Retailers often use the promotion strategy of offering supplementary products (e.g., free gift, bundle) to attract consumers and increase sales. Despite the growing literature on the promotions that are differently framed but offer economically identical values, little research has examined the link between promotion framing and consumer product returns. The current article sheds light on this relationship, hypothesizing that a free gift promotion would be superior to a bundle promotion in reducing consumer product returns. The findings suggest that a gift‐framed promotion leads to a lower product return intention than an economically equivalent bundle promotion, because consumers tend to perceive more loss from giving up the gift‐framed (vs. bundle‐framed) deal. Further, this study examines a moderating role of brand familiarity (familiar vs. unfamiliar) and shows that the merits of free gift framing on product return intention via perceived loss are amplified (attenuated) when the promoted brand is familiar (unfamiliar). Overall, the investigations of this study imply that it is better to frame a promotion as a “free gift” than a “bundle” to increase perceived loss in returning the purchase and thus to decrease consumer product returns. This strategic intervention works especially when the gift is offered by familiar brands. 相似文献
This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight S&P500 index and a newly constructed index, Asian Tigers Malaysia Index (ATMI). 11 years historical data of stock indexes futures and the economic statistics are studied; 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.
