Internal crowdsourcing offers new opportunities for ideas to gain legitimacy in idea development as not only managers but also experts are able to provide feedback to improve and subsequently accept ideas for further realisation. Given this far limited knowledge about how ideas gain legitimacy in idea development, this paper focuses on the influence of feedback, a main source of idea legitimacy in internal crowdsourcing. More specifically, this study aims to explore how feedback from different feedback providers (i.e., managers and experts) influences idea selection in internal crowdsourcing idea development. Based on text mining methods including sentiment, topic-model, and expert identification analyses, results reveal that feedback sentiment, feedback diversity and feedback amount play key roles in idea legitimisation. Specifically, these aspects of feedback influence idea acceptance in idea selection through the mediating role of idea revision in idea development and that this relationship is moderated by feedback providers. This study extends previous knowledge about the legitimisation process from the perspective of feedback in idea development by offering both a more comprehensive view, including different feedback aspects, and a more granular measurement of these, through the use of text analysis. Based on insights from the study, practical implications are presented for how to gain legitimacy from feedback for idea selection in internal crowdsourcing.

Decision biases reinforce firms’ tendency to develop innovations based on narrow economic motivations. Consequently, sustainability-oriented ideas explicitly targeting social and environmental issues are easily discarded in idea selection when trade-offs between economic and sustainability values are faced. Given the so far limited knowledge about how sustainability-oriented ideas are developed and selected in organizations today, this research aims to explore how managerial biases affect selection of sustainability-oriented ideas in internal crowdsourcing. It does so through an empirical study drawing on data collected from a Swedish multinational company using internal crowdsourcing for different types of innovation ideas. The empirical study explicitly identifies sustainability-oriented ideas based on machine learning and captures managerial biases for ideas based on sentiment analysis. Regression analyses reveal that managerial biases potentially affect the selection of sustainability-oriented ideas through the mediating role of managerial attention in idea development. Furthermore, this mediating relationship is moderated by search pattern in terms of directed search. The study contributes to the literature on both innovation and sustainability, shedding new light on the effects of managerial bias, managerial attention, and innovation search for decision making and provides managerial implications enabling a fruitful adoption of sustainability-oriented innovation ideas.

The soaring demand, inevitable changes, and substantial change costs associated with complex mechanical products (CMPs) have accelerated the need to reasonably and accurately respond to changes in customer requirements during the product design process. However, current related studies cannot provide a simple and intuitive decision reference for decision-makers (DMs) to respond to these changes. In this work, a complex network theory-based methodology is proposed. First, a complex network model of CMPs is constructed; this model is processed unidirectionally through analysis of the constraint relation and affiliation among parts. Second, all nodes in this network are divided into levels, and all feasible change propagation paths are selected by breadth-first search. Furthermore, to quantify the change losses of paths, a novel “change workload” is proposed, which is a comprehensive indicator, and a distinct decision reference. The “change workload” is composed of “network change rate,” “change magnification node rate,” and “change magnification rate,” whose weights are evaluated by The Entropy Method and Technique for Order Preference by Similarity to an Ideal Solution. Due to the independence of the “change workload” from expert experience, the proposed methodology is reasonable and capable of outputting a list of affected parts and a preferred ordering of propagation paths, which could provide clearer and more direct guidance for DMs. This presented method is fully proven through a real-world case study of a wind turbine. 

Solvers’ participation is essential for successful implementation of crowdsourcing contests for problem solving (CCPS). Many efforts have been made to investigate solvers’ various participation behaviours in CCPS. Whether or not a solver will conduct a behaviour is the result of decision making. However, to our knowledge, few studies concentrated on solvers’ participation from a decision process perspective and little is known about the factors that influence the decisions that solvers are likely to make. This study aims to develop a framework for demonstrating solvers’ decisions and their relations, thereafter identify the factors that affect each of decision makings. It does so through the qualitative structured interviews conducted with solvers in a crowdsourcing platform. The interviews capture four major interrelated solvers’ decisions that are decisions of participation in CCPS, platform selection, contest selection and determination of effort level, respectively. Moreover, the factors including solvers’ motives, solvers’ individual characteristics and incentives, and their roles in each of solvers’ decision makings are presented. The findings improve the understanding of solvers’ participation in CCPS from a decision process perspective. With the further comprehending of factors that affect solvers’ decision makings, this study provides practical implications for crowdsourcing platforms to improve their services for solvers.

Purpose: Despite the fact that user participation (UP) has been highlighted as an important aspect in innovation, previous findings on its relationship with service innovation performance (SIP) are inconsistent. This study aims to investigate the relationships among UP, knowledge management capability (KMC) and SIP, especially in the digital age, inspired by the theories of knowledge-based and absorptive capacity.

Design/methodology/approach: Based on a sample of 252 Chinese e-commerce enterprises, this study adopts a hierarchical regression analysis and bootstrap method to test the theoretical framework and research hypotheses.

Findings: UP and KMC have positive effects on SIP, respectively. KMC plays a mediating role in the effect of UP on SIP. Furthermore, the intermediary role of KMC varies in different sub-paths between UP and SIP.

Originality/value: First, this study provides some explanations for inconsistent arguments on the relationship between UP and service innovation. Second, with the consideration of specific dimensions of UP and SIP, the mediation role of KMC varies in different sub-paths has been recognized, which provides a deeper understanding of the relationship between UP and SIP. Third, this study opens the discussion about how to realize SIP more effectively in the digital age, advancing theoretical and practical developments on service innovation.

Purpose New opportunities to nurture good ideas for innovation arise as firms use web-based ideation platforms for collective idea generation and development. What influences creative performance in firm-internal collective idea development is however not as well researched as idea generation and thus an important area of research is the feedback and commenting on ideas. More specifically, the purpose of this paper is to explore the role of feedback timeliness and knowledge overlap between feedback providers and ideas in collective firm-internal online idea development. Design/methodology/approach An empirical study has been performed, drawing on data collected from a Swedish multi-national company using a web-based system for collective firm-internal ideation. The investigation explicitly captures the effects on ideation performance played by idea development contributions, in terms of feedback timeliness and knowledge overlap between feedback providers and ideas. Findings The empirical results show that idea development is significantly influenced by feedback timeliness as well as by the knowledge overlap between feedback providers and ideas. Specifically, it is found that longer time to feedback and an increased knowledge overlap result in an increased likelihood of idea acceptance. However, beyond a certain point, the positive effects of a longer time to feedback and increased knowledge overlap decrease, resulting in curvilinear relationships with idea acceptance. Originality/value The results from the empirical study reveal the effects of feedback timeliness and knowledge overlap on idea development. This provides us with new insights on the complex dynamics at place in collective firm-internal idea development and offers implications for how we can fruitfully manage this process.

With the increased usage of internal crowdsourcing for ideas, many firms are able to receive feedback from the voluntary collective wisdom with diverse expertise instead of a limited amount of specified experts, resulting in a large amount, diverse and complex input to ideas. Given the so far limited knowledge about it, firms face challenges to fruitfully manage feedback to ideas. Two aspects playing critical roles in the feedback on proposed ideas for innovation are arguably feedback sentiment and its source expertise. This paper aims to explore the roles of feedback sentiment and expertise in internal crowdsourcing for ideas. It does so through an empirical study drawing on data collected from a Swedish multi-national company using internal crowdsourcing for innovation ideas. The investigation explicitly captures the effects of feedback sentiment and source expertise in terms of: 1) positive and negative feedback sentiment; and 2) feedback sentiment in conjunction with expertise of feedback source. The effects of these two dimensions are investigated by performing a logistic regression analysis. Regression results reveal that both feedback sentiment and expertise are potentially impacting ideas in internal crowdsourcing and expertise is a moderator. More specifically, it is found that negative feedback outperforms positive feedback in an overall view, but also that the effects of feedback sentiment are influenced by the expertise of the feedback provider. As one of the first studies to explore the feedback sentiment in conjunction with expertise, this study does not only extend previous knowledge about feedback mechanisms, but also provides practical implications to manage contributors as well as their contributions to internal crowdsourcing of ideas.