Social Science Research 40 (2011) 299–311

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Social Science Research

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The paradox of de-coupling: A study of flexible work programand workers’ productivity

Song Yang a,*,1, Lu Zheng b,1

a Department of Sociology and Criminal Justice, University of Arkansas, USAb Department of Sociology, Texas A&M University, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 23 April 2009Available online 10 April 2010

Keywords:Sociology of organizationsNeo-institutional theoryDe-couplingFlexible workFlextimeProductivity

0049-089X/$ - see front matter � 2010 Elsevier Incdoi:10.1016/j.ssresearch.2010.04.005

* Corresponding author. Address: Department of S+1 479 575 7981.

E-mail address: yangwang@uark.edu (S. Yang).1 These authors contributed equally to this work.

Organizational de-coupling occurs when organizational adoption of programs is separatedfrom ongoing activities. Neo-institutional theory asserts it is an effective coping strategyfor modern organizations to deal with increasingly elaborated environments. In this paper,we investigate the consequences of organizational de-coupling from an inward-lookingwithin organizational perspective. Specifically, we study how the de-coupling of flexiblework program affects workers’ actualization of productivity. Our data analysis suggeststhat the organizational de-coupling, in which organizations only ceremonially adopt flex-ible work program without making it available to all workers, is detrimental to the actual-ization of productivity for workers who were bereft of the opportunity. In contrast, we findthat the highest level of productivity actualization is associated with workers who enjoy afactual flexible work schedule which is nevertheless not formally adopted by the employer.The implications of the findings are discussed.

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1. Introduction

The issue of workers’ productivity has been a major concern for scholars as well as practitioners over the last few decades.Many early employer or employee surveys have been focusing on how various participation and incentive programs can im-prove workers’ productivity. The New York Stock Exchange 1982 survey investigated over a 1000 employers regarding theiremployment of different stimulus programs and their effects on productivity gains. The Bureau of National Affairs PersonnelPolicies Forum 1983 Survey conducted interviews with almost 200 firms to examine the effectiveness of productivityimprovement programs such as team, training, and job design. Although later research has been somehow shifting their fo-cus from productivity to workers’ commitment, involvement, and morale (Delaney et al., 1989; Lawler et al., 1992), studies ofworkers’ productivity never abate. In fact, the topic of workers’ productivity only gains new momentum as U.S. work orga-nizations have been increasingly offshoring and outsourcing many production and service functions on the one hand, andrestructuring their internal production mode and employment relations on the other hand (Cappelli et al., 1997). Indeed,the globalization of economy is accelerating, fueled by modern technologies of computerization, communication, and trans-portation, as well as the insatiable corporate appetite for maximum profitability. Given the fact that American workers areamong the most expensive work force worldwide, they have to churn out as the highest level of productivity as possible tokeep American companies competitive (Appelbaum and Batt, 1994). This situation makes the employment of any

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ociology and Criminal Justice, University of Arkansas, 211 Old Main, Fayetteville, AR 72701, USA. Fax:

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productivity improvement program imperative and tempting. However, the effects of these programs as well as how theyare implemented in workplaces are largely understudied.

American employers in recent years have restructured work and organizations in response to not only external compe-titions with their domestic and international peers (Yang, 2008) but also internal pressures brought by an increasingly diver-sified workforce. Mirroring the general demographic shift in U.S. population, American workplaces have witnessedconsiderable increase in the number of women and minority workers (Murrell and James, 2001; Estlund, 2005). Propelledby federal supports and professional organizational push (Kelly, 1999), many organizations adopted various family friendlyprograms (Chawla, 1992; Davis and Kalleberg, 2006). They believe these programs could not only help workers keep the bal-ance between work and life, but also increase workers’ productivity.

One of the family friendly/productivity improvement program is flexible work program (henceforward flextime), which inits most typical form stipulates that as long as employees work during certain core hours and fulfill total working hoursrequirement, they can choose start and end time for the non-core hours at their discretion (Kelly and Kalev, 2006). Rationalesfor offering flextime abound. First, workers receiving flextime allegedly work harder and smarter; flextime may also reduceshirking, absenteeism and turnover (Glass and Estes, 1997). Second, flextime could keep or attract talent employees, whosepersonal or familial circumstances may otherwise prevent them from working on a regular schedule (Hobson et al., 2001).Third, flextime could facilitate healthy work-family balance. Its benefit is particularly pronounced for women workers whoin general shoulder more household or child-care responsibilities (Scandura and Lankau, 1997). However, the overall evi-dences so far on flextime’s return to workers’ productivity are mixed in that some reported positive effect and some foundno association at all (Shepard et al., 1996; Johnson and Provan, 1995; Kim and Campagna, 1981; Narayanan and Nath, 1982).

Extant studies have approached the question, i.e., the impact of flextime on workers’ productivity, either at the organi-zational level (e.g., Shepard et al., 1996) or at the individual level (e.g., Johnson and Provan, 1995). They nevertheless sharean assumption that equates an organization’s adoption of flextime with its actual implementation to or participation by indi-vidual workers. Such presumption is problematic in that organizations may claim the adoption of flextime without actuallyimplementing it; equally plausible, some workers may enjoy flextime informally without a formally sanctioned flextime pro-gram at the organizational level. Insofar as such inconsistencies exist in reality to a large extent, it is important to distinguishorganizational adoption from implementation. Only by examining these two distinct processes in combination, while not sim-ply conflating them as one thing, can we understand the impact of flextime on workers’ productivities.

In doing so, we bring the neo-institutional concept of de-coupling to explicate the effects of organizational adoption and(or lack of) implementation of flextime program on workers’ productivity. Neo-institutional theorists offered a compellingargument that in order to secure legitimacy and outside support, organizations may develop symbolic response to environ-mental pressures without disrupting its ongoing activities (Meyer and Rowan, 1977). DiMaggio and Powell (1983, p. 151)specifically pointed to the ritual aspect of quality-of-work-life programs in American corporations, which ‘‘adopt these ‘inno-vations’ to enhance their legitimacy, to demonstrate they are at least trying to improve working conditions.” For instance, theadoption of flextime at the organizational level could be decoupled from access to the program at worker level. Thereforehow and in which way people inside an organization would respond to the de-coupling remains unanswered by the neo-institutional theory, which mainly concerns the relation between the organizations and its external environment – howorganizations respond to institutional pressures. This paper aims to fill this gap in neo-institutional concept of de-couplingand bring its unexplored insight to examine the consequences of de-coupling within organizations. The case here is to showthe extent to which the de-coupling (or not) of flextime adoption from implementation has consequences on workers’ pro-ductivity actualization.

To the best of our knowledge, this study is perhaps the first attempt to examine the effects of de-coupling occurred at theorganizational level on outcome at organizational participant’s individual level. Our analysis on a unique employer–employ-ee matched national representative dataset reveals that de-coupling the adoption of flextime from implementation is wide-spread in American organizations. Preliminary results suggest that the practice of de-coupling, i.e., organizations onlyceremonially adopt flexible work program without making it available to all workers, could be detrimental to the actualiza-tion of productivity for workers who were bereft of the opportunity. On the other hand, the highest level of productivityactualization is found among workers who enjoy flextime despite the fact their employer actually does not have such pro-gram formally adopted. The rest of the paper proceeds as follows. The next section gives a critical review of neo-institutionaltheory’s single-sided dealing with organizational de-coupling. We will then review the literature on flextime and productiv-ity. Two specific hypotheses informed by our theoretical framework will be formulated accordingly. We will then discussdata, measurement, and findings in order. We will conclude and discuss future directions in the end.

2. Previous theory and research

2.1. Consequences of ‘‘de-coupling”: neo-institutionalism revisited

Neo-institutionalists argue that when facing pressure from institutional environment, organizations adopt certain formalstructure or program to gain legitimacy, to protect the organization from being questioned (e.g., Edelman, 1990, 1992), tostrengthen its support, and to secure its survival (Meyer and Rowan, 1977). Meyer and Rowan (1977, p. 349) argued that‘‘[i]ncorporating externally legitimated formal structures increases the commitment of internal participants and external

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constituents”. In other words, organizations that do not meet institutionalized expectations for how they should look and actare viewed as illegitimate, and such deviations can incur real costs to organizations. For example, diversified public compa-nies that are hard to fit into legitimate product categories tend to be eschewed by securities analysts. As such companies failto attract reviews from relevant critics, their stock prices are discounted, a penalty of being illegitimate (Zuckerman, 1999).

In the meantime, organizations purposely decouple its formal structure from day-to-day work by avoiding integration,ceremonializing inspection and evaluation, or neglecting implementation. De-coupling may manifest in different ways, rang-ing from separating one part of an organization from other part(s) of the organization, to separating a symbolic formal struc-ture from operational practices, to adopting a program that may or may not be implemented such as our inquiry of flextimein question. De-coupling enables organizations to maintain legitimating, formal structures while adjusting their actions inresponse to practical considerations (Meyer and Rowan, 1977). Taking this matter more explicitly, Oliver (1991) developeda framework specifying the conditions under which different responses might be selected by organizations in coping withcomplex and conflicting environments. These strategic responses range from acquiescence, compromise, avoidance, defi-ance, to manipulation. In the case of ‘‘de-coupling” (or ‘‘concealment” using Oliver’s terminology), an organization may‘‘establish elaborate plans and procedures in response to institutional requirements in order to disguise the fact that it doesnot intend to implement them” (Oliver, 1991, p. 154). Admitted that a strategic response openly challenging the taken-for-grantedness in the prevailing cognitive-cultural system may likely incur sanctions from outside constituents, ‘‘de-coupling”might save organizations from that fate because an appearance of compliance is oftentimes sufficient for the attainment oflegitimacy (Edelman, 1992).

Relatively little research has been devoted to examining organizational de-coupling in a systematic way. One notableexception is Westphal and Zajac’s series of work (1994, 1998 and 2001) in investigating the determinants of organizationalde-coupling. In their study of long-term CEO incentive plans, they found that, among corporations which had announced toadopt these plans, many failed to implement them within a subsequent 2-year period (Westphal and Zajac, 1994). The mereannouncement of adopting these plans, no matter whether or not they were actually used, boosted these corporations’ shareprice nevertheless. To account for the variation in corporations’ implementation of these plans, they pointed out becauseimplementation of these plans could negatively affect CEO compensation, firms with more powerful CEOs vis-à-vis the boardof directors were more likely to decouple these incentive plans from actual practice (Westphal and Zajac, 1998). In their sub-sequent study on stock buyback programs, Westphal and Zajac (2001) built up a comprehensive institutional framework byextending their explanatory factors from the political (i.e., CEO power) to social structural (i.e., interlock ties) and experien-tial ones (i.e., prior experiences in de-coupling). These studies have greatly strengthened our understanding about when andunder what conditions organizational de-coupling is more likely to occur. Yet, an equally important question reminds unan-swered. That is, what are the consequences of organizational de-coupling?

Institutional scholars in general consider organizational de-coupling as beneficial or at least benign to organizations be-cause they assume that the appearance rather than the fact of conformity is sufficient for the attainment of legitimacy. Webelieve this assertion is oversimplified or incorrect; it should not be taken as a given but, rather, as an empirical question. Forone thing, despite the fact that it requires extensive effort and investigative skill for outside constituents to distinguish ‘‘sym-bolic adoption” from ‘‘substantive implementation,” academics or market analysts may possibly ‘‘see through” de-couplingefforts of organizations in a long run. Organizations would lose any benefits from engaging organizational de-coupling as aresult. More importantly, it is myopic to pay attention only to the consequences originated from the environment (or outsideconstituents) but fails to take into account what may arise from within the organization. It is especially true when the pro-gram in question bears direct relevance to organizational participants.

Organizations consist of groups of people with different interests. Something to which top managers only want to pay lipservices may be what ordinary employees hope to put into action. ‘‘Window dressing” may be good enough to satisfy out-siders who do not have full knowledge about the discrepancy between ceremonial adoption and thorough implementation.But information asymmetry poses less a problem to those who experience organizational practices on a daily basis. Thinkwhat employees would feel when they find out a much celebrated flextime in their company only exists on paper andthe employer does not have the real intention to provide them access to this program. Such deployment of de-coupling,or hypocrisy if you will, could cause a greater level of dissatisfaction than if the company simply neglects employees’ de-mand for instituting a flextime program. As a result, the de-coupling of the flextime – either outright non-implementationor selective implementation after an organization’s formal adaptation – may backfire among workers who hope to benefitfrom the program. Negative consequences may in turn reflect on these employees’ lower level of commitment and produc-tivity. It is our contention that to gauge the consequences of organizational de-coupling, we should not only pay attention towhat may happen outside an organization, i.e., interactions between the organization and its environments, but also exam-ine what may occur inside organizations, e.g., how organization participants perceive and react to the de-coupling. To expli-cate this proposition, we empirically examine the adoption and implementation of flextime in American organizations andthe consequences on worker’s productivity.

2.2. Flextime and productivity

An important workplace restructuring has been going on in the domain of working time, as American employers strive toaccommodate increasingly diversified workforce, particularly women employees (Epstein and Kalleberg, 2006). Flextime,also dubbed as flexible work hours or flexible scheduling, provides employees with some discretion over the specific hours

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of the day or week when the work is to be performed (Golden, 2001). A study shows that workers in general would preferhaving more control over the time spent at work to shorter working hours that are rigidly scheduled (Fenwick and Tausig,2001). According to Bureau of Labor Statistics, about 27.5% of American workers have been on flextime by 2004, and it ismost widely used in financial services and professional and business services (BLS, 2005).

Proponents of flextime argued that the program can be beneficial to both employers and employees, creating a win–winsituation (McGuire and Liro, 1986). For one thing, since not everybody is most productive from eight to five, flextime allowsworkers to adjust their working schedules to their bio-clock to work the hours they prefer and feel most productive. As indi-vidual workers make more efficient use of their own circadian rhythm, their job performances improve. In line with the per-son-job fit literature, congruence between the individual workers and the job environments leads to greater performance(Caldwell and O’Reilly, 1990). Studies also report that flextime can increase worker productivity through intermediateand indirect effects, such as increase in workers’ job satisfaction and job autonomy (deCarufel and Schaan, 1990), and de-crease in their absenteeism and work-related stress (Baltes et al., 1999). Granted, flextime does not come without costs. Likeany other new programs in workplace, executing flextime incurs costs associated with learning and training (mostly formanagers for their understanding and implementing the new program), extra overhead expenditures, and managerial mon-itoring due to varied and spread working hours of workers.

Empirical studies provide mixed evidences on the effects of flextime on workers’ productivity. Some reported productiv-ity gains for companies with flexible work hours (Schein et al., 1977; Shepard et al., 1996). Others found no correlation be-tween flextime and productivity increase (Kim and Campagna, 1981; Narayanan and Nath, 1982), probably because thatalthough flextime improves performance, administrative cost ensues from the implementation of the program, whichmay offset the benefits. In any case, employees on flextime are, at least, at the same productive level, if not greater thanare those on traditional fixed-work schedules (Hammer and Barbara, 1997; Kossek et al., 1999).

2.3. Program adoption and individual access: the interactive impact

Institutional theorists propagate that modern organizations adopt various programs to signal compliance with their insti-tutional environment, which draws them much needed resources, such as legitimacy, reputation, and talented individuals(DiMaggio and Powell, 1983). However, as organizations become outward looking in their design of internal control and coor-dination, their formal policy and informal practice can be decoupled from each other (Meyer and Rowan, 1977). For example,educational institutions formally adopt standards and procedures in response to government mandates and community de-mands but decouple them from the on-going routines of teaching and administration (Meyer and Rowan, 1977); corporationsformally adopt long-term incentive plan or stock repurchase program when facing pressures from shareholders but may ormay not actually implement them, so that the plans remain more symbolic than substantive (Westphal and Zajac, 1994, 2001).

De-coupling is more likely to happen when there are high symbolic gains from adoption but equally high costs associatedwith implementation (Scott, 2008, p. 172). Moreover, when implementing a formally adopted policy threatens the discretionof organizational actors, they may favor a symbolic response that involves separating substantive activities of the organiza-tion from the formally adopted policy in order to preserve their discretion (Oliver, 1991; Westphal and Zajac, 2001). Thesefactors also contribute to the de-coupling of flextime. Firstly, flextime may be a free lunch for employees, but not for theorganization. To illustrate, under a flextime program, some workers may prefer to start at 6 am and work until 2 pm, whereasothers may start at noon and finish at 8 pm. The longer spread of working time adds costs to workplace maintenance andother supporting services, not to mention managerial efforts required to coordinate workers’ schedules. Secondly, higher le-vel managers may be resistant to implementing flextime. Previous studies have shown that the flextime benefits more tolower level employees than higher levels employees, who may already have greater flexibility with respect to arrival andleaving times (Narayanan and Nath, 1982). Another research documented that managers often complain that flextime in-volves disruption in the normal coordination of work and therefore makes their jobs more difficult (Nollen and Martin,1978). In short, both added costs of implementing flextime to the organization and lack of incentive for mangers to do somake the de-coupling of flextime a workplace reality. This study is to examine the extent to which such de-coupling existsin workplace and, more importantly, how the de-coupling affects actualization of workers’ productive potentials.

A cross-tabulation between employer’s programmatic adoption of and workers participation in the flextime producesfour possible scenarios as shown in Table 1. Modules 1 and 4 in Table 1 represent the situations of consistency – Module1 refers to the situation where both organizational adoption and worker participation take place, whereas Module 4indicates the opposite scenario where neither adoption nor participation occurs. Modules 2 and 3 represent two types of

Table 1A diagrammatic depiction of interaction between organizational programmatic adoption of and workers participation in flexible work program.

Workers participation Organizational programmatic adoption

Adopted Not adopted

Participated Module 1 Module 2Consistency-adopted and participating Inconsistency-not adopted but participating

Not participated Module 3 Module 4Inconsistency-adopted but not participating Consistency-not adopted and not participating

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inconsistency (or de-coupling). Module 2 denotes the situation where organizations do not adopt flextime, but workers indi-cate their usage of flextime nevertheless. Module 3 reveals yet another type of de-coupling – organizations indicating adop-tion of flextime but workers reporting no participation.

These two modules of de-coupling (i.e., Module 2 and Module 3) are of particular interest in our study. Module 3 suggestsorganizational non-implementation or workers’ unequal access to flextime. In either case, workers’ lack of access to flextime,despite the fact that their employer has allegedly adopted it, reflects much of organizational internal labor control and coor-dination, which are often stratified and unequal. For example, one study revealed that organizations adopted flextime toinstitutionalize managerial discretion rather than creating outright prerogative for employees (Kelly and Kalev, 2006). Evenwhen organizations write a formal policy of flextime, employers pick and choose valuable employees when it comes todetermining who can actually have the flextime.

Therefore, organizational adoption of certain programs and individual access to the programs reflect two distinctiveunderlying processes: the former reflecting environmental pressures and the latter corresponding with organizationalinternal control and allocation. Such disjuncture between programmatic adoption and individual access creates a much com-plicated structure. To the extent that flextime facilitate workers’ actualization of their productivity, the disjuncture – orga-nizations adopt those programs, in which individual workers cannot participate, may have detrimental impact on workers’productivity. Insofar as workers cannot participate in flextime that presumably is facilitative to productivity, their exclusionmay make salient the notion that workplace is a stratified structure that perpetuates inequality. Workers may feel left-out,left-behind, mistreated, or victimized by managerial hegemony. We therefore expect that the disjuncture between organi-zational adoption of the programs and workers’ access to them – workers cannot participate in those programs despite thepresence of those programs in the workplaces – would dampen workers’ realization of productivity potentials. We hypoth-esize that:

Hypothesis I: Workers who can participate in flextime that has been adopted by their employers (Module 1 in Table 1)are more likely to actualize their productivity potentials than are workers who cannot participate in flextime that hasbeen adopted by their employers (Module 3 in Table 1).

The Module 2 in Table 1 reveals another scenario of de-coupling in workplace that workers can work on a flexible sche-dule despite that there is no formal employer-sponsored flextime. A study on flextime by the Bureau of Labor Statistics(2005) suggests its widespread existence, which may reflect a couple of underlying processes. First, it could be a voluntaryrebellion from the workers. When a formal policy of organization goes against the interests of internal participants, opposinginformal norms and practice will emerge to ‘‘bend the bars of the iron case” of the formal rules if organizational sanctions areweak (Nee and Ingram, 1998, p. 36). Meanwhile, employers concerning about the costs and learning curve associated withformally implementing the flextime may yield their stands to allow flexibility in work scheduling without writing it intotheir formal HR policy. Second, it could also be reflecting ‘‘occupational prerogative” (Leicht and Wallace, 1988), in whichthe nature of certain jobs bestows the job incumbents with flexible work, regardless of the employer’ adoption of such pro-grams. One notable example would be university faculty, who rarely hold eight to five work schedules, regardless of whethertheir schools have flextime. Comparing this privileged group who enjoy the benefits of flextime as an informal practice withthe disadvantaged group who could not access to flextime even when their employer has formally adopted it, we expect theformer would be more likely to realize their productivity potential. Here, we hypothesize that:

Hypothesis II: Workers who can participate in flextime that has not been adopted by their employers (Module 2 in Table1) are more likely to actualize their productivity potentials than are workers who cannot participate in flextime that hasbeen adopted by their employers (Module 3 in Table 1).

3. Date and measurement

The sample we use to test above hypotheses is a combined dataset of two national representative datasets: the 2002 Gen-eral Social Survey (GSS) (Davis et al., 2002) and the 2002 National Organization Survey (NOS) (Smith et al., 2002). Thus, theunit of analysis is worker–organization as each case has both workers’ attributes and their workplace characteristics. Thiscombined worker–employer dataset provides a unique research opportunity to take account of both workers’ individualtraits and their workplace characteristics.

Because the respective codebooks of the 2002 GSS and the 2002 NOS provide further detail on research design, data col-lection, and questionnaire items, we offer here a brief description of our combined dataset. The GSS is an ‘‘omnibus” personalinterview survey of U.S. households that is conducted annually by the National Opinion Research Center (NORC). The 2002GSS contains core questionnaire items and modules on prejudice, doctors and patients, quality of working life, employeecompensation, altruism, adult transition, and mental health. The 2002 NOS draws it sample from the informants of the2002 GSS. During the 2002 GSS interviews, the NORC asked half of all household respondents to provide contact informationabout their employers. Of the total 888 GSS respondents who were asked to provide their employer contact information, 14were duplicates in that more than one GSS respondent worked at the sample physical location. Thus, the final sample of the2002 NOS consisted of 874 unique physical locations. The NORC then conducted telephone interviews to collect completeinformation on 516 of these 874 locations. The unadjusted response rate is 59% (516/874 = .59). The response rate is 62%

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(516/827 = .62) if adjustments are made for cases that were not located, found to no longer exist, or found to be a duplicate ofanother physical location.

By combining the GSS 2002 and the NOS 2002, we obtain a dataset of 516 cases encompassing both employer-level vari-ables such as organizational adoption of flextime, organization size, age, and other structural properties and employee-levelvariables such as race, sex, age, and in particular their participation in flextime. The total number of cases included in ourdata analyses is 415 after listwise deletion of cases with one or more missing values. A comparison between the originalsample of 516 cases and the resulted sample of 415 cases does not reveal any systematic difference between them; thereforethe exclusion of missing data does not cause any serious sample selection bias. Also note that to maintain comparability, allour data analyses are based on the same subsample of 415 cases.

The dependent variable is the workers’ realization of their productivity potentials, which comes from the 2002 GSS. Thesurvey asked respondents to indicate their level of agreement to the statement that ‘‘conditions on my job allow me to beabout as productive as I could be.” Although responses are grouped as ‘‘strongly agree, agree, disagree, and strongly dis-agree”, we dichotomize the responses into agreeing (coded as 1 and including strongly agree and agree) and disagreeing(coded as 0 and including disagree and strongly disagree). Among the total 415 complete responses, those agreeing orstrongly agreeing to the statement accounts for 85% of the sample (352 cases), while the remaining 15%, i.e., 63 respondents,disagree or strongly disagree. We note that our dependent variable is a subjective measure rather than an objective or factualmeasure of productivity. Although someone may prefer objective measures of productivity to subjective ones, we believethat self-assessment of productivity actualization is as equally important as objective measures. Who else, after all, is in abetter position than one self to know about her or his productivity potential?

The main independent variables are derived from two dummy variables, one indicating whether respondent’s employerhas adopted flextime program, and the other indicating respondent’s participation in the program. The 2002 NOS measuredorganizational adoption of flextime with the question ‘‘Does your establishment offer flexible hours or flextime scheduling?”Responses are dichotomous as 1 is for ‘‘yes” and 0 for ‘‘no”. Individual worker’s participation in flextime is captured with anitem in the 2002 GSS, ‘‘How often are you allowed to change your starting and quitting times on a daily basis?” The responsesare ‘‘often, sometimes, rarely, and never.” We dichotomize the responses into allowed/participating (1 = ‘‘often” or ‘‘some-times”), and not allowed/not participating (0 = ‘‘rarely” or ‘‘never”).2

To examine the potential impacts of the ‘‘de-coupling” between ‘‘organizational adoption” and ‘‘worker participation,” itis essential to include the interaction term of these two variables in regression analysis. A conventional model thereforewould include the first-order terms of ‘‘organizational adoption” and ‘‘worker participation” and the interaction term. Tomake the analysis less technical and easier to understand, we substitute the two variables and their interaction term withfour dummy variables, which correspond to the four modules in Table 1, i.e., ‘‘Module 1: adopted and participating,” ‘‘Module2: not adopted but participating,” ‘‘Module 3: adopted but not participating,” and ‘‘Module 4: not adopted and not partici-pating”. This alternative specification is mathematically equivalent to the conventional one, but enables us to compare theproductivity outcomes directly between Module 1 and Module 3 as our first hypothesis is set out to test and that betweenModule 2 and Module 3 in the second hypothesis. Module 3 is defined as the reference category and not included in theregression model.

Control variables include both individual and organizational level variables, which may affect productivity actualizationin workplace. At the individual level, we control for respondents’ gender, race, age, education, occupation, and work tenure.Gender and racial inequalities in labor processes are important subjects in social scientific research (e.g., Becker, 1957; Wil-son, 1978; Cole and Zuckerman, 1984). Research in this line suggests that women and blacks are often subject to statisticaldiscrimination when perceptions are based on different average productivity between groups. Such biases persist despiteresearch that shows that women on average allocate more effort to work than men (Bielby and Bielby, 1988), and as faras race is concerned, productivity is higher in industries in which high proportions of workers are blacks (Tomaskovic-Devey,1988). Given the significance of gender and race in workplace, we include them as control variables in the model althoughwe expect neither of them by itself has effect on productivity if other factors, such as education and occupation, are takeninto account adequately. Respondent’s age and age-squared are included to capture the potential curvilinear effect of age onproductivity (e.g., Shirbekk, 2004). The Human Capital Economists have long been arguing for the educational effects onworkers’ productivity (Schultz, 1961; Becker, 1993; Mincer, 1994). The raging debates between Human Capital Theoryand other theories about the true effect of education on productivity – mainly the credential theory, only demonstratethe importance of considering education when examining workers’ productivity (Chevalier et al., 2004). We thus includecontrols for individual educational achievement. Work tenure, measured by the number of years working for current em-ployer, is also included to account for its effect on productivity. A longer tenure, reflecting accumulation of firm-specificon-the-job training, may lead to higher productivity (e.g., Topel, 1991). To the extent that workers’ productivity may be alsoaffected by organizational contexts (Porter and Steers, 1973), we control for organizational size, age, a measure fororganizational formalization, and a measure for organizational hierarchy. Previous research has shown organizational sizeand hierarchy are negatively associated productivity, formalization has a positive effect on productivity, and findings on

2 Throughout our paper, we use different terms interchangeably to describe individual employee’s participation in flexible work programs, which include‘‘worker’s participation,” ‘‘flexible work implementation,” ‘‘employee access,” ‘‘workers’ usage,” ‘‘make use of,” ‘‘employees have,” ‘‘take advantage of,” and‘‘benefit from.” Here, we clarify that, in light of the verbatim of the survey question, all those terms denote the same meaning, that is, employees are allowed touse the flexible work scheduling.

Table 2Cross-tabulation of organizational programmatic adoption of and workers participation in flexible work program (N = 415).

Workers participation Organizational programmatic adoption

Adopted Not adopted

Participated Module 1 Module 2Consistency-adopted and participating Inconsistency-not adopted but participatingN = 168 N = 59(62.9%) (39.9%)

Not participated Module 3 Module 4Inconsistency-adopted but not participating Consistency-not adopted and not participatingN = 99 N = 89(37.1%) (60.1%)

Note: Numbers in parentheses are column percentages, i.e., frequency divided by its column total.Source: GSS 2002 and NOS 2002.

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the effect of organizational age are at best mixed (e.g., Glisson and Martin, 1980; Haltivanger et al., 1999). The Appendix pro-vides detailed descriptions of those variables.

3.1. Findings

The data analysis is organized into three parts. First, we examine the extent to which ‘‘organizational adoption” of and‘‘worker participation” in flextime is consistent (Module 1 and 4) or inconsistent (Module 2 and 3) in our sample of 415 orga-nizations. We then report results from a bivariate analysis of the impacts of flextime adoption/implementation on worker’sself-reported productivity actualization. The third part reports results from multivariate regression analysis of the question.

To indicate the consistency/inconsistency between organizational adoption of and worker’s participation in flextime, wecreate Table 2 by cross-tabulating organizational adoption and worker’s participation using our dataset. We calculate theratio of inconsistency/consistency by dividing the pairs of inconsistency, computed by multiplying numbers in the inconsis-tency cells (i.e., Modules 2 and 3), by the pairs of consistency, computed by multiplying the numbers in the consistency cells(Modules 1 and 4). Thus, complete consistency should be zero, i.e., the closer the result is to zero, the greater level of theconsistency. Table 2 shows that the ratio for flextime is :39 59�99

168�89 ¼ :39� �

), suggesting a considerable level of inconsistency(or de-coupling) between organizational adoption of flextime and workers actual access to it. In particular, the cell for Mod-ule 3 in Table 2 reveals that among workers whose respective employer has adopted a formal flextime program, 37% of theseworkers could not participate. The cell for Module 2 in Table 2 indicates that among workers whose respective employerdoes not adopt a formal flextime program, almost 40% of those workers actually work on a flexible schedule. Given the wide-spread existence of organizational de-coupling of flextime, it is very important to take it into account when we study theimpacts of flextime on workers’ productivities.

To assess how the interactions of organizational adoption of and work participation in flextime affect actualization ofworkers’ productivity potential, we conduct a bivariate analysis calculating the means of the dependent variable – ‘‘condi-tions in my job allow me to be as productive as I could (yes or no)” for each of the four modules. Table 3 shows that thepercentages of workers agreeing to the statement, ranked from the highest to the lowest, are Module 2 (i.e., no-adoption/participation: 93.2%), Module 1 (i.e., adoption/participation: 85.1%), Module 4 (i.e., no-adoption/no participation: 83.1%),and Module 3 (i.e., adoption/non-participation: 80.8%). The results provide preliminary support to the Hypothesis I thatworkers who participate in flextime that has adopted by their employers (85.1% for Module 1) are more likely to actualizetheir productivity potential than are workers who cannot participate in flextime that has adopted by their employers (80.8%for Module 3). The evidence for Hypothesis II is even stronger in that workers who participate in flextime that was notadopted by their employers (93.2% for Module 2) are more likely to actualize their productivity potential than are workerswho cannot participate in flextime that was adopted by their employers (80.8% for Module 3). Although bivariate statisticalanalysis does not rule out the issue of spuriousness for not controlling for possible confounding variables, it does reveal thattaking into account of the juncture of organizational adoption and worker participation in flextime explains much variationin worker’s actualization of their productivity potential.

Table 3Cross-tabulation of the four adoption-participation modules and actualization of productive potentials (N = 415).

Flexible work program adoption and worker participation

Module 1: adopted andparticipating

Module 2: not adoptedbut participating

Module 3: adopted butnot participating

Module 4: not adoptedand not participating

Yes N = 143 N = 55 N = 80 N = 74(85.1%) (93.2%) (80.8%) (83.1%)

No N = 25 N = 4 N = 19 N = 15(14.9%) (6.8%) (19.2%) (16.9%)

Note: Numbers in parentheses are column percentages – frequency divided by its column total.Source: GSS 2002 and NOS 2002.

Table 4Coefficients from logistic regression of actualization of productivity potentials.

Model 1 Model 2 Model 3b

Organizational adoption �.362 (.317) – –Worker participation – .436 (.318) –Combinations of organizational adoption and worker participationa

Adopted and participating (Module 1) – – .409 (.383)Not adopted but participating (Module 2) – – 1.214* (.609)Not adopted and not participating (Module 4) – – .303 (.410)

Racea

White .626 (.391) .652 (.388) .593 (.391)Others .300 (.683) .364 (.685) .351 (.687)

Male �.472 (.318) �.502 (.317) �.479 (.320)Age �.181* (.090) �.176* (.089) �.183* (.090)Age-squared .002* (.001) .002* (.001) .003* (.001)

Occupationa

Managerial .845 (.500) .671 (.499) .733 (.503)Clericals .022 (.432) �.048 (.436) �.032 (.437)Manual/labor .587 (.409) .633 (.414) .656 (.415)

Educationa

Graduate .360 (.673) .203 (.672) .307 (.682)BA .945 (.568) .729 (.563) .832 (.580)Some college .992 (.648) .755 (.651) .845 (.661)High school 1.001* (.473) .951* (.476) .996* (.481)

Work tenure .006 (.022) .008 (.022) .005 (.022)Organization age/10 �.284 (.186) �.261 (.184) �.293 (.187)Organization age/10-squared .011 (.009) .010 (.008) .011 (.009)Organization size (log) �.054 (.084) �.055 (.084) �.055 (.085)Formalization .034 (.080) .053 (.080) .049 (.383)Hierarchy .028 (.033) .033 (.034) .033 (.033)Model Chi-square (v2) (df) 32.20* (19) 32.75* (19) 35.51* (21)N 415 415 415

Note: Source: GSS 2002 and NOS 2002.Numbers in parentheses are standard errors.

a Reference category is ‘‘adopted but not participating,” ‘‘black,” ‘‘part-time,” and ‘‘less than high school”, respectively.b The specification of Model 3 is the equivalent to including the first-order term of the variables ‘‘adoption” and ‘‘implementation” as well as their

interaction. But the current specification which compares the four modules directly is intuitively easier for interpretation.* p < .05 (two-tailed test).

306 S. Yang, L. Zheng / Social Science Research 40 (2011) 299–311

For a more rigorous analysis of the relation between flextime adoption/implementation and actualization of productivitypotential, we employ a series of logistic regressions, all of which controlled for individual and organizational variables. Notethat although Hierarchical Linear Model (HLM) seems to be a better choice to analyze this two-level (i.e., individual and orga-nization) dataset (Raudenbush and Bryk, 2002), our dataset with one on one matched structure (one employee per organi-zation) prevents us from applying this method. Model 1 and Model 2 in Table 4 show that neither organizational adoption offlextime nor individual worker’s participation in the program affects workers’ actualization of productivity as long as onlyone of them is taken into account. This suggests the merit of considering the interactions between the two factors. In termsof control variables, both models show the curvilinear effect of age on actualization of productivity – initially age decreasesthe productivity actualization, and then at certain cut-off point, age turns around to increase actualization of productivity.3

Given the average respondent in the sample is about 42 years old, the turning point at the age of 45 suggests the boost in work-ers’ productivity may come from the relief from caring young kids. In addition, educational effect on productivity actualizationappears to be non-linear: only one of the educational categories, high school degree, has a significant and positive effect on thedependent variable – high school graduates are more likely to report actualization of their productivity potential than thosewith lower educational achievement. The directions of other higher education levels, i.e., some college, BA, and graduate degree,point to the expected positive direction but fail to reach the significance level. This seems to echo previous research that sug-gests workers’ education in excess of what their jobs require do not have positive effects on their productivity (Tsang et al.,1991; Kelleberg, 2007).

Model 3 in Table 4 intends to examine how the interaction of organizational adoption of flextime and individual partic-ipation affects workers’ actualization of their productive potentials. In light of the findings from Table 3, which shows theworst case scenario being Module 3 – organizations adopting flextime but workers cannot participate, the regression leavesit out as the reference category, to be compared with other three modular scenarios, e.g., organizations adopting and workersparticipating (Module 1 in Table 3), organizations not adopting but workers can participate (Module 2 in Table 3), and orga-nization not adopting and workers cannot participate (Module 4 in Table 3).

3 The exact cut-off point varies, depending on the magnitude of the coefficients. For example, model 1 shows Y0 = (�.181) age + (.002) age2. Taking derivativeof both sides: (�.181) + .004 (age) = 0, thus age = 45.25. Whereas applying the same procedure to Model 2 and Model 3 yields age = 44, and age = 30.5,respectively.

S. Yang, L. Zheng / Social Science Research 40 (2011) 299–311 307

The regression results from Model 3 (see the third results column in Table 4) fail to provide further support to ourHypothesis I that workers who can participate in flextime that has been adopted by their employers (Module 1) are morelikely to actualize their productivity potentials than are workers who cannot participate in flextime that has adopted by theiremployers (Module 3). Although the coefficient points to the expected direction, it does not reach the significance level. TheHypothesis II, in contrast, receives additional support. Model 3 in Table 4 shows that employees who have flexible workscheduling despite none-adoption of flextime by their employers (Module 2) are more than twice (exp (1.214) � 1 = 2.37)as much to report that they can actualize their productive potentials than are those who cannot have flextime despite adop-tion of the program by their employers (Module 3). The analysis divulges a delicate picture: the inconsistency between pro-gram adoption and worker participation in flextime bi-polarizes workers into high and low camps in their actualization ofproductivity. On the high end, employees achieve superior productivity actualization by either rebelling to have flextimeinformally or receive such flexibility as de facto job prerogative, despite the absence of flextime in their organizational code-book. On the low end, employees respond with dampened productivity over their inabilities to have flextime when theiremployers claim to have adopted the program.

Moreover, the fact that Hypothesis I does not receive supportive evidence whereas Hypothesis II does may imply thatusing flextime as an informal reward to employees could be more effective in boosting workers’ productivity than whenthe employer actually adopts it as a formal policy for the organization. As our analysis shows, contrary to what we expectin Hypothesis I, there is not significant difference in productivity actualization between those who can take advantage offlextime and those who cannot in organizations where flextime is formally adopted (i.e., Module 1 vs. Module 3). One plau-sible explanation for this finding is that once the program is formally adopted by the organization, those who benefit from itmay take it for granted and therefore this does not translate to higher productivity. As one practitioner commented, ‘‘contin-uing to add one work/life initiative after another could be problem down the line if workers start to see all the extra help andflexibility as simply another entitlement” (Laabs, 1998). In contrast, a greater likelihood of productivity actualization is foundamong workers who enjoy flextime informally while it is not adopted by their employers (as in Module 2), compared withthose in organizations which have formally adopted the program regardless whether these workers can participate the pro-gram or not (as in Module 1 and Module 3). This suggests that when flextime is awarded informally to workers as a privilegeas opposed to an entitlement, it is more likely to achieve its intended outcome: higher productivity.

As for control variables, respondent’s sex, race, occupation, and work tenure do not have any significant effects on pro-ductivity actualization. Nor do the organizational characteristics such as age, size, number of hierarchical levels, and de-gree of formalization. Consistent with other studies, respondent’s age exerts a curvilinear effect on actualization ofproductivity (Shirbekk, 2004). The finding that respondents with high school education are more likely to report produc-tivity actualization than are workers with less than high school education holds in the full model (Model 3 in Table 4) asin previous ones. The non-significant effects of the educational levels above high school, i.e., some college, BA, and grad-uate, may suggest that surplus education beyond what the job requires does not have further effects on productivity(Tsang et al., 1991; Kelleberg, 2007).

We shall caution that our data does not allow us to control for other more detailed variations in worker’s job conditionsthat may mitigate our findings. Admittedly, some jobs are much more adaptable to flexible work scheduling than are others,engendering great benefits to their incumbents when flexible schedule is provided. Other jobs may not be attuned to theflexible work schedule, which can lead to managerial decision not to provide those job holders with flexible schedules. Inother words, the differential between those who have the flexible work without the official adoption of the policy by theiremployers, and those who do not have the flexible work despite adoption of the policy by their employers may reflect theirjob condition differences. Managers make discretionary decision on flexible work schedule necessitated by job conditions,irrespective of organizational level adoption of the program. The ensuring gap in productivity between the two groups inour finding may reflect the efficient allocation of flexible work schedule by the managers. Those jobs that are adaptableto the flexible work scheduling do receive it, producing an increase in their job performance. Unfortunately, the data cur-rently available to us does not enable us to test or rule out this competing hypothesis.

4. Conclusions

Scholars of work and organization studies have stressed changes in work and strategic transformation in current work-places in response to the global competition (Cappelli et al., 1997; Kalleberg et al., 2006; Appelbaum and Batt, 1994). Domes-tically, American workforce has become much more diversified – more women and racial/ethnic minority gain employmentin various workplaces (Leicht and Fennell, 2007). Such new workforce demography engenders a new wave of scholarly scru-tiny on the change of employment practices including those posed as family friendly (Davis and Kalleberg, 2006) and thosepromoting racial equalities (Kalev et al., 2006). However, while policies to support work and family integration are formallyadopted at the organizational level, variations in how they are implemented across different individuals, work units, andlocations exist. One study on flexible work arrangement documents that the formalized policies institutionalize managerialdiscretion rather than creating outright rights for employees, that managers pick and choose valuable workers to offer thoseprograms (Kelly and Kalev, 2006). Another study discusses four implementation attributes (the level of supervisorial sup-port, the universality of the policies, the negotiability of the policies, and the quality of communication) that contributeto the variability (Ryan and Kossek, 2008).

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This paper focuses on one important work restructuring practice, flextime, which is designed to increase work perfor-mance through harmonization of work and other personal (mostly familial) duties and its impact on worker’s actualizationof productivity potential. This paper firstly challenges the premise of extant empirical research that ignored the disjuncturebetween organizational adoption of and worker’s participation in the flextime program. Our analysis of an employer–em-ployee multilevel dataset from the GSS and NOS in 2002 reveals the extent to which the de-coupling of flextime within orga-nizations exists in the real world (recall that the ratio of inconsistency/consistency is about 40% in Table 2). Given this, wemaintain that any rigorous study which aims to examine the impact of flextime on workers’ productivity has to take intoconsideration of the organizational de-coupling of flextime. Our analysis has shown the interactive effects between the orga-nizational adoption of and individual participation in flextime on workers’ productivity. Compared with workers who cannotparticipate in flextime, which was adopted by their employers, workers who participate in flextime, especially when theirorganizations did not adopt such program, are more likely to actualize their productivity potential. In contrast, workerswho cannot use flexible work schedule that was already in place in their organizations are the least likely to report actual-ization of their productivity potential. This suggests that employer’s mere adoption of such program without providing equalaccess to workers is detrimental to the productivity of those who are left-out.

On the theoretical front, our study expands the application of neo-institutional concept of de-coupling. This paper usesthe disjuncture between organizational adoption of the flextime and worker’s participation in the program to embody theexistence and extent of organizational de-coupling. Our analyses of such disjuncture and its effects on worker’s actualizationof productivity demonstrate the consequences of de-coupling on organizational work. Neo-institutional sociologists havelargely been silent on the consequences of de-coupling or ceremonial adoption of human resource programs on work,and for a good reason – the theory is not developed out of concerns of organizational structuration and its effect on in-tra-organizational issues. Rather, the theory majorly concerns the relation between organizations and their operating envi-ronments – most research by neo-institutional scholars adopts the outward looking perspective while standing on theorganization level. To further understand the consequences of organizational de-coupling, we take an inward looking per-spective and bring the work back as the research focus, investigating how ceremonial adoption and de-coupling in flextimeaffects worker’s actualization of their productivity potential.

By bringing the concept of de-coupling from neo-intuitional theory to the domain of work, our study points to a greatpotential in applying this approach to other workplace programs. Future research could examine to what extent the findingreported here – the disjuncture between flextime adoption and workers’ nonparticipation and its impact on workers’ pro-ductivity – could inform us about other work programs. For example, despite abundant studies of many family friendly pro-grams such as parental leaves, dependent care benefits (see Davis and Kalleberg, 2006), researchers frequently compareoutcomes between organizations with the programs and those without, or between workers who participate in the programsand who do not. Our study establishes the importance of their interaction between organizational adoption and individualparticipation in affecting various outcomes, which should stimulate future studies on similar work programs.

Some limitations of current study also call for further inquiries in following directions. First, to the extent organizationaladoption of certain programs can signal its compliance with external pressures, thus bringing in legitimacy, resources, andtalented workers, this study suggests that the efficacy of those programs can be impaired if their adoption remains largelyceremonial and implementations stratified. As such situation of adoption without complete implementation appears to beharmful to workers’ productivity, we underscore that our data do not permit a further investigation into an important dis-tinction between two likely scenarios. In particular, when our respondent report that s/he cannot have flextime that wasadopted by their employers (totally 99 cases or 23.86% in our sample – see Module 3 in Table 2), we cannot discern between,on the one hand, none of the employees, including our respondent, has the flexible schedules, and, on the other hand, someemployees have the flexible work scheduling, while others, including our respondent, do not. The former scenario indicatesadoption without implementation – employers adopt flextime as a complete ceremonial gesture, outrightly rejectingemployees’ right to using such program. Adoption without implementation may lead to workers’ collective resentmenttoward the employer, feeling as a group they are rejected of the flexible work benefit. The latter scenario is adoption withdifferential implementation – employers adopt the flextime program, but only provide to a group of selective employees.Adoption with differential implementation may lead to workers’ personalizing their inabilities to have flexible work or feel-ing of relative deprivation (Walker and Smith, 2002). Such distinction is important as they may have different consequenceson the workers’ productivity – while both scenarios cause employee resentment, the latter one can be more severe as it oftenleads to agonizing soul-searching as to why not only ‘‘I am left-behind,” but also ‘‘I am singled-out and left-behind.”

Second, we acknowledge that the cross-sectional dataset we used in this study prevents us from pinpointing the exactcause of the productivity gap between the high group (those who have flexible work schedule while their employers donot adopt the program officially) and the low group (those who cannot have the flexible schedule despite the program-matic adoption by their employers). We cannot determine whether the gap comes from the decline in productivity of thelow group, from the increase in the high group, or both processes occurring simultaneously. Similarly, current analysisdoes not allow us to rule out the possibility that differences in workers’ productivity may not be the consequence ofbut rather a cause as some employers selectively grant flextime to strong performers (Kelly and Kalev, 2006). Then thequestion becomes whether granting flextime to strong performance may solicit further improvement in productivity.Although such questions are beyond the scope of current paper, future studies with longitudinal data or employing anexperimental design could enable researchers to precisely measure within-subject changes in productivity level inducedby changes in working schedule.

S. Yang, L. Zheng / Social Science Research 40 (2011) 299–311 309

Third, further research is required in delineating the processes through which organizational de-coupling is unfolded, i.e.,how informal practices emerge and operate (e.g., Goudner, 1954; Dalton, 1950; Burawoy, 1979). For example, our studyfound that employees are able to benefit from flextime, despite non-existence of such program in their organizational HRprograms. We speculate that employee rebellion or occupational prerogative may account for their actual usage of the pro-grams without formal adoption by their employers. However, the two underlying processes may unfold via different paths.Employee rebellion may originate from a few disgruntled employees who consider flextime an essential part of their em-ployee benefit packages. Those who end up having flexible work benefits are victors of the power struggle and negotiationwith their employers, who yield their stand to award those negotiators with flextime. In contrast, occupational prerogativedoes not go through intra-organizational power struggles or labor negotiations. Rather, it cuts across organizationalboundaries, awarding those in certain positions with flextime. It is not a product of power negotiation, but rather a productof occupational privileges. Further ethnographic or fieldwork study should move us beyond above speculations of aboveintra-organizational dynamics. Insofar as such inconsistency – workers are de facto beneficiaries of flextime that is not for-mally adopted as an official HR policy, is associated with greater productivity for workers, more work needs to be done toreveal exactly how such inconsistencies emerge and what they actually entail.

Acknowledgments

The authors appreciate assistance from Tom Smith and Peter Marsden, who provide the datasets for this study. We appre-ciate comments from Hiroshi Ono and Harland Prechel on early drafts of the paper. We are grateful for the cogent commentsfrom anonymous Social Science Research reviewers and the editor.

AppendixConstruction items of independent variables.

Variable Description Descriptivestatistics

Source

Respondent age Respondent’s age Mean = 41.75SD = 12.98

GSS2002

Respondent sex Respondent’s sex Male = 185Female = 230

GSS2002

Respondent race To which racial group the respondent belongs White = 333Black = 61Others = 21

GSS2002

Respondent education Respondent’s education Less than highschool = 37Highschool = 216Juniorcollege = 40Bachelor = 84Graduate = 38

GSS2002

Respondent tenure The number of years respondent worksfor the organization

Mean = 6.90SD = 8.55

GSS2002

Respondent Occupation Respondent’s occupation Managerial = 72Professional/technical = 103Clerical = 85Manual/labor = 155

GSS2002

Organization size The number of full time employees (log) Mean = 3.09SD = 2.14

NOS2002

Organization age Year 2002 minus the year in which theorganization was founded

Mean = 34.16SD = 37.17

NOS2002

Hierarchy Number of levels from the lowest to the highest hierarchicalladder

Mean = 5.33SD = 11.25

NOS2002

Formalization Documentations for job description, job performance, safety,violence, weapons, and dispute resolution. Existence of eachdocument is coded as 1, and results summed up

Mean = 4.22SD = 2.49

NOS2002

310 S. Yang, L. Zheng / Social Science Research 40 (2011) 299–311

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