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Producers’ brand-dealer dual loyalty to capital equipment

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Abstract

This paper aims to identify the factors that affect agricultural producers’ loyalty to brand and dealer, jointly and individually, when buying agricultural capital equipment. A second objective is exploring the influence of one type of loyalty over the other, for which we resort to different settings. Our findings show distinctive determining factors for each type of loyalty, the bi-univocity of these two loyalties, and the necessary farm profile to achieve dual loyalty. Agricultural equipment companies demand this information to target farmers and design marketing strategies to make farmers loyal or disloyal, strengthen their distribution channels, and identify the best communication lines to reach potential and current customers.

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Availability of data and materials

The dataset supporting the conclusions of this article is available from the Universidad Austral. Restrictions apply to the availability of this data, which were used under license for this study. Data are available from the authors with the permission of the Universidad Austral.

Notes

  1. These were built through cluster analysis. For details, see Appendix A.

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Contributions

All authors contributed to the design of the work. PH and JRP analyzed and interpreted data. RF and PH drafted the work. JRP revised it. All authors read and approved the final manuscript.

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Correspondence to Josefa Ramoni-Perazzi.

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Appendix A: Cluster analysis for the construction of the dependent variables

Appendix A: Cluster analysis for the construction of the dependent variables

As it has been done in previous work such as Roucan-Kane et al. (2011), we apply cluster analysis to identify groups of producers based on their loyalty to capital equipment brands and dealers, using the options available in questions 40 and 41 of the “The Needs of the Argentine Agricultural Producer” questionnaire:

  1. a.

    I will do more business with this brand/dealer.

  2. b.

    I endorse this brand/dealer to my neighbors.

  3. c.

    I try products from other brands/dealers.

  4. d.

    I would switch to another brand/dealer to save 5%.

  5. e.

    I would switch to another brand/dealer to save 10%.

  6. f.

    I am loyal to this brand/dealer (I do not change brands/dealers even if prices increase more than 10%).

To interpret the results, options c, d and e are considered responses associated with disloyalty, since they show a disposition to switch brands (even when this switching may not happen in practice). On the other hand, options a, b and f are considered replies associated with loyalty, since they imply that the producer is somehow involved with the brand (he would not switch even with higher prices, recommend it to neighbors or seek to do more business with it).

We use a hierarchical clustering agglomeration method called linkage by averages between groups or inter-groups, proposed by Sokal and Michener (1958). We calculate the similarities between the individuals in the sample based on the binary Euclidean distance since the variables selected to carry out the cluster analyzes are all qualitative, each one with two response categories, 0: no and 1: yes.

In Table

Table 8 Cluster solution for capital equipment brand loyalty with two groups

8, we observe a first group of 182 producers who will continue doing business with the brand and show a strong commitment to it. They recommend the brand to their neighbors, and none of them try different products nor will switch brands facing a 5% or even a 10% price increase. Additionally, 85% of them claim that they would stick to the brand even when facing a price increase above 10%. This group of producers can be considered ‘empirically loyal’ to capital equipment brands. On the other hand, a second group includes 382 producers, which are ‘empirically disloyal’ to capital equipment brands. With respect to dealer loyalty, as we see in Table

Table 9 Cluster solution for capital equipment dealer loyalty with two groups

9, the first cluster is a group of 299 producers, whom we call ‘empirically loyal’ to the dealer. In the second cluster, there is a different group of producers, whom we call the ‘empirically disloyal’.

In both cluster solutions, we may see that the t-tests for percentage comparison (assuming that the population variances are different) indicate that the differences for each variable between the groups are statistically significant. Therefore, we can conclude that there are two significantly different groups in terms of brand loyalty and dealer loyalty, as defined by the combination of six proposed variables; that is, the cluster solutions are statistically valid. Consequently, in terms of dual loyalty, we identify 156 producers considered loyal to both brands and dealers, 26 loyal to brands and disloyal to dealers, 143 loyal to dealers and disloyal to brands, and 239 producers considered disloyal to both brands and dealers.

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Harmath, P., Feeney, R. & Ramoni-Perazzi, J. Producers’ brand-dealer dual loyalty to capital equipment. J Market Anal 10, 390–407 (2022). https://doi.org/10.1057/s41270-021-00137-4

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  • DOI: https://doi.org/10.1057/s41270-021-00137-4

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