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This paper investigates the buy online and pick up in-store cooperation (BOPSC) of online and offline retailers. Specifically, this study solves the following questions: (1) What is the impact of BOPSC on their optimal price and sales volume of products? (2) When should an online retailer and an offline retailer conduct the BOPSC strategy with each other?

The paper first establishes two game models to explore the equilibriums of online and offline retailers in non-BOPSC and BOPSC. Then the condition for online and offline retailers to implement BOPSC strategy are determined. Furthermore, the applicability of the BOPSC strategy is enhanced by incorporating numerical analysis.

The study’s findings reveal that BOPSC strategy will not always beneficial to online and offline retailers, which depends on the total cost of online shopping and the product valuation of consumers. BOPSC strategy leads to the increase of prices and online orders, and the demand of offline retailer is eroded. Moreover, BOPS cooperation between different retailers is easier to achieve than omni-channel integration strategy. When the convenience difference between offline shopping and BOPSC pick-up is moderate, the effectiveness of BOPSC strategy can be improved.

This study has the following two main contributions: Firstly, the authors investigate the effects of BOPSC strategy on the prices of online and offline retailers. The study results show that the BOPSC strategy alleviates price competition and promotes a win–win situation between online retailers and offline retailers. Secondly, this paper mainly studies the cooperative behavior between online and offline retailers and reveals the optimal conditions for online and offline retailers to adopt BOPSC strategy. It can help small- and medium-sized online and offline retailers to choose suitable products for BOPSC strategy, so as to achieve the purpose of increasing profit.

Omnichannel sales have provided new impetus for the development of catering merchants. The authors thus focus on how catering merchants should manage capacities at the ordering, production and delivery stages to meet customers’ needs in different channels under third-party platform delivery and merchant self-delivery. This is of great significance for the development of the omnichannel catering industry.

This paper formulates the capacity decisions of omnichannel catering merchants under the third-party platform delivery and merchant self-delivery mode. The authors mainly use queuing theory to analyze the queuing behavior of online and offline customers, and the impact of waiting time on customer shopping behavior. In addition, the authors also characterize the merchant’s capacity by the rate in queuing model.

The authors find that capacities at ordering stage and food production stage are composed of base capacities and safety capacities, but the delivery capacities only have the latter. And in the self-delivery mode, merchants can develop higher safety capacities by charging delivery fees. The authors prove that regardless of the delivery mode, omnichannel sales can bring higher profits to merchants by integrating demand.

The authors focus on analyzing the capacity management of omnichannel catering merchants at the ordering, production and delivery stages. And the authors also add the delivery process into the omnichannel for analysis, so as to solve the problem of capacity decision-making under different delivery modes. The management of delivery capacity and its impact on other stages’ capacities are not covered in other literature studies, which is one of the main innovations of this paper.

Electronic‐retailing is the buzzword of 2000. Every other press release I receive relates to electronic commerce or Internet shopping. Therefore, it seems appropriate to focus this summer issue of Retail Insights on the subject. The first article by Rowley discusses the phenomenon of shopping bots, the intelligent agents designed to support comparison shopping across a number of Internet sites. She reviews the functions and evaluates the coverage of different shopping bots. In the second article, Wee and Ramachandra assess the level of cyberbuying activities in China, Hong Kong and Singapore by concentrating on the who, why and what of online retailing.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Relatively little research explicitly considers how a firm’s channel decisions affect consumer tradeoffs between the cognitive costs of online search and the physical costs of visiting a retail store. This study aims to argue that opening a new retail store affects both cognitive and physical search costs, but that the magnitude of these effects depends on the location of the consumer relative to the new store. In addition, individual differences in consumers’ cognitive costs that should moderate these effects.

This study takes the opening of multiple retailer stores on the same day as the nature experiment. Combining with data from other sources, this study tests the hypotheses using conditional logit for the correction of incidental problem in the traditional fix-effect logit model. Endogeneity issue is tested with the control function approach.

This study finds that opening a new store leads to a 136% increase in the odds of online visiting by consumers who live in the broader metropolitan area of the new store, while it leads to a 43% decrease in the odds of online visiting by consumers who live in the nearby shopping region. However, the effect of store entry on increasing website search is weaker for consumers who are more loyal, and the effect of store entry on decreasing website search is weaker for consumers who are more efficient in searching online.

This study adds to prior research by identifying when, and for which consumers, opening a physical store has complimentary versus substitutive effects on online search behavior.

The purpose of this paper is to study how cost efficiency and the reliability of a physical distribution network are affected by changes in online shopping demand and to suggest how logistics service providers can respond to such changes.

Based on a discrete event simulation approach, possible adaptive measures to online shopping demand increase are tested at three levels of decision making in parcel distribution network: priority assignment in the main hub (operational), introduction of sub‐hubs (tactical), and increase in the hub‐terminal capacity (strategic). The feasibility of the simulation is tested with parameters adopted from the logistics service data of an existing major parcel carrier in South Korea.

Findings from the simulation model suggest that the existing physical distribution network can improve its cost efficiency and service reliability by evolving into a more centralized network structure with increased capacity of transshipment facilities if the online shopping demand is expected to increase consistently over the long run.

This research will help logistics service providers to have good insights into performances of their distribution networks at different levels of demand and to devise a plan for adaptation to meet future demand.

This paper provides a framework to understand the complex relationship between network configurations, service levels, costs, and other decision‐making processes with respect to changes in online shopping demand based on a simulation approach.