Widespread use of mobile devices has resulted in the creation of large amounts of data. An example of such data is the one obtained from the public (crowd) through open calls, known as crowdsourced data. More often than not, the collected data are later used for other purposes such as making predictions. Thus, it is important for crowdsourced data to be recent and accurate, and this means that frequent updating is necessary. One of the challenges in using crowdsourced data is the unpredictable incoming data rate. Therefore, manually updating the data at predetermined intervals is not practical. In this paper, the construction of an algorithm that automatically updates crowdsourced data based on the rate of incoming data is presented. The objective is to ensure that up-to-date and correct crowdsourced data are stored in the database at any point in time so that the information available is updated and accurate; hence, it is reliable. The algorithm was evaluated using a prototype development of a local price-watch information application, CrowdGrocr, in which the algorithm was embedded. The results showed that the algorithm was able to ensure up-to-date information with 94.9% accuracy.
Automated algorithm, big data, crowdsourcing application, crowdsourced data, data deletion, data management, price information
Mobile devices have seemingly become a necessity in people's daily life. They have significantly changed the way people communicate and perform their day-to-day activities. In line with this scenario, there is a practice nowadays that is gaining more and more attention from mobile application developers called crowdsourcing. The combination of the two innovations, i.e. mobile devices and crowdsourcing, promises great potential for the advancement of business and society. Despite the popularity of mobile crowdsourcing applications, special attention needs to be given to user participation, since user participation is one of the main factors that determine the success of a mobile crowdsourcing application. This study, therefore, aims at identifying the factors that influence user participation in mobile crowdsourcing application. The interview method involving 13 mobile crowdsourcing application users was used to collect the required information. The constant comparison method comprising open coding, axial coding and selective coding techniques was used to analyse the results. Findings from the analysis showed that user participation in mobile crowdsourcing applications is mainly influenced by the personal benefits that the applications can bring to the user rather than the benefit it can bring to others. These benefits cover five dimensions: financial impact, useful information provided, interaction with other users, rewards offered and features of the applications.
Crowdsourcing, influencing factor, mobile crowdsourcing, mobile crowdsourcing application
Crowdsourcing gathers the world's software engineering experts on a specific subject matter, and allows organisations and individuals to employ the combined effort of these 'experts' to accomplish the software task at hand. However, leveraging the knowledge of experts will not be achieved without online crowdsourcing platforms, which makes communication possible. This study intends to evaluate the performance of four Crowdsourced Software Engineering (CSE) platforms (TopCoder, InnoCentive, AMT and Upwork) based on the criteria of the Web of System Performance (WOSP) model. The WOSP criteria include functionality, usability, security, extendibility, reliability, flexibility, connectivity and privacy. Findings from the analyses showed that the four CSE platforms vary in all of their features, and at the same time, they all lack the requirements of flexibility. The results provide insight into the current status of CSE platforms and highlight the gaps inherent in these platforms while offering a more complete picture. This study contributes to work on enhancing the design of current and future platforms.
Collaborative environment, crowdsourcing platform, crowdsourced software engineering (CSE), web of system performance (WOSP)
The collaborative and competitive nature of multi-agent systems (MAS) is visible through the simple social mode of communication that emerges between human-agent interactions or agent-to-agent interactions. A simple mode of communication involves the fundamental actions carried out by individual agents in achieving their desired goal. The sum of these achievements contribute to the overall group goal. Comparatively, the collective intelligence (CI) of a MAS simply means that these agents should work together to produce better solutions than those made possible when using the traditional approach. In designing MAS with CI properties, formalisation of a higher level deliberation process is essential. A high level deliberation process refers to the judgement comprehension of tasks, reasoning and problem solving and planning. In this paper, we propose our Collective Intelligence Model, CIM, which has the potential to control and coordinate a high-level deliberation process of a MAS. CIM is inspired by the emerging processes of controlled discussion, argumentation and negotiation between two or more intelligent human agents. These processes screen and validate the deliberation process through a cross-fertilisation approach. The emergent property of the cross-fertilised ideas results in an intelligent solution that solves optimisation-related tasks.
Giving computers the ability to understand the user's mood and feelings is the aim for affective computing field. This ability would enhance the interaction between the user and his/her computer to create advanced systems for education, commerce, security and mental disorder diagnosis, among other functions. To achieve this goal, computer software needs to be trained on big data using emotion measures. These emotions should be elicited by a standardised, replicable and validated medium. However, collecting and rating such emotion elicitation media is not a trivial task, as it involves several factors. This research aims at designing a crowdsourcing platform to collect and rate emotion elicitation media. The platform is designed such that registered users can add, recommend and rate emotion election clips, whereas researchers can access and statically analyse data about the rated clips. This crowdsourcing platform can be used by emotion researchers to collect highly- rated emotion elicitation media, and by individuals through social media platform to share emotion elicitation media. The highly-rated clips could be used to elicit emotions, which then could be used to create models for automatic emotion recognition. The automation of emotion recognition will benefit different fields such as health (physical and mental), education and technology.
Crowdsource platform, emotion elicitation, emotion recognition, human-computer interaction, media collection
Crowdsourcing introduces new perspectives in innovation, allowing for new products and services to shift away from the traditional manufacture-centric model to a more user-centric one. In order for businesses to reap the benefits of open innovation, it is necessary to understand the factors that motivate ideators to contribute valuable ideas. Equally, there is an urgency to identify the challenges faced by ideators in crowdsourcing for open innovation to retain the participants of crowdsourcing communities. This paper presents a structured review to address the aforementioned issues. Our findings reveal that the intrinsic factors that drive participation in open innovation are related to the learning experience that results from sharing ideas. Extrinsic factors like social motivation are frequently mentioned in different studies. This study also highlights the need for organisations to develop strategies for interacting with their contributors in order to sustain their participation and idea contribution. In conclusion, this paper can serve as a guideline for practitioners to improve crowdsourcing platforms with the inclusion of important motivational features. It can also serve as reference for organisations for formulating policies to regulate idea contribution.
Crowdsourcing, open innovation, motivational factors, crowdsourcing challenges
The increasing adoption of social media is a viable means in crowdsourcing. It can facilitate the connectivity of collaboration between different organisations, people and society to produce innovative and cost-effective solutions to many problems. Social media have opened up unprecedented new possibilities of engaging the public in meaningful ways through crowdsourcing. However, the growing number of security and privacy issues in social media may weaken the efficacy of crowdsourcing. This study aims to provide a basic understanding of security and privacy issues in line with the growth of crowdsourcing using social media platforms. This study also illustrates how crowdsourcing and social media data can lead to security and privacy issues in different environments. Lastly, this study proposes future works that may serve as direction for scholars to explore security and privacy in crowdsourcing through social media platforms. Secondary sources obtained from journals, conference papers, industry reports and books were reviewed to gather information.
Crowdsourcing has changed the way people conduct business. It provides access to work, and employers can source for the best talent, at the best price, with the shortest turnaround time. Research so far has focussed on crowdsourcing implementation. Hence, there is a need to conduct research that can contribute towards crowdsourcing sustainability. Thus, the objectives of this paper are to identify current practices of crowdsourcing in Malaysia and the challenges that face it. A conceptual model for crowdsourcing sustainability ecosystem is then proposed. This study adopted the case-study approach. Two crowdsourcing platforms were examined in the case study. Two techniques were used to obtain the data: observation and interview. Observation was carried out to observe how the crowdsourcing platforms worked. The interviews helped to uncover current practices, challenges in using crowdsourcing and identification of sustainability factors. It is hoped that the proposed conceptual model will facilitate better planning of the ecosystem supporting crowdsourcing and ensure sustainable growth for crowdsourcing. Future research into crowdsourcing can test the proposed conceptual model to validate its components.
Crowdworkers, ecosystem, job provider, platform, sustainable model
Crowdsourcing is an initiative implemented by the Malaysian government to support its National Key Result Area (NKRA) agenda to improve the lives of citizens with low household income in the B40 group. Crowdsourcing activities are done on mobile crowdsourcing platforms that enable workers to perform micro tasks at any time for a fixed payment. However, without active and constant participation from the crowd, this initiative might not be successful. This paper describes a preliminary study in identifying motivation factors for participating in mobile crowdsourcing platforms. This study identified intrinsic and extrinsic motivation factors that can attract crowds to participate in mobile crowdsourcing platforms. Technology efficacy factors that interlink with motivation factors were also identified in this study. The preliminary study employed the qualitative method where in-depth interviews were conducted among 30 crowdsourcing participants in Peninsular Malaysia. The findings of this study are the basis for a motivation model that can attract crowdworkers from among the B40 group of household-income earners to participate in crowdsourcing to procure and perform available micro-tasks. The findings will also help improvise mobile platforms for crowdsourcing.
Crowdsourcing, interlinked motivation model, mobile crowdsourcing platform