The potential of crowd sourcing applications in organizational context - A railroad case study

Motivation Since the introduction of Web 2.0, the characteristics of it become more and more visible in our everyday life. Moreover, these characteristics also become visible in organizational contexts, often labelled Enterprise 2.0. This phenomenon makes organizations more connected, both internally and externally. Organizational leaders claim to benefit from Enterprise 2.0. One of the techniques that makes this possible is crowd sourcing, which is the use of large undefined groups or customers within the business process. In this thesis, we research the potential of using crowd sourcing applications in organizational context. Since NS currently encounters problems, and customers notice these problems, NS is an interesting context to conduct a case study. Results We performed a literature study to find the most important characteristics of crowd sourcing applications, the best practices and success factors. Based on this literature study we developed a complete and validated set of requirements for a crowd sourcing application for NS for feedback on the passenger information in the train. To test and validate our ideas we created a prototype, a feedback system for passenger information in the train. This prototype application was tested by 63 passengers in the train and a questionnaire was conducted. From the test and questionnaire we drew the following conclusions: 1. There seems to be enough support from passengers to make in-train crowd sourcing a success. 2. It is crucial for the success of such a crowd sourcing application that complaints and suggestions made by passengers are acted upon and some form of feedback is given to these passengers. 3. There are different channels to implement in-train crowd sourcing: portal website, mobile application, or both. Results indicate that the portal website alone is not enough. 4. Publicity and incentives are key factors in the success of the crowd sourcing application. 5. In-train crowd sourcing can be implemented as stand-alone application or part of a social media environment. 6. Previous results by van der Wees and Moonen are confirmed: o The most valuable data for NS is a better understanding of who is travelling their trains and at what trajectories. Within the train [. ], feedback on the sentiment of the journey, the attractiveness of the train environment, and the feeling of safety are most sought after. o Most people clarified that if they felt that it was useful, they would likely keep using the application. o Customers are most likely to adopt applications that give them the feeling they contribute to something worthwhile. [. ] Applications closer to the core business are more likely to get adopted. Since our research shows that passengers have the willingness to participate in a crowd sourcing application, our advice is to implement such a crowd sourcing application for use in the train. However, we advise to wait with the implementation until there is a near-to-perfect back-end for the application. Results indicate the willingness to participate, but if NS does perform action, the willingness and number of contributions will decrease. Our final advice is to implement this crowd sourcing idea as part of a bigger whole, the social media environment. Page 5 of 94 Consequences All of this could contribute to NS having a better brand image to the outside world and an improvement in customer satisfaction. However, certain preconditions need to be fulfilled for this to come true. If passengers do not know how to find the application, it is not effective. Worse, if passengers do find it but realize there comments are not noticed by NS, it will backfire and reduce customer satisfaction. In order to avoid these risks, NS should wait with the implementation until there is a good process for processing and solving the passengers comments. Additionally, NS should choose the right channel for implementing such an application: mobile, portal website or both. Finally, it is important that NS finds a good fit between the back-end process and the number of responses; the more responses, the better the back-end needs to be.