This research demonstrates that the type of product option framing (additive vs. subtractive) and the temporal distance between an option choice and later buying behavior can influence decision difficulty. In two studies, the authors show that consumers who engage in additive option framing experience greater difficulty in making decisions for the near future than for the distant future, whereas consumers who engage in subtractive option framing experience greater difficulty in making decisions for the distant future than for the near future. In addition, by using theories of mental simulation, the authors show that communication strategies that promote process simulations for distant‐future choices in the subtractive option framing condition and those that promote outcome simulations for near‐future choices in the additive option framing condition are most effective in reducing decision difficulty. These effects hold across varying product categories and varying option prices.
PurposeThe purpose of the study is to clarify the quality of home delivery logistics services from the perspectives of customers and provide insight to aid the prioritization of service quality improvements and guide managerial strategic planning.Design/methodology/approachThe study used a three-dimensional model that integrated Kano model, goal difficulty (GD) and importance–performance analysis (IPA) for investigating service quality aspects emphasized by customers and determine which attributes should be prioritized according to an enterprise's resource and capability constraints. Data were collected through questionnaires administered to the customers and managers of five primary home delivery logistics service enterprises and six small to medium-sized enterprises in Taiwan. Improving the quality of home delivery logistics services has become of increased interest for enterprises.FindingsThe three most important attributes, ranked in order of priority for improvement, were the protection of customers' personal information, delivery of products without damage and reasonable compensation standards for product damage. The study concludes that enterprises should prioritize the improvement of these attributes. Implications, detailed explanations and directions for further investigations are also proposed.Originality/valueThe study discusses the importance and relevant satisfaction levels of service quality attributes from the perspective of customers while also considering the limitations of companies' resources and capabilities. The results indicate that the method can be used to identify service quality attributes of home delivery logistics and formulate strategies for enhancing customer satisfaction.
In the rapid development of public transportation led, the traffic flow prediction has become one of the most crucial issues, especially estimating the number of passengers using the Mass Rapid Transit (MRT) system. In general, predicting the passenger flow of traffic is a time-series problem that requires external information to improve accuracy. Because many MRT passengers take cars or buses to MRT stations, this study used external information from vehicle detection (VD) devices to improve the prediction of passenger flow. This study proposed a deep learning architecture, called a multiple-attention deep neural network (MADNN) model, based on historical MRT passenger flow and the flow from surrounding VD devices that estimates the weights of the vehicle detection devices. The model consists of (1) an MRT attention layer (MRT-AL) that generate hidden features for MRT stations, (2) a surrounding VD (SVD) attention layer (SVD-AL) that generate hidden features for SVD devices, and (3) an MRT-SVD attention layer (MRT-SVD-AL) that generate attention weights for each VD device in an MRT station. The results of the investigation indicated that the MADNN model outperformed the models without multiple-attention mechanisms in predicting the passenger flow of MRT traffic.
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