The primary aim of this study is to address techniques for this thesis. My study relies on the trust in the crowdsourcing. I did a literature review in my first assignment while the second part demonstrates the methodology of research and also provides a relevant solution. This assignment is categorized into different tasks that have some sub-tasks. This research is structured as follows. The initial task is to define the research issues, structured in sub-problem and assess the gathered literature. The second task is the detail of different methodologies for the chosen issues (Liu, and Baras, 2015). The third one is to analysis, review, and summarizes the problems and literature. The last assignment is to demonstrate the intention of my own methodology, benefits, limitation or reasons. The key aim of this research is to analyze the suitable methodology for completing the investigation.
Crowdsourcing system categories the large issue into smaller projects through computers and also outsource the function to a different set of people in order to solve the issue. They facilitate a way in which mass collaboration can exist in order to resolve the different issues (Christensen, et al., 2015). In previous research, several kinds of crowdsourcing system have originated. In this way, there is a different system which has crowdsourcing characteristics such as Youtube, Mob4hire, 99designs, and Amazon’s Mechanical Turk (AMT) and Wikipedia (Ye, et al., 2015).
It is evaluated that trust management mechanisms have been exercised by different research. It is a viable technique in order to identify the issue of wicked employees in crowdsourcing system. Trustworthiness is the subjective chance where individual A expects that person B will perform a given act in their welfare. It is analyzed that trust-aware HIT allocation techniques can improve the social welfare of the entire CS system. This research report focuses on the trust management in the crowdsourcing system. Under this research, different trust management models will use to implement the HIT allocation in CS system and conduct the extensive experimental investigation to assess the performance of current trust management strategies in crowdsourcing system in terms of improving the system-wide social welfare (Yu, et al., 2015).
|
Research Problem |
Sub-problem |
Collected Literature |
|
Crowd Trust: A Context-Aware Trust Model for Worker Selection in Crowdsourcing Environments |
Worker Selection in Crowdsourcing Environments |
Intext |
|
Challenges and Opportunities for Trust Management in Crowdsourcing |
· Challenges for Trust Management in Crowdsourcing · Opportunities for Trust Management in Crowdsourcing |
Intext |
|
Trust-based Modeling of Multi-criteria Crowdsourced Data |
Trust-based Modelling of Multi-criteria Crowdsourced Data |
Intext |
It is another component of research methodology that is vital for completing the objective of investigation in a valid as well as reliable way. Furthermore, certain tools are implemented by a research to complete the investigation. These tools are the qualitative, quantitative and mixed design of research (Gaikwad, et al., 2015). The qualitative research design is used in interpretative philosophy and enhances the understanding of investigation in depth. This design of research depends on the opinion, belief, and language of respondents. This research design focuses on certain strategies such as focus group, experiment, questionnaire, and interview method.
In opposed to this, quantitative research design evaluates the different events according to a number of responses. In this tool, the question is started with how often and how many. It is a scientific tool in order to resolve the complex group of data. Moreover, mixed research design includes the benefits of both qualitative and quantitative design of research (Wang, et al., 2016).
Mix research design is practiced by a research to complete the objective of the investigation. Because, this research design emphasizes on the features of both design of research such as quantitative and qualitative research design. In this way, the quantitative design of research is used to assess the number of responses of candidates. As well as, qualitative designing of research is applied for defining the relation between trust and crowdsourcing (Sun, et al., 2015).
Compare and Contrast of Methodologies
|
|
Qualitative research |
Quantitative research |
|
The basic objective of the research |
To increase the broad qualitative knowledge of the fundamental cause and motivation |
To quantify the facts and figures to generalize the outcome from the sample to the population of interest |
|
Sampling type |
A small amount of non-representative |
A large number of participants |
|
Data gathering technique |
Unstructured |
structured |
|
Nature of data assessment |
Non-statistical |
Statistical |
Mix design of research provides a researcher in order to eliminate the issues from the investigation that could be created during the research (Wang, et al., 2017). As a result, mix research design facilitates the conceptual as well as theoretical data regarding trust in crowdsourcing (Miao, et al., 2016).
There are different issues associated with trust in crowdsourcing. However, here, I would like to interpret the concern of Worker Selection in Crowdsourcing Environments. It is not adequate to select the Trust Model within crowdsourcing atmosphere, however; it is also significant to monitor the Challenges and Opportunities for Trust Management in Crowdsourcing (Yu, et al., 2016).
Crowdsourcing platform involves both employees and publishers. It is difficult for task publishers to choose the trustworthy employees in order to deal with the human intelligence tasks. In modern times, the ubiquitous trust evaluation system employs to assess the approval rate of HITs as dishonest employees can simply accomplish something and maximizes profit by promptly possible answer and counterfeiting HITs approval rates. A trustworthiness of employees could vary in the crowdsourcing atmosphere. It can be distinct in certain sort of tasks and different reward of tasks. Thus, this literature proposes two classifications as per the task type and amount of task reward respectively. As per the classification, this literature review proposes a trust evaluation model that contains two type of context-aware trust; reward amount based trust as well as task type based trust (Ye, et al., 2015). It also defines that trustworthy model is practiced into the selection of employees as a multi-objective combinatorial optimization issue that is NP-hard. For solving this challenging issue, this research proposes an evolutionary algorithm MOWS GA based on NSGA-II. The outcome of experiments demonstrates that trust evaluation model could appropriately differentiate the dishonest and honest employees when both of them have high overall HITs rate of approval.
This literature review depicts that crowdsourcing system provides a new technique for organization and individuals in order to leverage the power of mass collaboration in order to attain the difficult projects in a divide and conquer way. Under the current crowdsourcing system, no facility is offered to assess the trustworthiness of employees and facilitating decision support to allocate the tasks to employees. It leads to the high dependency of the work quality and behavior of employees in the crowdsourcing system. To identify the issue, trust management technique is required for an organization. Traditional trust management tool is focused on addressing the trustworthy service providers as feasibly as possible (Yu, et al., 2012). This literature also provides the data regarding how to use SPs because of two common assumptions such as an SP can deal as an indefinite number of request in the one-time unit and a service consumer only needs to choose one SP for interaction in order to complete the tasks. But, these two assumptions are no longer applicable in CS system. Therefore, existing models cannot be directly applied for trust management in crowdsourcing system.
This literature review indicates the trust based modeling of Multi-criteria Crowdsourced Data. Single criteria are used to predict the classification of items for a given user. There are different techniques could be used to predict the data such as collaborative filtering and historical user information. But, different application domains can get advantage from the assessment of multiple criteria such as tourists attraction rate (restaurants, hotel, and attraction) using multiple criteria. This research argues that the personalized integration of multi-criteria data, as well as application of trust models, should not only refine the tourist profile but also enhance the quality of the mutual recommendations. The key contributions of this performance are a novel profiling approach that takes benefit of multi-criteria crowdsourced information and develops pairwise trust models and K-NN estimation of user rating by using the trust based neighbor choice (Leal, et al., 2017). This literature review also illustrates that the significant experimental work has been acted via crowdsourced dataset from the TripAdvisor platforms and Expedia.
Literature 1 is appropriate to investigate the Crowdsourcing platform. This literature review also considers a trustworthy model that is practiced into the choice of employees as a multi-objective combinatorial optimization issue (Dwarakanath, et al., 2016). It was highly associated with my selected research concern.
Literature 2 was related to Challenges and Opportunities for Trust Management in Crowdsourcing. This literature review also considers the high dependency of work quality and behavior of employees. It is highly relevant to my chosen research issue (Martinez, 2017).
Literature 3 is related to Trust-based Modeling of Multi-criteria Crowdsourced Data. It also focuses on different techniques that could be applied to estimate the data like collaborative filtering and historical user facts and figures. This research is highly related to my chosen research issue.
|
Literature # |
Research Problem |
Methodology |
Your Sub-problem |
Relevance |
|
Literature1 |
Crowd Trust: A Context-Aware Trust Model for Worker Selection in Crowdsourcing Environments |
Mixed research design |
Worker Selection in Crowdsourcing Environments |
Strongly relevant |
|
Literature2 |
Challenges and Opportunities for Trust Management in Crowdsourcing |
Mixed research design |
Challenges for Trust Management in Crowdsourcing Opportunities for Trust Management in Crowdsourcing |
Strongly relevant |
|
Literature 3 |
Trust-based Modeling of Multi-criteria Crowdsourced Data |
Mixed research design |
Multi-criteria Crowdsourced Data |
Strongly relevant |
Under this section, I am purposing a methodology that is a combination of other methodologies. I have chosen certain papers that are based on my research issue and also associated with an issue which I required to interpret. Thus, here I categorized the section 1.4 into subsection that involves the justification of research methodology and limitation and benefits of research methodology. It also demonstrates the structure or process of my methodology.
As I had already discussed that I would like to apply the hybrid methodology because it is not only limited to quantitative or qualitative research design however it involves the characteristics of both research designs in one methodology (Hellström, 2015). Hence, it is feasible to use both of them except only one. I will conduct the survey through a questionnaire for testing and get the feedback from the employees who work in crowdsourcing environment.
From the above framework, it can be illustrated that different technique could be used by a researcher in order to complete the research in an effective manner. For this research, certain techniques could be used such as survey, inductive approach, and observation.
References
Christensen, H.S., Karjalainen, M. and Nurminen, L., 2015. Does crowdsourcing legislation increase political legitimacy? The case of Avoin Ministeriö in Finland. Policy & Internet, 7(1), pp.25-45.
Dwarakanath, A., Shrikanth, N.C., Abhinav, K. and Kass, A., 2016, May. Trustworthiness in enterprise crowdsourcing: a taxonomy & evidence from data. In Proceedings of the 38th International Conference on Software Engineering Companion(pp. 41-50). ACM.
Gaikwad, S.N., Morina, D., Nistala, R., Agarwal, M., Cossette, A., Bhanu, R., Savage, S., Narwal, V., Rajpal, K., Regino, J. and Mithal, A., 2015, November. Daemon: A self-governed crowdsourcing marketplace. In Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (pp. 101-102). ACM.
Hellström, J., 2015. Crowdsourcing as a tool for the political participation?-the case of Uganda watch. International Journal of Public Information Systems, 11(1).
Leal, F., Malheiro, B., González-Vélez, H. and Burguillo, J.C., 2017. Trust-based Modelling of Multi-criteria Crowdsourced Data. Data Science and Engineering, 2(3), pp.199-209.
Liu, X. and Baras, J.S., 2015, December. Trust-aware crowdsourcing with domain knowledge. In Decision and Control (CDC), 2015 IEEE 54th Annual Conference on (pp. 2913-2918). IEEE.
Martinez, M.G., 2017. Inspiring crowdsourcing communities to create novel solutions: Competition design and the mediating role of trust. Technological Forecasting and Social Change, 117, pp.296-304.
Miao, C., Yu, H., Shen, Z. and Leung, C., 2016. Balancing quality and budget considerations in mobile crowdsourcing. Decision Support Systems, 90, pp.56-64.
Sun, Y., Wang, N., Yin, C. and Zhang, J.X., 2015. Understanding the relationships between motivators and effort in crowdsourcing marketplaces: A nonlinear analysis. International Journal of Information Management, 35(3), pp.267-276.
Wang, K., Gu, L., Guo, S., Chen, H., Leung, V.C. and Sun, Y., 2017. Crowdsourcing-based content-centric network: a social perspective. IEEE Network, 31(5), pp.28-34.
Wang, Y., Cai, Z., Yin, G., Gao, Y., Tong, X. and Wu, G., 2016. An incentive mechanism with privacy protection in mobile crowdsourcing systems. Computer Networks, 102, pp.157-171.
Ye, B., Wang, Y. and Liu, L., 2015, June. Crowd trust: A context-aware trust model for worker selection in crowdsourcing environments. In Web Services (ICWS), 2015 IEEE International Conference on (pp. 121-128). IEEE.
Ye, B., Wang, Y. and Liu, L., 2015, June. Crowd trust: A context-aware trust model for worker selection in crowdsourcing environments. In Web Services (ICWS), 2015 IEEE International Conference on (pp. 121-128). IEEE.
Yu, H., Miao, C., Liu, S., Pan, Z., Khalid, N.S.B., Shen, Z. and Leung, C., 2016, February. Productive Aging through Intelligent Personalized Crowdsourcing. In AAAI (p. 4405).
Yu, H., Miao, C., Shen, Z., Leung, C., Chen, Y. and Yang, Q., 2015, January. Efficient Task Sub-Delegation for Crowdsourcing. In AAAI (pp. 1305-1312).
Yu, H., Shen, Z., Miao, C. and An, B., 2012, December. Challenges and opportunities for trust management in crowdsourcing. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on (Vol. 2, pp. 486-493). IEEE.
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