Purpose This paper aims to explore the market creation practices of farm-based entrepreneurs in the local food sector. Alternative marketing channels for farm-based products increase, but it is not known how entrepreneurs work to position their products in the marketplace. By expanding on the research of farm-based entrepreneurship and entrepreneurial marketing (EM), this study explores the entrepreneurial practices that farm-based entrepreneurs use through the lens of the EM mix (EMM) and its constituent dimensions: person, purpose, practice and process. Design/methodology/approach The study uses a multiple case study design and follows a phenomenological approach in conducting in-depth retrospective interviews with 11 successful farm-based entrepreneurs in the local food sector in Norway. Findings The thematic analysis revealed four key EM practices of the study’s farm-based entrepreneurs: transferring the farm or transforming the farm as the primary purpose; legitimising a local brand through the uniqueness of person, purpose and place; using a personal networking approach in the market development process and flexible and controllable market expansion practices. These elements constitute the pillars of successful, creative and resource-efficient market development. Originality/value The study represents a pioneering attempt to explore and conceptualise EM within farm-based entrepreneurship. The findings ultimately give rise to a novel framework: the farm-based entrepreneur’s marketing mix (FEMM).

Online communities can be an attractive source of ideas for product and process innovations.However, innovative user-contributed ideas may be few. From a perspective of harnessing "big data" for inbound open innovation, the detection of good ideas in online communities is a problem of detecting rare events. Recent advances in text analytics and machine learning have made it possible to screen vast amounts of online information and automatically detect user-contributed ideas. However, it is still uncertain whether the ideas identified by such systems will also be regarded as sufficiently novel, feasible and valuable by firms who might decide to develop them further. A validation study is reported in which 200 posts from an online home brewing community were extracted by an automatic idea detection system. Two professionals from a brewing company evaluated the posts in terms of idea content, idea novelty, idea feasibility and idea value.The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of ideas, and that the profile of the identified ideas (in terms of novelty, feasibility and value) follows the same pattern identified in studies of user ideation in general. | INTRODUCTIONBig data has been predicted to revolutionize innovation and how firms will create value for themselves, their customers and society (see, e.g., McAfee & Brynjolfsson, 2012). Artificial intelligence systems that leverage big data allow more and more tasks to be solved in an automatic manner. Whilst in the past these were predominantly tasks of a mundane and repetitive nature, advances in text analytics and machine learning have also made it possible to solve more complex problems (Christensen, Nørskov, Frederiksen, & Scholderer, 2017).A problem that continues to occupy scholars and practitioners of new product development is how to obtain and select ideas for new products (e.g., di Gangi, Wasko, & Hooker, 2010;Frederiksen & Knudsen, 2017;van den Ende, Frederiksen, & Prencipe, 2015). In the context of inbound open innovation, Ooms, Bell, and Kok (2015), for example, argue that firms can enhance their receptivity-i.e., their capacity to absorb more diverse external knowledge from more varied sources-by engaging with social media. Whilst this can in theory expand a firm's boundaries for information absorption, the extent of engagement with social media is still constrained by available staff time. Such constraints can to some degree be overcome if companies develop or adopt systems that automate parts of the absorption process.The aim of the research presented here is to show how the performance of automated systems in areas such as inbound open innovation can be evaluated. On one hand, the study should be seen as a feasibility study of whether automated detection of ideas for product and process innovations is actually possible. On the other hand, it should also be regarded as a validation study that probes the "veracity"and "value" aspects of big data (Gandomi ...

This paper expands and contextualises social perspectives on entrepreneurial learning by considering the informal learning dynamics and outcomes in a facilitated learning network (FLN) targeting micro-entrepreneurs within the local food sector. This research builds new theoretical and empirical knowledge on the contributions of FLN as a community of inquiry (CoI) to support entrepreneurial knowledge acquisition. Our research strategy was a single embedded case study with the units of analysis consisting of 12 micro-firms within the local meat industry in Norway. In retrospective in-depth interviews, founder-managers reflected on their learning from others from participation in a local-food learning network. Three main themes emerged from our analysis, reflecting the informal regulating mechanisms for knowledge sharing and how entrepreneurs acquired new entrepreneurial knowledge: (1) cultural norms stabilising the community of inquiry, (2) engagement in the practices of others regulates access to community knowledge and (3) from community inquiry to individual entrepreneurial knowledge. Based on these themes, we built a conceptual framework showing informal knowledge-sharing mechanisms and the individual micro-entrepreneurs’ entrepreneurial knowledge acquisition in a CoI. Our study contributes to the research stream on social entrepreneurial learning and how learning from others in a CoI enhances entrepreneurial learning.