Managing Financially Distressed Suppliers: An Exploratory Study

MANAGING FINANCIALLY DISTRESSED SUPPLIERS: ANEXPLORATORY STUDY

CHRISTOPH BODE, DENIS H€UBNER AND STEPHAN M. WAGNERSwiss Federal Institute of Technology Zurich

The early anticipation and proactive management of financially distressedsuppliers have become crucial for buying firms to protect themselvesagainst supplier default risk. To this end, firms need to be able to readthe early warning signals from suppliers that are running into financialdistress, to take appropriate remedial actions, and to gather such experi-ences in repositories of organizational knowledge for future actions. Theobjective of this study is to explore how and why firms differ in thesebehaviors and capabilities. We use the organizational information-processing perspective as a guiding theoretical framework and analyzequalitative data obtained from interviews conducted with 14 global casestudy manufacturing firms. By comparing these firms, behaviors in termsof scanning and interpreting warning signals, reacting to distressed ordefaulting suppliers, and learning from such experiences, we are able toisolate several risk management archetypes and develop four sets of prop-ositions that describe the occurrence of these archetypes.

Keywords: supply chain risk; supplier financial distress; supplier default; organiza-tional learning and knowledge; qualitative data analysis; case studies

INTRODUCTIONTo protect themselves against costly supply chain

disruptions, buying firms need to proactively assessand manage the risks in their supply base (Kraljic,1983; Sheffi, 2005). These risks may originate fromvarious sources, ranging from unsolved problems inthe suppliers’ manufacturing operations to suppliersthat do not comply with certain ethical, social, orenvironmental standards (Hofmann, Busse, Bode, &Henke, 2013; Rao & Goldsby, 2009; Wagner & Bode,2008). A particularly important topic is the risk offinancially distressed suppliers and the associatedthreats of sudden supplier defaults and bankruptcies(Carter & Giunipero, 2010). In the midst of financialwoes, distressed suppliers have incentives to cut costsby postponing capital expenditures (e.g., process

improvements, R&D), liquidating assets, or reducingproduct quality (Maksimovic & Titman, 1991; Phillips& Sertsios, 2013) and to act opportunistically and“hold-up” dependent customers (Lester, 2002). Oncebankrupt, suppliers impose direct costs on their cus-tomers, either in the form of switching costs or in theform of subsidies to maintain operations.The need to manage the risk of financially distressed

suppliers has been fueled by the latest economic crisis(Blome & Schoenherr, 2011; Finley, 2009). Duringthe crisis, banks and other financial intermediariestightened their credit and loan policies, making itboth more difficult and more costly for firms to raisethe liquidity needed for their daily operations. Forexample, in the automotive supplier industry—whereconstant price pressures from the buying automotiveOEMs had eroded suppliers’ financial health—the eco-nomic downturn triggered many supplier defaultsincluding those of Contech in February 2009 (Larson,2009) or Visteon in May 2009 (McCarty & Orolani,2009). Despite the rebound of the world economy in2010, many automotive suppliers are still strugglingto shift gears and provide supplies to meet theincreasing demand. The German automotive supplierHonsel, despite having employees that are busilyworking to supply automotive OEMs such as BMW or

Acknowledgments: We are grateful for financial support from

CAPS Research, Tempe, AZ. On earlier drafts of this article, we

received helpful comments from participants of the 2012

Research Seminar of the International Supply Chain Risk Man-

agement Network (ISCRiM) and the 2012 Production & Opera-

tions Management (POM) Annual Conference. We would also

like to thank the Co-Editor-in-Chief Chad Autry, the anonymous

associate editor, and three reviewers for their significant contri-

butions to the improvement of this article.

Volume 50, Number 424

Volkswagen, had to file for bankruptcy in 2010,because it had divested too many of its assets duringthe financial crisis (Handelsblatt online, 2010). Theseexamples from the automotive industry illustrate thateven in a sound economic market environment, therisk of financially distressed suppliers is a seriousthreat and supplier financial risk management is atopic of enduring importance (Murphy, Schweinberg,& Pope, 2008).A large body of literature has explored the predic-

tion of corporate failures (Altman & Hotchkiss, 2006;Duffie & Singleton, 2003) and the management ofsupply risk (Blackhurst, Scheibe, & Johnson, 2008;Ellis, Shockley, & Henry, 2011; Zsidisin, 2003; Zsidisin,Ellram, Carter, & Cavinato, 2004), yet we knowremarkably little about what firms actually do toavoid supply chain disruptions triggered by supplierdefaults and, more specifically, how they differ inmanaging financially distressed suppliers that are onthe verge of defaulting. This is astonishing, becausewithout a solid understanding of what firms do, it isimpossible to propose the supply chain risk manage-ment initiatives that consider the distinctive prefer-ences of firms. To address this gap and shed light onbuying firms’ behaviors toward financially distressedsuppliers, this study offers detailed insights into howbuying firms manage this important risk. Specifically,we focus on prebankruptcy financial distress, a condi-tion which precedes a supplier’s default and bank-ruptcy petition (Hertzel, Li, Officer, & Rodgers, 2008;Wruck, 1990), and seek to provide exploratoryanswers to the following three questions: (1) Howdo buying firms screen their supply base to assess therisk of financial distress? (2) Which actions do theyapply when they identify a financially distressed sup-plier? (3) What lessons do they learn from theirexperiences with distressed suppliers?We use an exploratory, theory-building approach

which is underpinned by literature that views firms asinformation-processing systems (Daft & Weick, 1984;Galbraith, 1973; Thompson, 1967) and by the organi-zational crisis literature (Mitroff, 2000; Pearson &Clair, 1998). By comparing 14 manufacturing firmsfrom Europe, the Middle East, North America, andLatin America in terms of scanning and interpretingwarning signals, reacting to financially distressed sup-pliers, and learning from such experiences, we are ableto isolate ten different information-processing and riskmanagement patterns (archetypes). Based on thesefindings and the information-processing perspective,we develop four sets of propositions that describe andqualify the occurrence of these archetypes and theirrelationships. The proposed typology of archetypesprovides a sound basis for future research and helpsmanagers to classify and benchmark their risk man-agement processes.

FAILURE, CRISIS, AND FINANCIALDISTRESS OF FIRMS

Corporate failure or “death” is an intrinsic part oforganizational life and has long attracted the attentionof researchers in economics, finance, and management(Anheier, 1996). Despite the large body of literature,there is still no coherent theory of corporate failuresand their causes. Anheier (1996, p. 956), for example,concluded that “the phenomenon of failure in organi-zations is too multifaceted” to support a “grand the-ory of organizational failures.” A prevailing approach,however, is to view failure as the result of an organiza-tional crisis which is understood as an unplanned andunintentional situation that endangers the survival ofan organization (Pearson & Clair, 1998). The organi-zational crisis view gained prominence in the manage-ment literature during the 1960s and 1970s(Hermann, 1963; Turner, 1976), although the firststudies date back to the early 1930s with scholars try-ing to derive managerial implications from the investi-gation of failing firms and the underlying causes(Fleege-Althoff, 1930).The major tenet of the organizational crisis literature

is that failure does not occur suddenly, but developsgradually and incrementally over time (Pearson &Clair, 1998). Various stage models have been pro-posed to identify distinct phases of organizational cri-ses (Fink, 1986; Mitroff, 2000; Turner, 1976).Although the number and labels of the stages vary,these models generally describe a sequential course ofescalation consisting of relatively quiet incubationstages during which latent problems accumulate, criti-cal stages which begin with the first manifestation ofsymptoms and which end with acute periods thatrequire immediate management attention to salvagethe firm, and aftermath stages where the firm mayeither recover or fold. Two implications, both ofwhich are important for our study, follow directlyfrom this perspective. First, due to the early crisisstages, red flags precede the outbreak of the moreacute stages. In this vein, Mitroff (2000, p. 102) arguedthat “[p]rior to their actual occurrence, all crises sendout a repeated train of early warning signals. If thesesignals can be picked up, amplified, and acted upon,then many crises can be averted before they happen.”Second and as a corollary to the previous quote: Acrisis does not have to end with a business failure.Whether or not a firm fails depends on the ability ofthe firm and its stakeholders to manage the crisis.An organizational crisis is inevitably associated with

deteriorating financial performance and health. Partic-ularly in the later stages of a crisis, firms suffer fromfinancial distress, a condition which precedes failure bysome period of time. A financially distressed firm hastrouble raising the cash to meet the payments on its

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Managing Financially Distressed Suppliers

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current financial obligations, particularly with regardto “hard” contractual agreements that are enforceableby law (such as loans, debts to suppliers, salaries ofemployees, and interest payments) (Lau, 1987). Oncethe firm cannot pay these obligations on time andcannot solve the problem within a certain grace per-iod, a financial default occurs (Wruck, 1990). A defaultdoes not mean that the firm is legally insolvent orbankrupt. In fact, the default situation could also bejust a temporary condition. However, if the conditionis chronic, a defaulting firm will be forced at somepoint to file a formal insolvency or bankruptcy petition(Altman & Hotchkiss, 2006). The definition of bothterms and the associated legal processes vary by coun-try. Typically, the filing firm loses some control rightsto its creditors. The purpose of an insolvency proceed-ing is to satisfy the outstanding claims against thefirm’s assets (United Nations Commission on Interna-tional Trade Law, 2005). If a firm files for bankruptcyprotection (e.g., Chapter 11 in the U.S.), restructuringactions are likely to be triggered. The bankruptcy canbe initiated either by the owners of the firm who canexercise their “right to default” or by its creditors. Liq-uidation is reached when the firm sells its assets to thepublic.

THEORETICAL FRAMINGTo structure and support our empirical study, we

developed a simple conceptual framework, shown inFigure 1, prior to the actual data collection. Thestarting point of this framework is the organizationalcrisis literature reviewed above which suggests that a

supplier default does not happen “out of the blue”but as the terminal outcome of an escalating organiza-tional crisis. Thus, prior to a supplier going out ofbusiness, a stream of early warning signs will beemitted which could be observed by interested stake-holders such as customers (buying firms). However,to devise effective countermeasures, buying firms needto detect and understand these signs.In light of this background, the core of our concep-

tual framework is informed by literature that viewsfirms as information-processing systems (Galbraith,1973; Thompson, 1967; Tushman & Nadler, 1978).We opted for the organizational information-processing perspective as our theoretical lens, becauseit offers a generalized view on how firms respond toinformation that they receive from their environment.It is embedded in the open systems view andacknowledges that a firm is never self-sufficient. Tosurvive and to operate, firms have to enter intoexchange relationships with their environment,consisting of competitors, suppliers, and customershaving direct transactions with the firm (Dill, 1958;Duncan, 1972). The supply chain and procurementfunctions are central to these boundary-spanning tasks(Katz & Kahn, 1978). Drawing from this perspective,we follow several other studies that have madeinformation processing the integrating theoreticalstructure in models that describe firm-level behaviorin response to exogenous events (e.g., Barr, 1998;Dutton, Fahey, & Narayanan, 1983; Thomas, Clark, &Gioia, 1993).Specifically, we use Daft and Weick’s (1984)

information-processing model which explains a firm’s

FIGURE 1Conceptual Framework

SUPPLY BASE

SupplierFinancial Distress

Incubationstage

Early critical stage

Clarity of signals /Probability of default

tLate

critical stage

SignalsScanning Inter-

pretation

BUYING FIRM

Systemizing knowledge

Learning

Experiences

Adap-tationsAction

Bridging

Buffering

Incremental

Radical

Non-intrusive

Intrusive

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reaction to changes in its environment as a result ofsequentially performed scanning, interpretation, andaction tasks. First, firms scan their environment tocollect data about actual or potential changes. Second,they analyze and interpret the collected data toidentify critical threats and assess the need for action.And third, firms take actions based on the gathereddata and corresponding interpretations. These actionscan be further distinguished in those that focus onsolving the problem at hand (i.e., the operative deci-sions and activities) and those that have a more long-term learning focus where firms gather experiencesand draw inferences from their historical experiencesin repositories of organizational knowledge for futureactions (Cyert & March, 1963; Levitt & March, 1988).To account for both, we refer to the former as“action” and the latter as “learning.” Hence, we struc-ture a buying firm’s information-processing path interms of scanning, interpretation, action, and learningstages.

ScanningThe information-processing perspective suggests that

firms scan constituents of their environment, if theyhave specific information-processing needs (Daft &Lengel, 1986). In the context of financially distressedsuppliers, information-processing needs arise from therisk of direct and indirect negative effects on the buy-ing firm’s performance objectives. This risk motivatesbuying firms to scan their supply base for informationabout their suppliers’ financial health and other earlywarning signals. As suggested by the organizationalcrisis perspective, suppliers will send out signals at theonset of an organizational crisis and often signifi-cantly before the occurrence of a financial distress sit-uation or a financial default (Mitroff, 2000; Platt &Platt, 2002). Such information may prevent losses forthe buying firm, which suggests that scanning is akey element of successful management of supplierdistress.Because the corresponding early warning signals

are often ambiguous and subtle, elaborated scanningprocesses are usually necessary to detect them. Inthis regard, the credit risk literature (e.g., Duffie &Singleton, 2003) offers a sizable amount ofapproaches which can be used to measure theprobability of default of suppliers, ranging fromAltman’s (1968) Z-score derived from discriminantanalysis to sophisticated credit portfolio risk modelssuch as CreditRisk+ (Wilde, 1997). The use of thesemodels requires extensive information about asupplier’s capital structure and the values of itsassets and liabilities or extensive historical data—information to which buying firms will not alwayshave outright access.

InterpretationInterpretation gives meaning to gathered data or sig-

nals. It usually takes place along with or immediatelyafter scanning. Based on the assessed information, thebuying firm constructs or modifies its beliefs aboutthe risk involved in the current situation and decideshow to proceed. The interpretation process may gener-ate insights that delegitimize a situation that had pre-viously been considered acceptable (Greening & Gray,1994; Meyer, 1982) because the assessed informationexposed the financial instability of the supplier. How-ever, the information-processing perspective stressesthat a firm will respond only if the interpreted infor-mation exceeds a certain response-justifying thresholdwhich is determined by the firm’s risk appetite andgoals (Cyert & March, 1963; Huber & Daft, 1987). Ifthe risk is believed to be below the threshold, thebuying firm does not act and accepts the situation;otherwise, it will proceed to the action stage.Upon receiving a warning signal from a supplier,

several issues make it difficult for buying firms to inferwhether or not a supplier is truly facing a financialdistress situation. Specifically, the different sources ofsignals vary in their levels of reliability. For example,a market rumor that a supplier might be in trouble isa less reliable signal than a supplier asking for an ear-lier payment policy. Early warning signals such aschanges in ownership structures have a long “dis-tance-to-distress” and are associated with more noiseand, thus, are more uncertain than clearer signalsreceived in the critical stages of a crisis.

ActionIf the scanning and interpretation process suggests

that the risk in the current situation has exceeded acertain threshold, there is a mandate for managers tobring their firms’ supply base back in line with theexpectations. Although firms may attempt to managedistressed supplier situations in any number of ways,there are generic ways in which firms behave, whichdiffer in their level of cooperativeness. For example,Bode, Wagner, Petersen, and Ellram (2011) suggestedthat, in response to supply chain disruptions, a buy-ing firm will strive to reduce or manage the vulnera-bility experienced by buffering and/or bridging. Bothstrategies are intended to enhance the security of thebuying firm vis-�a-vis its supply, but they differ in theirlevel of cooperativeness.The strategy of buffering is an uncooperative

approach. It aims at reducing the buying firm’s exter-nal resource dependencies and at achieving moreautonomy (Galbraith, 1973; Thompson, 1967). A buf-fer insulates the firm from the exchange relationshipswith suppliers and mitigates the detrimental conse-quences of external disturbances. A common example

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of the application of this strategy is the diversificationof the supply base by the installation of redundantsuppliers (Anupindi & Akella, 1993). The firm canalso build up various forms of slack (inventory, flexi-bility, or time buffers) to act as “shock absorbers”(Bourgeois, 1981, p. 30).In contrast, bridging is a more cooperative approach.

It aims at managing the external resource dependenciesby improving the relationships with the suppliers orby enlarging the control and influence over them(Aldrich, 1979; Katz & Kahn, 1978). Unlike buffering,bridging encompasses actions that alter or strengthenexisting exchange relationships with suppliers. To cre-ate a bridge between itself and its suppliers, the buy-ing firm can modify these relationships, ranging fromforming links with influential individuals in the sup-plier firms to vertical integration (Ulrich & Barney,1984). Bridging may also imply more active monitor-ing of suppliers and enhanced exchange of informa-tion between the firms. Not only do these actionsreduce uncertainty; they also enable the buying firmto read the early warning signals about critical envi-ronmental changes and to develop adequate andprompt responses.

LearningAs buying firms scan their environment and

interpret and act upon the collected data, theyreceive feedback that affects current and futurebehavior (Cyert & March, 1963; Daft & Weick, 1984;Thompson, 1967). The organizational learning litera-ture is relatively fragmented, but it generally recog-nizes that hazardous experiences, such as dealingwith financially distressed suppliers, can be a valu-able source of organizational learning (Lampel,Shamsie, & Shapira, 2009; Rerup, 2009). These expe-riences may expose the latent flaws and vulnerabili-ties of internal and external structures and processes,thus helping the affected buying firms to improvetheir strategies for the future (Levitt & March, 1988;Nathan & Kovoor-Misra, 2002).At its most basic level, learning creates the potential

for behavioral change (Huber, 1991). With regard tothe outcome of such learning, there is a widespreadacceptance that at least two different qualities are tobe distinguished: (1) incremental changes in routineswithin existing assumptions and schemes, termedincremental learning, lower-level learning, or single-looplearning, and (2) more drastic changes of rules, norms,strategies, or structures with a long-term impact,termed radical learning, higher-level learning, or double-loop learning (Argyris & Sch€on, 1978; Fiol & Lyles,1985; Lant & Mezias, 1992). The first form comeswith relatively low risk, because only small behavioralchanges are added to a familiar routine. Here, the aimis to improve upon the initial situation without

substantially changing it. The second, more complexform of learning involves more drastic changes andoften requires an external stimulus (Fiol & Lyles,1985) which stems from the idea that strong negativeexperiences force a firm to question existing schemesand structures and to unlearn habitual behaviors (e.g.,Hedberg, 1981).

METHODOLOGYGiven the nature of our research questions, an

exploratory, qualitative methodology is most appro-priate to compare buying firms’ behaviors toward dis-tressed suppliers. With this and the delineatedinformation-processing framework as an initialstructure, we consider our study as part of thedescription/mapping stage of theory building (Stuart,McCutcheon, Handfield, McLachlin, & Samson, 2002)in which scholars describe observable phenomenaand develop insights on critical variables that influ-ence them. We use a multiple case study design andfollow established case study research methods (Yin,2009) to fit the requirements of this early stage ofepistemology.

Sampling and Data CollectionA theoretical sampling approach—the identification

of “places, people, or events that will maximizeopportunities to discover variations among conceptsand to densify categories in terms of their propertiesand dimensions” (Strauss & Corbin, 1998, p. 201)—was applied. We sampled firms from differentmanufacturing industry sectors and regions. The par-ticipating firms produce and market products such ascars and commercial vehicles, electronics, fashion,food, and industrial goods and are based in Europe,the Middle East, North America, and Latin America.Only firms whose annual sales volumes exceededUS $100 million were considered, to ensure that firmshave well-established organizational routines. Thediverse nature of our sample (Table 1 provides anoverview of the investigated firms) enabled us tomaintain external validity.Our informants were specialists in the areas of pur-

chasing and supply (chain) management and at differ-ent hierarchical levels including middle (e.g.,managers or specialists), senior, and top management(e.g., head of purchasing, COO). Some informantswere in charge of a specific business unit, but theseunits were profit centers that had profit and lossresponsibilities as well as their own independent pur-chasing organizations. Hence, within the scope oftheir responsibilities, our informants reflect theirfirms’ behavior toward suppliers. In such cases, it islegitimate to rely on well-informed key informants(Rosenberg & Stern, 1970).

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Following Eisenhardt (1989), we used a sequentialprocess and ceased data collection after attaining theo-retical saturation (the point at which no significantadditional insights emerged) after 14 interviews. Eachinterview was initiated by a personal invitation sentby email. When an affirmative response was received,appointments were set either for a face-to-face (wherefeasible) or a telephone interview. The interviews wereconducted by two researchers between June and Octo-ber 2011 and lasted, on average, approximately60 min. All gathered data were carefully documentedin a structured database that was accessible to theresearchers. Following this procedure, we ensured ahigh reliability of our data.The interviews were based on a previously fixed semi-

structured interview instrument (shown in theAppendix), which was itself based on the outlinedinformation-processing framework and followed thestages of scanning, interpretation, action, and learning.Prior to data collection, both the conceptual frameworkand the interview guide were discussed with managersand researchers in purchasing and supply management.

During the interviews, we explained the researchobjectives to our informants and discussed the riskof supplier financial distress and supplier default.We asked the informants to base their answers solelyon ongoing buyer–supplier relationships which arenot in exploration or decline, because the life cycletheory of interfirm relationships suggests that buyer–supplier relationships develop over time alongdistinct phases that exhibit systematic differences inpatterns of behavior, goal congruence, and processesof the two parties involved (Dwyer, Schurr, & Oh,1987; Ring & Van de Ven, 1994). To reduce thechances of eliciting responses that were sociallydesirable or consistent with how informants believeresearchers want them to respond, we guaranteedanonymity and confidentiality. All interviews wererecorded and transcribed. Subsequently, thesetranscripts were sent back to the informants to checkfor accuracy. Finally, for the purposes of triangula-tion and establishing construct validity, we consultedsecondary data sources (e.g., annual reports, businesspress, corporate websites, general terms and

TABLE 1

Sample Demographics

FirmIndustrySector

Revenues[million US-

$]Total

Employees

InformantManagement

Level HeadquartersInterviewLocation

AntennaCo Electronics 1,800 11,500 Specialist USA MexicoAutomotiveCo Automotive 130,000 260,000 Middle

ManagementGermany Germany

BeverageCo Foodprocessing

8,800 42,000 SeniorManagement

Greece Switzerland

BreweryCo Foodprocessing

27,000 70,000 Specialist England Switzerland

BusCo Automotive 340 1,200 MiddleManagement

Turkey Turkey

CementCo Construction 540 1,000 SeniorManagement

Turkey Turkey

DefenseCo ICT 7,000 21,000 MiddleManagement

Germany Mexico

ElectronicsCo Industry 100,000 335,000 Specialist Germany SwitzerlandFashionCo Fashion 1,300 7,000 Senior

ManagementItaly Mexico

HealthcareCo FMCG 60,000 116,000 SeniorManagement

USA Mexico

PowerCo Industry 30,000 130,000 SeniorManagement

Switzerland Switzerland

RadiatorCo Automotive n.a. 700 MiddleManagement

Turkey Turkey

TurbinesCo Industry 27,000 93,500 MiddleManagement

France France

VanCo Automotive 4,900 10,000 Specialist Turkey Turkey

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conditions of purchase, internal documentation) inaddition to the interview data.

Data AnalysisOur data analysis was based on standard coding

procedures (Miles & Huberman, 1994) and consistedof data reduction and matrix displays as well asdetailed within-case and between-case analyses.While conducting the initial review of the interviewnotes, we found several recurring behavioral patternswhich we used to preliminarily organize the data.This initial review provided a basic classification ofcodeable behaviors (Miles & Huberman, 1994), butthe definitions and examples of the coding schemewere iteratively refined and modified with all codesemerging from the data. Once the data collectionceased, the interview transcripts were coded into thefinal categories. Each interview was coded by tworesearchers working independently. An intercoderagreement rate of 95 percent and a Cohen’s j of.89 indicated almost perfect agreement between theraters (Landis & Koch, 1977). Any remaining dis-crepancies were discussed until full agreement wasreached. In combination with the theoreticallygrounded research framework, the high intercoderagreement suggests a high level of internal validityof our results. After consolidating the data, wecategorized the behaviors along the information-processing stages of our framework. For each stage,we examined the corresponding behaviors and itera-tively fleshed out distinct and coherent groups.During this process, we realized that scanning and

interpretation tasks are interwoven and coalesce insuch a way that it is almost impossible to separatethem on an empirical basis. Our informants indi-cated that intentional scanning tasks would alwaysbe connected to some specific interpretation activity.Additionally, supply base monitoring is a routinetask for specialists in purchasing and supplymanagement which differs from Daft and Weick(1984) who described nonroutine decision-makingprocesses of strategic importance at the topmanagement level. The decision horizon of ourresearch is significantly narrower. As informationcan be distinguished as tactical and strategic withthe former being concerned with “large volume ofrelatively routine day-to-day problems and situationsconfronting an organization” (Egelhoff, 1991, p.350) and the latter dealing with “smaller volume ofrelatively nonroutine, and usually more important,problems” (Egelhoff, 1991, p. 351), we collapsedthe two stages of scanning and interpretation intoone. Eventually, our coding procedure led tobehavioral archetypes or gestalts (Strauss & Corbin,1998) along these three information-processingstages.

ANALYSIS AND RESULTS

Information-Processing StagesIn a first step, we report separate analyses for (1)

scanning and interpretation, (2) action, and (3) learn-ing. The findings, presented in Table 2, help us toidentify archetypal behavior on the level of theinformation-processing stages.

Scanning and Interpretation. Most of our infor-mants stated that they consider supplier default risksas a threat to their supply chains and that they payattention to corresponding warning signals in theirsupply bases. They named several information sourceswhich they use in this regard, including publicly avail-able financial statements (corporate disclosures), creditand market data, and third-party reports and credit rat-ings (provided by credit rating agencies). However, aswe coded and matched scanning and interpretationpatterns among the participating firms, we observed ahigh level of heterogeneity in scanning and interpreta-tion behaviors toward financially distressed supplierswhich ranged from highly reactive postures to sophisti-cated, proactive risk management systems. In particu-lar, we found significant differences in the firms’ levelof intrusiveness vis-�a-vis their suppliers. This finding isin congruence with Daft and Weick (1984) who pur-ported that intrusive firms are vigilant to their environ-ment, show a proactive and assertive behavior, and actupon, rather than react to, environmental events. Incontrast, nonintrusive firms accept the environment asgiven, scan it within narrow limits, and are reluctantto engage in proactive scanning. Three major arche-types of firms emerged from the data which we refer toas Reactors, Observers, and Guards:

Proposition 1a: In the scanning and interpretationstage, buying firms differ in their level of intrusive-ness and can be clustered as Reactors (leastintrusive), Observers (medium intrusiveness), andGuards (most intrusive).

Reactors are nonintrusive in their scanning and toler-ate a high level of signal urgency (i.e., high thresholdof action). Instead of scanning their environmentsproactively, they prefer reactive, focused scanning, andinterpretation activities which they initiate on an adhoc basis. When receiving a fuzzy early warning sig-nal, Reactors will refrain from specific activities untilthe corresponding supplier shows clear and unambig-uous operational warning signals such as cost-cuttingprograms or explicit requests for modification of pay-ment terms. DefenseCo, for example, establishedlong-term, collaborative relationships with most of itssuppliers and relies solely on nonformalized informa-tion exchanges and clear operational warning signs.HealthcareCo controls the quality of received goodsand argued that “deviation in quality may be

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interpreted as a consequence of financial problems, asthe supplier might reduce the quality of their rawmaterials to decrease its costs.”Observers are significantly more intrusive than Reac-

tors. While the latter might detect a financial distresssituation only late in a supplier’s crisis process,Observers try to anticipate such situations and have aconsiderably lower threshold of action. The intrusive-ness of Observers is only limited insofar as they do notrequest quantitative financial data from their suppli-ers. For example, the purchasing departments ofAntennaCo and ElectronicsCo track market rumors.Based on its own prior experiences with distressedsuppliers, AntennaCo stated that “Before a firm has tofile for insolvency usually a trail of rumors foreshad-ows this event.” ElectronicsCo relies on third-partyrating agencies, although it knows that this informa-tion provides only a rear-window perspective (Bhatta-charyya, Datta, & Offodile, 2010). BeverageCo relieson its strict supplier accreditation process and on reg-ular site visits. Our informant claimed that due to thethorough accreditation process, chances of a “certifiedsupplier” becoming financially distressed would benegligible. Indeed, supplier accreditation may decreasethe information asymmetry and provide the buyerwith a better understanding of the financial health ofthe supplier (Zsidisin & Ellram, 2003).

Guards represent the most intrusive archetype. Incontrast to Observers, these firms have formalizedsupplier default risk management systems whichreceive data inputs from various sources includingaccreditations, credit ratings from third-party ratingagencies, monthly supplier surveys, financial quickcheck analyses performed by in-house specialists, anddetailed qualitative data gathered through regularaudits. All informants in this cluster agreed with thestatement “Our staff regularly visits our suppliers andthus receives formal and informal information.”AutomotiveCo, PowerCo, and TurbinesCo track thestaff turnover rates of their suppliers and consider lay-offs a bellwether of financial problems. CementCo,BusCo, and VanCo monitor changes of key employees(e.g., R&D personnel) and stakeholder conflicts. Fur-thermore, they are well informed about asset utiliza-tion rates of their core suppliers, as they “know themonthly capacity and how it is booked,” in the wordsof one informant. Suspicious signals will always trig-ger focused scanning and interpretation activities. If apossible supplier financial distress is identified,CementCo assembles a management committee whichdecides whether and how to act. However, not eventhe Guards apply sophisticated and intensive scanningactivities to all suppliers. Only a small number of sus-picious or highly critical suppliers are put on watch

TABLE 2

Information-Processing Archetypes

Scanning and Interpretation Action Learning

(a) GuardMonitors suppliers thoroughly.Performs analyses of quantitativedata as well as of qualitativeenvironmental data. Might acceptonly accredited suppliers.

(a) PartnerEstablished good long-termrelationships with few suppliers.Supports suppliers financiallyand beyond, followingdeveloped contingency plans.Switches suppliers onlyif unavoidable.

(a) EvolverPrepared for radical learning.Performs root-cause analysesof adverse experiences. Severedisruptions are discussed atsenior management level.Structural changes could be orwere applied.

(b) ObserverTakes operative signals intoaccount, but also tracksmarket rumors and performsqualitative analyses.

(b) CooperatorTries to support suppliers byadaption of payment terms orcredits. Noncritical supplierswill be replaced.

(b) AdaptorApplies single-loop learningand adjusts existing processeson an experience basis.

(c) ReactorWaits for operationalsignals or explicitsupplier requests beforethe financial distressis acknowledged.

(c) SubstitutorWill replace distressedsuppliers.

(c) IgnorerHas no learning routines inplace and is reluctant to learn.

(d) Lucky FellowDid not experience a financiallydistressed supplier yet.

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lists which are updated every three or six months.Table 3 summarizes the scanning and interpretationtools reported by the investigated firms.Two additional findings can be inferred from our

data. First, a corollary to Proposition 1a followsdirectly from the previous discussion:

Proposition 1b: The spectrum of possibleresponses to financially distressed suppliers isbroadest for Guards, second broadest for Observ-ers, and smallest for Reactors.

Next, we found that some firms refrain from moni-toring particularly large or powerful suppliers thatact in monopolistic or oligopolistic markets. AsCementCo explained, “There are only a few globalcompanies that control the fuel supply globally andwhich we cannot influence at all. Thus, we do notmonitor our fuel suppliers, although they account for45 percent of our purchasing volume.” In a similarvein, AutomotiveCo stated, “A few of our suppliers arehighly critical for the entire Western automotive indus-try. We do not believe it makes sense to closely moni-tor them, because, if they run into financial problemsthe entire industry would have to find a solution.”These arguments are somewhat analogous to the “toobig to fail” theory in the context of financial institu-tions and suggest that firms adjust their scanning andinterpretation efforts when there is little possibility todirectly influence the financial distress of a supplier.

Proposition 1c: In the scanning and interpretationstage, buying firms reduce their activities towardsuppliers that act in monopolistic or oligopolisticmarkets, that is, when their power to influence theoutcome of a financial distress is low.

Action. Once the received signals exceed a thresh-old and a supplier is considered risky due to its poor

financial health, a broad repertoire of coping strategiescan be applied. Our data suggests that a broad rangeof approaches can prevent supply chain disruptionscaused by financially distressed suppliers, from replac-ing the supplier to the provision of credits and con-sulting services to improve its financial situation. Inextreme cases, even vertical integration may take place.Our analysis of the response behaviors of the inter-viewed firms lead to three archetypes. The key differ-entiating feature suggested by our data was the levelof cooperativeness in the buying firm’s responsebehavior:

Proposition 2a: In the action stage, buyingfirms differ in their level of cooperativeness andcan be clustered in Substitutors (dominantlyuncooperative), Cooperators (selectively cooperativeand noncooperative), and Partners (dominantlycooperative).

Substitutors, the least cooperative group of firms,have a strong preference for buffering strategies, par-ticularly the immediate substitution of distressed sup-pliers. Once a supplier has been identified asfinancially distressed, Substitutors tend to cancel openorders and transfer the volume to alternative suppli-ers. Obviously, this behavior can exist only whenswitching costs are sufficiently low. BreweryCodescribed this as follows: “We have a low resourcedependence on our suppliers. Thus, we will terminateour business relationship and blacklist a supplierwhen it causes high-impact disruptions.” The Substitu-tors in our sample had a dominant position in theirsupply chains and produced rather simple products(food and apparel). They rely mainly on arm’s lengthbuyer–supplier relationships with little or no depen-dence on their part. Although they refrain fromcooperative bridging measures, Substitutors may offer

TABLE 3

Scanning: Classification and Tools/Procedures

Applied Tools and Procedures Guards Observers Reactors

Observation of operational signals (e.g., quality issues, deliveryaccuracy, problems with plant utilization, cost-cutting programs)

7 3 3

Financial analysis (e.g., financial quick check) 7 – –Regular supplier audits 6 – –Tracking of market rumors 4 2 –Listening to supplier requests (e.g., changes in payment terms) 4 1 1Accreditation 3 1 –Third-party credit risk/financial health ratings 2 1 –

Firm count 8 3 3

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short-term support to avoid supply disruptions duringthe transition phase from the distressed supplier to analternative source. For example, FashionCo andBreweryCo stated that they would agree—uponrequest—to modify their payment terms for high-per-forming suppliers. Other Substitutors might return to asupplier once the supplier’s financial situation has sta-bilized. FashionCo, for example, “[…] had to cancelan order from a financially distressed local supplierand to shift to a more expensive Italian one. Appreci-ating the local supplier’s honesty, we returned to itonce it recovered its financial stability.”Firms that matched our second archetype, Coopera-

tors, rely on strong relationships with selected parts oftheir supplier base. With the exception of CementCo,these firms produce complex or high-tech productsand tend to be more dependent on their suppliers, asproduct-specific ramp-up times are required for newsuppliers to become operational. AntennaCo providedan example of its interpretation of the buyer–supplierrelationships: “Though we pay special attention tohaving a highly diversified supplier base, we want tooffer an equal win-win relationship to all of our busi-ness partners—even for small businesses.” Cooperatorswould not refuse to modify their payment terms toassist their preferred suppliers. ElectronicsCo is evenwilling to provide short-term credits to its strategicsuppliers to weather temporary problems with liquid-ity. Still, if a supplier is noncritical, Cooperators alsogravitate toward buffering actions and seek healthy,alternative suppliers. However, to avoid the risk ofsupply chain disruptions, they would be willing tosupport a distressed supplier until a replacement isfound; as CementCo explained, “Although we did notexpect a supplier to overcome its financial crisis, wesupported it by paying wages for its employees inorder to keep our production running. As soon as analternative supplier was found, we stopped the sup-port for the distressed one.” When continuing busi-ness with financially distressed suppliers, CementCohedges its financial risks demanding the issuance ofan indemnity letter. Cooperators pursue a selectivestrategy between critical and uncritical suppliers. Wefound manufacturing firms from different sectors andsizes in this category; all firms deliver their productssolely to B2B markets.Third, Partners are similar to Cooperators but have

sophisticated contingency plans that they activate,when confronted with a financially distressed supplier.All automotive firms fit this archetype, as do Health-careCo and PowerCo. These firms rely on strong rela-tionships with a relatively small number of accreditedsuppliers which they expect to be “honest and trans-parent, if they are facing problems,” as HealthcareCoexplained. Once HealthcareCo detects a signal suggest-ing supplier financial distress, the typical first response

is to arrange a meeting at the senior managementlevel to understand the severity of the situation and todetermine appropriate countermeasures. The lattercould include modification of payment terms, award-ing additional business volume, buying of the sup-plier’s relationship-specific assets (e.g., tools),prefinancing input purchases, and knowledge transfer(e.g., through in-house consulting departments). Auto-motiveCo explained that its internal consultancywould provide “guidance and support for necessaryrestructuring processes,” to improve the strugglingsupplier’s financial performance. For suppliers of criti-cal inputs, PowerCo is willing to provide upfront pay-ments for raw materials, to enable the start of theproduction during a liquidity shortage. Still, even Part-ners meticulously estimate and compare the costs ofbridging and costs of buffering in determining theirresponses. Although these firms generally prefer tobridge, they admit that sometimes even a criticalsupplier must be replaced. VanCo, for example, said,“One of our suppliers had been going through finan-cial problems for some years. Several times a consor-tium of OEMs had saved it from bankruptcy. Whenwe learned about an additional shareholder conflict,we eventually decided to develop an alternative sup-plier. One year later the initial supplier filed insol-vency due to the conflict and other OEMs sufferedseriously, while we could continue our production.”Table 4 summarizes our findings for the action stage.Overall, eleven of the 14 investigated firms respond

more cooperatively (Cooperator or Partner) to finan-cially distressed suppliers. Two additional findings,however, qualify this observation. First, our interviewdata suggest that cooperative postures are almostalways driven by calculative and economic consider-ations rather than by affective commitment(Gundlach, Achrol, & Mentzer, 1995). The informantof AutomotiveCo (Partner) stated pointedly “There areno white knights in supply management,” indicatinghis belief that there is little room for true loyalty infinancial distress situations. If switching to an alterna-tive supplier is a viable option, most buying firms willpursue this route. Thus,

Proposition 2b: In the action stage, a buying firm’slevel of cooperativeness is largely driven by calcula-tive/economic motives rather than by affectivemotives and loyalty.

Second, a buying firm’s actions toward a distressedsupplier seem to depend critically on the stage of thesupplier’s crisis process. As indicated by our infor-mants, when the crisis has advanced to the moreacute stages so that the chances of a successfulturnaround appear to be low, firms tend to behaveless cooperatively. AutomotiveCo explained: “Mostof our suppliers also sell products to our direct

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competitors—and we certainly do not want to cross-subsidize our competitors by keeping a severelydistressed supplier alive. […] If we expect the worstcase, an insolvency, we extend our stock of itemssourced from the struggling supplier, in order to keepour production lines running without interruptions.”This suggests a mix of short-term (e.g., increasingstock) and long-term (switching the supplier) buffer-ing actions. In sum,

Proposition 2c: In the action stage, the moresevere the supplier’s financial distress, the morelikely the buying firm will behave in a noncoopera-tive way.

Learning. Supplier defaults are usually considered“unfortunate facts of corporate life cycles,” but thisview neglects the positive learning potential inherentin such experiences: Buying firms can learn from theirexperiences with distressed suppliers and improvetheir future scanning, interpretation, and action pro-cesses. Some of the interviewed firms had developedworking environments conducive to organizationallearning where employees share their lessons learned.Having dealt with financially distressed suppliers inthe past, buying firms may have developed corre-sponding rules and routines. As discussed, learningtakes on different forms and different qualities.Indeed, the learning behavior of the interviewed firmsranged from no occurrences of learning, all the wayup to radical learning where experiences with a singlesupplier led to the development of completely newrisk management processes. Clustering the firmsaccording their quality of learning leads to the follow-ing proposition:

Proposition 3a: In the learning stage, buying firmsdiffer in their quality of learning and can be clus-tered as Lucky Fellows and Ignorers (no learning),

Adaptors (incremental learning), and Evolvers (incre-mental and radical learning).

Two of the investigated firms—RadiatorCo andBreweryCo—which we call Lucky Fellows, have notfaced a financially distressed supplier recently. Hence,there was no possibility for learning to occur; a possi-ble reason for this might be that these firms imple-mented sophisticated scanning processes and areGuards in the scanning and interpretation stage. Radi-atorCo developed contingency plans and would preferto keep a supplier from defaulting than to recruit anddevelop a new one. BreweryCo, in contrast, wouldrather replace a distressed supplier.The second archetype with a high distance to organi-

zational learning is the Ignorers. These firms are reluc-tant to change in their general scanning or actionbehavior. Although PowerCo and DefenseCo imple-mented knowledge management systems and storetheir experiences, they do not deliberately exploit thiscontent. DefenseCo carries out regular audits toensure that defined processes follow internal guide-lines, but our informant was not able to give a singleexample of learning from experiences with distressedsuppliers. FashionCo does not even document itsprior experiences, but acknowledges potential forimprovement in this field. The informant stated thatfirm culture and everyday routines without slack aremajor roadblocks to learning: “We are so busy withour daily business that we don’t have time for report-ing procedures.”In contrast, firms that fit the Adaptor archetype draw

conclusions from past incidents of supplier financialdistress or supplier defaults. They fine-tune theirscanning, interpretation, and action processes, if spe-cific experiences exposed shortcomings. To this end,they use what has happened to modify their proce-dures either on an event-based or periodic basis. This

TABLE 4

Action: Classification and Tools/Procedures

Applied Tools and Procedures Partners Cooperators Substitutors

Adaption of payment terms 6 5 2Substitution of noncritical suppliers 4 5 3Substitution of critical suppliers – 2 2Provision of credits 5 1 –Meetings with suppliers on senior management level 4 1 –Prepared contingency plans 3 – –Provision of consulting services 2 – –Provision of references to potential investors 1 – –Request of indemnity letters – 1 –

Firm count 6 5 3

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involves minor changes in scanning and interpretationprocedures but also updates of standardized contractterms or supplier selection profiles. CementCoexplained that its purchasing department constantlyreviews and updates its contract terms to protect itselfin the case of a supplier default. BusCo gave an exam-ple of such incremental learning: “Based on recentexperiences, we improved our scanning process byadding and refining indicators.”Finally, the Evolver archetype consists of firms whose

organizational culture promotes structural changesand which are prepared to depart from their estab-lished routines after having analyzed the root causesof supply chain disruptions. They run formalizedknowledge management systems that encourageemployees to discuss their experiences with financiallydistressed suppliers. After having dealt with a dis-tressed supplier, these firms perform in-depth analyseswhich may involve top management. “We have aninternal online database to share experiences aboutdisruptions leading to small consequences. However,if a supplier’s financial distress led to a severe impact,a detailed business case is prepared and discussed bythe management board,” explained AutomotiveCo.BeverageCo established quality circles (a quality man-agement technique) which include root-cause analysesof supply chain disruptions caused by financially dis-tressed suppliers. Once a root cause has been identi-fied, actions are taken to prevent recurrence. Bycarrying out such self-reflective techniques, Evolvers areprepared to apply major changes in their routines asneeded. We summarize our findings regarding thelearning archetypes in Table 5.Although we found firms in which no learning took

place, only a minority appear to be averse to learning.Most of the informants agreed that a negative experi-ence with a distressed supplier can and should triggerchange. Besides the general posture toward learning,the data suggest that the quality of learning is alsoinfluenced by the severity of prior experiences. Elec-tronicsCo offered an example of shock-based radical

learning. After having experienced a severe supplychain disruption caused by an unforeseen supplierdefault, the firm designed and implemented a moni-toring tool. Formally,

Proposition 3b: In the learning stage, more severenegative experiences with financially distressedsuppliers will lead to a higher quality of learning(i.e., more radical, less incremental).

Information-Processing PathThe information-processing stages do not exist in iso-

lation but act in concert. Hence, in a second step and tounderstand the relationships among the stage-levelarchetypes, we investigated the entire information-processing path from scanning and interpretation tolearning. Table 6 summarizes the observed behaviorswhich are neither uniform nor uniformly distributed.To reveal overarching patterns and constellations of

archetypes, we applied a hierarchical clusteringapproach using the complete linkage (farthest neigh-bor) method which is commonly used because itleads to conservative results and creates clusters ofmaximum similarity between the cluster elements(Jain & Dubes, 1988). The method is based on theEuclidian distance metric, and cluster similarity ismeasured by the maximum distance between entitiesin each cluster. Initially, all clusters contain only sin-gle entities. Then, in a stepwise fashion, the algorithmcombines those entities (or groups of entities) thatshare most similarities into larger clusters until a sin-gle cluster consisting of all entities is reached (Brusco,Steinley, Cradit, & Singh, 2012). Figure 2 shows theresulting dendrogram as a graphical representation ofthe constellations of information-processing arche-types in our sample.Although the dendrogram shows a significant

amount of heterogeneity, some branches reveal con-sistent patterns (which also remain robust if otherclustering algorithms such as Ward, average linkage,or single linkage are applied). A first pattern surfaces

TABLE 5

Learning: Classification and Tools/Procedures

Applied Tools and Procedures Evolvers Adaptors Ignorers Lucky Fellows

Formal knowledge management system 4 – 1 1Adjustment of existing processes 1 3 – –Case discussions on senior management level 4 – – –Structures for radical change 4 – – –Formal knowledge management system in preparation 1 – 1 –No recent supplier default experienced – – – 2

Firm count 6 3 3 2

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when jointly investigating the archetypes of scanning/interpretation and action. Five of the 14 studied firmsshare a Guard–Partner–X constellation: BusCo andVanCo are identical (Guard–Partner–Adaptor), whilePowerCo, RadiatorCo, and AutomotiveCo differ only

in terms of their learning postures. A possibleexplanation for the Guard–Partner–X constellationcould lie in Propositions 1b and 2c which suggest thatGuards, because they detect supplier financial distressrelatively early, have large room to maneuver, which

TABLE 6

Archetype Classification of Case Study Firms

Firm

Archetype Classification

Scanning and Interpretation Action Learning

AntennaCo Observer Cooperator EvolverAutomotiveCo Guard Partner EvolverBeverageCo Observer Substitutor EvolverBreweryCo Guard Substitutor Lucky FellowBusCo Guard Partner AdaptorCementCo Guard Cooperator AdaptorDefenseCo Reactor Cooperator IgnorerElectronicsCo Observer Cooperator EvolverFashionCo Reactor Substitutor IgnorerHealthcareCo Reactor Partner EvolverPowerCo Guard Partner IgnorerRadiatorCo Guard Partner Lucky FellowTurbinesCo Guard Cooperator EvolverVanCo Guard Partner Adaptor

FIGURE 2Cluster Dendrogram

DefenseCo (R-C-I)

FashionCo (R-S-I)

HealthcareCo (R-P-E)

BeverageCo (O-S-E)

AntennaCo (O-C-E)

ElectronicsCo (O-C-E)

TurbinesCo (G-C-E)

CementCo (G-C-A)

BreweryCo (G-S-L)

PowerCo (G-P-I)

RadiatorCo (G-P-L)

AutomotiveCo (G-P-E)

BusCo (G-P-A)

VanCo (G-P-A)

Note: The complete linkage (farthest neighbor) hierarchical clustering method was used (with Lance–Williams dissimilarity update formula).

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includes bridging. In the early stages of a supplier cri-sis, a cooperative response is likely to be relativelycheap. Inverting these arguments, one could expectthat firms behaving like Reactors in the scanning/inter-pretation stage have to behave like Substitutors in theaction stage. This prediction would also tie in withProposition 2c. With the exception of FashionCo, wefind less consistent evidence for this pattern. In sum,we propose tentatively:

Proposition 4a: Buying firms that behave likeGuards (Reactors) in the scanning and interpretationstage are more likely to behave like Partners(Substitutors) in the action stage.

A second pattern can be found when comparing thescanning/interpretation and learning stages. Clearly,scanning/interpretation and learning seem to correlateregardless of the choice of actions taken. First, the twofirms that did not have to cope with financially dis-tressed suppliers in the past (Lucky Fellows) are bothGuards in relation to scanning and interpretation.The implementation of sophisticated information-processing systems without significant prior experi-ences suggests that these firms learned vicariouslyfrom the experiences of other firms’ (Kim & Miner,2007; Nathan & Kovoor-Misra, 2002). Moreover, itcould be conjectured that these Guards were particu-larly successful in addressing supplier financial distresssituations very early in the course of the supplier’sorganizational crisis. Second, five of the six Evolvers(learning) in our sample are either Reactors or Observ-ers (scanning/interpretation), in that they demonstraterelatively little sophistication in terms of scanning andinterpretation. Conversely, four of the eight Guards(scanning/interpretation) in our sample are eitherIgnorers or Adaptors (learning), in that they show arelative lack of sophistication in terms of learning.Taken as a whole, these findings suggest a high levelof learning motivation among Reactors and Observersand a low level of learning motivation among Guards.In other words, firms that consider their processes tobe sound and mature see little reason to change.Therefore, we propose:

Proposition 4b: Buying firms that behave likeEvolvers (Ignorers or Adaptors) in the learningstage are more likely to behave like Observers orReactors (Guards) in the scanning and interpretationstage.

In addition to these two clusters, we observe thatfirms in the same business sectors are often groupedinto related branches of the dendrogram. For example,most of the consumer goods manufacturers arelocated in the upper half, while most of the automo-tive and industrial goods manufacturers are located in

the lower branch of the lower half. In addition, cul-tural effects might be a driving factor as the upperbranches are associated mainly with firms outside ofEurope and the United States. Not surprisingly, largerfirms, with abundant resources, information-process-ing capacities, and internal standards, tend to be morethorough in their scanning and more supportive intheir actions (Guard–Partner–X). Overall, these obser-vations suggest that similar firm-specific factors(industry, supply chain characteristics, culture) mayfoster similar procedures to cope with suppliers’ finan-cial distress and constellations of stage-level arche-types. In addition, most of our informants alsoindicated that they would adjust their general posturesdepending on the relationship with the supplier(trust, dependence). Taking these two aspects intoaccount, we propose:

Proposition 4c: The constellations of stage-levelarchetypes will be influenced by firm-specific andrelationship-specific factors.

DISCUSSION AND IMPLICATIONSDespite a plethora of studies which provide norma-

tive statements of how firms should manage supplychain risks (e.g., Chopra & Sodhi, 2004; Manuj &Mentzer, 2008) or supplier default risk (e.g., Finley,2009) and although failure and distress of firms arewell-researched phenomena (e.g., Altman & Hotchkiss,2006), our study addresses an important gap in the lit-erature by presenting a detailed analysis of firms’actual behavior in response to financially distressedsuppliers. Our findings offer several theoretical andmanagerial implications.

Theoretical ImplicationsOur main research objective was to explore buying

firms’ information-processing behaviors in the contextof supplier financial distress and to identify commonpostures or configurations. Archetypal classificationshave proven to provide valuable insights for theory andpractice in various fields of business research (e.g.,Miles & Snow, 1978; Wu & Choi, 2005). To this end,we adapted the generic information-processing frame-work from Daft and Weick (1984) and combined itwith the organizational crisis literature. Our findingslead to a typology of archetypical behavior toward therisk of supplier financial distress and highlight that thebehavior of buying firms follows certain patterns asthey pass through the information-processing stagessuggested by Daft and Weick (1984). We observed andtheorized the role of three variables which explain asignificant portion of the observed heterogeneity inbuying firms’ information-processing behaviors toward

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their supply bases: intrusiveness, cooperativeness, andquality of learning. These variables identify three arche-types in the scanning/interpretation stage, three arche-types in the action stage, and four distinct archetypalpatterns in the learning stage.Intrusiveness is instrumental to explain the behavior

in the scanning and interpretation stage. We foundthat firms differ in terms of how thoroughly and vig-orously they scan their suppliers and how they reactto warning signals. While Reactors behave passivelyand wait until clear operational signals emerge, Guardsproactively and assertively scan their supply base forearly warning signals. Their approach toward scan-ning/interpretation enables Guards to execute preemp-tive countermeasures. Observers are located in-betweenthe two extremes; they perform qualitative but lesssophisticated quantitative analyses.Our data revealed that once a financially distressed

supplier has been identified, there are several possibleresponses that can be classified according to their levelof cooperativeness. The continuum ranges from Substi-tutors (dominantly uncooperative) to Partners (domi-nantly cooperative). The former prefer to switch andreplace a distressed supplier to avoid possible disrup-tions and consequential losses. The immediate transferof orders to reliable suppliers supports the theoreticalsuggestion of contingent rerouting as a possible meansof avoiding supply chain disruptions (Tomlin, 2006).More supportive are Cooperators who may help impor-tant suppliers by providing short-term liquidity (i.e.,earlier payments). The highest level of cooperativenessis displayed by Partners who are even willing to grantlong-term credits and to transfer knowledge to help thesupplier to improve its situation. All of these counter-measures can be understood as specific variations ofthe generic buffering and bridging strategies to avoidsupply chain disruptions (Bode et al., 2011).With regard to the last information-processing stage

(learning), the results suggest that when firms facefinancially distressed suppliers, scanning/interpretationand response-formation (action) processes do notstart from scratch with every warning signal received.We observed that the learning stage indeed influencesfuture behavior toward suppliers in financial distress.Warning signals will elicit different responses, depend-ing on the firm-specific interpretative postures throughwhich they are filtered. Two of our responding firmshave not experienced a distressed supplier recently.Still, the two Lucky Fellows showed sophisticated scan-ning and interpretation processes which suggests vicar-ious learning that is based on observations ratherthan on experience (Kim & Miner, 2007; Nathan &Kovoor-Misra, 2002). In contrast, Ignorers neithermake use of stored knowledge nor intend to changetheir processes. Examples of experience-based learningwere provided by Adaptors who continuously seek to

fine-tune their processes and by the Evolvers who arethoroughly reviewing their practices and willing tomake drastic changes if necessary. For the learningstage, many informants stated that firm culture andworkload of the involved managers inhibit the thor-ough debriefing of supplier defaults. This finding is inline with the learning literature which purports thatculture is an important factor for a firm’s ability tolearn (e.g., Fiol & Lyles, 1985). Finally, Proposition3b suggests that the strength of a single experiencewith supplier financial distress affects the willingnessto learn. This finding supports Norrman and Jansson(2004) who described how the Swedish telecommuni-cations equipment provider Ericsson, in the wake of asevere supply chain disruption, reassessed and trans-formed its supply chain risk management processesand organizational culture.We acknowledge that the general behavior described

by the proposed archetypes has to be examined in itsrelational and organizational context. For example,informants insisted that they distinguish noncriticalfrom critical suppliers when crafting their response,suggesting resource dependence as a key determinantfor the chosen action. Furthermore, trust in the sup-plier and in its financial outlook is a factor that likelyaffects a buying firm’s information-processing. Thisfinding adds to existing research which suggests thatinter-organizational relationship climate is crucial forthe perception and resolution of disruptions (Grewal,Johnson, & Sarker, 2007).

Managerial ImplicationsIn addition to taking a first exploratory step for the-

ory building to explain firms’ behavior in this field,our results have several practical implications.As the underlying causes for financial distress are

manifold, it is not possible to provide guidance forremedial actions prima facie and the specific supplychain context must always be taken into consider-ation. However, our findings should urge managers tocollect and to analyze both qualitative and quantita-tive data, to fully understand their suppliers’ financialsituation and to gain broad room for maneuver incase of supplier financial distress.The delineated archetypes provide managers with a

scheme to classify their risk management processesand to use as a benchmark against competitors. Thisallows communicating and structuring risk manage-ment activities and supports managers in aligningtheir risk management procedures according to theirsupply chain objectives and needs. For example,Guards should focus on efficient resource allocations,while Reactors should evaluate whether their level ofintrusiveness is sufficient for their business environ-ment. In contrast, firms with highly sophisticatedprocedures should focus on incremental learning and,

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conversely, firms that have less sophisticated proce-dures should seek to improve through rather radicalforms of learning.

Limitations and Future Research DirectionsThere are several limitations of this study that should

be considered in the interpretation of its results. Froma methodological standpoint, we used an exploratory,qualitative research design. For this reason, andalthough we carefully selected our informants in thehope of gathering a diverse set of cases, the resultsmay lack external validity and the conclusions may beidiosyncratic (Eisenhardt, 1989). Furthermore, ourstudy considers only one side of the supplier–buyerrelationship dyad, neglecting which specific signalssuppliers may send out intentionally or unintention-ally to ask for help from buying firms. We used infor-mation-processing as a theoretical lens to shed lighton the buying firms’ behavior; however, our data didnot reveal a clear distinction between the scanningand interpretation stages. Finally, we did not analyzethe antecedents that drive the observed archetypes.Several additional directions for future research can

be outlined. Our exploratory study has highlighted aresearch gap concerning firms’ behavior toward finan-cially distressed suppliers. Based on the proposedtypology of archetypes, future empirical or conceptualresearch should be able to narrow this gap. Above all,it would be interesting to further investigate thevariables that determine the archetypes explored inthis study and to further theorize why buying firmsbehave according to certain archetypes. Futureresearch should also explore the effectiveness of theobserved archetypes under various environmental con-ditions. In addition, we found some evidence (LuckyFellows) that firms observe the behavior of other firmsand use such second-hand experience to change theirsupply chain risk management. Little is known aboutsuch vicarious learning in the context of supply chainrisk. Likewise, Proposition 1c hints at an interestingphenomenon which might need further research,namely that certain suppliers are so central to anentire industry that they become virtually “too big tofail.” We also encourage scholars to validate and gen-eralize our results with quantitative samples of firmsfrom a variety of sectors and of different sizes. Datagathered from both sides of dyadic relationshipswould allow further analyses of the interorganiza-tional nature of supply chain disruptions caused byfinancially distressed suppliers.

CONCLUSIONA great deal of research has focused on developing

concepts and frameworks for supply chain risk man-agement and the prediction of financial default. Yet

the literature to date has failed to integrate thesetopics and to answer the question of what buyingfirms actually do when their suppliers are in finan-cial distress. The objective of our study was toexamine this question and to present a first system-atic empirical investigation of how buying firms dealwith the risk of financially distressed suppliers.Using an exploratory research design and drawingon insights from the organizational information-processing literature, we delineate several archetypalsets of behaviors for the three sequential informa-tion-processing stages of scanning and interpretation,action, and learning. In sum, these findings proposea typology of risk management behavior to thesupply chain risk management literature, whichmight also be applicable to other sources of supplychain risk.

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Christoph Bode (Ph.D., WHU–Otto Beisheim Schoolof Management) is an assistant professor in the Depart-ment of Management, Technology and Economics atthe Swiss Federal Institute of Technology in Zurich,Switzerland. His research interests lie in the areas ofsupply chain, logistics, purchasing, and operationsmanagement. In particular, he focuses on disruptionsand risk management, inter-firm relationships, strate-gies and performance, innovation, entrepreneurshipand sustainability. Dr. Bode has published articles in avariety of journals including the Academy of Manage-ment Journal, Business Strategy and the Environment, theEuropean Journal of Operational Research, DecisionSciences and the Journal of Business Logistics. He alsoserves on the editorial review boards for the Journal ofOperations Management and the Journal of Supply ChainManagement; he has received Best Reviewer awards fromboth publications.

Denis H€ubner (Dipl. Wirtsch-Ing., Otto von GuerickeUniversity-Magdeburg) is a research assistant and Ph.D.candidate in the Department of Management, Technol-ogy and Economics at the Swiss Federal Institute ofTechnology in Zurich, Switzerland. His research inter-ests include supply chain risks and inter-firm conflicts.

Stephan M. Wagner (Ph.D., University of St. Gallen)is a professor and the Kuehne Foundation Chair ofLogistics Management in the Department of Manage-ment, Technology and Economics at the Swiss FederalInstitute of Technology in Zurich, Switzerland. He alsoserves as the Director of the Executive MBA Program forthe Department. Dr. Wagner’s research interests are inthe areas of supply chain management, logistics andtransportation management, and purchasing and sup-ply management. His research focuses on strategy, net-works, relationships, behavioral issues, risk, innovationand entrepreneurship. Dr. Wagner’s articles haveappeared in many outlets, including the Academy ofManagement Journal, the Journal of Management, theJournal of Operations Management, Decision Sciences, and

the California Management Review. He serves as an Asso-ciate Editor for the Journal of Supply Chain Management.

APPENDIXINTERVIEW INSTRUMENT

Profile(a) What is the major business and globalpresence of your firm?

(b) What are the main responsibilities of yourjob? What is the name of your position?

(c) How many years of experience do you havein supply chain management?

(d) Does your firm actively manage supplychain risks in general? If yes, please specify.

(e) Do you consider financial risks of suppliersin your supply chain risk management?

Monitoring the supply base(a) Please characterize your sourcing strategy.(b) How is your supply base characterized?Please describe the supplier selection process.

(c) Do you actively track the suppliers’ financialcondition? If yes, which early warning signalsdo you consider and which tools do you usewhen scanning suppliers for financial distress?

(d) Do you apply the same means to allsuppliers?

(e) How do you determine whether a supplieris financially distressed or not?

Managing a financially distressed supplier(a) Could you describe a situation where oneof your suppliers was under financial distress?

(b) Did you act immediately or reassessed thesituation prior to action?

(c) What actions did you pursue toward yourdistressed supplier?

(d) Did your firm offer any kind of support toyour distressed supplier? If so, pleasedescribe the support.

(e) Were these actions part of any fixed internalorganizational routine, process or guidelines?(f) Why did you choose such types of actions?What was your rationale behind them?

(g) Could you avert supply chain disruptions?If no, please specify the disruption.

Aftermath(a) What was your learning experience afterdealing with the case you just mentioned?

(b) Have you fixed documenting procedures tosave knowledge gained from such events?(c) Did your firm apply means to avoid futuredisruptions alike?

(continued)

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(d) Due to a financial distress of a supplier,have there been any changes in processes,routines, supply chain risk management,sourcing strategy or culture?

(e) Who did pursue these changes?Thinking back(a) Do you want to add something to theanswers?

(b) Is there anything that we didn’t talk aboutthat appears relevant?

(continued)

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Documents

Managing Financially Distressed Suppliers: An Exploratory Study