The sustainability of agri-food chain relationships is examined for six different EU countries (Germany, UK, Spain, Ireland, Finland and Poland), two different commodities (meat and cereals) and two chain stages (upstream: farmer–processor, and downstream:
processor–retailer). While the results of a pooled data analysis, combining all countries into a single dataset, has already been reported in Fischer et al. (2009), this chapter describes disaggregated findings.
Data
Based on pilot study findings involving expert interviews (see Chapter 5), a company survey was developed. The questionnaire was pre-tested separately in each participating country. Where feasible, personal interviews were conducted (mostly with farmers) or respondents were interviewed by telephone. In addition, questionnaires were sent by mail (followed-up by telephone calls and/or a subsequent reminder mailing).
Most of the obtained samples were drawn from existing sampling frames, i.e., all businesses whose contact details were available were approached.1 The data were collected from November 2006 to April 2007. In total, the surveys yielded 1442 usable responses (see Table 1 for the sample profile).
The representativeness of the obtained sample was assessed using two criteria for which complete target population information was available across the different countries:
first, geographic distribution of farm/company location and second, farm/company size.
Despite some differences across the countries,2 overall the collected responses reflect existing population variation with regard to these two indicators.
As to data quality, in total 86% of survey respondents claimed to be in upper management positions or (part-) owners of the surveyed businesses. Non-response bias (Armstrong and Overton, 1977) was assessed by comparing early survey responses with later ones, using multivariate analysis of variance on key demographic characteristics (company/farm size, geographic location and business activity). However, no significant differences were found.
1 In the Finnish case a sample of contact details was randomly selected from the total population of relevant businesses.
2 The Spanish sample was collected in the Aragón region. In the UK sample more than 80% are from Scotland.
Despite these regional biases we refer to these samples as Spanish and UK ones.
Methodology
The relevance of different factors potentially affecting the sustainability of agri-food chain relationships were analysed by estimating a structural equation model (SEM).3 SEM in its most general form consists of a set of linear equations that simultaneously test two or more relationships among directly observable and/or un-measurable latent variables (Bollen, 1989).
Table 1. Number of collected survey responses by country, agri-food chain and activity.
Country and
agri-food chain Farmers Processors Economic activity Retailers N.s.† or others Total
Spain 206 79 51 336
pig meat 102 35 25 162
cereals 104 44 26 174
Poland 209 35 91 335
pig meat 100 17 48 165
beef 109 18 43 170
UK 229 12 6 7 254
beef 171 5 6 2 184
cereals 58 7 4 69
n.s.† or mixed 1 1
Finland 156 43 24 3 226
pig meat 75 16 8 2 101
cereals 81 27 16 124
n.s.† or mixed 1 1
Ireland 120 14 17 151
pig meat 49 7 6 62
beef 71 7 11 89
Germany 42 88 9 1 140
pig meat 13 26 3 42
cereals 24 60 6 1 91
n.s.† or mixed 5 2 7
Total 962 271 198 11 1442
†N.s. = not specified (i.e., respondent did not reveal affiliation).
All SEMs discussed in this chapter were estimated as multi-group models, keeping the model specification constant for all groups. The selected generic model performed best on theoretical and statistical grounds for the overall (pooled) dataset. One of the drawbacks of this approach is, however, that a generic model may not necessarily be the best specification for each group (country, commodity chain, chain stage). Optimized country-specific SEMs have been estimated, and their findings are discussed, in FOODCOMM (2008). Here, a generic SEM is most appropriate because the aim of the analyses in this chapter is to compare the relevance of central determinants of Relationship Sustainability between countries, commodities and chain stages.
The generic model specification is depicted in Fig. 1. Based on the theoretical work in Part I of this volume, and a qualitative study (Chapter 5), Relationship Sustainability is assumed to be a function of Effective Communication, the Existence of Personal Bonds, the
3 The AMOS software package (version 6.0) was used with unbiased covariances as the input matrix. Given the existence of missing values in the dataset maximum likelihood estimation was conducted.
Impact of Key People Leaving, and Equal Power Distribution among chain partners. For a detailed discussion of the underlying hypotheses, and the data for these variables, see Fischer et al. (2009).
Fig. 1. Determinants of Relationship Sustainability – the generic model.
Two constructs (multi-item, latent variables) were used in the specified model. All other variables were measured as single items. The Relationship Sustainability construct was specified as a one-level, four-item latent variable.4 For the generic model, construct reliability (Anderson and Gerbing, 1988) was assessed by Cronbach’s alpha. Construct validity was assessed using principal component analysis (PCA) on the four items. Group-specific factor loadings and significance levels for these items are reported in the results section below. For the Effective Communication construct, two items were used – Adequate Communication Frequency and High Information Quality. Given only two items, PCA is not meaningful for this construct. Finally, common method bias was assessed, i.e., whether significant measurement error due to multiple-item questions limited the validity of our results, despite great care undertaken in questionnaire design and item wording to minimize this problem. Following Podsakoff et al. (2003), the ‘single-common-method-factor approach’ was used to test whether the generic SEM results would significantly change when explicitly controlling for the effects of an unmeasured latent methods factor.
This was not the case. Hence, taken together these statistical tests suggest that there is no major problem with either the reliability or validity of the two constructs.
4 We also tested a two-level, six-item Relationship Sustainability construct, made up from two latent factors (Relationship Quality and Relationship Stability, both measured by three different items, as discussed in Chapter 4). However, the final overall model fit was less satisfactory, thus we decided to estimate the structural equation model based on the one-level, four-item Relationship Sustainability construct.
Existence of Personal Bonds
Impact of Key People Leaving
High Trust in buyer/supplier
Relationship Sustainability
High Commitment towards buyer/supplier
High Satisfaction with buyer/supplier
Positive Collabora-tion History with b/s Effective
Communication Adequate
Comm-unication Frequency High Information Quality
Equal Power Distribution