Purpose
The purpose of this paper is to examine the effects of final delivery solutions on e-shopping usage behaviour by modelling their interaction across residents living in different neighourhoods with availabilities of different facilities, including automated parcel stations (APSs), collection and delivery points (CDPs), and the direct-to-home delivery stations of parcel express firms (PEFs).
Design/methodology/approach
The study is based on a survey on e-shopping behaviour and delivery awareness. A mixed structural equation model is used to predict the interactions among availability of final delivery facilities (AFDF), level of satisfaction with delivery services and e-shopping usage after controlling individual socioeconomic attributes and retail environment.
Findings
Compared with AFDF, individual socioeconomic attributes are the most influential factors contributing to e-shopping spending and frequency. Improving AFDF has only a slight effect on e-shopping spending, while a larger impact on e-shopping frequency and perceived satisfaction to delivery services is observed. The quantity of PEF delivery stations has a relatively large influence on e-shopping usage but the effects of APSs and CDPs are not as strong as expected.
Research limitations/implications
The causality between final delivery solutions and e-shopping behaviour can be further tested by using social experiments or longitudinal data.
Practical implications
All findings will help business and public policy decision makers to derive a balanced and effective deployment of final delivery solutions, which is also referential for other emerging markets similar to China.
Originality/value
This study theoretically contributes to the international literature by examining the heterogeneous effects of final delivery solutions on different aspects of e-shopping engagement.
Introduction: Current studies estimated a general incubation period distribution of COVID-19 based on early-confirmed cases in Wuhan, and have not examined whether the incubation period distribution varies across population segments with different travel histories. We aimed to examine whether patients infected by community transmission had extended incubation periods than the early generation patients who had direct exposures to Wuhan. Methodology: Based on 4741 patient case reports from municipal centers of disease control by February 21, 2020, we calculated the incubation periods of 2555 patients with clear epidemiological survey information and illness development timeline. All patients were categorized into five groups by their travel histories. Incubation period distributions were modeled for each group by the method of the posterior Weibull distribution estimation. Results: Adults aged 30 to 59 years had the most substantial proportion of confirmed cases in China. The incubation period distribution varied slightly across patient groups with different travel histories. Patients who regularly lived in Wuhan and left to other locations before January 23, 2020 had the shortest posterior median value of 7.57 days for the incubation period, while the incubation periods for persons affected by local community transmission had the largest posterior median of incubation periods, 9.31 days. Conclusions: The median incubation period for all patients infected outside Wuhan was 9 days, a bit of more extended than the early estimated 5-day incubation period that was based on patients in Wuhan. Our findings may imply the decreases of virulence of the COVID-19 virus along with intergenerational transmission.
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