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The aim of this paper is to explain the organizational changes along supply chains when a geographical brand, i.e. a place name that has value for commercial purposes…
The aim of this paper is to explain the organizational changes along supply chains when a geographical brand, i.e. a place name that has value for commercial purposes, becomes a geographical indication (GI).
Using a case study research design, this paper compares GI vs non-GI supply chains in the European Union and describes the organizational changes that occur in supply chains when a GI is adopted.
When a GI is adopted, an additional “public” level of governance is added along the supply chain that forces it to reallocate and specialize quality controls between the public and private levels of governance to avoid redundancies and to adopt more market-oriented mechanisms of governance in dyadic relationships. The paper argues that these changes occur because the private and public levels of governance complement one another.
More aspects of supply chain management (the power balance or relationship stability) and a more systematic longitudinal analysis using supply chains in various agrifood industries should be considered to generalize the conclusions. An econometric analysis formally testing the main conclusions (propositions) is also required.
The changes needed to successfully adopt a GI are identified, and an explanatory map of these changes is offered.
The structural governance tensions created by the use of common-pool resources within supply chains are explored. It is hypothesized, first, that when a “common-pool resource”, namely, a geographical name, is used in a supply chain, some type of public level of governance that promotes cooperation is required to preserve its value. Second, this public level of governance complements the dyadic mechanisms of governance, requiring the specialization and reallocation of quality controls and the move toward more market-oriented transactions.
Aims to provide the entities and institutions that train retailers with a series of recommendations to improve the quality of the courses they organise and give…
Aims to provide the entities and institutions that train retailers with a series of recommendations to improve the quality of the courses they organise and give, especially concerning the aspects where those actually receiving training detect the greatest shortcomings. Hence, the perceptions of the individuals attending the courses as well as the importance they give to each of the aspects considered to be relevant for evaluating the quality of training received have been analysed. To this end, a measurement instrument based on the SERVPERF scale has been developed and, according to the quality evaluations obtained, the retailers have been classified into homogeneous groups. Evaluating the quality of the training received, grouping the retailers, and detecting significant differences among the groups will enable the bodies organising and/or giving courses to diversify their offerings in terms of the characteristics of those receiving training. They will also be able to determine the strengths and weaknesses of these courses at an overall level as well as for each of the groups. Likewise, clues can be found to improve aspects of the training courses to which due attention may not be being paid at the present time.
Analyses the training demand in the retail sector based on the study of a representative sample of small‐sized retailers. By means of probit models a set of hypotheses…
Analyses the training demand in the retail sector based on the study of a representative sample of small‐sized retailers. By means of probit models a set of hypotheses developed from the basic assumptions of the human capital theory are tested. Four models of training demand are considered: probability of attending a training course in the near future, probability of having attended in the past, probability of being willing to follow multimedia and correspondence courses, and probability of repeating the experience of attending another training course in the near future. In particular, we determined how the retailer’s age, sex, educational level and the business sector, location and size, the quality of training previously received, the suitability of the costs and scheduling of the training courses, among other variables, significantly influence the probability of small‐sized retailers attending training courses.