Innovative Brand Community Members and Their Willingness to Share Ideas With Companies

Nørskov, Sladjana; Antorini, Yun Mi; Jensen, Morten Berg

doi:10.1142/s1363919616500468

Cited by 13 publications

(14 citation statements)

References 49 publications

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“…It allows to find the relationship between the variables which are surveyed on the different scale, so it provides to analyze the data measured on a different scale in the one type of analysis. This method helped specify, in detail, the issues of co-existence of the categories of variables (objects) measured on a nominal scale [59,60]. The procedure for this method involves the following several steps.…”

Section: Description Of Mathematical Methods Used In the Workmentioning

confidence: 99%

User-Driven Innovation in Poland: Determinants and Recommendations

et al. 2019

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For many years different scientists have published the results of research related to the usage of UDI (user-driven innovation) concept and its impact on innovation activity of enterprises. None of them, however, concerned the relationship between various determinants for developing the R&D (Research and Development) sphere. It is the reason why the main purpose of the study is to present the empirical evidence in finding the determinants for usage of “user-driven innovation” concept (UDI). The work focuses on using this concept in R&D departments, which are very often responsible for introducing this kind of innovation in a firm’s practice. The use of the UDI concept in R&D in the enterprise will be influenced by various factors which, in the literature, are divided into external, independent of the enterprise; and internal, which can be a result of adopted strategies, action plans and/or operating standards. The implementation of UDI in enterprises gives a number of benefits resulting from interaction with the environment, but also encounters barriers and internal problems that need to be neutralized. It is why, in this paper, the internal factors will be considered. To present the relationship between the selected variables, the correspondence analysis which constitutes one of the methods for multidimensional statistical analysis, was applied. The results presented in the paper allowed to formulate several important conclusions about the potential level of implementation of UDI concept in R&D departments in Poland. Firstly, it should be noted that in Poland, the UDI concept is still seen as new. This is why there are no scientific publications on this topic, as well as a lack of system solutions and strategic documents encouraging entrepreneurs to conduct research and development activity based on contacts with users. That is why authors have made some recommendations regarding the promotion of activities related to the use of UDI in research and development processes. Our research supplements the current knowledge in the field of internal conditions affecting the implementation of the UDI concept in enterprises which are starting to implement this concept.

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Section: Description Of Mathematical Methods Used In the Workmentioning

confidence: 99%

User-Driven Innovation in Poland: Determinants and Recommendations

et al. 2019

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“…Prominent examples of the role of user communities in open inbound innovation are the communities hosted by Dell (di Gangi et al, 2010;Poetz & Schreier, 2012), Lego (Antorini, 2007;Antorini, Muñiz, & Askildsen, 2012;Nørskov, Antorini, & Jensen, 2016), Propellerhead (Jeppesen & Frederiksen, 2006) and IBM (Mahr & Lievens, 2012). Firm-hosted communities such as these have the advantage that the hosting firm can retain a certain degree of control.…”

Section: Online Communities As Idea Reservoirsmentioning

confidence: 99%

How good are ideas identified by an automatic idea detection system?

et al. 2018

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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 ...

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“…The community was (Antorini, 2007). AFOLs are known for their ability to develop innovations (Nørskov, Antorini & Jensen, 2016), and they have generated new products and new product lines and created new market opportunities for Lego (Antorini, Muñiz & Askildsen, 2012). The AFOLs' innovations have created value both for the user innovators and the company.…”

Section: Training Datamentioning

confidence: 99%

In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining

Christensen¹,

Nørskov

Frederiksen

et al. 2016

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Online communities are attractive sources of ideas relevant for new product development and innovation. However, making sense of the 'big data' in these communities is a complex analytical task. A systematic way of dealing with these data is needed to exploit their potential for boosting companies' innovation performance. We propose a method for analysing online community data with a special focus on identifying ideas. We employ a research design where two human raters classified 3,000 texts extracted from an online community, according to whether the text contained an idea. Among the 3,000, 137 idea texts and 2,666 non-idea texts were identified. The human raters could not agree on the remaining 197 texts. These texts were omitted from the analysis. The remaining 2,803 texts were processed by using text mining techniques and used to train a classification model. We describe how to tune the model and which text mining steps to perform. We conclude that machine learning and text mining can be useful for detecting ideas in online communities. The method can help researchers and firms identify ideas hidden in large amounts of texts. Also, it is interesting in its own right that machine learning can be used to detect ideas.

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Innovative Brand Community Members and Their Willingness to Share Ideas With Companies

Cited by 13 publications

References 49 publications

User-Driven Innovation in Poland: Determinants and Recommendations

User-Driven Innovation in Poland: Determinants and Recommendations

How good are ideas identified by an automatic idea detection system?

In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining

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