time measured on a six-point scale (i.e., ACAD) to assess subjects’ effort toward achieving high academic standards. We scale ACAD from 0 to 1 so that 0 indicates little time spent outside of class and 1 indicates a significant amount of time14 with a mean of 0.44. These variables attempt to measure two dimensions of professionalism among students in an academic setting: (1) recognition of higher professional standards of behavior (HIGHSTAN) and (2) dedicated effort to meet professional (i.e., academic) standards (ACAD).

Hypothesis 5: Moral Competence Hypothesis

To test Hypothesis 5, we consider three proxies for an individual’s moral competence. First, we use the Moral Competency Index (MCI) (Lennick and Kiel 2005). MCI measures subject resolve to have moral courage (Kim and Kim 2012). The MCI is measured as the sum of a subject’s response to forty statements15 scaled from 0 to 1 so that 0 indicates low moral competency to 1 indicates high moral competency.16 We find a sample mean of 0.79.17 According to Lennick and Kiel (2005), this level of MCI represents a moderate level of moral competency. Further, the highest decile of subjects would be classified as having a very high level of moral competency and no subjects ranked lower than a low level of competency. This suggests there is a significant variation in moral competency. We expect that individuals with a lower MCI will be more likely to remain silent. Second, we include worship attendance (WORSHIP) as a proxy for religious involvement using a question with a seven-point scale.18 Bloodgood et al. (2007) found that more religious people were less likely to engage in unethical conduct. WORSHIP is scaled from 0 to 1 so that 1 indicates high religious involvement and 0 indicates low religious involvement. WORSHIP’s sample mean is 0.47. Third, IMAGINECHEAT has subjects indicate if they could imagine a scenario where they would cheat (1 = yes, subject can imagine a scenario where he/she would cheat; 0 = otherwise) with a reported sample mean of 0.34.19 We expect individuals who can imagine cheating would be more likely to overlook the bad acts of others.

Other Controls

A meta-analysis by Mesmer-Magnus and Viswesvaran (2005) finds similar gender differences across studies: Women blew the whistle more often than men did. Consistent with prior literature we include a control (GENDER, 1 = male; 0 = female) for gender with 47 % of our sample male.

Research Design

The direction of hypothesized relationships is indicated in parentheses in the visual diagram of our research design in Figure 2. We use our independent and dependent variables to test the conceptual relationships of interest in this study between individual characteristics and whistleblowing decisions. This diagram of our research design provides an overview summary of our statistical models and tests in the next section.
Fig. 2

Diagram of investigated relationships

Results

Preliminary Analyses

The correlation analyses in Table 2 provide some preliminary evidence supporting our hypotheses. Consistent with Hypothesis 1, the perceived awareness (RESP) that an action should be prohibited is strongly (p < 0.05) negatively correlated with the corresponding willingness to remain silent. However, the correlation analyses provide mixed support for Hypothesis 2, a positive relationship between community and group influences and the willingness to engage in fallacious silence. While COM is positively associated (p < 0.05) with silence, we find no evidence SOC is associated with silence. The influence of a performance culture (PERF) is strongly (p < 0.05) positively associated with the willingness to remain silent providing some initial support for Hypothesis 3 although the importance of grades (GRADE) is not significantly associated with the dependent variable. Both perceived higher institutional standards (HIGHSTAN) and time spent on academics (ACAD) are negatively associated (p < 0.10) with the dependent fallacious silence variable providing some support for the professional standards hypothesis (H4). The correlations also provide strong initial support for the moral competence hypothesis (H5). Subjects’ Moral Competency Index (MCI) is strongly (p < 0.05) negatively correlated with the dependent fallacious silence variable, and imagining cheating scenarios (IMAGINECHEAT) is strongly (p < 0.05) positively correlated with the dependent variable.
Table 2

Correlation matrix

  

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(1)

DECISION

−.49**

.42**

.07

.38**

−.08

−.24*

−.25*

−.34**

−.12

.35**

−.17

(2)

RESP

1

−.24*

−.27*

−.50**

.14

−.05

.18

.02

−.07

−.16

.15

(3)

COM

 

1

.04

.19

.12

−.29**

−.00

.06

.06

−.17

.11

(4)

SOC

  

1

.30**

−.01

.19

.16

.03

.19

.20

.11

(5)

PERF

   

1

−.11

.063

−.16

.03

.03

.26*

.04

(6)

GRADE

    

1

−.03

.35**

.15

.17

−.24*

.19

(7)

HIGH STAN

     

1

.07

.03

.04

.01

.05

(8)

ACAD

      

1

.09

.18

−.09

.19

(9)

MCI

       

1

.23*

−.24*

.12

(10)

WOR

        

1

−.06

−.02

(11)

IMAGINECHEAT

         

1

−.14

(12)

GENDER

          

1

*** p value < .01, ** p value < 0.05, * p value < 0.10

All variables defined in Table 1

A mean-difference analysis, presented in Table 3, provides a univariate analysis of our variables of interest. Consistent with H1, individuals with lower than median awareness that an action should be prohibited (Low RESP) have significantly (p < 0.01) higher mean willingness to remain silent. The mean-difference results also provide some support for our community and group influences hypothesis (H2). Individuals with above median use of social networks and technology (High COM) have significantly (p < 0.01) higher mean willingness to remain silent. The mean-difference tests for SOC provide no additional support for Hypothesis 2.20 Similarly the insignificant mean differences for PERF and GRADE provide no additional support for Hypothesis 3, the performance culture hypothesis. Perceived high institutional standards (HIGHSTAN) has a significant negative mean difference (p < 0.05) supporting Hypothesis 4, the professional standards hypothesis. Academic studies time (ACAD) also has a marginally (p < 0.10) significant negative mean difference supporting Hypothesis 4. The mean difference results also provide additional support for the moral competence hypothesis (H5). While worship attendance (WOR) does not have a statistically significant mean difference, both MCI and imagining cheating (IMAGINECHEAT) have strong (p < 0.01, p < 0.01, respectively) negative mean differences with the willingness to remain silent.
Table 3

Tests of mean differences

 

DECISION

n

Mean

Std. Dev.

RESP

   

 Low

39

0.74

0.25

 High

40

0.47

0.28

 t Test (p value)

 

4.59

(0.00)

COM

   

 Low

47

0.50

0.27

 High

32

0.76

0.27

 t Test (p value)

 

−4.35

(0.00)

SOC

   

 Low

55

0.61

0.29

 High

24

0.60

0.31

 t Test (p value)

 

0.13

(0.90)

PERF

   

 Low

40

0.57

0.27

 High

39

0.64

0.32

 t Test (p value)

 

−1.07

(0.29)

GRADE

   

 Low

62

0.60

0.31

 High

17

0.62

0.26

 t Test (p value)

 

−0.26

(0.80)

HIGH STAN

   

 Low

21

0.72

0.29

 High

58

0.56

0.29

 t Test (p value)

 

2.17

(0.03)

ACAD

   

 Low

47

0.66

0.27

 High

32

0.53

0.32

 t Test (p value)

 

1.92

(0.06)

MCI

   

 Low

40

0.70

0.27

 High

39

0.51

0.29

 t Test (p value)

 

2.96

(0.00)

WOR

   

 Low

45

0.62

0.26

 High

34

0.58

0.34

 t Test (p value)

 

0.62

(0.54)

IMAGINECHEAT

   

 Low

52

0.53

0.29

 High

27

0.75

0.26

 t Test (p value)

 

−3.29

(0.00)

GENDER

   

 Low

42

0.65

0.29

 High

37

0.55

0.29

 t Test (p value)

 

1.51

(0.13)

All variables defined in Table 1

Regression Analyses

We further test our hypotheses with a regression analysis using the following regression model.
$$ {\text{DECISION}}_{\text{i}} = \beta_{0} + {{\upbeta}}_{ 1} {\text{RESP}}_{\text{i}} + \beta_{2} {\text{COM}}_{\text{i}} + \beta_{3} {\text{SOC}}_{\text{i}} + \beta_{4} {\text{PERF}}_{\text{i}} + \beta_{5} {\text{GRADE}}_{\text{i}} + \beta_{6} {\text{HIGHSTAN}}_{\text{i}} + \beta_{7} {\text{ACAD}}_{\text{i}} + \beta_{8} {\text{MCI}}_{\text{i}} + \beta_{9} {\text{WOR}}_{\text{i}} + \beta_{10} {\text{IMAGINECHEAT}}_{\text{i}} + \beta_{11} {\text{GENDER}}_{\text{i}} + \varepsilon_{\text{i}}$$
where DECISIONi is the sum of student i’s reported willingness to remain silent for 12 rule-violating situations. The sum is scaled from 0 to 1 with 1 indicating a high willingness to remain silent. RESPi is the sum of student i’s 12-reported beliefs that each of the twelve rule-violating situations is prohibited. The sum is then scaled from 0 to 1 with 1 indicating that all activities should be prohibited. COMi is student i’s the use of social community technology like social networks and text messages measured from 0 to 1 where 1 indicates a high level of community technology use. SOCi is student i’s time spent per week on other university-related activities (e.g., clubs, Greek organizations). Student i’s response is scaled from 0 to 1 where 1 indicates high university involvement. PERFi is student i’s belief that the priority is finding the ‘right’ answer and academic behaviors are permissible unless explicitly prohibited. Student i’s responses are summed and scaled from 0 to 1 where 1 indicates a high performance culture. GRADEi is the importance of grades to student subject i measured on a seven-point scale. Student i’s response is scaled from 0 to 1 where 1 indicates grades are very important. HIGHSTANi equals 1 if subject i believes they are held to a higher standard than other students, 0 = otherwise. ACADi is student i’s time spent per week on academic work outside of class time. Student i’s response is scaled from 0 to 1 where 1 indicates a significant amount of out-of-class academic work. MCIi is student i’s moral competency index. The sum of student i’s response to forty questions. The sum is scaled from 0 to 1 with 1 indicating high moral competency. WORi is the frequency of student i’s worship attendance measured on a seven-point scale. Student i’s response is scaled from 0 to 1 where 1 indicates high religious involvement. IMAGINECHEATi equals 1 if subject i can imagine a scenario where he/she would cheat, 0 = otherwise. GENDERi is student i’s gender: 1 = male, 0 = female.
Our regression results provide support for most of our hypotheses (Table 4). The awareness hypothesis (H1) is supported. RESP is strongly (p < 0.01) negatively significant. COM is strongly (p < 0.01) positively significant providing support for the group and community influences hypothesis (H2), but SOC is not significant. There is also support for the performance culture hypothesis (H3). While GRADE is only marginally significant (p < 0.10), PERF is significant at p < 0.05. Both HIGHSTAN (p < 0.10) and ACAD (p < 0.05) are negative and provide support for our professional identify hypothesis (H4). There is strong support for the moral competence hypothesis (H5). Both MCI and IMAGINECHEAT are strongly positive and significant (p < 0.01) and WOR is marginally significant at p < 0.10.
Table 4

Regression analyses

 

Exp. sign

B

t

Sig.

CONSTANT

?

1.03

3.47

***

RESP

−0.39

−2.84

***

COM

+

0.58

4.28

***

SOC

+

−0.02

−0.15

 

PERF

+

0.29

1.83

**

GRADE

0.14

1.13

*

HIGHSTAN

−0.13

−1.29

*

ACAD

−0.10

−1.76

**

MCI

−0.91

−3.04

***

WOR

−0.10

−1.21

*

IMAGINECHEAT

+

0.14

2.63

***

GENDER

−0.08

−1.58

**

Adj. R 2

 

0.56

  

n

 

79

  

*** p value < .01, ** p value < 0.05, * p value < 0.10

All variables defined in Table 1

Inspecting the regression coefficients more closely indicates that certain factors are more influential in the rationalization to engage in fallacious silence. MCI, COM, and RESP seem to have the most influence based on the regression coefficients. In particular, the subject with the highest MCI is 31 percent less likely to remain silent than the subject with the lowest MCI,21 ceteris paribus. The subject with the highest RESP is 34 percent less likely to remain silent than the subject with the lowest RESP,22 and the subject with the highest COM rating is 58 percent more likely to remain silent than the subject with the lowest COM rating.23 We conduct a stepwise regression to further analyze the importance of hypothesized rationalization factors. This analysis in Table 5 reveals that the most important individual factors are, in ranked order of influence, RESP, COM, MCI, and IMAGINECHEAT with these four factors explaining 52 % of the variation.
Table 5

Step-wise regression analyses

Model

Adj-R 2

B

t

 

1

0.23

   

CONSTANT

0.99

11.68

***

RESP

−0.73

−4.94

***

2

0.34

   

CONSTANT

0.57

4.10

***

RESP

−0.60

−4.28

***

COM

0.54

3.65

***

3

0.45

   

CONSTANT

1.53

5.59

***

RESP

−0.59

−4.57

***

COM

0.57

4.18

***

MCI

−1.24

−3.96

***

4

0.52

   

CONSTANT

1.19

4.34

***

RESP

−0.50

−4.07

***

COM

0.65

5.04

***

MCI

−1.02

−3.39

***

IMAGINECHEAT

0.18

3.37

***

All variables defined in Table 1

We next transition to consider whether the severity of the observed rule-violating action affects our results. We use the average reported willingness to remain silent to measure and rank each of the twelve rule-violating actions from most severe to least severe (Table 6). The most severe rule-violating action has the lowest level of silence; the least severe rule-violating action has the highest level of silence. The rankings reveal that subjects are most likely to remain silent when a peer is violating the rules by consulting his/her textbook and least likely to remain silent when a peer violates the rules by purchasing a solution manual, downloading a solution manual or paying a graduate student to assist with the project. A t test confirms that the mean willingness to remain silent is statistically significantly different between these two groups. When we replicate our regression analysis for each of these groups, we find largely consistent results (Table 7). Notably, we still find COM and IMAGINECHEAT influence both decisions, but RESP and MCI do not influence the rationalization to remain silent in the least-severe rule-violating scenario, consulting a textbook.
Table 6

Propensity to engage in fallacious silence for particular scenarios

 

Mean

Rank

Consulting their textbook

81.15

1

Consulting their parents

69.89

2

Consulting a message board

67.67

3

Consulting other professors

65.14

4

Comparing answers with their classmates

61.66

5

Working in a group

59.57

6

Asking a graduate student to help understand the issue

59.32

7

Asking a former student for guidance

57.99

8

Asking a professional for guidance

57.67

9

Purchasing a solution manual off e-bay

50.46

10

Downloading a solution manual from a free website

48.43

11

Paying a graduate student to help understand the issue

46.03

12

Subjects’ scores indicated their willingness to remain silent when they observed a peer engaging in the specific prohibited activity while completing an assignment

n = 79

Table 7

Regression analyses for extreme fallacious silence decisions

 

Exp. sign

High level of fallacious silence

Low level of fallacious silence

 

B

t

Sig.

B

t

Sig.

Constant

?

0.10

0.30

 

1.27

3.30

***

RESP

0.05

0.39

 

−0.31

−2.59

***

COM

+

0.62

3.73

***

0.74

4.06

***

SOC

+

−0.22

−1.52

**

0.04

0.22

 

PERF

+

0.26

1.45

**

0.30

1.52

**

GRADE

0.19

1.22

*

0.10

0.56

 

HIGHSTAN

−0.02

−0.25

 

−0.11

−1.49

**

ACAD

−0.26

−2.08

**

−0.08

−0.55

 

MCI

0.14

0.36

 

−1.48

−3.55

***

WOR

−0.01

−0.07

 

−0.08

−0.66

 

IMAGINECHEAT

+

0.16

2.43

***

0.15

2.02

**

GENDER

−0.03

−0.60

 

−0.06

−0.97

*

Adj. R 2

 

0.23

  

0.42

  

N

 

79

  

79

  

*** p value < .01, ** p value < 0.05, * p value < 0.10

All independent variables are defined in Table 1 with the exception of RESP. High level of fallacious silence, a dependent variable, is subjects’ willingness to remain silent when they observe a peer violating an assignment’s rule by consulting a textbook scaled from 0 to 1. Low level of fallacious silence, a dependent variable, is subjects’ willingness to remain silent when they observe a peer violating an assignment’s rule by paying a graduate student to assistant them understand the issues, downloading a solution manual, or purchasing a solution manual off e-bay, scaled from 0 to 1. A test of the mean response reveals that subjects are statistically significantly more likely to remain silent for matters classified as “high level of fallacious silence” than “low level of fallacious silence” (t stat 8.04, p value < 0.001). RESP is recalculated for each dependent variable scenario (i.e., “High level of fallacious silence” and “Low level of fallacious silence”). RESP for the High Level of Fallacious Silence is subjects’ belief that it is acceptable to consult their textbook to complete an assignment when the professor does not provide any instructions on textbook usage scaled from 0 to 1. RESP for the Low level of fallacious silence is subjects’ belief that it is permissible to pay a graduate student to help them understand issues, download a solution manual, or purchase a solution manual off e-bay when the professor does not provide any specific guiding instructions, scaled from 0 to 1

These interesting results provide potential insights into how the severity of the rule-violating actions influences the factors that impact the rationalization to remain fallaciously silent. When the situation involves a more severe rule-violating action, awareness and moral competence become strongly significant (p < 0.01) and important factors influencing the willingness to rationalize silence. Awareness is important because more severe rule-violating actions are more clear-cut and easier to identify. Once identified, moral competence matters because it guides the decision to remain silent. Less-severe rule-violating actions involve more ambiguity. Awareness becomes statistically insignificant due to the challenge introduced by ambiguity. Without awareness, moral competence also becomes insignificant. Instead, individuals rely more on community influences (both COM and SOC are strongly significant at p < 0.05 or better) to guide their willingness to remain silent. Also, individual concerns like the importance of grades (p < 0.10) and time spent on academic studies (p < 0.05) become important factors. We further examine the implications of these results in our discussion.

Discussion and Implications

This study considers factors that influence individuals’ fallacious silence rationalizations. To guide our study, we first use the fraud triangle (Cressey 1953) and models of moral behavior (Rest 1984) to build a model of fallacious silence and summarize prior literature around the notion that the fallacious silence decision requires opportunity, incentives, and rationalization. While prior literature has extensively considered how incentives and opportunities influence the fallacious silence decision, we focus this study on factors that influence how individuals rationalize silence.

In general, we find evidence that the willingness to rationalize fallacious silence is related to community and cultural influences and personal traits (i.e., awareness, professional standards, and moral competence). Based on additional step-wise analyses, we further find that awareness, community, and moral competence dimensions most influence the willingness to remain silent, respectively. We also find that the importance of these factors varies with the severity of the rule-violating action. Personal traits like awareness and moral competence are more important factors with more clear-cut, severe rule-violating actions. Community influences and personal concerns become more important when considering less severe and more ambiguous rule-violating actions.

Several implications follow from these findings. The importance of community and cultural influences has implications for an organization’s cultural development. Effort should be focused on developing organization loyalty (Taylor and Curtis 2010) and building a supportive culture and whistleblowing program (Vandekerckhove and Lewis 2012). The whistleblowing program should support and develop employee awareness of inappropriate activities and whistleblowing responsibilities (Vandekerckhove and Lewis 2012). The importance of personal characteristics like awareness and moral competence has implications for the education and development of individuals and employees. Academic education and business training and development programs should work together to develop educated professionals. Wilkerson (2010) recently proposed significant professional education reforms. Education should go beyond technical and cognitive development and include moral and professional development as well. Developing professional standards and ideals should be the foundation of these professional education reforms (Wilkerson 2010) and develop moral awareness, competence and character.

These education and personal development reforms complement recent regulatory reforms. While regulatory reforms focus on limiting opportunities and modifying incentives, our suggestions focus on developing and supporting the individual to limit rationalization. Although these recommendations should improve whistleblowing program effectiveness, it is important to note that whistleblowing programs, like other governance and control mechanisms, have limitations and may never fully work perfectly.

Limitations and Future Research

As with every study, our research is subject to a number of limitations. First, our study employs students. It is possible that our results do not generalize to other settings where the incentives for silence may be significantly different. Second, our dependent variable measures subjects’ intent to remain silent, but prior research suggests that there are different correlates between action and intention (Mesmer-Magnus and Viswesvaran 2005). Future research should consider using an alternative to student survey-based self-reports such as a behavioral experiment. Third, we considered a limited set of factors that may influence the fallacious silence decision, but it is likely that there are other factors that also influence this decision. For example, future research could consider how time pressure, broader community involvement, and the strength of the relationship with the rule violator might influence the fallacious silence decision. Fourth, we used survey scenarios with technically no ambiguity as to whether or not the observed actions were wrong. Uncertainty around the presence of fraud and the possibility of “crying wolf” complicates the decision. Lastly, we present some results suggesting the factors that influence the decision to remain silent vary across decisions. We suggest that future research more formally consider how factors such as the moral intensity (Jones 1991; Singer et al. 1998) of decisions may influence the decision to engage in fallacious silence. However, there is also the possibility that whistleblowing will never achieve the effectiveness that regulators aspire it to have.

We believe that our research raises several important issues for future research to consider. The fallacious silence model presented here offers many possible avenues for future work. For example, how does the effectiveness of the whistleblowing program influence whistleblowing opportunities and the decision to remain silent? Responsive corrective action is important; the blown whistle needs to be heard and acted on. Arguably, if there is no confidence that reporting an incident will lead to action, then there is no opportunity to effectively blow the whistle. Another avenue could explore how the presence of multiple witnesses influences the decision. It is possible that multiple witnesses allow an individual to assume it is someone else’s moral responsibility. Alternatively, it may create social pressures to report lest one be identified as a complicit member of the fraud. Future work could also explore how moral intensity impacts factors that contribute to fallacious silence rationalizations building on our work in Tables 6 and 7. Future work like this can continue to develop our understanding of the opportunities, incentives and rationalizations involved in fallacious silence decisions.

Footnotes

  1. 1.

    For example, in 2012, Harvard University experienced a major scandal involving around 50 percent of the students enrolled in a class (http://en.wikipedia.org/wiki/2012_Harvard_cheating_scandal).

  2. 2.

    Also known as “inactive observers’’ (Miceli and Near 1992) or ‘‘silent observers’’ (Rothschild and Miethe 1999).

  3. 3.

    The False Claims Act of 1863 allows whistleblower lawsuits when the government is being defrauded. Whistleblowers are entitled to a portion of assessed penalties. In 1986, Congress made it easier for whistleblowers to bring cases and gave them a larger share of assessed penalties. For example, whistleblowers recently benefited from uncovering widespread mortgage fraud by some of America’s biggest banks (see, http://money.cnn.com/2012/07/02/news/economy/whistleblowers-foreclosure-settlement/index.htm?source=cnn_bin).

  4. 4.

    Psychological costs can include humiliation, anger, stress, and resentment which can lead to reduced creativity and productivity.

  5. 5.

    Vandekerckhove and Tsahuridu (2010) argue that the duty to whistleblow cannot be legislated.

  6. 6.

    For example, Keil et al. (2010) found that the judgments of potential whistleblowers were influenced by risk of detection (i.e., economic incentives), trust in the supervisor and perceived management response (i.e., authoritative social incentives), organizational climate (i.e., collegial social incentives), and duty to report (i.e., moral incentives).

  7. 7.

    Kohlberg’s (1969) work closely links judgment with incentives. Note that judgments at different stages of moral development are influenced by different types of incentives in Kohlberg’s (1969) model: pre-conventional (motivated by economic self-interest incentives), conventional (motivated by social and legal compliance incentives), post-conventional (motivated by moral incentives of identified duties and obligations).

  8. 8.

    All students in the graduate program were enrolled in a five-year joint undergraduate/masters’ degree program.

  9. 9.

    The subject response to the question “How many text messages do you send a month?” Is measured on this scale: 0–15, 16–50, 51–100, 100–500, and more than 500.

  10. 10.

    The subject response to the question “How often do you use a social network website (such as facebook) a week?” is measured on the following scale: never, once a week, a few times a week, daily, a few time a day, and as much as I can.

  11. 11.

    The subject response to the question “How many hours per week do you normally spend on other University related activities (clubs, Greek organizations)?” is measured on the following scale: 0–5, 6–10, 11–15, 15–20, 21–25, and more than 25.

  12. 12.

    The scale’s low point is “not important” and the scale’s high point is “very important”.

  13. 13.

    Specifically, we ask the subjects “Do you believe this university holds its students to higher standards than other universities would?”

  14. 14.

    The subject response to the question “How many hours per week do you normally spend on academic work outside of class time?” is measured on the following scale: 0–5, 6–10, 11–15, 15–20, 21–25, and more than 25.

  15. 15.

    Each statement is measured on a 101-point scale with 0 indicating the statement is “always true” and 100 indicating the statement is “never true”.

  16. 16.

    See Lennick and Kiel (2005) for the specific statements used to assess MCI.

  17. 17.

    This is comparable to Christensen et al. (2007)’s reported MCI score in an experiment involving accounting students.

  18. 18.

    The subject response to the question “How often do you attend worship services or Bible Study?” is assessed on the following scale: Never, On special occasions, Monthly, A few times a month, most weeks, weekly, and multiple times a week.

  19. 19.

    Specifically, we ask the subjects “Can you imagine a situation when you would cheat?”.

  20. 20.

    The lack of significant correlations is likely attributable to low variability in the SOC variable. SOC’s low variability also likely affects its mean-difference and regression results.

  21. 21.

    Given the minimum MCI is 0.59 and the maximum MCI is 0.94, with a coefficient correlation of −0.91, the model indicates the difference between the highest and lowest willingness to remain silent is 0.31 ((0.94–0.59) × 0.91).

  22. 22.

    Given the minimum RESP is 0.00 and the maximum RESP is 0.88, with a coefficient correlation of −0.39, the model indicates the difference between the highest and lowest willingness to remain silent is 0.34 ((0.88–0.0) × 0.39).

  23. 23.

    Given the minimum COM is 0.00 and the maximum COM is 1.00, with a coefficient correlation of 0.58, the model indicates the difference between the highest and lowest willingness to remain silent is 0.58 ((1.00–0.00) × 0.58).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Hankamer School of BusinessBaylor UniversityWacoUSA

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