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.
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.