An Explorative Analysis of Innovation Activities in the Agricultural Sector : An Association Rule Mining Algorithm Approach

The purpose of this study is to analyze innovation activities in the agricultural sector using an association rule mining algorithm to understand the different types of innovation activities and their combinations that ultimately result in product, process, organizational, and marketing innovations.Dynamic capabilities theory was used to inform the Association Rule Mining algorithm to analyze a set of innovation activities in the agricultural sector. This allowed to identify the key innovation activities or capabilities necessary for agricultural businesses to successfully develop product, process, organizational, and marketing innovations in response to their external the agricultural sector environment.The findings indicate that certain combinations of innovation activities tend to be more successful than others in resulting in different product, process, organizational, and marketing innovation types. This suggests that investing in a combination of intramural Research and Experimental Development (R&D) and software acquisition is likely to result in product innovation. In addition, R&D may also be an effective way for agricultural businesses to produce organizational innovations, while combinations of multiple innovation activities are more effective in leading to marketing innovation than single activities.The findings suggest that policy makers should consider supporting and promoting a combination of intramural R&D and acquisition of computer hardware or software, or engaging in training, particularly for animal- and crop-producing farms in the agricultural sector, as they may help to drive process innovation and improve the competitiveness of these businesses