Interactive eshopping experience: an empirical investigation

Utilizing an experimental design, the study investigates the effects of eshopping behavior (experiential, utilitarian, or mixed) and interactivity level (low or high) on the consequences of eshopping (site attitude and future purchase intentions), as mediated by eshopping experience (sensory, affective, and cognitive) and flow experience (control, attention focus, and cognitive enjoyment). Structural equation modeling was used for data analysis. Eshopping behavior had a weak negative effect, and interactivity level had a weak positive effect, on eshopping experience. Experiential eshopping behavior decreased eshopping experience more than mixed or utilitarian eshopping behavior did. The latter two behaviors were not significantly different from each other in terms of eshopping experience. High interactivity level web sites increased eshopping experience more than low interactivity level sites did. Interactivity level had a weak negative effect on flow's control dimension and a moderate positive effect on flow's cognitive enjoyment component. High interactivity level sites moderately increased cognitive enjoyment more than low interactivity level sites did. Eshopping experience strongly and positively influenced flow experience in terms of control and cognitive enjoyment, and moderately impacted attention focus. Cognitive enjoyment had a strong positive effect on site attitude and future purchase intentions. However, control and attention focus did not significantly affect future purchase intentions. The study found an indirect effect of eshopping behavior on site attitude, instead of the traditional effect of attitude on behavior based on the theory of reasoned action and technology acceptance model. The results of the pilot study (N = 105) were consistent with the final study (N = 310). The study attempts to add to the small base of existing studies that examine eshopping experience and flow theory in an ecommerce setting (Novak et al. 2003; Skadberg and Kimmel 2004). The present study contributes to the online consumer behavior literature by utilizing flow theory and investigating the mediating effects of eshopping experience and flow experience on the consequences of eshopping. The findings should help inform web site design, facilitating the creation of sites which are more responsive to users by providing interactive features and understanding eshopping behaviors which users exhibit

Alternativas sostenibles para la logística urbana en la era del comercio electrónico: sistema M4G (Metro for Goods)

Las ciudades están en el centro de la vida humana y están viviendo un intenso proceso de transformación en distintos ámbitos (urbanización, medioambiente, movilidad, economía, digitalización) que, de forma conjunta, tratan de innovar para mejorar la calidad de vida de sus habitantes. Dentro de las ciudades, la distribución urbana de mercancías aparece como uno de los grandes retos para el sector del transporte y la logística, afectando directamente a este proceso de transformación de la ciudad. La aparición de la covid-19 ha tenido especial relevancia e impacto en la movilidad de la ciudad y en el comercio electrónico. El B2C, o negocio de empresa a consumidor, ha crecido notablemente en los últimos años y se ha acelerado a raíz de la pandemia, aumentando el acceso de la población a Internet y produciendo cambios en los hábitos de comportamiento de los consumidores. En las áreas metropolitanas, el “efecto Amazon” (amplia selección de minoristas online, envío rápido, devoluciones gratuitas y precios bajos) ha llevado a un mayor uso de vehículos ligeros en el reparto del comercio electrónico en las ciudades. Este hecho está afectando el funcionamiento racional del sistema de transporte urbano de mercancías, incluyendo un alto grado de fragmentación, baja optimización de carga y, entre otras externalidades, mayor congestión del tráfico. Esta tesis investiga el potencial uso de la red de metro, en una gran ciudad como Madrid, para proporcionar servicios de entrega de paquetes de e-commerce aprovechando su actual capacidad de transporte disponible y utilizando las estaciones para la entrega de los paquetes. Se definen las características de un nuevo modelo de distribución mixto (M4G: Metro for Goods) para entregas de última milla asociadas al comercio electrónico a través de trenes compartidos con viajeros o trenes exclusivos de carga de paquetes. La investigación cuantifica la demanda de paquetes para las tres alternativas de entrega al cliente e-commerce: taquillas inteligentes, centros de recogida o domicilio del cliente. Los resultados muestran que el coste total por paquete de los escenarios propuestos del modelo M4G es menor que el coste actual de reparto a través de furgonetas, siendo significativamente menores las externalidades sociales y los costes medioambientales derivados del nuevo modelo. Con relación a los costes operativos, se demuestra que la utilización del modelo M4G podría ser una alternativa eficiente en las entregas que se puedan realizar dentro de la estación. Esta tesis doctoral contribuye al estado del arte e identifica una alternativa viable que permita emplear sinergias entre el transporte público de personas y la distribución urbana de mercancías. La tesis muestra las diferentes medidas e iniciativas que se están proponiendo para la mejora de la logística urbana y como el modelo M4G puede ser una opción a considerar por los grupos de interés públicos y privados

Header for SPIE use eShopper Modeling and Simulation

The advent of e-commerce gives an opportunity to shift the paradigm of customer communication into a highly interactive mode. The new generation of commercial Web servers, such as the Blue Martini’s server, combines the collection of data on a customer behavior with real-time processing and dynamic tailoring of a feedback page. The new opportunities for direct product marketing and cross selling are arriving. The key problem is what kind of information do we need to achieve these goals, or in other words, how do we model the customer? The paper is devoted to customer modeling and simulation. The focus is on modeling an individual customer. The model is based on the customer’s transaction data, click stream data, and demographics. The model includes the hierarchical profile of a customer’s preferences to different types of products and brands; “consumption ” models for the different types of products; the current focus, trends, and stochastic models for time intervals between purchases; product affinity models; and some generalized features, such as purchasing power, sensitivity to advertising, price sensitivity, etc. This type of model is used for predicting the date of the next visit, overall spending, and spending for different types of products and brands. For some type of stores (for example, a supermarket) and stable customers, it is possible to forecast the shopping lists rather accurately. The forecasting techniques are discussed. The forecasting results can be used for on-line direct marketing, customer retention, and inventory management. The customer model can also be used as a generative model for simulating the customer’s purchasing behavior in different situations and for estimating customer’s features