Lead User Identification Research Articles

A machine learning and clustering-based methodology for the identification of lead users and their needs from online communities

Nowadays, online community platforms provide firms with an important source of information for conducting dynamic marketing research. High-technology companies, in particular, rely heavily on lead users for the development of very novel products or easily adjustable services. In this paper, we present a three-phase methodology that integrates a machine-learning-based algorithm with a sophisticated clustering technique. The purpose of this methodology is to systematically identify lead users and their needs from a complex online community network. We also aim to identify important features, perceptions, and preferences for different groups of lead users. To validate the effectiveness of our approach, we conduct a real-world case study.

Automated identification of different lead users regarding the innovation process

Lead users are often established in an organizational innovation process to attenuate the difficulties a company faces, such as high costs or the obscurity of customers’ needs. But to benefit from these lead users a major challenge is to characterize and identify them especially in the fast-moving world of social media. Therefore, we aim to design a tool to identify lead users automatically for the two innovation phases (“Idea generation” and “Development”) by combining different approaches such as social network analysis, topic modeling and sentiment analysis. Thus, we consulted the design science approach and applied our artifact to 11,481 contributions of an online digital platform. The technical realization of the six different characteristics and their respective weighting according to the different phases of the innovation process resulted in different lead users and showed the necessity of distinguishing between them. Our results were evaluated and confirmed by the identified lead users and an expert. Hence, our investigation contributes to both practice and theory (kernel theories and design theory) alike.

LEAD USER IDENTIFICATION THROUGH TWITTER USING MICRO-BLOG DATA—A CASE STUDY IN THE AVIATION INDUSTRY

To date, one of the most prominent methods to integrate users into the innovation process is the lead user method. However, due to its high resource intensity, it is less used than its potential would allow. In times of the internet and especially social media, this is changing since methods like netnography allow to efficiently identify potential lead users. Hence, based on a case study in the aviation industry, targeted screening and pyramiding are combined to identify lead users. Three users with sufficient characteristics to be lead users were included based on their degree-centrality and relevant contribution on Twitter. This study proposes the combination of these methods and Twitter as a suitable approach for lead user identification.

A Machine Learning and Clustering-Based Methodology for the Identification of Lead Users and Their Needs from Online Communities

A Machine Learning and Clustering-Based Methodology for the Identification of Lead Users and Their Needs from Online Communities

An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI.

The 18th Open & User Innovation Conference (OUI 2021): Book of Abstracts

The International Open and User Innovation (OUI) Conference is the leading academic conference on user innovation and open innovation, two closely related paradigms that have changed our thinking about innovation and creativity in the last decades. Organized by the Open & User Innovation Society since 2002, the OUI Conference brings together about 300-400 researchers from around the world to exchange recent research in open innovation, user innovation, open source/open hardware, collaborative innovation, citizen/free innovation, open innovation policies, and related topics. In 2021, the Institute for Technology & Innovation Management at RWTH Aachen University, Germany, hosted the 18th edition of this conference. The community of scholars meeting at OUI is characterized by a very open, developmental academic culture sharing plenty of feedback and ideas. The conference targets mainly an academic audience of faculty, senior researchers, and PhD students, but is also open to reflected practitioners and policy makers interested in open and user innovation. The OUI is truly multidisciplinary, covering disciplines such as innovation management, strategic management, organization design, marketing, intellectual property rights, entrepreneurship, and public policy, but also health care, psychology, and industrial engineering and design. In the center of the conference are its academic tracks, presenting both latest research (full presentations) and research ideas & proposals – hence providing a glimpse into the future of OUI: Open Social Innovation: Healthcare, Sustainability, and Societal Challenges; Open and User Innovation Platforms; Lead users, Toolkits, and User Identification; Firms‘ Capabilities and Interaction for Open and User Innovation; Crowdsourcing and Distributed Problem Solving; Crowdfunding and User Entrepreneurship; Institutions, Innovation Law, and Policy; Open Software, Open Hardware, and Makers; Diffusion of Open and User Innovation; The Internet of Production: Industry 4.0, User Process Innovation, and Data Ecosystems.

Identification of network behavioral characteristics of high-expertise users in interactive innovation: The case of forum autohome

With the development of mobile internet, an increasing number of enterprises have made use of online forum, communities and other social media to closely cooperate with users for interactive innovation. Thereby, how to use users’ network behavioral data to identify those high-expertise users is an issue of great concern for enterprises’ identification of valuable lead users and implementation of products’ interactive innovation. In this paper, a questionnaire is conducted to divide automobile knowledge and ability into five grades. Following that, a 5-level automobile terminology word bank containing 560 terminologies is built based on statistical results of samples. According to the terminology word bank, the corpuses of 399 users are selected from the autohome.com to analyze and quantify their expertise. Finally, models based on the two-stage least square method is adopted for correlation analysis of users’ expertise and their network behavioral characteristics. Empirical results suggest that users with high expertise in the network generally demonstrate the following six characteristics: a high centrality in the social networks, a high frequency of replies, a high length of posts, a large number of post replies, a large number of fans, activeness of users in multiple subgroups. The main contribution of this study is twofold: propose a method to quantify the user’s expertise based on user’s corpus; provide a new perspective for finding high-value users in enterprise interaction innovation practice.

Do pro-environmental values, beliefs and norms drive farmers' interest in novel practices fostering the Bioeconomy?

A transition towards a bio-based economy is accompanied by a growing demand for biomass resources as fossil fuels need to be replaced for the more sustainable production of consumer goods, chemicals and energy. To increase the supply of renewable biomass and avoid a conflict with food production, currently underutilized by-products (i.e. leaves, stems) from horticultural production could be valorised as feedstock. The success of this approach depends on farmers' willingness to adopt novel practices like the collection and treatment of plant leaves. However, literature on factors influencing farmers' decisions to adopt novel practices aimed to foster the Bioeconomy is limited. This paper addresses this gap by exploring drivers of farmers' interest in the valorisation of by-products. To this aim, the Value-Belief-Norm theory was used and expanded by contextual factors, such as the perceived market demand for biomass and future environmental policies. A survey with German fruit and vegetable farmers (N = 96) has been carried out and data have been analysed with a Structural Equation Model. Findings suggest that the Value-Belief-Norm theory is a relevant framework for the agricultural domain to predict farmers' interest in the valorisation of horticultural by-products. Results further indicate that an internal ecological worldview is potentially relevant for farmers' perception of contextual conditions aimed to foster the Bioeconomy. These outcomes could have managerial and policy implications associated with the identification of potential lead users to trigger the diffusion of innovative sustainable practices and generally foster the Bioeconomy.

Involving Lead Users in Innovation: A Structured Summary of Research on the Lead User Method

Research on the lead user method has been conducted for more than thirty years and has shown that the method is more likely to generate breakthrough innovation than traditional market research tools. Based on a systematic literature review, this paper shows a detailed view on the broad variety of research on lead user characteristics, lead user processes, lead user identification and application, and success factors. The main challenge of the lead user method as identified in literature is the resource issue regarding time, manpower, and costs. Also, internal acceptance and the processing of the method have been spotted in literature, as well as the intellectual property protection issue. From the starting point of the initial lead user method process introduced by Lüthje and Herstatt (2004), results are integrated into a revisited view on the lead user method process. In addition, concrete suggestions for corporate realization options are given. The article closes with limitations and future research suggestions.

Lead User Projects in Practice — Results from an Analysis of an Open Innovation Accelerator

Earlier research on the lead user method is focused on individual case studies and how the method was applied in a specific context. In this paper, we take a broader approach, analyzing a sample of 24 lead user projects, which included working with 188 lead users. These projects were analyzed regarding their specific processes, resources, challenges, and success factors, using the data of an open innovation intermediary. The key finding is that a refinement and elaboration phase and the integration of designers are crucial success factors which, to date, have not been included in the literature. Moreover, crowdsourcing contests and netnography proved to be of significant value for the need, trend, and lead user identification phases. This paper concludes by discussing theoretical and practical implications, the limitations of this study, and recommendations for future studies.

Digital anthropology as method for lead user identification from unstructured big data

This paper focuses on lead user identification in an open data context through the principles of digital anthropology (DA). The approach is demonstrated using a case study from the entertainment industry: the festival of Tomorrowland. Following the principles of DA, the specific data structure of the case selected is studied. This structure follows the tribal roles of Cova and Cova, which are then compared with the lead user characteristics of Belz and Baumbach. The different characteristics and roles are used afterwards to establish the user types that may be found in the big data set of the case study. Unlike earlier methods, such as netnography, which focuses on a specific user type as the main source of insights, our results show that DA requires a change of focus from specific individuals to groups of individuals with the capacity to produce the insights of a lead user. DA shows how all the roles in the data structure present different possibilities for innovation. The application of DA to big data enables the extension of analysis to whole populations instead of specific samples, bringing about new possibilities to model and extract the insights of consumers publicly exposed in the digital media.

Social Media for Identifying Lead Users? Insights into Lead Users’ Social Media Habits

An important and frequently researched concept in the field of open innovation is the integration of lead users into the process for developing innovative product concepts. Social media has offered promising new possibilities for companies in terms of supporting lead user integration. This paper provides an overview of relevant aspects of lead user research, as well as the identification of lead users. Furthermore, we connect lead user theory with social media and discuss possible ways to identify lead users. Based on the conclusion that existing research does not yet sufficiently provide a detailed understanding on identifying lead users in social media, we conducted an empirical study to gain deeper insights in the social media habits of lead users; this involved a case study comprising 20 interviews. This study’s findings present a rather ambivalent picture, concluding that identifying lead users via social media may work with some limitations. Social media can contribute complementary with established approaches like pyramiding to the identification of lead users, but it is not a standalone solution. It strongly depends on the content of the project and on the group of individuals where lead users should be originated. Based on these findings, we derive theoretical and managerial implications and show how companies can utilize social media in addition to established methods for identifying lead users. Finally, the paper concludes with a discussion of study limitations and recommendations for areas of further research.

Automated feature extraction from social media for systematic lead user identification

ABSTRACTManufacturers strive to rapidly develop novel products and offer solutions that meet the emerging customer needs. The Lead User Method, emerging from studies on sources of innovation by the scientific community, offers a validated approach to identify users with innovation ideas to support rapid and successful new product development process. The approach has been more recently applied on online communities, where collection and analysis of rich user data are performed by expert practitioners. In this paper, feature extraction techniques are outlined, that enable automated classification and identification of lead users that are present in online communities. The authors describe two case studies to construct a classification model that is then used to identify online lead users for confectionery products, and to evaluate the outlined feature extraction techniques. The presented research points to opportunities in automated identification within the lead user approach that further reduce the resource and time costs.

IDENTIFYING OPEN INNOVATION PARTNERS: A METHODOLOGY FOR STRATEGIC PARTNER SELECTION

By opening a company’s innovation process, and allowing purposeful collaboration with external partners, Open Innovation (OI) offers several advantages, such as the use of external expertise, shorter time-to-market and reduced failure rates. However, the success of OI projects is directly linked to the selection of suitable partners, who operationally or strategically contribute to reach the goal of the OI project. Despite this relevance of OI-partner selection, methodical support has hitherto been limited — it is either too abstract to be helpful in practice, or too focused on single aspects. This paper presents a methodical approach to close this gap by combining identification and selection approaches from different fields, such as stakeholder analysis, lead-user identification and systems engineering. The methodology was evaluated in an industrial OI-project with an SME from plant manufacturing.

The Social Network Position of Lead Users

The field of lead user research has seen a great deal of attention from academics and practitioners alike. However, we still lack a full understanding of the nature of users with high potential for innovation. In this paper, we employ a social network perspective on lead users. Increasing the realism of our research in three empirical studies with different empirical settings and methods, we provide robust evidence that lead users have a distinctive social network position: They exhibit an unusually high level of “betweenness centrality,” meaning that they are positioned as bridges between different social groups. This finding has two major implications for lead user theory. First, it consolidates seminal conceptual work on lead users and their embeddedness in social networks. And second, the findings extend and validate prior work on the social network perspective of lead users by combining theoretical insights from cognitive psychology, research on creativity, and network theory. As the social network positions of individuals can be mapped quickly and at low cost with modern Web mining tools, our findings may point to a new and readily applicable approach for the efficient and effective identification of lead users in real‐life projects, an aspect that is usually emphasized as the most crucial activity in lead user projects.

Who is at the cutting-edge of market trends? Development of a scale for identifying “Lead-users”

Users are, by no means, an insignificant source of innovative and attractive products. 5% of our non-representative sample of consumers from Gipuzkoa innovates, which is quite similar to findings in other relevant studies on the subject. This would suggest that innovation management and policies that are exclusively manufacturer-oriented and fail to take the user into account will always be unsatisfactory and incomplete.However, it is equally true that not all consumers innovate. The challenge, therefore, is for companies who wish to develop co-creation strategies with consumers to be able to identify those consumers who do.To this end, the authors present a reliable and valid scale called “Consumers at the Cutting-Edge”. We offer a 31-item scale divided in three dimensions (“User leadership”, “Curiosity/creativity”, and “Availability of time and skills for product development”) designed specifically for the identification of lead users so that they can contribute to the innovation process.

Towards Automatic and Accurate Lead User Identification

Identifying product development opportunities is one of the initial phases in the fuzzy front end of a creative innovation process. A number of approaches have been developed to systemize this process, e.g., lifestyle trends, documenting customer complaints, TRIZ trends, and lead user identification and interviewing. Lead users are an essential group of users for companies to involve as part of knowledge management in product design and evaluation. A lead user is an active and experienced individual engaged in modifying and developing products for personal gain, making systematic identification of a lead user the principal basis to precipitate product innovation and development. Currently, some companies, e.g., 3M, employ an interactive screening method, a time intensive activity consisting of semi-structured telephone interviews and written questionnaires by expert users to select lead users [1]. Another more recent approach, netnography, entails systematic analysis of online communities by observing, collecting and laboriously interpreting data by expert users [2]. In contrast to these two time and cost sensitive methods, in this paper a Fast Lead User Identification (FLUID) systematic approach for an automated and accurate discovery of lead users, by exploiting data mining methods and algorithms on rich social networking sites like the micro blogging site Twitter, is proposed. Social sites provide easy access to rich data for extraction and analysis of extended user information and behavior, enabling assessment and classification of lead users with dissimilar expertise as an early algorithmic step of creative innovation. In addition to the discussion of the three aforementioned methods, steps for validating the FLUID method are outlined.

Fast Lead User Identification Framework

Large portion of product innovation and development is accomplished by customers and only a small segment of the customer population engages in such innovation activities. Empirical research has shown that users in this subgroup, called lead users, tend to experience needs before the rest of the marketplace and stand to benefit greatly by finding solutions to those needs. To meet the challenge of quickly and effectively identifying lead users and uncovering their innovation ideas, the authors propose a fast and systematic approach, called Fast Lead User IDentification (FLUID), utilizing data mining techniques to identify lead users on social networking sites. The paper describes the steps taken to build and optimize the FLUID system to effectively identify lead users on the micro-blogging site Twitter. This entails studies using validated lead user questionnaires resulting in clusters of lead and non-lead Twitter users for a single product. The gathered online user metadata and behavior are then used as training data for the automated system. An overview of data processing techniques and relations to the empirically derived lead user characteristics are presented. Finally, classification algorithms that help to separate lead users from non-lead users are discussed, including optimization leading to the validation of the proposed approach. By making use of data-mining techniques on data rich sites like social networking sites, the FLUID approach minimizes the resource and time costs in identifying lead users and this provides a step towards systematizing the fuzzy-front end of the new product development process.

How to assess actors for an Open Innovation-project?

Companies are confronted with an increasing variety of challenges such as shortened development cycles or higher demand of market orientation. One potential solution is collaboration with external partners/actors in terms of Open Innovation (OI). This allows several advantages such as the utilization of external expertise and reduced risk of market fails. However, OI itself bears “new” risks which are often related to the choice of external actors, e.g. insufficient effort-benefit ratio or knowledge drain. So far, adequate methodical support is limited. To allow an efficient identification and selection of actors, established approaches from other fields, such as stakeholder (SH) analysis, are enhanced by OI-specific methods. This paper presents an integrated assessment approach for determining relevant actors within a previously identified pool of SH. The approach combines elements from SH-analysis and Lead-User identification, enhanced by elements from complexity management.

Lead Users and Non-Lead Users: Do Their Preferences Really Differ in Product Design?

Nowadays, the lead user method (von Hippel, 1986; Luthje et al., 2005) and conjoint analysis (Green and Srinivasan, 1990) are widely used methods for new product development. Both methods collect and analyze customers' preferences and use them for (optimal) product design. However, whereas the lead user method primarily is intended to create breakthrough innovations (see von Hippel et al., 2001), conjoint analysis is more capable for incremental innovations (Baier and Brusch, 2009). The main objective of this paper is to measure if lead user preferences differ to non-lead user preferences by extending traditional conjoint analysis with a lead user identification technique and whether this fact can be used in (new) product design for generating breakthrough innovations. This combined procedure is compared to traditional conjoint analysis. The well-known field of mountain biking is used as the empirical setting. The study showed thirty new improvements and led indeed to different preferred attribute-levels.