Corruption’s impact on organizational outcomes

Lisa A. Kihl (School of Kinesiology, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA)
Mansour Ndiaye (University of Connecticut, Storrs, Connecticut, USA)
Janet Fink (University of Massachusetts Amherst, Amherst, Massachusetts, USA)

Social Responsibility Journal

ISSN: 1747-1117

Publication date: 5 March 2018



This study aims to develop a model of corruption that measures the impact of sports corruption on organizational outcomes (i.e. win difference and attendance) and the mediating role of institutional reputation. How the form and extent of the corruption impacted these organizational outcomes was also examined.


Archival data were collected and then structural equation modeling (i.e. path analysis) was used to analyze the model and estimate the parameters.


The model was a good fit. The extent of the violation served as an antecedent to sanctions and institutional reputation mediated the relationship between the extent of the violation and organizational outcomes. A weak but negative correlation between sanctions and institutional reputation was also found.

Practical implications

Rather than seeking to prevent specific forms of corruption carried out by individuals, managers should consider developing and implementing reform interventions that concentrate on eliminating corrupt networks (e.g. a group of interrelated individuals that abuse their power for private or public gain) who facilitate systematic malfeasance acts.


The study is the first to operationalize the extent and form of corrupt acts and measure their impact on organizational outcomes. The results demonstrate how different variables interact to determine the effects on organizational outcomes.



Kihl, L., Ndiaye, M. and Fink, J. (2018), "Corruption’s impact on organizational outcomes", Social Responsibility Journal, Vol. 14 No. 1, pp. 40-60.

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Corruption’s impact on organizational outcomes

Corruption is a complex and multifaceted phenomenon (Aidt, 2003; Andvig and Fjeldstad, 2001; Argandona, 2003; Bac, 1998; Luo, 2004) that impacts organizational performance (Baucus and Baucus, 1997; Luo, 2004; Zeidan, 2013). The complexity of organizational corruption is demonstrated through its various components. Corruption contains different forms of malfeasance, and its extent is measured in terms of intensity (i.e. quantity and gravity) and hierarchies (i.e. acts and levels of organizational involvement). Corruption occurs in various contexts (e.g. sport industry and corporate settings) (Andvig and Fjeldstad, 2001; Luo, 2004), and it varies in how outcomes are measured. For example, research has shown that the consequences of corruption on organizational performance differ by the form (e.g. fraud, bribery, lack of oversight) (Davidson and Worrell, 1992; Luo, 2004; Strachan et al., 1983), and extent of the corruption, as well as the number of sanctions levied (Baucus and Baucus, 1997). The complexity of corruption is further illustrated by the notion that its impact on organizational performance is mediated by an organization’s reputation (Greve et al., 2010). Highly reputable institutions engaged in corruption can generally rely on their reputation to mitigate the negative impact on organizational performance post corruption (Karpoff et al., 1999). In addition, inconsistencies exist in assigning sanctions across organizations that commit corrupt acts, and it appears organizations with high reputations or power do not always receive draconian type penalties (Davis and Hairston, 2014).

The corruption literature also varies in how organizational performance is operationalized and measured (Gaviria, 2002), which makes comparisons challenging. For example, in sport, Mazanov et al. (2012) measured the impact of fraud (match fixing) in an Italian soccer league on share price, and Smith (2015) measured the impact of sanctioned football and men’s basketball athletic programs on winning percentage. While, in business contexts, organizational performance has been measured in terms of return of assets (Van Vu et al., 2016), reduction in sales growth (Baucus and Baucus, 1997) and investment growth (Gaviria, 2002; Mazanov et al., 2012). The lack of a uniform definition and means for measuring organizational performance across diverse contexts, in particular intercollegiate athletic institutions, create challenges in conducting research on the consequences of corruption.

For this study, corruption is broadly defined as those practices that violate organizational rules and norms for personal or group gain (Fleming and Zyglidopoulos, 2008). A broad definition of corruption was selected to capture its complex nature in terms of the form and extent of malfeasance activities (Andvig and Fjeldstad, 2001). While the complex and multifaceted nature of corruption is well established in the corruption literature (Cuadrado and Arce, 2005; Luo, 2004), organizational corruption scholars including sport corruption, have provided little theoretical and/or empirical support regarding the nature of the various relationships that exist among specific forms of corruption, extent of violations, sanctions imposed and the role of an establishment’s reputation on organizational outcomes. The National Collegiate Athletic Association (NCAA) offers a fertile context to enhance our understanding of the nature of these relationships and the ability to further explain the complexity of corruption and, in this study, its impact on organizational outcomes. Further, such understanding will help develop suitable reform strategies that limit organizational corruption.

NCAA Division I intercollegiate athletic sports programs epitomize the complex and multidimensional characteristics of corruption. While technically classified as non-profit organizations, in reality, intercollegiate athletics is a highly commercialized for-profit billion-dollar industry where the largest five conferences[1] reported a combined $1.534bn in total revenue and operation budgets averaged over $100m for 2012-2013 (NCAA, 2014a). The commercialized college sports model has created a culture of corruption where malfeasant acts are rationalized for the potential of earning enormous revenues owing to enhanced organizational outcomes (Nixon, 2014). From 2009 to 2014, 62 major infraction violations[2] cases were reported by NCAA Division I institutions; 90 per cent of these occurred in the revenue sports of men’s basketball and football (NCAA, 2014b). These cases included various types of bribery (e.g. inappropriate inducements to recruits and impermissible benefits), fraud (e.g. academic and reporting) and lack of oversight (e.g. university officials failing to monitor programs or failing to stop corrupt acts). Sanctions imposed on programs ranged from years of probation, ban on post-season competition, expungement of records, financial penalties and/or restrictions in recruiting and scholarship privileges (NCAA, 2014b).

However, inconsistencies exist in institutions with high (top 30 nationally, University of Southern California and the University of Michigan) or low (unranked nationally, University of Nevada, Las Vegas and University of Arkansas) institutional reputations that were involved in different corrupt acts. Their organizational outcomes as measured in this study (i.e. winning and attendance) varied – that is sometimes the organizational outcomes were impacted by the malfeasance and subsequent sanctions while other times they were not. We acknowledge that the research context is limited to NCAA Division I team sports of men’s basketball and football, nonetheless, the multifaceted characteristics of corruption in these intercollegiate sports provide an ideal context to measure the effect of corruption and the nature of the relationships among form, extent, penalties, reputation and organizational outcomes.

The purpose of this study was to propose and test a model of corruption in intercollegiate athletics that measured the impact of corruption on organizational outcomes (Figure 1). First, we investigated the effects of the form and the extent of NCAA violations on the sanctions imposed. Second, we explored the mechanism through which the form and extent of the violations impact organizational outcomes (in this case, winning percentage and attendance) and anticipated that this relationship would be mediated by institutional reputation. The model was tested with data derived from the NCAA major infractions database from 1953 to 2013 (NCAA, 2014b).

The contribution of our paper to the broader corruption literature is threefold. First theoretically, the a priori developed model that was tested in the study with preexisting data at various intervals contributes to a better understanding of relationships among elements of corruption and their enduring effects on organizational performance metrics (i.e. winning percentage and attendance outcomes). Said differently, the approach taken provided a glimpse into the complexity of corruption and generated an understanding of the mechanisms through which corrupt acts affect organizational outcomes, specifically in NCAA Division I revenue sports. Second, empirically we addressed calls for further research about measuring corruption and how the variables of form, extent, sanctions and institutional reputation might impact organizational outcomes (Langseth, 2006; Luo, 2004). Rather than measuring corruption through perception indexes or surveys, which are limited in terms of measuring actual corruption (Olken, 2009), this study examined preexisting data that objectively captured the multidimensional nature of corruption and its impact on the organization outcomes of the NCAA Division I sports of men’s basketball and football. Before we can develop effective strategies to combat corruption, it is important to understand its complexity (Cuadrado and Arce, 2005) and our study accomplishes this.

The paper is organized as follows. First, we review the literature related to corruption and organizational performance. This review is followed by the proposed theoretical model and respective hypotheses statements. Second, the research methods are outlined including data collection and the structural equation modeling used to test our hypotheses and model fit. Third, the empirical results are presented, including a description of the structural equation model results. Fourth, we provide a discussion and recommendations for future research followed by managerial implications. Last, limitations of the study and conclusions are discussed.

Related literature

Corruption and organizational performance

Literature examining the impact of corruption on organizational performance is mixed (i.e. both positive and negative consequences) (Sahakyan and Stiegert, 2012). Earlier studies have shown that corruption is positively associated with firm growth and performance (Huntington, 1968; Leff, 1964). Individuals and organizations engage in corrupt activities to maximize benefits and more efficiently conduct business through circumventing rules and regulations (Beck and Maher, 1986; Huntington, 1968; Leff, 1964). In particular, Beck and Maher (1986) found increased efficiency when officials allocated contracts to the highest bidder. Firms also seek to improve quality of governance by bribing government officials through extra perks and unreported compensation (Bailey, 1966). In contrast, more current literature asserts a negative association between corruption and firm performance and growth (Gaviria, 2002; Murphy et al., 2009; Zeidan, 2013). Luo (2002, 2004) contends that evolutionary hazards and competitive disadvantage are two main consequences of corruption on firms. Malfeasant activities create evolutionary hazards by impeding firm growth, firm value and business development (Gaviria, 2002; Murphy et al., 2009; Zeidan, 2013) through the associated pathways of the punishment effect, image effect and cost effect (Luo, 2002, 2004). Typically, corruption results in severe penalties including institutional and criminal punishments and economic sanctions (Baucus and Baucus, 1997; Kihl et al., 2008; Luo, 2002, 2004), which impose substantial financial costs to a firm that ultimately negatively affects revenue (Murphy et al., 2009).

In combination with the punishment effect, an organization’s reputation and/or image is extremely damaged by corruption (Baucus and Baucus, 1997; Gunthorpe, 1997; Luo, 2004). Framed from both stakeholder theory and resource-based view theory, research has demonstrated that organizations engaged in corruption sustain damaged reputations that harm stakeholder relationships (Gunthorpe, 1997; Mazanov et al., 2012; Waddock and Graves, 1997). Corrupt acts erode citizen trust and potentially affect ongoing contractual ties with employees and customers (Lewis, 2003; Neil, 2004), impacting future business relationships.

Corporate corruption is also associated with long-term effects on a firm’s performance in terms of decreased sales growth (Baucus and Baucus, 1997; Buraimo et al., 2012) and harming current and future clients and business partner relationships (Cialdini et al., 2004; Luo, 2004; Neil, 2004). Various studies have shown that corruption can cost organizations between 1 and 6 per cent of annual sales (Hogsett and Radig, 1994; Schnatterly, 2003; Touby, 1994). Thus, corruption appears to result in many negative outcomes relative to organizational performance.

Organizational outcomes versus organizational performance

The ability of an organization to measure its performance in any setting is an important endeavor, as performance is a key indicator and a central element of organizational success. But how do we measure organizational performance? Many scholars (Cunningham, 2002; Winand et al., 2010) have argued that one must assess an organization’s strategic objectives against its deliverables when evaluating its performance. Consequently, the recognition and understanding of the environmental context in which organizations exist is imperative. That is, to blindly view performance similarly across contexts without regard to organizational strategy and goals would be misguided.

In traditional business settings, there are key metrics (e.g. stock prices, revenue, profits) that lend themselves well to measuring performance in competitive marketplaces. In other areas such as sports, and more specifically intercollegiate athletics, the term “organizational performance” does not always fit. One of the differences seems to be grounded in the fact that traditional business entities are more focused on efficient allocations of resources and maximizing value for stakeholders whereas sports organizations, particularly those in intercollegiate athletics, present themselves with a plurality of organizational objectives that may have competing values at times (e.g. athletic achievement versus academic achievement). Hence, performance measurement in the sport arena becomes more complex.

In fact, Cameron (1980) argued organizational performance is a multi-dimensional construct that is socially constructed. In keeping with this view, the focus of this paper centers on two proxies of organizational performance in sports – attendance and win difference – and uses the term “organizational outcomes” instead of “organizational performance.” The choice of these outcome variables does not imply that win difference and attendance are universal measures of organizational outcomes in sports. They are instead two viable dependent variables given the focus of this paper and considering some of the strategic goals of athletic departments in the sports of football and men’s basketball (e.g. winning and generating revenues). Attendance figures provide athletic departments with the ability to increase ticket sales, generate excitement, experience greater fan involvement and revenues and create more sponsorship opportunities for athletic organizations. Meanwhile, a team’s winning record provides a tangible measure of recognition, conference stature and athletic achievement in intercollegiate athletics. For example, Frick and Simmons (2008) used domestic championship titles in the German Soccer League – Bundesliga – and Champions’ League trophies to assess performance in their study of managerial quality on organizational performance.

Any governing body sanction would certainly put tremendous restraints on organizational objectives about winning and increasing ticket sales, hence the choice behind these two variables to assess the impact of corruption-driven sanctions on organizational outcomes in sports.

The current approach to organizational outcomes is also consistent with how sport scholars have measured similar constructs with the combination of various indicators. For example, Cunningham (2002) used athletic achievement, graduation rates and gender equity to measure the organizational effectiveness athletic departments. Meanwhile, Rocha and Turner (2008) relied on athletic achievement, student-athletes’ education, social performance and financial performance to assess the organizational effectiveness of athletic departments and coaches’ extra-role behaviors.

Corruption and organizational outcomes in sport

While sport is founded on the ideals of meritocracy and fair play, one needs to look no further than the daily sports headlines to observe the propensity of corruption within its realm. In May of 2015, 14 prominent soccer officials from the Federation Internationale de Football Association (FIFA) were indicted by US Department of Justice on criminal corruption charges including bribery, fraud and money laundering with allegations that the corruption spanned decades (Fortunato, 2016; Robinson, 2015). In October 2015, longtime FIFA President, Sepp Blatter, and former Vice-President and member of the Executive Committee of FIFA and President of the Union of European Football Association (UEFA), Michel Platini, were banned from the sport for eight years (later reduced to six years upon appeal to the FIFA Appeal Committee (British Broadcasting Corporation, 2016; Fortunato, 2016) by FIFA’s ethics committee owing to illegal payment made by Blatter to Platini in 2001 and criminal cases for both men are looming (British Broadcasting Corporation, 2015).

It is too early to project the impact of corruption on FIFA’s organizational outcomes. However, Vox Media (Taub, 2015) reported that over a period of 20 years, the association lost millions of dollars of revenues from marketing proceeds that went to corrupt officials instead of FIFA, money that would have been used to help support the growth of soccer through the 209 member associations that come from various countries around the world. Further, FIFA has lost five prominent sponsors in the past year (i.e. Sony, Emirates, Castrol, Continental, and Johnson & Johnson), and while none have publicly stated their departure is the result of the indictments, the timing would suggest the corruption allegations weighed in their decisions (Fortunato, 2016; Rumsby, 2015).

Empirical evidence in various sport contexts provides mixed results of corruption’s impact on organizational outcomes. In intercollegiate athletics, Mahony, Fink and Pastore (1999) found that NCAA probation had minimal impact on team performance. For example, teams given one to three years of probation for committing a major violation still averaged approximately 0.500 winning percentage. Similarly, Smith’s (2015) examination of NCAA Division I football and men’s basketball teams sanctioned for NCAA major violations winning percentages were not significantly impacted. Conversely, Perry (2002) found a reduction in on-the-field success of men’s basketball and football programs under NCAA major violation penalties; however, significance varied by sport and penalties. In professional sport, Carmichael et al. (2014) also found Italian football programs clubs’ sanctioned for corruption did not significantly impact team performance (i.e. individual and playing performance measures and league success).

In terms of financial outcomes, Rhoads and Gerking (2000) found that overall university alumni contributions were lower when the men’s basketball team was on NCAA probation for rules infractions than when football programs were on probation. Alumni contributions were measured by real voluntary educational-related contributions received from individuals, charitable foundations, businesses and religious organizations. Similarly, research examining intercollegiate athletic programs under NCAA sanctions showed various financial losses due to a reduction of income as a result of game forfeits, returned revenue, a reduction of future competitions (Mahony et al., 1999) and decreased attendance (Smith, 2015). Smith also found that universities’ reputations were also diminished after their athletic programs were penalized for NCAA major violations.

Professional sport organizations involved in corruption have experienced financial harm in terms of decreased home game attendance, charitable giving, sponsorship and broadcast revenue (Buraimo et al., 2012; Carmichael et al., 2014; Grimes and Chressanthis, 1994; Hughes and Shank, 2008; Leeds and von Allmen, 2011). Presumably corruption drives spectators away from sporting events (Carstairs, 2003), resulting in decreased gate revenue/attendance, which, in turn, impacts sponsorship and broadcast revenue (Buraimo et al., 2012; Leeds and von Allmen, 2011). Italian football teams guilty of corruption had decreased home attendance (Buraimo et al., 2012; Carmichael et al., 2014) and lost a combined revenue estimated to be €66m over four seasons. However, Mazanov et al. (2012) found an increase in long-term value of sport clubs involved in corruption as the share prices of clubs involved in corrupt activities actually increased by 30 per cent. They argued the strong fan identification with the clubs, coupled with reform efforts, diminished uncertainty among investors. However, inconsistencies in the literature highlight the need to investigate the impact of corruption on organizational outcomes.

Despite this literature, to date, there has been no studies that examine how the form and extent of corruption impact sport organizational outcomes. Surprisingly, in the context of intercollegiate athletics, where many serious cases of corruption occur in men’s basketball and football, we do not have a sound understanding of the varying aspects of corruption and the extent to which the violations impact these respective programs’ organizational outcomes.

Form and extent of corruption and organizational outcomes

There have been few studies examining how the form of corruption impacts organizational outcomes (Button et al., 2011; Khatri and Tsang, 2003; Van Vu et al., 2016; Zahra et al., 2005). Various forms of corruption exist such as fraud, bribery, collusion, embezzlement, lack of oversight, extortion, money laundering, abuse of authority and so forth. Much of the literature depicting the consequences of corruption does not distinguish between different forms of corruption and their specific impact on organizational outcomes. Rather, the research tends to discuss the consequences of “illegal behavior” of corporations (Baucus and Baucus, 1997; Zeidan, 2013) rather than emphasizing the specific form of corruption (e.g. fraud, bribery) and its related consequences. Van Vu et al.’s (2016) examination of different types of bribery and the effect on firm performance is one exception. They found that costs associated with different varieties of bribery, in particular, informal paying of tax collectors and public official payments for obtaining permits, negatively impacted businesses’ financial efficiencies. In another exception, Zahra et al. (2005, p. 817) argued that “top management fraud is more pervasive and wide reaching in its impact” because of the various financial consequences (decrease in stock price, reputation, decrease in credit rating and fines) these corrupt acts cause. However, absent in the literature are comparisons to determine whether consequences of corruption differ by the form of the corrupt act (Davidson and Worrell, 1992; Luo, 2004; Strachan et al., 1983), which is one of the intentions of this research.

Similarly, the extent of the corruption may impact organizational outcomes. Firms with multiple convictions experienced both decreased rate of assets and rate of sales (Baucus and Baucus, 1997; Zeidan, 2013). However, additional research on the seriousness (i.e. intensity and number of hierarchical levels involved) of corrupt acts has shown a non-significant impact on organizational outcomes in the long term (Baucus and Baucus, 1997; Zeidan, 2013). In the sport context, Hughes and Shank (2008) found that scandals involving more individuals and violations that occurred repeatedly over a period of time were perceived more negatively than isolated incidents by individual agents.

Institutional reputation as a mediator

Institutional reputation has and continues to be a popular research endeavor in corporate settings (Barnett et al., 2006; Fombrun et al., 2000; Fombrun and Shanley, 1990), although little empirical enquiry has taken place in sports’ environments. Within the literature, institutional reputation (including organizational) is a contested concept and several differences exist in how it is defined and measured (Ertug and Castellucci, 2013; Lange et al., 2011; Nguyen and LeBlanc, 2001). However, we draw from the economic and organizational literature that argues that reputation is conceived as “a collective representation of a firm’s past actions and results that describes the firm’s ability to deliver valued outcomes to multiple constituents” (Fombrun, 2012, p. 100). Reputation is an “enduring status categorization of the quality of an organization as perceived by external audiences and stakeholders” (Elsbach, 2006, p. 17), and it is particularly important to stakeholders because it seems to be a tangible indicator of the quality of service(s) that an organization provides or the value a consumer receives. Quality in higher education is usually measured by several attributes that represent the quality of institutions’ products and services (Lange et al., 2011). Thus, higher education institutional reputations (i.e. US News & World Report) are based on ranking the quality of seven attributes including academic reputation, student selectivity, faculty resources, graduation and retention rate, financial resources, alumni-giving and graduation rate performance) (Bastedo and Bowman, 2010). Patterson et al. (2014, p. 78) argued that institutional rankings therefore are “a powerful tool stakeholders can use to influence performances” and thus affect an organization’s competitive advantage (Patterson et al., 2014; Rindova and Fombrun, 1999).

General reputations theory presumes that various forms of corruption will damage a firm’s general reputation, defined as the evaluation across a range of socially valued properties including quality, efficiency and honesty (Alexander, 1999; Greve et al., 2010). General reputations also claim that firm status and size predict the extent of reputational damage. Alexander (1999) and Karpoff et al. (1999) found smaller firms were punished more harshly by the stock market for engaging in fraud than more powerful firms. Furthermore, highly ranked and/or reputable organizations engaged in corruption were less likely to experience negative stigmas because of their social status (Wiesenfeld et al., 2008). Research has shown that organizations that have boards with high reputation tend to possess close associations with influential community members that allows them to draw on these relationships when corruption occurs (Davis et al., 2003; Westphal, 1998; Wiesenfeld et al., 2008). Greve et al. (2010, p. 87) argued that “organizations with strong networks “can rely on their connections to disseminate information that contradicts the stereotyping” and negative characterizations that generally occur when corruption occurs, which can decrease the negative consequences experienced by highly reputable institutions whose sports programs engaged in corrupt activities.

Research (Beatty and Ritter, 1986; Milgrom and Roberts, 1990) has also shown that favorable reputation may facilitate higher premium charges (e.g. tuition and fees), attraction of a talented pool of applicants (e.g. faculty and staff) or greater ability to raise money (e.g. donors). Arpan et al. (2003) also found in their study of university images that both academic and athletic factors contributed significantly to university images. Other studies have documented that athletic success, actually contributes to the attraction or recruitment of a greater pool of talented students (McCormick and Tinsley, 1987; Tucker and Amato, 1993), increases in alumni donations (Coughlin and Erekson, 1984; Goff, 2000), positive public perceptions of graduates (Lovaglia and Lucas, 2005) and enhances goodwill (Toma, 1999). The identified benefits of athletic success favorably impact or enhance the reputation of an institution.

Given the literature reviewed above, we proposed the model in Figure 1 and developed the following hypotheses:


(a) Form and (b) extent of corruption will be directly related to the type of sanction.


Sanction will have a negative impact on organizational outcomes.


Institutional reputation will mediate the relationship between sanction and organizational outcomes (operationalized as attendance and winning average).



Data were collected using the NCAA Legislative Services website to collect information regarding all violations in football and men’s basketball from 1953 to 2013. We identified and included all reported major infraction cases that occurred in NCAA Division I Athletics during this period. The data collected provided a final sample of 143 cases, although 17 had missing data points. Cases with missing data were eliminated, and the final sample was 126 – a medium sample for structural equation modeling (SEM) studies based on Kline’s recommendations (Kline, 2005). In total, 45 per cent (n = 63) of the infractions involved football programs, while 55 per cent (n = 80) came from basketball programs.


Each infraction was examined for form and extent. Consistent with the literature, the form of the violation was classified based on the following three different categories: fraud, bribery and lack of oversight. In our measure, we also accounted for violations that contained more than one “form” category (i.e. 1 = fraud; 2 = bribery; 3 = fraud and bribery; 4 = fraud and lack of oversight, 5 = bribery and lack of oversight; 6 = all three). We elected not to have a separate coding category for “lack of oversight” because our data analysis revealed that lack of oversight is always accompanied by fraud or bribery except in one instance out of the 143 cases. To operationalize “extent,” we first created three different categories based on the level of gravity of the violations. Classification of the levels of extent below shows a summary of the classification at each level:

  1. Level 3:

    • Repeat offender in past five-year automatic

    • At least two of the following three:

      • – three or more incidents of violations;

      • – six or more individuals AND three or more units; and

      • – greater than $10,000 exchanged.

  2. Level 2:

    • At least two of the following three:

      • – two to three violations;

      • – two to six individuals AND at least two units involved; and

      • – $5,000-9,999 exchanged.

  3. Level 1:

    • One to three of the following:

      • – one to two violations;

      • – one to two individuals; and

      • – Less than $5,000 exchanged.

Once the researchers agreed on these classification levels, each investigator individually reviewed all violations cases and classified them based on the metrics in the table. The inter-rater reliability among the researchers was 97.6 per cent. The sanctions levied by the NCAA for the various violations were operationalized based on their severity, which again, was determined by the researchers (“4” for any post season ban, “3” for programs that received probation greater than three years or with any additional sanctions, “2” for programs that were put on probation for less than three years with any additional sanctions and “1” for programs that were only put on probation with no additional sanctions). Institutional reputation was obtained from the US News and World Report academic ranking data (i.e. 1 = top 50; 2 = top 100; 3 = top 150; 4 = lower than 150 or unranked). The US News and World Report is an accepted measure of national institutional ranking systems by higher educational scholars and policymakers (Usher and Savino, 2006).

Organizational outcomes were measured by two variables, win difference and average attendance. Win difference was calculated using a difference score between a five-year pre- and post-sanction period (i.e. five-year average wins prior minus five-year average wins after). Attendance figures were derived from the compilation of the immediate first five-year period figures following the NCAA sanction. We were unable to obtain an attendance “difference” variable due to too many missing data points.

Data analysis

Path analysis

A SEM technique known as path Analysis (PA) was used to analyze and test the proposed model in this study. PA involves the estimation of possible causal relationships among constructs of a given model. Bootstrapping was later used to test direct and indirect effects (mediation). The proposed model in this study is shown below and all of our variables including form and extent are shown as observed variables. A covariance is also modeled among the exogenous constructs. Means and bivariate correlations were calculated for all study variables.

Path models

Three nested models were analyzed:

  • Model 1: A just-identified model or full model where all possible relationships between exogenous and endogenous are modeled. Indicators within exogenous constructs co-vary with each other.

  • Model 2: The specified and proposed model in this study where indicators within exogenous and control constructs co-vary.

  • Model 3: Or an alternative model which accounted for additional paths that were shown to be significant (e.g. form and attendance) from the analysis of the just-identified or full model.

These relationships were added to our specified model to build the third or alternative model. Model 3 became the alternative model.

Scale validity and reliability

Fit indices statistics, specifically Chi-square, the Comparative fit index (CFI), the robust root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) were used to estimate the structural model. CFI measures the level of divergence between the hypothesized model and the null or baseline model. RMSEA measures the extent to which there is a lack of fit per degree of freedom (Byrne, 2006). Finally, SRMR assesses the difference between the observed data and reproduced data matrices (Byrne, 2006; Kline, 2005). Values for acceptable levels of good fit based on these indices are as followed: CFI > 0.90; SRMR < 0.10; RMSEA < 0.08. According to Byrne (2006) and Kline (2005), residuals of good fitting models should be small.


Descriptive statistics

Descriptive statistics are presented in Table I. As expected, sanctions are negatively related to reputation. Moreover, the fact that antecedents of sanctions (i.e. form and extent) is also negatively correlated with reputation is consistent with theoretical arguments made in this study.

More, specifically, the data also show that extent has a stronger relationship with sanction than form, and both hold positive relationships with our organizational outcome variables, although the strengths of these relationships are fairly negligible.

Hypothesis testing

The analysis of path estimates in this study followed current SEM practices and compared three models. Model 1 was a just-identified model or a full model. Model 2 was the specified model in this study and Model 3 was the alternative model where three new paths were added to the proposed model (i.e. extent → reputation, type → attendance and type → reputation).

The just-identified model was a poor fitting model. Results indicated the proposed model had good fit statistics (χ2 = 25.503, p < 0.008, RMSEA = 0.10, CFI = 0.92 and SRMR = 0.0855). However, the alternative model demonstrated to have improved fit with the added paths, and it was the best fitting among all the model statistics (χ2 = 8.827, p < 0.357, RMSEA = 0.03, CFI = 0.99 and SRMR = 0.0423). Therefore, although the proposed model was good fitting, we chose to test our hypotheses with the improved model because scholars (Kline, 2005) argue that the omission of causal variables would cause inaccurate or biased estimates in models. A chi-square difference test between the two models was also conducted, and results of the significant p-value (p < 0.000) further indicated that the addition of factors improved the fit to the data. Table II provides a summary of the comparison between the proposed and alternate models based on their fit indices.

The examination of the structural paths in our final model provided support for various relationships advanced in this study. H1a stated that the form of the violation serves as an antecedent to NCAA sanctions after controlling for the sport type. Results showed this hypothesis was not supported (β = 0.095, p = 0.10). Hence, that relationship was dropped from the final model. H1b stated that the extent of the violation serves as antecedent to NCAA sanctions after controlling for the sport type. Results show this hypothesis was supported (β = 0.628, p = 0.000). H2 posited that NCAA sanctions have a negative impact on institutional reputation. While there was a negative correlation between sanction and reputation (r = −0.071), the correlation was insignificant.

H3 contended that reputation would mediate the relationship between sanction and the organizational outcomes (win difference and attendance). Evidence of such a relationship was not supported. However, because our findings demonstrated that extent has a very strong relationship with sanction, we probed the mediational relationship from extent through reputation, and found that mediation was supported. Results showed that the two-tailed significance test for the direct effects of sanctions on win difference (p = 0.076) and attendance (p = 0.627) to be statistically non-significant. However, the indirect effects of extent on win difference (p = 0.040) and attendance (p = 0.011) through reputation proved to be statistically significant. Overall, these results provide support for the conceptual model that was advanced and tested in this study. The model also explained 18 per cent of the variance in NCAA sanctions, 10 per cent of the variance in institutional reputation, 11 per cent of the variance in win difference and 62 per cent of the variance in attendance.


The purpose of this study was to propose and test a model of corruption, explaining the multidimensional relationships involved in assessing corruption’s effect on organizational outcomes in an intercollegiate sport setting. In particular, our objectives were twofold:

  1. examine relationships between form and extent of malfeasant activities in relation to sanctions; and

  2. assess the mechanism through which institutional reputation serves as a mediator between sanctions and organizational outcomes.

Results showed that our general contentions regarding the model of corruption and organizational outcomes withstood tests reasonably well and contributed to the literature in various ways.

The literature on corruption has generally used sanction as a global variable to investigate acts of corruption on organizational outcomes (Mahony et al., 1999; Perry, 2002), yet our results suggest that a more specific view of this variable should be taken. Namely, it is important to consider the multidimensionality of the sanction variable. The model in this study explained 18 per cent of the variance in sanctions and provided a good understanding of how the form and the extent of violations served as antecedents to sanction. The form of misdeed (e.g. bribery versus fraud) had no impact on the sanction levied to the respective violating teams. Instead, it was the extent of the acts that influenced the type of sanction given.

The corruption literature suggests that the form of corruption, for example, fraud, bribery or favoritism impacts the sanction imposed on an organization (Luo, 2004). Various categories of fraud (e.g. academic) and bribery (e.g. improper payments) would arguably result in more severe sanctions in comparison to different types of corruption (e.g. improper recruiting calls) because of the potential harm fraud and bribery might cause an organization (Bruhn et al., 2002). Clearly this would be true in intercollegiate sports as well; a case of academic fraud would conceivably receive greater penalties than nepotism because academic integrity is the foundation of institutions of higher education and thus potentially has a greater negative impact on society by limiting its public good (Kezar, 2004). However, the form of violations we measured (fraud, bribery, lack of oversight and various combinations), had no impact on the sanction imposed – and thus apparently were viewed similarly by the NCAA agents who determined the sanctions. There may be a couple of different explanations for this finding. First, it is possible that the form of the corrupt acts (bribery, fraud or lack of oversight) is technical and/or abstruse concepts that are harder to decipher for the non-legal mind as compared to the concrete description of extent – number of violations and number of people and/or amount of money involved – in the corrupt acts. Hence, extent may trigger stronger reactions (i.e. sanctions) than the elemental indicators of form.

Second, the findings show a need to engage in a more granular analysis of the corrupt act. The category of a particular form of corruption (e.g. fraud: academic versus match fixing) could influence the sanctions imposed rather than just the general form of corruption (e.g. fraud). Different categories of fraud (e.g. academic, match-fixing, illegal gambling, money laundering, etc.) for instance, could influence the number and extent of penalties assigned to an organization found guilty of malfeasant acts. Van Vu et al. (2016) found different categories of bribery impacted firms’ financial performances in the context of Vietnamese firms. In addition, the context within which the form and category occur might also influence outcomes. Match-fixing and doping offenses may or may not be associated with a similar scope of penalties for varying reasons (e.g. governance, legal system). Thus, sport corruption scholars should further examine the form variable to enhance our understanding of the multidimensional nature of this concept and its impact on organizational outcomes across different sport contexts (countries, for-profit and non-profit sports and team and individual sports).

In understanding the complexity of corruption’s effect on organizational outcomes, our findings demonstrate that sanction cannot be perceived as a stand-alone variable. Rather the seriousness of the corrupt activities’ (i.e. number of hierarchies involved and quantity) relationship with sanctions should also be considered. While the literature has broadly discussed these distinct constructs in separate studies (Baucus and Baucus, 1997; Button et al., 2011), this is the first time that the effects of both constructs on organizational outcomes had been concomitantly investigated.

The findings showed that NCAA sanctions had an inverse relationship between institutional reputation and sanction, which is consistent with previous arguments and empirical evidence in the corruption literature relative to size (Alexander, 1999; Karpoff et al., 1999), status of an organization and/or the public profile of board members (Davis et al., 2003; Wiesenfeld et al., 2008). The small size of the inverse relationship in our study suggests that the sanctions had little impact on an institution’s reputation. Relatedly, the mediating effects of institutional reputation between sanction and organizational outcomes was non-existent, but a deeper inquiry using the extent of the violations showed the influence on organizational outcomes to be exclusively mediated by institutional reputation.

The seriousness or egregiousness (i.e. extent) of the corrupt acts, and not the actual sanction, indirectly impacted organizational outcomes through the mediation of institutional reputation. The NCAA governance and infractions system could also contribute to our understanding of the findings. Prior to 2013, the NCAA had a two-tier system of violations, major and secondary (NCAA, 2013). Within this system, all serious cases of corruption were classified into the major violation category, without any consideration for severity or form of corruption. In addition, the NCAA has been extensively criticized for imposing penalties that appeared arbitrary and lacking severity (Davis and Hairston, 2014). Davis and Hairston (2014) argued the NCAA was reluctant to impose strict sanctions on programs who could impact the Association’s financial interests (e.g. television and postseason competition bans). Therefore, the NCAA’s approach to imposing sanctions in our study may have influenced relationships between extent, sanction, reputation and organizational outcomes.

These findings were similar to Mahony et al. (1999) and Smith (2015) who determined that sanctions minimally impacted winning percentage as winning decreased in the short term (one to three years) but increased after the fourth and fifth year. Conversely, our findings were inconsistent with Perry (2002) who found that men’s basketball sanctioned programs experienced a decrease in winning percentage, while football programs did not. Both of these studies, nonetheless, examined a direct relationship between sanctions and outcomes. Evidence in the current investigation suggests the outcomes’ variables are impacted indirectly through other intervening variables (e.g. mediators or moderators). Therefore, researchers should include relevant moderators or mediators (e.g. institutional reputation) when probing effects of corruptive or non-compliant behaviors on organizational outcomes.

Institutions with high reputations (e.g. University of California Los Angeles, University of Michigan) did not experience the same decrease in attendance or winning percentage in comparison with lower reputation institutions (e.g. Coastal Carolina University) engaged in corruption. This finding lends credence to the view that highly regarded organizations engaged in corruption can generally rely on their reputation to mitigate the negative impact on post-corruption organizational outcomes (Karpoff et al., 1999). That is, the effects of the extent/seriousness of the violations on winning percentage and attendance were diminished by the reputation of the institution. High status/symbolic organizations within society (e.g. world ranked institutions of higher education) are perceived as less discreditable when found guilty of committing corrupt acts (Wiesenfeld et al., 2008). Research shows that stakeholders (e.g. board members, financial supporters, alumni, fans) within prestigious organizations possess a certain status within society that results in strong relationships and thus can rely on their networks when faced with instances of corruption (Greve et al., 2010; Wiesenfeld et al., 2008). The minimal decrease in attendance can also be related to fan loyalty or family loyalty, as Connor (2007) maintains that when sport teams experience corruption, fans will display unconditional support because the team is part of their identity. Organizations should therefore work diligently to maintain their high status and reputations by providing high-quality products and services and by sustaining various relationships within society. However, this recommendation is made with caution, as we strongly assert that equally important is educating organizations about corruption prevention.

Beyond these findings, the current study contributed to the corruption literature by expanding methodological approaches to the study of malfeasant behaviors in sport organizations, in particular developing an objective measure of corruption’s effect on organizational outcomes. Measuring corruption is one of the foremost challenges for scholars and “without accurate and reliable” measures of corruption’s impact “theories cannot be meaningfully tested” to help understand the variables and/or fundamentals of corruption’s impact on an organization (Sequeira, 2012, p. 146). While inquiries in the sport corruption literature have mostly used descriptive data and case-oriented studies to analyze the economic effect of corruption or statistical patterns to measure the extent of corruption, the current study used historical archival data at various time intervals, which lent more support to the strength of our findings. In particular, the findings have “improved the breadth and quality of available measures to capture relationships between wrongdoing and performance” (Baucus and Baucus, 1997, p. 147). The use of archival data demonstrated that corruption’s impact can be measured in a rigorous manner and that causality can be inferred. We encourage future research to examine other variables that can serve as indicators of outcomes and compare them against our results.

The proposed model was, indeed, theoretically derived and tested for consistency with observed data and is a replicable and adaptive measure of corruption’s effect on organizational outcomes. The proposed and tested model contributes to both the broader corruption literature and specifically to the sport management literature. The model goes beyond addressing Kaufmann’s (1998) call for corruption measurement that examines different forms of corruption and its factors, to testing the relationships between variables. The model helps explain the multidimensional nature of corruption’s impact on organizational outcomes within a particular sport industry context. The model is the first step in making comparisons between sport industry contexts (e.g. local, national and international sports such as football, cricket, cycling, international sport federations and professional sport leagues) and conducting comparative analyses. Obtaining valid and reliable corruption data is challenging. The NCAA’s infractions database is an important resource for both practitioners and scholars who are seeking to measure corruption’s effect on organizational outcomes. We recommend that other national and international governing bodies make these types of infractions data available to objectively measure the multidimensional nature of corruption and its affect across the sport industry.

Practical implications

The findings in this study have several main practical implications pertaining to managing the impact of corruption in relation to organizational outcomes. A key contribution demonstrated in this study was that the extent of corrupt acts influenced the sanctions imposed and thus had a significant effect on organizational outcomes. Rather than seeking to prevent specific forms of corruption carried out by individuals, managers should consider developing and implementing reform interventions that concentrate on eliminating corrupt networks (e.g. a group of interrelated individuals that abuse their power for private or public gain) who facilitate systematic malfeasance acts. Nielsen (2003) argued that networks within and across organizations can negatively influence and perpetuate unethical behavior within an organization. For example, reform programs that focus on only detecting individual unethical behaviors are less effective than reform programs that focus on both the organization and intra- and inter-organizational relationships. Similar to many organizations, NCAA institutions do have compliance programs; however, these programs focus on rules education and adherence, which, as Brass et al. (1998) argued, are limited in effectiveness of curbing systematic corruption. To prevent systematic corruption requires an analysis of the communication and interaction patterns (types and structure) of organizational stakeholders to assess how organizational values, norms or codes of ethics are transmitting and diffused (Brass et al., 1998). The logistics of implementing such a monitoring system in practice and its effectiveness lack research. Nevertheless, reform strategies should incorporate both organization systems (compliance programs) and social relationship oversight (auditing stakeholder network patterns) within and across organizations to combat systematic corruption (Brass et al., 1998; Nielsen, 2003).

In relation to the literature on institutional reputation and its mediating effect of the consequences of corruption, a second contribution of this study was to show that an institution’s reputation can serve as a buffer and can create a halo effect (Coombs and Holladay, 2006) in terms of impacting organizational outcomes owing to systematic corruption. An institution’s prior reputation can serve as a shield that softens the impact on organizational outcomes. An important practical implication from this finding is for organizations to develop a reputation strategy that either maintains or develops its image. For example, highly reputable sport departments could use a reputation strategy that communicates through various media outlets and public relations outlets the quality of its products (e.g. quality of education, technology, conference play) and services (game day experiences, customer service, atmosphere, facilities), how employees uphold the organization’s mission and values (e.g. through compliance and ethical training and ethical behavior) and how they develop and use human resource practices that attract, retain and develop talented employees. Conversely, organizations seeking to build reputation might incorporate a rebranding strategy aimed at differentiating the organization from its competitors including redesigning websites, sending text messages and focusing on the emotional side of the brand (Burke et al., 2011). Another emerging strategy is corporations and sport teams developing and implementing corporate social responsibility initiatives to enhance their stature within a league and/or community. Marketing strategies communicating the social impact on communities can build and shape an institution’s reputation (Amis, 2003), which ultimately can serve as a mediating effect in the event of case of corruption.

Last, our findings also have practical implications for organizational governance and infractions. Sanctions should be consistently applied to corruption cases that are similar in seriousness and form. In relation, organizations should also institute accountability and penalty guidelines that provide a defined range of sanctions that are associated with different levels of severe corrupt activities. The NCAA recently implemented a four-tier violation classification system based on severity of infraction (NCAA, 2013). Accountability and penalty guidelines that were also implemented arguably aim to provide consistency in imposing comparable sanctions for similar severe cases (Davis and Hairston, 2014).

Scholars have previously documented rampant and entrenched cultures of corruption across various business (e.g. Enron, Volkswagen Emissions, Toshiba accounting) and sport contexts such as in soccer (e.g. FIFA), baseball (e.g. steroids) or cycling (e.g. blood doping). The pervasiveness of these violations is troubling, but it does not have to be that way. The more we understand the corruption phenomenon, the better situated we are to best monitor these organizations, find the right solutions and bring back notions of integrity, fairness and fair-play, both of which are sacrosanct to business and sports. It is therefore important that organizations, in particular sports, develop and implement a consistent governance and infractions system (e.g. World Anti-Doping Code; World Anti-Doping Agency, 2016), otherwise it can encourage organizational stakeholders to engage in a risk–reward analysis where the reward of winning far outweighs the risk of being held accountable.

Limitations and directions for future research

Although the findings of this study seem to provide strong support for the contentions made in this paper, one must be careful when interpreting the results. First, as with all structural equation models, it is perceivable that alternative mediational conceptualizations not considered here may fit the data equally well. Therefore, there is a need to further replicate this study in various organizational settings. It is only through the accumulation of empirical evidence that the proposed model can verifiably sustain its consistency. A larger sample size would also have provided more power to investigate the various relationships in our model. Efforts should also be made in future studies to include variables that have previously served as good proxies of organizational outcomes (e.g. revenues, booster donations, alumni giving). Second, while US News & Report World is commonly used to measure institutional reputation (Hutchinson et al., 2016), we acknowledge the shortcomings in the ranking system (Bastedo and Bowman, 2010), as higher education has changed over time. However, the categories used to evaluate quality as a reflection of reputation have generally remained constant. Future research could use additional measures of reputation to assist with examining its mediating role in examining the impact of corruption on organizational outcomes.

While our study demonstrated a statistical association between the independent, mediating and dependent variables, this quantitative approach cannot explain why people may or may not have attended games, the specific reasons behind team success or how the institution’s reputation impacted their willingness to maintain their relationship with the organization post corruption. To answer these questions, future research using qualitative methods could provide first-hand accounts of individuals’ understandings of how corruption, sanction and reputation influenced their thinking and/or behaviors. Finally, given the unique governance structure of the NCAA, it would be good to replicate and test the model in domestic and international professional sport or corporate contexts.


In conclusion, this study used archival data combined with the inferential power of path analysis to propose and test a model of corruption that has, to date, been an elusive academic endeavor for many scholars, particularly in sport settings. Toward that end, this study has advanced the literature and deepened our knowledge and understanding of some of the variables and key processes at play when investigating the impact of corruption on organizational performance. To fully understand the nature of corruption so that it can be combated, it is important to understand the complexity of corruption within different contexts as strategies to address the consequences of corruption do not automatically apply across contexts. This study has shown that corruption is not just about cause and effect, the relationship is multidimensional based on the context, extent, sanction and reputation. Having a better understanding of the multidimensional nature of variables and process related to corruption’s effect on organizational outcomes can assist both academics and practitioners in their efforts to manage the impact of corruption.


Model of corruption

Figure 1

Model of corruption

Descriptive statistics

1 2 3 4 5 6 7
1. Extent 1.000
2. Type −0.166* 1.000
3. Sanction 0.428*** −0.071 1.000
4. Form 0.260** −0.208** 0.111 1.000
5. Reputation −0.224** −0.193* −0.071 −0.019 1.000
6. Attendance 0.055 0.704*** 0.015 −0.107 −0.477*** 1.000
7. Win difference 0.023 0.086 0.005 −0.256** −0.201* 0.125 1.000
Mean 2.437 1.381 2.540 4.389 2.968 22504.389 −0.540

= p < 0.05;


= p < 0.01; and


= p < 0.001

Comparisons between Models 2 and 3

Model 2 25.503 11 0.008 0.919 0.10 0.0855
Model 3 8.827 8 0.357 0.995 0.029 0.0423



The five NCAA major conferences include the Big 12, Atlantic Coast Conference, Southeastern Conference, Big Ten, and Pacific 12.


Prior to August 2013, the NCAA classified violations into major and secondary. A major violation was any violation that provides an extensive recruiting or competitive advantage, is not isolated or inadvertent in nature, and/or is a repeated secondary violation. A secondary violation involved gaining a limited recruiting or competitive advantage and is isolated or inadvertent in nature (NCAA, 2012/2013). New enforcement guidelines were instituted in 2013 where violations were classified into four levels and sanctioning guidelines were revised where a matrix succinctly articulates the consequences for breaching specific rules within in each level of violation (Davis and Hairston, 2014).


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Corresponding author

Lisa A. Kihl can be contacted at:

About the authors

Lisa A. Kihl is Associate Professor at the School of Kinesiology, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA.

Mansour Ndiaye is based at the University of Connecticut, Storrs, Connecticut, USA.

Janet Fink is based at the University of Massachusetts Amherst, Amherst, Massachusetts, USA.