This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members.
As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation.
The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association.
This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus.
The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA.
Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs.
This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.
Moraes, J., Schaefer, J., Schreiber, J., Thomas, J. and Nara, E. (2020), "Algorithm applied: attracting MSEs to business associations", Journal of Business & Industrial Marketing, Vol. 35 No. 1, pp. 13-22. https://doi.org/10.1108/JBIM-09-2018-0269Download as .RIS
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