European Journal of Operational Research 231 (2013) 245–256

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European Journal of Operational Research

journal homepage: www.elsevier .com/locate /e jor

Invited Review

A review of trade credit literature: Opportunities for researchin operations

0377-2217/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.ejor.2013.03.016

⇑ Corresponding author at: Ecole Polytechnique Fédérale de Lausanne, 1015Lausanne, Switzerland. Tel.: +41 21 69 300 22; fax: +41 69 300 20.

E-mail address: ralf.seifert@epfl.ch (R.W. Seifert).

Daniel Seifert a, Ralf W. Seifert a,b,⇑, Margarita Protopappa-Sieke c

a Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerlandb IMD, Chemin de Bellerive 23, P.O. Box 915, 1001 Lausanne, Switzerlandc Department of Supply Chain Management and Management Science, University of Cologne, Albertus-Magnus-Platz, D-50923 Cologne, Germany

a r t i c l e i n f o

Article history:Received 31 March 2010Accepted 13 March 2013Available online 27 March 2013

Keywords:Trade creditPermissible delay in paymentInterface operations and finance

a b s t r a c t

Trade credit arises when a buyer delays payment for purchased goods or services. Its nature has predom-inantly been an area of inquiry for researchers from the disciplines of finance, marketing, and economicsbut it has received relatively little attention in other domains. In our article, we provide an integrativereview of the existing literature and discuss conflicting study outcomes. We organize the relevant liter-ature into seven areas of inquiry and analyze four in detail: trade credit motives, order quantity decisions,credit term decisions, and settlement period decisions. Additionally, we derive a detailed agenda forfuture research in these areas.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

Trade credit is a regular component of market transactions andconstitutes a major source of short-term financing. Estimates sug-gest that more than 80% of business-to-business transactions in theUnited Kingdom (UK) are made on credit (Wilson and Summers,2002), while about 80% of United States (US) firms offer their prod-ucts on trade credit (Tirole, 2006). Large, non-financial businessesin the US generate 15% of their financing from accounts payableand small businesses rely even more on it (Elliehausen and Wol-ken, 1993; OECD, 2006). Internationally, these levels can be evenhigher and trade credit exceeds, by far, short-term bank credit(De Blasio, 2005; Rajan and Zingales, 1995). The average level oftrade credit in use, however, varies significantly from country tocountry. A comparison of accounting data of industrialized nationsshows that median accounts receivable range from 13% to 40% ofsales and, with the exception of Italy, are relatively stable over time(Fig. 1). Similarly, trade credit varies from industry to industry. USdata suggest that relative accounts receivable increase in the dis-tance to the end-consumer (Fig. 2). While accounts receivable tendto exceed accounts payable in most sectors, the retail sector is anotable exception; this is most likely due to the proximity toend-consumers. Ng et al. (1999) find that credit terms show highvariation between industries but low variation within them. Thedegree of within-variation seems to differ from industry to indus-try, at least as indicated by actual payment delays (Seifert and

Seifert, 2011). In this study, one of the roughly 30 managers inter-viewed stated: ‘‘When we began [our project] last year, we discov-ered that we had over 1000 different credit terms globally!’’ Thus,in some industries firms seem to vary credit terms from customerto customer (Wilson and Summers, 2002).

Such a widespread phenomenon of varied credit terms is likelyto raise questions. Its existence is already perplexing. Why do sup-pliers offer credit when there are specialized financial intermediar-ies? If trade credit is cheaper than bank credit, the question is howdo suppliers generate the competitive advantage? If bank credit ischeaper than trade credit, then why do banks ignore the opportu-nity? Additional questions revolve around the practical manage-ment of trade credit. How do firms set credit policies and whichcredit policies are optimal? While generous credit terms mightspur sales, the additional working capital and bad debts may out-weigh the benefits. At what point in time is it optimal to pay?Although late payment might entail additional interest costs, re-duced inventory costs might outweigh these financial drawbacks.

The wide range of these potential questions has led researchersfrom various domains to analyze trade credit. While finance schol-ars have contributed the most to the research, trade credit has alsoreceived considerable attention in the literature of economics andmarketing. Researchers from other domains, however, have pro-vided relatively little input. This is especially remarkable for oper-ations management since financial flows are considered a keyelement of purchasing and supply chain management (Mentzer,2001). Recognizing this deficit, a growing number of operationsmanagement researchers have begun to investigate the interfacebetween operations and finance (Birge et al., 2007; Protopappa-Sieke and Seifert, 2010; Kouvelis and Zhao, 2011; Gupta and Dutta,

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140366 observations on 15798 firmsUnited States

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43867 observations on 3698 firmsJapan

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9373 observations on 935 firmsGermany

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9314 observations on 952 firmsFrance

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2874 observations on 316 firmsItaly

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22958 observations on 2570 firmsUnited Kingdom

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14425 observations on 2020 firmsCanada

Fig. 1. Accounts receivable across countries. Note. The figure is based on Compustat and Global Vantage data. Firms in the financial sector (SIC 6000–6999) have beenexcluded. Lines indicate median accounts receivable/sales and shades indicate quartiles.

1 Trade credit, trade debt, vendor finance, vendor financing, trade finance, suppliercredit, relationship lending, direct lending, accounts receivable, accounts payable,delay in payment, delayed payment, late payment, payment delay, payment terms,payment policy, payment period, payment time, settlement period, credit terms,credit policy, credit period, credit time, two-part terms, payment discount, cashdiscount, and credit risk.

246 D. Seifert et al. / European Journal of Operational Research 231 (2013) 245–256

2011). The aim of this article is to support this investigation by pro-viding researchers who wish to contribute to this field with (1) anintegrative review of the existing literature and (2) a tabulation ofuntapped areas for future research.

The rest of this article is organized as follows. Section 2 presentsthe search strategies and classification methods. Section 3 reviewstrade credit works from the finance and operations managementliterature. Section 4 discusses avenues for further research. Sec-tion 5 concludes.

2. Methodology

In order to ensure a comprehensive accumulation of literaturesources we began with the identification of the search terms andkeywords, as suggested by Tranfield et al. (2003). Our aim was toshowcase the range of the existing knowledge base by drawingfrom a significant sample of articles. We identified 27 relevant

search terms and keywords to be applied to titles, abstracts, andfull text articles.1 We used three strategies to search both the theo-retic and the empirical literature on trade credit both in publishedjournals and unpublished work. First, we conducted an exhaustivesearch of the EconLit database, which reaches back to 1969, to findrelevant references to trade credit in published articles. Second, wecompleted a search of the Social Science Research Network database,dating back to 1995, to identify unpublished work from universitiesand public institutions. Finally, we carried out a manual search toscreen sources cited in reference sections, as well as relevant jour-nals. During our search we perceived different research domains

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640 observations on 67 firmsAgriculture

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7252 observations on 889 firmsMining

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1871 observations on 219 firmsConstruction

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64631 observations on 6790 firmsManufacturing

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18064 observations on 1914 firmsTransportation & Utilities

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6013 observations on 710 firmsWholesale

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10210 observations on 1132 firmsRetail

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29763 observations on 3821 firmsServices

Fig. 2. Accounts receivable across industries. Note. The figure is based on Global Vantage data for the United States. Lines indicate median accounts receivable/sales andshades indicate quartiles.

D. Seifert et al. / European Journal of Operational Research 231 (2013) 245–256 247

(economics, marketing, finance, operations) to address similar re-search questions with different methodologies. In order to capturethe diversity of the topic we included 14 highly rated journals2 fromthese four aforementioned research domains (please refer to foot-note 2 for a complete list of the journals considered). Following Coo-per (1998), we aspired to be overly inclusive during our search andjudged conceptual relevance during data evaluation only. Our searchproduced a significant number of highly cited articles published inthese journals. Overall, our searches yielded 205 related studies,182 of which we included in our analysis. We excluded the remain-

2 European Journal of Operational Research, IIE Transactions, International Journalof Production Economics, Journal of Business Finance & Accounting, Journal ofFinance, Journal of Financial and Quantitative Analysis, Journal of Financial Econom-ics, Management Science, Managerial and Decision Economics, Manufacturing &Service Operations Management, Naval Research Logistics, Operations Research,Production and Operations Management, and Review of Financial Studies.

ing 23 studies for several reasons: Eleven of the studies consideredtrade credit only peripherally, five offered only limited contributionscompared to included works, and seven of the unpublished studieslacked rigor or originality.

Throughout our search we continuously sought to classify arti-cles. To develop a taxonomy, we first grouped articles by researchdomain, e.g., economics, because research domains were likely toaddress similar research questions. Whenever we perceived a do-main to contain multiple questions, we split the domain into thesegroups. Because our aim was to provide an integrative review, weperiodically checked if there was any overlap between groups fromdifferent research domains. Whenever we found two or moregroups that overlapped, we merged them. At the end of our search,we were left with seven groups: Monetary policy implications, fac-toring economics, credit risk models, trade credit motives, order quan-tity decisions, credit term decisions, and settlement period decisions.The first, monetary policy implications, investigated how monetary

Table 1Studies on trade credit motives.

Author(s) Cit. Motive Country focus Firm/industrycharacteristics

Methodology Main propositions/findings

Schwartz (1974) 171 Capital access – – Equilibriummodel

� Firms leverage capital access to induce buyers toincrease purchases

Ferris (1981) 153 Transactionpooling

United States – Panel-dataregression

� Trade credit reduces treasury uncertainties

Emery (1984) 148 Credit information – – Equilibriummodel

� Suppliers have information and collection advantages

Smith (1987) 197 Non-salvageableinvestment

– – Equilibriummodel

� Trade credit is a screening device that helps suppliersprotect non-salvageable investments

Brennan et al. (1988) 153 Price elasticity – – Equilibriummodel

� Suppliers use trade credit to price discriminate

Chant and Walker(1988)

22 Credit rationing United States Manufacturers anddistributors

Panel-dataregression

� Trade credit can be both a substitute for and a comple-ment to bank credit

Elliehausen andWolken (1993)

57 Transactionpooling

United States Small firms Survey � Trade credit serves to reduce transaction costs

Wilner (2000) 120 Control protection – – Equilibriummodel

� Managers demand trade credit in anticipation of largerconcessions in case of financial distress

Jain (2001) 43 Credit information – – Optimizationmodel

� Banks and suppliers profit from trade credit lending atthe expense of buyers

Ono (2001) 21 Credit rationing Japan Large manufacturers Panel-dataregression

� Trade credit is a complement to bank credit

Summers and Wilson(2002)

12 Transactionpooling

United Kingdom Small firms with oneproduct line

Survey � Trade credit serves to reduce transaction costs� Trade credit allows owner-managers to stay in control

Wilson and Summers(2002)

46 Product marketposition

United Kingdom Small firms with oneproduct line

Survey � Trade credit is a result of industry pressure� Two-part terms are used to improve cash flow

Howorth and Reber(2003)

8 Credit rationing United Kingdom Small firms Survey � Credit rationing drives firms to pay late

Danielson and Scott(2004)

14 Credit rationing United States Small firms Survey � Trade credit is a substitute for bank credit

Frank andMaksimovic(2005)

90 Collateral value United States – Equilibriummodel

� Suppliers have collection advantages

Huyghebaert (2006) 11 Control protection Belgium Manufacturing start-ups

Panel-dataregression

� Trade credit offers control benefits in the early stagesof a venture

Van Horen (2007) 1 Product marketposition

Europe andCentral Asia

– Survey � Suppliers offer trade credit if buyers have marketpower

Fabbri and Klapper(2008)

1 Product marketposition

China – Survey � Trade credit is a competitive gesture to maintain/increase sales� Trade credit increases after new product introductions

or price decreases

Table 2Studies on order quantity decisions.

Author(s) Cit. Decision variablesa Objective functionb Dem. Sup.

T Q n p q L v d h A M cr kr N pD a cs ks h ct a s

Goyal et al. (2007) 6 � � � � � � � D IJaggi et al. (2007) 0 � � � � � � � � � D IChung (2008) 0 � � � � � � � � � D IHuang and Hsu (2008) 2 � � � � � � � � � D ILiao (2008) 3 � � � � � � � � D FOuyang et al. (2008) 0 � � � � � � � � � � � � D FSana and Chaudhuri (2008) 11 � � � � � � � � D ISoni and Shah (2008) 6 � � � � � � � D IChung (2009) 0 � � � � � � D IChung and Liao (2009) 0 � � � � � � D IOuyang et al. (2009) 2 � � � � � � � � � D ITeng (2009) 0 � � � � � � � � � D ITeng and Chang (2009) 0 � � � � � � � � D ITsao (2009) 0 � � � � � � � � � � � � D IHuang et al. (2010) 0 � � � � � � � � � � D NChen and Kang (2010a) 0 � � � � � � � � � � D IChen and Kang (2010b) 0 � � � � � � � � � � � � � D I

Note: Column Cit. stands for citations received, Dem. for demand, Sup. for supply, D for deterministic demand, I for infinite and instantaneous replenishment, F for finite andinstantaneous replenishment, N for infinite and non-instantaneous replenishment.

a T stands for buyer cycle time, Q for buyer order quantity, n for lot size multiplier, p for retail price, q for promotional effort, L for lead time, v for warranty cost, d for pricediscount.

b h stands for holding cost, A for major setup cost, M for supplier credit period, cr for buyer procurement cost, kr for buyer capital cost, N for buyer credit period, pD forrevenue, a for partial payment factor, cs for supplier production cost, ks for supplier capital cost, h for deterioration rate, ct for transportation cost, a for minor setup cost, s forscreening cost.

248 D. Seifert et al. / European Journal of Operational Research 231 (2013) 245–256

Table 3Studies on credit term decisions.

Author(s) Cit. Decision variablesa Objective functionb Dem. Sup.

M Q n T p s w d N t k h A ks kr pD cr cs h B ct

Kim et al. (1995) 36 � � � � � � � � D IBoyaci and Gallego (2002) 53 � � � � � � � � � D IAbad and Jaggi (2003) 45 � � � � � � � � � � D IJaber and Osman (2006) 25 � � � � � � � � � D IShi and Zhu (2006) 3 � � � � � � � � D IYang and Wee (2006) 8 � � � � � � � � � � D FChen and Kang (2007) 11 � � � � � � � � D ILuo (2007) 9 � � � � � � � � D ISarmah et al. (2007) 10 � � � � � � � � D IShi and Zhang (2007) 0 � � � � � � � D IShi et al. (2007) 0 � � � � � � � � D IJaggi et al. (2008) 5 � � � � � � � � D ISarmah et al. (2008) 10 � � � � � � � D FGupta and Wang (2009) 2 � � � � � � � � S NKouvelis and Zhao (2012) 0 � � � � � � � S ICharharsoogi and Heydari (2010) 0 � � � � � � � � S NLee and Rhee (2010) 0 Not applicablec � � � � � S IShi and Zhang (2010) 0 � � � � � � D I

Note: Column Cit. stands for citations received, Dem. for demand, Sup. for supply, D for deterministic demand, S for stochastic demand, I for infinite and instantaneousreplenishment, F for finite and instantaneous replenishment, N for infinite and non-instantaneous replenishment.

a M stands for supplier credit period, Q for buyer order quantity, n for lot size multiplier, T for buyer cycle time, p for buyer price, s for settlement period, w for supplierprice, d for cash discount, N for buyer credit period, t for supplier cycle time, k for safety factor.

b h stands for holding cost, A for setup cost, ks for supplier capital cost, kr for buyer capital cost, pD for revenue, cr for buyer procurement cost, cs for supplier production cost,h for deterioration rate, B for backorder cost, ct for transportation cost.

c Lee and Rhee (2010) characterize optimal trade credit rates but do not formulate an optimization model in the traditional sense.

Table 4Studies on settlement period decisions.

Author(s) Cit. Dec. var.a Objective functionb Dem. Sup.

s T Q h A ks kr cr h cQd B pD

Haley and Higgins (1973) 78 � � � � � � � D IJamal et al. (2000) 75 � � � � � � � D ISarker et al. (2000) 96 � � � � � � � � D IChang and Wu (2003) 13 � � � � � � � D IHuang and Chung (2003) 32 � � � � � � � � D ILiao and Chen (2003) 4 � � � � � � � D IHuang (2005) 4 � � � � � � � � D IOuyang et al. (2005) 13 � � � � � � � D NSong and Cai (2006) 8 � � � � � � D IHo et al. (2008) 10 � � � � � � � � � � D IChung (2010) 0 � � � � � � � D N

a s stands for settlement period, T for buyer cycle time, Q for buyer order quantity.b h stands for holding cost, A for setup cost, ks for supplier capital cost, kr for buyer capital cost, cr for buyer procurement cost, h for deterioration rate, cQd for cash discount

benefit, B for backorder cost, pD for revenue Note. Column Cit. stands for citations received, Dem. for demand, Sup. for supply, D for deterministic demand, I for infinite andinstantaneous replenishment, N for infinite and non-instantaneous replenishment.

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policy should account for trade credit (Meltzer, 1960; Jaffee andModigliani, 1969; Gertler and Gilchrist, 1993). The second, factor-ing economics, examined which credit administration functionsshould be subcontracted to third party specialists (Mian and Smith,1992; Smith and Schnucker, 1994; Summers and Wilson, 2000).Since neither of these two seemed anchored in operations manage-ment or directly related, we excluded them from the review. Thethird, credit risk models, studied the amount3 of credit to begranted to potential debtors. Dating back to at least the 1950s, thisgroup formed a well-established stream in the operations manage-ment literature (Cyert and Trueblood, 1952; Cyert et al., 1962;Mehta, 1968; Bierman and Hausman, 1970; Beranek and Taylor,1976; Kolesar and Showers, 1985). Reviews of this literature were

3 In contrast to timing, which is the main concern in works on credit term andsettlement period decisions.

readily available (Rosenberg and Gleit, 1994; Thomas, 2000; Crooket al., 2007). We therefore excluded this group from the review, too.

For each of the remaining four groups, we developed codingsheets (Tables 1–4) and included relevant characteristics and ex-cluded irrelevant ones as the coding progressed. We sorted worksby their order of appearance. Additionally, we included the numberof citations received by each work to facilitate access for research-ers new to the field. Furthermore we distinguish between firm andindustry characteristics (e.g., size and type), methodology (e.g.,type of analysis), and main propositions and findings.

3. Literature review

3.1. Trade credit motives

The reasons why firms offer credit has been actively researchedfor more than 30 years. While the issue is still far from being

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resolved, researchers have provided several motives4 as potentialanswers.

One way to structure these motives is by differentiating be-tween supply-side and demand-side motives. On the supply side,the finance literature has identified six major motives: capital ac-cess, product market position, price elasticity, collateral value, creditinformation, and non-salvageable investment. The first, capital ac-cess, views trade credit as a marketing expense that enables buyersto increase their purchases because the time value of money effec-tively lowers the price. Then, the attractiveness of trade credit as amarketing tool increases with a supplier’s access to capital markets(Schwartz, 1974). Consequently, well-established firms should of-fer relatively more credit due to their better access to capital.Empirical data, however, suggests the opposite. Wilson and Sum-mers (2002), Fabbri and Klapper (2008), and Van Horen (2007)therefore argue that product market position determines tradecredit offer. The product market position argument, however, begsthe question why do suppliers not simply compete on price. Schol-ars have therefore hypothesized that trade credit contains addi-tional functions: First, price elasticity differences between buyersmight enable suppliers to price discriminate and increase overallsales (Brennan et al., 1988; Petersen and Rajan, 1997). Second, sup-pliers might have an advantage in salvaging value from sold goods(Frank and Maksimovic, 2005). Third, suppliers might act as inter-mediaries between buyers and banks because the first possesssuperior information (Emery, 1984; Jain, 2001). Finally, trade creditmight protect non-salvageable investments in buyers by acting asa screening device that elicits information about buyer default risk(Smith, 1987). The author presents a trade-credit model consistingof buyers, sellers and financial institutions to argue that defaultrisk information is valuable for non-salvageable investments. Gian-netti et al. (2011) present a simple formal framework from the sup-ply side to explain why suppliers are willing to give trade credit.Even though the authors use a static framework, they identify col-lateral liquidation, moral hazard, information advantage andimperfect competition as main reasons. Along the same lines, Leeand Rhee (2011) also discuss trade credit from a supplier’s perspec-tive but they further present trade credit as a tool for supply chaincoordination.

On the demand side, the finance literature has identified threemajor motives: transaction pooling, credit rationing, and control pro-tection. The rationale of reducing costs by pooling transactions wasfirst identified by Ferris (1981) and subsequently tested by Ellie-hausen and Wolken (1993) and Summers and Wilson (2002). Tradecredit in this view is an instrument that facilitates trade by provid-ing a contractual alternative to immediate money use. Uncertain-ties in firms’ exchange of goods lead to uncertainties in moneyflows and thus, stochastic money holding costs. Firms reduce thesecosts by agreeing on payment terms and creating informationabout money requirements and expected receipts. Another reasonfor demanding trade credit might be credit rationing. If informa-tion asymmetries cause banks not to be able to distinguish riskyborrowers from safe ones and if borrower liability is limited, bor-rowers with risky projects are better able to bear the ensuing high-er interest rates. Therefore, charging higher interest rates does nothelp banks in sorting borrowers so banks resort to credit rationing(Chant and Walker, 1988; Biais and Gollier, 1997; Ono, 2001; Ho-worth and Reber, 2003; Danielson and Scott, 2004). A final reasonfor demanding trade instead of bank credit might be control pro-

4 Unfortunately, researchers have used this term somewhat arbitrarily, sometimesdescribing goals (e.g., increase revenues) and sometimes describing enablers (e.g.,credit access). Because there are many potential enablers but only two goals (increaserevenues, reduce costs), it is – for the purposes of this review – more useful to discussenablers. Nevertheless, we use the term motive throughout the text becauseresearchers are likely to encounter this term in the literature.

tection. Wilner (2000) and Huyghebaert (2006) argue that tradecredit is commonly more costly than institutional credit becausesuppliers, desiring to maintain an enduring product market rela-tionship, grant more concessions to buyers in financial distressthan lenders in competitive credit markets. Therefore, the desireto protect control may drive buyers to use trade credit.

3.2. Order quantity decisions

The presence of trade credit affects the holding cost of inventoryand thus the economic order quantity (Beranek, 1967). Extensiveoperations management literature has therefore been developed,which studies economic order quantities under permissible delayin payment (Haley and Higgins, 1973; Kingsman, 1983; Chapmanet al., 1984; Goyal, 1985; Aggarwal and Jaggi, 1995; Jamal et al.,1997; Hwang and Shinn, 1997; Teng, 2002; Teng et al., 2011; Su,2012). Even though these models are based on deterministic envi-ronment assumptions, they provide interesting insights. A first re-view of this literature may be found in Chang et al. (2008). Weprovide an update of their review because optimal order quantitydecisions under permissible delay in payment represent a highlyactive area of research. For compatibility purposes, we continuethe previous review’s classification into models (1) without short-age and without deterioration, (2) without shortage but with dete-rioration, (3) with shortage, (4) with trade credit linked to orderquantity, and (5) with inflation. Our results suggest that most con-tributions fall in the first category. Here, models in which bothbuyers and suppliers offer credit (so called two-stage models)seem to have been a particular focus. In such models a supplier of-fers a credit period to the retailer who in turn offers a credit periodto customers. Other categories, however, seem to have receivedless attention.

In the first category of inventory models with no deteriorationand no shortage, Jaggi et al. (2007) present an economic orderquantity model for a retailer that simultaneously receives tradecredit from a supplier and offers trade credit to his customerswhere the customer demand depends on the credit period. Numer-ical examples suggest that as the credit elasticity of demand facedby the retailer increases, the optimal order quantity decreases. Theauthors also investigate the effect of differences between interestearned and interest paid on the optimal order quantity. In mostcases, the optimal order quantity is less than the traditional eco-nomic order quantity. Huang and Hsu (2008) present a similartwo-stage trade credit model (for which Chung (2008) providesadditional proofs) but assume end-consumer demand to be creditinelastic. Teng and Chang (2009) relax the assumption that thecredit period offered by the retailer is shorter than the credit per-iod offered by the supplier. Chen and Kang (2010b) compare one-stage and two-stage trade credit financing and conclude thattwo-stage financing lowers supply chain profits. Thangam (2012)presents a two-stage trade credit model for perishable items thatfurther extends not only the existing literature of determiningthe optimal replenishment policy but also the optimal price dis-counting policy.

Goyal et al. (2007) and Chung (2009) extend Haley and Higgins(1973) by introducing a ‘‘regular interest’’ period after the net per-iod. Soni and Shah (2008) introduce a three-stage interest schemeconsisting of an interest-free period, a period costing interest rateIc1, and a period costing interest rate Ic2 > Ic1. Sana and Chaudhuri(2008) model a similar progressive scheme and additionally testseven different demand functions. They find that different demandfunctions lead to similar outcomes.

Chung and Liao (2009) study a discounted cash flow approach,Chen and Kang (2010a) study imperfect items, Huang et al. (2010)investigate order-processing cost reductions, and Teng (2009)analyzes a retailer who offers full trade credit to ‘‘good’’ and partial

D. Seifert et al. / European Journal of Operational Research 231 (2013) 245–256 251

trade credit to ‘‘bad’’ customers. Tsao (2009) investigates two typesof trade allowance, promotional effort cost sharing and price dis-counting in a multi-item setting. Increased trade credit reducesthe need for effort cost sharing or price discounting to achievechannel coordination.

In the second category of inventory models with deteriorationand no shortage, Liao (2008) investigates the economic productionquantity under exponential decay and two-stage trade credit.Numerical examples suggest that the retailer’s order quantity in-creases as the retailer offers more credit to its customers.

Finally, in the category of models with trade credit linked to or-der quantity, Ouyang et al. (2008) present an integrated inventorymodel with variable production rate, price sensitive demand, and afreight charge according to a weight schedule. Ouyang et al. (2009)additionally generalize a number of previous works by incorporat-ing deterioration and partial payments. Liao et al. (2012) present amodel that combines the second and the last category (withoutshortage but with deterioration and trade credit linked to orderquantity). Their contribution to the existing literature is the con-sideration of two storage facilities (an owned warehouse and arented warehouse). Apart from identifying the optimal replenish-ment cycle time and the optimal order lot-size to minimize, theyfurther discuss the decision of the retailer to rent an additionalwarehouse.

3.3. Credit term decisions

The exchange of goods and/or services usually requires buyersand suppliers to agree on trade terms. These trade terms often con-sist of prices and delivery and payment conditions. While paymentconditions may be exceedingly simple (e.g., cash on delivery), sup-pliers may also grant payment delays. The optimal length of thesepayment delays is the core concern of credit term decisions. The fi-nance literature5 investigates optimal payment delays mainly byevaluating the net benefits of credit term changes. Researchers haveidentified two alternative approaches: The opportunity cost ap-proach which analyzes whether the net benefits exceed the opportu-nity cost of the funds to be used for a credit policy change (Oh, 1976;Dyl, 1977; Walia, 1977) and the net present value approach whichdiscounts the net benefits (Lieber and Orgler, 1975; Atkins andKim, 1977; Kim and Atkins, 1978; Hill and Riener, 1979; Sartorisand Hill, 1981). After several years of active discussion, it is nowwidely accepted that the net present value approach leads to betterdecisions (Kim and Feist, 1995). The formulae developed by the fi-nance literature, however, still require estimates in how changesof credit terms impact demand. Scholars – predominantly from oper-ations management – have therefore begun to apply insights frominventory control to predict these changes. Therefore, we reviewthese works in the following paragraphs, which are presentedroughly in order of increasing complexity.

Schiff and Lieber (1974) develop an integrated model for thecredit and production decisions of a single firm. Demand is mod-eled as a deterministic function of time, credit period, and inven-tory. An oscillating parameter introduces seasonality into thedemand function, which prompts the firm to change its productionand credit decisions. When demand increases, production in-creases but credit decreases. When demand decreases, the oppo-site pattern occurs. Kim et al. (1995) develop a deterministicmodel to find the optimal credit period. Assuming a lot-for-lot pol-icy, fixed wholesale price, and no collaboration, they solve a sup-plier’s problem based on expected retailer behavior. The authorsdo not make any assumptions about the above-mentioned trade

5 Because our aim is to support researchers from operations management, werestrict ourselves to a short summary of the finance literature and review theoperations management literature in detail.

credit motives. However, their work documents a trade credit mo-tive that has largely gone unnoticed in the finance literature: thestimulation of end-consumer demand. Based on the impact ofthe supplier’s credit on the retailer’s pricing, they show that tradecredit increases end-consumer demand and thus the retailer’s or-der size. Therefore, trade credit increases both the supplier’s andthe retailer’s profits. Shi and Zhang (2010) incorporate default riskinto the supplier’s decision. If the buyer defaults, the supplier sal-vages the remaining inventory. Numerical examples suggest thatthe optimal credit period increases with the buyer’s capital costand decreases with the supplier’s capital cost. Moreover, the opti-mal credit period decreases with the demand rate, the probabilityof default, and the buyer’s inventory holding costs. Of these factors,the probability of default has the highest impact on the supplier’sprofits but the lowest impact on its credit period. Therefore, theauthors conjecture that the role of default risk is to determinewhether to grant trade credit. Du et al. (in press) present a modelfor the coordination of a two-echelon supply chain under a deter-ministic operating environment and determine the optimal retailprice, order quantity, and credit period offer. The authors find thata policy that incorporates quantity discount and a credit paymentoption is superior as it increases total supply chain profitability.

Abad and Jaggi (2003) introduce cooperative behavior, modeledas a set of Pareto-efficient solutions. The particular solution de-pends on a profit-sharing parameter that the supplier and the re-tailer establish through negotiation. Example problems suggestthat under both behaviors it is advantageous to offer trade credit.However, under non-cooperative behavior this result holds onlyif the supplier has a lower cost of capital. Abad and Jaggi (2003)also find that homogeneous policies, e.g., low price/short creditperiod, are more profitable than mixed policies, suggesting thatunit prices and credit periods should be optimized simultaneously.Jaber and Osman (2006) relax the lot-for-lot assumption. Yang andWee (2006) focus on deterioration and finite replenishment rates.Their sensitivity analyses indicate that credit-induced surplusprofits increase with the price elasticity of consumer demand, withsetup costs, and with the deterioration rate of inventory but de-crease with holding costs and with the supplier’s replenishmentrate. Chen and Kang (2007) provide sensitivity analyses for theprofit-sharing parameter in an imperfect quality setting. Shi andZhu (2006) and Shi and Zhang (2007) address the negotiation pro-cess with an Aumann–Shapley solution6 and Shi et al. (2007) withdata envelopment analysis. Sarmah et al. (2008) study the negotia-tion process between a manufacturer and multiple buyers, and theoptimal credit period that entices buyers to accept coordinated ship-ments. Jaggi et al. (2008) investigate a situation in which both thesupplier and the retailer offer trade credit. Assuming the supplier’scredit period as given, they present an algorithm to jointly deter-mine the retailer’s optimal cycle time and credit period. Numericalexamples suggest that changes in the supplier’s credit period donot greatly affect either the retailer’s cycle time or the retailer’s cred-it period.

Kouvelis and Zhao (2012) model only non-cooperative behaviorbut introduce stochastic demand. Using a capital-constrainednewsvendor model, the authors find – differing from Abad and Jag-gi (2003) – that trade credit is always advantageous for the sup-plier. They also find that a supplier will always offer interestrates below the risk-free rate and that a retailer will always prefertrade credit to bank credit when offered an optimally structuredscheme. Furthermore, trade credit improves supply chain profits,and, while the supplier always improves its profits, the retailer’sbenefits depend on its working capital endowment. Gupta and

6 A review of cooperative bargaining solutions such as the Aumann–Shapley andother solutions may be found in Thomson (1994).

252 D. Seifert et al. / European Journal of Operational Research 231 (2013) 245–256

Wang (2009) formulate a basestock model to investigate the rela-tionship between cash discount and discount period. A smallercash discount implies a longer discount period. If the discount per-iod exceeds the shelf life of the goods, offering a cash discount be-comes uneconomic. Charharsoogi and Heydari (2010) introducecooperative behavior and uncertain lead times. As in Kouvelisand Zhao (2012) trade credit is always advantageous for the sup-plier. Numerical examples suggest that, in general, the optimalcredit period decreases with the lead time uncertainty and theshortage cost. The authors also investigate the sensitivity to setupand holding costs both at the buyer’s and the supplier’s end. Over-estimation by the buyer (supplier) decreases (increases) the creditperiod and is thus an undesirable strategy. Underestimation resultsin credit-related gains but distorts operational decisions. Thus, nei-ther the buyer nor the supplier have incentives to misjudge theirparameters.

Luo (2007) and Sarmah et al. (2007) compare trade credit andquantity discount contracts in an economic order quantity frame-work. Trade credit is more efficient if the supplier’s cost of capitalis lower than the buyer’s cost of capital; otherwise quantity dis-counts are more efficient. Lee and Rhee (2010) consider quantitydiscounts, buy backs, two-part tariffs, and revenue sharing in anewsvendor framework. Under the assumption of positive inven-tory financing costs and if both the buyer and the supplier relyon bank credit, all four contracts fail to coordinate. However, add-ing trade credit restores coordination in the presence of quantitydiscount and two-part tariffs, and restores coordination in thepresence of buy back contracts if the supplier has a sufficientlylow cost of capital. Revenue sharing contracts are always less prof-itable. Boyaci and Gallego (2002) compare trade credit and inven-tory consignment contracts. They conclude that consignmentcoordination is more desirable because trade credit coordinationcauses the buyer’s and the supplier’s capital costs to convergeand thus renders the contract infeasible in the long term.

3.4. Settlement period decisions

Once firms have contractually agreed upon credit terms, theystill face a decision problem regarding actual collection and pay-ment. Although accounts receivable may be overdue, stringent col-lection may damage buyer relationships. And although accountspayable may exist, early payment may reduce liquidity and createunnecessary financial costs. The finance literature has tackledthese decision problems primarily by correlating measures ofworking capital management with measures of firm profitability(Soenen, 1993; Shin and Soenen, 1998; Fisman, 2001; Wang,2001; Deloof, 2003; Cull et al., 2007; García-Teruel and Martínez-Solano, 2007). While nearly all authors conclude that early collec-tion increases profitability, the consequences of early payment arecontroversial. Although theory suggests that early payment shoulddecrease profitability, data suggest the opposite. Deloof (2003)therefore argues that this observation may be a consequence ofendogeneity (payment delay affecting profitability but, at the sametime, profitability also affecting payment delay). This shortcomingof the financial literature creates opportunities for analytical re-search, which we present in the following.

Haley and Higgins (1973) consider a buyer’s lot-sizing problemunder trade credit financing. They derive the optimal order quan-tity and settlement period when the supplier offers two-partterms. Payments are assumed to occur within the net period7. Ifthe permissible delay in payment is longer than the cycle time, thestandard solution of separate optimization holds. Otherwise, how-

7 Two-part terms are usually described as a combination of cash discount, discountperiod, and net period, e.g., 2/10 net 30.

ever, order quantity and settlement period must be determinedsimultaneously. Huang and Chung (2003) develop decision rules tofacilitate the determination of the settlement period in practice.They first calculate the optimal cycle time and then ‘‘deltas’’ thathelp to determine the optimal settlement period quickly. Huang(2005) provides an efficient solution procedure. Ouyang et al.(2005) relax the assumption of an infinite replenishment rate anddevelop a shorter decision rule, for which Chung (2010) providesan improved search procedure.

Jamal et al. (2000) investigate the optimal settlement period fora retailer in a deteriorating-item inventory situation where awholesaler allows a specified credit period to the retailer for pay-ment without penalty. The authors model the retailer’s decisionas a cost minimization problem and solve it via iterative search.Their results indicate that depending upon several parameters itcan, in some cases, be advantageous for the retailer to pay afterthe permissible credit period and incur late payment penalties.Song and Cai (2006) improve the previous authors’ work by show-ing that a single decision variable suffices to solve the optimizationproblem. Using the same example, they show that the optimal pay-ment time is in fact shorter.

Liao and Chen (2003) additionally include inflation. Inflationprovides an incentive for the retailer to defer payment as it effec-tively reduces the wholesale price. Based on a numerical example,the authors find that the retailer’s optimal settlement periodequals the permissible delay when the delay is short and the cycletime when the delay is long. The authors conclude that retailersshould devote time to finding the inflection point for suppliernegotiations. However, this binary behavior is caused by theassumption that M 6 P⁄ 6 T⁄, where M is the permissible delay inpayment, P⁄ is the optimal settlement period and T⁄ is the optimalcycle time. Future research should relax this assumption, espe-cially because the authors indicate that the settlement period asso-ciated with minimal total costs violates this constraint in manyinstances. Sarker et al. (2000) continue under the same assumptionand find that inventory cycle time and order quantity are concavewith respect to inflation. Intuitively, total inventory costs decreasewith inflation. The decline is steeper when unit costs are higher.Chang and Wu (2003) build on their work and provide an efficientalgorithm that yields lower total costs.

Ho et al. (2008) present a model that integrates credit term andsettlement period decisions. In their study, a supplier offerstwo-part terms to a retailer who faces price sensitive demand.Numerical examples suggest that integration yields higher profitsolutions than isolated decision-making.

4. Research agenda

As noted in the introduction, and as expressed by the number ofrecent contributions in the previous sections, research at the inter-face of operations and finance is growing fast. In this section, wetherefore develop an agenda for future research to address remain-ing shortcomings in this domain. We build this agenda around twocore themes: opportunities arising from inside operations manage-ment (e.g., based on insights from existing operational models) andopportunities arising from outside operations management (e.g.,based on insights from the finance literature). The first theme,opportunities arising from inside operations management, suggeststhat multiple product settings, alternative credit schemes, opera-tional uncertainty, cash flow timing, and incomplete informationare promising avenues for future research. The second theme,opportunities arising from outside operations management, sug-gests that the study of multiple echelons, default risk, and late pay-ment presents exciting opportunities for future research.Weexclude opportunities for works that are primarily concerned with

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order quantity decisions. Such opportunities may be found in thereview by Chang et al. (2008). Our update of their review suggeststhat, except for supplier buyer coordination, these opportunitiescontinue to be relevant.8

4.1. Opportunities arising from inside operations management

Inventory models may be classified along 13 dimensions: prod-uct, decision variables, decision making structure, time, demand,objective function, ordering/production costs, holding, shortages,supply, physical system, information structure, and resource usage(Porteus, 1990). Most of these dimensions range from simple (e.g.,one decision maker) to complex (e.g., multiple decision makers)but, perhaps, more realistic modeling choices. Therefore, opportu-nities for future research arise in dimensions in which extant mod-els do not fully reflect business realities. In the following, wediscuss such opportunities. We restrict our discussion to dimen-sions that, in our eyes, require attention. Although this selectionrepresents an arbitrary choice, we believe that the risk of omittingimportant dimensions is limited as Tables 3 and 4 document thestate of most of these dimensions.

1. How should credit terms be structured in the presence of multipleproducts?Most of the credit term models in Section 3.3 develop recom-mendations for firms with one product. While such firms exist(startups, for example), they may not be representative of theeconomy. Typically, firms carry several products and offerindustry-specific credit terms (Smith, 1987). The possibility ofcarrying several products directly raises two questions: (1)Are credit terms calculated from single product models stilloptimal? and (2) How should credit terms be structured inthe presence of multiple products? For example, there may beone credit term per product, there may be one credit term perbuyer, there may be one credit term per group of productsand/or buyers, or there may be only one credit term regardlessof products and buyers. Since some cost parameters are prod-uct-specific (e.g., holding costs) but others are buyer-specific(e.g., capital costs), research is needed to investigate if averagesof these parameters continue to be optimal. Similarly, andlinked to this question, research is needed that investigates ifand how credit terms may be structured.

2. Are there other relevant decision variables?Table 3 reveals that, while certain decision variables are part ofmost credit term models (e.g., credit periods, order quantities,lot size multipliers), other decision variables are discussed lessoften (e.g., prices, cash discounts). However, these decision vari-ables may always be present or at least be very common. Nget al. (1999), for example, report that 11 out of 27 industriesin the US offer cash discounts. Furthermore, these decision vari-ables affect the buyer’s and the supplier’s profits. Therefore,future research should study additional decision variables. Nextto price, cash discounts and two-part terms in general, futurework should investigate alternative credit terms such as pro-gressive interest schemes and date terms (e.g., Robb and Silver,2006). Finally, research is needed that compares the perfor-mance of these credit terms and develops recommendationsfor when to use which type of credit term.

8 To add to their opportunities, we screened works that had been published aftertheir review. These works recommended to additionally model shortages (mentionedsix times), to include other trade terms (5), to model other demand functions (4), tomodel other deterioration functions (4), to introduce stochastic demand (3), tointroduce inflation (2), and to model finite replenishment rates (2). Furthermore,authors recommended to investigate multi-buyer settings, progressive interestschemes, other shipping options, and unequal shipping lots.

3. Can trade credit be used to alleviate the effects of operationaluncertainty?While the consideration of stochastic demand may not beimportant in itself (cf. the heuristics literature, e.g., Zheng,1992), there is reason to believe that trade credit may alleviatethe effects of demand uncertainty. If demand is deterministic,buyers may calculate inventories, cash, and borrowings pre-cisely. If demand is stochastic, however, buyers may experiencecash flow shortfalls and may be unable to purchase raw mate-rials. In such cases, trade credit may help buyers to carry outproduction decisions, avoid supply disruptions (Fisman, 2001)and reduce transaction costs for suppliers (e.g., for call centersthat handle overdue accounts). Trade credit should thereforeincrease with demand uncertainty. Similarly, trade credit mayincrease with lead time uncertainty. Numerical examples inCharharsoogi and Heydari (2010), however, suggest that tradecredit decreases with lead time uncertainty. More research istherefore needed to investigate how trade credit relates to oper-ational uncertainty and if trade credit may be used to alleviateits effects.

4. Is it important to account for cash flow timing in the objective func-tion?The objective function of credit term and settlement perioddecision models may be formulated in either of two ways:based on the opportunity cost approach or based on the netpresent value approach. The opportunity cost approach addsthe trade credit benefits to the objective function. Therefore,under the opportunity cost approach, the objective function clo-sely resembles the original economic order quantity model, canoften be solved in closed form, and is perceived to be simple toanalyze. The net present value approach, on the other hand,adds the discounted trade credit benefits to the objective func-tion. Therefore, it is an accurate measure of value creation,which maximizes wealth. The net present value approach, how-ever, is much less popular; perhaps because its objective func-tion is more complex or perhaps because its analysis is moredifficult. Similar to the discussion in the finance literature (cf.Kim and Feist, 1995), research is therefore needed that com-pares the outcomes of these two approaches. If there is no sig-nificant cost improvement linked to the net present valueapproach, future research may use the more popular opportu-nity cost approach. While this comparison may be a stepstoneonly, the results would represent an important foundation forother contributions.

5. How do credit terms change when information is incomplete?Often, credit term and settlement period models analyze buy-ers and suppliers that use trade credit to coordinate orderquantities or to share capital access. Most of these modelsassume complete information, meaning that the buyer andthe supplier know all parameters and have the same informa-tion. In reality, however, buyers and suppliers may not havethe same information. The contracting literature suggests thatif information is held asymmetrically, joint profits may beeither lower or higher depending on the contract type(Corbett and Tang, 1999; Corbett et al., 2004). Interestingly,the most common trade term, i.e., the combination of a fixedwholesale price and a net credit term, may be interpreted asa two-part nonlinear contract (with a fixed wholesale price)suggesting that joint profits are higher when trade credit isthe supply contract. Thus, one may conjecture that tradecredit alleviates the consequences of asymmetric information.Although interesting, neither the argument nor the contract-ing literature (since it ignores capital costs) explains how toset credit terms under incomplete information. Therefore,future research should investigate optimal credit terms underincomplete information.

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4.2. Opportunities arising from outside operations management

The models reviewed here, and summarized in Tables 3 and 4,build on a number of the trade credit motives in Table 1. Some mo-tives are at the core of several models, e.g., product market positionand price elasticity of demand. Other motives, however, have beendiscussed less often, e.g., collateral value and credit information.Yet, these motives and the insights associated with them may beimportant to incorporate. We, therefore, outline how operationsmanagement research may be advanced by building on the addi-tional trade credit motives. For this purpose, we sort these remain-ing motives into three groups (capital access; collateral value,credit information, and non-salvageable investment; transactionpooling, credit rationing, and control protection) and propose cor-responding research questions.

1. How should credit terms be structured in supply chains?Similar to the recent interest in two-stage credit models inorder quantity decisions (cf. Section 3.2), research should inves-tigate how trade credit should be structured in supply chains.While we possess knowledge about how order quantities reactto changes in capital cost parameters in the presence ofaccounts receivable and payable (Teng and Chang, 2009), welack insights into how credit offered should react to creditreceived, especially across several echelons. Data suggest thataccounts receivable increase relative to the distance to theend-consumer (cf. Fig. 2). However, this observation may bedue to price rather than credit term differences. Even if differingcredit terms are at the heart of this observation, it is not wellunderstood if cascading credit terms are optimal. While theymay serve as equity stakes in the buyer’s production process(Kim and Shin, 2007) or as instruments to protect non-salvage-able investments (Smith, 1987), cascading credit terms may justbe a relict that leads to inefficient capital allocation (Long et al.,1993). Research is therefore needed that investigates how toallocate capital along supply chains with the help of creditterms.

2. How does the possibility of default affect the credit term decision?Recent research suggests that the buyer’s risk of default affectsthe credit granting rather than the credit term decision (Shi andZhang, 2010). Thus, their functional separation in practice maybe efficient and the incorporation of default risk into credit termmodels may not be a priority. However, this research is basedon net terms. Other credit terms, e.g., two-part terms, may besensitive to default risk. For example, the theoretical modelsin Smith (1987) and Wilner (2000) suggest that cash discountsin two-part terms depend on buyer default risk and predict thatcash discounts will be deep when buyers are deemed risky orwhen suppliers make significant non-salvageable investments.Therefore, future research should investigate if buyers that dif-fer with respect to default risk should be offered different creditterms or if, perhaps, a limited set of credit terms may besufficient.

3. How does the possibility of late payment affect the settlementperiod decision?Howorth and Reber (2003) document that late payment, i.e., thesettlement of invoices after the due date, is common in firmsthat lack cash to finance their operations. They also documentthat suppliers react to late payments by withholding furthersupplies or levying penalty charges. The current literature onsettlement period decisions reflects these successive activitiesonly partially. For example, some works assume that paymentscan never be late (Haley and Higgins, 1973; Huang and Chung,2003; Ouyang et al., 2005; Ho et al., 2008), while other worksassume that payments are always late but entail no supplier

reaction (Jamal et al., 2000; Sarker et al., 2000; Liao and Chen,2003). While the validity of the second assumption dependson the business environment (Shi and Zhang, 2010, for example,describe that supplier reactions to late payment are less com-mon in developing countries), the first assumption may be eas-ily criticized. Therefore, future research should relax theassumption of timely payment and integrate supplier reactions.The absence of such reactions may then be regarded as a specialcase of a more general model.

5. Summary

This article provided an integrative review of several streams ofthe trade credit literature and deduced questions for future re-search. We began by highlighting the well-developed literatureon trade credit motives. This literature proposed six major sup-ply-side motives (capital access, product market position, priceelasticity, collateral value, credit information, and non-salvageableinvestment) and three major demand-side motives (transactionpooling, credit rationing, and control protection). We also pre-sented three emerging literature streams that covered order quan-tity, credit term, and settlement period decisions. One of the mainfindings of the literature on order quantity decisions was that thepresence of trade credit increased the economic order quantity.One of the main findings in the literature on credit term decisionswas that trade credit could serve as a buyer–supplier coordinationmechanism. Finally, one of the main findings in the literature onsettlement period decisions was that in many instances orderquantities and settlement periods needed to be determined jointly.

Following the literature review, we derived an agenda for futureresearch. We built this agenda around two core themes: Opportu-nities arising from inside and opportunities arising from outsideoperations management. For order quantity decisions, our analysissuggested that the research opportunities in Chang et al. (2008)continued to be relevant and we therefore focused our researchagenda on credit term and settlement period decisions. The firsttheme, opportunities arising from inside operations management,suggested that multiple product settings, alternative creditschemes, operational uncertainty, cash flow timing, and incom-plete information were promising avenues for future research.The second theme, opportunities arising from outside operationsmanagement, suggested that the study of multiple echelons, de-fault risk, and late payment presented exciting opportunities forfuture research.

Acknowledgements

The authors thank the Editor Prof. Robert Dyson, University ofWarwick, as well as the anonymous referees for their constructivefeedback that improved this paper.

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