r Academy of Management Discoveries2015, Vol. 1, No. 2, 165–179.Online onlyhttp://dx.doi.org/10.5465/amd.2014.0045

PAY DISPERSION, SORTING, AND ORGANIZATIONALPERFORMANCE

JASON D. SHAWThe Hong Kong Polytechnic University

The author takes a sorting perspective to explore relationships among pay dispersion,good- and poor-performer quit rates, and organizational performance in a multiwavestudy of independent grocery stores. Under high pay-for-performance, pay dispersionhas a significant positive relationship with poor-performer quit rates and, further, theindirect effects of pay dispersion on organizational performance via poor-performer quitrates are stronger when pay-for-performance is high. The relationship between paydispersion and good-performer quit rates is negative when pay-for-performance is low,such that the highest good-performer quit rates are found when pay is compressed andpay-for-performance is not used. Pay dispersion is also found to be directly and posi-tively related to organizational performance among organizations that emphasize pay-for-performance. Implications for sorting theory and related perspectives are addressedand future research directions are outlined.

INTRODUCTION

Much debate and controversy surrounds horizontalpay dispersion, when employees doing similar jobs

arepaiddifferent rates.Theorists and researchershavedescribed the consequences using bifurcated imageryranging from motivation, effort, windfall gain, fair-ness, and productive competition, on the one hand, tosocial loafing, relative deprivation, politics, conflict,and disunity, on the other hand (Shaw, 2014). It isperhaps not surprising then that meta-analytic evi-dence suggests that horizontal pay dispersion andorganizational performance essentially have a zerorelationship (20.01) (Park & Sung, 2013).

Recent theoretical developments have begun toresolve many underlying conceptual tensions in theliterature. Shaw, Gupta, and Delery (2002) first ad-vanced the idea that widely dispersed pay generatesbetter organizational outcomes when organizations

I wish to thank the anonymous reviewers, associ-ate editor Peter Bamberger, Yoshio Yanadori, AnneTsui, and seminar participants at Peking Universityand the University of South Australia for helpfulcommentsonpreviousversions.Thestudywas fundedby the SHRM Foundation. The interpretations, con-clusions, and recommendations, however, are thoseof the author and do not necessarily represent thoseof the foundation.

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use normatively accepted practices to generatedispersion, that is, when the dispersion is based oninputs relevant to productivity (Trevor, Reilly, &Gerhart, 2012). Explained pay dispersion (Shawet al., 2002) resolves many of the literature’s variousconundrums emanating from competing universal-istic theoretical views. Researchers are increasinglyassociating wider horizontal pay ranges accompa-nied by justifiable dispersion-creating practicessuch as performance-based pay with higher produc-tivity, lower accident rates, and better financial per-formance (e.g., DeBrock, Hendricks, & Koenker, 2004;Fredrickson, Davis-Blake, & Sanders, 2010; Kepes,Delery,&Gupta, 2009;Shaw&Gupta, 2007). Explainedpay dispersion has been shown to relate positively toperformance even in highly interdependent task envi-ronments such as professional hockey (Trevor et al.,2012).

Despite advances, the literature remains at a some-what nascent stage (Gupta & Shaw, 2014). As Shaw(2014: 538) stated: “to date [the pay dispersion litera-ture] ismore extensive in terms of answering questionsof when rather than why or how” (Shaw, 2014: 538).Theoretical and empirical development (e.g., Shawet al., 2002) and replications (Trevor et al., 2012) doappear in the literature, but precise formulations andadditional evidence are needed for several reasons.First, compensation costs are immense, accounting forthe vast majority of total expenses in many organiza-tions (Gerhart, Rynes, & Fulmer, 2009). Second, meritand bonus budgets in many societies are relativelymeager (Mitra, Tenhiala, & Shaw, in press), increasingthe importance of distributing and structuring pay pru-dently to obtain desired retention and performanceoutcomes.Third, intermsofsorting, intensecompetitionfor talent, clustered turnover patterns, and the preva-lence of poaching draw weight to the issue of func-tional and dysfunctional turnover patterns (Heavey,Hausknecht, & Holwerda, 2013; Park & Shaw, 2013).

Here, I explore the potential explanatory mecha-nisms between explained pay dispersion and organi-zational performance. In particular, compensationresearchers typically assume that properly designedpay structures enhance motivation (e.g., Jenkins,Mitra, Gupta, & Shaw, 1998) and create sorting effects(Gerhart & Rynes, 2003; Shaw, 2011). Drawing onsorting theory, I advance the literature by outlininga general sorting perspective that can explain the re-lationships among horizontal pay dispersion, norma-tively accepted dispersion-creating practices, andorganizational performance through the quit patternsofgoodandpoorperformers. I test themodel ina three-wave data set drawn from independent grocery stores.

I made several decisions in conducting this study.As noted, I focus on horizontal dispersion—thespread of pay among employees holding similar

jobs, rather than vertical dispersion—the spreadof pay across organizational echelons (Gupta,Conroy, & Delery, 2012; Shaw, 2014). Vertical paydispersion is better suited for studies of larger, hier-archical organizations. I gathered data from a sampleof small single-unit grocery stores and focused onpay structures and quit patterns among full-time flooremployees. Second, I base the concept of justifiable orexplained variation on whether pay-for-performancesystems are used. Many organizations in the UnitedStates and other Western hemisphere countries so-cially accept the use of pay-for-performance practices.As such, a focus on pay-for-performance shouldreasonably test the general theory. I return to cross-cultural applications and other potential justifiablereasons for dispersion later in the article. Third, Iconduct analyses concerning pay structures and quitpatterns at the organizational level. The study wasdesigned as a complementary and alternative per-spective to the larger literature on the relationshipbetween performance and turnover at the individuallevel (e.g., Cadsby, Song, & Tapon, 2007; Harrison,Virick, & William, 1996; Nyberg, 2010; Salamin &Hom, 2005).

THEORETICAL FOUNDATIONS

The Concept of Explained Pay Dispersion

Pay may be dispersed horizontally for various rea-sons. Theories claiming that within-job pay disper-sionmotivatesworkers tend to assume that legitimatedispersion practices create pay differences betweenemployeesdoing similarwork (Shawet al., 2002); thatis, differences in relevant employee inputs explainthe pay differences (Trevor et al., 2014). When paydispersion practices appear to be legitimate, individ-ual behavior is more strongly aligned with outcomes(Gerhart et al., 2009), individuals perceive higher payfairness (Heneman, Greenberger, & Strasser, 1988)and perceive compensation differentials to be largeenough to be meaningful (Mitra, Gupta, & Jenkins,1997; Mitra et al., in press). Relevant dispersion-creating practices, however, do not always explainhighly dispersed pay. Instead, other factors such asorganizational politics, lack of formal or inconsis-tently applied procedures, nepotism, and game-playing can cause employees who are doing similarwork to be paid differently (Kepes et al., 2009). A casein point is the increasing prevalence of personalized,idiosyncratic agreements (or i-deals) (Rosen, Slater,

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Chang, & Johnson, 2013). Nonstandard by definition,these agreements can result in pay distributions thatdonot reflect standardpracticeandincertaincasesmaynot be readily explained by general, relevantworkforceinputs. High levels of pay dispersion in the absence ofnormativelyaccepteddispersion-creatingpracticescanbe considered unexplained pay dispersion. Pay dis-persion that is unexplained by performance-relatedinputs is likely to engender negative or idiosyncraticresponses. Employees may see horizontal pay disper-sion that cannot be readily explained by legitimateemployee-level inputs or organizational practices asunfair, arbitrary, and demotivating. In essence, the“motivating aspects of pay dispersion will be realizedonly when accompanied by legitimate or normativelyaccepted factors” (Shaw et al., 2002: 492).

Although the literature clearly defines explainedversus unexplained pay dispersion as a conceptualvariable, researchers have used different approachesfor assessing whether pay dispersion is legitimatelyexplained.Researchdesignanddataavailability seemlargely to drive the approach. The most common ap-proach has been to assess pay dispersion and prac-tices that create pay dispersion independently (e.g.,Kepes et al., 2009; Shaw et al., 2002; Shaw & Gupta,2007) using organizational-level data to create sepa-rate pay dispersion and employment practices vari-ables. These components then interact to indicateexplained or unexplained pay dispersion. For exam-ple, Shaw et al. (2002) used horizontal pay dispersionand performance-based pay practices interactions toassess whether pay dispersion was explained orunexplained. Kepes et al. (2009) explored pay dis-persion as it interacts with separate measures oflegitimate (performance-based pay) and illegiti-mate (politically based pay) practices to determinewhether pay dispersion was explained or unex-plained. This approach indicates that pay dispersionis explained when pay is highly dispersed anddispersion practices are legitimate. The person–organization literature provides an analogous ap-proach: researchers routinely use the interaction ofobjective and desired work characteristics (e.g., jobcomplexity) to address fit or misfit (Edwards, 1996;Kristof, 1996). This approach has a number ofadvantages. Dispersion and sources of dispersionare independent, and therefore researchers can as-sess overall pay dispersion and dispersion-creatingpractice effects both independently and jointly. Inaddition, this approach allows researchers to exam-ine explained and unexplained pay dispersion andassess the consequences of compressed pay struc-tures with or without dispersion practices such aspay-for-performance.

Researcherswho have access to data on individualperformance and pay levels have used an alternative

operational approach. For example, Trevor et al.(2012) used an individual performance metric (e.g.,points, assists) to predict pay levels across a pro-fessional hockey league. The within-team variationin the predicted pay-level scores indicated the de-gree to which legitimate inputs could explain paydispersion. This approach is advantageous in thatpay levels can be tied to individual-level perfor-mance inputs. However, in this conceptualizationunexplained and explained pay dispersion reside ona separate continuum; this contradicts a conceptualdefinitionwhich assumes that paydispersion cannotbe concurrently explained and unexplained.

As such, I take the former conceptual and opera-tional approach here. Pay dispersion is explainedwhen it results from a legitimate dispersion-creatingpractice—here, pay-for-performance. I assume thatpay dispersion is unexplained when pay is highlydispersed but the organization does not use pay-for-performance.

A Sorting Theory View on Explained PayDispersion, Quit Patterns, and Performance

Compensation systems are primarily used to moti-vate workers, attract high-quality talent pools, andretain the best performers (Bishop, 1987; Lawler &Jenkins, 1992). Researchers in various disciplineshave well-explored motivation and attraction goals(e.g., Gerhart & Rynes, 2003; Jenkins et al., 1998), butorganizational-level research on the sorting effects ofpay structures and pay-for-performance has been,with exceptions (e.g., Shaw&Gupta, 2007), “virtuallyignored in thepaydispersion literature” (Trevor et al.,2012: 586). A sorting theory perspective suggests thatgood and poor performers will show different quitpatterns partly depending on whether normativelyaccepted practices explain pay variation. Sortingtheory suggests that after individuals work in an en-vironment for a time, they will use various factors todetermine whether it suitably matches their abilityand preferences, and whether the organization willultimately meet their goals and needs (Lazear, 2000).Applied to the issue of explained versus unexplainedpay dispersion, sorting theory suggests that goodperformers should realize a number of relativeadvantages over poor performers under wide paystructures and performance-based pay. First, underhighly explained pay dispersion, good performerswill view the system as potentially maximizing theirself-interest. They should enjoy higher pay, positivefeedback about their performance, and positive af-fective responses. They should show high reten-tion rates and low affective and calculative forcesfor quitting (Shaw, Dineen, Fang, & Vellella, 2009).Individual-level and experimental research provides

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some ancillary evidence supporting this view.Cadsby et al. (2007) found experimental results in-dicating that higher performing participants preferpay-for-performance, whereas other individual-levelresearch has found higher survival rates amonggood performers under performance-based pay (e.g.,Harrison et al., 1996; Salamin & Hom, 2005; see alsoBelogolovsky&Bamberger, 2014).Highexplainedpayvariation should have a different relation with poor-performer quits. Poor performers are likely to eithersettle for low pay or depart for potentially better al-ternatives elsewhere (Bloom&Michel, 2002; Lambert,Larcker, & Weigelt, 1993).

The extant literature offers few direct tests of thesorting potential of explained pay dispersion. In onestudy, quit patternswere traced after a piece-rate planreplaced the seniority system; the best performersshowed significantly lower quit rates, whereas theworst performers showed higher quit rates (Lazear,1999, 2000). Although that studydidnot examinepaydispersion directly, piece rates largely depend on in-dividual performance differences. Consequently, thenew system likely stretched the pay distribution,which reasonably indicates that piece-rate pay in-creased explained pay variation, making the sortingeffects evident. In another study using a sample oflong-haul trucking companies, good-performer quitsin explainedpaydispersionpay systemswerehighestunder high pay dispersion and nonperformance-based pay (Shaw & Gupta, 2007). Those findingsyield more equivocal quit patterns for poor per-formers. Professional hockey teams were also foundmore likely to retain high-input players when priorplayer performance could explain within-team payvariations (Trevor et al., 2012). Like Shaw and Gupta(2007), the Trevor et al. (2012) findings were less de-finitive for the retention of poor performers.

The sorting perspective, in toto, suggests an in-tegrative approach for understanding how pay dis-persion accompanied by the legitimate dispersionpractice of pay-for-performance can enhance orga-nizational performance. Viewing pay dispersion asa system of relationships, sorting theory suggests thatexplainedpaydispersionmayenhanceorganizationalperformance indirectly by creating divergent turnoverpatterns for good and poor performers. Performance-based pay dispersion should result in lower quit ratesamong good performers, while good performer quitrates should be higher when pay is compressed andpay-for-performance is not used. Good-performer quitrates should, onbalance, benegativelyassociatedwith

organizational performance (Shaw, 2011). Thus, paydispersion should have positive indirect effects onorganizational performance through “lower” good-performer quit rates under high pay-for-performance,that is when a normatively accepted practice explainspay dispersion. Conversely, explained pay dispersionshould indirectly affect organizational performanceby increasing poor-performer quit rates. Under highpay-for-performance, pay dispersion should havestronger positive relationships with poor-performerquit rates and positive indirect effects on organiza-tional performance through “higher” poor-performerquit rates, that is, when the normatively acceptedpractice explains pay dispersion.

METHOD

Two waves of survey data from key informantquestionnaires and archival data on company char-acteristics and performance provided data sourcesfor this study. Thedata usedherewere part of a largerstudy of employment systems and organizationalperformance (e.g., Shaw et al., 2009; Shaw, Park, &Kim, 2013). The primary data source was a survey ofsingle-unit grocery stores in the United States. Arandom sample of 1,000 stores was drawn from theindependent groceries edition of the Chain StoreGuide, which provides archival data on retail andfoodservice organizations. The survey research pro-cess unfolded according to the guidelines for en-hancing response rates in key informant research(Gupta, Shaw, & Delery, 2000). A member of the re-search team called each store in the sample to obtainthe name of the store manager and to verify themailing address. The key informant was then senta letter detailing the goals of the study and encour-aging participation. About aweek after the letter wasmailed, a member of the research team called eachstoremanager to verify that the letterwas received, toanswer any questions and to further encourage partic-ipation. Immediately following the telephone call, theteammailed the questionnaire (Time 1). After a monthpassed, a reminder letter and another copy of thequestionnaire were mailed to nonresponding stores.The team received 320 returned questionnaires—a32 percent response rate. About 18 months later, theresearch teammailed feedback reports to respondingorganizations, along with a short follow-up ques-tionnaire (Time 2), and received 135 returned ques-tionnaires—a 42 percent Time 2 participation rateand an overall 14 percent response rate. The teamobtained the third wave of data (Time 3) from theChainStoreGuide for thecalendaryearafter theTime2 follow-up questionnaire (Time 3).

All measures used in this study from the Time 1and Time 2 questionnaires were specific to full-time

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employees. In field work prior to the administrationof the questionnaires, the research team assessedthe nature of the work and the structure of the hu-man resource management (HRM) systems withina number of stores. With the exception of thestore manager, who was also frequently the storeowner, full-time employees performed work of a“generalist” nature. Employees were assigned tostocking, cashiering, cleaning, organizing, unload-ing, and other customer-service-related activities, asneeded. In addition, the stores had a single pay sys-tem for full-time employees and did not have “paybands” and other HRM structures that would signifymoving through the formal hierarchy, as would beexpected in a vertical pay dispersion situation. Fur-thermore, informants were instructed to excludetheir own pay information in the reports of pay in-formation in the questionnaire. Thus, the focus onpay differences among full-time floor employees inthese small groceries is most similar to a horizontalpay dispersion situation, as prior researchers havedefined it (e.g., Shaw, 2014).

Measures: Independent Variables (Time1, Questionnaire)

Pay dispersion. The literature offers a number ofoperationalizations of pay dispersion, although theyare frequently highly interrelated (Shaw, 2014). Iused the Gini coefficient, the most commonly usedmeasure of income inequality in management andeconomics (Bloom, 1999; Donaldson & Weymark,1980; Shaw et al., 2002), defined as:

Gini coefficient5 111n2

2n2�y

�y1 1 2y2 1 . . . 1nyn

�

where y1 to yn are the annual pay levels of employeesin a given store arranged indecreasing order of size; �yis themeanpay level in the store; andn is thenumberof full-time employees in the store. The maximumGini coefficient is 1.0, indicating absolute inequalityof pay; theminimumis zero, indicatingcomplete paycompression. As a consequence of the estimateddistribution, the Gini coefficient estimates are un-derestimated in this study.

As a validity check, I also calculated two otherdispersion measures: the coefficient of variation(i.e., the standard deviation of pay levels withina store divided by the mean) and the pay range(i.e., thehighest pay levelminus the lowest pay level)

(Gupta et al., 2012). The correlation between theGinicoefficient and the coefficient of variation was 0.57(p, .01); the correlation between theGini coefficientand the pay range was 0.59 (p , .01). The multivar-iate results were substantively identical when thecoefficient of variation operationalization was used.The results were somewhat weaker when pay rangewas used as the measure of dispersion.

Pay-for-performance. This variable was mea-suredwith four items fromColquitt (2001) (a5 0.92).The items were: “To what extent do rewards reflectthe effort employees put into their work?”; “To whatextent do rewards reflect what employees havecontributed to the organization?”; “To what extentare rewards appropriate given the work employeeshave completed?”; and “To what extent are rewardsjustified, given employees’performance?”The itemshad five response options from 1 (Not at all) to 5(To a very great extent).

Measures: Mediators and Dependent Variable(Time 2 and Time 3)

Good- and poor-performer quit rates (Time 2). Inthe Time 2 questionnaire, key informants first re-ported the number of total quits in the past year.Next, they reported the number of full-time em-ployee quits whose job performance was in thelowest 20 percent and the highest 20 percent. Thenumber of poor- and good-performer quits was di-vided by the total number of full-time employees tocreate the poor- and good-performer quit rates.

Organizational performance (Time 3). Perfor-mance was operationalized as the total sales dividedby the square meters of the store’s retail area. Thedata were obtained from the Chain Store Guide atTime 3, covering the calendar year after the quit-ratemediators were collected.

Measures: Control Variables (Time 1 and Archival)

Several controlsmay relate to the independent anddependent variables in the equations. Average payrates and unionization are related to quit rates andorganizational performance (Gerhart & Rynes, 2003;Osterman, 1987; Shaw, 2014). “Average pay rates”was measured with key informant Time 1 responsesto the item “how do pay rates for employees in theirstore compare with pay rates in the local labor mar-ket?” Response options range from 1 (Ours are muchlower) to 5 (Ours are much higher). “Unionization”was coded 1 if full-time employees were covered bya collective bargaining agreement and 0 otherwise.“Discharge rates”may signal underlying differencesin the quality of the workforce and performanceinstability (Batt & Colvin, 2011) and was therefore

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controlled using Time 1 questionnaire information:the number of full-time employees discharged orfired at Time 1 divided by the total number of full-time employees. I accounted for the possibility thatthe quality of human capital stocks may influencequit patterns and organizational performance bycontrolling for staffing practices (Shaw, 2014). “Se-lective staffing”wasa five-itemmeasurebasedon thevalid selection procedures index (Shaw, Delery,Jenkins, & Gupta, 1998). Informants reported theextent to which they used structured interviews,physical ability tests, reference checks, drug testing,and background checkswhen hiring employees. Theitemshad five responseoptions from1 (Not at all) to 5(To a very great extent).

RESULTS

Response Bias Checks and Measurement Issues

The data used in these analyses were obtained inthreewaves: Time1 (questionnaire andarchival), Time2 (questionnaire), and Time 3 (archival). To assure thatthe final analysis sample was representative relative tothepopulationof single-unit grocery stores, I used threesetsofempiricalanalyses.First, Ipredictedresponses totheTime1questionnaire across five variables availablein the Chain Store Guide (store age, store sales, totalsquare feet, and number of specialty departments)using a logistic regression. I coded responding stores1 and nonresponders 0. One variable—specialtydepartments—was significant; responding organiza-tions had a fraction more specialty departments (6.7versus 6.2) than did nonresponding organizations.Second, I assessed whether the stores in the finalanalysis sample differed from the nonrespondingorganizations in the sampling frame. None of thevariables was a significant predictor in this logisticequation. Third, I compared responding and non-responding organizations against the same array of

archival characteristics available in the Time 3 ar-chival database. No characteristic was a significantpredictor in the equation. In all, the checks suggestedno marked differences between responding andnonresponding stores.

Regression Results

Table 1 shows descriptive statistics and correla-tions. Table 2 reports the regressionswhen good- andpoor-performer quit rates were the dependent vari-ables. Table 3 shows the regression analyses for or-ganizational performance.

The left-most columns in Table 2 show the re-gression results when good-performer quit rates isthe outcome variable. In Model 1, pay dispersionis significantly and negatively related to good-performer quit rates (b 5 20.01, p , .05), but pay-for-performance is not significantly related (b50.01,n.s.). Model 2 shows the interaction of pay disper-sion and pay-for-performance when good-performerquit rates is the dependent variable. The productterm is significant (b5 20.01, p, .05), explaining anadditional 4 percent of the variance in good-performerquit rates. Figure 1 shows a plot of the interaction.When pay-for-performance is low, pay dispersionand good-performer quit rates have a significantlynegative relationship (bHigh pay-for-performance520.03,p , .01), with the highest estimated good-performerquit rates occurring when pay dispersion andpay-for-performance are both low. When pay-for-performance is high, pay dispersion and good-performer quit rates are not significantly related(bLow pay-for-performance 5 20.01, n.s.).

The right side of Table 2 reports the regressionresults when poor-performer quit rates is the de-pendent variable. In Model 1, pay dispersion (b 50.01, p , .05) and pay-for-performance (b 5 0.02,p, .05) are positively related to poor-performer quit

TABLE 1Correlations and Descriptive Statistics

Mean SD 1. 2. 3. 4. 5. 6. 7. 8.

Average pay rates 2.97 0.72Unionization 0.04 0.19 0.00Discharge rates (Time 1) 0.03 0.08 20.23* 0.03Selective staffing (Time 1) 2.34 0.71 0.13 0.10 20.10 0.92Pay dispersion (Time 1) 0.10 0.05 20.03 0.04 20.07 20.03Pay-for-performance (Time 1) 3.42 0.90 0.09 20.17 0.02 0.23* 0.05 0.92Good-performer quit rates (Time 2) 0.04 0.06 20.11 0.05 20.03 20.02 20.08 0.12Poor-performer quit rates (Time 2) 0.04 0.10 0.06 20.02 0.14 20.03 0.05 0.21* 20.04Organizational performance (Time 3) 119.24 208.84 20.18 20.01 0.03 212 0.11 0.08 0.05 0.04

Notes. N 5 111. Internal consistency reliability estimates on the main diagonal.* p, .05

** p, .01

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rates at Time 2. The product term in Model 2 issignificant (b 5 0.02, p , .01), explaining 6 percentof the variance in the equation. Figure 2 shows theform of the interaction. Under high pay-for-performance, pay dispersion and poor-performerquit rates have a significant and positive relation-ship (bHigh pay-for-performance 5 0.04, p , .01), but therelationship is not significant under low pay-for-performance (bLow pay-for-performance 5 0.00, n.s.).

Model 1 of Table 3 shows that pay dispersion (b536.23, p , .01) and pay-for-performance (b 5 39.57,p , .01) are significantly related to organizationalperformance. The interaction between pay dispersionand pay-for-performance is significant inModel 3 (b554.40 p, .01), explaining 17 percent of the variance inorganizational performance. Figure 3 depicts the inter-action. Under high pay-for-performance, pay disper-sion and organizational performance has a significantandpositive relationship (bHigh pay-for-performance5 82.73,p , .01). Under low pay-for-performance, therelationship has a nonsignificant negative slope(bLow pay-for-performance 5 218.35, n.s.).

The column labeled Model 3 in Table 3 includesthe main effects of good- and poor-performer quitrates in predicting organizational performance. Thisequation shows that good-performer quit rates is notsignificantly related to organizational performance(b 5 253.33, n.s.), but poor-performer quit rates issignificantly related (b 5 206.03, p , .05). Poor-performer quit rates explain about 3 percent of theunique variance (the semipartial r2) in organizationalperformance.This significant coefficient suggests thatpoor-performerquit ratesmaymediate the interactionbetween pay dispersion and pay-for-performance onorganizational performance—amoderatedmediationeffect. The data fail to indicate that good-performerquit rates mediate the relationship between pay dis-persionandorganizationalperformanceathigh levels

of pay-for-performance. To assess this possibility, Iused a nested-equations path analytic approach to testfor indirect-effects moderated-mediation (Edwards &Lambert, 2007). The approach involves substitutingthe regressionequation(s) for themediatingvariable(s),when appropriate, into the equation for the dis-tal dependent variable (here, organizational per-formance). The coefficients derived from theseregressions were then used to estimate direct, in-direct, and total effects of pay dispersion acrosspay-for-performance levels.

The relationships are described using path ana-lytic conventions (Edwards & Lambert, 2007): PMX

refers to the path between X (pay dispersion) andthemediator; PYM is the path from themediator to Y

TABLE 3Regression Results: Organizational Performance

Organizational Performance(Time 3)

Model 1 Model 2 Model 3

Average pay rates 16.63 9.17 5.49Unionization 74.01 52.28 48.95Discharge rates (Time 1) 30.26 29.86 232.27Selective staffing (Time 1) 235.23* 239.51** 240.13*Pay dispersion (Time 1) 36.23** 38.56** 39.49**Pay-for-performance (Time 1) 39.57** 33.38** 31.99**Pay dispersion * pay-for-performance

54.40** 55.26**

Good-performer quit rates(Time 2)

253.33

Poor-performer quit rates(Time 2)

206.03*

Total R2 0.22** 0.39** 0.42**DR2 0.22** 0.17** 0.03*

Note. N 5 111.* p , .05

** p , .01

TABLE 2Regression Results: Good- and Poor-Performer Quit Rates

Good-PerformerQuitRates(Time2) Poor-Performer Quit Rates (Time 2)

Model 1 Model 2 Model 1 Model 2

Average pay rates 20.01 20.01 0.01 0.01Unionization 20.01 20.01 0.03 0.02Discharge rates (Time 1) 20.02 20.02 0.11 0.11Selective staffing (Time 1) 0.00 0.00 20.01 20.01Pay dispersion (Time 1) 20.01* 20.02* 0.01* 0.02**Pay-for-performance (Time 1) 0.01 0.01 0.02* 0.01Pay dispersion * pay-for-performance 20.01* 0.02**Total R2 0.09 0.13* 0.14* 0.20**DR2 0.09 0.04* 0.14* 0.06**

Note. N 5 111.* p, .05

** p, .01

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(organizational performance); PYX is the direct pathfrom X to Y (pay dispersion on organizational per-formance); PYM * PMX refers to the indirect effectsof pay dispersion on organizational performancethrough the quit-rate mediator, and PYX 1 PYM *PMX is the total effect (direct and indirect) ofpay dispersion on organizational performance.Because Type 1 error rate may be inflated whentesting product terms for significance (Shrout &Bolger, 2002), I followed suggestions (Edwards &Lambert, 2007) to construct confidence inter-nals derived from a bootstrapping procedure with10,000 samples.

Table 4 shows the path analysis results forpoor-performer quit rates. Under low pay-for-performance, neither the indirect effect(PymPmxLow pay-for-performance 5 0.00, n.s.) nor thetotal effects (Pyx 1 PymPmxLow pay-for-performance 5215.77, n.s.) of pay dispersion on organiza-tional performance through poor-performer quitrates are significant. In contrast, the indirect(PymPmxHigh pay-for-performance 5 8.24, p , .01) andtotal (Pyx 1 PymPmxHigh pay-for-performance 5 102.99,p, .01) effects of pay dispersion on organizationalperformance via poor-performer quit rates aresignificant under high pay-for-performance. Thus,the results show significant indirect and totaleffects of pay dispersion on organizational per-formance through poor-performer quit rates are

significant only when pay-for-performance is high.Finally, the results also show that the interactionof pay dispersion and pay-for-performance sig-nificantly predicts organizational performance inthe presence of the poor-performer quit rates me-diation effect. This finding suggests direct-effectmoderation (Edwards & Lambert, 2007); theinteraction with organizational performance is uni-que in that it does not operate via the mediators.Figure 4 shows the empirical model observed fromthese tests.

DISCUSSION

The results show that pay dispersion and organi-zational performance are indirectly related via poor-performer quit rates when pay-for-performance isused. The findings are consistent with recent meta-analytic evidence showing that the main effect re-lationshipbetweenpaydispersionandorganizationalperformance is near zero (Park & Sung, 2013); theyconfirm that researchers must apply, develop, andextend theory to explain how and when pay disper-sion can lead to outcomes important for organi-zational decision makers. In a multiwave study ofindependent grocery stores, pay dispersion is posi-tively related to poor-performer quit rates under highpay-for-performance, but is not related under lowpay-for-performance. Furthermore, pay dispersion

FIGURE 1Interactionof paydispersionandpay-for-performance

in predicting good-performer quit rates

10%

0%

Low(-1 SD)

High(+1 SD)

Low Pay-for-performance

High Pay-for-performance

Pay Dispersion

Goo

d P

erfo

rmer

Qu

it R

ates

FIGURE 2Interactionof paydispersionandpay-for-performance

in predicting poor-performer quit rates

10%

0%

Low(-1 SD)

High(+1 SD)

Low Pay-for-performance

High Pay-for-Performance

Pay DispersionP

oor

Per

form

er Q

uit

Rat

es

172 DecemberAcademy of Management Discoveries

has stronger indirect and total effects on organiza-tional performance via poor-performer quit ratesunder high pay-for-performance. Pay dispersion isnegatively related to good-performer quit rates whenin the absence of pay-for-performance, such that thehighest good-performer quit rates are observed whenpay is compressed and pay-for-performance is notused. Good-performer quit rates are not related toorganizational performance and therefore good-performer quit rates do not transmit the effects ofexplained pay dispersion on organizational perfor-mance.Finally, the results showthat the interactionofpay dispersion and pay-for-performance relates di-rectly to organizational performance: under high pay-for-performance, pay dispersion is positively related

to organizational performance; under low pay-for-performance, the relationship is not significant.

Theoretical Implications

The results offer qualified support for a sortingtheory perspective on the relationships among paydispersion, pay-for-performance, and organizationalperformance via differential quit rates. Sorting the-ory suggests two related processes. The first sortingprocess occurs when organizational conditionsdifferentially attract qualified or unqualified appli-cants; some organizations attract higher-quality andmore-capable workforces. The second process oc-curs when organizations create environments thatfit the capabilities and preferences of certain em-ployees so that quit patterns sort the workforce. Theindividual-level literature offers fairly convincingevidence that good performers prefer and per-form better under transparent, incentive-based com-pensation (e.g., Belogolovsky & Bamberger, 2014;Cadsby et al., 2007) and are more likely to stay whenrewards are maximally contingent (e.g., Harrisonet al., 1996). Using pay dispersion and pay-for-performance as cases in point, I expected that theinteractionofpaydispersionandpay-for-performancewould predict quit rates of good and poor performersand that thesedifferential quit patternswouldmediatethe effect of this interaction on organizational perfor-mance. These notions have some support here, withstipulations.

Pay dispersion and pay-for-performance interactin predicting good-performer quit rates, such that thehighest rates are observed when pay is compressedand pay-for-performance is low. This point esti-mate is consistent with sorting arguments, but theexpected lowest good-performer quit rates underhighly dispersed pay and pay-for-performance arenot observed here. Further, good-performer quitrates are not significantly related to organizationalperformance and therefore there is no evidencethat these rates mediate the relationship between

FIGURE 3Interactionof paydispersionandpay-for-performance

in predicting organizational performance

350

100

Low(-1 SD)

High(+1 SD)

Low Pay-for-performance

High Pay-for-performance

Pay Dispersion

Sto

re P

erfo

rman

ce(S

ales

per

Lab

or H

our)

TABLE 4Path Analytic Results: Direct, Indirect, and Total Effects of Pay Dispersion (Time 1) on Organizational Performance (Time 3)

via Poor-Performer Quit Rates (Time 2) at Low and High Levels of Pay-for-Performance (Time 1)

Pmx Pym Direct Effects (Pyx)Indirect

Effects (PymPmx)Total Effects

(Pyx 1 PymPmx)

Simple paths for low pay-for-performance 0.00 206.03* 215.77 0.00 215.77Simple paths for high pay-for-performance 0.04** 206.03* 94.75* 8.24** 102.99**

Notes. N5 111. Coefficients in bold are significantly different across pay-for-performance levels. PMX5path fromX (paydispersion, Time1)to M (poor-performer quit rates, Time 2), PYM 5 path from M (poor-performer quit rates, Time 2) to Y (organizational performance, Time 3),PYX 5 path from X (pay dispersion, Time 1) to Y (organizational performance, Time 3).

* p, .05** p, .01

2015 173Shaw

explained pay dispersion and organizational per-formance. This finding is noteworthy, especiallygiven the literature’s recent emphasis on star per-formers (e.g., Aguinis & O’Boyle, in press, 2014)suggesting that performance levels are not normallydistributed and that a few outlying, star performersmay disproportionately influence organizationalperformance. The sorting-based findings here sug-gest, as utility studies at the individual level havefound, that occupations differ in terms of the leveland range of performance variability, as does thevalue associated with high performance (e.g., theSDy$). The notion of “star performers” in the litera-ture likely does not apply well to my sample of in-dependent grocery stores. Thus, it is not entirelysurprising that explained pay dispersion predictedgood-performer quit rates, but these rates, in turn,did not relate to organizational performance.

Instead, the full sorting model held with poor-performer quit rates as the mediator. In particular,poor-performer quit rates appear to partially trans-mit the effects of pay dispersion on organizationalperformance when pay-for-performance is high.When pay is dispersed and explained by the use ofpay-for-performance, poor performers quit at higherrates which, in turn, is associated with higher orga-nizational performance. Among the full-time floorworkers at the center of this study of independentgrocery stores, employee performance was likelyconstrained at the upper limits. Star performerswere unlikely to contribute disproportionally to or-ganizational performance as Aguinis and O’Boyle(2014) arguedwill occur in knowledge-intensive andtechnology-driven 21st century work contexts. In-stead, the findings indicate that in low-skill occu-pations, explained pay dispersion that encourages

poor performers to quit may be more effective thanefforts to retain good performers.

Taken together, the results should be considered inthe broader context of sorting theory and the assess-ment of the utility or functionality of turnover. Thestudy of the utility of separations and replacementshas a long history in applied psychology (e.g.,Boudreau & Berger, 1985). Although these treatmentshave not appeared recently in the literature, the cur-rent findings should be case in the light of organiza-tional investments in pay-for-performance systemsand the payoffs of rewarding and retaining high per-formers (e.g., Sturman, Trevor, Boudreau & Gerhart,2003). In the case ofmy findings, good-performer quitrates were low across most combinations of pay dis-persion and pay-for-performance and were only highwhen pay was compressed and pay-for-performancewas not emphasized. Further, the predicted media-tion of good-performer quit rates was not evidentin the findings, although there was support for theutility of explained pay variation in terms of in-creasing organizational performance via increasedpoor-performer quit rates.As an anonymous reviewersuggested, a fruitful endeavor would be to mergeorganizational-level research on explained pay dis-persionwith the existing base of theory and empiricalfindings to isolate the conditions (e.g., environmental,industry, organizational) under which “winning thetalent war” (Sturman et al., 2003) through manipula-tions of the pay structure is worth it.

A final direct implication of the findings is thatpay dispersion significantly interacted with pay-for-performance in predicting performance in thepresence of the proposed quit-rate mediators. Thenature of the interaction was such that I observedthe highest organizational performance levelswhen

FIGURE 4Observed empirical model

Pay-for-performance

Pay Dispersion

Poor-performerQuit Rates

Good-performerQuit Rates

OrganizationalPerformance

174 DecemberAcademy of Management Discoveries

pay dispersion and pay-for-performance were bothhigh—an explained pay dispersion effect. This sug-gests that although poor-performer quit rates seemedto transmit part of the interactive relationship on or-ganizational performance, other potential mediatorsare possible. One possibility is that explained dis-persionmay raise general performance or effort levelswithin the organization. Research has shown, for ex-ample, that effectively managed financial incentivesare positively related to performance (Jenkins et al.,1998) and that meaningful differences in pay canevoke greater employee effort (e.g., Mitra et al., inpress). A second possibility is that explained paydispersion may cause the workforce as a whole toperceive stronger fairness or improve other collectivejob attitudes. As the pay dispersion literature haspreviously suggested (e.g., Shaw, 2014; Shaw et al.,2002), such positive attitudinal reactions may trans-late directly into better organizational performance.Future researchers would be well served to designstudies that examine whether other factors can ex-plain the direct-effects moderation observed here.

Other theoretical lenses might be used to analyzethe results of this study. Tournament theory focuseson vertical pay dispersion or pay structures acrossorganizational hierarchies (see Connelly, Tihanyi,Crook, Gangloff, 2014, for a review). The tournamentmodel shows that individuals within and across or-ganizational echelons compete for higher pay andpromotions. As competition intensifies and pro-motion opportunities become rarer, the likelihood ofpromotions to higher levels decreases, so that expo-nentially wider pay differences are necessary to mo-tivate employees. If viewed as a single round ina sequential tournament, my findings could alignwith tournament theory, but the horizontal pay dis-persion context does not allow direct testing of moti-vation and sorting facets of tournament perspective.Similarly, expectancy theory suggests that individualevaluations of pay spreads and bases for pay coulddrive their efforts and perhaps withdrawal. From anexpectancy theory perspective, pay dispersion andpay-for-performance may map roughly on the con-cepts of valence and instrumentality, or the de-sirability of rewards and the likelihood of obtainingthose rewards. Extending the approach, individualsmay react negatively to pay dispersion because paydifferences are not desirable enough or because theyhave little likelihood of obtaining pay increases, suchas in the absence of pay-for-performance practices.Although expectancy-theory language might explainthe findingshere, andsomeresearchershaveextendedthe theory to organizational-level applications (e.g.,Kepeset al., 2009), itmaybedubious toapplyawithin-personmotivation theory to cross-organizational testsof pay practices, quit rates, and performance.

I also add to the management literature’s in-creasingly popular conversation about explainedand unexplained pay distributions (e.g., Kepes et al.,2009; Shaw et al., 2002; Shaw & Gupta, 2007; Trevoret al., 2012). The universalistic notions that paydispersion has either functional or dysfunctionalorganizational consequences have previously dom-inated the literature. These notions should be aban-doned. My findings should be combinedwith recentevidence about pay dispersion consequences bothexplained andunexplainedbynormatively acceptedpractices.

Future Research Directions

In this study, I initially outline mechanisms be-tween pay dispersion and organizational perfor-mance outcomes, a direction researchers in this areahave highlighted as a pressing need (e.g., Conroy,Gupta, Shaw, & Park, 2014; Shaw, 2014). The litera-ture has, however, identified several other possiblemediators yet to be examined. Clearly, this studycould be extended by examining employee-relatedmotivation and effort as a function of explained andunexplained pay dispersion. As noted above, highereffort or motivationmay be responsible for the direct-effectsmoderationobservedhere.Another possibilityis that more favorable job attitudes and justice per-ceptions mediate explained pay dispersion effects onorganizational performance, independent of with-drawal patterns. In addition, the literature has sug-gested various collective outcomes of pay dispersion,but has made little progress in empirical testing.

My researchdesign anddata limitationsprecludedmediation tests using data aggregated from individ-ual or team-level reports. In one initial step, re-searchers examined several team-related mediatorsof pay dispersion and performance, including con-flict, cohesion, and potency, and found some evi-dence that pay dispersion related negatively to teamprocesses, especially among family-owned organi-zations (Ensley, Pearson, & Sardeshmukh, 2007).Although that study was a positive step, the authorsdid not examine whether legitimate dispersion prac-tices could explain the pay dispersion.

The logic underlying explained pay dispersion isthat any practice or policy that is normatively ac-cepted in the context can generate positive organiza-tional outcomes. Pay-for-performance has mostlybeen examined as a legitimate reason for variation,but various factors can justifiably be used to create

Author’s voice:What would I do differently?

2015 175Shaw

pay differentials (Conroy et al., 2014). Pay-for-performance is common in the United States andsome other Western cultures, but organizations maycreate pay differences through measures such asskill, seniority, knowledge, competencies, family size,special allowances, and cost-of-living. For example,“skill-based pay dispersion is nonperformance based,but individuals may believe that as people’s skillsimprove, so should their pay. In this sense, skill-basedpay dispersion is legitimate, and should not result innegative equity perceptions” (Downes & Choi, 2014:58). It would be interesting to examine whether thefactors that areused tocreateexplainedpaydispersionand subsequent positive outcomes in one context re-late negatively to the same outcomes in other contexts.Future research should also develop alternative pre-dictions for outcomes and reactions to horizontal andvertical pay dispersion. Effort- and withdrawal-relateddynamics may differ when employees compare paydifferences within and across levels. One study, forexample, found that wide horizontal pay distributionsnegatively affected pay fairness perceptions, but onlyamong those at low pay distribution levels (Trevor &Wazeter, 2006). We might reasonably presume thatexplained and unexplained pay dispersion will havestronger equity-related consequences in horizontalcomparisons than it will have across organizationallevels where employees complete different and per-haps more complex and significant assignments. Inaddition, although pay dispersion studies often usetournament theoryand individualmotivation theory inparallel fashion, the perspectives do not treat motiva-tion synonymously. Tournament theorists argue thathuge pay differentials within organizations can com-pensate for near-zero probabilities of winning higherpay prizes. Indeed, the theory suggests that extremelylargedifferentials arenecessary tomotivate individualswhen the competition is difficult towin. Psychologicalmotivation theories such as expectancy theory suggest,however, that motivation is lowered if employees per-ceive thatperformance isweakly linkedwithoutcomes,when valence is held constant. Expectancy theoryfails to clearly indicate whether ever-increasingpayouts result in ever-increasing desirability of re-wards. If valence of rewards reaches a maximumthreshold, any reductions in instrumentalitieswill thenlower employee motivation. Future research could ex-amine when each perspective holds predictive verac-ity, perhaps by comparing the dynamics of horizontalversus vertical pay dispersion.

Limitations

Readers should consider several caveats whenevaluating this research. First, organizational keyinformants completed the questionnaires. For years,

macro-HRM and strategy researchers have debatedthe strengths and weaknesses of using key infor-mants (e.g., Gupta et al., 2000; Wright, Gardner,Moynihan, Park, Gerhart, & Delery, 2001), partic-ularly whether they have the motivation, ability,and capability to respond accurately and reliably(Tomaskovic-Devey, Leiter, & Thompson, 1994). Bycarefully identifying independent grocery storemanagers who were responsible for selection andperformance management, and, in most cases, forsetting and adjusting workforce pay levels, I ensuredthat respondents had reasonable or expert knowl-edgeof thevariables.The independent grocery storesin the sample were also small, so that managers weremore likely to know about the objective informationthey were reporting (Batt, 2002), and the stores werenot run bymultiple HRM systems that can introduceerror into the measurement of workforce variables(Lepak & Snell, 2002).

The pay variables can also be questioned. Paydispersion (Gini coefficient) was estimated frommanager-reported ranges. The research design madeit infeasible to collect the pay level of every full-timeemployee. To estimate the pay structure spread, Iasked the managers to report minimum, average,and maximum pay levels and also the number ofemployees making those general rates. When re-searchers have been unable to obtain full pay ranges,they have estimated dispersion using various alter-native operationalizations that tend to be highlycorrelated. When comparisons have been possible,alternative operationalizations have shown quitesimilar patterns (Shaw, 2014). Clearly readers shouldevaluate the results considering that the pay dis-persion calculation here underestimated the actualintraorganizational pay variation. As an anonymousreviewer pointed out, the pay-for-performance mea-sure did not refer specifically to financial rewards.Readers should consider this limitation when evaluat-ing the current results.

The analysis sample was rather small after match-ing data from two waves of surveys and a third wavefrom an archival source, which raises concerns aboutresponse bias, generalizability, and result stability. Iperformed response bias checks that revealed fewdifferences between the stores in the final analysissample and the randomly selected sample, but I can-not rule out the possibility that the analyses weresomehow biased. The small sample raises questionsas to whether the results can be generalized to thebroader population of single-unit grocery stores andto other similar organizations such as independentretail stores. Again, the response bias checks suggestthat these concerns may be minimized, but externalvalidity threats cannot be dismissed. The theoreticalmodel was also rather involved and many estimates

176 DecemberAcademy of Management Discoveries

were made relative to the sample size. The use ofbootstrapped standard errors reduces some concernsabout the stability of the findings, but replications ofthe full model with larger sample sizes and in othercontexts might generate additional confidence.

Key informants reported quit rates by prior perfor-mance levels. Meta-analytic results reveal minimaldifferences between findings generated from key in-formant reports of quit rates or quit rates from archivalsources (Park & Shaw, 2013). In this case, however, Ifurther asked informants to report the number of goodand poor performers who quit. To reduce error, I usedperformance bands to guide responses (e.g., top 20percent and bottom 20 percent), an improvement overinitial studies of functional and dysfunctional quitrates (e.g., Park, Ofori-Dankwa, & Bishop, 1994; Shaw& Gupta, 2007), but future researchers should pursuealternative operationalizations of differential quitrates. For example, obtaining performance evaluationrecords would allow calculations of good- and poor-performerquitsor theaveragequalityofhumancapitallost through turnover (e.g., Dess & Shaw, 2001; Shaw,Duffy, Johnson, & Lockhart, 2005). Finally, I examinedonly good- and poor-performer voluntary turnoveroutflows. Recent literature has considered the quan-tity and quality of replacements (e.g., Hausknecht &Holwerda, 2013). Some stores in the samplemay haveused replacement strategies that ameliorated turn-over’s negative effects. For example, the findingssupported the prediction that explained pay disper-sion would relate to good-performer quit rates, but Ifound no evidence that good-performer quit ratesmediated the effects of explained pay dispersion onperformance. Several explanations may be offered:one possibility is that certain stores timely and effec-tively hired high-quality workers to replace goodperformers who departed.

CONCLUSION

I advance the literature by testing a sorting-theory-based process model of the relationships amongpay dispersion, pay-for-performance, quit patternsof good and poor performers, and organizationalperformance. I find a stronger relationship betweenpay dispersion and organizational performance viapoor-performer quit rates when organizations usehigh pay-for-performance schemes. The pay disper-sion and pay-for-performance interaction also di-rectly relates to organizational performance suchthat the positive pay dispersion→organizationalperformance relationship is stronger when pay-for-performance is high. I find further that pay disper-sion and pay-for-performance interact in predictinggood-performer quit rates. Good performers aremost likely to quit when pay is compressed and

pay-for-performance not emphasized. The findingssupport several major themes in sorting theory, haveimplications for related theories, and highlight sev-eral areas needing future theory refinements andadditional empirical tests.

REFERENCES

Aguinis, H., & O’Boyle, E. 2014. Star performers in twenty-first century organizations. Personnel Psychology,67: 313–350.

Aguinis, H., & O’Boyle, E. 2015. Cumulative advantage: Con-ductors and insulators of heavy-tailed productiv-ity distributions and productivity stars. PersonnelPsychology. DOI: 10.1111/peps.12095.

Batt, R. 2002. Managing customer services: Human re-source practices, quit rates, and sales growth. Acad-emy of Management Journal, 45: 587–597.

Batt, R., & Colvin, A. J. S. 2011. An employment systemsapproach to turnover: HR practices, quits, dismissals,and performance.Academy ofManagement Journal,54: 695–717.

Belogolovsky, E., & Bamberger, P. A. 2014. Signaling insecret: Pay for performance and the inventive andsorting effects of pay secrecy. Academy of Manage-ment Journal, 57: 1706–1733.

Bishop, J. 1987. The recognition and reward of employeeperformance. Journal of Labor Economics, 5: s36–s56.

Bloom,M. 1999. Theperformance effects of pay dispersionon individuals and organizations. Academy of Man-agement Journal, 42: 25–40.

Bloom, M., & Michel, J. G. 2002. The relationships amongorganizational context, pay dispersion, and managerialturnover.AcademyofManagement Journal, 45: 33–42.

Boudreau, J. W., & Berger, C. J. 1985. Decision-theoreticutility analysis applied to employee separations andacquisitions. Journal of Applied Psychology, 70:581–612.

Cadsby, C. B., Song, F., & Tapon, F. 2007. Sorting and in-centiveeffectsofpay forperformance:Anexperimentalinvestigation. Academy of Management Journal, 50:387–405.

Colquitt, J. A. 2001. On the dimensionality of organiza-tional justice: A construct validation of a measure.Journal of Applied Psychology, 86: 386–400.

Connelly, B. L., Tihanyi, L., Crook, R. R., & Gangloff, K. A.2014. Tournament theory: Thirty years of contests andcompetitions. Journal of Management, 40: 16–47.

Conroy, S., Gupta, N., Shaw, J. D., & Park, T. Y. 2014. Amultilevel approach to the effects of pay variation.Research in Personnel and Human Resources Man-agement, 32: 1–64.

2015 177Shaw

DeBrock, L., Hendricks, W., & Koenker, R. 2004. Pay andperformance: The impact of salary distribution onfirm-level outcomes in baseball. Journal of SportsEconomics, 5: 243–261.

Dess, G. G., & Shaw, J. D. 2001. Voluntary turnover, socialcapital, and organizational performance.Academy ofManagement Review, 26: 446–456.

Donaldson, D., &Weymark, J. A. 1980. A single-parametergeneralization of the gini indices of inequality. Jour-nal of Economic Theory, 22: 147–149.

Downes, P. E., & Choi, D. 2014. Employee reactions to paydispersion: A typology of existing research. HumanResource Management Review, 24: 53–66.

Edwards, J. R. 1996.Anexaminationof competing versionsof the person-environment fit approach to stress.Academy of Management Journal, 39: 292–339.

Edwards, J. R., & Lambert, L. S. 2007. Methods for in-tegrating moderation and mediation: A general ana-lytical framework using moderated path analysis.Psychological Methods, 12: 1–22.

Ensley, M. D., Pearson, A. W., & Sardeshmukh, S. R.2007. The negative consequences of pay dispersionin family and non-family top management teams:An exploratory analysis of new venture, high-growth firms. Journal of Business Research, 60:1039–1047.

Fredrickson, J. W., Davis-Blake, A., & Sanders, W. M. G.2010. Sharing thewealth: Social comparisons and paydispersion in the CEO’s top team. Strategic Man-agement Journal, 31: 1031–1053.

Gerhart, B., & Rynes, S. L. 2003. Compensation: Theory,evidence, and strategic implications. ThousandOaks, CA: Sage.

Gerhart, B., Rynes, S., & Fulmer, I. S. 2009. Pay andperformance: Individuals, groups, and executives.Academy of Management Annals, 3: 251–315.

Gupta, N., Conroy, S. A., & Delery, J. E. 2012. The manyfaces of pay variation. Human Resource Manage-ment Review, 22: 100–115.

Gupta, N., & Shaw, J. D. 2014. Compensation: The neglectedarea of HRM research.Human ResourceManagementReview, 24: 1–4.

Gupta, N., Shaw, J. D., & Delery, J. E. 2000. Correlatesof response outcomes among organizational keyinformants. Organizational Research Methods, 3:323–347.

Harrison, D. A., Virick, M., & William, S. 1996. Workingwithout a net: Time, performance, and turnover undermaximally contingent rewards. Journal of AppliedPsychology, 81: 331–345.

Hausknecht, J. P., & Holwerda, J. A. 2013. When does em-ployee turnovermatter?Dynamicmemberconfigurations,

productive capacity, and collective performance. Orga-nization Science, 24: 210–225.

Heavey, A. L., Holwerda, J. A., & Hausknecht, J. P. 2013.Causes and consequences of collective turnover: Ameta-analytic review. Journal of Applied Psychol-ogy, 98: 412–453.

Heneman, R. L., Greenberger, D., & Strasser, S. 1988. Therelationship between pay-for-performance percep-tions and pay satisfaction. Personnel Psychology, 41:745–759.

Jenkins, G. D., Jr, Mitra, A., Gupta, N., & Shaw, J. D. 1998.Are financial incentives related to performance? Ameta-analytic reviewof empirical research. Journal ofApplied Psychology, 83: 777–787.

Kepes, S., Delery, J. E., & Gupta, N. 2009. Contingencies inthe effects of pay range on organizational effective-ness. Personnel Psychology, 62: 497–531.

Kristof, A. L. 1996. Person-organization fit: An integrativeview of its conceptualizations, measurements, andimplications. Personnel Psychology, 49: 1–55.

Lambert, R. A., Larcker, D. F., & Weigelt, K. 1993. Thestructure of organizational incentives. Administra-tive Science Quarterly, 38: 438–461.

Lawler, E. E. III, & Jenkins, G. D. Jr. 1992. Strategic rewardsystems. In M. D. Dunnette & L. M. Hough (Eds.),Hand-book of industrial and organizational psychology(3rd ed.): 1009–1055. Palo Alto, CA: ConsultingPsychologists Press.

Lazear, E. P. 1999. Personnel economics: Past lessons andfuture directions. Journal of Labor Economics, 17:199–215.

Lazear, E. P. 2000. Performance pay and productivity.American Economic Review, 90: 1346–1361.

Lepak, D. P., & Snell, S. S. 2002. Examining the humanresource architecture: The relationships among hu-man capital, employment, and human resource con-figurations. Journal of Management, 28: 517–543.

Mitra, A., Gupta, N., & Jenkins, G. D., Jr. 1997. A drop in thebucket: When is a pay raise a pay raise? Journal ofOrganizational Behavior, 18: 117–137.

Mitra, A., Tenhiala, A., & Shaw, J. D. In press. Smallestmeaningful pay increases: Field test, constructive repli-cation, and extension.Human Resource Management.

Nyberg, A. 2010. Retaining your high performers: Moder-ators of the performance-job satisfaction—Voluntaryturnover relationship. Journal of Applied Psychol-ogy, 95: 440–453.

Osterman, P. 1987. Turnover, employment security, andthe performance of the firm. In M. Kleiner (Ed.), Hu-man resources and the performance of the firm:275–317. Madison, WI: Industrial Relations ResearchAssociation.

178 DecemberAcademy of Management Discoveries

Park, H. Y., Ofori-Dankwa, J., & Bishop, D. R. 1994.Organizational and environmental determinants offunctional and dysfunctional turnover: Practical andresearch implications.HumanRelations, 47: 353–366.

Park, T. Y., & Shaw, J. D. 2013. Turnover rates and orga-nizational performance: A meta-analysis. Journal ofApplied Psychology, 98: 268–309.

Park, T. Y., & Sung, L. K. 2013. The effects of pay dis-persion: Ameta-analysis. Working paper, VanderbiltUniversity.

Rosen, C. C., Slater, D., Chang, C.-H., & Johnson, R. E. 2013.Let’s make a deal: Development and validation of the expost I-Deals scale. Journal ofManagement, 39: 709–742.

Salamin, A., & Hom, P. W. 2005. In search of the elusiveU-shaped performance-turnover relationship: Arehigh performing Swiss bankers more liable to quit?Journal of Applied Psychology, 90: 1204–1216.

Shaw, J. D. 2011. Voluntary turnover and organizationalperformance: Review, critique, and research agenda.Organizational Psychology Review, 1: 187–213.

Shaw, J. D. 2014. Pay dispersion. Annual Review of Or-ganizational Psychology and Organizational Be-havior, 1: 521–544.

Shaw, J. D., Gupta, N., & Delery, J. E. 2002. Pay dispersionand workforce performance: Moderating effects ofincentives and interdependence. Strategic Manage-ment Journal, 23: 491–512.

Shaw, J. D., Delery, J. E., Jenkins, G. D., Jr, & Gupta, N. 1998.An organizational-level analysis of voluntary and in-voluntary turnover. Academy of Management Jour-nal, 41: 511–525.

Shaw, J. D., Dineen, B. R., Fang, R., & Vellella, R. V. 2009.Employee-organization exchange relationships, HRMpractices, and quit rates of good and poor performers.Academy of Management Journal, 52: 1016–1033.

Shaw, J. D., Duffy,M. K., Johnson, J. J., & Lockhart, D. 2005.Turnover, social capital losses, and performance.Academy of Management Journal, 48: 594–606.

Shaw, J. D., & Gupta, N. 2007. Pay system characteristicsand quit patterns of good, average, and poor per-formers. Personnel Psychology, 60: 903–928.

Shaw, J. D., Park, T. Y., & Kim, Y. 2013. A resource-based perspective on human capital losses, HRMinvestments, and organizational performance. Stra-tegic Management Journal, 34: 572–589.

Shrout, P. E., & Bolger, N. 2002. Mediation in experi-mental and nonexperimental studies: New proce-dures and recommendations. Psychological Methods,7: 422–445.

Sturman,M.C., Trevor, C.O., Boudreau, J.W.,&Gerhart, B.2003. Is itworth it towin the talentwar? Evaluating theutility of performance-based pay. Personnel Psy-chology, 56: 997–1035.

Tomaskovic-Devey, D., Leiter, J., & Thompson, S. 1994.Organizational survey nonresponse. AdministrativeScience Quarterly, 39: 439–457.

Trevor, C. O., Reilly, G., & Gerhart, B. 2012. Reconsideringpay dispersion’s effect on the performance of in-terdependent work: Reconciling sorting and payinequality. Academy of Management Journal, 55:585–610.

Trevor, C. O., & Wazeter, D. L. 2006. Contingent viewof reactions to objective pay conditions: Inter-dependence among pay structure characteristicsand pay relative to internal and external refer-ents. Journal of Applied Psychology, 91: 1260–1275.

Wright, P. M., Gardner, T. M., Moynihan, L. M., Park,H. J., Gerhart, B., & Delery, J. E. 2001. Measurementerror in research on human resources and firmperformance: Additional data and suggestionsfor future research. Personnel Psychology, 54:875–901.

Jason D. Shaw (jason.shaw@polyu.edu.hk) is Chair Professor,Associate Dean for Research and Postgraduate Studies, andCo-director of the Centre for Leadership and Innovation in theFaculty of Business at TheHongKongPolytechnicUniversity.He received his doctoral degree from the University ofArkansas. His research interests include team effectiveness,employment relationships, financial incentives, and turnover.

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