Extending research on organizational learning to multi-product environments is of particular importance given that the vast majority of products are manufactured in such environments. We investigate learning in a multi-product facility drawing on exceptionally rich data for a manufacturing firm that is a leading producer of high technology components. Weekly data for 10 years from the firm's production and human resource tracking systems are augmented by surveys of managers and engineers and by extensive first-hand observation. We find that productivity improves when multiple generations of the firm's primary product family are produced concurrently, reflecting the firm's ability to augment and transfer knowledge from older to newer product generations. No significant transfer of knowledge is evident between the primary product family and other products. Productivity is, however, decreased when the production facility is faced with extensive within-product buyer-specific customizations.We develop the implications of these findings for theory and practice.
{"title":"Learning by Doing in a Multi-Product Manufacturing Environment: Product Variety, Customizations, and Overlapping Product Generations","authors":"Carolyn D. Egelman, D. Epple, L. Argote, E. Fuchs","doi":"10.3386/W19674","DOIUrl":"https://doi.org/10.3386/W19674","url":null,"abstract":"Extending research on organizational learning to multi-product environments is of particular importance given that the vast majority of products are manufactured in such environments. We investigate learning in a multi-product facility drawing on exceptionally rich data for a manufacturing firm that is a leading producer of high technology components. Weekly data for 10 years from the firm's production and human resource tracking systems are augmented by surveys of managers and engineers and by extensive first-hand observation. We find that productivity improves when multiple generations of the firm's primary product family are produced concurrently, reflecting the firm's ability to augment and transfer knowledge from older to newer product generations. No significant transfer of knowledge is evident between the primary product family and other products. Productivity is, however, decreased when the production facility is faced with extensive within-product buyer-specific customizations.We develop the implications of these findings for theory and practice.","PeriodicalId":259514,"journal":{"name":"ERPN: Other Product Strategies (Sub-Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129717268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kurt Matzler, Christoph Grabher, J. Huber, J. Fueller
The prediction of new product success is still a challenging task. Traditional market research tools are expensive, time consuming, and error prone. Prediction markets have been introduced as a viable alternative. Utilizing inputs from various participants in game‐like environments, they have been shown to produce accurate results by combining dispersed knowledge via market‐based aggregation mechanisms. While most previous studies use employees or experts as a sample, we test whether online consumer communities can be used to predict the sale of new skis via prediction markets. Sixty‐two users took part in the study. The prediction market was open for 12 days before the main skiing season 2010/2011 began. The outcomes of the prediction markets were compared with the actual sales numbers provided by the ski producers. The mean average errors were between 2.74% and 9.09% in the four markets. Overall, it can be concluded that the prediction markets based on consumer communities produce accurate results.
{"title":"Predicting New Product Success with Prediction Markets in Online Communities","authors":"Kurt Matzler, Christoph Grabher, J. Huber, J. Fueller","doi":"10.1111/radm.12030","DOIUrl":"https://doi.org/10.1111/radm.12030","url":null,"abstract":"The prediction of new product success is still a challenging task. Traditional market research tools are expensive, time consuming, and error prone. Prediction markets have been introduced as a viable alternative. Utilizing inputs from various participants in game‐like environments, they have been shown to produce accurate results by combining dispersed knowledge via market‐based aggregation mechanisms. While most previous studies use employees or experts as a sample, we test whether online consumer communities can be used to predict the sale of new skis via prediction markets. Sixty‐two users took part in the study. The prediction market was open for 12 days before the main skiing season 2010/2011 began. The outcomes of the prediction markets were compared with the actual sales numbers provided by the ski producers. The mean average errors were between 2.74% and 9.09% in the four markets. Overall, it can be concluded that the prediction markets based on consumer communities produce accurate results.","PeriodicalId":259514,"journal":{"name":"ERPN: Other Product Strategies (Sub-Topic)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131428196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the highly dynamic global marketing system and the increasing importance of specialized products, the position and worth of brands has also increased. Each brand tries to provide a competitive advantage to its customers so they buy the product the first time and then keep buying it. This dissertation attempts to measure the influence of the competitive advantage provided by the brands on the loyalty the consumers feel for that brand. The main aim of this thesis is to elaborate the features competitive advantages which influence the consumer loyalty in order to prefer branded cosmetics & toiletries products and then measure the relationship between features of competitive advantage and brand loyalty of consumer. The quantitative approach of study was adopted to collect data for analysis. The data collection technique was the survey method which means the respondents filled in questionnaires. After the explanation of the research methodology the analysis of findings of research was carried out in the next chapter. It was done through making frequency distribution and plotting the data on line charts and bar graphs. Finally the conclusion and recommendations for the future researchers are also given. The findings reveal that the factors of brand competitive advantage do have an influence on the consumers’ loyalty for that brand in case of cosmetics and toiletries. However some factors have a greater influence then the others.
{"title":"The Influence of Brands Competitive Advantage on the Consumer Loyalty","authors":"Rawan Basrawi","doi":"10.2139/ssrn.1572821","DOIUrl":"https://doi.org/10.2139/ssrn.1572821","url":null,"abstract":"With the highly dynamic global marketing system and the increasing importance of specialized products, the position and worth of brands has also increased. Each brand tries to provide a competitive advantage to its customers so they buy the product the first time and then keep buying it. This dissertation attempts to measure the influence of the competitive advantage provided by the brands on the loyalty the consumers feel for that brand. The main aim of this thesis is to elaborate the features competitive advantages which influence the consumer loyalty in order to prefer branded cosmetics & toiletries products and then measure the relationship between features of competitive advantage and brand loyalty of consumer. The quantitative approach of study was adopted to collect data for analysis. The data collection technique was the survey method which means the respondents filled in questionnaires. After the explanation of the research methodology the analysis of findings of research was carried out in the next chapter. It was done through making frequency distribution and plotting the data on line charts and bar graphs. Finally the conclusion and recommendations for the future researchers are also given. The findings reveal that the factors of brand competitive advantage do have an influence on the consumers’ loyalty for that brand in case of cosmetics and toiletries. However some factors have a greater influence then the others.","PeriodicalId":259514,"journal":{"name":"ERPN: Other Product Strategies (Sub-Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132973964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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