Nowadays, 85% of international transactions are being implemented by banks, due to the fact that people rely on their trust as well as lack of knowledge about the possible alternatives (Lemon, 2014). Though banks provide higher security services and facilitate payments in any required currency, it has the main shortcoming that high amount of fees is incurred on each transaction without the availability of clear and precise information prior to agreement. In the UK, standard payment for each transaction can reach to £20-25 per and 5-7% margin is gained from loaded exchange rates to those banks who claimed they offer “commission-free” transaction option. In total, the hidden fees incurred in the international money transfer can range from 4% to 15% (Lemon, 2014).
Creating and writing up an excellent script is already hard enough, however raising funding to make the screenplay to reality and brining the film to the cinema in front of audience represent another big challenge to the filmmaker. In recent years, heat waves of crowdfunding shed a light on film industry as well. The emergence of film crowdfunding has made some breakthrough in assisting film projects especially for independent filmmakers to raise funding.
Crowdsourcing information systems aims at delivering informational products and services by harnessing a large group of online users. Individuals motivated intrinsically (e.g., enjoyment) or extrinsically (e.g., reward) can contribute to the system through picking among a variety of open tasks on crowdsourcing platforms. As the huge amount of tasks posted ever day, matching individual with an appropriate task that meet up individual’s personal preference and skill is crucial to the success. However, in reality, the ever-increasing amount of opportunities engaging individuals on crowdsourcing platforms lead to an information overload situation. Therefore, how to assist contributors in finding a suitable task in line with self-identification principle has attracted scholars and practitioners’ attentions. Geiger and Schader (2014) review and analyze the current state of personalized task recommendation in crowdsourcing context which shed a light on designing the relevant mechanisms on crowdsourcing platforms.