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Journal of Operations Management 32 (2014) 268–280

Contents lists available at ScienceDirect

Journal of Operations Management

jo ur nal ho me pa ge: www.elsev ier .com/ locate / jom

HSAS 18001 certification and operating performance: The role ofomplexity and coupling�

hris K.Y. Loa,∗, Mark Pagellb, Di Fana, Frank Wiengartenc, Andy C.L. Yeungd

Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong KongSchool of Business, University College Dublin, IrelandESADE Business School, Ramon Llull University, SpainFaculty of Business, The Hong Kong Polytechnic University, Hong Kong

r t i c l e i n f o

rticle history:eceived 15 February 2013eceived in revised form 19 April 2014ccepted 22 April 2014vailable online 10 May 2014

eywords:

a b s t r a c t

Today, manufacturing firms encounter pressure from multiple stakeholders to manage occupationalhealth and safety issues properly, systematically and transparently. While manufacturing firms com-monly use internally developed Occupational Health and Safety Management Systems, there is growingpressure to adopt externally certified system such as OHSAS 18001. However, there are conflicting viewsand little empirical evidence that examines the linkage between OHSAS 18001 certification and operat-ing performance. Hence, this paper examines the impact of OHSAS 18001 on operational performancethrough three theoretical lenses: Institutional Theory, Normal Accident Theory, and High Reliability The-

HSAS 18001vent studyccupational health and safetyontextual factors

ory. We also investigate how complexity and coupling moderate the relationship between OHSAS 18001and operational performance. Based on a sample of 211 U.S. listed manufacturing firms with OHSAS18001 certification, we find that certification leads to significant increases in abnormal performance onsafety, sales growth, labor productivity, and profitability and that these benefits increase as complexityand coupling increase.

. Introduction

Manufacturing firms encounter increasing pressure from mul-iple stakeholders to manage Occupational Health and SafetyOHS) issues systematically and improve workplace conditions. Fornstance, multi-nationals such as Apple, Cisco, and Tata motorsow request that their suppliers obtain Occupational Health andafety Management System (OHSMS) certification to ensure OHSssues are managed systematically and transparently. Occupationalealth and Safety Assessment Series 18001 (OHSAS) is the favoredertification because the system has to be audited by an inde-endent organization. OHSAS certification appears to follow theissemination pattern of ISO 9001 and then 14001, suggesting that

n a fairly short period of time this certification could become the

orm.

Brown (1996) and Pagell et al. (2013) both argued that safetyust be treated as a key operational priority alongside cost, qual-

� The work described in this paper was substantially supported by a grant fromhe Research Grants Council of the Hong Kong Special Administrative Region, ChinaProject No. PolyU, 5452/11H).∗ Corresponding author. Tel.: +852 27665609.

E-mail addresses: tcclo@polyu.edu.hk, kwan.yu.lo@polyu.edu.hk (C.K.Y. Lo).

ttp://dx.doi.org/10.1016/j.jom.2014.04.004272-6963/© 2014 Elsevier B.V. All rights reserved.

© 2014 Elsevier B.V. All rights reserved.

ity, flexibility, delivery and innovation. Moreover, safety is not justa priority of a few large corporations, a majority of governmentsand citizens demand it as a basic human right. Workplace safety isa legal and moral expectation that must be met by an organizationin order to maintain its license to operate. Poor safety performanceindicates an organization’s failure to meet the expectations of cus-tomers, regulators and other stakeholders. Most safety incidentsoccur in operational settings, therefore poor safety performance isindicative of poorly managed operations; safety is an operationalpriority.

Operational workers suffer the majority of occupational illnessand accidents in manufacturing settings. The manufacturing sec-tor provides about 9% of employment in the United States, butit accounts for about 30% of occupational accidents and illness(Bureau of Labor Statistics, 2013). However, OHS issues receive littleattention in the operations management literature (Brown, 1996;de Koster et al., 2011; Pagell et al., 2013). Instead, the safety ofoperational workers is mostly studied in various other academicfields including occupational medicine and ergonomics. Absent anoperational perspective, the present understanding of safety is lim-

ited, with unknown implications for both the safety of operationalworkers and other operational performance outcomes (for a fulldiscussion see Das et al., 2008 or Pagell et al., 2013). Worse, the lit-

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rature provides divergent predictions as to how safety relates tother operational priorities and outcomes such as cost and quality.

Our research takes a theory-driven approach to increase ournderstanding of the operational priority of safety by examininghe impact of OHSMS certification, in the guise of OHSAS 18001,n operational performance outcomes. Specifically, this researchsks what is the general impact of OHSAS 18001 certification on oper-tional performance. Additionally, because context has been showno influence operational performance in general as well as thempact of other certifications (Nawrocka et al., 2009; Lo et al., 2013)his research also asks what is the role of context in the OHSAS 18001perational performance relationship.

We explore four dimensions of operational performance thatHSAS could impact: safety, sales, labor productivity, and prof-

tability. We examine safety since this is what OHSAS certifications intended to improve. We examine sales because certification iseing driven by customers and could therefore impact sales. Wexamine labor productivity because it functions as a good proxy forperating effectiveness (e.g., Levine and Toffel, 2010) and becausemprovements in safety are likely related to labor productivity.

e examine profitability, measured as ROA, because ROA is anfficiency-based metric of profits that is reflective of overall oper-tional effectiveness (e.g., Corbett et al., 2005; Swink and Jacobs,012). In order to simplify the remainder of the document we usehe term “economic performance” to refer to the more traditionalndicators of operational performance (profitability, sales and laborroductivity) and “safety” to refer to the worker well-being com-onent of operational performance.

The research questions are addressed via a longitudinal analysisf the long-term impact of certification on operational per-ormance. Answers gained from these questions contribute toperations management practice and theory. The results provideanagers considering OHSAS certification a clearer understand-

ng of how managing a production system to be safe influencesther operational performance outcomes, such as productivity androfitability. The results also contribute to our understanding ofheory by empirically testing the conflicting predictions abouthanges in operational performance that emerge from institutionalheory, normal accident theory (NAT) and high reliability theoryHRT).

. Theoretical development and hypotheses

OHSAS 18001 remains the most popular externally certifiedHSMS (Fernández-Muniz et al., 2012). Since the certification was

ntroduced in 1999, it has diffused rapidly, growing from 8399 cer-ifications in 2003 to 56,251 in 2009 (a compound annual growthate of 37.46%) (OHSAS Project Group, 2011). Over the same periodSO 14000 certifications grew from 64,996 to 222,974 (and nowre at 267,457) and ISO 9000 certifications exceeded one millionInternational Organization for Standardization, 2012). So whileHSAS 18001 grows rapidly and could eventually become the de-

acto safety standard, today it is relatively rare compared to otherertifications such as ISO 9001.

OHSAS 18001 aims to certify that a firm’s OHSMS can developnd maintain a safe workplace; protecting workers from acci-ents and illness (British Standards Institution, 2012). Certificationequires firms to identify hazards, assess safety risks, and estab-ish and measure safety controls to continuously enhance safetyerformance (Fernández-Muniz et al., 2012). OHSAS 18001 is thenimilar to other externally certified management systems such as

SO 9001 and 14001, both of which have been linked to improvedrm operational performance (Corbett et al., 2005; Lo et al., 2012).

Despite these similarities, OHSAS 18001 differs from otherxternal certifications and could influence operational perfor-

anagement 32 (2014) 268–280 269

mance differently. ISO 9001 and 14001 certifications were oftendriven by customer demand to create management systems wherelittle previously existed. However, most firms have an OHSMS andmany have been actively managing safety for decades, both becauseof their values and because safety regulation has existed in theUnited States since the 1930s. OHSAS 18001 then generally aug-ments or replaces existing, and often effective, systems. Equallyimportant, while ISO 9001 clearly benefits customers directly andISO 14001 is linked to resource efficiency, the literature lacks a busi-ness case for safety (see Pagell et al., 2013) in general and safetycertifications specifically.

Three different theoretical perspectives could be used to pre-dict the relationship between OHSAS certification and operationalperformance. Institutional theory suggests that firms get certifiedmainly to gain legitimacy, with limited impact on operational per-formance; NAT and the concept of role overload suggests thatfirms sacrifice safety for other operational performance outcomes;finally, HRT and the human capital perspective suggests that certi-fication should improve all operational performance outcomes.

2.1. Institutional theory

From an institutional perspective, OHSAS certification helpsa firm signal legitimacy to major customers (Staw and Epstein,2000; Suchman, 1995). Previous research showed that ISO 9001and 14001 were both adopted partially for legitimating reasons(Boiral, 2007; Qi et al., 2011), hence it is likely that OHSAS certifi-cation would have a similar effect. OHSAS 18001 is interpreted asa signal of a firm’s commitment to health and safety management.Given the increasing demand for organizations to at least appear tomeet expectations about health and safety, such pressure could bea powerful driver toward certification.

If this perspective is correct, OHSAS certification improves thefirm’s sales performance, since certification may meet customers’safety requirements (Law et al., 2006). However, if the only benefitfrom certification is the ability to signal, then all other componentsof operational performance would remain unchanged after certifi-cation. Based on this perspective, the process of certification wouldnot alter production processes or the production system’s reliabil-ity. The primary benefit of certification would be increased salesonce certification was granted.

2.2. Normal accident theory: coupling leads to role overload

Normal accident theory (Perrow, 1981, 1984) proposes thataccidents in complex and tightly coupled systems are inevitable,regardless of efforts to control them. A system’s complexity isdriven by the level of variability in interactions, the number ofmulti-functional processes or jobs, the level of specialization oftasks that can limit awareness of interdependencies, and theneed to deal with the unfamiliar or unintended (Perrow, 1984;Shrivastava et al., 2009). A system is tightly coupled if there is min-imal slack between steps, if processes could only be done in onesequence, if substitution of labor, supplies and processes are diffi-cult if not impossible, and if work could only be done in one way(Perrow, 1984; Shrivastava et al., 2009).

Shrivastava et al. (2009) noted that all systems that transformraw materials are relatively complex and tightly coupled. Hence,NAT predicts that in manufacturing organizations, accidents areinevitable, with the risk of an accident increasing as complexityincreases or coupling becomes tighter (Wolf, 2001). NAT alsosuggests that more formal work rules (e.g., standard operating

procedures) in tightly coupled environments do not necessarilylower the risk of an accident, because the rules might have seriousunintended consequences in unpredictable situations, for whichthe rules were not designed to anticipate (Rijpma, 1997).

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70 C.K.Y. Lo et al. / Journal of Operat

The predictions from NAT are in-line with the conclusionsrom numerous researchers who proposed that there is a trade-ff between safety and other operational outcomes such as qualityr productivity (e.g., Brenner et al., 2004; Ford and Tetrick, 2008;odard, 2004; Landsbergis et al., 1999; Lewchuk et al., 2001; Parker,003; Paté-Cornell and Murphy, 1996; Zohar, 2002; Zohar anduria, 2005). Safety and other operational priorities are likely inonflict because operational workers often feel pressure to takehortcuts on safety, in order to meet production quotas or even torotect their jobs (e.g., Brown, 1996; Brown et al., 2000; Mclain,995; Barling et al., 2002; de Koster et al., 2011).

From an operational perspective, slack resources are oftenndicative of waste and hence many operational best practices, suchs lean, are focused on removing slack. However, from the perspec-ive of operational workers, reduction in slack means less time too tasks, creating “role overload” (e.g., Mclain, 1995). As productionystems become more efficient, there is less time for workers toomplete tasks and workers often feel pressured to take shortcuts.lack resources are a critical way to decrease coupling (Wolf, 2001),educing slack increases coupling. The theoretical explanation forhe trade-off perspective would be that operational improvementsncrease coupling, create role overload, which leads to an increasen accidents.

Similar logic applies for improving safety. Improvement inafety takes organizational resources and requires operationalorkers to spend time on safety rather than production. If this per-

pective is correct, then if OHSAS certification leads to improvedafety, it must do so by harming other operational performanceutcomes. Specifically, workers would be less productive, whichould then lead to decreased profits. However, sales would likelyot change since an organization would gain some customers whoill pay the higher price for proper safety conditions and lose thoseho will not. Based on this perspective, organizations pursuing

ertification would see trade-offs between safety and productiv-ty from the moment they began to pursue certification, and thoserade-offs would continue through and beyond certification

.3. High reliability theory and the human capital perspective

The argument that safety is in conflict with other operationalerformance outcomes finds compelling support in both the safetye.g., Zohar, 2002) and operations literature (e.g., de Koster et al.,011), but remains generally untested. There is an equally com-elling perspective in the literature based on the human capitalerspective and HRT.

The operations management community has generally con-luded that continuous improvement-based management systemsre best practice and that they require well-trained and empow-red workers. In an optimized production system, human capitaleeds to be valued and leveraged. Workers who are not safe areot valued, and cannot be leveraged.

The human capital perspective fits well within the predictionsf HRT. HRT posits that accidents are preventable in highly reli-ble organizations. These organizations are described as “mindful”,enoting that they make safety a strategic priority, pay carefulttention to design and processes, have mechanisms for valuinguman capital, and have a safety culture (Reason, 1998; Weickt al., 1999). Critically HRT suggests that high reliability benefitsll operational performance outcomes, not just safety (e.g., Weickt al., 1999); highly reliable organizations can prevent accidentsnd other failures such as quality defects or late deliveries.

The benefits of high reliability extend beyond safety for numer-

us reasons. First, in highly reliable organizations workers can trusthat management regards safety as a priority, which establishes

climate whereby the workers focus on their tasks rather thanelf-protection (Colquitt et al., 2011). A safer working environment

anagement 32 (2014) 268–280

fulfills the workers’ basic safety needs and allows the workers topursue operational goals (Das et al., 2008).

Second, control of processes is fundamental to HRT. Numerousauthors (e.g., Shrivastava et al., 2009; Farjoun, 2010) have notedthat as complexity and coupling increase, the need for controlincreases, because the potential for catastrophic failure expands.Control of transformation processes is also a fundamental compo-nent of safety and operations management, and increased controlof processes means that there is less variance and a reduction innon-productive tasks such as rework.

Finally, workplace injuries and illnesses have direct and indi-rect costs. The direct costs include workers’ compensation, medicaltreatment (Loeppke et al., 2007) and insurance premiums (Starr andWhipple, 1984). The indirect costs include overtime wages, laborturnover, new employee training, lost working days (Oxenburghand Marlow, 2005) and reputational damage (de Koster et al., 2011).Previous studies have highlighted that an OHSMS can be an effec-tive tool to reduce accidents and illnesses among workers, leadingto lower absenteeism and labor turnover and a general reduction inthese costs (e.g., de Koster et al., 2011; Loeppke et al., 2007). Fromthis it could be concluded that improved safety performance shouldimprove a firm’s profitability (Robson et al., 2007).

HRT can then explain the literature which suggests that safetyand other operational outcomes move in tandem. When a systemis safe, workers trust management and can focus on their jobs; theproduction system should be in control which is a critical conditionfor outcomes such as quality; therefore costs will be reduced.

This set of predictions could apply to all effective OHSMS, notjust OHSAS certification. If all OHSMS improved reliability, then theinstitutional perspective’s prediction that certification was mainlya way to signal should be supported. However, the literature on cer-tification suggests that external certifications has added benefitsover internal systems including: an increased awareness of pro-cesses, greater measurement and control, less room for backslidingsince an external assessor must be satisfied on a regular basis, andincreased training and participation of workers (e.g., Curkovic andPagell, 1999; Sroufe and Curkovic, 2008).

OHSAS 18001, when properly implemented, fosters manyattributes of a highly reliable organization, which on averageimproves all operational outcomes, not just safety. If OHSAS cer-tification is not purely ceremonial then it could provide benefitsbeyond an internally developed OHSMS. Based on this perspective,organizations that pursue certification realize improvements in atleast productivity and safety from the moment they begin to pur-sue certification, and those benefits would continue through andbeyond certification, leading to improved sales and profits.

Based on the discussion above, we propose:

H1. There is a significant relationship between OHSAS certifica-tion and safety performance.

Institutional theory predicts that certification leads to increasedsales, but would not impact ROA1 or productivity. NAT predicts thatif certification did improve safety then productivity and ROA woulddecrease, while HRT suggests that improvements in safety due tocertification would occur with simultaneous improvements in theother operational performance outcomes. Following the same logic

1 Increased sales could improve ROA because fixed costs are distributed over moreunits; depending on the sales expenses and relative capital intensity. However, theadditional operational costs to achieve and maintain the certification as well asadditional investments in safety training and equipment could offset the impactfrom increased sales to ROA.

C.K.Y. Lo et al. / Journal of Operations Management 32 (2014) 268–280 271

Table 1Predictions on the relationships between OHSAS 18001 certification and operational performance.

Safety (H1) Sales growth (H2) Labor productivity (H3) ROA (H4)

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izes the predictions each theory would make for each dimensionf operational performance.

2. There is a significant relationship between OHSAS certifica-ion and sales growth.

3. There is a significant relationship between OHSAS certifica-ion and labor productivity.

4. There is a significant relationship between OHSAS certifica-ion and ROA.

.4. Contextual factors and the effectiveness of OHSAS 18001ertification

If OHSAS certification is more than a signaling device, thennstitutional explanations are insufficient to explain the OHSASertification operational performance relationship. However, bothAT and HRT based explanations could still be useful in predicting

he role of context in the certification/performance relationship.We follow much of the extant literature and present NAT and

RT as competing theories (Wolf, 2001; Rijpma, 2003; Shrivastavat al., 2009). Yet Shrivastava et al. (2009) propose that once oneccounts for “time” the two theories can be complimentary: orga-izations that are not constantly mindful will over time drift (Snook,000) from reliable behavior into behaviors that lead to accidents.estated an organization can stay highly reliable as long as it main-ains mindfulness and does not drift away from the prescribedehaviors. Maintaining high reliability is however difficult becauserift or entropy are typical outcomes in organizations.

Shrivastava et al. (2009) then develop the work of Leveson2004) and Rasmussen (1997) to conclude “the higher the abilityo control transformation processes, the higher the level of safety inhe workplace” (pg. 1380). Shrivastava et al. (2009) build on bothAT and HRT to note that as complexity increases and couplingets tighter, the need for control increases because of the potentialor harm. Shrivastava et al. (2009) synthesis of NAT and HRT woulduggest that as complexity increases and coupling becomes tighterhe benefits of OHSAS certification would increase. We use this syn-heses to develop hypotheses for our second research question onhe impact of context on the certification/operational performanceelationship. Complexity is explored by examining R&D and laborntensity, while coupling is explored by looking at inventory levelsnd volatility.

Because the benefit of R&D investment is distant (Ravenscraftnd Scherer, 1982) and its influence on operations is ambigu-us (Hoskisson and Johnson, 1992), R&D intensive firms oftenncounter uncertainties in meeting customer demand (Palmer andiseman, 1999). These uncertainties create complex circumstance

or operations, for example difficulty in scheduling (Prater et al.,001). Furthermore, the production processes of R&D intensiverms are often technologically complex (Singh, 1997). If produc-ion is not outsourced, operational workers at these firms willave higher exposure to hazardous circumstances, because it isore difficult to take immediate corrective action in complicated

rocesses. This complexity increases the risk of accidents andystems failures (Perrow, 1984; Singh, 1997). In such settingsHSAS 18001’s potential to enhance control could be of increasedenefit relative to firms with less complex production settings.

No change No changeDecreases DecreasesImproves Improves

Similarly, when labor intensity increases, a firm’s operationsbecome more variable and complex (Swink and Jacobs, 2012). Laborintense firms’ production processes are often complicated and notautomated; therefore these firms rely heavily on workers in theiroperations. Variability and complexity increase the likelihood ofaccidents (Perrow, 1984) and high levels of labor intensity increasethe potential impact of each of these accidents on the system.OHSAS 18001 should continuously improve working conditionsand lead to lower turnover, lower recruitment and training costs,and improved labor productivity (de Koster et al., 2011; Mclain,1995). Additionally, OHSAS 18001 may allow a firm to recruithigher quality workers relative to non-adopters, because skilledworkers have more power to bargain for better welfare and workingconditions (Cahuc et al., 2006). These benefits will be most usefulto firms that are relatively labor intense.

H5. As operational complexity (measured by R&D and labor inten-sity) increases the benefit of OHSAS certification also increases.

NAT predicts that as coupling increases the risk of safety fail-ures also increases (Perrow, 1984). One of the primary means ofdecoupling in a manufacturing setting is via the use of inventorybuffers. Inventory can buffer for stochastic demand (Minner, 2001)or keep the supply chain running when disruptions occur. Inven-tory creates slack for the workers so that they do not have to reactto every change in demand or problem in delivery, allowing themto do their jobs as planned and reducing the odds of drift. Inven-tory is then an indicator of coupling, with low levels of inventorybeing associated with high levels of coupling. As coupling increases(inventory decreases) the risk of failure increases and, hence, thevalue of OHSAS 18001 could also increase.

The assumption that more inventory means looser coupling ispremised on holding the correct inventory. Firms that do a poor jobwith tasks such as forecasting and planning may have the wronginventory. Firms that have the wrong inventory will make morefrequent set-ups, spend time that was set aside for maintenanceon production, do more expediting, and have to move more partsin and out of storage. Holding the wrong inventory, as evidencedby increased inventory volatility, is indicative of increased cou-pling. Fig. 1 summarizes the relationship between certification andoperational outcomes, and the influence of context.

H6. As operational coupling increases (measured by increasedinventory volatility and decreased inventory level), the benefit ofOHSAS certification also increases.

3. Methodology

In this research we sampled all U.S. listed manufacturing firms(SIC 2000-3999). We collected financial data from Standard andPoor’s COMPUSTAT database, industry characteristics data (e.g.,labor working hours and wages) from the U.S. Census Bureau(2013), and safety violation data from U.S. Department of Labor.

As there is no publicly available database containing the fulllist of OHSAS 18001 certified firms, data on OHSAS certificationwere collected from three sources: official company websites,

annual reports (from SEC filings), and media reports (from Factivabased on the keywords OHSAS, 18001, Occupational Health andSafety Management System, and the company name). This was amulti-step process with built in redundancy. First, one member of

272 C.K.Y. Lo et al. / Journal of Operations M

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The Roles of Com plex and Coupling Opera�ons Context

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Labor Produc�vity

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Fig. 1. The research framework.

he research team searched all three sources for announcementsf certification for each of the 8477 listed manufacturing firms.econd, a different research team member verified the announce-ents identified. Third, firms without an announcement were

e-checked to ensure that all the certified firms had been includedn our sample. If we found no OHSAS certification informationn a firm’s website or in annual or media reports, the firm wasonsidered a non-adopter. A clear certification date was neededor an adopter to be included in our sample. This research followshe practice of previous certification literature (e.g., Corbett et al.,005) by focusing only on the first OHSAS certification.2

Out of the 8477 U.S. listed manufacturing firms (both active andnactive), 374 had secured at least one OHSAS certification between999 and 2011. Seventy-eight of the 374 firms had to be discardedecause there was no information on the year of their first cer-ification. Additionally, 85 firms did not have financial data fromhe period of OHSAS certification (these firms had obtained certi-cation prior to listing on the stock exchange), so these were alsoiscarded. Finally, 211 firms were included in our analysis.

Certification patterns in the sample followed the overall trendor OHSAS 18001. Between 1999 and 2004, 71 firms (35.6% of theample) had obtained certification. Certification has become moreopular in recent years (2005–2011) with 140 firms (64.4% of theample) obtaining certification in this period. OHSAS certification inhe sample appears to be more prevalent in recent years. However,

uch like the overall population, there are still relatively few firmsith certification, especially when compared to ISO 9001 and ISO

4001. Firms in the electronics industry (SIC: 36) had obtained theost OHSAS certifications, with chemical products (SIC: 28) and

ndustrial machinery manufacturing (SIC: 35) being the second andhe third respectively.

.1. Event study approach for longitudinal analysis

To explore the causal relationship between OHSAS certificationnd operational performance, we used a long-horizon event study

2 OHSAS 18001 certification is facility specific so a firm with multiple facilitiesay obtain multiple certifications.

anagement 32 (2014) 268–280

approach. Consequently, this research defines the event year (year0) as the year when the firm had first acquired OHSAS certification.Furthermore, the long-horizon event study approach requires us todefine the base year of the event (i.e., the starting year of the eventperiod), which is the year when the firm was free from the impact ofimplementing OHSAS 18001. On average it takes between 6 and 18months to implement a management certification prior to registra-tion (Corbett et al., 2005; Lo et al., 2012). Thus, we define year −2 asthe base year, which means that the firm has yet to start the imple-mentation process. As we are interested in the long-term impactof OHSAS 18001, this research investigates abnormal performancechanges over the four years from the start of the certification pro-cess (year −1, year 0, year 1 and year 2).

We matched each sampled firm with a portfolio of compa-rable control firms. Following previous research (e.g., Hendrickset al., 2007), a separate portfolio was created for each firm for eachof the economic performance indicators: sales, labor productivityand ROA. Each portfolio was based on three factors: industry, firmsize, and performance on that specific element of economic perfor-mance. Following Barber and Lyon (1996), the two-digit SIC codewas used to match with industry. To control for size, each sampledfirm was matched to control firms within 50–200% (a factor of 2)of the sample firm’s total assets (Hendricks and Singhal, 2008). Forthe specific element of economic performance, the control firm’schosen were those with 90–110% of the sampled firm’s pre-eventperformance (the firm’s sales, labor productivity, and ROA in year−2).

Creating a similar portfolio for safety would have been optimal,but was not feasible because collecting safety data for all listed firmswas beyond our resources. Therefore, the portfolio of control firmsused for safety performance assessment was the same as that usedfor ROA. ROA was chosen because sales and productivity shouldcontribute to it and because it is the economic performance metricused in our cross sectional analysis.

On average, each sampled firm matched with 28.25 controlfirms, and 97.15% of the sample matched with 5 or more controlfirms. Following this matching process, we estimated the abnor-mal change in performance of the sample firms as compared to thecontrol group. The formulas used for calculating abnormal perfor-mance are

AP(t+j) = PS(t+j) − EP(t+j)

EP(t+j) = PS(t+j) + (PCk(t+j) − PCk(t+j))

where AP is the abnormal performance, PS is the actual perfor-mance, and EP is the expected performance of the sample firms, PCis the median performance of control firms, t is the OHSAS 18001certification year, i is the base year (i = −2) and j is the endingyear of comparison (j = −1, 0, 1 or 2). For the economic perfor-mance indicators, we used ROA for profitability, sales growth forsales performance, and the ratio of operating income to number ofemployees for labor productivity. We followed previous researchand used the number of violations of safety regulations as a proxyfor safety performance (see, for instance, Pagell and Gobeli, 2009).We collected violation data from the OHSA Integrated Manage-ment Information System (IMIS) database of the U.S. Departmentof Labor (OSHA IMIS, 2013), which contains information on fourtypes of violations; (1) normal, (2) repeated, (3) serious, and (4)others. We followed previous practice (Pagell and Gobeli, 2009)and treated all violations as equal. Correcting for employment is thenormal practice followed by many government agencies (Bureau of

Labor Statistics, 2013) and researchers (Pagell and Gobeli, 2009) inassessing how likely it is for any one employee to get injured. Ourmeasure of safety performance was therefore the ratio of violationsper every 10,000 employees in a given year.

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Following Barber and Lyon (1996) and Corbett et al. (2005),e trimmed the datasets, eliminating outliers at each tail of theopulation of each performance outcome. Furthermore, we con-ucted the Wilcoxon sign-rank (WSR) test and significant test toxamine abnormal performance. Following common practice (e.g.,endricks and Singhal, 2008), we will mainly discuss the resultsased on the WSR test, as this test is less influenced by outliershan the parametric t-test. However, to verify the robustness of ourndings, we conducted additional parametric t-tests for the meansf abnormal performance.

.2. Cross-sectional analysis of contextual factors

To test H5 and H6, both abnormal safety and abnormal ROAere used as dependent variables. We used abnormal ROA as

he dependent variable for economic performance (instead ofabor productivity and sales growth) in the cross-sectional analysisecause it was believed to represent overall operational effec-iveness. This is consistent with recent research using a similarpproach (e.g., Swink and Jacobs, 2012). To be consistent andomparable to other studies using a similar methodology (e.g.,endricks and Singhal, 2008), we used OLS to test H5 and H6.

The OLS regression models were specified as follows:

Pk = �0 + �1(PPk) + �2(FSizek) + �3(Yeark) + �4(ISO 9001k)

+ �5(ISO 14001k) + �6(ISizekh) + �7(Wagekh)

+�8(Unionkh) + �9(Hourkh) + �10(RDk) + �11(LIk)

+�12(IVk) + �13(ILk) + ek

here k refers to the kth sample firm and h refers to the hth industryhat the kth firm operates in.

The outcome variable APk is either abnormal ROA or abnormalafety performance. RDk is the firm’s R&D intensity, which is theatio of R&D expense to total assets (Naveh and Marcus, 2005; Russond Fouts, 1997). LIk is the firm’s labor intensity, equaling the ratiof the number of employees to total assets of the firm (Dewenternd Malatesta, 2001). These two indicators are the independentariables for H5. IVk is a firm’s yearly inventory volatility, which ishe ratio between the standard deviation of the firm’s inventory byuarter and its mean quarterly inventory value in the year (Steinkernd Hoberg, 2013). ILk refers to the average inventory scaled byotal assets. IVk and ILk are the independent variables for H6.

To ensure the rigorousness of our model, we controlled for sev-ral firm and industry level factors that could impact on the samplerms’ abnormal performance. First, since more profitable firmsould have more resources to achieve higher profitability in theuture, we controlled their ROA in year −2 (PPk). Second, becausearger firms could have more resources, but larger firms may alsoave more difficulty coordinating employees when implementingHSAS 18001, we controlled for firm size (Douglas and Fredendall,004). Firm size (FSizek) was the natural logarithm of the numberf employees employed by the firm in year −2 (Kull and Wacker,010). Finally, institutional theory suggests that the motivation fornd outcomes of certification could be different for early and latedopters (Westphal et al., 1997; Zhu and Sarkis, 2007). Therefore,e controlled for the year of OHSAS 18001 certification (Yeark). Therm level variables were based on data in year −2.

It is also possible that the firms sampled had adopted an inte-rated management system, which included ISO 9001, ISO 14001nd OHSAS 18001. Therefore, we created two dummy variables

hat showed whether the firm had also received ISO 9001 or ISO4001 certification during the same event study period (year −2 toear +2). The dummy variables were coded 0 for firms without ISO001/14001 and 1 for firms with ISO 9001/14001.

anagement 32 (2014) 268–280 273

Industry level control variables were also used in the model.First, we controlled for the size of sample firms’ industry h (ISizekh).There are various proxies for industry size, including industry out-put (Moomaw, 1988), number of firms (Weiss, 1963) and numberof employees (Rushing, 1967). We used the number of employ-ees (Rushing, 1967) because our research focused on workers andworker related outcomes. Second, we controlled for the averageproduction worker’s wages in the industry (Wagekh), equal to thenormalized annual wage per production worker. We also con-trolled for the influence of labor unions in the industry (Unionkh).Production workers are more likely to be unionized in danger-ous industries (Hirsch and Berger, 1983) and unionized workerscan bargain collectively with employers to secure higher risk pre-miums in comparison with non-unionized workers. Therefore,firms in industries with high levels of union influence wouldencounter additional operations and administrative costs (Freemanand Medoff, 1981; Olson, 1981). We used the ratio of the numberof workers covered by collective agreements to the total number ofworkers in industry as the labor union influence indicator. Finally,we used the industry average working-hours of production workersto control for the association between health and safety problemsand long working hours (Hourkh) (Spurgeon et al., 1997). All indus-try level control variables were based on data collected for year−2.

4. Results

First, we compared the sample to their industry and the controlfirms on the four operational performance indicators as well as thecontrols. We conducted paired-sample t-tests which indicated that,compared to their industry two-years (year −2) prior to certifica-tion, the certified firms (0.089 violations per 10,000 employees)had significantly better safety performance (0.641 violations per10,000 employees, p < 0.001). Not only was the propensity for viola-tions lower than industry norms, this group exhibited less variancewith respect to violations, suggesting greater control of safety pro-cesses and safety outcomes prior to OHSAS certification. However,when compared with the matched control firms’ safety perfor-mance there was no significant difference, indicating that using theROA control portfolio for safety was not a concern. Prior to certifi-cation, the sample and the matched control firms (based on ROA)exhibited significantly better safety performance than the corre-sponding industry.

4.1. Results from the event study analysis

The result of the cumulative abnormal performance analysisis shown in Table 2 and the year-to-year abnormal performanceanalysis is shown in Table 3. We followed convention and usedthe largest sample available for each period and each dependentvariable (e.g., Corbett et al., 2005; Swink and Jacobs, 2012). Thesample varied across tests for two reasons. The primary reason wasmissing data. Firms may be missing information on one or moreof the performance outcome variables in any given period. Thisproblem is more common in firms with more recent certifications(2010 and 2011) because the COMPUSTAT database did not containyear 1 or year 2 financial data for all recently certified firms. Thesecond reason was survivor bias, particularly among firms thathad gone out of business or delisted after being certified in year−2. Four firms in the sample were delisted over the event horizon.

However, the results for each hypothesis remained the same afterremoving these 4 delisted firms. The results show the impactof OHSAS certification on all 4 dimensions of the sample firms’abnormal operational performance.

274

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Table 2Results of sample firms’ cumulative abnormal performance.

Pre-certification (year −2 to year 0) Post-certification (year 0 to year 2) Full event window (year −2 to year 2)

N Median Percentage Mean N Median Percentage Mean N Median Percentage Mean

Safety Performancea,b 194 −0.00 41.23% −0.21 163 −0.00 39.26% −0.03 163 −0.00 45.40% −0.20Statistic −2.67** 3.14** −1.67+ −1.69+ 2.93** −0.84 −1.14 2.07* −1.18Sales growth 180 0.63 51.67% −0.31 148 2.10 56.08% 2.66 148 1.95 56.76% 2.92Statistic 0.27 0.53 −0.29 1.69+ 1.58 1.96+ 1.65+ 1.65+ 2.10*

Labor productivity 177 2.26 58.19% 4.82 143 −0.09 49.65% 2.62 143 3.11 58.74% 7.18Statistic 2.39* 2.11* 3.09** −0.69 0.00 1.39 2.55* 2.01* 3.18**

ROA 194 0.82 58.24% 1.04 163 0.71 54.60% 0.83 163 1.41 58.94% 1.68Statistic 2.83** 2.23* 3.39** 1.801+ 1.097 2.168* 3.27** 2.59** 3.86**

Note: Z-statistics for WSR test (median) and sign test (percentage), t-statistics for t-test (mean).Percentage indicates the percentage of firms achieving positive abnormal changes in safety performance, sales growth, labor productivity and ROA.

+ Note a statistically significant difference from 0 at 0.1 level (two-tail).* Note a statistically significant difference from 0 at 0.05 level (two-tail).

** Note a statistically significant difference from 0 at 0.01 level (two-tail).a The percentage of negative safety performance between sample and control firms in the pre-certification, post-certification and the full event window period are 22.09%, 20.86% and 30.67%, respectively.b The absolute number of violations in the pre-certification, post-certification and the full event window period are −41.80, −4.89, and −32.60 per 10,000 employee, respectively.

Table 3Results of sample firms’ year-to-year abnormal performance.

Year −2 to year −1 Year −1 to year 0 Year 0 to year 1 Year 1 to year 2

N Median Percentage Mean N Median Percentage Mean N Median Percentage Mean N Median Percentage Mean

Safety performancea,b 211 −0.00 30.81% −0.12 194 −0.00 30.41% −0.08 180 −0.00 31.11% −0.03 163 −0.00 26.30% 0.10Statistic −2.26** 3.25** −1.12 −2.90** 3.60** −1.97+ −1.78+ 3.07** −1.20 −0.52 1.79+ 0.15Sales growth 197 1.48 54.31% 0.31 180 0.86 51.67% 0.97 165 2.60 58.18% 3.63 148 −0.91 47.29% −0.69Statistic 0.55 1.29 0.28 0.47 0.60 0.59 2.35* 2.28* 2.40* −0.66 −0.50 −0.37Labor productivity 192 1.58 56.50% 2.04 177 1.97 57.63% 2.53 160 2.10 56.88% 3.34 143 0.45 51.75% 0.55Statistic 1.79+ 1.66+ 2.03* 1.68+ 1.95+ 1.71+ 2.05* 1.66+ 2.45* 0.07 0.33 0.42ROA 211 0.42 55.92% 1.01 194 0.73 56.19% 0.73 180 0.54 58.33% 0.51 163 0.76 57.06% 1.03Statistic 2.93** 1.65+ 2.43* 1.89+ 1.65+ 2.03* 2.30* 2.16* 2.16* 1.89+ 1.72+ 2.35*

Note: Z-statistics for WSR test (median) and sign test (percentage), t-statistics for t-test (mean).Percentage indicates the percentage of firms achieving positive abnormal changes in safety performance, sales growth, labor productivity and ROA.

+ Note a statistically significant difference from 0 at 0.1 level (two-tail).* Note a statistically significant difference from 0 at 0.05 level (two-tail).

** Note a statistically significant difference from 0 at 0.01 level (two-tail).a The percentage of negative safety performance between sample and control in the period of year −2 to year −1, year −1 to year 0, year 0 to year 1 and year 1 to year 2 are 15.16%, 12.89%,15.00% and 16.64%, respectively.b The absolute number of violations in the period of year −2 to year −1, year −1 to year 0, year 0 to year 1 and year 1 to year 2 are −25.32, −15.52, −5.4 and 16.3 per 10,000 employee, respectively.

ions Management 32 (2014) 268–280 275

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Table 4Results of systematic bias tests.

Year −3 to year −2

N Median Percentage Mean

Safety performance 204 0.00 26.07% 0.02Statistic 1.46 0.28 −0.51Sales growth 192 0.56 52.82% 1.44Statistic 0.99 0.79 1.12Labor productivity 186 0.92 53.44% 1.28Statistic 1.44 0.87 1.44ROA 204 0.34 52.45% −0.23Statistic 0.67 0.63 −0.51

Note: Z-statistics for WSR test (median) and sign test (percentage), t-statistics for

C.K.Y. Lo et al. / Journal of Operat

The sample firms’ abnormal safety performance (H1) at the 1%evel was significantly better (based on the WSR tests) compared tohat of the control firms during the implementing period (year −2o year 0). OHSAS 18001 increased abnormal safety performanceor the sampled firms even though, prior to certification, they (andhe controls) were performing better than the industry average.his eliminated the possibility that OHSAS certification was only aeremonial action. This observation provided support to H1.

H2 predicted that OHSAS certification had a significant impactn a firm’s sales performance. The results shown in Table 2 indi-ated that the cumulative abnormal sales growth from year −2 toear 0 was not statistically significant, while the results for botheriods (year 0 to year 2) and (year −2 to year 2) were statisti-ally significant. For instance, the median (mean) change from (year2 to year 2) was 1.95% (2.92%), which was significant at the 10%

5%) level. In this period, 56.76% of the sample firms achieved posi-ive abnormal change. Table 3 indicated significant abnormal salesrowth in the period (year 0 to year 1) but not in the years prioro or after certification. In the period (year 0 to year 1) the medianmean) change was 2.60% (3.63%), which was significant at the 5%5%) level. In total, 58.18% of the sample firms achieved positivebnormal change in this period. This result suggests that obtain-ng certification provided firms with opportunities to attract newustomers. Further, they indicate that OHSAS 18001 had a positivempact on the sample firms’ long run abnormal sales performance.his observation supported H2.

H3 predicted that OHSAS certification should have had a sig-ificant impact on a firm’s labor productivity. This hypothesis wasased on actual changes in processes rather than just on signaling,o the improvement should have been realizable across all periods.he cumulative results shown in Table 2 on abnormal labor produc-ivity were significantly positive during the pre-implementationeriod as well as in the full event window (year −2 to year 0 andear −2 to year 2). For instance, in the period (year −2 to year 2), theedian (mean) change was 3.11 thousand (7.18 thousand), whichas significant at the 5% (1%) level. In this period, 58.74% of the

ample firms achieved positive abnormal change. Table 3 showshat the sample firms had significant abnormal labor productiv-ty in the year they started to implement OHSAS 18001 (year −2 toear −1). The median (mean) abnormal increase was 1.58 thousand2.04 thousand), which was significant at the 10% (5%) level. In thiseriod, 56.50% of the sample firms had achieved positive abnormalhange. Abnormal labor productivity continued to be significant inhe (year −1 to year 0) and (year 0 to year 1) periods. These resultsuggested that OHSAS 18001 had a significant (and positive) impactn a firm’s long run abnormal labor productivity. This observationupported H3.

H4 predicted that OHSAS certification should have had a signifi-ant impact on firm profitability. Tables 2 and 3 show that abnormalhanges in ROA were significantly positive across all year-to-yearnd cumulative observation periods. For instance, in the periodyear −2 to year 2), the median (mean) abnormal ROA was 1.41%1.68%), which was significant at the 1% (1%) level. In this period,8.94% of the sample firms achieved positive abnormal change.hese results suggested that implementing OHSAS 18001 had aignificant (and positive) impact on a firm’s long run abnormalrofitability. Therefore, this observation supported H4.

.1.1. EndogeneityThere could be other factors affecting changes in operational

erformance that were not captured through the matching pro-ess. Therefore, we conducted additional tests to assess if the

elationship between OHSAS 18001 and operational performancemprovement is free from endogeneity issues. First, we performed-tests on the sample and the control firms’ R&D intensity and laborntensity in year −2. Results indicated no significant differences

t-test (mean).Percentage indicates the percentage of firms achieving positive abnormal changesin safety performance, sales growth, labor productivity and ROA.

(p > 0.1). Next, we conducted similar tests for all performance indi-cators over the period (year −3 to year −2) using the same sampleand control firms (see Table 4). This test examined whether theimpact of OHSAS 18001 on abnormal performance during the eventperiod (year −2 to year 2) was actually driven by earlier perfor-mance gains (year −3 to year −2). Table 4 shows that there was nosignificant change among all performance indicators over the (year−3 to year −2) period. The performance changes appeared onlyafter the firms had started implementing OHSAS 18001 in year −2.These tests suggested that the causal relationship is not due to a sys-tematic bias in operational performance prior to pursuing OHSAS18001. Therefore, we believe our sample selection was robust andfree from endogeneity issues.

4.2. Results of regression analysis

Table 5 shows the correlations between the regression variableswhile Table 6 displays the regression analysis examining the con-textual factors under which firms could benefit more from OHSAS18001. The control models contained the firm and industry levelcontrols, the complexity model included the controls along withR&D intensity and labor intensity (H5), and the full model includedinventory volatility and inventory level (H6) along with all the otherindicators. The results showed that firms with higher R&D intensityand labor intensity achieved higher profitability and safety perfor-mance after OHSAS certification (p < 0.05). Thus, H5 is supported.The complexity variables improved the explanatory power of theabnormal ROA model by 3.1% (based on adjusted R-square), and theabnormal safety performance model by 18.5%.

Inventory volatility is a significant predictor of firms’ abnormalROA, while inventory level is not. By contrast, inventory volatilityis not a significant predictor of firms’ abnormal safety performance,while inventory level is a significant predictor. These two variablesimprove the explanatory power of the abnormal ROA model by3.4%, and the abnormal safety performance model by 2%. Hypoth-esis 6 (coupling) is thus supported, but the mechanism by whichcoupling is increasing (inventory level vs. volatility) determines therelationship with a specific DV.

5. Discussion

Our research examined the impact of OHSAS 18001 on multipledimensions of abnormal operational performance, taking contex-tual factors into consideration. We found that OHSAS 18001 leads tosignificant increases in abnormal operational performance in terms

of safety, sales, labor productivity, and profitability. OHSAS certifi-cation leads to increased abnormal safety performance for the firmsin the sample, even though they had above average safety perfor-mance, relative to their industry norm, prior to certification. When

276 C.K.Y. Lo et al. / Journal of Operations MTa

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compared to similar firms, without certification, these firms alsosaw increased abnormal economic performance. The results areunequivocal in showing that OHSAS 18001 certification increasedboth the economic and safety components of abnormal operationalperformance. These results have important implications for theoryand practice.

5.1. Implications for theory

The literature provides three sets of predictions on the opera-tional performance implications of OHSAS certification. Our resultsprovide no support for the NAT based perspective, limited supportfor institutional theory, and significant support for the HRT basedperspective.

The literature suggested that OHSAS 18001, like ISO 9001 and14001 (e.g., Jiang and Bansal, 2003; Boiral, 2007; Martinez-Costaet al., 2008; Qi et al., 2011), could be mainly a signaling device.Our results do provide limited evidence of signaling, with certifyingfirms seeing significant abnormal growth in sales after certificationeven though these firms had above average safety performance fortheir industries prior to certification. The abnormal sales growthonly occurred after certification, so improvements in internal pro-cesses during the implementation period are likely not driving salesor the increases would accrue earlier (i.e., year −1). The bulk of thedirect abnormal increase in sales for this sample occurred mainly inthe year after certification rather than continuing into the secondyear, which is also consistent with a signaling hypothesis. Obtainingthe actual certification seems to be driving the increase in abnormalsales, providing support to the proposition that firms use OHSAScertification as a signaling or legitimation tool.

However, there are real and lasting increases in all of the otherabnormal operational performance metrics; so institutional theoryalone makes poor predictions for this sample. Our results do notallow us to fully discount institutional effects, but they seem to bea small component of why a firm would pursue certification. Stud-ies of other certifications have found that some percentage of firmspursue ISO 9001 or 14001 mainly for institutional or marketing rea-sons (Boiral, 2007), but that there are potentially real performancebenefits from certification (e.g., Corbett et al., 2005). This studycontributes to the overall certification literature by showing thatlegitimation or signaling is generally a component in the behaviorof organizations. However, certifications such as ISO 9001/14001and OHSAS 18001 that are premised on developing production sys-tems with mindful (Weick et al., 1999) processes and cognitionscan deliver significant increases in abnormal operational perfor-mance.

The results also inform the debate between researchers whoposit that safety must be traded-off with other elements of opera-tional performance (e.g., Zohar, 2002) and those who propose thatsafety and other dimensions of operational performance are com-plements (e.g., Das et al., 2008). Unlike many previous tests (e.g.,Das et al., 2008) this research explores the relationship betweensafety and other operational outcomes using longitudinal data. Theresults are clear; the firms in this sample saw increases in abnor-mal safety performance that led to simultaneous and subsequentimprovements in the economic components of abnormal opera-tional performance.

This does not mean that the observations made by authors suchas Brown (1996), who noted that in some settings, workers feelforced to take safety shortcuts to meet production quotas and/orprotect their jobs are wrong. Instead, these results are in line with

recent theorizing (Shrivastava et al., 2009; Farjoun, 2010) andresults such as Pagell et al. (2013) which showed that when firmshave mindful processes and cognitions they can create systemsthat are in control and reliable; allowing improvements in all

C.K.Y. Lo et al. / Journal of Operations Management 32 (2014) 268–280 277

Table 6Estimated coefficients from regression analysis for the indicators in year −2.

Dependent variable: abnormal ROA (year −2 to year 2) Dependent variable: abnormal safety performance(year −2 to year 2)

Control model Complexitymodel

Full model Control model Complexitymodel

Full model

Intercept −7.391+

[−1.841](4.014)

−8.798*

[−2.205](3.990)

−11.704**

[−2.883](4.060)

8.315[0.978](8.504)

8.902[1.154](7.714)

11.029[1.356](8.134)

Prior performance −0.042[−0.567](0.075)

−0.053[−0.715](0.074)

−0.059[−0.819](0.073)

0.656+

[1.740](0.377)

0.563[1.630](0.346)

0.421[1.203](0.350)

Size −0.007*

[−1.974](0.003)

−0.005[−1.561](0.003)

−0.005[−1.440](0.004)

0.007[1.011](0.007)

−0.001[−0.144](0.007)

0.003[0.419](0.007)

Year of adoption 0.004+

[1.850](0.002)

0.004*

[2.207](0.002)

0.006**

[2.885](0.002)

−0.004[−1.019](0.004)

−0.005[−1.191](0.004)

−0.006[−1.391](0.004)

ISO 9000 −0.016[−0.560](0.028)

−0.024[−0.845](0.028)

−0.015[−0.552](0.028)

0.069[1.150](0.060)

0.074[1.340](0.055)

0.069[1.261](0.055)

ISO 14001 −0.003[−0.288](0.012)

−0.001[−0.049](0.011)

0.002[0.210](0.011)

−0.003[−0.125](0.025)

−0.015[0.652](0.022)

−0.010[−0.417](0.023)

Size (Industry) 0.002[0.260](0.007)

0.001[0.138](0.007)

0.001[0.131](0.007)

0.026+

[1.714](0.015)

0.028*

[2.080](0.014)

0.024+

[1.710](0.014)

Wage level (Industry) −0.002[−0.320](0.005)

−0.001[−0.191](0.005)

−0.002[−0.302](0.005)

0.007[0.681](0.011)

0.002[0.193](0.010)

0.004[0.349](0.010)

Union influence (Industry) <−0.001[−0.246](0.001)

<0.001[0.516](0.001)

<0.001[0.446](0.001)

−0.001[−1.268](0.001)

−0.002+

[−1.968](0.001)

−0.002*

[−1.987](0.001)

Length of working hour (Industry) 0.004[0.745](0.006)

0.008[1.414](0.006)

0.007[1.246](0.006)

0.004[0.313](0.012)

0.002[0.150](0.011)

0.007[0.647](0.011)

R&D intensity 0.262*

[2.333](0.112)

0.255*

[2.303](0.111)

−0.343**

[−2.691](0.127)

−0.302**

[−2.316](0.131)

Labor intensity 0.004+

[1.830](0.002)

0.005*

[2.271](0.002)

−0.007**

[−3.485](0.002)

−0.009**

[−3.975](0.002)

Inventory volatility 0.026**

[2.754](0.009)

−0.021[−1.029](0.021)

Inventory level −0.029[−0.372](0.078)

0.345*

[2.071](0.166)

R2 5.6% 9.7% 14.1% 6.9% 25.5% 28.4%

Incremental R2 4.1% 4.4% 18.6% 2.9%

Adjusted R2 0.1% 3.2% 6.6% 1.7% 20.2% 22.2%

Incremental adjusted R2 3.1% 3.4% 18.5% 2.0%

Incremental F-test 3.43* 3.79* 19.38** 2.54+

+ Indicates statistically significant at 0.1 level (two-tail).* Indicates statistically significant at 0.05 level (two-tail).

** Indicates statistically significant at 0.01 level (two-tail).P 2 in atN

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ans

rior performance: ROA at year −2 in abnormal ROA model; violation rate at year −-Statistics in the bracket; standard error in the parenthesis.

= 163.

perational performance outcomes relative to firms that do notave such systems.

The findings also have important implications for the use of NATnd HRT in operations management research. While our results doot support the proposition that firms make trade-offs betweenafety and other operational performance outcomes, the results for

bnormal violation model.

hypotheses 5 and 6 do show that NAT can provide useful predic-tions. The results indicate that OHSAS 18001, while effective in all

settings, is more effective in more complex or tightly coupled con-texts. The firms that NAT would suggest have the greatest risk offailure also accrue the most benefits from certification.

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78 C.K.Y. Lo et al. / Journal of Operat

These results align well with the syntheses of HRT and NAT. Ouresults suggest that complexity and coupling are useful in predict-ng when the increased control that certification can create wille most beneficial. NAT has previously been used to predict whenafety failures were most likely to occur. We suggest that oper-tions management researchers might be better served by usinghe coupling and complexity components of NAT to predict whennd what types of operational controls would be most effective toeduce/prevent safety failures.

The proposition that increased control will reduce safety fail-res, which is imbedded in HRT, should be familiar to operationsanagement researchers. Our results also provide additional sup-

ort to that argument. While we do not show that all future failuresre eliminated via certification, we do provide evidence that adopt-ng many of the processes and cognitions associated with HRTan reduce the number of failures and increases operational effec-iveness. These cognitions and processes are common to manyertifications suggesting that these processes can form a core ofest operational practice.

.2. Implications for practice

The findings verify that OHSMS certification does not induce arade-off between safety and economic performance. The empiri-al evidence provides a relevant reference for senior managers ofanufacturing firms who are considering OHSAS certification, even

n those firms with effective internally generated safety manage-ent systems. Management can expect that certification will lead

o increased abnormal operational performance, especially whenhe firm has complex or tightly coupled operations.

Not only does the study show that OHSAS 18001 certificationas positive benefits for operational performance, the results alsoeveal the pattern as to how OHSAS 18001 certification is linkedo increased abnormal performance. This pattern helps to explainow certification both allows firms to improve their processes andignal their achievements to the market. Specifically, abnormalales increases are associated with certification, not the increasesn abnormal safety and productivity that occur due to improvingrocesses. However, abnormal productivity and safety increasesccrue throughout the event window. From a managerial perspec-ive, our results suggest that as soon as the implementation processegins the firm can expect to see increases in productivity andafety relative to uncertified peers, but that these changes will notranslate directly into increased sales until the firm is certified.

The benefits of certification accrue to all organizations, but aretronger for those in complex or tightly coupled settings. The con-ingent nature of certification’s operational performance benefitsould provide important insight for practitioners and the organi-ations that develop standards. Managers who are not yet facingtakeholder pressure to adopt OHSAS 18001 can assess their ownevel of complexity and coupling to get a better sense of the likelyenefits of certification. Standard development organizations (e.g.,SI and ISO) would be well served by tailoring OHSAS to various set-ings. We would hope that managers of labor intensive firms woulde aware of the benefits of protecting their workforce. But man-gers at R&D intensive firms do not have such obvious signals anday be more likely to miss the benefits OHSAS 18001 would pro-

ide. Similarly, managers might not make an association betweennventory level or volatility and safety. Standard setting organiza-ions can help this process along by adapting the certification tohese settings.

Presently, OHSAS certification is relatively rare and done mainly

n a voluntary basis by firms who are already performing betterhan industry norms in terms of safety. As OHSAS 18001 becomes

ore common its relative performance implications could change.he ability to generate sales growth from certification will dissipate

anagement 32 (2014) 268–280

as the standard disseminates. But we posit that as the standardbecomes more widespread, the safety and productivity benefitsevidenced in our sample will remain or even increase. As certi-fication becomes the norm or expected, firms with poor safetyperformance will be forced to get certification and given their rela-tively poor starting point performance wise they may see largersafety and productivity benefits than the early adopters in thissample.

Finally, the results provide another piece of evidence for man-agers trying to navigate the conflicting literature on the relationshipbetween the safety and economic components of operational per-formance. OHSAS 18001 inculcates practices and cognitions thatare in line with operational best practices such as a focus on pro-cesses and a continuous improvement philosophy. Recent research(e.g., Das et al., 2008; Pagell et al., 2013) posits that when firms man-age a system to be safe and productive using such practices thatmultiple dimensions of operational performance can be simulta-neously improved. Firms who proactively manage safety as a keycomponent of operations via the processes imbedded in OHSAS18001 or something similar need not trade-off safety for otherelements of operational performance.

While the results are robust, this study like all studies suf-fers from limitations. The major limitation of this study is thatthe sample includes only U.S. listed firms who reported their cer-tification. However, the effectiveness of a management practicesuch as OHSAS 18001 might vary in countries with a differentcultural context (Kull and Wacker, 2010) or where governmentregulations were quite different. Moreover, listed firms are usu-ally large firms that typically have basic OHSMS systems in place;a supposition that seems confirmed by the above average safetyperformance of the sample. Thus, the findings could be differentfor small firms. The measures of complexity and coupling are alsolimited. Future research should look at different and more encom-passing measures of both, such as complexity in product design,and work in progress inventories between key operations. Finally,firms in export-oriented economies (e.g., China, Ireland, and India,etc.) may pursue OHSAS certification mainly to fulfill customers’requirements with no intention of improving working conditions.Therefore, the impact of certification might be dependent on themotivations of the certifying firms.

6. Conclusions

This research sought to answer two questions. First, what is thegeneral impact of OHSAS 18001 certification on operational perfor-mance? The results showed that OHSAS certification has significantbenefits for the safety and economic components of abnormal oper-ational performance, even for firms operating in environments withstringent safety regulation who already had above average safetyperformance relative to their industry. The second research ques-tion asked what is the role of context in the OHSAS 18001 operationalperformance relationship. The results showed that as complexityand coupling increased, the benefits of certification also increased.OHSAS certification led to significant increases in abnormal opera-tional performance and these benefits accrued most to firms withhighly complex or coupled production systems.

The results also clarified the theoretical confusion surroundingthe relationship between safety certification, safety performanceand other operational performance outcomes. The institutionaltheory perspective suggests that certification may be pursuedmainly as a signaling device. The results provided some support

for this contention with all of the significant increases in abnormalsales growth occurring after certification, even though the firmshad above average safety performance for their industries prior tocertification. However, the results also showed significant increases

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n abnormal performance in terms of safety, ROA, sales growth androductivity meaning that certification had more than ceremonialenefits.

The existence of increased abnormal safety and economic per-ormance also provides insight into the ongoing debate about theelationship between safety and the economic elements of opera-ional performance. The results showed no evidence of trade-offs,hich is more in line with HRT than NAT. However, the results

howed that the complexity and coupling components of NAT dorovide good predictions as to when increased control, in the guisef OHSAS certification, will be most beneficial. Therefore we followhrivastava et al. (2009) and suggest that rather than treating HRTnd NAT as conflicting theories, operations management scholarsould be better served by building on the predictions of NAT toetermine where enhanced control, be it in the guise of OHSASertification, statistical process control, training or something else,ill be most valuable to prevent failure.

OHSAS certification is unique in that firms pursuing certificationhould be replacing existing OHSMS and hence a priori there is noeason to expect certification to improve operational performance.owever, the results are broadly in-line with previous studies ofther forms of certification, suggesting that certified managementystems that inculcate the processes and cognitions associated withRT will generally improve operational performance.

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