IMPROVING MARKETING FORECASTING THROUGH COLLECTIVE MARKET INTELLIGENCE
dc.contributor.advisor | Bharadwaj, Neeraj | |
dc.creator | Lang, Mark Frederick | |
dc.date.accessioned | 2020-10-27T15:14:04Z | |
dc.date.available | 2020-10-27T15:14:04Z | |
dc.date.issued | 2012 | |
dc.identifier.other | 864885856 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12613/1688 | |
dc.description.abstract | New product development and management are critical to the long-term success of the firm. New product development is also an area where the firm needs to improve performance. Two important new product decisions are selecting new concepts and estimating their future market potential and demand. Forecasting is a critical activity in supporting these two decisions. Unfortunately, forecasting is an activity where firms often struggle to be proficient. Recent advances in forecasting methods offer opportunities for improvement. One of the techniques is prediction markets, an emerging methodology that taps collective intelligence. Despite widely reported application and promise of prediction markets, they have yet to be adopted in marketing practice or examined in marketing academia. This dissertation addresses two research questions: do prediction markets produce better marketing forecasts than methods traditionally employed by firms, and if they do, how do they do it? To answer these research questions, two field studies are completed. The first is an empirical test of prediction markets compared to traditional forecasting methods implemented within a Fortune 100 firm. The second, based on a post survey, is an analysis of how market knowledge factors in combination with prediction markets design factors produce superior results. Study I finds that prediction markets do provide superior results in 67% of the forecasts and reduce error levels and ranges. Study II finds that out of several design factors, prediction market forecast accuracy is driven most by new information acquisition and knowledge heterogeneity. These findings contribute to MSI 2012-2014 Research Priorities and calls in the marketing literature to develop, better, real-time, intelligent decision support tools to help solve problems of the big data era and support improved demand forecasting. | |
dc.format.extent | 264 pages | |
dc.language.iso | eng | |
dc.publisher | Temple University. Libraries | |
dc.relation.ispartof | Theses and Dissertations | |
dc.rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available. | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Marketing | |
dc.subject | Collective Intelligence | |
dc.subject | Forecasting | |
dc.subject | Market Knowledge | |
dc.subject | New Product Development | |
dc.subject | Prediction Markets | |
dc.title | IMPROVING MARKETING FORECASTING THROUGH COLLECTIVE MARKET INTELLIGENCE | |
dc.type | Text | |
dc.type.genre | Thesis/Dissertation | |
dc.contributor.committeemember | Di Benedetto, C. Anthony | |
dc.contributor.committeemember | Fong, Nathan | |
dc.contributor.committeemember | Stanton, John L. | |
dc.description.department | Business Administration/Marketing | |
dc.relation.doi | http://dx.doi.org/10.34944/dspace/1670 | |
dc.ada.note | For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu | |
dc.description.degree | Ph.D. | |
refterms.dateFOA | 2020-10-27T15:14:04Z |