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AN EMPIRICAL EXAMINATION OF SUPPLY CHAIN PERFORMANCE ALONGSEVERAL DIMENSIONS OF RISK

by

Stephan M. WagnerSwiss Federal Institute of Technology, Zurich

and

Christoph BodeWHU—Otto Beisheim School of Management

INTRODUCTION

In the past years, a fairly new research area has emerged on the supply chain management scene and has gainedconsiderable attention from both academics and practitioners: Supply chain risk management. This new interest isñieled by two parallel issues.

First, a recent series of crises and catastrophes has attracted public attention. Natural disasters like HurricaneKatrina devastating the Gulf Coast of the United States in 2005, terrorist acts such as the attacks of September 11,2001, and epidemics like S ARS in South-East Asia in 2003 are violent reminders that we live in an unpredictableand increasingly unstable world. Moreover, there is strong evidence that such catastrophic events are becomingmore frequent (Coleman 2006). Elkins, Handfield, Blackhurst, and Craighead (2005) observed that there has been anmcrease both in the potential for disruptions and in their magnitude. Likewise, Munich Re (2007, p. 46) stated in itsannual report on natural hazards that "[s]ince 1950, there has been a long-term upward trend in the number of eventsand the amount of economic and insured losses."

Second, modem supply chains seem to be more vulnerable than ever. Over the last decade, almost all industrieshave seen increased competitive pressure in the business environment and the globalization of markets. Thesechanges have compelled firms to make their intra-firm business processes and inter-firm supply chains either moreefficient or more responsive, for instance, by outsourcing and offshoring many manufacturing and R&D activities,sourcing in low-cost countries, reducing inventories, or collaborating more intensively with other supply chainactors (Fisher 1997; Huit, Ketchen, and Slater 2004; Lee 2002; Wisner 2003). Although such supply chain designchanges and supply chain management initiatives have great potential to make operations leaner and more efficientin a stable environment, they simultaneously increase the fragility and vulnerability of supply chains to disruptions(Craighead, Blackhurst, Rungtusanatham, and Handfield 2007; Wagner and Bode 2006; Zsidisin, Ragatz, andMelnyk 2005a).

In summary, we find a relatively unstable world on the one hand, and increasingly sensitive supply chains onthe other. Many supply chain decision-makers were caught off-guard by the intensity of the recent disasters whichhighlighted the lack of preparedness in many supply chains. For this reason, many firms have started to take supplychain disruptions more seriously and to rethink their supply chain strategy and design. Accordingly, 38% of 247CFOs surveyed said that their corporations were "sitting on too much unmanaged supplier risk" (Katz 2004, p. 1).

At a glance, it seems obvious that firms are now compelled to tackle supply chain risks just as vigorously asthey tackle other business risks. However, supply chain risk management comes at a cost and before firms engage inexpensive actions they need to have information about the (1) probability of occurrence of supply chain disruptions

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and (2) the effect of these disruptions on performance. In particular, supply chain risk management activities areonly justified if supply chain risks interfere with supply chain performance. To our knowledge, only the study byHendricks and Singhal (2003, 2005a, 2005b) investigated the relationship between supply chain risks andperformance. Their study was based on a sample of public ad-hoc announcements from the Wall Street Journal andthe Dow Jones News Service concerning supply chain disruptions. In two articles (Hendricks and Singhal 2003,2005a) they showed how media announcements on supply chain disruptions affect the observable share price andshareholder value of the announcing firm. The results demonstrated that stock markets severely penalizeannouncements of supply chain disruptions. A third article (Hendricks and Singhal 2005b) focused on operatingperformance metrics observable through financial statement analysis (e.g., sales, operating income, ROA). Bycomparing the financial statements in the year preceding and following an ad-hoc announcement, they were able toshow that such announcements have a substantial long-term negative effect on operating performance. WhileHendricks and Singhal investigated the relationship between announced supply chain disruption and performance(shareholder value and metrics derived from financial statements), they did not differentiate among types ofdisruptions and did not consider the probability of occurrence. The relationship between supply chain risk andsupply chain performance has not yet been investigated empirically. Although, risks are inherent in supply chains,both their impact and their appropriate management are now under greater scrutiny, current knowledge is still quitelimited as most articles on supply chain risks are rather anecdotal or case study-based. Results from large-scaleempirical research are scarce and mostly descriptive (Jüttner 2005; Peck and Jüttner 2002; Svensson 2002; Zsidisinand Ellram 2003).

Therefore, the goal of this research is twofold. First, we provide a detailed and empirically-derivedoperationalization of supply chain risk sources. For this purpose, a set of the most relevant supply chain disruptionswas singled out from the literature, discussed with practitioners and experts, and then incorporated into aquestionnaire. Second, we examine the link between those supply chain risk sources and supply chain performance.

The rest of the article is organized as follows. In Section 2, we review the literature on contingency theory andstrategic choice theory. We apply both frameworks to the relationship between supply chain risk and supply chainperformance and use them to develop our model and hypotheses. Moreover, we delineate the applied nomenclatureregarding the terms risk, risk source, and disruption. Section 3 describes the empirical study and the methodologyused to test the hypothesized relationships. Section 4 presents the findings. Section 5 discusses the results, and thefinal section suggests implications for managerial practice and future research.

CONCEPTUAL BACKGROUND AND HYPOTHESES

A Contingency Perspective on Supply Chain Risk Management

A classical approach in strategic management research applicable to supply chain management is to divide theconcept of strategy into two distinct aspects: process (how strategy is formed) and content {what is decided).Numerous researchers focused on either process or content and investigated the relationship between certainstrategic variables and performance (Ketchen, Thomas, and McDaniel 1996). Although this distinction and itsusefulness have been extensively discussed, the inquiry of Pettigrew and Whipp (1993) indicated that, in addition tocontent and process, the intemal and external context of the organization plays an important role for decision-making and should therefore be incorporated in this framework. Empirical research by Ketchen, Thomas, andMcDaniel (1996) confirmed this perspective.

This view is supported by contingency theory that builds on the central assumption that high organizationalefficiency and perfonnance result when firms consider the context in which strategy is crafted and implemented. Forhigh efficiency and performance, organizations must match structure to the context and environment, i.e., forcesoutside the decision-maker's control. If this "fit" is not achieved "opportunities are lost, costs rise, and themaintenance of the organization is threatened" (Child 1972, p. 8). Contingency theorists empirically tested directrelationships between particular contextual variables and organizational structure or performance (Lawrence andLorsch 1967). However, from the contingency theory perspective, strategies are merely necessary responses to theenvironment. Therefore, Child (1972) proposed strategic choice theory as a corrective to the classic contingencyapproach. The strategic choice perspective negates the pure deterministic function between context and

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organizational structure, arguing that organizations have strategic choice when designing their structure. Whilestrategic decision-makers are constrained by contextual factors, they still have some room for strategic maneuvering.

The role of the constructs "context" or "environment" has received a great deal of attention both in strategicmanagement research and in orjganizational theory. Various concepttializations of the construct and its constituentelements exist. This article applies Duncan's (1972, p. 314) notion of "environment" as "the totality of physical andsocial factors that are taken directly into consideration in the decision-making behavior of individuals in theorganization." This definition includes factors that are intemal and external to the firm. We argue that supply chainrisk sources are critical contextual variables that can be internal and external to supply chains and to the acting firmsin a supply chain network.

From a strategic management perspective, matching or aligning organizational resources with the organization'scontext, and especially to environmental opportunities and threats, is a major task for decision-makers (Miles andSnow 1978; Venkatraman and Camillus 1984). As stated in the introduction, the literature has suggested that supplychain risk sources pose a threat for which many organizations are not prepared. If this assumption is correct,decision-makers must now reconsider their strategy and, if necessary, align the organization to this changed'environment in order to achieve a strategic fit.

This reasoning brings us to our hypotheses. We posit that the risk deriving from the various supply chainsources undermines supply chain performance. If this assumption finds support then the call for an organizationaladaptation towards supply chain risk is substantiated.

Supply Chain Risk, Disruptions, and Risk Sources

Risk is an elusive construct that has a variety of different meanings, measurements and interpretationsdepending on the field of research (Jemison 1987). There is an extensive body of literattire concerning risk indecision theory (Arrow 1965), finance (Altman 1968), marketing (Cox 1967), management (March and Shapira1987), and psychology (Kahneman and Tversky 1979).

Several researchers in the field of supply chain management have defined supply chain risk. Here - but also inthe general discussion of risk - there are two distinctive meanings. There is a persistent tension between, on the onehand, risk purely as danger and, on the other hand, risk as both danger and opporttinity (Mitchell 1995). Accordingto classical decision theory and in fields such as finance, the fluctuations around the expected value (mean) of aperformance measure are used as proxy for risk. That is, risk is equated with variance and consequently has both apotential "downside" and "upside." Following these considerations, Jüttner, Peck, and Christopher (2003, p. 200)defined supply chain risk as a "variation in the distribution of possible supply chain outcomes, their likelihood, andtheir subjective value."

In contrast, the notion that risk inherits primarily negative consequences corresponds to the common humanperception. March and Shapira (1987) examined how managers perceive and respond to risk. They found that themajority tend to exaggerate its "downside." Several scholars in the supply chain management and supplymanagement fields share this view. Harland, Brenchley, and Walker (2003, p. 52), for instance, discussed severaldefinitions and concluded that supply chain risk is associated with the "chance of danger, damage, loss, injury or anyother undesired consequences."

For the purpose of this sttidy and considering the impact of recent disruptions on supply chains, we find that thelatter notion of risk as purely negative corresponds best to supply chain business reality. For this reason, we willconsider neither "happy disasters" nor situations in which managers intentionally "gamble" on risk. Here, weunderstand risk as the negative deviation from the expected value of a certain performance measure, resulting inundesirable consequences for the focal firm. Hence, risk is equated with the damage or loss resulting from a supplychain disruption.

We define a supply chain disruption as the combination of (1) an unintended, anomalous triggering event thatmaterializes somewhere in the supply chain or its environment, and (2) a consequential sittiation which significantlythreatens normal business operations of the firms in the supply chain. For the affected firms, it is an exceptionalsituation in comparison to every-day business. The disruption has a certain probability of occurrence and is

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characterized both by its severity and by its direct and indirect effects. Since the resulting detriment is usually afunction of time, supply chain disruptions involve time pressure, implying that decisions for mitigation must bemade swiftly. Depending on its severity, other terms might be applied, such as, glitch, disturbance, accident,disaster, or crisis.

Supply chain disruptions can materialize either inside or outside of a supply chain. Consequently, they can behighly divergent. For instance, the financial default ofa supplier and an earthquake that destroys production capacityare situations with completely different attributes and therefore have different effects on the supply chain.

In attempting to differentiate supply chain risks from other business risks, many scholars have proposedtypologies and/or taxonomies of risks (Chopra and Sodhi 2004; Christopher and Peck 2004; Hallikas, Karvonen,Pulkkinen, Virolainen, and Tuominen 2004; Jüttner 2005; Jüttner, Peck, and Christopher 2003; Norrman andLindroth 2004; Spekman and Davis 2004; Svensson 2000). The categories of supply chain disruptions are oftenlabeled "supply chain risk sources." As such, Svensson (2000) identified two categories (quantitative andqualitative), Jüttner (2005) delineated three (supply, demand, and environmental), and Chopra and Sodhi (2004)proposed nine (disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory, andcapacity). In the following, for the sake of brevity, we call a negative deviation from the expected value of aperformance measure (resulting in negative consequences for the focal firm) a "supply chain risk" when thisdeviation is the result of a supply chain disruption.

For our purpose, we will divide supply chain risk sources into five distinct classes: (1) demand side; (2) supplyside; (3) regulatory, legal and bureaucratic; (4) infrastructure; and (5) catastrophic. While the first two risk sourcecategories deal with supply-demand coordination risks that are internal to the supply chain, the latter three focus onrisk sources that are not necessarily internal to the chain.

Demand Side Risks

Demand side risks result from disruptions emerging from downstream supply chain operations (Jüttner 2005).These include, on the one hand, disruptions in the physical distribution of products to the end-customer, usually intransportation operations (e.g., a truck driver strike) (McKinnon 2006) and the distribution network (e.g., a delay ina distribution center). On the other hand, demand side risks can originate from the uncertainty caused by customers'unforeseeable demands (Nagumey, Cruz, Dong, and Zhang 2005). Disruptions occur here from a mismatch betweena company's projections and actual demand as well as from poor supply chain coordination. The consequences ofsuch disruptions are costly shortages, obsolescence, and inefficient capacity utilization. An important issue in thiscontext, affecting forecast quality and therefore demand side disruptions is the bullwhip effect, characterized by anamplification of demand volatility in the upstream direction ofthe supply chain.

Lee, Padmanabhan, and Whang (1997) identified delayed and distorted information, sales promotions, orderbatching, price fluctuations and rationing or shortage gaming as major causes of the bullwhip effect. Other factorsintensifying the bullwhip effect are over-reactions, unnecessary interventions, second guessing, and mistrust(Christopher and Lee 2004). Although demand side risk management is in some respects the "bread-and-butter" ofsupply chain management, these issues still present a major risk source for many firms. Spekman and Davis (2004)cite the example of Cisco Systems Inc. that wrote off US $2.5 billion in inventory in 2001 due to a lack ofcommunication among its downstream supply chain partners. Consequentially, we draw the hypothesis:

Hi: The higher the demand side risk, the lower the supply chain performance.

Supply Side Risks

Firms are exposed to numerous risks associated with the upstream side of their supply chains. Supply side risksreside in purchasing, suppliers, supplier relationships, and supply networks. These include supplier business risks,production capacity constraints on the supply market, quality problems, and changes in technology and productdesign (Zsidisin, Panelli, and Upton 2000). Kraljic (1983) was among the first who emphasized that firms shouldproactively assess and manage the uncertainties in their supplier portfolio in order to guard against costly supply

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disruptions. The need to assess and manage supply side risks carefully has been intensified hy stronger reliance onextemal sources for critical materials and components (Giunipero and Eltantawy 2004).

Supplier business risks relate to events that affect the continuity of the supplier and result in the interruption ortermination of the buyer-supplier relationship. This concerns particularly the threat of financial instability ofsuppliers and the consequences of supplier default, insolvency, or bankruptcy (Wagner and Johnson 2004).

Another type of disruption occurs when a supplier is vertically integrated by a direct competitor of the customerfirm, forcing the termination of the relationship (Chopra and Sodhi 2004). In ongoing and cooperative buyer-supplier relationships, opportunistic behavior from suppliers has also been reported in the literature as a source ofsupply risk (Spekman and Davis 2004; Stump and Heide 1996). Organizational lock-in is a particular threat when apurchasing organization is so dependent on a supplier that it has little room for maneuvering.

Capacity constraints or shortages as well as poor logistics performance (delivery reliability) derive fromunsolved problems in the supplier's production and operations management (Lee and Billington 1993). ThebuUwhip effect also plays a role here and has to be countered by the suppliers. Furthermore, poor quality in thepurchased products or services is a significant risk and can have a domino effect through the supply chain to thefinal customer (Zsidisin, Panelli, and Upton 2000).

Finally, the inability of suppliers to adapt to technological or product design changes may have detrimentaleffects on the customer's costs and competitiveness (Zsidisin and Ellram 2003). With the increased importance ofand reliance on outsourcing, the cited risks are greater (Giunipero and Eltantawy 2004). Accordingly, wehypothesize:

H2: The higher the supply side risk, the lower the supply chain performance.

Regulatory, Legal and Bureaucratic Risk

In many countries, authorities (administrative, legislative, regulatory agencies) are an important factor ofuncertainty in the setup and operation of supply chains. Regulatory, legal and bureaucratic risks refer to the legalenforceability and execution of supply chain-relevant laws and policies (e.g., trade and transportation laws) as wellas the degree and frequency of changes in these laws and policies. This includes the ability to obtain approvalsnecessary for supply chain design activities and supply chain operation. This risk source is external to the individualsupply chain or firm.

With the exception of government initiatives for security facilitation such as the Customs-Trade PartnershipAgainst Terrorism (C-TPAT) or Authorized Economic Operators (AEO) certifications (Sarathy 2006; Zsidisin,Ragatz, and Melnyk 2005b), little attention has been paid to risks stemming from changing legal stipulations and'conditions. According to Hendricks and Singhal (2003, 2005a, 2005b) supply chain disruptions can be associatedwith the actions or decisions of authorities. Administrative barriers (e.g., customs, trade regulations) may restrict thedesign and influence the operative performance of supply chains. Legal changes are often sudden and difflcult toanticipate. Examples are the new road pricing schemes for fieight vehicles in European countries; these schemeshave substantially increased transportation costs. Environmental legislation now requires product traceability and theestablishment of reverse logistics systems. In order to meet such environmental requisites, firms frequently getinvolved in more complex supply chains and incur higher supply chain costs. In summary, we decided to considerthese risks in our study and posit:

H3: The higher the regulatory, legal and bureaucratic risk, the lower the supply chain performance.

Infrastructure Risks

The risk source "infrastructure" includes disruptions that materialize ft-om the infrastructure that a firmmaintains for its supply chain operations. It includes socio-technical accidents such as equipment malftinctions,machine breakdowns, disruptions in the supply of electricity or water, IT failures or breakdowns, in addition to localhuman-centered issues (vandalism, sabotage, labor strikes, industrial accidents) (Chopra and Sodhi 2004; Spekman

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and Davis 2004). It is important to note that this class subsumes only localized events referring to the extent towhich other organizations are affected.

IT-related problems are highly relevant to supply chain management since many supply chain managementfunctions build on information processing and sharing. In the last years, organizations have become increasinglytechnology-dependent and, consequently, vulnerable to IT problems or breakdowns (Chopra and Sodhi 2004). Theseevents can be the result of malicious actions by individuals or groups (cyber-attacks, virus attacks), software bugsand hardware failures (Warren and Hutchinson 2000). Moreover, modern Enterprise Resource Planning (ERP)systems force firms to open their internal processes and databases both to their suppliers and customers, thusincreasing their exposure to IT-related threats. Based on these considerations, we hypothesize:

H4: The higher the infrastructure risk, the lower the supply chain performance.

Catastrophic Risks

This class subsumes pervasive events that, when they materialize, have a severe impact on the area of theiroccurrence. Such events can be epidemics or natural hazards (force majeure), socio-political instability, civil unrest,and terrorist attacks (Kleindorfer and Saad 2005; Martha and Subbakrishna 2002; Swaminathan 2003).

In many regions of the world, tsunamis, droughts, earthquakes, hurricanes, and floods are a constant threat totheir societies in general and to their firms in particular (Helferich and Cook 2002; Munich Re 2007). The negativeconsequences on supply chains are obvious since production facilities and transportation systems are highlyvulnerable to natural disasters. Due to the globalization of markets and a surge in globe-spanning supply chainoperations, local catastrophes have increasingly indirect global repercussions.

The destructive impact of terrorism on flrms' supply chains has received much attention since 2001 (Rice andTenney 2007; Sheffi 2001). Terrorist acts affect supply chains either directly (e.g., destruction of logisticsinfrastructure) or indirectly (e.g., port closures for security reasons imposed by the government) (Czinkota, Knight,Liesch, and Steen 2005). Taking these aspects into account, we formulate the last hypothesis:

H5: The higher the risks from catastrophes, the lower the supply chain performance.

METHODS

Data Collection and Sample

Data were collected through a cross-sectional survey administered in Germany to a sample of 4,946 top-levelexecutives in logistics and supply chain management. The mailing and two follow-ups generated 760 usableresponses, yielding a relatively high response rate of about 15.4%, considering the demands on the time of top-levelexecutives (Tomaskovic-Devey, Leiter, and Thompson 1994). Non-response bias was assessed on the notion thatlater respondents would be similar to non-respondents (Armstrong and Overton 1977). For all questionnaire items,the responses of later respondents were compared to those of earlier ones. This comparison indicated absence ofnon-response bias.

The sample covered industrial (71.7% of the sample), service (19.5%) and trade (8.8%) firms. The firms'annual sales ranged from less than US$ 10 million to US$ 90 billion (mean US$ 60.3 million), and the number ofemployees from fewer than 100 to 430,000 (mean 2,913), thus yielding a heterogeneous sample. Given the rangeand size of the firms studied and the diversity of industries, there was no prima facie reason to expect any systematicbias in the results. Most of the respondents held management positions in logistics and supply chain management(37.5%), or were in higher-level senior management positions (e.g.. Executive VP, Senior VP) or owners of thebusiness (23.8%). On average, the respondents had worked in this position for 7.0 years and had been with the firmfor 10.9 years. A more detailed breakdown of the sample and informants can be found in Table 1.

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TABLE 1

SAMPLE CHARACTERISTICS

Percent of total sampleSector and industry

Industry sectorAutomotiveElectro/ElectronicsMachineryChemicals and pharmaceuticalInformation technologyMaterials and metal productionFoodPaper, pulp, and printingConstructionConsumer goodsAerospace and defenseMedical devicesOther industry

Service sectorLogistics servicesOther services

Trade sector

11.210.19.58.46.66.25.54.23.02.52.11.30.9

17.12.4

71.7

19.5

8.8Sales (in US$)

Less than 10 million10 million - under 50 million50 million - under 100 million100 million - under 250 million250 million - under 500 million500 million - under 1 billion1 billion - under 10 billion10 billion and moren.a.

14.923.916.314.78.76.77.25.02.5

Number of employeesLess than 100100-499500 - 9991,000-4,9995,000 - 9,99910,000 and moren.a.

21.442.211.615.32.83.73.0

Position of informantLogistics/Supply chain managementHigh-level senior management or ownersPurchasing/ProcurementProduction/OperationsSales, distribution, and serviceOther management positionsAccounting/Financen.a.

37.523.815.013.93.63.42.10.7

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Questionnaire and Measure Development

As indicated earlier, most previous studies on supply chain risk management were based on anecdotal evidenceor case studies. Except for the supply chain performance measure, items with formal scale characteristics were notavailable for our purpose. Therefore, new measures and a ñiUy standardized survey instrument were developedincrementally.

First, we started developing the risk source measures based on an initial pool of scale items that were generatedthrough an extensive review of the academic and practitioner literature on supply chain risk management.Operational definitions for the risk measures and construct items were derived from these sources. A preliminaryquestionnaire was drafted. Second, the scale items included in the questionnaire, their relevance, their wording anddirections, and the format of the questionnaire were refined on the basis of comments from practitioners andacademicians. Third, to further refme the survey instrument, it was pre-tested through interviews with supply chainmanagement executives from a small number of firms. Again, their comments were incorporated into the finalversion of the questionnaire.

Respondents were asked to indicate how their firms had been affected during the last three years by supplychain disruptions, and to specify their firms' supply chain risk management activities and supply chain performance.Five-point Likert-type items were used to operationalize all constructs. All items were scored so that higher numbersrefiect increases in the underlying constructs. Translations of the individual scale items and response cues for eachmeasure are listed in Appendix 1.

Our demand side risk measure consists of two items that capture the risk deriving from the interaction (or lackthereof) with customers and volatility of the market (Jüttner 2005; Lee, Padmanabhan, and Whang 1997). Likewise,the supply side risk measure consists of six items that capture the risks stemming from events and actors in theupstream supply chain, for instance, the supply market conditions and the performance of suppliers (Zsidisin 2003;Zsidisin and EUram 2003). Regulatory, legal and bureaucratic risks were assessed with a two-item scale related tochanges in the political environment as well as administrative barriers imposed by governmental authorities(Hendricks and Singhal 2003). We operationalized infrastructure risk with a four-item scale directed towards IT,equipment, and facility malfunctions (Chopra and Sodhi 2004; Spekman and Davis 2004). For the catastrophic riskmeasure, we generated a four-item scale that captures risks that originate from terrorism, socio-political crises,natural disasters, and epidemics, for instance (Helferich and Cook 2002; Kleindorfer and Saad 2005). In order tomeasure the dependent variable, supply chain performance, we adopted the scale from Rodrigues, Stank, and Lynch(2004) that focuses on the "downstream" supply chain performance. The four performance items relate to deliverydependability, order fill capacity, delivery speed, and customer satisfaction.

As the intention of this research was to investigate the effects of various supply chain risk sources on supplychain performance, but not the infiuence of supply chain risk management activities performed by the firms, it wasnecessary to include a risk management control variable. We operationalized this control variable along six itemsbased on risk management or mitigation strategies proposed in the literature (Chopra and Sodhi 2004; Kleindorferand van Wassenhove 2004; Tang 2006).

Traditional psychometric approaches were used to evaluate each scale's reliability and validity (Mentzer andFlint 1997; Nunnally and Bernstein 1994). They included correlation analysis, reliability evaluation, and principalcomponent factor analysis using varimax as the method of rotation. Factor analysis results and reliabilities areincluded in Appendix 1. Reliabilities for all dependent variables were evaluated via item-to-total correlations andCronbach's alpha coefficient (Malhotra 2004; Nunnally and Bernstein 1994). All item-to-total correlations are above0.5 (i.e., have values greater than 0.35, a threshold that would indicate that an item should be deleted from the scale).Cronbach's alpha coefficients range from 0.691 to 0.854. As a rule of thumb, coefficients above 0.6 are acceptable,especially for new scales. All items meet established standards for convergent validity (i.e., all items load on uniquecomponents with factor loadings larger than 0.5).

In summary, the evidence provided in these analyses suggests that the measures included in this study possesssufficient reliability and validity to proceed with hypotheses testing. For hypotheses testing analysis, summatedcomposites of the multi-item measures used in this study were calculated. Correlations of constructs and descriptivestatistics are summarized in Table 2.

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TABLE 2

SUMMARY STATISTICS AND CORRELATION MATRIX

Variables

(1) Demand side risk

(2) Supply side risk

(3) Regulatory, legal andbureaucratic risk

(4) Infrastructure risk

(5) Catastrophic risk

(6) Supply chain performance

(7) Risk management

Mean

Standard deviation

** Significant at the 0.01 level.

* Significant at the 0.05 level.

(1)

n.a.

0.40

0.14

0.20

0.13

-0.13

-0.03

3.25

0.99

**

**

**

**

**

(2)

n.a.

0.36

0.37

0.31

-0.11

0.12

2.47

0.77

(3)

** n.a.

** 0.37 **

** 0.33 **

** -0.02

** 0.12 **

2.20

0.94

RESULTS

(4)

n.a.

0.29

-0.10

0.06

1.73

0.68

(5)

** n.a.

** -0.04

* 0.11 **

1.55

0.73

(6)

n.a.

0.17

3.77

0.62

(7)

** n.a.

2.73

0.87

In order to test our research hypotheses, an ordinary least square (OLS) regression model was estimated. Table3 presents the standardized parameter estimates and the t-values.

TABLE 3

OLS REGRESSION FOR SUPPLY CHAIN PERFORMANCE

Variables

Control variable

Risk management

Predictor variables

Demand side risk

Supply side risk

Regulatory, legal andbureaucratic risk

Infrastructure risk

Catastrophic risk

Model summary: F(688, 6) =

** Significant at the 0.01 level.

* Significant at the 0.05 level.

Standardizedestimate

0.18

-0.08

-0.09

0.03

-0.07

-0.01

6.96**;/?^ = 0.06

/ value

4.75**

-1.98*

-1.99*

0.61

-1.57

-0.33

P

.000

.048

.047

.546

.118

.743

Hypothesis

—

H,

H2

H j

H4

Result

—

Support

Support

No support

No support

No support

The supply chain risks explained 6% ofthe variance of supply chain performance (F = 6.96, df= 688, 6). Hiposits a relationship between demand side risk and supply chain performance. With a standardized parameterestimate of-0.08, this hypothesis was significant (/ = -1.98, p < 0.05), indicating support for H,. Hj, that supply siderisks have a negative impact on supply chain performance, was also supported with a statistically significant

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estimate of -0.09 (/ = -1.99, p < 0.05). However, for the effect of regulatory, legal and bureaucratic risks,infrastructure risks, and catastrophic risks on supply chain performance, as proposed in H3, H4, and H5, the data didnot reveal statistically significant relationships. Therefore, H3, H4, and H5, were not supported.

DISCUSSION

Based on a sample of 760 top-level executives in logistics and supply chain management, the results of thisstudy notahly shed light on the relationship between supply chain risks and supply chain perfonnance and therelevance of various supply chain risk sources as contextual variables in supply chain decision-making.

Supply chain risks only partially explain the variance in supply chain performance, and in particular, there is nosignificant relationship between regulatory, legal and bureaucratic risks, infrastructure risks, and catastrophic risksand supply chain performance. The rather low R^ is not astonishing since a firm's supply chain perfonnance isclearly dependent on many factors other than supply chain risks. With regard to the last two risk sources, disruptionsfrom these classes are, in general, "outliers" or exceptional events that are characterized by a low probability ofoccurrence (Stauffer 2003). Therefore, an obvious explanation is that their probability is low enough to have anegligible impact on supply chain performance. In sum, authority actions, potential infrastructure disruptions, andcatastrophes are not contextual variables that must necessarily be factored into strategic supply chain decisions(Mintzberg, Raisinghani, and Théorêt 1976; Pettigrew and Whipp 1993).

This seems somewhat "counterintuitive" in light of the intensive interest of practitioners and academics tounderstand and manage the various sources of supply chain risks. However, there is a widely accepted psychologicalrationale for the misjudgment of the impact of supply chain disruptions in general and the hypothesized but non-significant relationships between the three risk sources and supply chain performance in particular. Research bypsychologists has shown that people, instead of using statistics, rely on a limited number of heuristics to predict theimpact of risks. These heuristics sometimes result in reasonable judgments and sometimes in serious errors(Kahneman and Tversky 1973). One such heuristic is called the "availability heuristic" (Slovic, Fischhoff, andLichtenstein 1982). Human beings make judgments based on what they can remember, not on complete data. Thisheuristic is commonly used for judging the frequency or likelihood of events, such as supply chain risk sources (e.g.,supply chain disruptions caused by floods).

News coverage also has a significant effect on decisions. After a news feature about a ñre in a manufacturingplant or an airport evacuation due to a bomb threat, managers will be more aware of the impact of such events ontheir firm's supply chains. Other factors can also affect managers' judgment. Things which are easier to imagine, forexample, are more available. It is easier for people to remember images of flooded harbors, empty airports andautoworkers on picket lines than the lack of communication with suppliers and/or customers. The attention that theseevents receive is much higher than they merit according to their probability (Stauffer 2003).

Our conclusion that supply chain risks do not have a large effect on supply chain performance needs to becontrasted with the flndings of Hendricks and Singhal (2003, 2005a, 2005b). As described above, Hendricks andSinghal analyzed the impact of announcements of supply chain disruptions on shareholder value and operatingperformance and show that both performance measures are substantially affected by supply chain disniptions. Acareful examination and comparison of their study and our results reveals that both approaches differ strongly andthat the results are not in conflict with each other. Hendricks and Singhal's work was based on a completelydifferent sample and measured a different issue. The observation of ad-hoc announcements yields two things: majorsupply chain disruptions (minor disruptions or problems are not announced in the media) and firms that were reallyaffected by such a major supply chain disruptions. Apart from the shortcomings of the applied event-studymethodology (MacKinlay 1997; McWilliams and Siegel 1997), their sample is biased in favor of large disruptions.They study "if-then" situations where "risk has struck" which sheds - undoubtedly important - light on the questionhow supply chain disruptions affect shareholder value and operational perfonnance under the condition that amassive disruption has already occurred.

Our sample is randomly drawn from a population and therefore includes both firms that may have beenconfronted with supply chain risks and firms that have not, or only to a small extent. Therefore, our researchindirectly acknowledges the frequency of supply chain disruptions experienced by the firms. In other words, our

JOURNAL OF BUSINESS LOGISTICS, Vol. 29, No. 1, 2008 57 7

results shed light on the importance of supply chain risk sources as a contextual variable in strategic decisions.Additionally, the present study is not restricted to major supply chain risks (that are publicly reported) Instead it isalso sensitive to minor supply chain risks. Finally, our study focuses on "supply chain performance" which is not ina direct relationship with share price or shareholder value.

Having illustrated that certain supply chain risks are less relevant as context in strategic decision-making ofGerman firms, it is of course more important for strategists and decision-makers to understand which supply chainrisk sources they should consider. Our findings reveal relationships between supply and demand side risks andsupply chain performance. Hence, supply and demand side risks are contextual variables that supply chain strategiesneed to account for (Mintzberg, Raisinghani, and Théorêt 1976; Pettigrew and Whipp 1993). This finding isconsistent with the literature on supply chain management. Primarily, it supports the assumption that supply anddemand coordination is the central issue in supply chain management (Kleindorfer and van Wassenhove 2004).

IMPLICATIONS AND CONCLUSIONS

The objective of this research was twofold: (1) to provide a detailed operationalization of the supply chain riskconstruct; and (2) to examine the relevance of various supply chain risk sources for strategic decision-making basedon the relationship between supply chain risks and supply chain performance.

First, the article contributes to the research on supply chain risk management by providing a detailedoperationalization of the supply chain risk constructs. Building on a thorough examination of the supply chain risktaxonomies proposed in the literature as well as on interviews with practitioners, we compiled qnd empiricallyvalidated constructs for different classes of supply chain risk sources.

Second, the findings support the assumption that there are negative associations between supply and demandside risks and supply chain performance, that is, that these risk sources are relevant contextual variables in strategicsupply chain decisions. However, the results relativize the current "passionate" discussion of the subject. In terms ofregulatory, legal and bureaucratic risks, infrastructure risks and catastrophic risks, the study yields no empiricalevidence for a negative relationship with supply chain performance. Overall, the data reveal a significant, but ratherlow impact of supply chain risks experienced by firms operating in Germany on supply chain performance.

Several managerial implications can be deduced from this study. First, supply chain risks have a negativeimpact on supply chain performance. As a consequence, they underscore the importance of supply chain riskmanagement concepts and measures. Second, while Hendricks and Singhal (2003, 2005a, 2005b) showed that severedismptions have substantial negative consequences on the health of the affected firms, our findings take intoconsideration the frequency of occurrence of those effects. Given that severe disruptions (e.g., caused by regulatorylegal or bureaucratic barriers, infrastructure breakdowns, or serious catastrophes) which lead to the release of ad-hocannouncements occur less frequently than "every-day" demand side and supply side disruptions, these latter risksources are m fact very important for achieving high supply chain performance. Thus, decision-makers should turntheir attention to these two risk sources. Third, supply chain managers should bear in mind an acceptable cost-benefit trade-off in their firms' mitigation endeavors concerning major contingency risks (Sarathy 2006). In supportof a better utilization of risk management resources, our study advocates the allocation of scarce resources to themitigation of demand side and supply side risks. This aspect ties in with the introduction where we explained thatthe series of recent catastrophes has stimulated the intense attention to supply chain risk management.

Several areas for future research can be highlighted. As described above, the data for this survey were collectedfrom firms based in Germany. Therefore, the results hold only true for firms based in countries with a similarpolitical, economic, and geographic setting. For example regulatory, legal and bureaucratic risks might be alleviateddue to Germany's fairly stable political and economic situation. Also, as shown by Helferich and Cook (2002)Germany is relatively immune to natural disasters. Therefore, a replication of this survey in other countries with'presumably different risk profiles (e.g., China or the US) would be a consequential next step.

Second, an empirical study that explains supply chain performance based on the strategy process and thestrategy content while taking into account supply chain risk as context (Ketchen, Thomas, and McDaniel 1996)would be highly interesting. Previous conceptual and qualitative research has focused on the strategy content (i.e..

WAGNER AND BODE

provides insights into a large set of mitigation strategies). Some discuss operational risks (Johnson 2001), otherdisruption risks (Lee and Wolfe 2003; Kleindorfer and Saad 2005; Sheffi 2001) and still others provide generalguidelines (Chopra and Sodhi 2004; Christopher and Peck 2004; Craighead, Blackhurst, Rungtusanatham, andHandfield 2007; Rice and Caniato 2003; Zsidisin, Ragatz, and Melnyk 2005a). However, the influence of thesestrategies on the relationship between supply chain risk and supply chain performance has neither heen thoroughlyunderpinned with theory nor analyzed through empirical research.

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JOURNAL OF BUSINESS LOGISTICS, Vol. 29, No. I, 2008 323

APPENDIX 1

MEASURESItems (response cues) Numher Cronhach

of items alphaFactor Item-to- Meanloading total

correlationTo what extend has your firm in the past three yearsexperienced a negative impact in supply chain managementdue to ... (5-point scale: not at all - to a very large extent)

Demand side risksUnanticipated or very volatile customer demand.Insufficient or distorted infonnation from your customersabout orders or demand quantities.

Supply side risksPoor logistics performance of suppliers (deliverydependability, order fill capacity).Supplier quality problems.Sudden default of a supplier (e.g., due to bankruptcy).Poor logistics performance of logistics service providers.Capacity fluctuations or shortages on the supply markets.

Regulatory, legal and bureaucratic risksChanges in the political environment due to theintroduction of new laws, stipulations, etc.Administrative barriers for the setup or operation ofsupply chains (e.g., authorizations).

infrastnictural risksDowntime or loss of own production capacity due tolocal disruptions (e.g., labor strike, fire, explosion,industrial accidents).Perturbation or breakdown of intemal IT infrastructure(e.g., caused by computer viruses, software bugs).Loss of own production capacity due to technical reasons(e.g., machine deterioration).Perturbation or breakdown of external IT infrastructure.

Catastrophic risksPolitical instability, war, civil unrest or other socio-political crises.Diseases or epidemics (e.g., SARS, Foot and MouthDisease).Natural disasters (e.g., earthquake, flooding, extremeclimate, tsunami).International terror attacks (e.g., 2005 London or 2004Madrid terror attacks).

0.724

0.799

0.691

0.748

0.854

0.8770.811

0.824

0.820

0.5680.568

0.695

0.7970.7110.6360.513

0.833

0.764

0.6870,5500.5040.478

0.530

0.530

0 589

0.724

0.705

0.649

0.841

0.809

0.806

0.793

0.576

0.500

0.512

0.756

0.672

0.684

0.672

3 25

2 47

2 20

1 73

1 55

324 WAGNER AND BODE

APPENDIX 1 (CONTINUED)

Items (response cues) Numher Cronhachof items alpha

Factor Item-to- Meanloading total

correlation

Evaluate the following supply chain performance indicatorscompared to your major competitor (5-point scale:significantly worse - significantly hetter)

Supply chain performanceOrder fill capacity: Provision of desired quantities on aconsistent basis.Delivery dependability: Meeting quoted or anticipateddelivery dates and quantities on a consistent hasis.Customer satisfaction: Meeting customer satisfactionwith supply chain performance on a consistent basis.Delivery speed: Time between order receipt andcustomer delivery.

Indicate how the following statements apply to your firm (5-point scale: does not apply at all - applies very much)

Risk managementIn collaboration with our customers and suppliers we areworking on transparent supply chains and an opensharing of information.Our firm has elaborated business continuity orcontingency plans addressing several supply chain risks.We regularly monitor our suppliers for possible supplychain risks.We reduce demand side risks through late productdifferentiation.In our firm, an employee or a team is dedicated to supplychain risk management.If possible, we insure against supply chain related risks.

0.852

0.794

0.872

0.843

0.769

0.735

0.782

0.725

0.632

0.637

0.803 0.662

0.773

0.752

0.631

0.629

0.623

0.625

0.601

0.467

0.472

0.466

3.77

2.73

JOURNAL OE BUSINESS LOGISTICS, Vol. 29, No. 1, 2008 325

ABOUT THE AUTHORS

Stephan M. Wagner (Ph.D. University of St. Gallen, Switzerland) is Professor and holds the Chair of LogisticsManagement at the Swiss Federal Institute of Technology, Zurich. Prior, he served on the faculty of WHU - OttoBeisheim School of Management, Vallendar, Germany. Prior to joining academia, he worked for 10 years as seniormanager for an intemational top-management consulting firm and as head of supply chain management for a Swiss-based technology group. He has conducted research on supply chain risk for several years and investigated, forexample, risks in supplier management, supplier default dependencies, or supply chain vulnerability. Recently heacquired research fiinds from the German Federal Ministry of Economics and Technology to develop a SupplyChain Risk Management Navigator for medium sized enterprises.

Christoph Bode (Dipl.-Wi.-Ing. University of Karlsruhe, Germany) is a Doctoral Student at WHU - OttoBeisheim School of Management, Vallendar, Germany. He has published several scholarly articles and bookchapters and gave presentations on supply chain risk issues on intemational academic conferences in Berlin,Brussels, Hamburg, Karlsruhe, Long Beach, San Diego, and Tempe. In his doctoral project he empiricallyinvestigates supply chain design issues across various countries.