New restaurants often do not manage to succeed within a reasonable amount of time. Exotic restaurants especially face the problem that price promotions may not attract new customers because prospective customers might associate very low prices for unfamiliar food with a high functional risk. This paper describes how Pay-What-You-Want (PWYW), a new pricing mechanism, was successfully implemented at Kish, a moderately priced Persian restaurant in downtown Frankfurt. After the initial testing phase, which had the characteristics of a promotional offer, the restaurant decided to permanently offer its buffet lunch under PWYW conditions. We report the long-term effects of this decision as well as a simulation demonstrating that profitability is mainly based on 'trading up' the continuous inflow of new customers to the more profitable dining offer where prices are fixed.
{"title":"Kish: Where Customers Pay As They Wish","authors":"Ju-Young Kim, M. Natter, Martin Spann","doi":"10.2202/1546-5616.1118","DOIUrl":"https://doi.org/10.2202/1546-5616.1118","url":null,"abstract":"New restaurants often do not manage to succeed within a reasonable amount of time. Exotic restaurants especially face the problem that price promotions may not attract new customers because prospective customers might associate very low prices for unfamiliar food with a high functional risk. This paper describes how Pay-What-You-Want (PWYW), a new pricing mechanism, was successfully implemented at Kish, a moderately priced Persian restaurant in downtown Frankfurt. After the initial testing phase, which had the characteristics of a promotional offer, the restaurant decided to permanently offer its buffet lunch under PWYW conditions. We report the long-term effects of this decision as well as a simulation demonstrating that profitability is mainly based on 'trading up' the continuous inflow of new customers to the more profitable dining offer where prices are fixed.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68628369","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}
The US population is changing dramatically, e.g. aging, migrating, becoming more racially diverse and overweight, etc. We examine how these major shifts affect snack consumption and, therefore, how Consumer Packaged Goods (CPG) manufacturers should adjust their business-as-usual practices. We present the development and implementation of DemoImpact, a consumption forecasting model and decision support system for all snacks available on the US market (900+). It captures the effects of 48 demographic variables (age, gender, marital status, obesity, education, income ), and accounts for the effects of significant holidays and seasons. DemoImpact is built on 6 years of historical snack consumption and uses the US Census demographic predictions to forecast consumption frequency and volume. The model was implemented at a leading CPG company and forecasted consumption well. The DSS enables top and mid-level managers first to understand how the consumer is changing, and then how to prepare for the change in tastes and how to develop accurate forecasts. DemoImpact has been used, validated and extended since 2006. We show how the CPG manufacturer embraced the results of the model, adjusted its business strategy and impacted retailers in its sales channel.
{"title":"DemoImpact: Modeling, Forecasting and Managing the Impact of Major US Sociodemographic Trends on Multi-Category Snack Consumption","authors":"Marina Girju, M. Adams, B. Ratchford","doi":"10.2202/1546-5616.1115","DOIUrl":"https://doi.org/10.2202/1546-5616.1115","url":null,"abstract":"The US population is changing dramatically, e.g. aging, migrating, becoming more racially diverse and overweight, etc. We examine how these major shifts affect snack consumption and, therefore, how Consumer Packaged Goods (CPG) manufacturers should adjust their business-as-usual practices. We present the development and implementation of DemoImpact, a consumption forecasting model and decision support system for all snacks available on the US market (900+). It captures the effects of 48 demographic variables (age, gender, marital status, obesity, education, income\u0085), and accounts for the effects of significant holidays and seasons. DemoImpact is built on 6 years of historical snack consumption and uses the US Census demographic predictions to forecast consumption frequency and volume. The model was implemented at a leading CPG company and forecasted consumption well. The DSS enables top and mid-level managers first to understand how the consumer is changing, and then how to prepare for the change in tastes and how to develop accurate forecasts. DemoImpact has been used, validated and extended since 2006. We show how the CPG manufacturer embraced the results of the model, adjusted its business strategy and impacted retailers in its sales channel.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627933","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}
Sound marketing decisions often require understanding the cause-and-effect relationships between treatment and outcomes. Market research traditionally approaches such questions by designing randomized experiments that aim to isolate the effects of the specific treatment from other effects. We review an alternate methodology that is well suited to observational studies, where the analyst cannot control how treatment is applied. The methodology uses propensity scoring and matching to emulate the randomization of treatment. It is well established in other fields, but not widely known among marketers in spite of the fact that non-experimental data is common in marketing studies. We present two applications as case studies to illustrate the value of the methodology and to describe how we addressed some of the practical issues, in sufficient detail for readers to be able to use the methodology in similar studies.
{"title":"Analyzing Causal Effects with Observational Studies for Evidence-based Marketing at IBM","authors":"S. Manganaris, Ruchi Bhasin, M. Reid, K. Hermiz","doi":"10.2202/1546-5616.1116","DOIUrl":"https://doi.org/10.2202/1546-5616.1116","url":null,"abstract":"Sound marketing decisions often require understanding the cause-and-effect relationships between treatment and outcomes. Market research traditionally approaches such questions by designing randomized experiments that aim to isolate the effects of the specific treatment from other effects. We review an alternate methodology that is well suited to observational studies, where the analyst cannot control how treatment is applied. The methodology uses propensity scoring and matching to emulate the randomization of treatment. It is well established in other fields, but not widely known among marketers in spite of the fact that non-experimental data is common in marketing studies. We present two applications as case studies to illustrate the value of the methodology and to describe how we addressed some of the practical issues, in sufficient detail for readers to be able to use the methodology in similar studies.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68628023","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}
We present a model that deals with the challenge of forecasting market acceptance and technology evolution along the product lifecycle, pre-launch. Market growth is driven by product's utility increase due to technology evolution, while firms' product improvements strategies are motivated by market growth and directed by market preferences. The interdependency between utility increase and market growth makes the problem inherently dynamic. To find the dependency of utility increase on market growth, we conduct an industry and technology analysis that follows industry financial policies, innovation orientation, industry players' inter-relations and technologic capabilities. For relating market preferences and purchase intentions to product's utility, we use data collected by a conjoint study. The ability to collect and interpret data about both demand and supply aspects, before the product is introduced, leads to a pre-launch forecasting. The evolution of the cumulative adoption level over time, as a result of the technology evolution, and vice versa, is based on both customer purchase decision processes and firms responses. We demonstrate the applicability of the model on the digital camera market.
{"title":"A Utility-Based Diffusion Model Applied to the Digital Camera Case","authors":"Yair Orbach, G. Fruchter","doi":"10.2202/1546-5616.1105","DOIUrl":"https://doi.org/10.2202/1546-5616.1105","url":null,"abstract":"We present a model that deals with the challenge of forecasting market acceptance and technology evolution along the product lifecycle, pre-launch. Market growth is driven by product's utility increase due to technology evolution, while firms' product improvements strategies are motivated by market growth and directed by market preferences. The interdependency between utility increase and market growth makes the problem inherently dynamic. To find the dependency of utility increase on market growth, we conduct an industry and technology analysis that follows industry financial policies, innovation orientation, industry players' inter-relations and technologic capabilities. For relating market preferences and purchase intentions to product's utility, we use data collected by a conjoint study. The ability to collect and interpret data about both demand and supply aspects, before the product is introduced, leads to a pre-launch forecasting. The evolution of the cumulative adoption level over time, as a result of the technology evolution, and vice versa, is based on both customer purchase decision processes and firms responses. We demonstrate the applicability of the model on the digital camera market.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627864","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}
This paper studies competition and financial market performance of the US grocery sector over a period of almost 50 years (1960-2007), using the Sharpe-Fama-French-Carhart standard performance attribution methodology. Following Hou and Robinson (2006), investors in industries with strong (resp. weak) competitive pressures demand a positive (resp. negative) return premium commensurate with the competitive risk incurred. We examine whether such a Hou-Robinson premium exists in the US grocery sector.We find no statistical evidence of a Hou-Robinson premium for the sector as a whole. The sector thus appears to be correctly priced. When, however, our sample is separated into larger and smaller firms, the portfolio of larger firms exhibits a positive Hou-Robinson premium, while that of smaller firms exhibits a negative premium. This result supports the dual nature of competition in the US grocery sector.Furthermore, when we compare the risk-adjusted average return of the larger retail firms in the grocery portfolio with that of the larger manufacturing firms in the same portfolio, we find no statistically significant difference. The larger manufacturing and retail firms in the grocery supply chain seem to share the same level of competitive risk. However, the portfolio of smaller manufacturing firms does not exhibit the negative premium seen in the portfolio of smaller retail firms, indicating that the latter are able to shield themselves from competitive pressures in a way that their manufacturing counterparts are not.
{"title":"Competition, Risk and Return in the US Grocery Industry","authors":"M. Corstjens, Ludo Vanderheyden","doi":"10.2202/1546-5616.1095","DOIUrl":"https://doi.org/10.2202/1546-5616.1095","url":null,"abstract":"This paper studies competition and financial market performance of the US grocery sector over a period of almost 50 years (1960-2007), using the Sharpe-Fama-French-Carhart standard performance attribution methodology. Following Hou and Robinson (2006), investors in industries with strong (resp. weak) competitive pressures demand a positive (resp. negative) return premium commensurate with the competitive risk incurred. We examine whether such a Hou-Robinson premium exists in the US grocery sector.We find no statistical evidence of a Hou-Robinson premium for the sector as a whole. The sector thus appears to be correctly priced. When, however, our sample is separated into larger and smaller firms, the portfolio of larger firms exhibits a positive Hou-Robinson premium, while that of smaller firms exhibits a negative premium. This result supports the dual nature of competition in the US grocery sector.Furthermore, when we compare the risk-adjusted average return of the larger retail firms in the grocery portfolio with that of the larger manufacturing firms in the same portfolio, we find no statistically significant difference. The larger manufacturing and retail firms in the grocery supply chain seem to share the same level of competitive risk. However, the portfolio of smaller manufacturing firms does not exhibit the negative premium seen in the portfolio of smaller retail firms, indicating that the latter are able to shield themselves from competitive pressures in a way that their manufacturing counterparts are not.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627495","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}
This paper examines the effect of privacy concerns on purchase likelihood. In many consumer-seller relationships, consumers are required to provide personal information to marketers in order to get better service. The law requires that marketer provide a statement of their privacy policies to consumers. Secondary disclosure is a key aspect of privacy policies. Consumers are concerned about giving up privacy, but the impact of such concerns on purchase behavior is unclear. By integrating several marketer-controlled variables like price, product, and channels with a marketers secondary disclosure policy, the study measures the relative importance of the latter along with the effects of secondary disclosure and internet attitudes. The results show that secondary disclosure attitudes influence the weight of secondary disclosure policies in purchase situations. The results also indicate that price sensitivity is influenced by secondary disclosure policies. The clear implications are that privacy concerns do matter and that privacy policies and marketing strategies cannot be set in isolation of each other. The findings are discussed in relation to prior research on privacy and future research directions. Statistical techniques used in the paper include structural equation models, conjoint analysis, and simultaneous equation regression.
{"title":"The Privacy Paradox: The Case of Secondary Disclosure","authors":"Giles D'souza, J. Phelps","doi":"10.2202/1546-5616.1072","DOIUrl":"https://doi.org/10.2202/1546-5616.1072","url":null,"abstract":"This paper examines the effect of privacy concerns on purchase likelihood. In many consumer-seller relationships, consumers are required to provide personal information to marketers in order to get better service. The law requires that marketer provide a statement of their privacy policies to consumers. Secondary disclosure is a key aspect of privacy policies. Consumers are concerned about giving up privacy, but the impact of such concerns on purchase behavior is unclear. By integrating several marketer-controlled variables like price, product, and channels with a marketers secondary disclosure policy, the study measures the relative importance of the latter along with the effects of secondary disclosure and internet attitudes. The results show that secondary disclosure attitudes influence the weight of secondary disclosure policies in purchase situations. The results also indicate that price sensitivity is influenced by secondary disclosure policies. The clear implications are that privacy concerns do matter and that privacy policies and marketing strategies cannot be set in isolation of each other. The findings are discussed in relation to prior research on privacy and future research directions. Statistical techniques used in the paper include structural equation models, conjoint analysis, and simultaneous equation regression.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627636","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}
In the online world, publishers place ads from advertisers adjacent to internet search results for a given keyword. To sell such advertising, web publishers auction multiple ad slots using a generalized second-price auction. In this paper, we compare two auction policies that publishers can use to determine the rank and payments of bidding advertisers. The first policy, the highest bid policy, ranks ads based on the bids submitted while the second policy, the highest profit policy, ranks ads based on the expected profit generated to the publisher. Interestingly, we find that the highest profit policy may generate lower publisher profits per keyword even though it uses more information. Subsequently, we use data from a search engine and empirically establish that the correlation between valuations and click through rates are positive, an important assumption in our theoretical model. This finding provides significant support for the theoretical results.
{"title":"A Theoretical and Empirical Analysis of Alternate Auction Policies for Search Advertisements","authors":"S. Balachander, Karthik N. Kannan, D. Schwartz","doi":"10.2202/1546-5616.1101","DOIUrl":"https://doi.org/10.2202/1546-5616.1101","url":null,"abstract":"In the online world, publishers place ads from advertisers adjacent to internet search results for a given keyword. To sell such advertising, web publishers auction multiple ad slots using a generalized second-price auction. In this paper, we compare two auction policies that publishers can use to determine the rank and payments of bidding advertisers. The first policy, the highest bid policy, ranks ads based on the bids submitted while the second policy, the highest profit policy, ranks ads based on the expected profit generated to the publisher. Interestingly, we find that the highest profit policy may generate lower publisher profits per keyword even though it uses more information. Subsequently, we use data from a search engine and empirically establish that the correlation between valuations and click through rates are positive, an important assumption in our theoretical model. This finding provides significant support for the theoretical results.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68628157","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}
Regression analysis with time series data is frequently used in marketing research. However, despite its popularity and ease of interpretation it cannot provide any information regarding the relations between marketing time series over different frequencies. This article proposes a new research tool, wavelet analysis, that when incorporated in regression analysis can provide some frequency domain insights about the effectiveness of marketing instruments over different cycles. In addition, by adopting appropriate regression-modeling techniques, wavelets can provide increased estimation and prediction accuracy of marketing causal effects.
{"title":"Regression Analysis of Marketing Time Series: A Wavelet Approach with Some Frequency Domain Insights","authors":"Antonis A. Michis","doi":"10.2202/1546-5616.1083","DOIUrl":"https://doi.org/10.2202/1546-5616.1083","url":null,"abstract":"Regression analysis with time series data is frequently used in marketing research. However, despite its popularity and ease of interpretation it cannot provide any information regarding the relations between marketing time series over different frequencies. This article proposes a new research tool, wavelet analysis, that when incorporated in regression analysis can provide some frequency domain insights about the effectiveness of marketing instruments over different cycles. In addition, by adopting appropriate regression-modeling techniques, wavelets can provide increased estimation and prediction accuracy of marketing causal effects.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627760","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}
This paper explores the link between quality, cost and concentration. Using concentration and cost data and product quality indicators for 2,244 products in over eighty industries in 1997 and 2002 in the US, a two-stage, ordered probit, random effects estimation explores the impact of concentration and cost on quality. The results demonstrate that overall market concentration and high fixed costs are both positively correlated with product quality across most industries. Generally, when either industry concentration or industry fixed costs increase, the likelihood of the product being higher quality increases and the likelihood of observing a lower quality decreases. Further, the authors confirm that prices are a good signal of product quality. A theoretical model is also derived.
{"title":"Quality, Sunk Costs and Competition","authors":"J. Crespi, S. Marette","doi":"10.2202/1546-5616.1096","DOIUrl":"https://doi.org/10.2202/1546-5616.1096","url":null,"abstract":"This paper explores the link between quality, cost and concentration. Using concentration and cost data and product quality indicators for 2,244 products in over eighty industries in 1997 and 2002 in the US, a two-stage, ordered probit, random effects estimation explores the impact of concentration and cost on quality. The results demonstrate that overall market concentration and high fixed costs are both positively correlated with product quality across most industries. Generally, when either industry concentration or industry fixed costs increase, the likelihood of the product being higher quality increases and the likelihood of observing a lower quality decreases. Further, the authors confirm that prices are a good signal of product quality. A theoretical model is also derived.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68628069","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}
Ralitza Nikolaeva, Manohar U. Kalwani, W. T. Robinson, S. Sriram
Survival determinants can influence important strategic decisions. Analysis of longitudinal data on 418 online retailers in fourteen Bizrate product categories yields insights into key survival determinants. Survival tends to be easier for introductory versus growth stage entrants. Survival, however, is more difficult for e-tailers who sell touch and feel products and for pure e-tailers who do not have the support of either brick and mortar stores or catalog operations. While introductory stage entrants have higher survival rates versus growth stage entrants, there is no significant impact for rank order of entry or pioneer leadtime. These mixed results point to modest early mover advantages in online retailing. While some e-tailers like Amazon.com benefited from moving early, many others suffered from pioneer burnout.
{"title":"Survival Determinants for Online Retailers","authors":"Ralitza Nikolaeva, Manohar U. Kalwani, W. T. Robinson, S. Sriram","doi":"10.2202/1546-5616.1075","DOIUrl":"https://doi.org/10.2202/1546-5616.1075","url":null,"abstract":"Survival determinants can influence important strategic decisions. Analysis of longitudinal data on 418 online retailers in fourteen Bizrate product categories yields insights into key survival determinants. Survival tends to be easier for introductory versus growth stage entrants. Survival, however, is more difficult for e-tailers who sell touch and feel products and for pure e-tailers who do not have the support of either brick and mortar stores or catalog operations. While introductory stage entrants have higher survival rates versus growth stage entrants, there is no significant impact for rank order of entry or pioneer leadtime. These mixed results point to modest early mover advantages in online retailing. While some e-tailers like Amazon.com benefited from moving early, many others suffered from pioneer burnout.","PeriodicalId":35829,"journal":{"name":"Review of Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1546-5616.1075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68627667","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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