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.

For better or worse, digital technologies are reshaping everything, from customer behaviors and expectations to organizational and manufacturing systems, business models, markets, and ultimately society. To understand this overarching transformation, this paper extends the previous literature which has focused mostly on the organizational level by developing a multi-level research agenda for digital transformation (DT). In this regard, we propose an extended definition of DT as "a socioeconomic change across individuals, organizations, ecosystems, and societies that are shaped by the adoption and utilization of digital technologies." We suggest four lenses to interpret the DT phenomenon: individuals (utilizing and adopting digital technologies), organizations (strategizing and coordinating both internal and external transformation), ecosystems (harnessing digital technologies in governance and co-producing value propositions), and geopolitical frameworks (regulating the environments in which individuals and organizations are embedded). Based on these lenses, we build a multi-level research agenda at the intersection between the bright and dark sides of DT and introduce the PIAI framework, which captures a process of perception, interpretation, and action that ultimately leads to possible impact. The PIAI framework identifies a critical research agenda consisting of a non-exhaustive list of topics that can assist researchers to deepen their understanding of the DT phenomenon and provide guidance to managers and policymakers when making strategic decisions that seek to shape and guide the DT.

Purpose – This paper aims to examine the impact of product innovation attributes (complexity, relative advantage, compatibility, trialability and observability) on brand equity, and whether these attributes exert a different effect on low- versus high-equity brands. The moderating role of consumer innovativeness in this relationship is investigated further. Design/methodology/approach – The study is based on survey data from users of two brands of digital audio players of different brand equity levels. Findings – Overall, it was found that innovation attributes have an effect on brand equity, and this effect differs between low- and high-equity brands, with a low-equity brand being benefited more than a high-equity brand from perceptions towards a product’s innovation attributes. Additionally, it was found that the impact of complexity and relative advantage on brand equity increases when consumer innovativeness increases in the case of a high-equity brand. However, no significant difference was found between low- and high-equity brands regarding the proposed moderating effect of innovativeness. Research limitations/implications – The study only examines two brands belonging to one particular industry, which limits the findings’ generalizability. Thus, the use of more test brands from different industries should be the goal for future research. Practical implications – Managers should consider the firm’s current brand equity level and its competitive position to maximize the effect of product innovation attributes. Originality/value – The study makes an original contribution to the research on the relationship between product innovation and brand equity and provides theoretical and managerial implications in the field of innovation and brand management.

With the aim of contributing to the existing knowledge of brand community members and their willingness to share ideas, we investigate whether and how brand community innovators’ (i) lead user characteristics, (ii) brand community identification, (iii) brand knowledge, (iv) brand loyalty and (v) preferences regarding the brand owner’s interference in community activities influence their willingness to share their ideas with the company. In contrast to earlier studies, which inquired into brand community members’ intentions to share their ideas [see Füller, J, K Matzler and M Hoppe (2008). Brand community members as a source of innovation. Journal of Product Innovation Management, 25, 608–619], we studied members who had already innovated and were actively involved in innovation processes. Using a survey of the Adult Fans of Lego (AFOL) community, we found that brand community members’ willingness to share their ideas is positively related to the ahead of the trend (AT) dimension of lead user characteristics, brand community identification and brand loyalty. Interference by the company in community activities also plays a role. Surprisingly, the brand community innovators perceive this role oppositely to what prior research on firm-hosted and open-source communities suggests. This study extends our knowledge of brand communities by demonstrating how brand community innovators’ interpersonal contexts, personal traits and brand perceptions may promote or demote willingness to share.

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It is well-established in the literature that biases (e. g., related to body size, ethnicity, race etc.) can occur during the employment interview and that applicants' fairness perceptions related to selection procedures can influence attitudes, intentions, and behaviors toward the recruiting organization. This study explores how social robotics may affect this situation. Using an online, video vignette-based experimental survey ( n = 235), the study examines applicant fairness perceptions of two types of job interviews: a face-to-face and a robot-mediated interview. To reduce the risk of socially desirable responses, desensitize the topic, and detect any inconsistencies in the respondents' reactions to vignette scenarios, the study employs a first-person and a third-person perspective. In the robot-mediated interview, two teleoperated robots are used as fair proxies for the applicant and the interviewer, thus providing symmetrical visual anonymity unlike prior research that relied on asymmetrical anonymity, in which only one party was anonymized. This design is intended to eliminate visual cues that typically cause implicit biases and discrimination of applicants, but also to prevent biasing the interviewer's assessment through impression management tactics typically used by applicants. We hypothesize that fairness perception (i.e., procedural fairness and interactional fairness) and behavioral intentions (i.e., intentions of job acceptance, reapplication intentions, and recommendation intentions) will be higher in a robot-mediated job interview than in a face-to-face job interview, and that this effect will be stronger for introvert applicants. The study shows, contrary to our expectations, that the face-to-face interview is perceived as fairer, and that the applicant's personality (introvert vs. extravert) does not affect this perception. We discuss this finding and its implications, and address avenues for future research.

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