Logistics
Supply chain relationships, supplier support programmes and stimulating investment: evidence from the
Armenian dairy sector
Liesbeth Dries
Wageningen University, Wageningen, The Netherlands
Matthew Gorton
Newcastle University Business School, Newcastle-upon-Tyne, UK
Vardan Urutyan
Agribusiness Department, Armenian National Agrarian University, Yerevan, Armenia, and
John White
School of Management, University of Plymouth, Plymouth, UK
Abstract Purpose – The purpose of this paper is to evaluate the determinants of supply chain
relationships, the provision of supplier support measures and the role that support measures play in
stimulating investment by suppliers in emerging economies. Design/methodology/approach – The paper
draws on survey evidence for 300 commercial dairy farms in Armenia. The identi?cation of potential
determinants of supply chain relationships and support programmes is based on literature on supply
chain management and transaction cost economics. Findings – Positive determinants of supplier support
programmes are the degree of exclusivity of the buyer-supplier relationship, initial capital of the
supplier, co-operation between suppliers, and foreign ownership of the buyer. Support programmes are
less likely to be offered in very competitive environments. Support measures such as loans, physical
inputs and guaranteed prices facilitate supplier investments. Research limitations/implications –
Research is limited to cross-sectional data for a single country and further testing would help assess the
generalizability of the ?ndings. Practical implications – The ?ndings highlight the gains that can be made
from openness to international ?rms. The negative competition effect suggests that buyers are
constrained in their ability to monitor use of the provided services in an environment where a lot of
buyers are competing for the same supply. Improving the enforcement capability of companies under
these circumstances is an important challenge for the industry and policy makers. Originality/value –
The novelty of the study lies in the investigation of the relationships between the nature of supply chain
linkages and suppliers’ investments. Keywords Dairy, Supply-chain management, Armenia, Contractual
choice, Supplier investment, Supplier support programmes Paper type Research paper
1. Introduction
Supply chain inef?ciencies and disruptions are common problems inhibiting the international
competitiveness of emerging and transitional economies. Buyers often face inadequacies in the quality
and quantities of supplies. This has led to enhanced vertical coordination in agri-food chains. Speci?
cally, enhanced vertical coordination involves buyers providing support measures to suppliers, such as
credit and physical inputs, as part of a long-term relationship. This may
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8546.htm
overcome market failures and provide a more conducive framework for investment by the supplier.
However, the provision of such supports is often costly and there is a potential for opportunistic
behaviour – for instance credit provided to enable the supplier to purchase inputs could be diverted to
other activities. Curtailing opportunistic behaviour may be particularly problematic where transaction
costs
The data used in this paper were collected in the SIDCISA (Supporting the International Development of
CIS Agriculture) research project. This project was funded by EU INTAS. The analysis described in this
paper was conducted as part of the COMPETE (International comparisons of product supply chains in the
agro-food sectors) research project that is funded by EU FP7. Received 6 December 2012 Revised 9 April
2013 2 August 2013 11 October 2013 Accepted 11 October 2013
Supply Chain Management: An International Journal 19/1 (2014) 98– 107 q Emerald Group Publishing
Limited [ISSN 1359-8546] [DOI 10.1108/SCM-12-2012-0380]
98
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Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
incurred in monitoring and enforcing contracts are high (Blanchard and Kremer, 1997; Gow and Swinnen,
1998; Boger et al., 2001). This paper analyses vertical coordination in the Armenian dairy sector, speci?
cally, farm – milk processor relationships and the provision and impact of supplier support measures as
part of these relationships. It addresses three important questions relating to the type of buyer-
supplier relationship, which suppliers receive support measures and whether such support induces
investments by suppliers. Econometric analysis thus models the determinants of: contractual choice;
supplier support measures and farm level investment, drawing on a sample of 300 commercial dairy
farms. The Armenian dairy sector, as in many other emerging and transitional economies, has suffered
from inadequate investment in the supply base, with, by international standards, poor quality and
productivity. Investment at the farm level is crucial to improving the ef?ciency and international
competitiveness of the whole supply chain. Recent supply chain management research emphasises the
critical role of buyer-supplier relationships for supply chain management strategies in general (Tan,
2001) and, speci?cally, within the agri-food sector (Fischer et al., 2010). This relationship may also include
supplier development, de?ned as any effort of a buying ?rm to increase the capabilities of the supplier
(Krause and Ellram, 1997). Although much has been written on buyer-supplier relationships and supplier
development (Ellram, 1991), there is a lack of empirical models for establishing successful buyersupplier
partnerships (Tan, 2001; Fischer and Reynolds, 2010). The study is thus opportune and contributes to the
wider literature on supplier development strategies as a key component of supply chain management
(Monczka et al., 1993; Krause and Ellram, 1997; Wagner, 2006; Rogers et al., 2007). As far as we are aware,
it is one of the ?rst to assess the linkages between supplier support measures and actual investment by
suppliers. The remainder of this paper is organised as follows. We ?rst provide an overview of the
potential nature of buyer-supplier relationships and the rationale for the choice of contract and the
provision of supplier support programmes. This is followed by a description of the Armenian dairy
supply chain and the rural ?nance sector to provide context for our study. We then discuss the dataset
and the data collection process. Next, we present descriptive statistics before introducing an
investment model and analysing the determinants of contractual choice, supplier support and
investment. Relevant conclusions are drawn in the ?nal section.
incorporating two stages of the supply chain within a single ?rm, should lower procurement risks and
increase control over quality. However, the dissipation of resources in vertical ownership integration
places a relatively high demand on capital and may dull managerial incentives (Bhuyan, 2002). Firms may
possess core competencies in one stage of the supply chain, but lack the knowhow and skills to be
effective in another. As a result, intermediate or hybrid arrangements, where autonomous ?rms maintain
contracting/partnership arrangements, have become increasingly common (Sporleder, 1992; Hobbs and
Young, 2000). When working effectively, hybrid forms should give downstream agents improved control
over product quality and quantity, without dissipating managerial efforts. For simplicity, and following
the approach of Fischer et al. (2009), we group the different types of exchange relationships into two
major contract types: spot markets and informal or relational contracts are referred to as implicit
contracts,1 while written contracts and cross-shareholding arrangements are referred to as explicit
contracts. The degree of vertical coordination or integration is higher for explicit, compared to implicit,
contracting relationships. 2.2 Determinants of exchange relationships and supplier support Tighter
vertical coordination can help reduce business uncertainty, improve access to essential resources and
result in higher business productivity (Dyer and Singh, 1998). In emerging and transition countries,
improved access to resources is crucial to overcome problems of quality and volume of supplies (Sahay
et al., 2003). Providing access to resources can be part of supplier development programmes, which may
include a range of “activities undertaken by a buying ?rm to improve either supplier performance, or
supplier capabilities, or both, and to meet the buying ?rm’s short- and/or long term supply needs”
(Krause et al., 2007, p.34). The most common support tools included in supplier development
programmes are: credit, loan guarantees, physical inputs, guaranteed prices and prompt payments (Gow
and Swinnen, 2001; Krause et al., 2000; Krause et al., 2007; Wagner, 2006): H1a. Improving access to
resources requires tight vertical coordination and therefore more explicit contracts. Access to
resources is more problematic for resourcerestricted suppliers so we expect these suppliers to have
more explicit contracts. H1b. Supplier support programmes are provided to overcome market failures
and restrictions faced by suppliers in ful?lling buyers’ requirements in terms of quantity and quality of
products. Therefore, more resource-restricted suppliers are more likely to bene?t from supplier support
programmes. Market competition represents environmental uncertainty because it increases the
opportunities for suppliers (or buyers) to switch to another buyer (supplier). Under such circumstances,
the use of more explicit contracts becomes attractive as it lowers the risk of losing a trading partner
(McCluskey and O’Rourke, 2000; Reynolds et al., 2009). Supplier development programmes may also
generate greater scope for opportunistic behaviour: suppliers may divert support measures to
unintended uses or they may switch buyers after having received support. However, in some cases 99
2. Supply chain relationships and supplier support programmes
2.1 Governance of exchange relationships Exchange relationships have been conceptualised as a
continuum, differing in their degree of interaction, from spot markets at one extreme to vertical
ownership integration at the other (Anderson et al., 1994). Spot markets, as a governance mechanism,
involve immediate market transactions with no prior or post purchase commitments beyond ful?lling the
given exchange. They provide buyers with ?exibility but limit their ability to control the quality and
quantity of goods available for sale. Vertical integration, by
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Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
tangible incentives – such as supplier support – may also be used to protect against violations of
contractual agreements (Klein et al., 1978; Zaharieva et al., 2003). For example, if input markets are inef?
cient, access to credit through a particular contract with a buyer acts as a powerful incentive not to
break an agreed arrangement. Furthermore, supplier support programmes may also emerge in a reactive
manner – a buyer may seek to prevent suppliers from switching to a competing ?rm which has
introduced such measures. In this case, the competition effect actually works in favour of the spread of
supplier support programmes. The spill over effects of one ?rm offering supplier support measures may
be considerable (Gow et al., 2000): H2a. Greater uncertainty in the buyer-supplier relationship due to the
possibility of opportunistic behaviour, increases the use of explicit contracts. H2b. Self-enforcement of
the buyer-supplier agreement is an important prerequisite for the sustainability of supplier support
programmes. A higher likelihood of opportunistic behaviour by suppliers decreases the availability of
supplier support programmes. On the other hand, competition between buyers may also increase the
availability of supplier support programmes through spill over effects. Agri-food supply chains are
especially prone to problems of information asymmetry between buyers and suppliers because many
food quality characteristics are credence attributes (Gorton et al., 2006; Young and Hobbs, 2002). To
overcome the problem of information asymmetry, companies are increasingly using explicit contracts
and ownership integration (Fischer et al., 2009). An example of information asymmetry in the case of
supplier development programmes is that buyers cannot easily observe if supplier support measures are
used in ways intended by the principal. This information asymmetry may deter the buyer from providing
supplier support (Gow and Swinnen, 2001). H3a. Information asymmetries lead to greater use of explicit
contracts. Conversely, initiatives that help to overcome the problem of asymmetric information may
reduce the need for explicit contracts. H3b. Asymmetric information between the buyer and the supplier
decreases the likelihood of support programmes. Initiatives that help to overcome the problem of
asymmetric information increase the likelihood of supplier development.
Table I Size distribution in the Armenian dairy sector
Size class < 8 cows 8-14 cows 15-99 cows > 99 cows Total 2,476 1.4 49 0.0 173,716 100
Number of farms 167,134 4,057 % of farms 96.2 2.3 Source: Hovhannisyan (2008)
Typically, less than one half of produced milk is sold to dairy processing companies. The remainder is
used to feed calves, sold unof?cially to consumers or consumed by farm households (Avetisyan, 2008).
The dairy processing industry in Armenia is comprised of about 250 companies. In order to overcome the
problems related to the seasonality of local milk supply, most processors rely heavily on imported milk
powder to stabilize their production levels year-round. While of?cial data suggest that during recent
years milk powder comprised only about 8-12 per cent of total milk procured by the dairy companies,
other studies show that there is also an important additional share of shadow imports of milk powder
into the country (Asatryan and Eroyants, 2010). 3.2 The rural ?nance sector Although the ?nancial and
banking sector has experienced steady growth in Armenia, options for agriculture are more limited. The
vast majority of Armenian banks refrain from ?nancing agriculture. By the end of 2012 there were 21
commercial banks with 467 branches operating in Armenia. Only about 5.5 per cent of total credit
investments of these commercial banks went to agriculture in 2012. Banks consider agriculture a high
risk sector and impose higher rates and shorter repayment periods for provided loans. On average the
interest rates on agricultural loans provided to farmers by commercial banks and credit organisations
vary between 8 and 22 per cent (Urutyan, 2012). The overall level of institutional development in Armenia
is an important limiting factor when it comes to establishing business relationships between agricultural
producers who require funds and banks which have those funds. These institutional failures include
limitations in the use of real estate as collateral due to the lack of properly functioning land registry
of?ces, the lack of regulations regarding other forms of loan security and uncertainty regarding the
enforceability of claims on borrowers in default. In addition, a market for agricultural insurance does not
exist in Armenia. As a result, local producers must contend not only with the risks inherent in the ?
uctuation of prices on the market but also the speci?c risks inherent in extensive agricultural
production and a low level of productivity. Hence, farmers who are considering taking out loans face
huge uncertainty regarding the pro?tability of planned projects if these projects have to be ?nanced
with funds obtained at high interest rates (Urutyan, 2012). Despite some signs of improvement in recent
years – , e.g. in the period 2005-2012 total agricultural credits grew at approximately 35 per cent per
annum – important credit constraints remain. This is especially true for the majority of Armenian farm
households, which are small-scale, poorly capitalized and face ?uctuating and uncertain cash ?ows
throughout the year. These conditions put a strain on the restructuring and upgrading – in terms of
quality production 100
3. Background on the Armenian dairy and rural ?nance sector
3.1 The Armenian dairy supply chain Animal husbandry is one of the main branches of Armenian
agriculture and is of critical importance for small-scale farms. The vast majority of producers own fewer
than eight cows (see Table I). Livestock farms face a number of obstacles, hampering their operational
ef?ciency. The absence of affordable, high quality feeds, expensive veterinary services, a lack of arti?cial
insemination and ineffective management practices make the industry highly volatile and seasonal. This
negatively affects productivity and the competitiveness of locally produced milk compared to
imported milk powder.
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Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
and volumes – of the agricultural sector. With the formal rural ?nance sector offering few opportunities
to small Armenian milk producers, it is crucial to improve comprehension of the role of supply chain
relationships in overcoming credit constraints in the sector (Urutyan, 2012).
Table III Number of milking cows per commercial farm
No. of milking cows <4 4-5 6-7 8-9 10 to 19 20 to 49 50 to 99 100 1 Total Mean SD Source: Survey data No.
of farms in the sample 4 66 72 39 77 29 8 5 300 13.4 18.5 Share of farms in total sample (%) 1.3 22 24 13
25.7 9.7 2.7 1.7 100 – –
4. Data and data collection methodology
To gain a better understanding of commercial milk buyersupplier relationships, a survey was conducted
in 2006. The population of interest was de?ned as primary producers which sold cows’ milk to another
supply chain actor. Therefore, farmers without dairy cows, those who did not sell any of the milk
produced or who processed all milk into cheese or other dairy products (i.e. did not sell any raw milk)
were excluded from the study. To obtain the sample, a quota of 300 responses was set with the
intention of including a representative cross-section of commercial dairy farms, including both
household producers that marketed at least part of their output and agricultural companies.
Respondents were drawn from all regions (Marzes) which have signi?cant commercial milk production,
based on proportions drawn from statistical data on milk production. A strati?ed random sample was
employed with quotas for each region based on statistical data from Milk Production in the Marzes of
Armenia (NSSA, 2004). Regions where milk production was not developed commercially were excluded
from the sample. On the other hand, visits to the villages revealed that there are regions with highly
commercialized farmers working with many dairy processors. In this case, it was decided to increase the
number of farmers to be surveyed from such regions. Table II details the sampling plan. Suitable farms
were identi?ed from contacts with national statistical agencies, local and regional authorities, village
mayors, local livestock experts and agricultural agencies. Data were collected through face-to-face
interviews. The dataset by herd size is presented in Table III, with the typical commercial dairy farm in
Armenia having between ?ve and 20 milking cows. Comparing the size distribution of farmers in the
sample (see Table III) with the overall size distribution of Armenian milk producers (see Table I), we
observe that the sample is not representative of the total sector. Small farms have been
underrepresented in the sample: more than 50 per cent of the farms in our sample have a herd size of
eight cows Table II Sampling plan
Milk production in Marzes Yerevan Aragatsotn Ararat Armavir Gegharkunik Lori Kotayk Shirak Syunik
Vayots Dzor Tavush Total In tsd tonnes 3.6 64.1 41.4 35.9 98.7 65.4 56.9 83 47.1 21 38.1 555.2
or more, while this is the case for less than 4 per cent of the total Armenian dairy sector. Given our
sampling strategy – speci?cally excluding non-commercial dairy farms in line with the objectives of our
research question – this is, however, unsurprising.
5. Supply chain relationships and on-farm investments in the Armenian dairy sector
5.1 Description of milk buyer-supplier relationships Before presenting the econometric analysis related
to supplier investment, we brie?y describe the vertical relationships between milk producers and buyers
in the Armenian dairy supply chain. Dairy processors are the most common main buyers of milk from
farmers. Over three quarters of sampled farms sell directly to dairy processors. Another 20 per cent of
dairy farms sell through a co-operative. The remainder of milk is collected by intermediaries: dairy
logistics or collecting ?rms. The majority of sampled farms sell milk based on a contractual arrangement:
38 per cent have signed a written contract with the milk buyer; 36 per cent sell milk based on an oral
contract; and only a quarter of the sampled farms have not entered into a contractual agreement.
Per cent in total 1 12 7 6 18 12 10 15 8 4 7 100
Proportional 3 36 21 18 54 36 30 45 24 12 21 300
Final sample 0 20 23 0 57 39 20 71 60 0 10 300
Source: National Statistics Year Book 2004 and INTAS Survey Guidelines
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Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
The relationships that exist between buyers and sellers are often much more extensive than simple
agreements on price, volumes and delivery conditions. Table IV shows that supply chain relationships in
the Armenian dairy sector also involve a wide range of support measures. The most prevalent types of
support are prompt payments and quality control, which are received by over 80 per cent of sampled
farms. Around 30 per cent also receive credit from their main buyer. Considering the investment
activities of the sampled farms reveals that the majority of respondents had made on-farm investments
in the previous ?ve years (period 2001-2006). Almost 50 per cent of the sampled farms have invested in
animal housing, 31 per cent reported purchasing cooling tanks, milk lines, cows and other dairy-speci?c
investments and 19 per cent have made general investments related to land and non-dairy speci?c
agricultural equipment. 5.2 Determinants of supply chain relationships and supplier support programmes
Firstly, the analysis considers the determinants of the supply chain relationship and the elements that
determine access to support programmes. The explanatory variables in these models are directly
related to the three hypotheses that were derived in section 2. The relationships that we estimate are
the following:
EXPLICIT i ¼ b0 þ b1 RESOURCES i þ b2 OPPORTUNISM i þ b3 INFORMATION i þ b4 BUYERi þ 1i ð1Þ
SUPPORT i ¼ b00 þ b01 RESOURCES i
þ b02 OPPORTUNISM i
þ b03 INFORMATION i þ b04 BUYERi þ 1i
ð2Þ
where EXPLICIT is a measure of the contractual relationship between buyer and supplier; SUPPORT is a
measure of access to farm support programmes; RESOURCES characterises how resource-restricted the
milk producer is; OPPORTUNISM is a measure of the potential for opportunistic behaviour; INFORMATION
identi?es the level of information asymmetry between buyer and supplier; Table IV Percentage of
suppliers receiving support from their main buyer
Support measure Prompt payments Quality control Guaranteed prices Market access Credit Veterinary
support Transportation Physical inputs Business and ?nancial management support Farm loan guarantees
Specialist storage Investment loans Machinery Source: Survey data Percentage of suppliers receiving
support 87.7 82.7 46.7 40.0 30.7 23.7 20.3 16.3 4.0 4.0 2.0 1.7 1.7
BUYER includes additional variables that relate to the characteristics of the buyer company; and ?nally
1 is the error term. EXPLICIT is a dummy taking the value of one if the buyersupplier relationship is based
on a written contract and it is zero in the case of an oral contract or a spot market relationship.
SUPPORT is a dummy taking the value of one if the farm received support from the main buyer of milk.
The support categories included are: credit; investment loans; farm loan guarantees; physical inputs.
RESOURCES is measured by the existing capital stock of the farm. Since the main capital assets in
Armenian dairy farms are the cattle, we follow the approach of Petrick (2004) by using the number of
cows on the farm in the year 2001 as an indicator. It is expected that smaller, hence more resource-
restricted, farms are more likely to have explicit contracts and have greater need for supplier support
measures. OPPORTUNISM is a vector of variables (COMPETITION and SHARE). COMPETITION measures the
number of potential buyers of milk that a farmer has access to and hence characterises the degree of
competition between buyers in the market. More competition in the market creates greater uncertainty
for the buyer who will be more inclined to engage in explicit contracts with suppliers. On the other
hand, contracts are more dif?cult to enforce in a competitive market and hence providing support is
more risky (Poulton et al., 2004). The expected effect of COMPETITION on the likelihood of support is
negative. SHARE refers to the share of the farm’s total milk production that is sold to the main buyer.
We expect that a more exclusive relationship with the main buyer (i.e. a higher share of milk sold to this
trading partner) leads to a higher degree of dependence and commitment and hence a lower probability
of opportunistic behaviour (Krause et al., 2000). An implicit contract may suf?ce for governing the
relationship under these circumstances. On the other hand, an explicit contract can formalise the
dependence and commitment within the relationship. We expect SHARE to increase the likelihood of the
supplier receiving support. The effect on contract choice is ambiguous. INFORMATION is a vector of
variables (COLLECT and CO-OPERATE) that relate to the asymmetry of information in the buyer-supplier
relationship. COLLECT is a dummy that takes the value of 1 if milk is collected by the buyer from the
farm instead of through a village collection centre. Onfarm collection should provide better
opportunities for the buyer to monitor and check up on the supplier. This should increase the likelihood
of support measures but decrease the need for explicit contracting. CO-OPERATE is a dummy that takes
the value of one if the farmer indicates that he/she is cooperating with other farmers to store, market
or process milk, to buy inputs, or to engage in lobbying activities. Greater cooperation between
farmers may lead to greater discipline among farmers, the development of good practices and a denser
network in which information ?ows more ?uently. This may also have a positive impact on the likelihood
of receiving support. Finally, BUYER includes two indicators of the characteristics of the buyer (TYPE and
FDI). Buyer type (TYPE) is a dummy that takes the value of one if the buyer is a corporate dairy
processor and zero if the buyer is a cooperative or an intermediary milk collecting company. FDI is a
dummy that takes the value of one if the buyer is a foreign 102
Supply chain relationships Liesbeth Dries, Matthew Gorton, Vardan Urutyan and John White
Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
owned company or if the buyer is exporting dairy products. In both instances we expect that
internationally focused buyers are better resourced, having the necessary ?nancial means to provide
support programmes (Dries and Swinnen, 2004; Dries et al., 2009). Furthermore, foreign companies may
be more inclined to use explicit contracts because local market and business uncertainty may be higher
for them. FDI is expected to have a positive effect on the likelihood of explicit contracts and support.
Table V provides summary statistics of the variables included in models (1) and (2). Tests show that
correlation between the explanatory variables is limited with a maximum correlation coef?cient
between TYPE and CO-OPERATE of 31 per cent. 5.3 Determinants of on-farm investment The model that
we employ to analyse the determinants of onfarm investment is based on Elhorst (1993) and Petrick
(2004). The model was adapted in keeping with Dries and Swinnen (2010) to answer the speci?c research
question related to the impact of support programmes on investments and to deal with a number of
data limitations. We estimate the following empirical model: INVEST i ¼ b0 þ b1 SUPPORT i þ b2 CONTROLi
þ 1i ð3Þ
where INVEST measures farm investment, SUPPORT is a vector of variables measuring support
programmes from the dairy; CONTROL is a vector of control variables; and 1 is the error term. INVEST is
a dummy taking the value of one if the milk supplier made an investment in farm assets2 over the ?ve
year period of study (years 2001 to 2006) and it takes the value of zero if no investment was made in the
speci?ed period.3 The ?rst set of variables (SUPPORT) includes several indicators of dairy assistance
programmes. PLOAN is a dummy that takes the value of one if a supplier is delivering to a dairy company
that offers ?nancial assistance, in other Table V Descriptive statistics for variables included in the
regression models
Variable EXPLICIT SUPPORT o.w. PLOAN o.w. INPUTS o.w. PRICE o.w. PAYMENT INVEST INVESTd Hypothesis 1
RESOURCES Hypothesis 2 COMPETITION SHARE Hypothesis 3 COLLECT COOPERATE TYPE FDI Variable
description
words, if the supplier has access to dairy processor loans, credit or bank loan guarantee programmes
that improve access to external ?nancial resources. INPUTS is a dummy that takes the value of one if the
supplier receives physical inputs from the buyer and is zero in the other case. Apart from the direct
impact on investments through processor loans, dairy input supply programmes are likely to have an
indirect impact on suppliers’ investments by enhancing the pro?tability of the farm by lowering input
costs, or reducing transaction costs in accessing inputs. We expect, therefore, PLOAN and INPUTS to
have a positive effect on INVEST. Other factors that may cause an indirect impact on the likelihood to
invest are the provision of guaranteed prices (PRICE) and prompt payments (PAYMENT). Price guarantees
and the absence of payment delays reduce the riskiness of the business environment for the supplier. As
a result, farms may be more inclined to invest than in more uncertain situations. The control variables
(CONTROL) are related to the farm and the farmer. The expected sign of the variable RESOURCES
depends on the optimal size of the capital stock. A negative sign implies that larger farms are less likely
to invest than small farms and consequently, that farm sizes are likely to converge. A positive sign
would lead to the opposite conclusion and farm sizes diverge. Apart from efforts to achieve the optimal
capital stock, RESOURCES may also capture a different effect on investment decisions. Larger farms may
bene?t from reputation effects and the availability of more collateral. Furthermore, larger farms may
bene?t from more frequent interactions with the dairy company (Fafchamps, 1997; Johnson et al., 2002;
McMillan and Woodruff, 1999). This second effect suggests that RESOURCES has a positive effect on the
likelihood to invest. CO-OPERATE is a dummy that takes the value of one if the farmer indicates that
he/she is cooperating with other farmers to store, market or process milk, to buy inputs, or to engage
in
Percentage sample (dummy) or average Std dev. 38.0 37.3 35.0 16.3 46.7 87.7 72.7 40.0 8.5 2.0 86.4 70.0
29.3 76.0 22.0 – – – – – – – – 13.2 1.9 18.3 – – – –
1 if written contract 1 if support provided by buyer 1 if credit, investment loan or loan guarantee
provided by buyer 1 if inputs provided by buyer 1 if guaranteed prices provided 1 if no payment delays
1 if on-farm investment in ?ve years 1 if dairy-speci?c investment in ?ve years Resource-restricted
suppliers Number of dairy cows Competition/opportunistic behaviour Number of potential milk buyers
for the farmer’s milk Share of total milk production delivered to the main buyer Information asymmetry
1 if on-farm milk collection 1 if farmer co-operates with other farmers to store, market or process milk,
to buy inputs, or to lobby 1 if buyer is a corporate dairy 1 if buyer is foreign-owned dairy or exporter of
dairy products
Source: Survey data
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lobbying activities. We expect that more co-operation generates countervailing power and leads to a
denser network that creates more social capital, which facilitates investment. Table V provides
summary statistics of the variables included in Model (3). 5.4 Regression results Equations (1), (2) and (3)
are estimated using logit regression models. The results for the determinants of the supply chain
relationship are presented in Table VI. Results for the provision of supplier support programmes are
shown in Table VII. Results of the investment model are presented in Table VIII. Table VI shows that the
use of explicit contracts is less likely in an environment with greater competition between buyers
(COMPETITION). This result rejects H2a which claimed that explicit contracts are more likely to occur in
competitive environments because they help to lower the risk of losing Table VI Determinants of supply
chain relationship
EXPLICIT Coef?cient 2 0.001 2 0.186 0.014 2 0.347 2 0.400 0.533 1.156 2 1.287 Standard Error 0.010 0.085
0.008 0.282 0.295 0.334 0.312 0.732
** *
Table VIII Determinants of on-farm investment
INVEST PLOAN INPUTS PRICE PAYMENT RESOURCES COOPERATE Constant Observations: 300 Pseudo R2:
0.169
* 10%
Coef?cient 1.126 1.682 0.959 0.363 0.038 1.059 2 0.696
Standard Error 0.402 0.783 0.302 0.408 0.022 0.377 0.423
Signi?cance
*** ** *** * ***
a
Notes: aThe signi?cance level is indicated as follows: * * * 1%; * * 5%; Source: Own calculations based on
survey data
Signi?cancea
Hypothesis 1a RESOURCES Hypothesis 2a COMPETITION SHARE Hypothesis 3a COLLECT COOPERATE
TYPE FDI Constant Observations: 300 Pseudo R2: 0.06
* 10%
*** *
Notes: aThe signi?cance level is indicated as follows: * * * 1%; * * 5%; Source: Own calculations based on
survey data
Table VII Determinants of farm support programmes
SUPPORT Coef?cient 0.032 2 0.170 0.030 2 1.054 0.711 2 2.376 1.920 2 1.335 Standard Error 0.012 0.085
0.010 0.332 0.317 0.373 0.367 0.847 Signi?cance
*** ** *** *** ** *** ***
a
Hypothesis 1b RESOURCES Hypothesis 2b COMPETITION SHARE Hypothesis 3b COLLECT COOPERATE TYPE
FDI Constant Observations: 300 Pseudo R2: 0.24
* 10%
Notes: aThe signi?cance level is indicated as follows: * * * 1%; * * 5%; Source: Own calculations based on
survey data
trading partners to competitors. The hypothesis, however, assumes the enforceability of explicit
contracts. In emerging and transition countries, public contract enforcement institutions may not be
adequately developed or may function inef?ciently (Gow and Swinnen, 2001). This can explain our result.
SHARE has a positive and signi?cant coef?cient and indicates that suppliers that are more dependent on
their main buyer of milk are more likely to have an explicit contract. In other words, explicit contracts
formalise the dependence and commitment that is present in the buyer-supplier relationship. In line with
our expectations, export-oriented ?rms and companies with foreign direct investments are more likely
to use explicit contracts than locally oriented companies (FDI). This may be because foreign ?rms are
more vulnerable to uncertainties about local business and market conditions, or foreign ?rms may just
adopt explicit contracts as it is the way of doing business in the home market. Another reason may be
that foreign ?rms are more demanding in terms of quality and reliability and explicit contracts are
offered to reward compliant suppliers. Table VII shows that larger suppliers (RESOURCES) have better
access to support from the buyer. This result rejects H1b which claims that support programmes are
provided to more resource-restricted suppliers to allow them to ful?l the buyer’s requirements in terms
of quality and quantity. One explanation for this result may be that ‘large suppliers’ in the context of
the Armenian dairy sector are still relatively small and hence resource-restricted. Another explanation
may be that relatively large suppliers bene?t from a stronger `-vis the buyer and are able to bargaining
position vis-a negotiate more favourable contract terms. Furthermore, transaction costs will be lower if
support is given to a small number of larger suppliers compared against dealing with a mass of small
suppliers. With respect to H2b, COMPETITION has a signi?cantly negative sign which suggests that milk
buyers that operate in a more competitive environment are less likely to offer farm support to their
suppliers. This result is in line with our hypothesis. The risk of losing suppliers to competitors after
support has been provided may be a genuine deterrent for buyers to implement assistance programmes.
This ?nding is also con?rmed by Poulton et al. (2004) who report a negative effect of a competitive
buying market on supplier support in the African cotton sector. Furthermore, we ?nd that suppliers 104
Supply chain relationships Liesbeth Dries, Matthew Gorton, Vardan Urutyan and John White
Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
that have a more exclusive relationship with the buyer (i.e. a higher share of milk being sold to the main
buyer, SHARE) have a higher likelihood of receiving support. This con?rms the hypothesis that a more
dependent relationship decreases the chance of opportunistic behaviour and hence improves the
possibilities for support. H3b is con?rmed by the signi?cantly positive impact of COOPERATE on the
likelihood of receiving supplier support. The positive effect of co-operation points to the value of being
part of an extensive network which might help to overcome information problems. Another reason might
be improved discipline among group members and the higher collective volume of output makes them
more attractive recipients of support. COLLECT, on the other hand, has a signi?cantly negative coef?
cient. Collecting milk from the farm lowers the likelihood of receiving support. The improvement in
monitoring capabilities of the buyer appears to be negligible for explaining better support
opportunities. Finally, the results show that corporate dairy companies (TYPE) are less inclined to offer
support to their suppliers. In other words, farmers supplying co-operative buyers are more likely to
bene?t from farm assistance. As expected, exportoriented ?rms and buyers with foreign direct
investments (FDI) offer more farm support to their suppliers than domestic ?rms. This ?nding is in line
with the hypothesis that international ?rms have easier access to the ?nancial resources that are
necessary to provide these programmes. Table VIII presents the estimations of the determinants of
supplier investment. We are particularly interested in the effect that different buyer support
programmes have on the likelihood of suppliers to invest. First, access to dairy loans, credit and bank
loan guarantees plays a signi?cant role in improving the probability of investments by suppliers. This
indicates that access to credit through formal channels (rural ?nance sector) may be restricted and that
dairy loans are crucial to overcome this market imperfection and the sector’s ?nancial constraints.
Apart from the direct impact of investments through loans and credit, buyers’ input supply programmes
increase the propensity to invest indirectly by enhancing the pro?tability of the farm by lowering input
costs, or reducing transaction costs in accessing inputs. As discussed previously, the Armenian dairy
sector faces serious obstacles with respect to access to quality inputs such as animal feed, veterinary
services and insemination material. This lowers productivity and the pro?tability of dairy farms.
Overcoming input constraints through supplier development programmes is therefore crucial to
improve pro?tability and consequently investment opportunities. Table VIII also provides evidence of
the importance of reducing the riskiness of the business environment to stimulate supplier investment.
Providing guaranteed prices signi?cantly increases the farmer’s likelihood to invest. As was shown in
section 3, credit constraints are signi?cant in the Armenian agricultural sector and the dairy sector may
suffer even more as a result of the lumpy nature of dairy farming investments. Fluctuating prices add to
the uncertainty of farm cash ?ows and signi?cantly hamper investments. Finally, farm size (RESOURCES)
plays only a minor role in explaining onfarm investment, while co-operation between suppliers increases
the likelihood to invest signi?cantly. 105
6. Conclusion
This study contributes to the supply chain literature by analysing the determinants of supply chain
relationships, the provision of supplier support measures and their impact on supplier investment. A
study of supplier development programmes in the Armenian dairy sector is relevant because the sector
suffers from a number of challenges. On the one hand, productivity and the quality of primary
production is low which puts the sector at a competitive disadvantage and leads to imports of milk
powder and a deteriorating self-suf?ciency rate. On the other hand, upgrading of the supplier base is
hampered by a number of institutional failures –, e.g. affecting the use of collateral and access to
external (bank) capital. Supplier support measures as a component of buyer-supplier relationships
therefore play a crucial role in the dairy sector restructuring process and in improving the sector’s
long-term prospects. The evidence suggests that the provision of supplier support measures is signi?
cant but that buyers discriminate in terms of who receives them, with the more costly the measure, the
fewer the number of suppliers that bene?t. Analysis of the determinants of the provision of supplier
support measures indicates that suppliers that have a more exclusive relationship with the buyer and
those that deliver to more internationally oriented buyers (be it exporters or FDI ?rms) are more likely
to receive support. In contrast, buyers that operate in a more competitive market are less likely to
provide support to their suppliers. These results provide support for the theoretical underpinnings of
the study, namely that restricting opportunistic behaviour of suppliers is crucial for enforcing supplier
development programmes as part of the contractual agreement. The ?ndings generate implications of
wider importance for practitioners and policymakers. On the one hand, our results seem to point to the
gains that can be derived from openness to international ?rms – which bring in the ?nancial means and
experience to provide supplier support programmes. On the other hand, the negative competition
effect may indicate that buyers are limited in their ability to enforce repayment of the provided
services in an environment where many buyers are competing for the same supply. To improve the
functioning of supply chains, policy makers should improve the enforcement capability of buyers under
these circumstances. Better legal enforcement would reduce the prevalence of opportunism (Johnson et
al., 2002). The latter is likely to induce signi?cant positive spill over effects, given that lower
opportunism improves the probability of supplier development. Evidence from other studies also
indicates that private initiatives can stimulate sustainable supplier development programmes, in
competitive markets, through special institutional arrangements such as frequent monitoring, buyer
coordination, or local information networks (Swinnen and Maertens, 2007). The second set of results
extends knowledge on the relationship between the provision of supplier support programmes and
supplier investment. The study ?nds that supplier support programmes play an important role in
stimulating investment by suppliers. However, the results point to a rather nuanced view recognising
that supplier assistance programmes affect investment decisions in several ways. First, dairy loans,
credit and loan guarantee programmes directly improve suppliers’ access to ?nancial
Supply chain relationships Liesbeth Dries, Matthew Gorton, Vardan Urutyan and John White
Supply Chain Management: An International Journal Volume 19 · Number 1 · 2014 · 98 –107
resources. Second, buyers’ physical input supply programmes increase the propensity to invest
indirectly by enhancing the pro?tability of the supplier by lowering input costs and by reducing
transaction costs in accessing inputs. Finally, support programmes reduce uncertainty and the riskiness
of the business environment by providing guaranteed prices and prompt payments. All these elements
have an important effect on the likelihood of investment by suppliers. This is of critical importance
given the structural problems that affect the Armenian dairy sector, which are common to many
industries in transitional economies. The ?ndings point to future research possibilities. For instance, the
results regarding the inadequacy of contract enforcement in competitive or uncertain environments
merits further investigation. In emerging economies, the legal framework for contract enforcement is
only slowly developing. Undertaking in-depth, case study based analysis of the conditions under which
contract enforcement is successful in Armenia or extending the analysis to a crosscountry survey of this
issue can improve our understanding of the private solutions that may exist to overcome public failure
in providing contract enforcement institutions. This would be an interesting extension to the current
study that has focused on private solutions to input and credit market failure.
Notes
1 There is an extensive literature on relational and implicit contracts that instead of using formal,
written agreements rely on reputation, trust and unwritten codes of conduct as a means of
coordination. Many observers, from Barnard (1938) and Simon (1947) onwards, have emphasized the
importance of such informal agreements. 2 Farm assets include investments in animal housing facilities
(building, enlarging or modernizing stalls, sheds and herdsman’s camps); dairy-speci?c investments
(buying new calves and cows, milk lines, cooling tanks and fodder mixers); and general investments that
are not speci?cally related to milk production such as buying new land, pastures, investments in fences
and general agricultural equipment. 3 Dries and Swinnen (2010) show that dairy-speci?c investments are
more likely to be ?nanced through supplier support programmes because the interdependency between
buyer and supplier is higher in the situation of transaction-speci?c investments. We therefore also
estimated an investment model with the dependent variable being a dummy that takes the value of one
if an investment was made in a dairy-speci?c asset. However, this did not lead to interesting, new
insights and we do not report these results here.
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Corresponding author
Liesbeth Dries can be contacted at: liesbeth.dries@wur.nl
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