Supplier Relationship Quality in the German Pork and Dairy Sector:
Theoretical Considerations and Empirical Evidence
Authors:
Birgit Schulze (corresponding author)
Georg-August-University Goettingen
Institute of Agricultural Economics
Platz der Goettinger Sieben 5
37073 Goettingen
Germany
Phone: 0049-(0)-551-394485
Fax: 0049-(0)-551-3912122
E-mail: [email protected]
Achim Spiller
Georg-August-University Goettingen
Institute of Agricultural Economics
Platz der Goettinger Sieben 5
37073 Goettingen
Germany
Phone: 0049-(0)-551-399897
Fax: 0049-(0)-551-3912122
E-mail: [email protected]
Christian Wocken
Georg-August-University Goettingen
Institute of Agricultural Economics
Platz der Goettinger Sieben 5
37073 Goettingen
Germany
Phone: 0049-(0)-551-394825
Fax: 0049-(0)-551-3912122
E-mail: [email protected]
Paper presented at the 16th Annual World Forum and Symposium “Agribusiness, Food,
Health, and Nutrition”, IAMA Conference, June 10 – 13, 2006 in Buenos Aires, Argentina
Supplier Relationship Quality in the German Pork and Dairy Sector:
Theoretical Considerations and Empirical Evidence
Abstract
The degree of vertical coordination in the German pork and dairy sector is rather low, even if
in both sectors long-term relationships are of great importance. However, with growing
concentration on the processor level, the remaining agribusiness enterprises compete for the
most valuable farmer suppliers, so that the introduction of a supplier relationship management
seems to be appropriate at least to handle the bigger farmers. In this paper we investigate the
current level of relationship quality through two large scale surveys in the pork and dairy
sector in order to give some hints for better supplier relationship management. We assume
that there are not only differences between the sectors but also between companies within the
sectors, because there are some important differences in governance structures and supply
management. This hypothesis is tested by comparing regression models for two enterprises in
each sector. Company specific analyses reveal important differences especially between the
slaughterhouses, but also between the dairies. We therefore argue for a company specific
approach when aiming at managerial implications.
Keywords: satisfaction, trust, company specific analysis, Supplier Relationship Quality-Index,
benchmarking
1
Introduction
In recent years, the concept of supply chain management has been more and more enlarged by
approaches of relationship management (Ellram and Cooper, 1993; New, 1996). For
agribusiness, there is a number of scientific contributions emphasizing the importance of trust
in vertical relationships (Fearne, 1998; Batt and Rexha, 1999; Batt, 2003; Batt and Purchase,
2004) and the necessity of stronger collaboration between the different stages of the supply
chain (Fearne, 1998; Hornibrook and Fearne, 2001). However, the situation of processors in
agribusiness is different from those in other sectors as they are working with a large number
of more or less small suppliers – farmers – who all deliver the same product. For these
enterprises the first challenge is to explore the present situation of supplier relationship
quality, to identify in a second step the critical points in the cooperation and to develop
strategies to improve the relationship.
The aim of this study is to investigate the current level of relationship quality through
two large scale surveys in the pork and dairy sector to give some hints for better supplier
relationship management. Our model is based on an extensive literature review and additional
qualitative interviews with managers and farmers. The pecularity of our approach is that we
do not only test a comprehensive model of relationship quality but also use it to compare
different sectors and even enterprises on a broad empirical basis consisting of 209 dairy
farmers and 357 pork producers. While we suggest the measurement scale and possible
influencing factors to be generally the same for most agribusiness chains, we assume that
there are differences in the importance of these antecedents of relationship quality not only
between the sectors but also between companies within the sectors, because there are some
important differences in governance structures and supply management. This hypothesis is
tested by comparing regression models for two enterprises in each sector.
1
After a short description of recent developments in both sectors and a review of the
relevant publications in the related fields of marketing research, we briefly present the
underlying model and hypothesis before extensively discussing the results with regard to
possible efforts of relationship improvement. Some proposals for better managing chain
relationships by introducing new tools of supplier relationship management are presented in
the last section of the paper. Based on our analyses, we recommend regular company specific
surveys on relationship quality.
2
Milk and pig production in Germany: recent developments
Germany is Europe’s biggest milk and pork producer (24 and 22 % of total EU production).
However, structural changes took place much slowlier than in other European countries
(Tables 1 and 2). Nevertheless, in recent years, concentration is increasing faster due to
changes in agricultural policies and also the market entrance of foreign competitors who
bought a number of German processors and now account for considerable market shares, such
as the Dutch enterprises Campina (dairy) and Vion Food Group (meat). This section will
provide some insights into the complex developments the sectors recently had to face.
Table 1: Structures of milk production: comparison of important European countries
(2003)
GER
121.8
4,380.8
No. of dairy farms [1,000]
No. of cows [1,000]
Share of farms with > 100
3.8 %
cows
Share of cows in farms with
24.0 %
> 100 cows
Source: Eurostat 2006, own calculations
NL
25.0
1,477.8
GB
28.2
2,191.9
F
113.9
4,051.0
DK
8.0
596.0
10.0 %
28.2 %
1.2 %
27.9 %
22.3 %
58.0 %
4.0 %
51.4 %
The number of dairies in Germany decreased from 360 in 1990 to 112 enterprises in 2003
(Gerlach et al., 2006). Until 2010, experts forecast a further reduction down to 30 dairies. The
market share of the five biggest dairies added up to 38 % in 2004. Still the dairy sector is
characterised by important co-operatives (Schramm et al., 2004). Traditionally, in the dairy
sector contractual relationships predominate with a duration of at least two years. A certain
level of contracting seems to be necessary because of the high frequency of transactions and
the connected logistic planning. Recently, this is questioned by dairy farmers who wish to
achieve higher prices by building countervailing power, which is not possible without
delivery flexibility. Thus, during the last decade the former stable relationship between dairies
and farmers increasingly came under pressure. Examples of this are the new initiatives to
build countervailing power such as the German Dairy Farmer Association “Bundesverband
Deutscher Milchviehhalter” or the Austrian “Interessengemeinschaft Milch”. These groups
act against processors and try to increase farmer prices (Gerlach et al., 2006). Farmers are told
to cancel their contracts and try to establish new, short-term marketing channels for milk.
In contrast, the pork industry in Germany is traditionally characterised by arm’s length
transactions (Spiller et al., 2005). In a highly competitive surrounding, the supply chain shows
a certain level of distrust, which leads to distinctive inefficiencies, e.g., the repeated failures to
establish Salmonella monitoring. Partly doubtful grading processes and a lack of price
transparency in the market cause conflicts and lead farmers to question the credibility of their
buyers. Practitioners complain of a high level of distrust for their processors, for example the
low level of reliability of carcass grading systems (Spiller et al., 2005: 287).
2
Table 2: Structures of pig production: comparison of important European countries
(2003)
GER
No. of pig producers
95.7
[1,000]
No. of pigs [1,000]
17,056.8
Share of farms with >
3.7 %
1,000 pigs
Share of pigs in farms with
36.6 %
> 1,000 pigs
Source: Eurostat 2006, own calculations
NL
GB
F
DK
10.5
8.9
49.4
10.9
5,500.8
3,074.7
8,465.9
7,527.8
14.7 %
10.9 %
4.2 %
23.1 %
52.5 %
69.0 %
39.0 %
63.7 %
The structure of German pig production shows the same deficits compared to the European
neighbours as the dairy sector (Table 2). Nevertheless, the two biggest German
slaughterhouses are among the Top 10 European meat processors, whereas the biggest
German dairy only keeps rank 10. The cumulated market share of the top 3 slaughterhouses
rose up to 46 % due to the acquisition of Suedfleisch AG by the Vion Food Group in the
summer of 2005 (Spiller et al., 2005).
The biggest slaughterhouse in German ownership is the private enterprise Toennies,
which accounted for 8.2 Mio pig slaughters (17 % of all German slaughters) in Germany in
2005 and thereby keeps rank two behind the Dutch producer-owned conglomerate Vion
(Moksel, NFZ, Suedfleisch). Westfleisch, now the last big cooperative in the German pork
sector, slaughtered 5.2 Mio pigs in 2005 and thus keeps rank three (ISN 2006).
Sourcing strategies differ between these companies: While Westfleisch assures
70 to 80 % of the pigs through marketing contracts and has an own transportation service at
its disposal, Toennies and Vion get the pigs mostly via independent buyers, who also provide
the transportation services and do the settlement with the farmers.
With growing concentration on the processor level, the remaining agribusiness
enterprises compete for the most valuable farmer suppliers, so that the introduction of a
supplier relationship management seems to be appropriate to bind bigger farms more closely.
Since consolidation at the farm level does not progress as quickly, the growth of processors
implies an increasing number of vendors. For example, the biggest German abattoir obtains
pigs from more than 8,000 farmers. A personal relationship with each of these naturally is
impossible. Thus, we suggest that along with the resulting power imbalance, this alienation
also leads to a decrease of commitment and to distrust on both sides, and consequently to a
loss of total chain performance. Reports of practitioners support this hypothesis. These
findings support the postulation of advancements in supplier relationship management which
builds the basis for our further research.
3
Relationship quality: literature review
During the last decade, the concept of supply chain management has been more enriched by
approaches of relationship management (Ellram and Cooper, 1993; New, 1996). The basis of
this extended debate is formed by a number of different research streams, from channel
marketing over industrial marketing to even consumer marketing. Behavioural approaches
from these fields were also transferred to the case of agribusiness supply chains. In this
section, we provide a comprehensive literature review, which is structured according to the
chronological order of the appearance of the research streams in science. Special emphasis is
put on the agribusiness literature.
3
The first attempts to measure relationship quality go back to the marketing channel
literature. With a special focus on manufacturer-retailer dyads, channel research highlights
conflict between supplier and retailer, the emphasis was placed on exploring sick rather than
healthy relationships (Young and Wilkinson, 1989: 109). In the 1990ies the focus changed
towards co-operation in general and trust and commitment in particular (Morgan and Hunt,
1994). All in all, marketing channel research highlights the potential benefits of co-operative
relationships, building the theoretical background for the huge efforts manufactures and
retailers undertake to improve collaboration through, for example “Efficient Consumer
Response”. Relationship quality is also analysed in the sales and service management
literature, where a people-based approach dominate and relationship quality is defined as the
ability of a customer to rely on the sales person's integrity (Crosby et al., 1990; Lagace et al.,
1991).
Another research stream depends on industrial marketing. The well-known
Scandinavian approach (IMP Group, 1982) concentrated on long-term network relationships
in business-to-business marketing. In initial studies, the focus was on the ongoing interaction
process, in which single transactions are embedded. Later on the attention shifted towards a
network approach (Håkansson and Snehota, 1995). Based on the IMP interaction model, Woo
and Ennew (2004) offer a conceptualization of business-to-business relationship quality and
explain the connection between relationship quality and service quality.
Previous studies on relationship quality in the context of procurement were undertaken
by Leuthesser (1997), Dorsch et al. (1998) and Naudé and Buttle (2000). Dorsch et al. (1998)
highlighted the relevance of trust, satisfaction, commitment, opportunism, customer
satisfaction, and ethical profile. Naudé and Buttle (2000) proposed five attributes of
relationship quality: trust, power, integration, mutual understanding of needs, and profit.
Lages et al. (2005) revealed the amount of information sharing, communication quality, longterm orientation and satisfaction with the relationship.
Similarly, in consumer marketing, the relationship perspective attracted great interest
which led to new business approaches, such as customer relationship management (CRM).
The measurement of customer satisfaction was first conceptualized through the work of
Parasuraman et al. (1988). A review of selected approaches explaining relationship quality in
consumer behaviour was presented by Hennig-Thurau et al. (2002). The ongoing work reveals
that satisfaction is a necessary, but not a sufficient requirement for customer bonding.
In the agribusiness literature, a growing number of studies dealing with relationship
management can be found. Most studies discuss selected constructs, such as trust, power, and
dependency (Morgan and Hunt, 1994). Means of collaboration beyond contracts and vertical
integration are revealed in Hobbs and Young (2001). Several empirical studies were
conducted by Batt and co-authors. The role of trust in supply chains is discussed in Batt and
Rexha (1999) and Batt (2003). Batt and Wilson (2001) studied the relationship between grape
growers and wine makers in Western Australia. Gusti et al. (2004) conducted a similar survey
with small farmers and their intermediaries in Indonesia. In all studies Batt et al. identified
factors affecting the respective buyer-seller relationship and accentuated the role of
collaboration and trust in various agricultural supply chains (Batt 2003; Batt and Purchase,
2004).
Hansen et al. (2002) suggested that trust between members of a co-op and the co-op
management is an important variable to enhance group cohesion. Farmers’ trust in co-ops and
private buyers also is investigated by James and Sykuta (2006) for the case of US soybean
and corn producers, who state a gap in empirical literature concerning farmers’ trust in
agribusiness processors depending on their legal form. They find significant higher trust in
co-ops only for the soybean case, while for corn marketing, farmers trusted both co-ops and
private companies equally. Matanda and Schroder (2004) analysed buyer-seller-relationships
in Zimbabwean horticulture with a comprehensive behavioural approach, focussing on a
broad model of relational constructs. They elaborated differences between small and large
4
primary producers in terms of satisfaction, dependency, conflict frequency, long-term
orientation, commitment and social bonding. Clare et al. (2005) evaluated the relationship
between farmers, livestock buyers and slaughterhouses in the New Zealand red meat industry,
and found that buyers have a far closer relationship with farmer-suppliers than both groups
have with slaughterhouses.
All in all, the literature review provides evidence for the growing importance of a
behavioural approach towards the relationship between primary producers and processors in
the agribusiness. In the following part, elements of the research streams described are used to
develop an approach for the measurement of relationship quality between farmers and
processors.
4
A comprehensive model of relationship quality
For model development the experiences of the research streams described above are
transferred to the industries researched. In addition, qualitative interviews with managers and
farmers were carried out to further identify more sector specific topics. After those
considerations, a first draft of items was discussed with managers of an agribusiness company
(sugar industry). A following first version of the scale was tested with 271 suppliers of sugar
processors in Germany (Gerlach et al., 2005). The study already showed important similarities
to the following surveys, especially concerning the importance of farmer orientation and the
low significance of prices for supplier satisfaction. After reviewing items and wording, and
testing validity, clarity and redundancy, the next steps were two studies in the dairy and
finally in the pork industry which are presented in this paper.
In line with past research (Smith, 1998; Lages et al., 2005), we define relationship
quality as a higher-order concept, composed of three different, although related elements.
Relationship quality is the overall assessment of the strength of a business relationship,
combining satisfaction, trust and commitment.
A basic element for nearly all relationship models is the outstanding importance of
customer satisfaction or, in our case, supplier satisfaction (Gerlach et al., 2005). Similar to
consumer marketing, satisfaction reflects experience with a business partner as a necessary
but not sufficient condition for an ongoing relationship (van Weele, 2002: 165). In addition to
own experiences, information from other business partners and other cues build the basis of
satisfaction (Homburg and Stock, 2001: 20). According to the disconfirmation model,
satisfaction in supplier relationship quality is the result of a comparison between a buyer’s
performance and the supplier’s expectations.
Other elements of relationship quality are trust and commitment (Crosby et al., 1990;
Bejou et al., 1996; Naudé and Buttle, 2000; Batt, 2003): Trust is defined as “(...) a willingness
to rely on an exchange partner in whom one has confidence“ (Moorman et al., 1993: 82). It is
combined with the belief that others will not act to exploit one’s vulnerabilities (Hansen et al.,
2002: 42). Trust reduces opportunistic behaviour and transaction costs (Ganesan, 1994;
Doney and Cannon, 1997; Batt and Rexha, 1999) thus is relevant if information asymmetries
are present.
Commitment “(...) is an implicit or explicit pledge of relational continuity between
exchange partners” (Dwyer et al., 1987: 19). Commitment is seen as an outcome of trust and
defined variously in the literature as the belief of a supplier that the relationship with a
processor is so important that it warrants maximum effort to maintain it even if problems
occur (Morgan and Hunt, 1994).
Satisfaction, trust, and commitment are common variables in relationship quality
research. However there is a lack of consensus about additional constructs. Other authors add
variables, such as communication quality or information sharing (Naudé and Buttle, 2000;
Lages et al., 2005). From our viewpoint, these are not integral elements of relationship
5
strength but management instruments thus determinants of relationship quality. In the
following section we present our suggestions about the determinants and benefits of
relationship quality.
To improve relationship quality, it is necessary to identify the crucial influencing
variables (figure 1). In the literature we find important hints on this subject. These are in
particular findings on the importance of (comparative) price satisfaction. Price satisfaction
refers to several relationship studies that state that the economic outcome is important for the
evaluation of the relationship (Jaervelin, 2001) and, thereby, positively affects the
development of trust. The construct comprises short- and long-term satisfaction as well as
relative price satisfaction when comparing the own price received with the price paid by other
dairies/slaughterhouses. Further often used variables in relationship management are shared
values (Heide and John, 1992; Morgan and Hunt, 1994), perceived performance of the
partner, communication quality and quantity (Matanda and Schroder, 2004: 534) and
friendships between partners (Wilson, 1995: 339; Rodriguez and Wilson, 2002: 55). As both
industries are facing an increasing concentration at the processor and retail level, the effects of
power asymmetries and coercion have to be considered as important for relationship
perception (Anderson and Narus, 1990; Dwyer et al., 1987).
A special factor influencing supplier relationship quality in agribusiness is the
processor’s orientation towards farmers’ interests. Due to the importance of co-operatives,
farmers are used to having some influence on the manufacturer (Gerlach et al., 2005).
Possible elements of farmer orientation are farmers’ influence, proximity to agricultural
problems, competence of farm advisory service etc. The construct farmer orientation is also
linked to the often used variable mutual goal, for example, “the degree to which partners share
goals that can only be accomplished through joint action and the maintenance of the
relationship” (Wilson, 1995: 338).
The economic success of a farmer finally depends on the performance of the
processor. This is especially true for those farmers who are shareholders or are bound by
contracts. Perceived management competences therefore are probably an important factor for
assessing the buyer. The management strategy of the manufacturer should be comprehensible
for the farmers because their prices depend on it. A somewhat similar factor may be the
overall image of the managers which is derived from their behaviour towards farmers (e.g.,
fairness, openness).
Figure 1: Conceptual model
6
Due to sectoral differences described above, it is necessary to carefully adjust the
questionnaire to the specific industries, while keeping the basic model as the conceptual
framework. In particular we introduce some sector-specific items in the pork survey, such as
trust in grading processes, transportation and logistics problems, and transparency and
fairness of price grids.
Aiming at more than a simple “feel-good-approach” (Dyer and Chu, 2000), the
potential outcomes of an improved relationship with the suppliers also have to be measured.
As output variables, the willingness to switch the buyer, the willingness to collaborate more
strongly with the business partner, recommendation of the farmer, and the absence of
countervailing power are defined. In the following study emphasis is placed on switching
behaviour because of its fundamental relevance for processors. Especially in a market where
processors are working below capacity, such as the pork industry or in politically restricted
environments such as the dairy market, gaining secured supply is an important objective of
procurement management. Hence, we derive the hypothesis that a high relationship quality
leads to loyalty on the farmer side.
5
Data, empirical methods, and results
A total number of 566 extensive face-to-face interviews were conducted in the summer of
2004 (dairy farmers) and in the spring of 2005 (pork producers) respectively. The interviews
took place in north-western Germany. The sample is a convenient one, concentrating on large
farms in both sectors. Table 1 shows some important sample characteristics for both surveys
and the four most important processors, which are deeper analysed later on.
The farms in our samples are larger than the average in Germany, which is 37 cows
per farm and 281 pigs per farm, including breeding sows (destatis, 2006a and 2006b). The
respondents are on average 42 (dairy) and 41 (pork) years old and well-educated (only 3.4 %
(dairy) and 4 % (pork) have no agricultural education). Thus, we can assume that mainly
future-oriented farmers were interviewed.
Table 3: Sample characteristics
Dairy
N
H
Pork
W
Sample size
209
66
70
357
66
Ø acreage (ha)
97
102
80
93
88
Ø herd size
73
62
72
1,413
1,220
Ø age
42
43
41
41
41
% farmers with
further agricultural
89 %
83 %
85 %
86 %
88 %
education
Ø share of milk/ pig
production in total
69 %
66 %
70 %
61 %
61 %
farm income
N = Nordmilch; H = Humana Milchunion; W = Westfleisch; T = Toennies
T
58
94
1,089
42
81 %
65 %
The dairy farmers in the sample delivered their milk to 22 different dairies. “Nordmilch e.G.”
(N) is buyer for 35.4 %, “Humana Milchunion e.G.” (H) for 22.5 %, and “Campina GmbH
Germany” for 14.8 % of the interviewees. The remaining farmers (27.3 %) supplied smaller
companies. For the pork producers, the proceeding is more difficult due to the freedom of
choosing the buyer again for each transaction. Thus, at the beginning of the interview, farmers
7
were asked to name their main buyer or the one they could evaluate best. The following
questions then were related to this buyer.
Amongst the suppliers of different slaughterhouses in the sample, there are relatively
big subsamples of suppliers of the biggest German meat packers, Westfleisch e.G. (19.2 %)
(W) and Toennies (16.3 %) (T). For these latter companies, we carried out specific analyses in
order to test the hypothesis that there are differences in the importance of the influencing
factors between the respective enterprises. In the sample there are 17.1 % farmers with
marketing contracts for their whole production; 1.7 % had only partially contracted their
production and the majority of 81.2 % did not have any contractual linkage to a processor.
The questionnaires mostly consisted of seven-point Likert scales ranging from
“strongly disagree” (scale = -3) to “strongly agree” (scale = +3) and some rating scales ranged
from 0 to 100. In addition to the differences in the questionnaires which are due to the sectoral
specifities described in section 2, the experience gathered from the dairy survey was used to
improve the measurement scales and variables in the pork survey. Thus, some constructs were
measured through different statements in the two surveys, and certain constructs only appear
in one of the models. Nevertheless, results are comparable for most parts.
The analysis of the data is divided into four parts. First we present selected descriptive
data, followed by four seperate factor analyses: For each sector, the unidimensionality of the
relationship quality scale is tested, then the dimensions of the potential influencing aspects are
checked. Multiple linear regression analysis is in a third step conducted to measure the impact
of the identified factors on relationship quality. We furthermore built a Supplier Relationship
Quality-Index (Schulze et al., 2006) over the respective items in order to compare the
enterprises’ current level of relationship quality, before in a last step we estimate company
specific multiple linear regressions.
5.1
Who is more satisfied with the relationship? First comparisons of the pork and
dairy sector
As indicated above, the surveys in the dairy and pork sector are not completely comparable
because of sector specificities and improvements in the second questionnaire (pork). Some
descriptive results for selected identical variables presented in Table 4 provide initial
impressions of the relationship evaluation in the two sectors and also differences between
enterprises in the same sector.
Whilst overall satisfaction with the relationship is similar in both sectors, there are
clear differences in trust and commitment, even if deficits emerge in both. Pork producers on
average indicate a higher level of confidence in agreements made with their buyers than do
the interviewed dairy farmers. In contrast, the latter feel more committed to the dairies than
pork producers to the slaughterhouses. One possible explanation is the greater importance of
co-operatives in the dairy sector and the preponderance of contracts. Price satisfaction is very
low in the dairy market. Consequently, the willingness to switch the buyer if another dairy
pays a higher milk price is high with 68.8 %.
8
Table 4: Summary statistics of selected statements
Dairy
N
H
Pork
µ
µ
µ
µ
Description
(σ)
(σ)
(σ)
(σ)
All in all, I am satisfied with the
1.40
.78
1.90
1.36
collaboration with XY.
(1.36) (1.54) (1.07) (1.06)
Rank the satisfaction with XY on a scale 71.64 62.54 78.34 74.86
from 0 to 100.
(20.98) (22.19) (16.33) (17.07)
.52
-.27
1.00
1.35
Promises made by XY are reliable.
(1.64) (1.51) (1.47) (1.18)
.47
.15
.68
-.38
I feel committed to XY.
(1.57) (1.62) (1.56) (1.68)
-2.28 -2.51 -2.16
.68
I am satisfied with the price XY pays
(.92)
(.77) (1.08) (1.23)
In comparison to other processors I am
-.55
-1.95
.26
1.09
… with the price of XY (strongly
(1.72) (1.14) (1.40) (1.13)
satisfied – strongly dissatisfied)
XY is always trying to take farmers for a
.12
.30
-.06
-.69
ride.
(1.70) (1.63) (1.82) (1.41)
.08
.32
-.34
-1.57
XY relies on me as supplier.
(1.86) (1.77) (1.85) (1.34)
.23
.42
.16
-.26
Farmer and XY strive for different goals.
(1.53) (1.43) (1.65) (1.55)
.94
.58
1.18
.13
XY takes farmers problems seriously.
(1.21) (1.19) (1.04) (1.34)
If the price is good it doesn’t matter to
1.28
1.32
1.40
.53
whom I deliver.
(1.58) (1.55) (1.73) (1.71)
I would advise other farmers to become
.50
-.57
1.22
.34
(1.67) (1.55) (1.36) (1.29)
suppliers of XY.
N = Nordmilch; H = Humana Milchunion; W = Westfleisch; T = Toennies
W
µ
(σ)
1.36
(1.04)
72.89
(19.12)
1.42
(1.20)
-0.02
(1.74)
0.38
(1.30)
0.58
T
µ
(σ)
1.07
(1.11)
68.75
(18.25)
1.02
(1.17)
-0.95
(1.33)
0.33
(1.24)
1.05
(1.23) (0.95)
-0.41
(1.41)
-1.45
(1.33)
-0.44
(1.55)
0.31
(1.25)
0.42
(1.84)
0.38
(1.28)
-0.29
(1.32)
-2.09
(0.98)
0.47
(1.22)
-0.18
(1.21)
0.93
(1.47)
0.23
(1.18)
The figures also show that there are important differences between enterprises of the same
sector. The dairy Humana Milchunion (H) and Westfleisch (W) receive better evaluations for
most of the aspects than their competitors. However, the distance between the two dairies is
much bigger than the one between the slaughterhouses. Du to these differences revealed, in
the following sections we will provide comparisons for both the sector and the enterprise
level.
5.2
Determinants of relationship quality: sector comparisons
A first factor analysis (using principal component analysis) is conducted for both sectors to
test the hypothesis that satisfaction, trust and commitment build an unidimensional scale. In
both cases the assumed variables, five in the dairy and six for the pork case, converge in one
factor which can be described as a higher-order construct (see appendix). KMO values of .78
(dairy) and .87 (pork) were obtained, and cronbach’s alpha indicates a high reliability in both
sectors (.84 and .87, see appendices 1 and 2). Thus, relationship quality is confirmed to be a
unidimesional construct consisting of satisfaction, trust, and commitment. For each sector, a
second factor analysis was also used to identify influencing factors as proposed in figure 1.
For the dairy sector, six factors were extracted. Some constructs of the initial model
(Figure 1) could not be confirmed. The factor “Farmer orientation” for example includes also
items measuring perception of power and coercion, which do not build an own dimension as
9
was hypothesized. The alpha values in the final solution are higher than .70 with the exception
of price satisfaction and willingness to switch the buyer which have an alpha of .69 and .67
respectively (see appendix 1 for detailed information about the factors, respective items and
reliability). According to measurement theory, this is an acceptable solution (Nunnaly, 1978).
The impact of the identified factors on the quality of the business relationship was
measured using multiple linear regression analysis (Table 5). We thereby reveal that the
quality of the business relationship is to a great extent determined by the factors, farmer
orientation (1), management image and satisfaction with communication (2), pricesatisfaction (3), performance satisfaction (4) and the relative importance of dairy farming (5).
All other factors and other moderating variables such as farm size, farmers’ education, their
willingness to invest or engagement in farmer’s organisations do not have significant
influence.
Table 5: Regression model to explain the quality of business relationship (dairy)
Independent variables
Farmer orientation
Management image and satisfaction with communication
Price satisfaction
Performance satisfaction (single item)
Relative importance of dairy farming (moderating
variable)
Depending variable: Relationship quality
Adj. R² = .68; F = 86.37***; *** p ≤ .001
Beta
.43
.25
.20
.19
t
6.75***
3.94***
4.45***
4.52***
.07
1.66
It is surprising that the price satisfaction does not have the strongest impact. Much more
important are farmer orientation and the perception of management competence.
Understanding of farmers’ problems as well as communication with the supplier are much
more relevant than price perception. When farmers believe that a dairy will be more
successful in the long run, the quality of the relationship is better from the farmers’ view. All
in all, the perception of the relationship is determined by “weak” criteria and not primarily by
economic ones, which might have been assumed before.
A further analysis was used to check the connection between relationship quality and
main outcome variables. High correlation coefficients prove a significant coherence between
relationship quality and willingness to switch the processor (r = -0.46**), building
countervailing power (r = -0.23***) and recommendation to other farmers (r = 0.69***),
which are very important from the dairy point of view.
A similar approach is used to analyze the pork data. The reliability of the determining
factors (see appendix) again is quite high for the most important factors. The factor structure
shows some comparable results but also remarkable differences between dairy and pork.
Farmer orientation, price satisfaction and switching behaviour are relevant factors in both
studies. Communication, which is connected with management reputation in the dairy survey,
builds a separate factor for pork. Here, it is linked with advisory service. Performance
satisfaction does not appear as an own factor in the pork survey, but is linked to management
competences. Furthermore, we can reveal reliability as a separate factor. The power abuse
items were deleted during the factor analysis, while two items build the new factor structural
bonds, including items which ask for the perceived number of alternative buyers in the region.
Finally, two factors deal with important sector specific problems (transportation, transparency
of price grids) which are an important source of distrust in the pork chain.
The regression analysis (Table 6) again reveals farmer orientation to be the dominating
variable, which leads us to the conclusion that many agribusiness processors do not meet
farmer’s requirements for co-determination and participation. Furthermore, in this case again
10
the processor’s image is highly relevant. Probably, farmers sway between the traditional cooperative concept (with high farmer influence) and appreciation for competent management,
which in some parts ignores farmers concerns.
Structural bonds, which cause coercion to supply a certain buyer don’t have a
significant impact on perceived relationship quality, which is not surprising due to the fact
that only few farmers complain about having no marketing alternatives. As hypothesised
previously, specific aspects as trust in neutral carcass grading as well as the transparency of
price grids also have an impact on farmers’ relationship evaluation.
Table 6: Regression model to explain the quality of business relationship (pork)
Independent variables
Farmer orientation
Management competences
Reliability
Price satisfaction
Communication and service
Neutrality of grading process (single item)
Lack of transparency of price grids
Depending variable: Relationship quality
Adj. R² = .72; F = 74.22***; *** p ≤ .001; ** p ≤ .01
Beta
.52
.31
.26
.26
.20
.18
-.10
t
12.34***
8.00***
6.88***
6.58***
5.20***
4.10***
-2.79**
A significant coherence between relationship quality and buyer switching behaviour (r =
-0.36***) as well as willingness to cooperate more closely with the buyer (r = 0.63***) is
revealed in the pork sector, too.
The highly significant differences in mean values between the two dairies Humana
Milchunion and Nordmilch, and the two slaughterhouses Westfleisch and Toennies, reported
in Table 4, lead us to the assumption that there might be differences in the rating of the
determinants of relationship quality between companies. In the following section we therefore
provide the results of company specific estimations concerning the determinants of
relationship quality. The underlying hypothesis is that because of some important differences
in governance structures and procurement strategies, the suppliers also have different claims
and expectations to their buyers’ management.
5.3
Towards a micro-level analysis: company specific results
First, the two dairies are compared (Tables 7 and 8). In both estimations, price satisfaction has
no significant impact on relationship quality. As in the sector model, for Nordmilch suppliers
the farmer orientation of their dairy is the most important factor explaining their satisfaction,
trust and commitment while performance satisfaction gained some importance compared to
the more general results.
Table 7: Determinants of relationship quality (Nordmilch)
Independent variables
Farmer orientation
Management image and satisfaction with communication
Performance satisfaction (single item)
Price satisfaction
Relative importance of dairy farming (moderating
variable)
Beta
.37
.25
.24
.09
t
3.14**
2.09*
2.55*
1.08
.09
1.07
Depending variable: Relationship quality
Adj. R² = .58; F = 19.78***; *** p ≤ .001; ** p ≤ .01; * p ≤ .05
11
For Humana Milchunion, a changed order of determinants is revealed. Management image
and satisfaction with communication here is of greatest importance, while farmer orientation
has fallen back on rank three behind suppliers’ performance satisfaction. Price satisfaction
and the socio-economic aspect “relative importance of dairy farming” do not have significant
coefficients in this estimation neither.
Table 8: Determinants of relationship quality (Humana Milchunion)
Independent variables
Management image and satisfaction with communication
Performance satisfaction (single item)
Farmer orientation
Price satisfaction
Relative importance of dairy farming (moderating
variable)
Beta
.36
.29
.26
.09
t
2.30*
2.52*
2.04*
1.05
.03
.38
Depending variable: Relationship quality
Adj. R² = .65; F = 18.83***; *** p ≤ .001; ** p ≤ .01; * p ≤ .05
Tables 9 and 10 show the results for Westfleisch and Toennies. The three most important
determinants in the sector model rest in the same order in the regression for Westfleisch (see
Table 6), whereas results for Toennies differ considerably from the sector estimation in terms
of relative importance (Table 10). For Westfleisch, 69 % of the variance in supplier
relationship quality can be explained, in the Toennies case the explanatory power is even
higher with an adjusted R² of .82.
Table 9: Determinants of relationship quality (Westfleisch)
Independent variables
Farmer orientation
Management competences
(Relative) Price satisfaction
Reliability
The grading process at XY is neutral.
Communication and service
Lack of price transparency
Beta
0.48
0.38
0.29
0.28
0.28
0.15
-0.15
t
5.32***
4.38***
3.34***
2.82**
3.13***
1.48
-1.83
Depending variable: Relationship quality
Adj. R² = .69; F = 15.26; *** p ≤ .001; ** p ≤ .01
While for both enterprises, farmer orientation is the most important influencing factor, the
price is more important for suppliers of Toennies than for Westfleisch suppliers.
“Communication and Service” is the third important factor for the evaluation of the
relationship with Toennies, while for Westfleisch, this aspect has no significant impact. With
a closer look at the average scores of the enterprises presented in the next section we
investigate whether this is rather due to deficient communication at Toennies or if Toennies
suppliers just pay more attention to information provided by their buyer. However, knowing
that Westfleisch publishes a newsletter for their farmers quarterly, while Toennies does not,
the first hypothesis seems more promising to us.
12
Table 10: Determinants of relationship quality at Toennies
Independent variables
Farmer orientation
(Relative) Price satisfaction
Communication and service
Management competences
The grading process at XY is neutral.
Reliability
Lack of price transparency
Beta
0.56
0.46
0.28
0.22
0.14
0.14
-0.11
t
5.04***
4.39***
3.22***
2.45**
1.23
1.57
-1.00
Dependent variable: Relationship quality
Adj. R² = .82; F = 17.37; *** p ≤ .001; ** p ≤ .01; * p ≤ .05
Possible causes for the revealed differences might be due to the differences in legal form and
also in sourcing strategies. For free suppliers, price satisfaction is of greater importance,
because the relationship probably is evaluated for a shorter period. A rather long-term
orientation of co-op members for the evaluation is also reflected by the high importance of
management competences, because the success of the enterprise will pay back at each end of
the year in terms of boni etc.
5.4
Managerial implications: proposals for improved supplier management
The results presented in the previous sections provide insights into farmers evaluation of the
processors buying their products. By comparing sector and company-specific results for the
pork and dairy sector, we can show that besides the instructive industry model, company
specific estimations provide managers with useful information for introducing a supplier
relationship management.
Based on these preliminary analyses, a benchmark analysis can be conducted summing
up the average scores of the respective items included in the factors. Doing this, the enterprise
with best management image, farmer orientation etc. in the sector can be identified. Together
with the information about company specific relative importance of the determinants,
improvements can be designed tailor-made. We first summarised the respective relationship
variables to a unique (unweighted) mean score. All variables were standardised on a -3 to +3level so that the maximum achievable score is three. The resulting Supplier Relationship
Quality Index (“SuReQual-index”) represents the overall assessment of an agribusiness
processor through the farmer-suppliers. Values above 1.5 indicate an excellent relationship.
This is only achieved by the category “other co-ops” for the dairy sector and by the
slaughterhouse “Boeseler Goldschmaus”, which are not listed here. The same calculations are
made for the influencing factors.
The results of the index for the two dairies are shown in Table 11. The evaluation of
farmer orientation is not good for both, even if Humana at least reaches a positive score in this
field, whereas Nordmilch has a slightly negative value. The management image and
satisfaction with communication, which is the most important influencing factor for
Nordmilch, is evaluated much better for both. However, there is still a lot of potential for
improvement at Nordmilch compared to its competitor Humana. As already indicated, the
performance of Nordmilch is clearly deficient in terms of supplier relationship management.
13
Table 11: Benchmarks for improved supplier management (Humana and Nordmilch)
Factor
SuReQual
Farmer orientation
Management image and satisfaction
with communication
Price satisfaction
Performance satisfaction
Scale from -3 to +3
Index
Humana Milchunion
1.26
.55
Nordmilch
.30
-.13
1.35
.80
-.50
-.63
-1.87
-1.30
The benchmark analysis for the slaughterhouses (Table 12) reveals that the differences
between Westfleisch and Toennies are not as strong as between the dairies. Farmer orientation
lacking at Toennies, and the satisfaction with communication and services also is very low, as
hypothesized above. This is especially due to the market intermediates used by Toennies, but
it is important to note that Westfleisch, who has a direct relationship and also publishes a
quarterly newsletter, also receives a slightly negative value in this aspect. Thus, we state that
efforts towards communication improvements have to be made at Toennies.
Table 12: Benchmarks for improved supplier management (Westfleisch and Toennies)
Factor
SuReQual
Farmer orientation
Management competences
Reliability
Price satisfaction
Communication and Service
Neutrality of grading process
Lack of transparency of price grids
Scale from -3 to +3
Index
Westfleisch
.92
.44
1.34
1.23
.45
-.18
1.32
.49
Toennies
.40
-.17
1.37
.93
.46
-1.27
.88
-.05
An increased, company-wide supplier orientation has to be carried by all members of a
processor, especially by those who have contact with farmers directly or, as the top managers,
mostly indirectly through press releases or newsletters. To achieve the necessary internal
acceptance it has to be demonstrated that supplier relationship quality is viewed as a part of
the company’s performance. Therefore, the SuReQual-score could be used as business ratio
for supplier management, which could be integrated into a company’s balanced scorecard.
For the pork sector for example, where strong efforts are made by farmers’ unions as
the Deutsche Bauernverband (DBV) to achieve more price transparency in the market, one
might also think of regular surveys aiming at identifiying and publishing the most supplieroriented slaughterhouse. By this, the sector culture could be turned away from an adversarial
interaction and towards (antagonistic cooperations), improving the overall performance of the
sector.
14
6
Conclusions and further research
The surveys presented in this paper provide insights into farmers’ evaluation of the processors
buying their products. Relationship quality itself is notably determined by rather emotional
criteria, whilst price-related aspects play a minor role. Results show furthermore that
relationship quality affects the willingness to switch the buyer. Hence, relationship quality can
represent a competitive advantage for agrifood supply chains and should therefore be
improved using tools of supplier relationship management. The identified factors show which
instruments are the most promising for the respective sector and company.
Though some differences between the sectors occur, we find many parallels in the
factors explaining relationship quality. Price satisfaction does not show the strongest impact
neither in the dairy nor in the pork chain. Much more important are the farmer orientation of
the processor and the perception of management competence. Moderating variables such as
farm size or farmers’ education do not affect the perceived quality of business relationships.
Overall, the model is able to explain the perceived relationship quality to a high extent.
By comparing sector and company-specific results for the pork and dairy sector, we
can show that only company specific estimations provide managers with the information
needed for introducing appropriate measures of supplier relationship management, because
there might be differences in the relative importance of certain aspects, thus leading to false
improvement measures. The advantage of a survey including also suppliers of the
competitors, as it is done here, is that a benchmark analysis can be conducted. By summing up
the average scores of the items included in the different factors to indices, the own
performance can compared with the competitors for each important field. As for each
company the importance of the determinants might vary, the regression analysis is necessary
to make sure which differences compared to competitors are important or not.
Limitations of the results presented here are of course small subsample sizes for the
company specific analyses, so that further research focussed on suppliers of only some
important processors should be conducted. Furthermore, other sectors with more vertical
coordinated chains (e.g., horticultural products or poultry) should be included to test the
reliability of the instrument
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18
Appendix 1: Factors and items in the dairy survey
Factors and items
Relationship quality; KMO: .77; Cronbach’s alpha: .84; 61 % explained variance
All in all, I am satisfied with the collaboration with XY.
Rank the satisfaction with XY on a scale from 0 to 100
Promises made by XY are reliable.
If there are difficulties within the collaboration, XY meets me halfway.
I feel committed to XY.
Factor analysis “Determinants and benefits of relationship quality”; KMO: .90.
63 % explained variance
Farmer orientation, Cronbach’s alpha = .89
The rural roots are still important to XY.
XY takes farmers’ problems seriously.
Farmers and XY pull together.
Farmer and XY strive for different goals.
Farmers’ vote still counts for XY.
XY does not take care about dairy farmers.
XY is always trying to take farmers for a ride.
I feel committed to the management of my dairy.
Management image and satisfaction with communication, Cronbach’s alpha = .86
XY informs me badly.
I feel bad informed about XY’s entrepreneurial politics.
I perceive the managers of XY as: open – close-mouthed.
I perceive the managers of XY as: condescend – like a partner.
I perceive the managers of XY as: uncooperative – cooperative.
I perceive the managers of XY as: honest – dishonest.
I perceive the managers of XY as: unfair – fair.
Price satisfaction, Cronbach’s alpha = .69
I am satisfied with the price XY pays.
In comparison to other processors I am … with the price of XY (strongly satisfied –
strongly dissatisfied).
If you think of the price, XY paid you during the last five years, where would you
rank your satisfaction with this price on a scale from 0 = “endangering my livelihood”
to 100 = “very satisfactory”?
Performance satisfaction, Cronbach’s alpha = .71
My dairy is profitable.
I think the dairy redeems enough of the milk.
My dairy should rationalize more in the future.
Evaluation of the co-operative idea, Cronbach’s alpha = .80
The co-operative idea is important to me.
The legal form of co-ops is obsolete.
If XY wasn’t a co-op, the enterprise would be able to better cope with competition.
The co-operative idea is still a competitive advantage.
Willingness to switch the buyer, Cronbach’s alpha = .67
If the price is good it doesn’t matter to whom I deliver.
Did you already think about switching your dairy?
Without the (long-term) contract with XY I would switch to another dairy.
Factor loading
.80
.89
.75
.73
.70
.62
.74
.80
-.73
.77
-.79
-.75
.77
.55
-.68
-.76
.73
.81
-.78
.84
.71
.83
.82
.83
.81
-.75
.66
-.66
-.79
.84
.76
.80
.77
19
Appendix 2: Factors and items in the pork survey
Factors and items
Relationship quality; KMO: .87; Cronbach’s alpha: .87; 63 % explained
variance
All in all, I am satisfied with the collaboration with XY.
From long-term experiences I know that I can trust my buyer.
Overall the collaboration with XY works well.
I can trust XY.
I feel committed to XY.
Even if something goes wrong I stay loyal to XY.
Factor analysis “Determinants of relationship quality”; KMO: .84; 69 %
explained variance
Farmer orientation, Cronbach’s alpha = .87
Managers of XY feel committed to agriculture.
I can be sure that XY will consider farmer’s problems.
Farmers and XY pull together.
XY takes farmers’ problems seriously.
I perceive the managers of XY as: uncooperative - cooperative
Farmers and XY strive for different goals.
XY takes my complaints seriously.
Price satisfaction, Cronbach’s alpha = .80
In comparison to other processors I am … with the price of XY (strongly satisfied –
strongly dissatisfied).
I am satisfied with the price XY pays.
When I calculate all premiums and discounts, XY pays a higher price compared to
other slaughterhouses.
I know that my production quality is rewarded by XY.
Communication and service, Cronbach’s alpha = .81
I often have contact with XY.
To some of the employees at XY I have a good, personal relationship.
XY often gives me good advice concerning my production etc.
Management competences, Cronbach’s alpha = .70
I perceive the managers of XY as entrepreneurial competent.
I perceive the managers of XY as: incompetent – competent
I perceive XY as successful in the long run.
Lack of transparency of price grids, Cronbach’s alpha = .59
Changes in price grids should be published earlier.
Bigger suppliers get more favourable conditions at XY.
Price grids change too often at XY.
Reliability, Cronbach’s alpha = .66
It often happens that engagements with XY are broken.
I can rely on engagements with XY.
Structural bonds, Cronbach’s alpha = .63
I have lots of different slaughterhouses I can deliver to.
In my region there are relatively few marketing alternatives.
Transportation problems, Cronbach’s alpha = .64
With transportations of XY there often goes something wrong.
XY exerts negative influence on my price through the transports.
Attitude towards contract farming, Cronbach’s alpha = .87
In the long run I will have to subscribe a contract in order to produce pigs profitably.
Contracts are only favourable for slaughterhouses, farmers do not profit at all.
I do not want to give up my entrepreneurial freedom due to contractual arrangements.
In my opinion it would be better if farmers entered in long-term contracts with
slaughterhouses.
Contracts provide me with more planning security.
Buyer switching behaviour, Cronbach’s alpha = .84
I often switch the slaughterhouse in order to take advantage of price differences.
Please evaluate your switching behaviour: the scale is from 0 = “I deliver my pigs to
XY for a long time already.“ to “I switch the slaughterhouse as often as possible.”
If the price is good it doesn’t matter to whom I deliver.
Factor loading
.84
.83
.81
.81
.71
.65
0.76
0.73
0.72
0.71
0.66
-0.60
0.52
0.86
0.76
0.63
0.56
0.84
0.75
0.73
0.82
0.76
0.74
0.81
0.68
0.57
-0.87
0.70
-0.80
0.79
0.84
0.78
0.83
-0.82
-0.81
0.78
0.77
0.864
0.756
0.615
20