adoption of workplace substance abuse prevention programs

娀 Academy of Management Journal 2005, Vol. 48, No. 6, 1125–1142.

ADOPTION OF WORKPLACE SUBSTANCE ABUSE PREVENTION PROGRAMS: STRATEGIC CHOICE AND INSTITUTIONAL PERSPECTIVES CHESTER S. SPELL Rutgers University TERRY C. BLUM Georgia Institute of Technology We considered the role of strategic choice and institutional factors in influencing how managers have addressed workplace substance abuse. Substance abuse prevention program adoption was examined in a sample of 360 establishments. Normative pressure, reflected by changes in media discourse about substance abuse, was related to adoption of Employee Assistance Programs (EAPs) and drug-testing programs. Institutional factors were relevant for adopters of EAPs, but strategic choice factors were more significant for drug testing in the earliest of the three periods of adoption examined here.

testing current employees), or terminate them, depending on the policy of the firm. These approaches coexist in many organizations today. Although alcohol rehabilitation programs have existed for decades, very few organizations had such programs before 1980, and drug-testing programs were virtually unknown until that time. But by the 1990s, most moderate to large organizations had such programs. Can organizational theory explain how such rapid change occurred? The purpose of this article is to explain the changes in the prevalence of workplace programs addressing substance abuse. We consider the role of organizational characteristics related to strategic choice and institutional factors, including media discourse, with respect to adoption of substance abuse programs. The analysis also focuses on the relative importance of these factors over the time studied. We make predictions about when institutional versus strategic choice variables will be most relevant to adoption, and we seek understanding of the timing of any period-specific effects.

Substance abuse by employees remains a big issue for organizations. The financial costs to organizations were purported to be over 200 billion dollars annually in the mid 1990s (Hersch, Cook, Deitz, & Trudeau, 2000). French, Roebuck, and Alexandre (2001) noted both that illicit drug use has diminished in the U.S. population and that the majority of illicit drug users are employed. Managers have, not surprisingly, searched for ways to address this issue, and politicians, officials in government agencies, consultants, and other stakeholders have encouraged managers to adopt techniques related to substance abuse in the workplace. The major ways organizations have responded is (1) by detecting the presence of illegal drugs through drug testing and (2) by having Employee Assistance Programs (EAPs), which are formal programs that assist employees with personal problems that may be affecting their work-related behaviors, EAPs reflect a rehabilitation approach, which considers substance abuse a treatable medical condition. Drug testing focuses on detecting users of illegal substances. Management may prevent entry into an organization (by applicant drug screens), refer employees to rehabilitation (through

THEORY AND HYPOTHESES The Strategic Choice Perspective Probably the most common explanation for why programs addressing substance abuse have become prevalent is based on economics, and it invokes prevention of accidents caused by workers under the influence of drugs as a means of enhancing productivity and efficiency (Hersch, Cook, Dietz, & Trudeau, 2000). This view reflects the strategic choice perspective on innovations (Pfeffer & Salan-

The authors would like to acknowledge support during the collection of the data from Grant R01-AA-07192, from the National Institute on Alcohol Abuse and Alcoholism, and from Grant R01-DA-07417, from the National Institute on Drug Abuse. Thanks go to Eric Abrahamson, Gayle Porter, Philip Stone, Sara Rynes, and the anonymous reviewers of this journal for their comments and assistance on drafts. 1125

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cik, 1978).The strategic choice view is that organizational programs and structures are shaped by managers’ interest in maximizing efficiency and in matching environmental needs to their organization’s capabilities (Bamberger & Sonnensthul, 1996). This formulation implies that adoption is internally derived, yet the decision to adopt may also be based on external signals, like societal concern over drugs. Another relevant perspective is based on the concept of punctuated equilibrium. According to this view, the pattern of change in organizations is radical, brief, and pervasive (Romanelli & Tushman, 1994). Further, inertia is a common state of affairs in organizations, so salient signals such as a crisis in performance or significant environmental changes are viewed as stimulating changes in the prevalence of management practices. Neoinstitutional perspectives on the adoption of innovations (Greenwood & Hinings, 1996) focus on managers’ attempts to maintain legitimacy in the face of pressure from their environment. In the case of substance abuse prevention initiatives, a desire to demonstrate compliance with goals of the government (goals exemplified by the Drug-Free Workplace Act of 1988) ) may drive adoption. Related to neoinstitutional theory is the view that adoption of some management practices is driven by media attention (Abrahamson & Fairchild, 1999). Although each of these perspectives contributes something to academic understanding of how management practices originate and diffuse throughout organizations, rarely have they been considered together in a single study. Using programs addressing workplace substance abuse as a lens, we integrated the various perspectives to understand how workplace substance abuse programs became common. A longitudinal data set based on 360 establishments was used to test hypotheses. A number of studies suggest that drug users are more likely to be injured on the job, to have jobrelated accidents, to be involuntarily terminated for job-related reasons, and to have higher absenteeism (Normand & Salyards, 1989). However, the evidence is mixed as to whether adopting a drug testing program or an EAP reduces turnover or accidents, or affects other workplace outcomes. Elmuti (1993) cited evidence that drug testing does reduce absenteeism rates, but other researchers (Hoffmann & Larison, 1999) have claimed that the results to date do not show whether drug testing deters drug use by employees. There is evidence that EAPs reduce health care and other costs (Fitzsimmons, 1992). Per resource dependence theory, managers will act to reduce uncertainty for their firms, acquire

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resources essential to their survival, and increase control over needed resources (Pfeffer & Salancik, 1978). For example, if managers believe that hiring drug users poses a safety risk or productivity concerns, they may try to reduce uncertainty by screening out drug users through drug testing of applicants. Managers of organizations where safety is a major issue may be concerned about industrial accidents caused by employee substance abuse. EAPs might also be adopted in such circumstances to help rehabilitate employees with problems and reduce the chance that they will cause accidents or diminish productivity. Thus, if managers already perceive a drug problem at their organization, they are more likely to adopt a drug-testing program or an EAP to gain a sense of control over this issue. The relationships, captured in our first hypothesis set, reflect the view that whether an organization adopts programs addressing substance abuse depends on strategic choices made within the organization to maximize efficiency. We reviewed research literature on adoption of innovations to identify the factors that may affect if and when an organization adopts a practice. We now turn to descriptions of those factors. Organizational size. Formalized promotion and hiring practices (Pfeffer & Cohen, 1984), alcohol treatment programs (Fennell, 1984), and the adoption of technological innovations (Rogers, 1995) have all been related to organizational size. Larger organizations should have more resources than smaller ones and are more likely to adopt many programs simply because they can afford to do so. Also, larger firms may have more concern with showing legitimacy because they have greater public visibility, resulting in greater normative and coercive pressures from the environment. Regarding substance abuse programs, small firms that test few applicants may not find drug testing to be cost-effective. MacDonald, Wells, and Fry (1993) noted that some small companies have abandoned drug testing. There is evidence that larger organizations are more likely to have drug-testing programs and EAPs. Blum, Fields, Milne, and Spell (1992) indicated that drug testing is primarily a large company practice with results showing that more than half the Fortune 1000 companies performed some type of drug test, as compared with less than 5 percent of small employers. Union presence. Managers may feel they need to control the entry of employees to their organization before they join a union, at which time discipline and control may become more difficult. This view would make applicant drug testing attractive. Unions may discourage drug testing of current em-

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ployees, as opposed to applicants. In fact, unionized work sites have been found to be more likely than nonunionized locations to have drug-testing programs (Blum et al., 1992). For EAPs, the situation may be more complicated. Bamberger and Sonnenstuhl (1996) wrote that until the late 1980s unions generally supported EAPs but after that union support declined, when drug testing and managed care began, from labor’s viewpoint, to use EAPs to restrict access to treatment and for punitive ends. In any case, we expected to find that unionized establishments were more likely to have EAPs at least through the 1980s. Even if only some of the workforce at a work site is unionized, these relationships should still hold, since treating unionized and nonunionized employees differently might affect morale (Freeman & Medoff, 1979). Turnover. Work sites with high turnover may find that the costs of EAPs or drug-testing programs are not worth the benefits since employees don’t remain in the organizations for very long. Bennett, Blum, and Roman (1994) reported that work sites with low turnover were more likely to engage in pre-employment drug testing. EAPs may be more likely to be found in organizations with low turnover, since it makes more sense to invest in an EAP if employees stay long enough to return the investment the organization has made. Proportion of male employees. Some research suggests that workplaces where the majority of employees are male may be more likely to drug-test in response to commonly held suspicions that men are more likely to use drugs. Several studies have indicated that drug use is more common among men (Mensch & Kandel, 1988). Organizations with predominately male workforces might be more likely to adopt EAPs as a way to gain control over an issue seen as more significant for male employees. However, we might not expect as strong a relationship with EAPs since EAPs can also be seen as a benefit for both male and female employees. Thus, although we predict relationships with the proportion of male employees for both drug testing and EAPs, we do not predict that the relationships will be equally strong for both programs. From this demographic perspective, drug testing can be seen as a way to eliminate drug problems if managers think their company is vulnerable to drug-using employees reducing productivity or safety. For EAPs, the situation is a little different, as they are also a way to treat employees with other personal problems. Organizations facing uncertainty and needing to gain a sense of control are in general likely to adopt programs addressing substance abuse before other organizations. Thus, internal organizational char-

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acteristics related to the strategic choice perspective would predict adoption. This discussion leads to the following hypotheses: Hypothesis 1a. Large organizations are more likely to adopt workplace substance abuse programs (drug testing and EAPs) than are other organizations. Hypothesis 1b. Organizations whose managers perceive an existing drug problem are more likely to adopt workplace substance abuse programs (drug testing and EAPs) than are other organizations. Hypothesis 1c. Organizations where a union is present are more likely to adopt workplace substance abuse programs (drug testing and EAPs) than are other organizations. Hypothesis 1d. Organizations where turnover is low are more likely to adopt workplace substance abuse programs (drug testing and EAPs) than are other organizations. Hypothesis 1e. Organizations where the percentage of male employees is high are more likely to adopt workplace substance abuse programs (drug testing and EAPs) than are other organizations. The Institutional Perspective Until recently, institutional theory has not been regarded as a way to explain how organizations change, but rather as a way to explain similarity between organizations. But Greenwood and Hinings (1996) disagreed with this view and argued that institutional pressure is a source of change. Bamberger and Sonnenstuhl (1996) compared institutional and strategic choice views to explain differences among local unions in adoption of assistance programs for union members. DiMaggio and Powell (1991) described institutional change in terms of coercive, mimetic, and normative pressures. Each of these sources, as described by DiMaggio and Powell, can be drawn upon to understand the adoption pattern of programs for addressing workplace substance abuse. First we turn to the role of coercive pressure in program adoption. Coercive pressure may result from laws, regulations, or persuasion directed toward organizations. Regulations about substance use may in turn arise from widespread concern about effects of drugs on workplaces and society. Managers may cite productivity and efficiency concerns as the reason they adopt substance abuse programs, but what

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prompted the concern in the first place? Complementing the institutional view, the concept of punctuated equilibrium is that innovations are adopted in response to salient events like crises in performance or environmental changes. Applying this idea to the pattern of adoption of workplace substance abuse programs is informative. A wealth of popular and academic literature indicates drug use began to be seen as a crisis in the 1980s. There is little evidence that drugs in the workplace were a concern in the United States until the early 1970s (Roman & Blum, 1992). EAPs had existed since the 1940s at companies such as Eastman Kodak and DuPont (CONSAD Research Corporation, 1999), but they were originally focused on handling alcohol problems, and there was no consensus that they could extend beyond addressing alcoholism. Drug testing first appeared in the United States in the 1960s and 1970s, when military personnel were tested. The concern over drugs in society, called a “moral panic “ by Goode and Ben-Yehuda (1994), intensified in the 1980s. Events such as the Exxon Valdez disaster and a fatal railroad accident in Maryland in 1987, both attributed to substance abuse, undoubtedly sparked concern. Meanwhile, in the 1980s federal policy and regulations in response to this concern was developing. President Reagan signed an executive order promoting a drug-free federal workplace and declared the “War on Drugs” in 1986. The federal Drug-Free Workplace Act passed in 1988 encouraged all organizations with federal contracts of at least $25,000 to have EAPs and drug-testing programs. The U.S. Department of Transportation began randomly testing employees in air, rail, trucking, and mass transit industries in 1989. In response to this coercive pressure, managers may have decided to drug-test job applicants to convey a good-faith effort to meet the guidelines. By 1988, over 90 percent of companies in the transportation and utility industries were using pre-employment drug testing (U.S. Department of Labor, 1989). Our point is that during the 1980s some organizations may have adopted programs because productivity concerns were salient. Also, the emergence of cheap, widely available technology for detecting the presence of drugs and the expansion of alcoholism treatment programs to cover treatment for drug abuse played a role in adoption. But an important trigger for adopting came from a growing concern about substance abuse and new regulations that pressured organizations to act. Since a key event that put pressure on workplaces was the declaration of the drug war in 1986, and other events mostly occurred during the mid 1980s, we

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expected to find that adoption of programs increased after 1986. Hypothesis 2. The likelihood of adoption of drug-testing programs and EAPs increases after 1986. Mimetic pressure can result when uncertainty causes organizations to imitate other firms (DiMaggio & Powell, 1991). For example, Goodstein (1994) suggested that organizations tended to adopt child care services when such services became the established norm within their industry. Likewise, organizations may adopt practices like EAPs and drug testing because organizations similar to them have already adopted. This notion is consistent with a key assertion of institutional theory, that concern with legitimacy leads organizations to adopt practices that other organizations already have. Thus, in our research we considered not only the pressures on individual organizations to adopt activities or practices, but also the later diffusion of newly adopted practices throughout organizations in the same industry or area. If enough organizations in the same geographical area have drug-testing programs or EAPs, organizations without them may feel mimicry pressure to also adopt these programs. This statement implies organizations are more likely to adopt if other firms in their local labor market have already adopted, or if most firms in their industry have adopted. Hypothesis 3a. Organizations are more likely to adopt EAPs when a high proportion of organizations in their industries or in their local labor markets have adopted EAPs. Hypothesis 3b. Organizations are more likely to adopt drug testing when a high proportion of organizations in their industries or in their local labor markets have adopted drug testing. DiMaggio and Powell (1991) proposed that organizations adopt new practices that are consistent with the norms and values of other organizations and key stakeholders, and such pressure can be connected with efforts to appear legitimate and progressive. Bamberger and Sonnenstuhl (1996) found that different local norms caused organizations to implement union programs tailored to individual needs of each work site rather than programs that were the same at each of their sites. Some management research based on the neoinstitutional view has focused on how popular media and other sources put normative pressure on managers to embrace ideas about managing and practices associated with those ideas. Management fashion theory is an attempt to explain how dis-

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course (what is written and said) about management beliefs originates, disseminates, and eventually influences managers to adopt techniques. Relatively little attention has been given to whether or how discourse is correlated with adoption of management practices. We were interested in whether media attention, and any changes in media content, played a part in the prevalence of substance abuse prevention programs. Basically, the idea is that to the extent they are influenced by favorable reviews of EAPs and drug testing, managers may choose to adopt these programs. Abrahamson and Fairchild (1999) described how discourse consisting of very positive arguments increased attention to quality circles in the media, and how counterevidence and more qualified discourse related to diminished popularity of quality circles. Alternatively, they noted that discourse may consist of unrealistic enthusiasm on the upswing, preceding the disillusionment that occurs when management techniques fail to live up to expectations. Although most management fashion research has focused on waves of increasing and declining attention to various management practices, here the theory is extended in a more general way, in that the type of discourse ultimately is related to changes in management behavior resulting from normative pressure. It was change in the tone of discourse over time that we expected to be related to adoption rates. We expected a positive correlation between an increase in positive discourse and an increase in adopting employee substance abuse programs. The hypotheses below follow from these arguments: Hypothesis 4a. Positive discourse about substance abuse treatment is associated with an increase in likelihood of the adoption of EAPs. Hypothesis 4b. Positive discourse about drug testing is associated with an increase in the likelihood of adoption of workplace drug-testing programs. It is possible that discourse about substance abuse treatment and drug testing may have become less positive over time, yet it does not necessarily follow that organizations will reject drug-testing and substance abuse treatment programs. Other institutional factors may encourage managers to retain such programs, even when what is written about them in the media takes a less positive tone. For example, an organization may want to demonstrate compliance with federal government drugfree workplace guidelines that specify EAPs and drug-testing programs as ways to achieve a drug-

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free workplace. Thus, it seems unlikely organizations abandon substance abuse programs just because media attention grows more negative. However, negative discourse might discourage managers at organizations that don’t have these practices from adopting. Thus, unlike our other hypotheses, the following hypotheses predict a reduction in likelihood of adoption. Hypothesis 5a. Negative discourse about EAPs is associated with a decrease in the likelihood of the adoption of EAPs. Hypothesis 5b. Negative discourse about drug testing is associated with a decrease in the likelihood of the adoption of workplace drugtesting programs. As positive discourse increases, managers in all industries and labor markets are exposed to media that increasingly encourage them to consider drug testing and EAPs. Thus, it is conceivable that in periods when positive discourse is high, the proportion of adopters in an industry or labor market will matter less as a predictor of prevalence, because everyone is exposed to positive discourse with respect to this issue. We expected an increase in positive discourse to moderate the relationship between both industry and labor market effects for EAP and drug-testing program prevalence. Hypothesis 6a. As positive discourse about EAPs increases, the relationship between industry and labor market proportions of adopters and the likelihood of EAP adoption diminishes. Hypothesis 6b. As positive discourse about drug testing increases, the relationship between industry and labor market proportions of adopters and the likelihood of the adoption of drug testing diminishes. The Moderating Effect of Adoption Period The timing of adoption is also an issue. Tolbert and Zucker (1983) found that the first municipalities to adopt civil service reforms did so because of competitive reasons. Later adoption was driven more by municipalities trying to conform to what had become accepted practices. Essentially, the early adopters made strategic choices associated with their internal characteristics. Later adopters instead responded to pressure to be seen as progressive and to maintain legitimacy as the reforms became more and more common. We predict that the period of adoption (early or late) moderates the effects of strategic choice and

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institutional factors on the probability of the adoption of programs addressing substance abuse. Although it may be difficult to pinpoint exactly when early, middle, and late periods of adoption begin and end, we expected that early adoption would be more closely related to strategic choice variables and that the relationship between institutional factors and adoption would become stronger later in the study period. We made no prediction about whether these effects would be found to have been stronger during early or late periods for media discourse as we expected media discourse to inform managers throughout the adoption period. Hypothesis 7a. The relationships between the strategic choice variables (organizational size, perception of drug problem, union presence, turnover, and percentage of male employees) and the likelihood of adoption are stronger in the earlier phases of the study period than in the later phases. Hypothesis 7b. The relationships between the proportion of employers within an establishment’s industry and local labor market that have adopted and the likelihood of adoption are stronger in the later phases of the study period than in the earlier phases. Overall, the sets of hypotheses may best be framed as constituting both a strategic choice perspective and an institutional or environmental determinism perspective. METHODS We tested hypotheses using two sets of data. We needed longitudinal, organization-level data on adoption of drug testing and EAPs that also considered changes in independent variables over time. We also needed data on trends in the discourse on substance abuse. Organizational Data on Adoption of Programs Addressing Substance Abuse The data for this analysis came from a convenience sample collected as part of a larger study that examined employer responses to substance abuse and other human resource (HR) practices. It involved HR managers at 360 establishments in Georgia. The sample was originally selected in 1987 from private sector workplaces with 250 or more employees listed in Dun & Bradstreet’s Dun’s Market Indicator. As a sampling frame this source has been found to compare favorably with others (Kalleberg, Marsden, Aldrich, & Cassell, 1990). The

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Georgia Manufacturing Directory and lists from chambers of commerce supplemented Dun’s. The sample was stratified by location to match population distributions over urban and rural settings in the state. Information was collected on the establishments four times (in four waves of data collection): 1987– 88, 1991, 1993, and 1995. Of 406 eligible establishments contacted during the study period, 360 participated, for an overall response rate of 89 percent. For the first wave, we contacted 343 eligible workplaces and obtained responses from the managers of 297 of these sites; thus, the response rate was 86.6 percent. By the time of the second wave, 11 of the sites had gone out of business, and the managers of 7 others refused to participate further, reducing the sample to 279. A supplementary data collection added 63 establishments in rural areas of Georgia in 1991, increasing the sample size to 342. The third wave included 299 work sites, the others having refused further participation (n ⫽ 30) or ceased operations (n ⫽ 13) between the second and third waves. Thus, there were 297 workplaces in the first wave, 342 in the second (response rate ⫽ 95% ), and 299 in the third (response rate ⫽ 89%). The number of work sites in the 1995 wave was 244. Out of the 299 responding in 1993, 55 either had closed or refused to participate by 1995, so our continued response rate was 82 percent. By the 1995 wave of data collection, all but 76 sites had adopted drug testing, and all but 134 had EAPs. Of the 116 worksites missing from the 1995 wave, we knew from the earlier data collection waves that 24 had gone out of business, and 48 had already adopted drug testing. Therefore, of the establishments that had dropped out by 1995, only 44 had not yet adopted applicant testing. Of establishments that had dropped out by 1995, 30 had not yet adopted EAPs. All major industrial classes found in the Standard Industrial Classification (the SIC, which is now called the North American Industrial Classification System, or NAICS) were represented. Two hundred and twenty-six of the sites (65%) were in manufacturing; 16 sites (5%) were in wholesale or retail trade; 23 sites (7%) were in the financial, insurance, or real estate industries; and 16 sites were hospitals (5%). The remaining sites (18%) were in communication, transportation, or other services. The sample overrepresents manufacturing compared to national averages. Nationally, for establishments with over 250 employees, 48 percent are manufacturers, according to 1992 U.S. Census data. However, the manufacturing establishments in the sample were comparable in average revenues. For the sample, the mean revenue in 1991

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was 121.34 million; nationally the mean for manufacturing firms of similar size was 113.93 million. Service establishments in the sample had mean revenues of 133.99 million, compared to 115.22 million nationally. Data on Substance Abuse Discourse We obtained data for a content analysis by examining abstracts of periodical articles published from 1970 through 2001 for which either substance abuse treatment or drug testing was listed as the subject. Abstracts were examined via searches of ABI/ Inform, an online database of journals, newspapers, and other business, scientific, and popular periodicals. We chose the period 1970 –2001 because little attention was paid to the issue of interest in the four decades prior to the 1970s (Roman & Blum, 1992). Also, ABI/Inform covers this time frame. Although we could have examined all articles with a reference to either topic (substance abuse treatment or drug testing), we chose to count only articles mentioning substance abuse treatment because we learned from consultation with ABI/Inform that trained content analysts, working in teams specializing in particular functional areas, assign the subject headings. This approach is the same as used by Abrahamson and Fairchild (1999), who found very similar results using both methods. Content categories refer to the tone and type of discourse in the articles. For this study, the content categories examined were “positive” and “negative.” The implication is that the more positive words in an article abstract, the more positive in tone the discourse. Following Abrahamson and Fairchild’s (1999) methodology, we counted the total number of words for each year as well as the total number of words in each of the categories using the program General Inquirer (Stone, Dunphy, & Ogilvie, 1966). This program uses the Harvard-IV dictionary and links words within articles to content categories. Examples of words that the program considers positive are “acceptable,” “fair,” and “suitable”; negative words are “abandonment,” “fail,” and “objection.” To check the reliability of the abstracts as measures of the content of the articles, we ran the content analysis program on a random sample of 100 abstracts of articles and the articles’ full texts. The counts for abstracts and full text were then correlated for each of the content categories. The correlations in each case were significant and positive (p ⬍ .05). Because the number of articles (which was strongly correlated with total words written) on

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EAPs and drug testing in the database increased so much over time, we measured discourse by counting the number of words in each content category and normalizing these counts by dividing by the number of articles for each year. An alternative way to measure changes in discourse described by Abrahamson and Fairchild (1999) is based on constructing regression equations to predict word counts and calculating error rates of the predicted minus the actual word counts. We also used this method and found the results were very similar to the ones we report here. Measures Year of adoption of applicant drug testing. Each HR manager was asked if his or her work site had adopted drug testing of applicants at all interviews. If the answer was yes, we asked the year of adoption. Since respondents were asked for the year drug testing was adopted in all four waves, we checked the reliability of this variable by comparing the responses. Instances in which the difference in responses was greater than one year were examined case by case until the inconsistency was resolved. Year of adoption of Employee Assistance Program. The other dependent variable was adoption of an EAP. The same respondents were asked for this information in all four waves, and reliability was checked by comparing responses. Figure 1 shows the cumulative number of employers with pre-employment drug-testing programs and those with EAPs by year of adoption. The data from each wave were coded into a separate array. The percentage of male employees was handled as time-invariant (averaged over time). The test-retest reliability for this variable was .82. The other variables varied over time and are thus time-varying covariates. Size. Size was the number of full-time employees at a work site. We applied a logarithmic transformation to induce normality. This variable was collected during all waves and was time-varying. Turnover. Turnover was measured as the average annual rate of voluntary employess exits (average annual voluntary turnover) for a work site. Turnover data were collected in all waves. This variable was time-varying. Percentage of male employees. Data on the percentage of male employees at each establishment were collected during all waves. These figures were averaged over time, and thus the variable was time-invariant. Perceptions of drug problem. Respondents were asked the following in all waves: “What degree of

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FIGURE 1 Prevalence of Drug-Testing and Employee Assistance Programs in a Sample of 360 Establishments

overall impact does employee drug abuse have on your workplace?” (4 ⫽ “major,” 3 ⫽ “moderate,” 2 ⫽ “minor,” and 1 ⫽ “none”). This was a timevarying covariate. Density of EAPs and drug-testing programs. Time-varying covariates for the density of the adoption of both EAPs and drug testing were calculated for all waves by both geographical area and industry. To calculate area densities, we first coded each workplace as located in 1 of the 12 labor markets in Georgia defined by Tolbert and Killian (1987). They used census-based commuting time to identify counties with shared commuting patterns of employees. We calculated the variable as the number of establishments with drug testing (or an EAP) divided by the total number of establishments in the sample from each focal labor market. Industry densities were the proportions of employers nationwide that had EAPs or drug tests. We obtained this information from the Bureau of Labor Statistics (BLS) (U.S. Department of Labor, 1989). It was supplemented with more recent industry percentages from Hartwell, Steele, French, and Rodman for the years 1992–93. Industry type for each workplace was defined via NAICS code, as were the BLS statistics. These codes are used to group employers into major industrial classes. Period effects. To test Hypothesis 2, we coded a period effect for the federal War on Drugs, assigning the years up to 1986 a value of 1 to represent the time before the War on Drugs declaration; other years were coded 0. Since all sampled establishments were in Georgia, we also needed to see if any state laws influenced adoption of either drug testing or EAPs. The Georgia Drug-Free Workplace Act, passed in 1990,

gave employers a 7.5 percent discount on worker’s compensation premiums if they adopted drug-testing programs and provided a list of substance abuse counseling services. In this case, a 0 represented adoption of either drug testing or EAP before 1990, and a 1 represented adoption after 1990. This period effect was not significant in any of the analyses and was dropped from the models shown. Control variables. Several variables were included as controls. For the drug testing models only, a dummy code indicated whether an EAP was present before drug testing was adopted. This variable was included since EAPs may drug-test employees as part of their rehabilitation. For the EAP models, dummy codes indicated whether an EAP was internally managed or managed through a contract with another organization, and whether a wellness or health promotion program existed at a workplace before an EAP was adopted there. Most firms offered EAPs as external programs. These may have been programs built into their insurance provider’s contract. As such, EAPs may have been adopted as part of an insurance package. The variable for wellness programs was included since managers’ responses to rhetoric about strategic HR management, viewing employees as assets, and building employee commitment may have supplemented concerns about substance abuse. Health promotion and wellness programs are indicators of a firm’s trying to build long-term commitment from employees.1 For both models, controls included whether an establishment had a federal contract of over

1

We thank an anonymous reviewer for suggesting the last two controls described.

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$25,000 (meaning it had to comply with the DrugFree Workplace Act). We also controlled for differences across industries via the NAICS codes reflecting industry groups, as described above. Industry was not significant in the analyses so it was dropped from the models. Since workplace safety is often cited as a rationale for drug testing and EAPs (Normand, Lempert, & O’Brien, 1994: 216), we initially included a twoitem measure of the extent to which safety was important at each workplace. Since there were concerns about the validity of the measure and the safety variable was not significant in any of the analyses, it was dropped. Data Description and Validation A nonresponse analysis looked for differences between organizations having missing data on particular instrument items. This preliminary analysis compared differences among independent variables on the interview variables between respondents and nonrespondents using a logistic regression analysis in which the dependent variable was “missing/not missing.” None of the independent variables reached significance. We elected to replace missing data by regression imputation. This technique has been found to be more accurate than the more common mean substitution method (Switzer, Roth, & Switzer, 1998). The largest amounts of missing data were for turnover; in the first wave, 10.8 percent of the sample had missing data on turnover, and in the third wave, 48 percent of the sample lacked turnover data. However, since the analysis only examined establishments up to the time they adopt, for the later waves we could consider missing data for only the establishments that still hadn’t adopted drug testing or EAPs. Of these, for the drug testing analysis, 20.3 percent of the sample had missing data on turnover by the third wave. For the EAP analysis, 22.5 percent of the data for turnover was missing by the third wave. All the other variables had fewer than 10 percent of the cases missing. The average percent missing for all variables was 6 percent. Roth (1994) concluded that the type of missing data technique used has little effect when less than 10 percent of data is missing. Analyses Morita, Lee, and Mowday (1993) described the applicability of regression analysis to longitudinal data. This approach, known as Cox regression, has been used in a variety of studies predicting prevalence of management practices over time. The risk

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set for such an analysis is defined as those establishments “at risk” of adopting a program (i.e., they don’t have the program at present) in a particular year. Thus, the size of our risk set decreased each year by the number of work sites adopting drugtesting programs or EAPs during that year. The chief reason for using this analytic approach was that, unlike separate regression analyses, it included independent variables that varied over time and addressed censored cases (cases that drop out of an analysis during a study period). Also, the Cox procedure is semiparametric and implies no assumption about the underlying functional form of an adoption pattern (Yamaguchi, 1991: 10). The situation may be complicated when adopted innovations can be discontinued or “exnovated,” since dropped practices can conceivably be adopted again later. Here, “exnovation” was not expected to be a significant issue. Although Bureau of Labor Statistics data show that many smaller work sites have dropped drug testing, few establishments with over 200 employees (such as the ones in this sample) have discontinued drug testing (Hayghe, 1991). The sample data support the national data. By the final wave in 1995, four work sites had dropped pre-employment testing (four “for-cause” testing programs and a random testing program had also been dropped). Six had dropped EAPs. Since once establishments adopted a program they almost always kept it, we were interested in what predicted adoption instead of what caused firms to drop the practices. Via the Cox regression analyses, we applied a hazard rate that was the probability (Pij) that workplace i would adopt a drug-testing program or an EAP during time period (single year) j, given that there is no program present at the beginning of year j. The hazard rate for a given year may be small, but over the entire study period the dependent variable is related to the cumulative adoption of drug testing and EAPs (and tells us the prevalence over time). This pattern is shown in Figure 1. We obtained two regression equations, one in which the dependent variable referred to EAP adoption, and the other in which the adoption of drug testing was the dependent variable. We assumed that the changes in variables were not significant within the time between waves. As Allison (1984: 38) suggested, data for the years between waves were estimated as the average of the waves before and after that year, weighted by the number of years from the time data were collected. For each of the dependent variables, a model was tested in which we entered first the controls and then the strategic choice variables. Then, neoinstitutional variables were added. Next, the variables

1134


associated with the interactions were entered. We used Aiken and West’s (1991) mean-centering procedure for interaction terms. The graphs in Figure 1 show that the rate of adoption changed over time and that the adoption curve is not linear. Although, as noted above, the proportional hazards model does not require specification of a functional form, an important consideration concerns cases in which the hazard rate may fit one of a variety of distributions or piecewise models (Morita et al., 1993). Here, since the rate of adoption was not linear, we decided to also examine the data in segments over time. For drug testing, Figure 1 suggests there were three phases: a period of very little adoption up to 1985, then an acceleration phase, when adoption was rapid (1986 –90), and a leveling-off phase from 1991 onwards. For EAPs, the initial phase of very little adoption lasted until 1982: an acceleration phase that was “shallower” than that for drug testing followed, existing until nearly the end of the study period in 1991–93; subsequently, a slight leveling off was apparent. We also wanted to see if the same variables were significant for both early and late adopters; in other words, we wanted to test for moderating effects of time (Hypothesis 7). Our first approach was to create subsamples (early adopters only, etc.) and analyze each separately, as finding that some effects were significant in one period and not in another did not tell us if they are statistically different from one another. However, we subsequently followed an approach similar to what Sherer and Lee (2002) used in their study of adoption of legal practices. We tested for moderation by creating variables in which each independent variable interacted with dummy variables for the period. We divided the study period into early diffusion, acceleration, and late diffusion. For drug testing, this was done via three dummy variables, one for 1970 – 85 adoption, one for 1986 –90 adoption, and one for 1991–95 adoption. For EAPs, the periods were constructed as described in the preceding paragraph (early adoption extended through 1982). A model testing the interaction of early adoption would thus include the main effects plus the early-period interaction terms. Also, like Sherer and Lee, we did a sensitivity analysis that changed the years defining early, middle, and late, as the changes in slope could not be pinpointed.

RESULTS Table 1 shows means, standard deviations, and correlations of the study variables. We averaged

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time-varying variables over the waves of data to obtain the statistics in this table. Preliminary Analyses The Cox proportional hazards model relies on the assumption that the hazard function for all organizations in a sample is some constant proportion of a baseline hazard function (Morita et al., 1993). If that assumption is met, a Cox regression procedure is appropriate. We tested this assumption by including covariate-by-time interactions in the model, as Allison (1984) suggested. None of these interactions were significant, so they were dropped from the model. Because in our study time was measured in yearly intervals, using a discrete event history analysis approach might seem appropriate, rather than using the Cox method with its assumption that time is continuous. Thus, we ran all the analyses both ways and found very similar results, with none of the significance levels changing. Allison (1984) commented on the robustness of the Cox method, so this finding is perhaps not surprising. Results of Cox Regression Analyses A Cox regression analysis was done using the procedure PHREG (SAS Institute, 1990). Table 2 shows the coefficients that resulted when EAP adoption was the dependent variable (in the four leftmost columns). The interpretation of coefficients in Cox regressions is like that in other multivariate regression equations. The statistic shown below the coefficients, the -2 log-likelihood chi-square, tests the global hypothesis that all the beta coefficients in a model are equal to zero. A significant result indicates that the null hypothesis is rejected. Multicollinearity was checked by examining variance inflation factors (VIFs) and condition indexes for the independent variables. Fox (1991) noted as a rule of thumb that the square root of the VIF should be under 4 to assure that multicollinearity is not a problem. The VIFs did not indicate multicollinearity (the highest was 1.5). The condition indexes (Morrow-Howell, 1994) showed the safety variable, originally included in the analysis, was collinear with the percentage of male employees, but after we dropped safety (as was noted earlier), the indexes were acceptable. When the dependent variable was adoption of EAPs, the control variables for wellness program and federal contract were significant (p ⬍ .05) after the strategic choice variables were added (column 2). Unlike the other controls, which had 0/1 cod-

0.94 0.47

0.50 6.56 0.44 23.90 16.08 0.44 0.39 0.10 0.05 0.11 0.09 121.11 85.16 73.98 40.20 6.91 5.15

0.94 0.68

0.45 6.63 0.27 23.10 52.66

2.24

0.81

0.47

0.21

0.71

0.29

97.00 74.45 43.32 25.72 91.39 90.40

.21 ⫺.02

⫺.09 ⫺.05 .04 ⫺.07 ⫺.19 ⫺.17 ⫺.04 ⫺.17 ⫺.18 ⫺.01

.10 ⫺.20 ⫺.17 ⫺.58 ⫺.62 .10 .05 ⫺.71 .12

.28

.27 .26 .06 .06 .28 .19

.02

⫺.18

.16

⫺.18

.04

⫺.11

.01

⫺.04

⫺.11 ⫺.18 .04 .16 .05

3

.12 .02 .05 ⫺.09 ⫺.05

⫺.14

2

.21 .18 ⫺.03 ⫺.02 ⫺.05

.13 ⫺.20

1

⫺.29 ⫺.29 ⫺.12 ⫺.05 ⫺.27 ⫺.18

.15

⫺.03

.10

.10

⫺.17

.03

.13 .03 ⫺.16 .23

4

⫺.17 ⫺.18 .09 .01 ⫺.27 ⫺.12

⫺.01

⫺.11

.11

.13

⫺.13

.12

.03 ⫺.11 .00

5

⫺.05 ⫺.05 ⫺.21 ⫺.03 ⫺.08 ⫺.21

.26

.05

.14

⫺.07

⫺.18

.30

⫺.05 .28

6

.15 .19 .13 .04 .20 .21

.04

⫺.02

⫺.18

.03

.21

.10

⫺.17

7

⫺.12 ⫺.14 ⫺.36 .02 ⫺.17 ⫺.42

.36

⫺.05

.17

.01

⫺.30

.21

8

⫺.06 ⫺.04 ⫺.23 ⫺.02 ⫺.04 ⫺.26

.28

.17

.03

⫺.17

⫺.06

9

.14 .14 .33 .45 .20 .54

⫺.19

⫺.02

⫺.14

.01

10

⫺.21 ⫺.22 .10 ⫺.07 ⫺.26 .07

⫺.33

⫺.62

⫺.03

11

⫺.12 ⫺.15 ⫺.19 .29 ⫺.16 ⫺.19

.46

.04

12

.11 .12 ⫺.22 .03 .15 ⫺.17

.35

13

.04 .04 ⫺.31 ⫺.01 .04 ⫺.34

14

b

Correlations above .09 are significant at the .05 level, and those above .13 are significant at the .01 level (two-tailed tests). Means reflect non-mean-centered values. c The mean and standard deviation for size (number of employees) was log-transformed. Actual mean was 759.1; standard deviation was 702.9.

a

0.48

0.34

1. EAP adopted before drug testing 2. Internally managed EAP 3. Wellness program present before adoption of EAP 4. Federal contract 5. Sizec 6. Union presence 7. Turnover 8. Percentage of male employees 9. Perceived extent of drug problem 10. Year prior to War on Drugs 11. Density of EAP adoption in labor market 12. Density of EAP adoption in industry 13. Density of drug testing adoption in labor market 14 Density of drug testing adoption in industry 15. Positive discourse, EAP 16. Negative discourse, EAP 17. Positive discourse, drug 18. Negative discourse, drug 19. Year of adoption of EAP 20. Year of adoption of drug testing

s.d.

Meanb

Variable

TABLE 1 Descriptive Statisticsa

.69 .22 ⫺.06 .66 .26

15

.21 ⫺.05 .78 .26

16

⫺.28 .18 .46

17

⫺.04 ⫺.17

18

.24

19

* p ⬍ .05 ** p ⬍ .01 One-tailed tests.

60.94** 13.99*

46.95

8

⫺0.31 (0.15)* 0.56 (0.14)** 0.21 (0.11)* 0.04 (0.16) ⫺0.01 (0.01)* 0.01 (0.01) 0.10 (0.09)

⫺0.41 (0.16)** 0.74 (0.15)**

3

0.46 (0.29)

0.57 (0.27)*

Externally managed EAP EAP adopted before drug testing Wellness program Federal contract Size Union presence Turnover Percentage of male employees Perceived extent of drug problem Year prior to War on Drugs Industry density Labor market density Positive discourse Negative discourse Labor market density ⫻ positive discourse Industry density ⫻ positive discourse Size ⫻ early adoption Union presence ⫻ early adoption Turnover ⫻ early adoption Percentage of male employees ⫻ early adoption Perceived extent of drug problem ⫻ early adoption Industry density ⫻ early adoption Labor market density ⫻ early adoption Likelihood-ratio chi-square Change in likelihood-ratio chisquare df

Model 2

Model 1

Variable

13

666.45** 605.51**

424.42** 398.26** 24

7

12

1.87 (2.67)

1.22 (0.27)**

2

⫺0.04 (0.08) 0.09 (0.05)*

21

530.59** 74.14**

0.22 (0.38)

0.55 (0.29)*

778.87** 112.42**

0.06 (0.02)** 0.08 (0.02)**

0.01 (0.01) 0.02 (0.01)

456.45** 22.03**

⫺0.22 (5.49) 0.03 (0.01)* 1.98 (0.67)* 0.57 (0.21)** ⫺0.02 (0.02) 0.00 (0.01)

0.18 (0.17) ⫺0.10 (0.12) ⫺0.22 (0.19) ⫺0.01 (0.00)** 0.02 (0.01)** 0.56 (0.11)**

⫺0.07 (0.15) 0.90 (0.69)

⫺0.22 (5.70) 0.02 (0.01)* 1.33 (0.60)* 0.72 (0.23)** ⫺0.08 (0.02)**

0.19 (0.17) ⫺0.05 (0.12) ⫺0.04 (0.18) ⫺0.01 (0.00)** 0.02 (0.01)** 0.54 (0.10)**

⫺0.13 (0.17)

Model 8

0.13 (0.08)* ⫺0.42 (0.50)

26.16

0.19 (0.17) 0.01 (0.10) 0.05 (0.17) ⫺0.01 (0.00)** 0.02 (0.01)** 0.53 (0.10)**

0.66 (0.14)**

⫺0.26 (0.17)*

Model 7

0.00 (0.01)

⫺0.15 (0.20) ⫺0.03 (0.15) 0.06 (0.72) 0.19 (0.02)** ⫺0.12 (0.02)** ⫺0.04 (0.01)**

⫺0.10 (0.22) 0.02 (0.02) 2.11 (0.87)* 0.02 (0.01)* ⫺0.08 (0.01)**

⫺0.38 (0.16)*

Model 6

⫺0.50 (0.15)**

Model 5

Drug-Testing Program

⫺0.03 (0.01)*

⫺0.06 (0.17) 0.05 (0.15) 0.04 (0.12) 0.11 (0.20) ⫺0.01 (0.02) 0.02 (0.04) 0.02 (0.09)

0.03 (0.28)

Model 4

⫺0.32 (0.19)* 0.18 (0.18) 0.20 (0.13) 0.12 (0.20) ⫺0.01 (0.01) 0.01 (0.01) ⫺0.01 (0.09)

0.08 (0.33)

Model 3

Employee Assistance Program

TABLE 2 Results of Cox Maximum-Likelihood Regression Analyses

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Spell and Blum

ings, wellness program was coded 1 for presence and 2 for absence. The negative coefficient thus means that establishments with wellness programs were more likely to adopt EAPs. Larger establishments and those with lower turnover were also more likely to adopt in the partial model. When the institutional variables were added to form the model with all the main effects (model 3), wellness remained significant, but size and turnover were not significant. Labor market density (p ⬍ .05) was significantly related to probability of adoption. Of the media discourse variables, level of negative discourse had a negative coefficient, and positive discourse had a significant, positive coefficient. An increase in positive discourse predicted increased adoption of EAPs, and an increase in negative discourse was related to a decreased adoption of EAPs. These results mean that Hypotheses 3a, 4a, and 5a received support, but not Hypotheses 1a–1e, or Hypothesis 2 (capturing the period effect for the War on Drugs). When we included interactions between early adoption and the independent variables (column 4), the main effects of positive discourse and negative discourse remained significant (p ⬍ .01). The interactions for labor market (p ⬍ .01) and for industry and positive discourse (p ⬍ .05) were significant. This pattern supports Hypothesis 6a. Figure 2 depicts the moderating effect of positive discourse on the relationship of labor market density and adoption of EAPs. To plot this interaction, we assigned positive discourse values of one standard deviation above (high) and one standard deviation below (low) the mean. For the interactions with early adoption (Hypothesis 7a), we found that although only 22 EAPs had been adopted by 1982, larger establishments were

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more likely to adopt during the early period (p ⬍ .05). This was the only significant interaction. The model shown in column 4 of Table 2 reflects extension of the early adoption period to 1984 (55 establishments had adopted by then). In this model, both labor market and size are significant (p ⬍ .01 and .05, respectively). Industry and the perceived extent of drug problem were also related to early adoption (p ⬍ .05). These findings mean that, along with size, labor market factors became predictors of EAP adoption around 1983– 84. Since Hypothesis 7b does not state that discourse variables are moderated by adoption period, we did not include interactions for these in the model. However, in separate analyses we did include discourse by early adoption interactions, and both positive and negative discourse interactions were significant (p ⬍ .01); the same was true for both interactions of discourse with adoption in the middle period (p ⬍ .01). The log-likelihood statistic for the model with interactions added was significant (p ⬍ .01); the change in log-likelihood over the main effect model was also significant (p ⬍ .01). We also analyzed interactions with adoption in the middle period (the acceleration phase), 1984 – 1990 (results are not shown). When the interactions of the independent variables with adoption in this period were considered instead of early adoption, size became insignificant. The interactions of labor market and industry (p ⬍ .05) were significant. None of the other interaction variables reached significance. Since EAP adoption leveled off around 1990, we analyzed a late adoption segment lasting from 1991 onwards (44 establishments adopted EAPs during that period). None of the interactions of independent variables with late adoption were significant.

FIGURE 2 Moderating Effect of Positive Discourse on the Relationship of Labor Market Density and Employee Assistance Program Adoption

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From this, it seems we can define a late adoption period beginning around 1991 in which the establishments adopting EAPs were not very different from nonadopters for the measures used here. The results for drug testing adoption are shown in Table 2 in the four rightmost columns (models 5 to 8). For both main effects models (6 and 7), among the strategic choice variables turnover had a significant (p ⬍ .01) and negative coefficient (in agreement with Hypothesis 1d). Also, perceived extent of drug problem and percentage of male employees were significant (p ⬍ .01), supporting Hypotheses 1b and 1e. Both industry density (p ⬍ .05) and labor market density (p ⬍ .05) were significant (Hypothesis 3b). Positive discourse was significant, supporting Hypothesis 4b (p ⬍ .01). Negative discourse had a significant and negative coefficient, supporting Hypothesis 5b. When the interactions were added (model 8, far right column) the main effect relationships stayed the same, except that negative discourse lost significance. The interactions of discourse with the labor market and industry variables were not significant. In all these models, the log-likelihood chi-square was significant (p ⬍. 01), as well as the change in log-likelihood. Model 8 also includes the interactions of early adoption with the independent variables (Hypotheses 7a and 7b). We constructed the early adoption window for this model by considering the years 1970 – 85 (66 had adopted by then). The significant interactions with early adoption included turnover (p ⬍ .01) and the percentage of male employees (p ⬍ .01). When the sensitivity analysis used an adoption window before 1985, turnover and percent male were not significant. Thus, it would appear turnover and percent male were the significant variables, but not before the mid 1980s. As with the EAP models, discourse-by-period interactions were not included as they are not predicted by Hypothesis 7b. For the drug testing model, including interactions of discourse with early adoption seemed inappropriate in any case, since unlike EAPs, drug testing was mentioned in very few articles during the early adoption years (in fact, the database we used did not even list drug testing as a subject for articles until the mid 1980s). We constructed a second middle-period adoption window by considering the years 1986 –90. Results including this period (not shown) revealed the interactions of middle-period adoption with the percentage of male employees, the perceived extent of a drug problem, and industry variables were significant (all at p ⬍ .01). Turnover became nonsignificant in the middle adoption period. If the interactions of drug testing discourse (both positive

December

and negative) with the middle time period are included, they are also statistically significant. Since adoption leveled off after 1990, we considered a final period of later adopters. This analysis included the 44 establishments that adopted in the years 1991–95 and those not adopting by that time. Only the interaction with perceived extent of the drug problem and the main effect discourse variables predicted adoption. DISCUSSION In this study, we attempted to explain changes in prevalence of workplace substance abuse prevention programs. Using programs addressing workplace substance abuse as a lens, we took different theoretical perspectives on the adoption of innovations and considered these to understand how the practices gained prevalence in the workplace. We have presented an integrated theory that addresses the effect of strategic choice and environmental influences on adoptions of measures addressing workplace substance abuse. We have also presented evidence that changes in the tone of relevant publications can predict changes in adoption patterns, even when one controls forstrategic choice and other environmental factors. It was found that though managers may see both Employee Assistance Programs and drug testing as parts of an integrated substance abuse program, EAP adoption was related to different factors than drug testing, and relationships varied over time. Looking at the results in Table 2 for EAPs, we see that larger organizations were more likely to adopt early. Larger organizations have more concerns about legitimacy and are more likely to respond first to pressures to appear progressive as well as more likely to have the resources needed to implement new initiatives. When the adoption period was extended to 1984, local labor markets and industry were found to be a source of pressure to adopt EAPs. Managers may be more likely to consider an EAP when competitors have them. It appears that enough EAPs existed by the mid 1980s to make institutional pressures important, a finding reminiscent of Tolbert and Zucker’s (1983) finding that institutional pressure is important after initial adopters make practices accepted. It was predicted that discourse would interact with other institutional factors (industry and labor market variables) in such a way that the normative pressure associated with the discourse would moderate the effect of labor market and industry density. The interactions showed that in the years when positive discourse was higher than in the past, the effect of labor market density and, to some

2005

Spell and Blum

extent, the effect of industry diminished. Although the evidence of interactions was not overwhelming empirically, it suggests there is merit to modeling second-order effects involving discourse. In contrast to EAPs, for drug testing the demographic variables (percentage of males and turnover) were related to early adoption, as managers were considering whether their own workplace might be affected by employee substance abuse. The subsequent-period analyses showed that by the acceleration (middle) period, institutional pressures became important as industry and labor market factors became significant. As noted earlier, unlike EAPs, drug testing generated little discourse until the mid-1980s, so the significance of discourse was felt in the middle and later periods. Why were demographic variables not very predictive of EAP adoption but related to drug testing? First, EAPs deal with more than drug problems. The gender variable is likely less important (while women may be seen as less likely to use drugs, they have other needs that may be served by EAPs). We would not expect severity of drug problems to be an equally strong predictor for EAPs and drug testing for this reason alone. The significant finding for the drug problems measure may be even more remarkable given the questionable reliability of the singleitem indicator. EAPs were present in a small number of workplaces before the mid 1980s. On the other hand, drug-testing programs were almost unknown, and their prevalence increased at a more rapid rate. Managers meanwhile began to see in the media stories of accidents and productivity losses due to drugs. Drug testing may have been seen as a way of preventing immediate problems more than EAPs and as a more direct response to employee substance abuse. Characteristics of their own workforces may have thus been more on the minds of managers with respect to drug testing. If drug testing was indeed seen as a more immediate and pointed response, then one would expect the adoption slope to be steeper than that for EAPs, as is evident from Figure 1. Period effects were not significant in any of the models. This may be surprising, given the increase in program prevalence during the mid 1980s. However, as this finding relates to coercive pressure, it may be explained more by the regulatory limitations of the Drug-Free Workplace Act. As part of the War on Drugs, this act didn’t require the programs, and only about half of the establishments in our sample had federal contracts and were actually covered by the act. The regulations may instead have given employers that had not adopted out of fear of lawsuits the license they needed to adopt. In

1139

the first wave, 10.8 percent of respondents with no drug testing indicated they had not adopted owing to legal issues; by the second wave (after the DrugFree Workplace Act had passed), less than 2 percent had reservations owing to legal issues. This explanation is of course only relevant to drug testing and not to EAPs. Limitations and Further Research Directions Like all research, this study has limitations. Since limitations can provide clues for future research, they are discussed together. First, we did not consider all types of media for the discourse measure, only periodicals, which paid little attention to drug testing in particular in the earlier phases. Books and broadcast media certainly also influence managers. Another limitation concerns whether the ABI/Inform database reflects what managers read. This database includes trade journals, newspapers, business periodicals, and management journals. One could compare the current results with results based on other databases, such as the Business Periodicals Index, but we have no reason to believe that one source is more valid than another. It would be worthwhile to consider the causeeffect relationships among the institutional factors, which are not addressed in the present study. For example, we don’t know if media discourse was a stimulus for coercive and mimetic pressures or a result of increased government intervention and increased prevalence of the programs themselves. Also, in the results presented here we considered only changes in the content of discourse (changes in the number of positive or negative words as a percentage of total words). The change in actual number of positive or negative words written about the programs could also be related to adoption. However, note that, given the substantial increase in the overall number of articles included in the database over the years, counts would need to be normalized for these increases. We did substitute total counts of positive and negative words for the content variables and found that they were also significantly related to adoption of both drug testing and EAPs. Although we examined interactions of environmental factors with discourse, strategic choice factors may interact with discourse as well. For example, positive discourse may be more strongly related to the adoption of EAPs in unionized firms, which may already be receptive to such programs. The percentage of male employees may be more strongly associated with the adoption of drug testing in periods of more positive discourse. Full con-

1140


sideration is beyond the modeling done here, but in an exploratory attempt, we examined interactions of the demographic variables with positive discourse. None of the interactions were significant for EAP adoption, but interactions with union presence and turnover were significant for drug testing (p ⬍ .05). Although the results in this data set may not be particularly compelling, a fruitful project would be to examine interactions between the strategic factors and discourse. Although we did include whether a workplace’s EAP was internally or externally managed as a control variable, we did not address several aspects of type of EAP that might play a role in predicting adoption. For example, negative media coverage may not lead to firms dropping EAPs altogether, but instead might encourage firms to shift to less comprehensive EAPs. Approximately 90 percent of the EAPs in the sample reported they periodically trained managers on how to use the programs, indicating that the programs were fairly comprehensive, in this study we did not fully consider the extent of the services the Employee Assistance Programs entailed, and if they changed over time. Another issue is the generalizability of the sample. It does have representatives from all major industrial groups, and although it was composed only of establishments with over 200 employees, the sample is representative of the type of work environment that many U.S. employees experience. Also, most firms with under 200 employees do not have drugtesting programs or EAPs (Hayghe, 1991; Hartwell et al., 1992). So the sample includes the type of organizations most likely to have the programs of interest. As noted earlier, some smaller companies have had and dropped programs, especially drugtesting programs. A study investigating smaller establishments would be worthwhile. Practical Implications These results might be of most interest to management scholars and policy makers, but they have practical implications for managers as well. It appears that, especially for drug testing, later adopters may have focused more on what other organizations were doing and on what was being written about workplace substance abuse prevention programs than on their own particular needs. Since we did not consider data on the effectiveness of the programs in this study, we don’t know if the decision-making process was connected to program performance. A more general implication concerns the mindfulness of managers with respect to decisions about the adoption of any program or practice. Some adopters might always consider their

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internal characteristics, but it could be that when certain programs or practices become commonplace, many managers adopt because of normative or other pressures. Conclusions Tying the present results back to the theoretical frames considered here, we note that the institutional variables associated with discourse were statistically significant predictors of adoption for both programs. This observation is notable in light of the differences in complexity of the two programs. One might expect the normative pressure associated with discourse to be exerted more directly on a narrowly focused practice like drug testing than on a multiple-purpose program such as an EAP. Although the findings here are in some ways are comparable to those of Tolbert and Zucker (1983), ours go beyond the earlier findings with regard to the multiperiod discourse effects. The analysis focusing on specific adoption phases suggests that each theory described in our initial arguments may be have different relevance for the various phases of the adoption curve and for the two types of programs. Drug testing, which arose very rapidly in workplaces, may be adopted first by organizations responding to strategic concerns (that is, they have demographic characteristics that indicate substance abuse is a critical concern for them). Later, mimicry variables grow in importance. For more comprehensive innovations like EAPs, which have a longer and more complicated history, mimicry is more relevant earlier. These results also show the value of considering sufficiently long time periods when studying program or innovation adoption. Otherwise, idiosyncracies of theories’ temporal relevance may hide the effects of strategic choice or institutional variables. We hope that this research will inspire others to consider the role of strategic choice and institutional factors in their efforts to understand how other management practices gain popularity. In this sense, our study is an attempt to bring together research about the effects of media on the popularity of management practices (Abrahamson & Fairchild, 1999) and research on employers’ responses to other institutional and strategic pressures (Goodstein, 1994). In this article we have also tried to shed light on how workplace substance abuse prevention programs have become so common. Although this study shows the challenges in getting and combining data from organizations and mediabased data, what can be learned from such studies may be very worthwhile.

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Spell and Blum

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Chester S. Spell ([email protected]) is an assistant professor of management at the School of Business– Camden, Rutgers University. He received his Ph.D. from the Georgia Institute of Technology. His research interests include the adoption and diffusion of management innovations, especially those related to employee mental health and substance abuse treatment. Other interests include work and family conflict and organizational justice. Terry C. Blum ([email protected]) earned a Ph.D. at Columbia University. She holds the Tedd Munchak Chair in Entrepreneurship at Georgia Tech, where she is also the dean of the College of Management. She previously served as the director of the College’s Center for Entrepreneurship and New Venture Development. Her research interests include innovation and technology transfer in health services related to behavioral health care, and organizational and entrepreneurial factors that mediate the transfer, adoption, and diffusion of innovation to for-profit and notfor-profit health treatment organizations.