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This dissertation studies how crowd development impacts crowd performance in crowdsourcing. It first develops a double-funnel framework on crowd development. Based on structural thinking and four crowd development examples, this conceptual framework elaborates different steps of crowd development in crowdsourcing. By doing so, this dissertation partitions a crowd development process into two sub-processes that map out two empirical studies.
The first study examines the relationships between elements of event design and crowd emergence and the mechanisms underlying these relationships. This study takes a strong inference approach and tests whether tournament theory is more applicable than diffusion theory in explaining the relationships between elements of event design and crowd emergence in crowdsourcing. Results show that that neither diffusion theory nor tournament theory fully explains these relationships. This dissertation proposes a contatition (i.e., contagious competition) perspective that incorporates both elements of these two theories to get a full understanding of crowd emergence in crowdsourcing.
The second empirical study draws from innovation search literature and tournament theory to address the performance variation puzzle through analyzing crowd attributes. Results show that neither innovation search perspective nor tournament theory fully explains the relationships between crowd attributes and crowd performance. Based on the research findings, this dissertation discovers a competition-search mechanism beneath the variation of crowd performance in crowdsourcing.
This dissertation makes a few significant contributions. It maps out an emergent process for the first time in supply chain literature, discovers the mechanisms underlying the performance implication of a crowd-development process, and answers a research call on crowd engagement and utilization. Managerial implications for crowd management are also discussed.
Road networks are valuable assets that deteriorate over time and need to be preserved to an acceptable service level. Pavement management systems and pavement condition assessment have been implemented widely to routinely evaluate the condition of the road network, and to make recommendations for maintenance and rehabilitation in due time and manner. The problem with current practices is that pavement evaluation requires qualified raters to carry out manual pavement condition surveys, which can be labor intensive and time consuming. Advances in computing capabilities, image processing and sensing technologies has permitted the development of vehicles equipped with such technologies to assess pavement condition. The problem with this is that the equipment is costly, and not all agencies can afford to purchase it. Recent researchers have developed smartphone applications to address this data collection problem, but only works in a restricted set up, or calibration is recommended. This dissertation developed a simple method to continually and accurately quantify pavement condition of an entire road network by using technologies already embedded in new cars, smart phones, and by randomly collecting data from a population of road users. The method includes the development of a Ride Quality Index (RQI), and a methodology for analyzing the data from multi-factor uncertainty. It also derived a methodology to use the collected data through smartphone sensing into a pavement management system. The proposed methodology was validated with field studies, and the use of Monte Carlo method to estimate RQI from different longitudinal profiles. The study suggested RQI thresholds for different road settings, and a minimum samples required for the analysis. The implementation of this approach could help agencies to continually monitor the road network condition at a minimal cost, thus saving millions of dollars compared to traditional condition surveys. This approach also has the potential to reliably assess pavement ride quality for very large networks in matter of days.