Omnichannel distribution is a retail innovation that provides a seamless purchasing experience to customers through cohesive experience across channels, cross-channel integration, and integrated assistance. Blurring the lines between offline and online shopping, concepts like “Buy-Online-Pickup-In-Store” (BOPIS) and “Buy-In-Store-Ship-Direct” (BSSD) are increasingly becoming accepted in retail operations. While many retailers are still in a nascent phase of integrating online channels and physical stores, consumer-centric studies are called for to investigate the diffusion of these new strategies in the evolving marketplace. Our study explores the key adoption determinants of these new omnichannel strategies, focusing on the case of India. A detailed online survey was used to collect data for a sample of 311 Indian consumers. Econometric analysis reveals the main purchase influencing factors. We find that a quick purchase process, elimination of product delivery delays, delivery and shipping costs, ease of receiving product, retail system reliability and, trust in retailer are key adoption determinants. Purchase returnability is only weakly associated with BOPIS purchase choices while payment security has no significant effect. Among six demographic variables, only gender and age are found to differ significantly between the two concepts. These insights from this study should be useful for retailers to design omnichannel strategies and for transport policy makers to predict the future growth of e-commerce related transport movements.

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that “activities” that inspire trip-making for passenger is equivalent to “freight orders” in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments’ preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models.