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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.

The purpose of this paper is to examine the nature and dynamics of crowd disorder from the perspective of the police in a Canadian context, as well as to extend this perspective to include the opinions of female police officers.

A total of 460 Vancouver police officers participated in this study. Following the 2011 Stanley Cup riot, police officers received mail-based questionnaires focussed on gathering information concerning police perceptions of the crowd and the police response in riot situations. A total of 15 response items were analysed using descriptive approaches and confirmatory factor analyses.

The study findings revealed that, in addition to being multidimensional, the police perspective of crowd disorder may be contingent upon certain officer characteristics. Although, the police perspective can generally be categorized by four overarching constructs: dichotomous crowd, homogeneous threat, strict policing and tactical response; it becomes more complex once the officers’ gender is taken into consideration. The results suggest that the male and female police officers may have some differing views about the nature of crowds and the type of police response required to manage disorderly crowd situations.

In addition to being the first study to analyse police perceptions of crowd disorder in a Canadian context, this research is the first to include the points of view of female officers.

The purpose of this paper is to explore implicit crowdsourcing, leveraging social media in real-time scenarios for intelligent systems.

A case study using an illustrative example system, which systematically used a custom social media platform for automated financial news analysis and summarisation was developed, evaluated and discussed. Literature review related to crowdsourcing and collective intelligence in intelligent systems was also conducted to provide context and to further explore the case study.

It was shown how, and that useful intelligent systems can be constructed from appropriately engineered custom social media platforms which are integrated with intelligent automated processes. A recent inter-rater agreement measure for evaluating quality of implicit crowd contributions was also explored and found to be of value.

This paper argues that when social media platforms are closely integrated with other automated processes into a single system, this may provide a highly worthwhile online and real-time approach to intelligent systems through implicit crowdsourcing. Key practical issues, such as achieving high-quality crowd contributions, challenges of efficient workflows and real-time crowd integration into intelligent systems, were discussed. Important ethical and related considerations were also covered.

A contribution to existing theory was made by proposing how social media Web platforms may benefit crowdsourcing. As opposed to traditional crowdsourcing platforms, the presented approach and example system has a set of social elements that encourages implicit crowdsourcing. Instances of crowdsourcing with existing social media, such as Twitter, often also called crowd piggybacking, have been used in the past; however, using an entirely custom-built social media system for implicit crowdsourcing is relatively novel and has several advantages. Some of the discussion in context of intelligent systems construction are novel and contribute to the existing body of literature in this field.

The purpose of this paper is to present a new concept – task affordance in crowdsourcing context, and build it as a theoretical lens to help the authors reconfigure the artifacts and process in task-oriented crowdsourcing projects. The paper differs from previous studies by focusing on the relationships between the task artifacts, systems and goal-directed actors in crowdsourcing process rather than on the pure examination of task properties.

An operational definition of task affordance was proposed and a pseudo-entity-relationship model based approach was employed to portrait the task affordance in online crowdsourcing context. Furthermore, the authors developed a typology of task affordance and decomposed the concept into five dimensions, namely, design affordance, presentation affordance, assignment affordance, task-platform fit affordance, and task-worker fit affordance. A preliminary analysis of task affordances across various crowdsourcing categories was also conducted to validate the proposed typological framework.

The findings show that the task affordances have varying degree and extend among the diverse crowdsourcing categories. For instance, task design affordances seem to be low in the crowd processing and crowd rating cases compared with that in the crowd solving and crowd creation cases. For another example, in terms of the task presentation affordance, crowd rating cases need the lowest affordance while the crowd creation cases need the highest affordance. Therefore, the authors would like to emphasize that the successful adoption, implementation, and design of the task-oriented crowdsourcing owes to the careful examination of the relationships among the actors, artifacts, and environment of the crowdsourcing projects.

To the authors’ best knowledge, this paper is the first study on conceptualizing the task affordance in online crowdsourcing context. The study contributes to the academic literature on a comprehensive overview of task-related studies in crowdsourcing, which are scattered in several information related fields. Furthermore, this research contributes directly to the area of information science and technology due to a common interest in studying the environments and contexts in which people, information and technology interact and interplay. Practically, this study may yield some implications for the requester and platform operator when designing the relevant tasks or developing the specific crowdsourcing platform.

This paper aims to define and demonstrate the importance of crowdsourcing for the improvement of diverse functions of hotels, the advantages and disadvantages of this technique and, specifically, the relevance of crowdvoting for enhancing hotel sales, and diverse hotel performance dimensions.

A total of 45,103 opinions from Booking.com, a sample of 184 questionnaires addressed to hotel managers and partial least squares (PLS)-path modeling were combined to contrast the hypotheses.

The results empirically show the direct and positive impact of the opinions of the crowd on the amount of hotel sales that do not depend on physical intermediaries, and the impact that this has on the performance dimensions of hotels.

The paper shows the relevance of following strategies addressed at increasing the customers’ recommendations in the main social media or virtual travel agencies, as a mechanism to reduce tourism and hospitality organizations’ traditional weaknesses and increase their long-term performance.

The novelty of crowdsourcing, means that few works (although with an explosive increase and with high impact) have focused on this aspect in the literature, especially in the tourism and hospitality literature. The results of our research open new areas of research and contrast the relevance of crowdsourcing and specifically crowdvoting for the success of hotels. In addition, the methodology employed, by mixing the data from a social media, with a questionnaire addressed to managers, can open new ideas for future works.

The causes for the formation of a bubble in the collateral market when agents are provided with homogeneous expectations are explored. This bubbly dynamics will define a sufficient condition for deleveraging.

Theoretical approach with neutral deleveraging.

Findings of the study are defined sufficient conditions for a behavioral rational bubble's formation in a market of collateral and the subsequent deleveraging. The crowd-in effect of the representative bubble is caused by errors in extrapolating information and thus by representativeness, while the crowd-out effect of deleveraging is set off by reverting to a rational heuristic.

The limit is that it is a homogeneous expectations approach, the implication is that cannot be rational speculation.

Even in a simple model of homogeneous expectations a bubble may arise with serious effect on the demand side: models that detect just rational mispricings cannot account for behavioral components that have financial and real effects.

The paper defines how deleveraging may occur even in case of homogeneous expectations. The latter should not be seen just as a limit but also as a signal of the importance of being aware of behavioral components.

The idea of “best practice” is very much built into information systems and the ways in which they organise and structure work. The purpose of this paper is to examine how “best practice” may be identified (produced) through a community-based evaluation process as opposed to traditional expert-based evaluation frameworks. The paper poses the following research questions: how does “best practice” (e)valuation in online communities differ depending on whether they are produced by community members or experts? And what role play these two practices of valuation for online community performance?

The paper is based on a three-year ethnographic study of a large-scale online community initiative run by the European Commission. Participant observation of online and offline activities (23 events) was complemented with 73 semi-structured interviews with 58 interviewees. The paper draws on Science and Technology Studies, and in particular actor-network theory.

Promoting the idea of “best practice” is not just an exercise about determining what “best” is but rather supposes that best is something that can travel across sites and be replicated. The paper argues that it is crucial to understand the work performed to coordinate multiple practices of producing “best practice” as apparatuses of valuation. Hence if practices are shared or circulate within an online community, this is possible because of material-discursive practices of dissociation and association, through agential cuts. These cuts demarcate what is important – and foregrounded – and what is backgrounded. In so doing new “practice objects” are produced.

The research was conducted in the European public sector where participants are not associated through shared organisational membership (e.g. as employees of the same organisation). An environment for determining “best practice” that is limited to an organisation’s employees and more homogeneous may reveal further dynamics for “best practice” production.

This paper sheds light on why it is so difficult to reach commensuration in crowd-sourced environments.

The paper provides an analysis of how online community members collaborate in order to identify relevant and meaningful user-generated content. It argues that “best practice” is produced through a process of commensuration.

This study is concerned with understanding the nature of police stereotypes and expectations and their potential role in shaping the intergroup dynamics of “hooliganism” involving England fans during the football European Championships in Belgium and Holland (Euro 2000). The paper uses a questionnaire survey of Belgian Gendarmerie officers to explore the extent to which England fans were seen as a dangerous social category who's normative behaviours were likely to be interpreted as a manifestation of hooliganism and therefore as posing a relatively uniform threat to “public order”. In so doing the study provides evidence to support a contention that the Gendarmerie at Euro 2000 held a view of England fans that was consistent with the use of relatively indiscriminate coercive force. The implications of the analysis for understanding the nature of public order policing, its role in shaping “public disorder” in football contexts and the need for interactive and historical studies of crowd events are discussed.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.