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This study aims to examine consumers’ responses to crowdsourcing campaigns in the request initiation stage using the signaling theory from economics. The purpose of the research is threefold. First, it provides a comprehensive classification of various task types within crowdsourcing. Second, it conceptualizes crowdsourcing announcements as signals of customer orientation and empirically tests the differential effects of the two most common crowdsourcing task types (product- and communication-related) on customer orientation perceptions. Third, it illuminates the downstream behavioral consequences of crowdsourcing campaign announcements.

The authors conducted secondary data analysis of 883 crowdsourcing campaigns (pilot study) to provide evidence on the differential effects of crowdsourcing task types. In addition, four laboratory experiments were conducted to test the theoretical arguments. To test the main effect of crowdsourcing task types, Study 1A (N = 252 MTurk workers) used a one-factor (product- vs communication-related crowdsourcing vs control) between-subject design, whereas Study 1B (N = 171 undergraduate students) used a 2 (task type: product- vs communication-related) by 2 (product category: restaurant vs fashion) between-subject design. Study 2 (N = 93 MTurk workers) explored the underlying mechanism using a one-factor (product- vs communication-related) between-subject design. Study 3 (N = 375 MTurk workers) investigated the boundary condition for the effect of task type with a 2 (task type: product- vs communication-related) by 3 (company credibility: low vs neutral vs high) between-subject design.

The pilot study provides evidence for the conceptualized typology and the differential effects of crowdsourcing task types. Study 1A reveals that product-related crowdsourcing tends to have a more substantial impact than communication-related crowdsourcing on how customer-oriented consumers perceive a company. Study 1B validates the results of Study 1A in a different product category and population sample. Study 2 shows that the differential customer-orientation effect is mediated by the perceived cost of implementing the crowdsourcing outcome and unravels the differences in consumers’ purchase and campaign participation intentions depending on task type. Study 3 highlights that the customer-orientation effect attenuates as company credibility increases.

This research contributes to the crowdsourcing literature by categorizing the various types of crowdsourcing campaigns companies undertake and revealing the differential impact of the different types of crowdsourcing campaigns on consumers’ perceptions and behavioral intentions. In doing so, this research converges two lines of consumer research on crowdsourcing, i.e. product- and communication-related crowdsourcing. The findings add to the debate over the returns from research and development (R&D) versus advertising and extend it from marketing strategy to crowdsourcing literature.

The findings highlight the importance of choosing specific task types for crowdsourcing and lead to practical recommendations on designing crowdsourcing campaigns to maximize their benefits to crowdsourcing brands.

To the best of the authors’ knowledge, this is the first study that differentiates crowdsourcing task types and compares their effectiveness from a consumer perspective.

This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.

The authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.

(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.

This is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.

With the advent of Internet technologies, shorter product life cycles and increasing competition, organisations have started looking for innovation sources outside the organisational boundaries. The external community of crowds can be used as a valuable source of co-creation in a company's innovation process to generate value. Despite its growing popularity, organisations often face difficulty capturing value from crowdsourcing due to the lack of proper mechanisms behind crowdsourcing-based value co-creation between a crowd and an organisation and their impact on organisational learning and innovation performance. The present study seeks to understand the crowdsourcing-based co-creation mechanism that influences knowledge transfer effectiveness and the organisation's absorptive capacity, resulting in improved innovation performance.

The model was empirically tested using online survey data received from 300 managers of IT firms. Partial least squares structural equation modelling was used to test the model.

The empirical results reveal that crowdsourcing-based value co-creation causes structural, cognitive and relational linkages between a crowd and a firm, among which crowdsourcing-based cognitive linkage contributes more to organisational value capture. Further, an organisation's effective knowledge transfer and absorptive capacity play an important role in influencing the crowdsourcing-based-co-creation organisational learning-innovation performance framework.

This is the first and foremost study that has developed an integrated model using social capital dimensions to understand the entire mechanism behind crowdsourcing-based value co-creation between a crowd and an organisation and their impact on organisational learning and innovation performance. The study provides organisations with theoretical and practical implications of using crowdsourcing as a value co-creation tool and its effects on enhancing organisational learning and value capture.

To obtain optimal deliverables, more and more crowdsourcing platforms allow contest teams to submit tentative solutions and update scores/rankings on public leaderboards. Such feedback-seeking behavior for progress benchmarking pertains to the team representation activity of boundary spanning. The literature on virtual team performance primarily focuses on team characteristics, among which network closure is generally considered a positive factor. This study further examines how boundary spanning helps mitigate the negative impact of network closure.

This study collected data of 9,793 teams in 246 contests from Kaggle.com. Negative binomial regression modeling and linear regression modeling are employed to investigate the relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

Whereas network closure turns out to be a negative asset for virtual teams to seek platform feedback, boundary spanning mitigates its impact on team performance. On top of such a partial mediation, boundary spanning experience and previous contest performance serve as potential moderators.

The findings offer helpful implications for researchers and practitioners on how to break network closure and encourage boundary spanning with the establishment of facilitating structures in crowdsourcing contests.

The study advances the understanding of theoretical relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

Firms increasingly rely on both external and internal crowdsourcing to capture ideas more broadly and enhance innovative problem-solving. Especially in internal crowdsourcing, knowledge sharing that contributes to develop or further the understanding of the problem the idea is focused on solving can take place between critical employees, and in that way improve ideas generated by others. This far, most crowdsourcing practices have focused on identifying solutions to proposed problems, whereas much less is known about how crowds can be used to share problem-related knowledge. There is thus an untapped potential in leveraging crowds not just to generate solution-oriented ideas but also to share knowledge to improve ideas and even to reframe problems. This paper aims to explore the effect of problem- and solution-related knowledge sharing in internal crowdsourcing for idea development.

Data on ideas and comments were collected from an idea management system in a Swedish multinational company. The investigation captures the influences of the problem- and solution-related knowledge sharing on ideas based on content analysis and logistic regression analysis.

The results from this study show that sharing knowledge related to solutions in idea development impacts idea acceptance positively, whereas sharing knowledge related to problems in idea development has a negative effect on the likelihood of idea acceptance and these effects of knowledge sharing are moderated by the active author responses.

This research provides managerial implications for firms to deliberately manage knowledge sharing in peer communities in internal crowdsourcing, especially by providing suggestions on problem reframing and solution refining for ideas.

The results contribute to existing theory in terms of extending the view of crowdsourcing in ideation to include how crowds contribute to the development of the problem and the solution during the development of ideas and providing new insights on knowledge sharing in internal crowdsourcing based on problem-solving theory.

This paper aims to examine how the relationship norms established between customers and brands influence customer perceptions of crowdsourcing (vs firm-generated) cues.

Four studies (N = 851) examine the moderating role of relationship norms on product labeling cues (crowdsourcing vs firm-generated) effects on brand engagement, and the underlying mechanism of self-brand connection.

The findings suggest that crowdsourcing (vs firm-generated) cues lead to higher brand engagement (Studies 1A–1B), mediated by self-brand connection (Studies 2–3). In addition, relationship norms moderate the effects (Study 3), such that under exchange brand relationships crowdsourcing (vs firm-generated) cues yield higher brand engagement, whereas communal brand relationships reverse such effects.

The findings provide valuable managerial implications by highlighting the importance of using relationship norms as diagnostic cues to successfully implement crowdsourcing initiatives.

This research adds to the customer-brand relationship literature by revealing an accessibility-diagnosticity perspective of consumers’ reactions to crowdsourcing (vs firm-generated) cues.

The research study intends to gain a better understanding of members' behaviors in the context of crowdsourcing contests. The authors examined the key factors that can motivate or discourage contributing to a team and within the community.

The authors conducted 21 semi-structured interviews with Kaggle.com members and analyzed the data to capture individual members' contributions and emerging determinants that play a role during this process. The authors adopted a qualitative approach and used standard thematic coding techniques to analyze the data.

The analysis revealed two processes underlying contribution to the team and community and the decision-making involved in each. Accordingly, a set of key factors affecting each process were identified. Using Holbrook's (2006) typology of value creation, these factors were classified into four types, namely extrinsic and self-oriented (economic value), extrinsic and other-oriented (social value), intrinsic and self-oriented (hedonic value), and intrinsic and other-oriented (altruistic value). Three propositions were developed, which can be tested in future research.

The study has a few limitations, which point to areas for future research on this topic. First, the authors only assessed the behaviors of individuals who use the Kaggle platform. Second, the findings of this study may not be generalizable to other crowdsourcing platforms such as Amazon Mechanical Turk, where there is no competition, and participants cannot meaningfully contribute to the community. Third, the authors collected data from a limited (yet knowledgeable) number of interviewees. It would be useful to use bigger sample sizes to assess other possible factors that did not emerge from our analysis. Finally, the authors presented a set of propositions for individuals' contributory behavior in crowdsourcing contest platforms but did not empirically test them. Future research is necessary to validate these hypotheses, for instance, by using quantitative methods (e.g. surveys or experiments).

The authors offer recommendations for implementing appropriate mechanisms for contribution to crowdsourcing contests and platforms. Practitioners should design architectures to minimize the effect of factors that reduce the likelihood of contributions and maximize the factors that increase contribution in order to manage the tension of simultaneously encouraging contribution and competition.

The research study makes key theoretical contributions to research. First, the results of this study help explain the individuals' contributory behavior in crowdsourcing contests from two aspects: joining and selecting a team and content contribution to the community. Second, the findings of this study suggest a revised and extended model of value co-creation, one that integrates this study’s findings with those of Nov et al. (2009), Lakhani and Wolf (2005), Wasko and Faraj (2000), Chen et al. (2018), Hahn et al. (2008), Dholakia et al. (2004) and Teichmann et al. (2015). Third, using direct accounts collected through first-hand interviews with crowdsourcing contest members, this study provides an in-depth understanding of individuals' contributory behavior. Methodologically, this authors’ approach was distinct from common approaches used in this research domain that used secondary datasets (e.g. the content of forum discussions, survey data) (e.g. see Lakhani and Wolf, 2005; Nov et al., 2009) and quantitative techniques for analyzing collaboration and contribution behavior.

The authors advance the broad field of crowdsourcing by extending the literature on value creation in the online community, particularly as it relates to the individual participants. The study advances the theoretical understanding of contribution in crowdsourcing contests by focusing on the members' point of view, which reveals both the determinants and the process for joining teams during crowdsourcing contests as well as the determinants of contribution to the content distributed in the community.

Academics and practitioners are increasingly discussing the role of crowdsourcing initiatives in dealing with the challenges imposed by the COVID-19 pandemic, along with their role in an emergency context in general. However, empirical evidence about the role of crowdsourcing initiatives in an emergency context is still scarce. This paper aims to address this gap in knowledge.

The authors adopted an inductive approach to investigate how companies leveraged crowdsourcing initiatives to address the issues posed by COVID-19. Data were collected through semi-structured interviews. The selection of the interviewees was based on a purposive sampling strategy. Data were analysed through thematic analysis.

The analysis led to the identification and examination of drivers (e.g. preserving relationships with customers), advantages (e.g. increased demand for services) and disadvantages (e.g. increased platform cost) of implementing crowdsourcing initiatives during COVID-19. By leveraging the findings, this paper suggests future research opportunities. Relevant future research opportunities include: (1) quantitatively evaluating the economic impact of crowdsourcing initiatives during a disruptive event and (2) examining how the sector in which the company operates influences the beneficial and detrimental effects of crowdsourcing initiatives on company performance.

The involvement of crowds as innovation partners has provided unique opportunities for companies to innovate and address the challenging scenario. The scientific literature about the role of crowdsourcing during COVID-19 is growing. However, there is still a scarcity of empirical evidence about the mechanisms underpinning the use of crowdsourcing during the pandemic. By leveraging semi-structured interviews, this paper addresses this relevant gap in knowledge.

This study aims to investigate the conditions for the financial feasibility of an incentive-based model for self-drop or crowdsourced drop of the product to be returned at designated drop boxes (thereby ensuring a contactless process).

Constraint-based non-linear mathematical modeling was done for cost differential with and without crowdsourcing. This was analyzed against returns on investment for the installed infrastructure. Scenarios were looked into from the linear, iso-elastic and logarithmic demand functions to identify the optimal incentive policy. The results were further evaluated using “willingness to return” for customer willingness for product returns via drop boxes.

Crowdsourcing is viable when product returns are no more than 15%–20% of the overall products, with a logistics cost differential of 15%–25%. These were only viable when the product return incentive was within the range of 15%–20% of the product cost, as well as the penalty was in the range of 25 to 40% for wrong returns.

The findings are expected to aid the organizations in successfully designing product return policies while adhering to the post-COVID-19 norms, including contactless transactions and social distancing.

The study provides a look into the viability sensitivity of effective gains/profitability against the required level of service for returns, wrong returns, penalties and incentives for crowdsourcing in a developing country like India.

This study examines how contributors with different achievement goals participate under the influence of two common motivators/demotivators on crowdsourcing platforms, namely system design features and task nature.

A free simulation experiment was conducted among undergraduate students with the use of a crowdsourcing platform for two weeks.

The results indicate that contributors with a strong performance-approach goal get better scores and participate in more crowdsourcing tasks. Contributors with a strong mastery-avoidance goal participate in fewer heterogeneous tasks.

Contributors with different achievement goals participate in crowdsourcing tasks to different extents under the influence of the two motivators/demotivators. The inclusion of the approach-avoidance dimension in the performance-mastery dichotomy enables demonstrating the influence of motivators/demotivators more specifically. This article highlights differentiation between the quality and the quantity of heterogeneous crowdsourcing tasks.

Management is advised to approach performance-approach people if a leaderboard and a point system are incorporated into their crowdsourcing platforms. Also, management should avoid offering heterogeneous tasks to mastery-avoidance contributors. System developers should take users' motivational goals into consideration when designing the motivators in their systems.

The study sheds light on habitual achievement goals, which are relatively stable in comparison to contributors' motives and states. The relationships between achievement goals and motivators/demotivators are more persistent across time. This study informs system designers' decisions to include appropriate motivators for sustained contributor participation.