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Self Control and Career Success

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Self Control and Career Success
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  Childhood self-control, adolescent behavior, and career success Patrick D. Converse ⇑ , Katrina A. Piccone, Michael C. Tocci School of Psychology, Florida Institute of Technology, United States a r t i c l e i n f o  Article history: Received 7 June 2013Received in revised form 1 November 2013Accepted 9 November 2013Available online 5 December 2013 Keywords: Self-controlInitiatory controlInhibitory controlAdolescent behaviorCareer successIncome Job satisfaction a b s t r a c t Research indicates that dispositional self-control is an important predictor of a wide range of behaviorsand outcomes but little research has examined this characteristic in the context of career success. Thiswork adds to the limited findings in this area and extends previous research by developing and examin-ing a model of self-control and career success. Specifically, drawing from the concepts of cumulative andinteractional continuity (Caspi, Bem, & Elder, 1989) and the recently proposed distinction between start/initiatory and stop/inhibitory self-control (e.g., de Ridder, de Boer, Lugtig, Bakker, & van Hooft, 2011), wedeveloped and tested a model of the pathways leading from childhood self-control to career outcomesfocusing on adolescent behavior that is positive (e.g., studying) versus negative (e.g., stealing), education,and job complexity. Results indicated that childhood self-control predicted positive and negative adoles-cent behavior; this behavior predicted educational attainment; education predicted the complexity andincome associated with one’s job; job complexity predicted income and job satisfaction; and income pre-dicted job satisfaction. These findings add to research on self-control and career success, further demon-strating the relevance of self-control in this context and highlighting key links connecting these variablesinvolving factors related to start and stop control.   2013 Elsevier Ltd. All rights reserved. 1. Introduction Self-control—involving effective regulation of thoughts, feel-ings, and behaviors—has been extensively examined across a rangeof contexts. Researchers investigating clinical, developmental, so-cial, personality, criminology, and health issues (among others),have developed substantial knowledge bases regarding the natureand implications of self-control in these areas (e.g., see Baumeister& Vohs, 2004; Gottfredson & Hirschi, 1990). This broad and sus-tained research attention stems in part from the notion that inef-fective self-control is a major factor responsible for a range of personal and social problems (e.g., Baumeister, Heatherton, & Tice,1994) and conversely effectiveself-controlisakey tosuccessinlife(e.g., Baumeister, Leith, Muraven, & Bratslavsky, 1998). Consistentwith this idea, dispositional self-control has been linked to anumber of important outcomes including academic performance,impulse control, psychological adjustment, and interpersonaloutcomes (e.g., Tangney, Baumeister, & Boone, 2004; Wolfe & Johnson, 1995).However, dispositional self-control has not received substantialattention in work contexts, particularly in terms of career success.Given the pervasive influence of this characteristic on significantbehaviors and outcomes, this may represent a notable gap inresearch on the factors influencing career-related outcomes. Thepurpose of the present study was to extend the limited researchon this issue. More specifically, this research draws from two the-oretical perspectives—involving the concepts of cumulative andinteractional continuity (Caspi, Bem, & Elder, 1989) and the recentnotion of a distinction between two aspects of self-control (stop/inhibitory and start/initiatory; de Boer, van Hooft, & Bakker,2011; de Ridder, de Boer, Lugtig, Bakker, & van Hooft, 2011)—to de-velop and test a model of the pathways leading from self-control tocareer outcomes. 1.1. Defining self-control and career success Self-control has been defined as ‘‘the exertion of control overthe self by the self’’ (Muraven & Baumeister, 2000, p. 247). Thisconcept thus entails internally focused active control tendenciesinvolving regulation of thoughts, feelings, or behaviors. Disposi-tional self-control has connections to other prominent personalitytraits, including the Big Five, but evidence indicates this character-istic is reasonably distinct. There is, for example, a clear link be-tween self-control and conscientiousness but thesecharacteristics appear to overlap only partially (e.g., Tangneyet al., 2004, reported a correlation of .54 between self-controland conscientiousness).Career success has been conceptualized in a variety of waysbut is often defined as the positive psychological or work-related 0191-8869/$ - see front matter    2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.paid.2013.11.007 ⇑ Corresponding author. Address: School of Psychology, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901-6975, United States.Tel.: +1 (321) 674 8104. E-mail address:  pconvers@fit.edu (P.D. Converse).Personality and Individual Differences 59 (2014) 65–70 Contents lists available at ScienceDirect Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid  outcomes accumulated as a result of one’s work experiences(e.g., Judge, Cable, Boudreau, & Bretz, 1995; Ng, Eby, Sorensen, &Feldman, 2005). Career-related success is typically characterizedas involving two primary components: extrinsic or objective andintrinsic or subjective (see Ng et al., 2005). Extrinsic career successinvolves observable outcomes such as salary, whereas intrinsiccareer success involves subjective outcomes such as satisfaction.Given previous evidence suggesting that these two types of outcomes are relatively independent (Ng et al., 2005), the currentstudy examined both extrinsic and intrinsic outcomes, focusingon income and job satisfaction. 1.2. Linking self-control and career success As noted, although a large number of studies have demon-strated connections between self-control and a variety of impor-tant behaviors and outcomes, research on this characteristic inthe career context has been quite limited. For instance, in a recentmeta-analysis, de Ridder, Lensvelt-Mulders, Finkenauer, Stok, andBaumeister (2012) reviewed evidence regarding the relationshipbetween trait self-control and behaviors across a range of domains.These researchers reported  k  (number of tests) of only 5 for the do-main ‘‘school and work performance.’’ Furthermore, although deRidder et al. did not provide detailed information regarding thespecific behaviors examined in the school and work performancedomain, it may be that these were largely school- or task-related,rather than work- or career-related, as these authors listed GPA,homework hours, and persistence at solving tasks as examples of behaviors in this domain. There have been a few studies examiningrelationships between self-control variables and unemployment(e.g., Kokko, Pulkkinen, & Puustinen, 2000) and ‘‘career orienta-tion’’ (involving occupational status, education, present work situ-ation, and career stability; Pulkkinen, Ohranen, & Tolvanen, 1999),but research investigating specific career success outcomes in em-ployed individuals has been limited.Two recent exceptions are studies by Moffitt et al. (2011) andConverse, Pathak, DePaul-Haddock, Gotlib, and Merbedone(2012). Moffitt et al. (2011) demonstrated that childhood self-con- trol (beginning at age 3) predicted negative adolescent behaviors(from age 13 to 21; e.g., smoking and dropping out of school),which, in turn, predicted income later in life (at age 32). Similarly,Converse et al. (2012) found that childhood self-control related tolater income and occupational prestige through educational attain-ment and later career satisfaction through occupational opportu-nity for achievement. These studies provide valuable initialevidence of the relevance of self-control in predicting career-re-lated outcomes. However, information regarding the specific path-ways linking self-control to these outcomes is somewhat limitedand, in particular, the above studies did not draw from the recentperspective suggesting that there are two aspects of self-control(start and stop). Thus, this work draws from Caspi and colleagues’(1989)ideas regardingcumulativeandinteractionalcontinuityandthenotionsofstartandstopself-controltodevelopandtest amod-el involving self-control measured in childhood, positive and neg-ative behaviors (related to start and stop control) in adolescence,and career-related outcomes in adulthood (see Fig. 1). Note thatthese perspectives provided the stimulus and conceptual back-ground for the current research but we are not directly testingthese theories. 1.2.1. Cumulative and interactional continuity The model developed in the present research is developmentalin that it involves pathways leading from self-control relativelyearlyin lifeto careeroutcomesexperiencedin adulthood. To devel-opthismodel,wedrewfromCaspiand colleagues’(e.g.,Caspi,Rob-erts, & Shiner, 2005; Caspi et al., 1989) ideas regarding continuitiesand consequences of interactional styles over time. Caspi et al.(1989) proposed two general processes involving person-environ-ment interactions across time. First, cumulative continuity in-volves the individual selecting, creating, and shaping his/herenvironments based partially on dispositional qualities, and theseenvironments then sustaining those dispositions. An extravertedindividual, for example, may often select environments involvingsocial interaction; these environments may then help to maintainthis individual’s extraverted tendencies. Over long periods of time,this type of process may lead to not only stable dispositions, butalso distinct life paths. Second, interactional continuity involvestransactions between the individual and the environment: theindividual acts, the environment reacts, and the individual re-sponds. Caspi et al. (1989) proposed that this process can also haveimplications for life paths and dispositional continuity in severalways including through reciprocal reinforcement, self-confirmingexpectations, and selective attention to information that confirmsone’s self-concept.In developing these ideas, Caspi and colleagues emphasizedcontinuity with respect to certain interactional styles, but theseprocesses also appear to be quite relevant to the current contextinvolving pathways leading to career outcomes. More specifically,the concepts of cumulative and interactional continuity suggestthat individual differences in an influential characteristic such asself-control present relatively early in development can producedifferent life paths, resulting in different career outcomes. For in-stance, self-control may influence the types of environments indi-viduals select, create, and shape (e.g., those higher in self-controlmay choose situations based on longer term implications ratherthan shorter term rewards). Similarly, this trait may also affectindividuals’ reciprocal interactions with the environment (e.g.,those higher in self-control may engage in more long-term desir-able behaviors that are then reinforced by the environment includ-ing parents and teachers). As these processes unfold over time,different life paths may be created, leading to differing career out-comes. Fig. 1 presents the current model based on this perspectiveand the following sections discuss the specific links. 1.2.2. Start and stop control Effectively exercising self-control involves both inhibitingundesirable behaviors and initiating desirable behaviors (Baumei-ster, Bratslavsky, Muraven, & Tice, 1998; de Ridder et al., 2011).Based on this notion, recent research by de Boer et al. (2011) andde Ridder et al. (2011) suggeststwo key dimensions of self-control:start (or initiatory) and stop (or inhibitory). The start dimension of self-control is expected to facilitate engaging in positive behaviors,such as goal-directed activities, that may not be desirable in theshort-term but are likely to benefit individuals in the long-term.Alternatively, the stop dimension of self-control is expected to in-hibit engaging in negative behaviors, such as illicit or harmfulactivities, that may be desirable in the short-term but can havenegative consequences in the long-term. Based on this research,it is likely that dispositional self-control predicts both positiveand negative behaviors. Indeed, previous research has demon-strated that self-control is positively related to positive behaviors,such as time spent studying, and negatively related to negativebehaviors, such as alcohol consumption and cigarette smoking(de Boer et al., 2011; de Ridder et al., 2011). This has also been sup-ported by a recent meta-analysis that found self-control predictsboth desirable and undesirable behaviors (de Ridder et al., 2012).Based on the concepts of cumulative and interactional continuity,these relationships are expected to hold over time, from childhoodto adolescence. For example, children higher in self-control may bemore likely to choose situations and behaviors that are seen asmore long-term desirable (e.g., following rules). This may then berewarded by parents and teachers, reinforcing those tendencies 66  P.D. Converse et al./Personality and Individual Differences 59 (2014) 65–70  over time such that they are sustained into adolescence, manifest-ing in more positive behaviors and fewer negative behaviors (interms of long-term desirability). Hypothesis 1.  Childhood self-control (a) positively relates topositive adolescent behavior and (b) negatively relates to negativeadolescent behavior. 1.2.3. Positive and negative behavior and education These behaviors are likely to then have implications for educa-tional attainment. First, success in school appears to require bothinitiating these long-term desirable behaviors (e.g., studying) andinhibiting these long-term undesirable behaviors (e.g., drinking/drug use). Thus, engaging in more positive behaviors and fewernegative behaviors (with respect to long-term goals) is likely tolead to more academic success. Clearly, this greater academic per-formance is then likely to be associated with greater ability to con-tinue one’s education (e.g., better grades increase the chances of admission to undergraduate institutions and graduate programs).Second, these behaviors may also influence motivation to continuewith school. The idea of interactional continuity suggests how thiscan occur. If, for example, an adolescent decides to do drugs or skipclass, the environment is likely to react negatively (e.g., criticismfrom teachers or parents). This change in environment may thenhave negative effects on the student’s subsequent motivation(e.g., trying less in school). In contrast, if an adolescent decides toavoid drugs and attend class regularly, the environment is likelyto react positively; this may then have positive effects on subse-quent motivation. Thus, these positive and negative behaviorslikely influence both the ability and motivation to continue withschool, which should influence educational attainment. Consistentwith this, there has been ample research showing how positivebehaviors can facilitate educational attainment (e.g., Lleras, 2008;Staff& Mortimer,2007) and hownegativebehaviorscan deteredu-cationalattainment(e.g.,Moffitt,Caspi,Harrington, &Milne, 2002). Hypothesis 2.  Positive adolescent behavior positively relates toeducational attainment. Hypothesis 3.  Negative adolescent behavior negatively relates toeducational attainment. 1.2.4. Education, job complexity, income, and satisfaction Indicators of career success, including income and job satisfac-tion, are likely to be predicted by education and job complexity.First, educational achievement may be an important factor influ-encing career success. According to a human capital perspective,higher education levels signal that individuals have desirable attri-butes, such as intelligence and self-motivation (Ng et al., 2005).Organizations may attempt to gain and retain individuals withthese desirable attributes by offering higher wages and moreworkplace opportunities. Congruent with these expectations, edu-cation level has been found to relate to salary (Ng et al., 2005).Additionally, individuals with higher education levels are likelyto be better prepared for complex jobs, which involve intellectualdemands (Oswald, Campbell, McCloy, Rivkin, & Lewis, 1999). Hypothesis 4.  Education positively relates to (a) job complexityand (b) income.Second, job complexity may also influence career outcomes.Highly complex jobs involve ‘‘lack of routine repetitive work in fa-vor of work involving high intellectual demands and/or frequentchanges in task-related requirements—often involving the synthe-sis or interpretation of complex data’’ (Oswald et al., 1999, p. 3).Put differently, these jobs involve a high degree of difficulty, whichis likely to relate to higher salaries. Indeed, previous research hasillustrated a job complexity-income relationship ( Judge, Klinger,& Simon, 2010). Furthermore, the job characteristics model sug-gests that complex jobs predict positive outcomes such as mean-ingfulness of work, responsibility for outcomes, and knowledgeof results (Hackman & Oldham, 1980), which positively impact job satisfaction ( Judge, Bono, & Locke, 2000). Finally, income is ex-pected to relate to job satisfaction, as higher income is associatedwith self and others’ perceptions of success (Ng et al., 2005). In-deed, previous research has supported this income-satisfactionrelationship (see Ng et al., 2005). Hypothesis 5.  Job complexity positively relates to (a) income and(b) job satisfaction. Hypothesis 6.  Income positively relates to job satisfaction. 2. Method  2.1. Participants Data for this study came from the US Department of Laborsponsored National Longitudinal Survey of Youth 1979 (NLSY79)Children and Young Adults database. The NLSY79 Children andYoung Adults database was started in 1986 and focuses on the   Self-ControlPositive Behaviors Negative BehaviorsEducation Job ComplexityIncomeJob Satisfaction.04**(.10).04**(.10)-.05**(-.10)-.05**(-.10)2.25**(.13)2.25**(.13)-3.10**(-.23)-3.10**(-.23).18**(.39).18**(.39).01**(.09).01**(.09).08**(.09).08**(.09).05**(.33).05**(.33).69**(.12).69**(.12) Fig. 1.  Model and path analysis results. Unstandardized coefficients are shown first, with standardized coefficients in parentheses.  ⁄⁄  p  < .01. P.D. Converse et al./Personality and Individual Differences 59 (2014) 65–70  67  children of the female participants from the srcinal 1979 study.Individuals in this database have been assessed every two yearssince 1986 (the most recent data available are from 2010). Thisdatabase includes information provided by the mothers aboutthe children and self-reports from children aged 10 and older.In the current study, participants were 4932 individuals whohad scores on at least two of the focal variables and were at least20 years of age (in 2010). Note, however, that sample size variedacross variables (see Table 1). The sample was 50% female; 56%Black or Hispanic, 44% Non-Black/Non-Hispanic; and the meanapproximate age as of 2010 was 25.64 ( SD  = 3.63).  2.2. Procedures and measures Self-control was measured with 21 items ( a  = .87) from theBehavior Problems Index (BPI; see Zill, 1990). These items havebeen included in various measures of self-control (e.g., Chapple,2005; McGloin, Pratt, & Maahs, 2004; Nofziger, 2008; Raffaelli,Crockett,& Shen, 2005) and significantlypredicted theoretically re-lated constructs such as peer relationships and delinquency. Addi-tionally, several of these items (e.g., He/She has difficultyconcentrating, cannot pay attention for long; He/She is impulsive,or acts without thinking) are very similar to those included in pre-vious measures of start and stop self-control that, consistent withexpectations, significantly predicted engagement in positive andnegative behaviors (de Boer et al., 2011; de Ridder et al., 2011).Mothers rated their children on these items in 1988. Responseswere based on a three-point scale ranging from 1 ( often true ) to 3( not true ). Lower scores indicated lower self-control.Positive and negative behaviors were measured in 1996. Basedon items used in previous research (de Boer et al., 2011; de Ridderet al., 2011), negative adolescent behavior was measured with 11items focusing on behaviors that may be more attractive in theshort-term but less desirable in the long-term (involving lying,stealing, damaging property, drinking, using drugs, smoking, skip-pingschool, andstayingout withoutpermission),and positiveado-lescent behavior was measured with seven items focusing onbehaviors that may be less attractive in the short-term but moredesirable in the long-term (involving spending time on homeworkduring school, after school, or during the summer, belonging toclubs/teams/activities, and working for pay). A log transformationwas used for both of these variables due to skew.Educational attainment was measured as the highest gradecompleted as of 2010, specified as first grade (coded 1) througheighth year of college or more (coded 20). Census codes for partic-ipants’ current or most recent occupations in 2010 were also avail-able. The Occupational Information Network (O ⁄ NET) was used toobtain the level of complexity associated with each of thoseoccupations. More specifically, a crosswalk between the Censuscodes and O ⁄ NET-SOC codes was used to identify correspondingO ⁄ NET occupations. Job complexity was then measured with theO ⁄ NET variable ‘‘Job Zone,’’ which represents the amount of educa-tion, experience, and training required to perform the job andranges from 1 ( little or no preparation needed ) to 5 ( extensive preparation needed ). Job Zones were developed using SpecificVocational Preparation from the Dictionary of Occupational Titles,and evidence supports the validity of this index (see Oswald et al.,1999). Using Job Zone as an indicator of job complexity is consis-tent with previous research (e.g., Le et al., 2011). Note that in thecrosswalk more than one O ⁄ NET occupation was linked to a givenCensus occupation in several cases. In those cases, average JobZone scores across the multiple O ⁄ NET occupations were used.Income was measured as hourly rate of pay assessed in 2010 forthe respondent’s current or most recent job (a log transformationwas used for this variable). Job satisfaction was measured withone item in 2010 that assessed how the individual felt about his/her current or most recent job, with responses ranging from 1 ( likeit very much ) to 4 ( dislike it very much ). Previous research has sup-portedthe use ofsingle-itemmeasuresofjob satisfaction(Wanous,Reichers, & Hudy, 1997). Scores were recoded so that lower valuesindicated lower satisfaction. 3. Results Table 1 presents descriptive statistics and correlations. Toexamine the hypotheses, a path analysis was conducted usingAmos. The model tested is that shown in Fig. 1 with two additions:age, race (Hispanic or Black vs. Non-Hispanic, Non-Black), gender,and conscientiousness (measured in 2010 using the Ten-Item Per-sonality Inventory; Gosling, Rentfrow, & Swann, 2003) were in-cluded as predictors of all endogenous variables, andrelationships between all exogenous variables were included.Model fit indices indicated reasonably good fit:  v 2 (12) = 68.37,  p  < .01;NFI = .973;CFI = .977;RMSEA = .031. Themodel v 2 was sig-nificant but this is affected by sample size and the current sampleis relatively large; the other index values appear to indicate goodfit (e.g., see Kline, 2005). We also examined a model in which thecontrol variables (age, race, gender, and conscientiousness) wereremoved. Model fit indices indicated somewhat poorer but stillreasonable fit:  v 2 (12) = 116.63,  p  < .01; NFI = .913; CFI = .920;RMSEA = .042.As shown in Fig. 1, all hypotheses were supported. Similar re-sults were obtained for the model excluding the control variablesin that all of the coefficients were significant, the same sign, andsimilar in magnitude. In addition, Table 2 shows proportion of var-iance accounted for in the endogenous variables ranged from .04 to  Table 1 Descriptive statistics and correlations. N M SD  1 2 3 4 5 6 7 8 9 101 Self-control 2182 2.52 0.312 Positive behavior 3382 0.25 0.12 .10 ** 3 Negative behavior 1741 0.13 0.13   .10 ** .034 Education 4682 12.85 2.07 .17 ** .14 **  .21 ** 5 Job complexity 4018 2.31 0.94 .10 ** .12 **  .08 ** .40 ** 6 Income 1672 3.00 0.14 .09 * .20 **  .11 * .20 ** .37 ** 7 Job satisfaction 4174 3.17 0.84 .02 .01   .01 .02 .14 ** .16 ** 8 Age 4932 25.64 3.63   .02 .32 ** .23 ** .05 ** .21 ** .33 ** .06 ** 9 Race 4932 0.44 0.50 .02   .03   .08 ** .16 ** .05 ** .10 ** .04 **  .10 ** 10 Gender 4932 0.50 0.50 .10 ** .04 *  .05 * .12 ** .04 **  .02   .03 * .01   .0111 Conscientiousness 4661 5.80 1.15 .05 * .05 ** .00 .06 ** .08 ** .03 .06 ** .05 **  .09 ** .07 ** Note:  Race coded 0 = Black or Hispanic, 1 = Non-Black/Non-Hispanic. Gender coded 0 = male, 1 = female. *  p  < .05. **  p  < .01.68  P.D. Converse et al./Personality and Individual Differences 59 (2014) 65–70
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