Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models

International Journal of Physical Distribution & Logistics Management

ISSN : 0960-0035

Article publication date: 14 March 2018

Issue publication date: 22 March 2018

The purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML’s diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics.


The systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a “snowballing” technique, a total of 47 articles form the basis of the review.

The literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions.

Research limitations/implications

To circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models.

Practical implications

The design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution.

Social implications

Improved LML performance would have positive societal impacts in terms of service and resource efficiency.


This paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions.

  • Literature review
  • Omnichannel
  • Digital supply chains

Lim, S.F.W.T. , Jin, X. and Srai, J.S. (2018), "Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models", International Journal of Physical Distribution & Logistics Management , Vol. 48 No. 3, pp. 308-332.

Emerald Publishing Limited

Copyright © 2018, Stanley Frederick W.T. Lim, Xin Jin and Jagjit Singh Srai

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at


Last-mile delivery has become a critical source for market differentiation, motivating retailers to invest in a myriad of consumer delivery innovations, such as buy-online-pickup-in-store, autonomous delivery solutions, lockers, and free delivery upon minimum purchase levels ( Lim et al. , 2017 ). Consumers care about last-mile delivery because it offers convenience and flexibility. For these reasons, same-day and on-demand delivery services are gaining traction for groceries (e.g. Deliv Fresh, Instacart), pre-prepared meals (e.g. Sun Basket), and retail purchases (e.g. Dropoff, Amazon Prime Now) ( Lopez, 2017 ). To meet customer needs, parcel carriers are increasing investments into urban and automated distribution hubs ( McKevitt, 2017 ). However, there is a lack of understanding as to how best to design last-mile delivery models with retailers turning to experimentations that, at times, attract scepticism from industry observers (e.g. Cassidy, 2017 ). For example, Sainsbury’s, Somerfield, and Asda established innovative pick centres, but closed them down within a few years ( Fernie et al. , 2010 ). eBay launched its eBay Now same-day delivery service in 2012, but in July 2015, it announced the closure of this programme. Google, likewise, opened and then closed its two delivery hubs for Google Express in 2013 and 2015, respectively ( O’Brien, 2015 ).

The development of these experimental last-mile logistics (LML) models, not surprisingly, created uncertainty within increasingly complicated and fragmented distribution networks. Without sustainable delivery economics, last-mile service provision will struggle to survive (as was the experience of Sainsbury’s, Somerfield, Asda, eBay, Google, and Webvan) with retailers increasingly challenged to find an optimal balance between pricing, consumer expectations for innovative new channels, and service levels ( Lopez, 2017 ; McKevitt, 2017 ).

Although several contributions have been made in the LML domain, the literature on LML models remains relatively fragmented, thus hindering a comprehensive and holistic understanding of the topic to direct research efforts. Hitherto, existing studies provide limited or no guidance on how contingency variables influence the selection of LML configurations ( Agatz et al. , 2008 ; Fernie et al. , 2010 ; Mangiaracina et al. , 2015 ; Lagorio et al. , 2016 ; Savelsbergh and Van Woensel, 2016 ). Our paper addresses this knowledge deficiency by reviewing the disparate academic literature to capture key contingency and structural variables characterizing the different forms of last-mile distribution. We then theoretically establish the connection between these variables thereby providing a design framework for LML models. Our corpus is comprised of 47 papers published in 16 selected peer-reviewed journals during the period from 2000 to 2017. The review is performed from the standpoint of retailers operating LML. As such, some LML research streams are deliberately excluded, including issues related to public policy, urban traffic regulations, logistics infrastructure, urban sustainability, and environment.

This paper is structured as follows. First, we provide a working definition for LML and introduce relevant terms. Second, we set out the research methodology and conduct descriptive analyses of the corpus. The substantive part of the paper is an analysis of the literature on LML models and the development of a design framework for LML. The framework synthesises a set of structural and contingency variables and explicates their interrelationships, shedding light on how these interactions influence LML design. Finally, we highlight the key gaps in the extant literature and propose future research opportunities.

Defining LML

The term “last-mile” originated in the telecommunications industry and refers to the final leg of a network. Today, LML denotes the last segment of a delivery process, which is often regarded as the most expensive, least efficient aspect of a supply chain and with the most pressing environmental concerns ( Gevaers et al. , 2011 ). Early definitions of LML were narrowly stated as the “extension of supply chains directly to the end consumer”; that is, a home delivery service for consumers ( Punakivi et al. , 2001 ; Kull et al. , 2007 ). Several synonyms, such as last-mile supply chain, last-mile, final-mile, home delivery, business-to-consumer distribution, and grocery delivery, have also been used.

Despite their nuances, existing LML definitions converge on a common understanding that refers to the last part of a delivery process. However, existing definitions (details available from the authors) appear incomplete in capturing the complexities driven by e-commerce, such as omission in defining an origin ( Esper et al. , 2003 ; Kull et al. , 2007 ; Gevaers et al. , 2011 ; Ehmke and Mattfeld, 2012 ; Dablanc et al. , 2013 ; Harrington et al. , 2016 ); exclusion of in-store order fulfilment processes as a fulfilment option ( Hübner, Kuhn and Wollenburg, 2016 ); and/or non-specification of the destination (or end point), including failure to capture the collection delivery point (CDP) as a reception option ( Esper et al. , 2003 ; Kull et al. , 2007 ). Without a consistent and robust definition of LML, the design of LML models is problematic.

For the purpose of this review, we examine existing terminology on last-mile delivery systems in order to create a working definition for LML. As part of this definition, we introduce the concept of an “order penetration point” ( Fernie and Sparks, 2009 ) as a way of defining the origin of the last-mile. The order penetration point refers to an inventory location (e.g. fulfilment centre, manufacturer site, or retail store) where a fulfilment process is activated by a consumer order. After this point, products are uniquely assigned to the consumers who ordered them, making the order penetration point a natural starting point for LML. The destination point is commonly dictated by the consumer, hence we use “final consignee’s preferred destination point” as the terminology to indicate where an order is delivered. The choice of destination point could be a home/office, a reception box (RB), or a pre-designated CDP.

Last-mile logistics is the last stretch of a business-to-consumer (B2C) parcel delivery service. It takes place from the order penetration point to the final consignee’s preferred destination point.

Extending the above definition, we reference Bowersox et al. ’s (2012) view of a supply chain as a series of “cycles”, with half the cycle being the product/order flow and the other, information flow. We also reference the Supply Chain Operations Reference model ( Supply Chain Council, 2010 ) recognising that LML operates within a broader supply network. In particular, the LML cycle coincides with the consumer service cycle, interfacing direct-to-consumer-goods manufacturers, wholesalers, retailers with the end consumer ( Bowersox et al. , 2012 ). The process may be divided into three sub-processes, namely source, make, and deliver.

These three sub-processes set the focus for this review and they align with the delivery order phase in LML, namely picking, packing, and delivery. This model is consistent with Campbell and Savelsbergh’s (2005) view of the business-to-consumer process comprising order sourcing, order assembly, and order delivery. Accordingly, this review focuses on the examination of LML models: LML distribution structures and the contingency variables associated with these structures. The term “distribution structure” covers the stages from order fulfilment to delivery to the final consignee’s preferred destination point. It includes the modes of picking (e.g. warehouse or store-based), transportation (e.g. direct delivery by the retailer’s own fleet), and reception (e.g. consumer-pickup) ( Kämäräinen et al. , 2001 ). The associated contingency variables provide guidance for decision support by highlighting the key characteristics of each distribution structure for the design and selection, matching product, and consumer attributes ( Boyer and Hult, 2005 ).

Research methodology

A structured literature review aims to identify the conceptual content of a rapidly growing field of knowledge, as well as to provide guidance on theory development and future research direction ( Meredith, 1993 ; Easterby-Smith et al. , 2002 ; Rousseau et al. , 2008 ). Structured reviews differ from more narrative-based reviews because of the requirement to provide a detailed description of the review procedure in order to reduce bias; this requirement thereby increases transparency and replicability ( Tranfield et al. , 2003 ). Therefore, undertaking a structured review ensures the fidelity, completeness, and rigour of the review itself ( Greenhalgh and Peacock, 2005 ).

Our review provides a snapshot of the diversity of theoretical approaches present in LML literature. It does not pretend to cover the entirety of the literature, but rather offers an informative and focused evaluation of purposefully selected literature to answer specific research questions. In the following sections, we discuss the execution of the four main steps (planning, searching, screening, and extraction/synthesis/reporting) as outlined by Tranfield et al. (2003) . We also incorporate literature review guidelines suggested by Saenz and Koufteros (2015) . Our study uses key research questions identified by an expert panel and we reference the Association of Business Schools journal ranking 2015 to decide which journals to include in this scholarship ( Cremer et al. , 2015 ). Our review includes the classification of contributions across methodological domains. In later sections, we utilise insights from the literature review to develop an LML design framework that captures the relationships between distribution structures via a set of structural and associated contingency variables.

What is the current state of research and practice on LML distribution in the e-commerce context?

What are the associated contingency variables that can influence the selection of LML distribution structures?

How can the contingency variables identified in RQ2 be used to inform the selection of LML distribution structures?

The academic material in this study covers the period from 2000 to 2017. This period coincides with critical industry events, such as the emergence and subsequent demise of the online grocer, Webvan. The review is limited to peer-reviewed publications to ensure the quality of the corpus ( Saenz and Koufteros, 2015 ) and considers 16 journals, including one practitioner journal ( MIT Sloan Management Review ), to capture theoretical perspectives on industry best practices. Only articles from the selected journals have been included in this review, with one exception, where we included the article by Wang et al. (2014) , published in Mathematical Problems in Engineering . The article was deemed critical as it represents the only piece of work to date that connects and extends prior research on the evaluation of CDPs.

The 16 journals were selected based on their primary focus on empirical and conceptual development, rather than on their discussion of analytical modelling. Although we appreciate that there are significant research studies in this area (e.g. operations research), the focus of this review led us to primarily consider how scholars conceptualise LML distribution structures and apply theoretical variables to LML design through quantitative, qualitative, or conceptual approaches, rather than through mathematic-based models. The mathematic-based model literature focuses on the development of stylised and optimisation models in areas of multi-echelon distribution systems, vehicle routing problems ( Savelsbergh and Van Woensel, 2016 ), buy-online-pick-up-in-store services ( Gao and Su, 2017 ), pricing and delivery choice, inventory-pricing, delivery service levels, discrete location-allocation, channel design, and optimal order quantities via newsvendor formulation for different fulfilment options ( Agatz et al. , 2008 ), amongst others. These studies typically employ a series of assumptions to simplify real-world operations in order to provide closed-form or heuristic-based prescriptive solutions ( Agatz et al. , 2008 ; Savelsbergh and Van Woensel, 2016 ). Therefore, this review excluded journals with a primarily mathematical modelling or operations research focus. However, it included relevant mathematical modelling articles – published in any of the 16 selected journals – as long as they explicated types of LML distribution structure and/or the associated contingency variables. Finally, this study also excluded general management journals in order to fit the operational focus of this research.

The literature search was conducted on the following databases: ISI Web of Science, Science Direct, Scopus, and ABI/Inform Global. Two search rounds were undertaken to maximise inclusion of all relevant articles. The first literature probe was performed using the following search terms: “urban logistics” OR “city logistics” OR “last-mile logistics” OR “last mile logistics”. To extend the corpus, we incorporated the “snowballing” technique of tracing citations backward and forward to locate leads to other related articles; this study used this process in the second round to supplement a protocol-driven methodology. This approach resulted in new search terms and scholar identification to refine the search strategy as the study unfolded ( Greenhalgh and Peacock, 2005 ). The following new search terms were identified: “home delivery”, “B2C distribution”, “extended supply chain”, “final mile”, “distribution network”, “distribution structure”, and “grocery delivery”. These new keywords were then used to create additional search strings with Boolean connectors (AND, OR, AND NOT). Finally, in order to cross-check the searches, we consulted with a supply chain professor from Arizona State University and one from the University of Cambridge. It is therefore posited that the review coverage is reasonably comprehensive.

Exclusion criteria: paper titles bearing the terms “urban logistics”, “city logistics”, “last-mile logistics”, or “last-mile” but with limited coverage on distribution structures and the associated design variables were excluded (e.g. public policy, urban traffic regulations, logistics infrastructure, urban sustainability, environment), as were editorial opinions, conference proceedings, textbooks, book reviews, dissertations, and unpublished working papers.

Inclusion criteria: papers with coverage of distribution structures and design variables in the e-commerce context were included, regardless of their actual study focus. We included multiple research methods to have both established findings as well as emergent theorising.

During the search phase, we identified 425 articles referencing our subject terms. We eliminated duplicates based on titles and name of authors and rejected articles matching the exclusion criteria. For example, while the paper by Gary et al. (2015) holds the keyword “urban logistics” in the title, it focuses on logistics prototyping, rather than LML models, as a method to engage stakeholders. This paper, therefore, was excluded. The elimination stage resulted in 100 articles being considered relevant for further review. Results were exported to reference management software, EndNote version X8, for further review and to facilitate data management. We then adopted the inclusion criteria to select the final articles. Finally, we grouped the articles into two categories: LML distribution structures and the associated contingency variables. Ultimately, a total of 47 journal articles form the corpus of this review.

Extraction, synthesis, and reporting

Following an initial review of the 47 articles, a summary of each article was prepared using a spreadsheet format organised under descriptive (year, journal, title), methodology (article type, theoretical lens, sampling protocol), and thematic categories (article purpose, context, LML distribution structures, design variables, others) as adapted from Pilbeam et al. (2012) .

Accordingly, we conducted three analyses: descriptive, methodological, and thematic ( Richard and Beverly, 2014 ). The descriptive analysis summarises the research development over the period of interest, and the distribution statistics of the journals. The methodological analysis highlights the research methods employed in the domain, while the thematic analysis synthesises the main outcomes from the extracted literature and provides an overview of the review structure. Reporting structures were organised in a manner that sequentially responds to the research questions posed earlier.

Descriptive analysis

Table I provides summary statistics of the papers reviewed, author affiliations c , identifying contributions, as well as those journals where surprisingly contributions have yet to be made.

Methodological analysis

Typology-oriented provision: owing to the recent proliferation of LML models, a typology-oriented approach was particularly conducive for understanding LML practices. Lee and Whang (2001) , Chopra (2003) , Boyer and Hult (2005) , and Vanelslander et al. (2013) each developed LML structural types to assist design under different consumer and product attributes. These studies mostly captured the linearly “chain-centric” LML models prevalent in the pre-digital era.

Literature review and conceptual studies: several reviews have contributed in this domain. Some papers focused on specific areas, such as the evolution of British retailing ( Fernie et al. , 2010 ) and distribution network design ( Mangiaracina et al. , 2015 ), whereas others discussed several topics at once ( Agatz et al. , 2008 ; Lagorio et al. , 2016 ; Savelsbergh and Van Woensel, 2016 ). Narrowly focused papers identified limited LML structural types or variables influencing distribution network design, while more broadly focused papers examined wider issues in urban, city, or multichannel logistics. Conceptual studies typically provided guidance on the selection of LML “types” based on certain performance criteria (e.g. Punakivi and Saranen, 2001 ; Chopra, 2003 ), or logistics service quality (e.g. Yuan and David, 2006 ).

Empirical studies: these studies mainly compared LML types or demonstrated the impact of particular variables upon LML. Studies undertaking the former research purpose (contrasting types) employed simulations, field/mail surveys, and econometrics to examine performance or CO 2 emissions (e.g. Punakivi et al. , 2001 ). One paper employed a mixed-method approach (case research and modelling) to understand the organisation of the physical distribution processes in omnichannel supply networks ( Ishfaq et al. , 2016 ). Empirical studies aiming at the latter research purpose (evidencing impact) used field and laboratory experiments and statistical methods on survey data to examine the interplay between operational strategies and consumer behaviour (e.g. Esper et al. , 2003 ; Boyer and Hult, 2005 ; Kull et al. , 2007 ). These studies also employed econometrics to examine the effects of cross-channel interventions (e.g. Forman et al. , 2009 ; Gallino and Moreno, 2014 ). Additionally, a few studies used case research to provide operational guidance via framework development, such as last-mile order fulfilment ( Hübner, Kuhn and Wollenburg, 2016 ) and LML design, to capture the interests of various stakeholders ( Harrington et al. , 2016 ).

Mathematical modelling: studies also employed a variety of mathematical tools and techniques to formulate LML problems and find optimum solutions, mostly for vehicle routing problems ( Campbell and Savelsbergh, 2005 ; McLeod et al. , 2006 ; Aksen and Altinkemer, 2008 ; Crainic et al. , 2009 ; Wang et al. , 2014 ). In their work, Campbell and Savelsbergh (2006) combined optimisation modelling with simulation to demonstrate the value of incentives. Other studies focused on identifying optimum distribution strategies (e.g. Netessine and Rudi, 2006 ; Li et al. , 2015 ), inventory rationing policy ( Ayanso et al. , 2006 ), delivery time slot pricing ( Yang and Strauss, 2017 ), and formulating new models to capture emerging practices, such as crowd-sourced delivery ( Wang et al. , 2016 ).

Thematic analysis

The grounded theory approach ( Glasser and Strauss, 1967 ) was used to code and classify emerging repeated concepts and terminologies via the qualitative data analysis software, MAXQDA version 12. The classification was based on the two categories defining LML models: LML distribution structures and their associated contingency variables. Coding of the data was conducted independently by two authors. The distinguishing terms and concepts were documented in a codebook; where their views differed, the issues were discussed until consensus was reached. Terminologies relating to each classification level were either derived from the extant literature or introduced in this paper to unify key concepts.

For the first category, the types of LML distribution structure are classified based on different levels of effort required by vendor and consumer: push-centric, pull-centric, and hybrid. A push-centric system requires the vendor to wholly undertake the distribution functions required to deliver the ordered product(s) to the consumer’s doorstep; a pull-centric system requires the consumer to wholly undertake the collection and transporting function; and a hybrid system requires some effort on the parts of both the vendor and consumer and is varied by the location of the decoupling point. A further breakdown divided the push-centric distribution system into modes of picking (manufacturer-based, distribution centre (DC)-based, and local brick-and-mortar (B&M) store-based); the pull-centric distribution system was divided into modes of collection from fulfilment point (local B&M store and information store); and the hybrid distribution system was divided into modes of CDP (attended collection delivery point (CDP-A) and unattended collection delivery point (CDP-U)).

The second category captures the associated contingency variables commonly used in existing studies. This study created a list of 13 variables that influence the structural forms of last-mile distribution: consumer geographical density, consumer physical convenience, consumer time convenience, demand volume, order response time, order visibility, product availability, product variety, product customisability, product freshness, product margin, product returnability, and service capacity. These variables determine the manner in which, or the efficiency with which, a distribution structure fulfils consumer needs while relating to the idiosyncrasies of product types.

These classifications facilitate the understanding of LML models and enabled future structural variables to be consistently categorised. Figure 1 serves to present a structural overview of the LML models reported in the literature.

Review of LML distribution structures

push: product “sent” to consumer’s postcode by someone other than the consumer;

pull: product “fetched” from product source by the consumer; and

hybrid: product “sent” to an intermediate site, from which the product is “fetched” by the consumer.

Table II summarises the corpus on LML distribution structures.

Push-centric system: n -tier direct to home

This study found that the push-centric system is the most commonly adopted distribution form. It typically comprises a number of intermediate stages ( n -tier) between the source and destination in order to create distribution efficiencies. The literature classifies three picking variants according to fulfilment (inventory) location: manufacturer-based (or “drop-shipping”), DC-based, or local B&M store-based (i.e. retailer’s intermediate warehouse or store). The destination can either be consumers’ homes or, increasingly, their workplaces ( McKinnon and Tallam, 2003 ). The mode of delivery can be in-sourced (using retailer’s own vehicle fleet), outsourced to a third-party logistics provider (3PL) ( Boyer and Hult, 2005 ), or crowd-sourced using independent contractors ( Wang et al. , 2016 ).

When selecting a distribution channel, retailers need to trade-off between fulfilment capabilities, inventory levels ( Netessine and Rudi, 2006 ), product availability and variety ( Agatz et al. , 2008 ), transportation cost ( Rabinovich et al. , 2008 ), and responsiveness ( Chopra, 2003 ). The nearer the picking site is to the consumer segment, the more responsive is the channel. However, this responsiveness comes at the expense of lower-level inventory aggregation and higher risks associated with stock-outs ( Netessine and Rudi, 2006 ).

Pull-centric system: consumer self-help

The literature also discussed two variants of the pull-centric system. Both variants require consumers to participate (or self-help) throughout the transaction process, from order fulfilment to order transportation. The first variant represents the traditional way of shopping at a local B&M store, with consumers performing the last-mile “delivery”. The second “information store” variant adopts a concept known as “dematerialisation”, substituting information flow for material flow ( Lee and Whang, 2001 ). This variant recognises that material or physical flows are typically more expensive than information flows due to the costs of (un)loading, handling, warehousing, shipping, and product returns.

This study found that despite the popularity of online shopping, there are still occasions where consumers favour traditional offline shopping. Perceived or actual difficulties with inspecting non-digital products, the product returns process, or slow and expensive shipping can deter consumers from online shopping ( Forman et al. , 2009 ). This study also demonstrates other benefits of a pull-centric system, including lower capital investments and possible carry-over (or halo) effects into in-store sales ( Johnson and Whang, 2002 ).

Hybrid system: n -tier to consumer self-help location

The rich literature here mainly compared different modes of reception. Variants typically entailed a part-push and part-pull configuration. For instance, the problem associated with “not-at-home” responses within attended home delivery (AHD) can be mitigated by delivering the product to a CDP for consumers to pick up. The literature discussed two CDP variants: CDP-A and CDP-U. It found that retailers establish CDP-A through developing new infrastructure development, through utilising existing facilities, or establishing partnerships with a third party ( Wang et al. , 2014 ). Other terminologies associated with CDP-A include “click-and-collect”, “pickup centre”, “click-and-mortar”, and “buy-online-pickup-in-store”. The literature showed that retailers establish CDP-U (or unattended reception) through independent RBs equipped with a docking mechanism, or shared RBs, whose locations range from private homes to public sites (e.g. petrol kiosks and train stations) accessible by multiple users ( McLeod et al. , 2006 ).

These CDP-A and CDP-U strategies are commonly adopted by multi/omnichannel retailers to exploit their existing store networks, to provide convenience to consumers through ancillary delivery services, and to expedite returns handling ( Yrjölä, 2001 ). Moreover, the research showed that integrating online technologies with physical infrastructures enables retailers to achieve synergies in cost savings, improved brand differentiation, enhanced consumer trust, and market extension ( Fernie et al. , 2010 ). Studies have also investigated the cost advantage and operational efficiencies of using CDP-U over AHD and CDP-A (e.g. Wang et al. , 2014 ). CDP-U reduces home delivery costs by up to 60 per cent ( Punakivi et al. , 2001 ), primarily by exploiting time window benefit ( Kämäräinen et al. , 2001 ).

Development of LML design framework

This section addresses the second and third research questions by developing a framework that contributes to LML design practice. The development process is governed by contingency theory ( Lawrence and Lorsch, 1967 ), in which “fit” is a central concept. The contingency theory maintains that structural, contextual, and environmental variables should fit with one another to produce organisational effectiveness. The management literature conceptualises fit as profile deviation (e.g. Jauch and Osborn, 1981 ; Doty et al. , 1993 ) in terms of the degree of consistency across multiple dimensions of organisational design and context. The probability of organisational effectiveness increases as the fit between the different types of variables increases ( Jauch and Osborn, 1981 ; Doty et al. , 1993 ). In this paper, the environmental and contextual variables are jointly branded as contingency variables since the object was to examine how these variables impact the structural form of LML distribution.

We developed the LML design framework in two steps. First, we synthesised a set of LML structural and contingency variables and established the relationship between these through a review of the LML literature. Second, we reformulated the descriptive (i.e. science-mode) knowledge obtained via the first step into prescriptive (i.e. design-mode) knowledge. We adopted the contingency perspective in combination with Romme’s (2003) approach to inform knowledge reformulation.

Synthesising LML structural variables

Product source refers to the location where products are stored when an order is accepted; it coincides with the start point of an LML network. It can be contextualised as a supply network member entity (manufacturer, distributor, or retailer). To illustrate, the computer manufacturer Dell (customisation services), online grocer Ocado (home delivery services), and the UK’s leading supermarket chain Tesco (click-and-collect services) source their products from manufacturer, distributor, and retailer sites, respectively.

Geographical scope concerns the distance separating the start point (product source) and the end point (final consignee’s preferred destination point) of an LML network. An LML network can be classified as centrally based (e.g. Dell Services) or locally based (e.g. Tesco’s click-and-collect).

Mode of distribution describes the delivery mode from the point where an order is fully fulfilled to the end point; it can be classified into three types: self-delivery (e.g. Tesco’s self-owned fleet for home deliveries), 3PL delivery including crowdsourcing (e.g. Dell Services), and consumer-pickup (e.g. Tesco’s click-and-collect services).

Number of nodes concerns the operations in which products are “stationary”, residing in a facility for processing or storage. As opposed to nodes, links represent movements between nodes. There are two variations in respect to this variable: two-node and multiple-node. For example, a two-node structure can be found in Dell’s direct-to-consumer distribution channel, where computers are assembled and orders fulfilled at the factory prior to direct home delivery. In contrast, multiple-node structures are reflected in “in-transit merge” structure where an order comprising components sourced from multiple locations are assembled at a common node. As a case in point, when consumer order a computer processing unit (CPU) from Dell along with a Sony monitor, a parcel carrier would pick up the CPU from a Dell factory and the monitor from a Sony factory, then would merge the two into a single shipment at a hub prior to delivery ( Chopra, 2003 ).

Synthesising LML contingency variables

Consumer geographical density: the number of consumers per unit area ( Boyer and Hult, 2005 ; Boyer et al. , 2009 ; Mangiaracina et al. , 2015 ).

Consumer physical convenience: the effort consumers exert to receive orders ( Chopra, 2003 ; Harrington et al. , 2016 ).

Consumer time convenience: the time committed by consumers for the reception of orders. This variable fluctuates according to the structural form of last-mile distribution ( Rabinovich and Bailey, 2004 ; Yuan and David, 2006).

Demand volume: the number of products ordered by consumers relative to the distribution structure ( Chopra, 2003 ; Boyer and Hult, 2005 ).

Order response time: the time difference between order placement and order delivery ( Kämäräinen et al. , 2001 ; Mangiaracina et al. , 2015 ).

Order visibility: the ability of consumers to track their order from placement to delivery ( Chopra, 2003 ; Harrington et al. , 2016 ).

Product availability and product variety: product availability is the probability of having products in stock when a consumer order arrives ( Chopra, 2003 ; Yuan and David, 2006).

Product variety is the number of unique products (or stock keeping units) offered to consumers ( Punakivi et al. , 2001 ; Punakivi and Saranen, 2001 ).

Product customisability: the ability for products to be adapted to consumer specifications ( Boyer and Hult, 2005 ).

Product freshness: the time elapsed from the moment a product is fully manufactured to the moment when it arrives at the consumption point ( Boyer and Hult, 2005 ).

Product margin: the net income divided by revenue ( Boyer and Hult, 2005 ; Campbell and Savelsbergh, 2005 ).

Product returnability: the ease with which consumers can return unsatisfactory products ( Chopra, 2003 ; Yuan and David, 2006).

Service capacity: the ability of an LML system to provide the intended delivery service and to match consumer demand at any given point in time ( Rabinovich and Bailey, 2004 ; Yuan and David, 2006).

Synthesising the relationship between LML structural and contingency variables

Firms that target customers who can tolerate a large response time require few locations that may be far from the customer and can focus on increasing the capacity of each location. On the other hand, firms that target customers who value short response times need to locate close to them.

This statement identifies the association between a structural variable, namely “geographical scope”, and a contingency variable, namely “order response time”. Within the literature, two variations emerged for each variable: centralised vs localised network for geographical scope and long vs short delivery period for order response time; i.e. centralised geographical scope corresponds to long response time, while localised scope is more responsive. As such, the findings demonstrate that by identifying connecting rationales and the variations at different levels for each variable, we can capture correlations between two sets of variables (i.e. structural and contingency). Continuing this procedure across relevant statements found in our corpus, Table IV summarises the outputs.

Reformulation from science-mode into design-mode knowledge

We adopted the approach by Romme (2003) to reformulate the descriptive knowledge (i.e. science-mode, developed in the previous section) into prescriptive (i.e. design-mode) knowledge so that the latter becomes more accessible to guide practitioners in their LML design thinking. This approach has previously been used to contextualise various design scenarios (e.g. Zott and Amit, 2007 ; Holloway et al. , 2016 ; Busse et al. , 2017 ). For example, Busse et al. (2017) employed a variant of the approach to investigate how buying firms facing low supply chain visibility can utilise their stakeholder network to identify salient supply chain sustainability risks.

if necessary, redefine descriptive (properties of) variables into imperative ones (e.g. actions to be taken);

redefine the probabilistic nature of a hypothesis into an action-oriented design proposition;

add any missing context-specific conditions and variables (drawing on other research findings obtained in science- or design-mode); and

in case of any interdependencies between hypotheses/propositions, formulate a set of propositions.

[If order response time delivered by an LML network is short, then the geographical scope of the LML network should be localised].
[For an LML network to achieve short order response time, localise the geographical scope].

Following similar procedures, the science-mode knowledge describing the relationships between structural and contingency variables can be reformulated to the design-mode shown in Table V . Collectively, the resulting design-mode knowledge constitutes a set of design guidelines for LML practitioners.

Main research issues, gaps, and future lines of research

Although the literature covered in this study thoroughly addresses LML structures, the extant literature has limitations. Based on this study’s findings, there are four main areas that require future study.

Operational challenges in executing last-mile operations

The extant literature has focused on the planning aspect of LML, rather than exploring operational challenges. Consequently, research often takes a simplistic chain-level perspective of LML in order to develop simplistic design prescriptions for practitioners. While this approach seems suitable in the pre-digital era, it is inadequate to capture the complexities of last-mile operations in the omnichannel environment ( Lim et al. , 2017 ). The focus on LML nodes as solely unifunctional is also inadequate ( Vanelslander et al. , 2013 ). Not acknowledging the multi-functionality of individual nodes limits understanding of how this variant works.

To address the limitations of extant research, we propose extending the current research from addressing linear point-to-point LML “chains” (e.g. Chopra, 2003 ; Boyer and Hult, 2005 ) to also addressing the “networks” context, where multiple chains are intertwined and more widely practised in the industry. A study of LML systems using 3PL shared by multiple companies is an example of necessary future research. We also recommend future research to address the multi-functionality of individual nodes in an LML system. A study that addresses the ability of an LML node to simultaneously be a manufacturer and a distributor introduces more structural variance and needs to be theoretically addressed.

Additionally, existing literature typically focuses on comparing structural variants’ performance outcomes and their corresponding consumer and product attributes. However, we argue that such focus limits our understanding of how LML distribution structures interact as part of the broader omnichannel system. Accordingly, an avenue for future research would employ configuration perspectives ( Miller, 1986 ; Lim and Srai, 2018 ) to complement the traditional reductionist approaches (e.g. Boyer et al., 2009 ) in order to more holistically examine LML models. Future studies could consider the structural interactions with relational governance of supply network entities, in order to promote information sharing and enhancing visibility, which are critical in omnichannel retailing ( Lim et al. , 2016 ).

Finally, while recent articles have started to examine the effects of online and offline channel integrations (e.g. Gallino and Moreno, 2014 ), limited contributions have been made to date to understand how retailers integrate their online and offline operations and resources to deliver a seamless experience for consumers ( Piotrowicz and Cuthbertson, 2014 ; Hübner, Kuhn and Wollenburg, 2016 ). We propose revisiting the pull-centric system variants in the context of active consumer participation to understand the approaches retailers can use to attract consumers to their stores. In this regard, the subject can benefit from insightful case studies to advance our understanding of the challenges retailers face, as well as the operational processes retailers adopt to meet these challenges.

Intersection between last-mile operations and “sharing economy” models

With the exception of one paper ( Wang et al. , 2016 ), the majority of the extant literature discusses conventional LML models. Given the rapidly growing sharing economy that generates innovative business models (e.g. Airbnb, Uber, Amazon Prime Now) in several sectors (e.g. housing, transportation, and logistics, respectively) and exploits collaborative consumption ( Hamari et al. , 2016 , p. 2047) and logistics ( Savelsbergh and Van Woensel, 2016 ), there is an immense research scope at the intersection between LML and sharing economy models. First, we propose empirical studies to examine how retailers can effectively employ crowdsourcing models for the last-mile and to show how they can effectively integrate these models into their existing last-mile operations, such as combining in-store fulfilment through delivery using “Uber-type” solutions. This type of study is critical for understanding the impact of crowdsourcing models on retail operations and for promoting their adoption. Second, papers addressing omnichannel issues ( Hübner, Kuhn and Wollenburg, 2016 ; Hübner, Wollenburg and Holzapfel, 2016 ; Ishfaq et al. , 2016 ) are emerging. The emergence of new omnichannel distribution models demands theoretical development and the identification of new design variables. These models include on-demand delivery model (e.g. Instacart), distribution-as-a-service (e.g. Amazon, Ocado), “showroom” concept stores (e.g., Warby Parker), in-store digital walls (e.g. Adidas U.S. adiVerse), unmanned delivery (e.g. drones, ground robots), and additive printing (e.g. The UPS store 3D print). Increasingly, we also observe the growing convergence of roles and functions between online and traditional B&M retailers, which suggests new integrated LML models. These new roles and functions demand future research. Finally, while collaborative logistics enable the sharing of assets and capacities in order to increase utilisation and reduce freight, its success rests on developing a logistics ecosystem of relevant stakeholders (including institutions). Consequently, exciting research opportunities exist to explore new design variables that capture key stakeholders’ interests at various levels ( Harrington et al. , 2016 ).

Data harmonisation and analytics: collection and sharing platforms

The literature review revealed that, to date, there has been a tendency towards geographical-based studies and the use of simulated data. For example, this review reports studies based in Finland ( Punakivi and Saranen, 2001 ), Scotland ( McKinnon and Tallam, 2003 ), the USA ( Boyer et al. , 2009 ), England ( McLeod et al. , 2006 ), Germany ( Wollenburg et al. , 2017 ), and Brazil ( Wanke, 2012 ), amongst others. While these studies contribute to generating a useful library of contexts, they are difficult to compare, given differences in geography and geographically based data collection and analysis methods. Moreover, the majority of the studies in this review (41.30 per cent) were based on modelling and simulated data with limited application to real-world data sets, which might suggest a lack of quality data sets. Simulated data limit the advancement of domain knowledge, thus the development of real-world data sets could significantly fuel progress. As such, more attention should be focused on developing data sets, e.g. through the use of transaction and consumer-level data, to gain insights into last-mile behaviours and to design more effective LML models.

Additionally, future studies should standardise data collection in order to address current trends in urbanisation and omnichannel retailing, which are changing retail landscapes and consumer shopping behaviours. This study recommends establishing a data collection framework to guide scholars in LML design, with scholars developing new competences in data mining analytics to exploit large-scale data sets.

Moving from prescriptive to predictive last-mile distribution design

Extant studies have derived correlations between variation of independent variables (e.g. order response time) and variation of dependent variables (e.g. degree of centralisation) to provide prescriptive solutions to the design of last-mile distribution structures. However, these relationships (both linear and non-linear) are often confounded by other factors due to the real-world complexities and they inherently face multicollinearity and endogeneity issues, including the omitted variable bias problem, which leads to biased conclusions. Moreover, model complexity increases as more variables are included, potentially causing overfitting. Given these complexities, researchers usually find immense challenges in untangling these relationships. In this regard, we offer several valuable future lines of research leveraging more advanced techniques for the design of last-mile distribution.

First, our review captured 13 contingency variables that influence the design of last-mile distribution. Future research could discuss other contingency variables and investigate the use of statistical machine and deep learning techniques to identify the most critical contingency variables and uncover hidden relationships to develop predictive models. Second, as urbanisation trends continue, more institutional attention is required on urban logistics focused on negative externalities (congestion and carbon emissions) driven by the intensification of urban freight. According to our review, there is insufficient attention paid to urban freight delivery, and we propose exploring archetyping of urban areas for the development of predictive models to guide the design of urban last-mile distribution systems.

Third, the developed design framework is based on the assumption that only one last-mile distribution structure may be adopted for a given scenario. As we observed in the omnichannel setting, it is common for retailers to concurrently operate multiple distribution structures. The interrelationships between the various structural combinations under the management of a single LML operator also present a potential future research direction.

Last, there is room for a combination of methods to more appropriately tackle the increasingly complicated and fragmented distribution networks in the omnichannel environment. Indeed, this research revealed only two papers in the corpus that have employed a mixed-method approach. Ishfaq et al. (2016) used case research and classification-tree analysis to understand the organisation of distribution processes in omnichannel supply networks, while Campbell and Savelsbergh (2006) combined analytical modelling with simulation to demonstrate the value of incentives in influencing consumer behaviour to reduce delivery costs.


This paper offers the first comprehensive review and analysis of literature regarding e-commerce LML distribution structures and their associated contingency variables. Specifically, the study offers value by using a design framework to explicate the relationship between a broad set of contingency variables and the operational characteristics of LML configuration via a set of structural variables with clearly defined boundaries. The connection between contingency variables and structural variables is critical for understanding LML configuration choices; without understanding this connection, extant knowledge is non-actionable, leaving practitioners with an overwhelming number of seemingly relevant variables that have vague relationships with the structural forms of last-mile distribution.

From a theoretical contribution perspective, this paper identifies attributes of delivery performance linked to product-market segments and the system dynamics that underpin them. This understanding of the interrelationships between LML dimensions enables us to classify prior work, which is somewhat fragmented, to provide insights on emerging business models. The reclassification of LML structures helps practitioners understand the three dominant system dynamics (push-centric, pull-centric, and hybrid) and their related contingency variables. Synthesising structural and contingency variables, the network design framework ( Table IV ) sets out the connections, which when reformulated ( Table V ), provide practitioners design prescriptions under varying LML contexts.

Accordingly, the literature review demonstrates that push-centric LML models driven by order visibility performance are ideally suited to variety-seeking market segments where consumers prioritise time convenience over physical convenience. Conversely, it shows that pull-centric LML models favour order response time, order visibility, and product returnability performance, which are widely observed in markets where consumers desire high physical convenience, low product customisability, and high product variety. Most interestingly of all, this study explains the emergent hybrid systems, where service capacity performance excellence is delivered through multiple clusters of contingency variables, which suits availability-sensitive markets and markets where consumers prioritise physical (over time) convenience.

This paper identifies four areas for further research: operational challenges in executing last-mile operations; intersection between last-mile operations and sharing economy models; data harmonisation and analytics; and moving from prescriptive to predictive last-mile distribution design. Research in these areas could contribute to consolidating the body of knowledge on LML models while maintaining the essential multidisciplinary character. We hope that this review will serve as a foundation to current research efforts, stimulate suggested lines of future research, and assist practitioners to design enhanced LML models in a changing digital e-commerce landscape.

literature review of e commerce

Classification of literature review on LML models

Journal pool for reviewed papers

LML design framework

Aksen , D. and Altinkemer , K. ( 2008 ), “ A location-routing problem for the conversion to the ‘click-and-mortar’ retailing: the static case ”, European Journal of Operational Research , Vol. 186 No. 2 , pp. 554 - 575 .

Agatz , N.A.H. , Fleischmann , M. and van Nunen , J.A.E.E. ( 2008 ), “ E-fulfillment and multi-channel distribution – a review ”, European Journal of Operational Research , Vol. 187 No. 2 , pp. 339 - 356 .

Ayanso , A. , Diaby , M. and Nair , S.K. ( 2006 ), “ Inventory rationing via drop-shipping in Internet retailing: a sensitivity analysis ”, European Journal of Operational Research , Vol. 171 No. 1 , pp. 135 - 152 .

Bell , D.R. , Gallia , S. and Moreno , A. ( 2014 ), “ How to win in an omnichannel world ”, MIT Sloan Management Review , Vol. 56 No. 1 , pp. 45 - 53 .

Bowersox , D.J. , Closs , D.J. , Cooper , M.B. and John , C.B. ( 2012 ), Supply Chain Logistics Management , 4th ed. , McGraw-Hill , New York, NY .

Boyer , K. and Hult , G. ( 2005 ), “ Extending the supply chain: integrating operations and marketing in the online grocery industry ”, Journal of Operations Management , Vol. 23 No. 6 , pp. 642 - 661 .

Boyer , K. and Hult , G. ( 2006 ), “ Customer behavioral intentions for online purchases: an examination of fulfillment method and customer experience level ”, Journal of Operations Management , Vol. 24 No. 2 , pp. 124 - 147 .

Boyer , K.K. , Prud’homme , A.M. and Chung , W. ( 2009 ), “ The last mile challenge: evaluating the effects of customer density and delivery window patterns ”, Journal of Business Logistics , Vol. 30 No. 1 , pp. 185 - 201 .

Busse , C. , Schleper , M.C. , Weilenmann , J. and Wagner , S.M. ( 2017 ), “ Extending the supply chain visibility boundary: utilizing stakeholders for identifying supply chain sustainability risks ”, International Journal of Physical Distribution & Logistics Management , Vol. 47 No. 1 , pp. 18 - 40 .

Campbell , A.M. and Savelsbergh , M. ( 2005 ), “ Decision support for consumer direct grocery initiatives ”, Transportation Science , Vol. 39 No. 3 , pp. 313 - 327 .

Campbell , A.M. and Savelsbergh , M. ( 2006 ), “ Incentive schemes for attended home delivery services ”, Transportation Science , Vol. 40 No. 3 , pp. 327 - 341 .

Cassidy , W.B. ( 2017 ), “ Last-mile business explodes on e-commerce demand ”, available at: (accessed 16 September 2017 ).

Chopra , S. ( 2003 ), “ Designing the distribution network in a supply chain ”, Transportation Research Part E: Logistics and Transportation Review , Vol. 39 No. 2003 , pp. 123 - 140 .

Crainic , T.G. , Ricciardi , N. and Storchi , G. ( 2009 ), “ Models for evaluating and planning city logistics systems ”, Transportation Science , Vol. 43 No. 4 , pp. 432 - 454 .

Cremer , R.D. , Laing , A. , Galliers , B. and Kiem , A. ( 2015 ), Academic Journal Guide 2015 , Association of Business Schools , London .

Dablanc , L. , Giuliano , G. , Holliday , K. and O’Brien , T. ( 2013 ), “ Best practices in urban freight management – lessons from an international survey ”, Transportation Research Record: Journal of the Transportation Research Board , Vol. 2379 , pp. 29 - 38 , available at:

De Koster , R.B.M. ( 2002 ), “ Distribution structures for food home shopping ”, International Journal of Physical Distribution & Logistics Management , Vol. 32 No. 5 , pp. 362 - 380 .

Doty , D.H. , Glick , W.H. and Huber , G.P. ( 1993 ), “ Fit, equifinality, and organizational effectiveness: a test of two configurational theories ”, Academy of Management Journal , Vol. 36 No. 6 , pp. 1196 - 1250 .

Dumrongsiri , A. , Fan , M. , Jain , A. and Moinzadeh , K. ( 2008 ), “ A supply chain model with direct and retail channels ”, European Journal of Operational Research , Vol. 187 No. 3 , pp. 691 - 718 .

Easterby-Smith , M. , Thorpe , R. and Lowe , A. ( 2002 ), Management Research: An Introduction , Sage , London .

Ehmke , J.F. and Mattfeld , D.C. ( 2012 ), “ Vehicle routing for attended home delivery in city logistics ”, Procedia – Social and Behavioral Sciences , Vol. 39 No. 2012 , pp. 622 - 632 .

Esper , T.L. , Jensen , T.D. , Turnipseed , F.L. and Burton , S. ( 2003 ), “ The last mile: an examination of effects of online retail delivery strategies on consumers ”, Journal of Business Logistics , Vol. 24 No. 2 , pp. 177 - 203 .

Fernie , J. and Sparks , L. ( 2009 ), Logistics and Retail Management , 3rd ed. , Kogan Page , London .

Fernie , J. , Sparks , L. and McKinnon , A.C. ( 2010 ), “ Retail logistics in the UK: past, present and future ”, International Journal of Retail & Distribution Management , Vol. 38 Nos 11/12 , pp. 894 - 914 .

Forman , C. , Ghose , A. and Goldfarb , A. ( 2009 ), “ Competition between local and electronic markets: how the benefit of buying online depends on where you live ”, Management Science , Vol. 55 No. 1 , pp. 47 - 57 .

Gallino , S. and Moreno , A. ( 2014 ), “ Integration of online and offline channels in retail: the impact of sharing reliable inventory availability information ”, Management Science , Vol. 60 No. 6 , pp. 1434 - 1451 .

Gao , F. and Su , X. ( 2017 ), “ Omnichannel retail operations with buy-online-and-pick-up-in-store ”, Management Science , Vol. 63 No. 8 , pp. 2478 - 2492 .

Gary , G. , Rashid , M. and Eve , C. ( 2015 ), “ Exploring future cityscapes through urban logistics prototyping: a technical viewpoint ”, Supply Chain Management: An International Journal , Vol. 20 No. 3 , pp. 341 - 352 .

Gevaers , R. , Van de Voorde , E. and Vanelslander , T. ( 2011 ), “ Characteristics and typology of last-mile logistics from an innovation perspective in an urban context ”, in Macharis , C. and Melo , S. (Eds), City Distribution and Urban Freight Transport , Edward Elgar Publishing, Inc. , Cheltenham , pp. 56 - 71 , available at:

Glasser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , Chicago, IL .

Greenhalgh , T. and Peacock , R. ( 2005 ), “ Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources ”, British Medical Journal , Vol. 331 Nos 7524 , pp. 1064 - 1065 .

Hamari , J. , Sjöklint , M. and Ukkonen , A. ( 2016 ), “ The sharing economy: why people participate in collaborative consumption ”, Journal of the Association for Information Science and Technology , Vol. 67 No. 9 , pp. 2047 - 2059 .

Harrington , T.S. , Srai , J.S. , Kumar , M. and Wohlrab , J. ( 2016 ), “ Identifying design criteria for urban system ‘last-mile’ solutions – a multi-stakeholder perspective ”, Production Planning & Control , Vol. 27 No. 6 , pp. 456 - 476 .

Holloway , S.S. , van Eijnatten , F.M. , Romme , A.G.L. and Demerouti , E. ( 2016 ), “ Developing actionable knowledge on value crafting: a design science approach ”, Journal of Business Research , Vol. 69 No. 5 , pp. 1639 - 1643 .

Hübner , A. , Kuhn , H. and Wollenburg , J. ( 2016 ), “ Last mile fulfilment and distribution in omni-channel grocery retailing ”, International Journal of Retail & Distribution Management , Vol. 44 No. 3 , pp. 228 - 247 .

Hübner , A. , Wollenburg , J. and Holzapfel , A. ( 2016 ), “ Retail logistics in the transition from multi-channel to omni-channel ”, International Journal of Physical Distribution & Logistics Management , Vol. 46 Nos 6/7 , pp. 562 - 583 .

Ishfaq , R. , Defee , C.C. and Gibson , B.J. ( 2016 ), “ Realignment of the physical distribution process in omni-channel fulfillment ”, International Journal of Physical Distribution & Logistics Management , Vol. 46 Nos 6/7 , pp. 543 - 561 .

Jauch , L.R. and Osborn , R.N. ( 1981 ), “ Toward an integrated theory of strategy ”, Academy of Management Review , Vol. 6 No. 3 , pp. 491 - 498 .

Johnson , M.E. and Whang , S. ( 2002 ), “ e-Business and supply chain management: an overview and framework ”, Production and Operations Management , Vol. 11 No. 4 , pp. 413 - 423 .

Kämäräinen , V. and Punakivi , M. ( 2002 ), “ Developing cost-effective operations for the e-grocery supply chain ”, International Journal of Logistics Research and Applications , Vol. 5 No. 3 , pp. 285 - 298 .

Kämäräinen , V. , Saranen , J. and Holmström , J. ( 2001 ), “ The reception box impact on home delivery efficiency in the e-grocery business ”, International Journal of Physical Distribution & Logistics Management , Vol. 31 No. 6 , pp. 414 - 426 .

Kull , T.J. , Boyer , K. and Calantone , R. ( 2007 ), “ Last-mile supply chain efficiency: an analysis of learning curves in online ordering ”, International Journal of Operations & Production Management , Vol. 27 No. 4 , pp. 409 - 434 .

Lagorio , A. , Pinto , R. and Golini , R. ( 2016 ), “ Research in urban logistics: a systematic literature review ”, International Journal of Physical Distribution & Logistics Management , Vol. 46 No. 10 , pp. 908 - 931 .

Lau , H.K. ( 2012 ), “ Demand management in downstream wholesale and retail distribution: a case study ”, Supply Chain Management: An International Journal , Vol. 17 No. 6 , pp. 638 - 654 .

Lawrence , P.R. and Lorsch , J.W. ( 1967 ), Organization and Environment , Harvard University Press , Cambridge, MA .

Lee , H.L. and Whang , S. ( 2001 ), “ Winning the last mile in e-commerce ”, MIT Sloan Management Review , Vol. 42 No. 4 , pp. 54 - 62 .

Li , Z. , Lu , Q. and Talebian , M. ( 2015 ), “ Online versus bricks-and-mortar retailing: a comparison of price, assortment and delivery time ”, International Journal of Production Research , Vol. 53 No. 13 , pp. 3823 - 3835 .

Lim , S.F.W.T. and Srai , J.S. ( 2018 ), “ Examining the anatomy of last-mile distribution in e-commerce omnichannel retailing: a supply network configuration approach ”, International Journal of Operations & Production Management (forthcoming) .

Lim , S.F.W.T. , Wang , L. and Srai , J.S. ( 2017 ), “ Wal-mart’s omni-channel synergy ”, Supply Chain Management Review , September/October , pp. 30 - 37 , available at:

Lim , S.F.W.T. , Rabinovich , E. , Rogers , D.S. and Laseter , T.M. ( 2016 ), “ Last-mile supply network distribution in omnichannel retailing: a configuration-based typology ”, Foundations and Trends in Technology Information and Operations Management , Vol. 10 No. 1 , pp. 1 - 87 .

Lopez , E. ( 2017 ), “ Last-mile delivery options grow ever more popular ”, Supply Chain Dive, available at: (accessed 2 July 2017 ).

McKevitt , J. ( 2017 ), “ FedEx and UPS to compete with USPS for last-mile delivery ”, Supply Chain Dive, available at: (accessed 2 July 2017 ).

McKinnon , A.C. and Tallam , D. ( 2003 ), “ Unattended delivery to the home: an assessment of the security implications ”, International Journal of Retail & Distribution Management , Vol. 31 No. 1 , pp. 30 - 41 .

McLeod , F. , Cherrett , T. and Song , L. ( 2006 ), “ Transport impacts of local collection/delivery points ”, International Journal of Logistics Research and Applications , Vol. 9 No. 3 , pp. 307 - 317 .

Mangiaracina , R. , Song , G. and Perego , A. ( 2015 ), “ Distribution network design: a literature review and a research agenda ”, International Journal of Physical Distribution & Logistics Management , Vol. 45 No. 5 , pp. 506 - 531 .

Meredith , J. ( 1993 ), “ Theory building through conceptual methods ”, International Journal of Operations & Production Management , Vol. 13 No. 5 , pp. 3 - 11 .

Miller , D. ( 1986 ), “ Configurations of strategy and structure: towards a synthesis ”, Strategic Management Journal , Vol. 7 No. 3 , pp. 233 - 249 .

Netessine , S. and Rudi , N. ( 2006 ), “ Supply chain choice on the Internet ”, Management Science , Vol. 52 No. 6 , pp. 844 - 864 .

O’Brien , M. ( 2015 ), “ eBay shuts down same-day delivery pilots ”, Multichannel Merchant, available at: (accessed 6 September 2015 ).

Pilbeam , C. , Alvarez , G. and Wilson , W. ( 2012 ), “ The governance of supply networks: a systematic literature review ”, Supply Chain Management: An International Journal , Vol. 17 No. 4 , pp. 358 - 376 .

Piotrowicz , W. and Cuthbertson , R. ( 2014 ), “ Introduction to the special issue information technology in retail: toward omnichannel retailing ”, International Journal of Electronic Commerce , Vol. 18 No. 4 , pp. 5 - 16 .

Punakivi , M. and Saranen , J. ( 2001 ), “ Identifying the success factors in e-grocery home delivery ”, International Journal of Retail & Distribution Management , Vol. 29 No. 4 , pp. 156 - 163 .

Punakivi , M. and Tanskanen , K. ( 2002 ), “ Increasing the cost efficiency of e-fulfilment using shared reception boxes ”, International Journal of Retail & Distribution Management , Vol. 30 No. 10 , pp. 498 - 507 .

Punakivi , M. , Yrjölä , H. and Holmström , J. ( 2001 ), “ Solving the last mile issue – reception box or delivery box ”, International Journal of Physical Distribution & Logistics Management , Vol. 31 No. 6 , pp. 427 - 439 .

Rabinovich , E. and Bailey , J.P. ( 2004 ), “ Physical distribution service quality in internet retailing: service pricing, transaction attributes, and firm attributes ”, Journal of Operations Management , Vol. 21 No. 6 , pp. 651 - 672 .

Rabinovich , E. , Rungtusanatham , M. and Laseter , T. ( 2008 ), “ Physical distribution service performance and Internet retailer margins: the drop-shipping context ”, Journal of Operations Management , Vol. 26 No. 6 , pp. 767 - 780 .

Randall , T. , Netessine , S. and Rudi , N. ( 2006 ), “ An empirical examination of the decision to invest in fulfillment capabilities: a study of Internet retailers ”, Management Science , Vol. 52 No. 4 , pp. 567 - 580 .

Richard , W. and Beverly , W. ( 2014 ), “ Special issue: building theory in supply chain management through ‘systematic reviews’ of the literature ”, Supply Chain Management: An International Journal , Vol. 19 Nos 5/6 , available at:

Romme , A.G.L. ( 2003 ), “ Making a difference: organization as design ”, Organization Science , Vol. 14 No. 5 , pp. 558 - 573 .

Rousseau , D.M. , Manning , J. and Denyer , D. ( 2008 ), “ Evidence in management and organizational science: assembling the field’s full weight of scientific knowledge through syntheses ”, Academy of Management Annals , Vol. 2 No. 1 , pp. 475 - 515 .

Saenz , M.J. and Koufteros , X. ( 2015 ), “ Special issue on literature reviews in supply chain management and logistics ”, International Journal of Physical Distribution & Logistics Management , Vol. 45 Nos 1/2 , available at:

Savelsbergh , M. and Van Woensel , T. ( 2016 ), “ 50th anniversary invited article – city logistics: challenges and opportunities ”, Transportation Science , Vol. 50 No. 2 , pp. 579 - 590 .

Småros , J. and Holmström , J. ( 2000 ), “ Viewpoint: reaching the consumer through e-grocery VMI ”, International Journal of Retail & Distribution Management , Vol. 28 No. 2 , pp. 55 - 61 .

Sternbeck , M.G. and Kuhn , H. ( 2014 ), “ An integrative approach to determine store delivery patterns in grocery retailing ”, Transportation Research Part E: Logistics and Transportation Review , Vol. 70 No. 2014 , pp. 205 - 224 .

Supply Chain Council ( 2010 ), “ Supply chain operations reference model: overview of SCOR version 10.0 ”, Supply Chain Council Inc., Pittsburgh, PA .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of a systematic review ”, British Journal of Management , Vol. 14 No. 3 , pp. 207 - 222 .

Vanelslander , T. , Deketele , L. and Van Hove , D. ( 2013 ), “ Commonly used e-commerce supply chains for fast moving consumer goods: comparison and suggestions for improvement ”, International Journal of Logistics Research and Applications , Vol. 16 No. 3 , pp. 243 - 256 .

Wang , X. , Zhan , L. , Ruan , J. and Zhang , J. ( 2014 ), “ How to choose ‘last mile’ delivery modes for e-fulfillment ”, Mathematical Problems in Engineering , Vol. 2014 No. 2014 , pp. 1 - 11 .

Wang , Y. , Zhang , D. , Liu , Q. , Shen , F. and Lee , L.H. ( 2016 ), “ Towards enhancing the last-mile delivery: an effective crowd-tasking model with scalable solutions ”, Transportation Research Part E: Logistics and Transportation Review , Vol. 93 No. 2016 , pp. 279 - 293 .

Wanke , P.F. ( 2012 ), “ Product, operation, and demand relationships between manufacturers and retailers ”, Transportation Research Part E: Logistics and Transportation Review , Vol. 48 No. 1 , pp. 340 - 354 .

Weltevreden , J.W.J. ( 2008 ), “ B2c e-commerce logistics: the rise of collection-and-delivery points in the Netherlands ”, International Journal of Retail & Distribution Management , Vol. 36 No. 8 , pp. 638 - 660 .

Wollenburg , J. , Hübner , A. , Kuhn , H. and Trautrims , A. ( 2017 ), “ From bricks-and-mortar to bricks-and-clicks – logistics networks in omni-channel grocery retailing ”, International Journal of Physical Distribution & Logistics Management , available at:

Yang , X. and Strauss , A.K. ( 2017 ), “ An Approximate dynamic programming approach to attended home delivery management ”, European Journal of Operational Research , Vol. 263 No. 3 , pp. 935 - 945 .

Yrjölä , H. ( 2001 ), “ Physical distribution considerations for electronic grocery shopping ”, International Journal of Physical Distribution & Logistics Management , Vol. 31 No. 10 , pp. 746 - 761 .

Yuan , X. and David , B.G. ( 2006 ), “ Developing a framework for measuring physical distribution service quality of multi-channel and ‘pure player’ internet retailers ”, International Journal of Retail & Distribution Management , Vol. 34 Nos 4/5 , pp. 278 - 289 .

Zott , C. and Amit , R. ( 2007 ), “ Business model design and the performance of entrepreneurial firms ”, Organization Science , Vol. 18 No. 2 , pp. 181 - 199 .

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Competitive pricing on online markets: a literature review

  • Research Article
  • Open Access
  • Published: 14 June 2022
  • volume  21 ,  pages 596–622 ( 2022 )

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  • Torsten J. Gerpott 1 &
  • Jan Berends 1  

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Cite this article

Past reviews of studies concerning competitive pricing strategies lack a unifying approach to interdisciplinarily structure research across economics, marketing management, and operations. This academic void is especially unfortunate for online markets as they show much higher competitive dynamics compared to their offline counterparts. We review 132 articles on competitive posted goods pricing on either e-tail markets or markets in general. Our main contributions are (1) to develop an interdisciplinary framework structuring scholarly work on competitive pricing models and (2) to analyze in how far research on offline markets applies to online retail markets.

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Setting prices relative to competitors, i.e., competitive pricing, Footnote 1 is a classical marketing problem which has been studied extensively before the emergence of e-commerce (Talluri and van Ryzin 2004 ; Vives 2001 ). Although literature on online pricing has been reviewed in the past (Ratchford 2009 ), interrelations between pricing and competition were rarely considered systematically (Li et al. 2017 ). As less than 2% of high-impact journal articles address pricing issues (Toni et al. 2017 ), pricing strategies do not receive proper research attention according to their practical relevance. This research gap holds even more for competitive pricing. In the past, the monopolistic assumption that demand for homogeneous goods mostly depends on prices set by a single firm may have been a viable simplification since price comparisons were difficult. Today, consumer search costs Footnote 2 shrink as the prices of most goods can be compared on relatively transparent online markets. Therefore, demand is increasingly influenced by prices of competitors which therefore should not be ignored (Lin and Sibdari 2009 ).

In the early 1990s, few people anticipated that business-to-consumer (B2C) online goods retail markets Footnote 3 would develop from a dubious alternative to conventional “brick-and-mortar” retail stores to an omnipresent distribution channel for all kinds of products in less than two decades (Balasubramanian 1998 ; Boardman and McCormick 2018 ). In 2000, e-commerce accounted for a mere 1% of overall retail sales. In 2025, e-retail sales are projected to account for nearly 25% of global retail sales (Lebow 2019 ). Traditional offline channels are nowadays typically complemented by online technologies (Gao and Su 2018 ). With digitization of various societal sectors in general and the COVID-19 pandemic in particular, the shift toward online channels is unlikely to stop in the future. Besides direct online shops, two-thirds of e-commerce sales are sold through online marketplaces/platforms like Alibaba, Amazon or eBay (Young 2022 ). The marketplace operator acts as an intermediary (two-sided platform) who matches demand (online consumers) with supply (retailers). Whereas the retailer retains control over product assortment and prices, he has to pay a commission to the marketplace operator (Hagiu 2007 ). However, these intermediaries often act as sellers themselves, thereby posing direct competition to retailers who have to decide between direct or marketplace channels (Ryan et al. 2012 ).

Online consumer markets fundamentally differ from offline settings (Chintagunta et al. 2012 ; Lee and Tan 2003 ; Scarpi et al. 2014 ; Smith and Brynjolfsson 2001 ). Factors which make competition even more prevalent for online than for offline markets are summarized in Table 1 .

To date, a number of scholarly articles reviews various aspects of pricing under competition or online pricing (Boer 2015a ; Chen and Chen 2015 ; Cheng 2017 ; Kopalle et al. 2009 ; Ratchford 2009 ; Vives 2001 ). Vives ( 2001 ) provides an overview of the history of pricing theory and its evolution from the early work of Bertrand ( 1883 ) who studied a duopoly with unconstrained capacity and identical products to Dudey ( 1992 ) who set the foundation for today’s dynamic pricing Footnote 4 research with constrained capacities and a finite sales horizon. Ratchford ( 2009 ) reviews the influence of online markets on pricing strategies. Although he depicts factors shaping the competitive environment of online markets and compares online versus offline channels, he does not include competitive strategies specifically. This also holds for review papers on dynamic pricing which treat competition rather novercally (Boer 2015a ; Gönsch et al. 2009 ). With emphasis on mobility barriers, multimarket contact and mutual forbearance, Cheng ( 2017 ) studies competition mechanisms across strategic groups. Kopalle et al. ( 2009 ) discuss competitive effects in retail focusing on different aspects such as manufacturer interaction and cross-channel competition. To the best of our knowledge, Chen and Chen ( 2015 ) are the only scholars who review existing competitive pricing research by classifying model characteristics along product uniqueness (identical vs. differentiated), type of customer (myopic vs. strategic), pricing policy (contingent vs. preannounced) and number of competitors (duopoly vs. oligopoly). However, competition is only one of three pricing problems they analyze forcing them to reduce scope and depth and to exclude online peculiarities. In addition, significant competitive pricing contributions were published since 2015 (chapter 2.2). Overall, given the limitations of previous reviews of the pricing literature makes revisiting the current state of research a worthwhile undertaking.

Most often, competitive pricing literature uses simplifying assumptions limiting the applicability of presented models. The simplifications are required to circumvent challenges like the curse of dimensionality (Harsha et al. 2019 ; Kastius and Schlosser 2022 ; Li et al. 2017 ; Schlosser and Boissier 2018 ), endogeneity problems (Cebollada et al. 2019 ; Chu et al. 2008 ; Fisher et al. 2018 ; Villas-Boas and Winer 1999 ), uncertain information (Adida and Perakis 2010 ; e.g., Bertsimas and Perakis 2006 ; Chung et al. 2012 ; Ferreira et al. 2016 ; Keskin and Zeevi 2017 ; Shugan 2002 ) and simultaneity bias (Li et al. 2017 ). As a consequence, early work on pricing strategies with competition was restricted to theoretical discussions (Caplin and Nalebuff 1991 ; Mizuno 2003 ; Perloff and Salop 1985 ). This holds especially true in combination with other practical circumstances such as capacity constraints, time-varying demand or a finite selling horizon (Gallego and Hu 2014 ).

Armstrong and Green ( 2007 ) find empirical evidence that competitive pricing, especially for the sake of gaining market share, harms profitability. Similarly, some researchers cursorily ascribe competitor-based pricing as a sign of a poor management because it signals a lack of capabilities to set prices independently (Larson 2019 ). Revenue management researchers therefore often assume that monopoly pricing models implicitly capture the dynamic effects of competition. The so-called market response hypothesis is the key rationale to neglect the effects of competition altogether (Phillips 2021 ; Talluri and van Ryzin 2004 ). According to this reasoning, competition does not have to be considered as all relevant effects are already included in historical sales data. However, this intuitive argument can be easily rebutted as Simon ( 1979 ) already showed that price elasticities change over time. Furthermore, Cooper et al. ( 2015 ) study the validity of the market response hypothesis and conclude that this monopolistic view is rarely adequate. Monopolistic pricing models can only be applied to stable markets with little time-varying demand and little expected competitive reactions.

Detrimental outcomes of ignoring competition in pricing strategies are shown by Anufriev et al. ( 2013 ), Bischi et al. ( 2004 ), Isler and Imhof ( 2008 ), Schinkel et al. ( 2002 ), and Tuinstra (2004). The negative effects are even more harmful in fierce competitive settings such as situations with a high number of competitors or price sensitive customers (van de Geer et al. 2019 ). Empirical evidence on the influence of competition on pricing decisions is provided by Richards and Hamilton ( 2006 ) who find that retailers compete on price and variety for market share. Li et al. ( 2017 ) observe that competition-based variables explained 30.2% of hotel price variations in New York—compared to 22.3% attributed to demand-side variables. Similarly, Hinterhuber ( 2008 ) assesses competitor-based pricing as a dominant strategy from a practical perspective. Li et al. ( 2008 ) argue that because of its relevance, competition should be considered in operational revenue management and not be treated stepmotherly as an abstract strategic constraint.

Although striving to simplify pricing models is desirable, researchers should thus not simply ignore effects of competition on price setting in a non-monopolistic (online) world. Blindly pegging pricing strategies to competitors or undercutting competitors to gain market share may favor detrimental price wars and not profit-maximizing market structures. Nevertheless, no significant market player can operate isolated on online markets—decisions made always affect competing firms and consumer demand (Chiang et al. 2007 ). In such dynamic markets (chapter 3.3), competition must be considered with time-varying attributes (Schlosser et al. 2016 ).

Against this background, we suggest a conceptual framework to structure research covering competitive online pricing. It can serve scholars as a map to direct future research on the one hand and provide practicing managers with a guide to locate relevant pricing contributions on the other hand. Although the framework can be applied to a variety of markets with competitive dynamics, we concentrate our review on research covering B2C online goods retail markets. Thus, related research with a focus on auction pricing, multichannel peculiarities, behavioral pricing and multi-dimensional pricing approaches such as Everything-as-a-Service (XaaS) or bundle pricing is only assessed when findings are crucial to the competition-related discussion. In the remainder, we proceed as follows. The next chapter provides a descriptive overview of the competitive pricing literature for the subsequent discussion. Chapter 3 puts the identified literature into the perspective of online retail markets considering product and environmental characteristics. Section 4 concludes with practical implications and directions for future research.

Overview of competitive pricing research

Initially, properties of the reviewed literature are briefly summarized. Besides (a) the journal representation, (b) the historical development of online market considerations and (c) research domains, we classify research according to (d) the geographical and industry context as well as (e) research design and empirical foundation.

We identified relevant references through a semi-structured multi-pronged search strategy. Following Tranfield et al. ( 2003 ), we firstly screened the literature reviews mentioned in chapter 1 to obtain an overview of existing research streams. Second, we created a set of potentially relevant contributions by searching multiple keywords in the journal databases EBSCO, Scopus and Web of Science (c.f. Baloglu and Assante 1999 ). Footnote 5 Third, high-impact journals (see Appendix 1) in the academic fields economics, marketing management, and operations were screened. With focus on highly cited (> 10 citations in Scopus), recent (published later than 2000) research, we identified an initial sample of 996 unique papers. Fourth, we studied the abstract and skimmed the text of all papers for relevance to competitive online pricing, reducing our initial set to 174 papers. Fifth, we screened the references of the papers and identified literature cited which we not already included in our set. Sixth, especially for research areas with limited coverage in peer-reviewed journals, we uncovered gray literature through searches with Google Scholar. As a result, this study concentrates on papers published between 2000 and 2022 and only sparsely utilizes literature from the pre-internet era. The final sample of the papers with relevance to competitive B2C online pricing encompasses 132 entries. A complete list of the papers reviewed in great depth is provided in Appendix 2. 94% are peer-reviewed articles. Book chapters, conference papers and preprint/working papers each account for 2%.

Journal representation

Competitive pricing literature is widely dispersed over a broad range of journals as roughly half of the articles considered are from journals with less than three articles in our review. Notably, journals with a higher density of competitive pricing contributions are from the fields of operations, economics or are interdisciplinary. Table 2 reports the distribution of articles among the journals with the highest representation. In addition, it provides the considered articles subject to a content analysis in chapter 3.

Online pricing contributions over time

Between 1976 and the end of the second millennium, the number of papers on competitive pricing in an internet context is naturally limited (Fig.  1 ). Parallel to the dissemination of online use among residential households, interest of researchers in online pricing in a competitive environment started to take off. 71.8% of the papers published from 2015 to 2022 consider online settings specifically. The corresponding statistic from 2000 to 2005 amounts to 43.8%.

figure 1

Competitive pricing literature and its consideration of online peculiarities accumulated by year

Development of research domains

Competitive pricing literature typically can be assigned to one of the following research domains:

The economics domain takes a market perspective across individual firms. It elaborates on the existence and uniqueness of competitive equilibria also including all subjects regarding econometrics.

The marketing management domain analyzes competitive pricing problems from the perspective of a single firm with a focus on customer reactions to pricing decisions. It includes all subjects linked to marketing, strategy, business, international, technology, innovation, and general management.

The operations domain considers quantitative pricing solutions for, among others, quantity planning, choice of distribution channels, and detection of algorithm driven price collusion. It includes all subjects regarding computer science, industrial and manufacturing engineering, and mathematics.

Separating the last 47 years of competitive pricing research into three intervals, all reviewed papers are assigned to their most affiliated research domain. Although the domains are similarly represented in our review (see Fig.  2 ), we see differences in their temporal change. Whereas rather theoretical economic subjects are covered relatively constant over time, more practice-oriented marketing management and operations subjects gained momentum since 2000. This suggests a shift from model conceptualization toward applicable research, frequently based on empirical data.

figure 2

Distribution of competitive pricing literature over research domain and time interval

Geographical and industry context

As the origin of revenue management lies in transportation and hospitality optimization problems, one could expect that competitive pricing research also originates in these dynamic sectors. However, our analysis reveals a different picture: Almost half of the papers in our review do not concentrate on a specific industry. Besides, most industry-specific competitive pricing articles focus on retail, with 38% concentrating on the retail industry versus 8% and 4% on transportation and hospitality, respectively (see Fig.  3 ). This supports our proposition in chapter 1 that effects of competition on industry-specific pricing are particularly relevant for online markets.

figure 3

Competitive pricing research by focal industry and location of lead authors’ institution

Competitive pricing literature is predominantly driven by researchers employed by U.S. institutions (60%). The remaining 40% consist of Europe (19%), Asia (17%) and Canada (4%).

Research approach

A lack of empirical testing is an issue that hampers competitive pricing research. Liozu ( 2015 ) reported that only 15% of general pricing literature include empirical data. For competitive pricing, the situation appears even more aggravated. In addition to parameters such as price elasticities and stock levels of the company under study, comprehensive, real-time information of other market participants is crucial to add practical value.

For instance, to solve a simple Bertrand equilibrium, Footnote 6 full information of all competitors is needed, which is rarely available in real-life settings. Therefore, many problems covered in the literature are of a theoretical nature. In accordance with Liozu ( 2015 ), we find that only 18% of reviewed articles use empirical evidence to validate hypotheses. An additional 23% strive to ameliorate this shortage through simulation data and numerical examples. The remaining 59% fail to bring any empirical evidence or numerical examples.

As can be taken from Fig.  4 , missing empirical support is particularly prevalent for equilibrium models which use empirical data in only 7% of all papers.

figure 4

Competitive pricing research by research design and empirical validation

Competitive pricing on online markets

In this chapter, we assess the applicability of competitive pricing work to online markets. Typical characteristics of competitive B2C pricing models were derived from literature described in chapter 2. Competitive pricing literature can be classified along four characteristics depicted in Table 3 that form the market environment in which firms compete for consumer demand.

In the remainder of chapter 3, we discuss the four key questions in more depth and elaborate on their applicability to online retail markets.

Product similarity

In general, products in competitive pricing models are either identical (homogeneous) or differentiated by at least one quality parameter (heterogeneous). In case of homogeneous products, pricing is the only purchase decision variable—a perfectly competitive setting (Chen and Chen 2015 ). However, many firms strive to differentiate their products as this shifts the focus from the price as competitive lever to other product-related features (Afeche et al. 2011 ; Boyd and Bilegan 2003 ; Thomadsen 2007 ). According to Lancaster ( 1979 ), there are two types of product differentiation: vertical and horizontal differentiation. Vertical differentiation Footnote 7 encompasses all product distinctions which are objectively measurable and quantifiable regarding their quality level. Horizontal differentiation Footnote 8 can manifest in many variants and includes all product-related aspects which cannot be quantified according to their quality levels. Footnote 9 A key difference in the modeling of substitutable yet differentiated versus identical goods is that customers have heterogenous preferences among products. Footnote 10 A recent stream of literature approaches unknown differentiation criteria by assessing online consumer-generated content (DeSarbo and Grewal 2007 ; Lee and Bradlow 2011 ; Netzer et al. 2012 ; Ringel and Skiera 2016 ; Won et al. 2022 ).

Besides the chosen price level, Cachon and Harker ( 2002 ) argue that firms compete with the operational performance level offered and perceived, i.e., service level in online retail, to differentiate an otherwise homogenous offering. In situations, where resellers with comparable service and shipping policies offer similar products, price is a major decision variable for potential buyers (Yang et al. 2020 ). Often, e-tailers do not possess the right to exclusively distribute a certain product. For example, Samsung’s Galaxy S21 5G was offered by 69 resellers on the German price comparison website Footnote 11 As some products in e-tail can be differentiated and others cannot, both identical and differentiated product research have their raison d’être for competitive online pricing.

Most competitive pricing models only address the effects of single-product settings. This simplification is reasonable if there is no interdependence between products of an e-tailer (Gönsch et al. 2009 ). Taking up on the smartphone example, the prices of close substitutes, such as Huawei’s P30 Pro, nonetheless have an impact on the demand of Samsung’s Galaxy S21 5G. To further extent product differentiation, price models have to incorporate multi-product pricing problems in non-cooperative settings (Chen and Chen 2015 ). Such models have to account not only for demand impact of directly competing products but also for synergies, cannibalization/substitution effects of (own) differentiated goods. Although there is a recent research stream regarding product assortment (Besbes and Sauré 2016 ; Federgruen and Hu 2015 ; Heese and Martínez-de-Albéniz 2018 ; Nip et al. 2020 ; Sun and Gilbert 2019 ), multi-product work is still underdeveloped. Thus, competitive multi-product pricing constitutes an area which should be addressed in future research.

Product durability

The durability of products is an important feature to differentiate between competitive pricing model types. Durable (non-perishable) products do not have an expiration date, for example consumer durables such as household appliances. Perishable products can only be sold for a limited time interval and have a finite sales horizon. After expiration date, unused capacity is lost or significantly devalued to a salvage value. Footnote 12 Combined with limited capacities, the firm objective is thus most often to maximize turnover under capacity constraints and finite sales horizon (Gallego and van Ryzin 1997 ; McGill and van Ryzin 1999 ; Weatherford and Bodily 1992 ).

Perishability can be of relevance for products with seasonality effects or short product life cycles (i.e., finite selling horizon) such as apparel, food groceries or winter sports equipment. This is especially relevant because online retailers of perishable products are severely restricted in their shipment, return handle policies and supply chain length (Cattani et al. 2007 ). Sellers cannot replenish their inventory after the planning phase and cannot retain goods for future sales periods (Perakis and Sood 2006 ). Some products like apparel—albeit reducing in value after a selling season—still have a certain salvage value and can be sold at reduced prices (Anand and Girotra 2007 ).

It depends on the type of product to decide whether perishability should be included in competitive pricing models. There is a fundamental distinction in the underlying optimization objective for models with or without perishability. Whereas models with perishable products tend to focus on revenue maximization over a definite short-term time horizon, models with durable products tend to focus on profit maximization over an indefinite or at least long-term time horizon by balancing current revenues of existing and future revenues of new customers. To account for this trade-off, models with durable products need to discount future cash flows incorporating time value of money, stock-keeping, opportunity and other costs related to prolonged sales (Farias et al. 2012 ). To conclude, perishability cannot be treated as an extension to durable models but rather as a separate class of pricing models. Depending on the product and/or setting in focus, both are relevant for online retailing. Further research could investigate the performance of models with and without consideration of perishability in various (online) settings to determine when it is appropriate to use which class of pricing models. Also, an interesting field of future studies arises around the question which instruments (e.g., service differentiations or price diffusion) are used by online retailers to differentiate otherwise homogeneous offerings.

Time dependence

A key differentiator of competitive pricing models is the consideration of either a static (time-independent) setup with definite equilibrium or a dynamic (time-dependent) constellation with changing environmental factors and equilibria. Albeit static pricing models have no time component, many consist of multiple stages to investigate the interplay of different factors. Footnote 13 In contrast, dynamic models allow for varying competitive (re-)actions over time. Footnote 14 Within the latter category, there are models with a finite (Afeche et al. 2011 ; Levin et al. 2008 ; Liu and Zhang 2013 ; Yang and Xia 2013 ) and an infinite (Anderson and Kumar 2007 ; Li et al. 2017 ; Schlosser and Richly 2019 ; Villas-Boas and Winer 1999 ; Weintraub et al. 2008 ) time horizon.

Historically, competitive pricing models assumed fixed prices over the considered time horizon. Limited computational power made it impossible to appropriately estimate models dynamically due to dimensionality issues (Schlosser and Boissier 2018 ). A lack of reliable demand information, high menu and investment costs to implement dynamic approaches were additional reasons why pricing models remained inherently static without incorporating changing competitive responses (Ferreira et al. 2016 ). The focus in retail has conventionally rather been on long-term profit optimization and to a lesser degree on dynamically changing price optimizations (Elmaghraby and Keskinocak 2003 ).

The literature disagrees on whether firms should opt for static or dynamic pricing strategies. A static environment allows to simplify and concentrate on a specific topic such as equilibrium discussions. For instance, Lal and Rao ( 1997 ) study success factors of everyday low pricing and derive conditions for a perfect Nash equilibrium between an everyday low price retailer and a retailer with promotional pricing. With Zara as an example for a company with a successful static pricing strategy, Liu and Zhang ( 2013 ) argue that with the presence of strategic customers who prolong sales in anticipation of price decreases, firms might even be better off to deploy static over dynamic price setting processes. Studying the time-variant pricing plans in electricity markets, Schlereth et al. ( 2018 ) suggest that consumers might prefer static over dynamic pricing because of factors like choice confusion, lack of trust in price fairness, perceived economical risk or perceived additional effort. Further support for a static pricing strategy is found in Cachon and Feldman ( 2010 ) and Hall et al. ( 2009 ).

Nevertheless, to generalize that static should strictly be preferred over dynamic pricing models could be short-sighted. Firms cannot generally infer future behavior of competitors from past observations to assess how competitive (re-) actions may influence the optimal pricing policy (Boer 2015b ). Corresponding to the surge of revenue management systems in the airline industry during the 70s and 80s, increased price and demand transparency, low menu costs and an abundance of decision support software created fierce competition among online retailers (Fisher et al. 2018 ). Taking up on the above mentioned example by Liu and Zhang ( 2013 ), Caro and Gallien ( 2012 ) show that even Zara does not solely rely on static pricing. They supported Zara’s pricing team in designing and implementing a dynamic clearance pricing optimization system—to generate a competitive advantage in addition to the fast-fashion retail model Zara mainly pursues (Caro 2012 ). Zhang et al. ( 2017 ) discuss various duopoly pricing models with static and dynamic pricing under advertising. They find that market surplus is highest when one firm prices dynamically, profiting from the static behavior of the other. Chung et al. ( 2012 ) provide numerical evidence that a dynamic pricing model with an appropriately specified demand estimation always outperforms static pricing strategies—also in settings with incomplete information. Xu and Hopp ( 2006 ) show that dynamic pricing outperforms preannounced pricing, especially with effective inventory management and elastic demand. Further support for advantages of dynamic pricing can be found by Popescu ( 2015 ), Wang and Sun ( 2019 ), and Zhang et al. ( 2018b ). Empirical evidence of the negative consequences of sticking to a static strategy in a changing environment is found in the cases of Nokia, Kodak, and Xerox.

While some scholars distinguish between discrete and continuous dynamic pricing systems (Vinod 2020 ), we suggest to classify dynamic pricing models according to their level of sophistication into two evolutionary stages: the (in e-commerce widely applied) manual rule-based pricing approach and the data-driven algorithmic optimization approach (Popescu 2015 ; Le Chen et al. 2016 ). Footnote 15 For the rule-based approach, “if-then-else rules” are defined and updated manually. Footnote 16 However, the mere number of stock-keeping units (SKUs) in today’s retailer offerings aggravate the initial setup and handling of rule-based pricing and make real-time adjustments unmanageable (Schlosser and Boissier 2018 ). In addition, rule-based approaches are rather subjective than sufficiently data-driven. Faced with a large range of SKUs, competitor responses and heterogeneous demand elasticities, canceling out the human decision-making process on an operational level is the next evolutionary step for competitive pricing systems (Calvano et al. 2020 ). Data-driven algorithmic pricing strategies use observable market Footnote 17 data to predict sales probabilities based on consumer demand and competitive responses (Schlosser and Richly 2019 ).

As online marketplaces benefit from an increased number of retailers on their platforms, they typically support sellers to establish automated dynamic pricing systems (Kachani et al. 2010 ). Footnote 18 However, Schlosser and Richly ( 2019 ) claim that current dynamic pricing systems are not able to deal with the complexity of competitor-based pricing and therefore most often ignore competition altogether or solely rely on manually adjusted rule-based mechanics. Challenges include the indefinite spectrum of changing competitor strategies, asymmetric access to competitor knowledge, a large solution space under limited information and the black-box character of dynamic systems, which exacerbates an intervention in case of a pricing system malfunction. Besides, researchers did not yet identify an algorithm which consistently outperforms other methodologies in competitive situations. Instead, it depends on the specific setting and other competitors’ pricing behavior to assess which pricing algorithm is optimal (van de Geer et al. 2019 ) exacerbating the application of such systems.

Reflecting the literature findings for both static and dynamic pricing strategies, we conclude that pricing managers should develop dynamic pricing models in most e-commerce situations. As long as demand and competitor price responses vary over time on online markets, dynamic models are naturally superior to time-independent approaches. Static models on the other hand are only appropriate in market constellations with little time-varying demand and competitor behavior. As static research can be expected to remain a vivid field of literature, further research with regard to the transferability of static models to dynamic settings is desirable. In addition, more research is needed that helps to better understand the implications of widely applied rule-based dynamic pricing methods and their transition toward algorithmic approaches (Boer 2015a ; van de Geer et al. 2019 ; Kastius and Schlosser 2022 ; Könönen 2006 ).

Market structure

The market structure describes the number of competing firms such as duopoly or oligopoly in a demand setting with an indefinite number of consumers. 60% of the reviewed papers studied duopolies, 49% oligopolies, 7% monopolistic competition, and 3% perfect competition. Footnote 19

Especially for research in the economics stream, many papers assume a perfectly competitive market. Pricing research with perfectly competitive markets (e.g., van Mieghem and Dada 1999 or Yang and Xia 2013 ) is likely to be of very limited value to online retailers. Building on the notion of Diamond ( 1971 ), Salop ( 1976 ) argues that if customers have positive information gathering costs, no perfect competition can occur as firms have room to slightly increase prices without losing demand. Christen ( 2005 ) found evidence that even with strong competition and low information costs, cost uncertainty could decrease the detrimental effect of competition for sellers and could increase prices above Bertrand levels. Similarly, Bryant ( 1980 ) showed that perfect competition is not possible in a market with uncertain demand, even if the number of firms is large and customers have no search costs. Rather, price dispersion reflects uncertain demand (Borenstein and Rose Nancy L. 1994 ; Cavallo 2018 ; Clemons et al. 2002 ; Obermeyer et al. 2013 ; Wang et al. 2021 ). Israeli et al. ( 2022 ) empirically show that the market power of individual firms does not only depend on the number and intensity of competitors but also on the firm’s ability to adjust prices in response to varying inventory levels of product substitutes, especially with low consumer search costs. This is of relevance for e-commerce as e-tailers could exploit this dependence by incorporating competitors’ stock levels into pricing decisions (Fisher et al. 2018 ).

Some papers discuss (quasi) monopolistic competition (e.g., Xu and Hopp 2006 ) in which small firms charge the (higher) monopoly price rather than the (lower) competitive price. From an empirical study in the U.S. airline industry, Chen (2018) concludes that, as firms can price discriminate late-arriving consumers, competition is softened, profits are increased, and the only single-price equilibrium could be at the monopoly price. This supports Lal and Sarvary ( 1999 ) who show that online retailers enjoy a certain amount of monopoly power in cases where buyers cannot switch suppliers for repeated purchases (e.g., technical incompatibility reasons). In such cases, switching costs could increase online prices (Chen and Riordan 2008 ). However, this contradicts Deck and Gu ( 2012 ) who empirically show that, although the distribution of buyer values of competing products might theoretically lead to higher prices through competition, intensity of competition rarely allows for an occurrence of this phenomenon in e-tail settings.

Although duopoly settings can serve to assess the relevant strength of pricing strategies, which is not directly possible for oligopoly markets due to the curse of dimensionality (Kastius and Schlosser 2022 ), they cannot be transferred to more competitive environments (van de Geer et al. 2019 ). In online retail, a duopoly market structure is a rare exemption. Like for perfectly competitive markets, findings of duopoly research must be carefully assessed in terms of their applicability to online retail oligopolies.

Bresnahan and Reiss ( 1991 ) found empirical evidence that markets with an increasing number of dealers have lower prices than in less competitive market structures such as monopolies or duopolies. Although applicable to many online retail markets, where retailers face dozens, if not hundreds of thousands of competitors (Schlosser and Boissier 2018 ), few research attention is currently given toward a structure with a large number of competitors in an imperfect market (cf. Li et al. 2017 ). A way to assess the current competitive structure of markets is the utilization of online consumer-generated content such as forum entries (Netzer et al. 2012 ; Won et al. 2022 ) or clickstream data (Ringel and Skiera 2016 ) and actual sales data (Kim et al. 2011 ).

In many countries with well-developed B2C online markets, one or few major retailers dominate on an oligopolistic market. For example, the top three online retailers in the United States accounted for over 50% of the revenue generated on the national e-commerce market in 2021. Footnote 20 Due to lower locational limitations in conjunction with substantial economies of scale and scope, online markets tend to become more concentrated than their offline counterparts (Borsenberger 2015 ). Although one could expect that increased market transparency leads to a higher intensity of competition (Cao and Gruca 2003 ), limiting the market power of established firms and leaving growth potential for smaller firms (Zhao et al. 2017 ), it appears reasonable to predict that most online markets will ultimately resemble an oligopoly setting with a with a relatively small number of players—enabling increased tacit pricing algorithm collusion in the future (Calvano et al. 2020 ). With few exceptions (e.g., Noel 2007 ), there is little research (Brown and Goolsbee 2002 ; Wang et al. 2021 ; Cavallo 2018 ) exploring what type of competitor-based pricing strategies are used and what competitive dynamics are found on e-tail markets. Thus, more research is needed to investigate the current state of market structure and intensity of competition in today’s e-commerce markets as drivers of the selection and the outcomes of pricing approaches.

Implications and directions

We contribute to the literature by providing an interdisciplinary review of competitive online retail research. Competitive pricing problems can most often be assigned to one of the academic fields of economics, marketing management or operations. In a first step, this review offered a descriptive portrayal of the relevant literature. Motivated by practical issues and common features in competitive pricing research, we then structured competitive pricing contributions along four properties of pricing models. First, do firms compete with identical or quality differentiated products? Second, are products to be considered as perishable or durable goods? Third, is the market setting to be regarded time-independent or not? Fourth, which market structure prevails on e-tail markets? The framework is derived from an analysis of pricing research not exclusively restricted to online retail settings. Therefore, it could be extended to other online or offline markets, with little loss of generalizability.

We focused on e-tail markets because the relevance of competition for pricing strategies is disproportionally higher in such environments. On e-tail online markets, products are rarely offered exclusively so that the likelihood of substitutive competition is high. Nevertheless, products can be differentiated through other factors than prices such as generous shipping, customer retention (e.g., loyalty reward programs) or return and issue handling policies. With a look on product similarities, accounting for product interdependencies and multi-product situations are important improvements of prevailing pricing models. Second, pricing models with both a focus on perishable and/or durable products are relevant on e-tail markets. However, further research is needed exploring which of the respective perishability considerations are appropriate for different settings. Third, we conclude that, albeit time-independent static models may occasionally serve to simplify pricing issues, dynamic models outperform their static counterparts in constantly changing market environments such as in e-commerce. Fourth, we show that in most practical settings, online markets resemble either an oligopolistic market structure or a structure with many firms under imperfect competition. Thus, future research should consider these two “real” competitive settings instead of further looking at simplifying market structures such as monopolistic or duopolistic competition. This should ease a transfer of theoretical insights into practical applications. To sum, firms should be able to improve their competitive position by developing a profit optimizing dynamic pricing strategy for identical products in an oligopolistic setting with a varying number and relevance of competitors.

Due to space limitations, we had to focus on competitive pricing model characteristics related to four overall product and market attributes. Thus, more work is needed on other characteristics of competitive pricing models, particularly firm- and consumer-related characteristics. Firm-related characteristics encompass various additional properties of interacting firms (e.g., similarity or capacity constraints). Similarly, consumer-related characteristics entail further properties of interacting buyers (e.g., certainty, discreteness, sophistication, and homogeneity of demand).

In the selection process of literature, this study only considered papers in peer-reviewed journals and conference proceedings in English. Subsequent research could complement our findings by including industry-funded, unpublished and non-peer-reviewed articles, also in other languages. In addition, we do not claim that our research captures all competitive pricing publications of the considered field. As our study spans almost 50 years of a frequently discussed topic in the domains of economics, marketing management, and operations, we had to constrain the scope to the most influential work. Although we mutually evaluated our selection decisions and consulted outside peers for validation and further input, we cannot eliminate the element of subjectivity. Consequently, other authors could have selected slightly different papers. However, this shortcoming is unlikely to significantly affect our results as our literature selection was derived from a broad array of competitive pricing research and would therefore be only marginally influenced by a few omitted articles.

Competitive pricing includes all activities and processes to price products with the consideration of competitors. This does not only include rigidly pegging prices to competitor prices but rather a comprehensive consideration of current and expected price (re-)actions of competing firms to sustainably ensure profit maximization. In this article, the terms competitive pricing, competitor-oriented pricing and competitor-based pricing are used synonymously.

Search costs are defined as the costs of time and resources to acquire information with respect to price, assortment, and quality characteristics of the goods provided by different sellers. The internet dramatically reduces search costs through price comparison websites such as Google Shopping, Shopzilla (USA) or Idealo (Germany).

Business-to-consumer (B2C) online retail sales encompass all forms of electronic commerce markets in which residential end customers can directly buy goods from a seller over the internet through a web browser or a mobile app. In this paper, the terms business-to-consumer (B2C) online goods retail, online retail, e-tail, e-retail and e-commerce markets are used synonymously.

In contrast to classical quantity-based revenue management, dynamic pricing, also known as surge pricing, is the practice of adjusting prices according to current market demand (Boer 2015a ). Revenue management, also known as yield management, is a type of price discrimination which originates from the airline and hospitality industries. Typically, revenue management models assume fixed capacities, low marginal cost, varying demand and highly perishable inventory (Talluri and van Ryzin 2004 ).

Keywords used for abstract, title and keyword screening were “competitive pricing”, “competitor-based pricing”, “competition” AND “pricing”. To find literature for online pricing in particular, the search was combined with the keywords “online”, “e-retail”, “ecommerce” and “e-commerce”. Whereas the combination was scanned in great depths, the three competitive keywords were screened for influential papers with implications for online markets.

Bertrand competition is a simplified model of competition to explain price competition among (at least) two firms for an identical product at equal unit cost of production. Prices are set simultaneously, and consumers buy without search costs from the firm with the lowest price. When all firms charge the same price, consumer demand is split evenly between firms. A firm is willing to supply unlimited amounts of quantities above the unit cost of production and is indifferent to supply at unit cost as it will earn zero profit. The only Bertrand equilibrium exists when prices are equal to unit cost (i.e., competitive price) as each firm otherwise would have an incentive to undercut all other competitors and thereby rake in the entire market demand. Therefore, there can be no equilibrium at prices above the competitive price and price dispersion cannot occur.

In vertical differentiation, consumer choice depends on specific quality levels of product attributes. At the same price, all consumers prefer one product over other products, for example because of superior design. In the simplest form, products differ in one attribute and customers are willing to pay marginal increments of this attribute.

In horizontal differentiation, consumer choice depends on preferences for products. At the same price, some customers would buy one product and others other products.

We consider product differentiation only to product-related differentiation attributes. However, in competitive pricing literature firm-related differences such as firm loyalty or distribution channels are occasionally attributed to differentiation. For example, Abhishek et al. ( 2016 ) differentiate online distribution channels of otherwise homogeneous products and firms.

Heterogeneous customer preferences are a key requirement for product differentiation, otherwise price constitutes the only driver of the buying decision (Li et al. 2017 ). Without heterogeneity in consumers’ marginal willingness to pay for different levels of product quality, there can be no product differentiation (Pigou 1920 ).

Accessed 14–03-2022.

Salvage value is defined as the residual cash-flow of a good after its expiration date.

For example, game settings on the foundation of Stackelberg games necessarily comprise ≥ 2 stages (Geng and Mallik 2007 ; Gupta et al.; Wang et al. 2020 ; Yao and Liu 2005 ). Another example would be Anand and Girotra ( 2007 ) who propose a 3-stage model in which they include the supply chain configuration and determination of production quantities in addition to the actual price setting.

As such, we classify n-stage models as dynamic models when not all individual stages serve a specific time-independent purpose.

For instance, the Brandenburg consumer advice center (Verbraucherzentrale Brandenburg) examined dynamic price differentiation in online retail and found that 15 of the 16 observed German online shops dynamically changed their prices in 2018 (Dautzenberg et al. 2018 ).

A typical rule would be to set prices always x% lower than competitor prices up to a certain profit threshold.

Observable market data include price and stock levels of competitors (Fisher et al. 2018 ) or clickstream and keyword data of customers (Li et al. 2017 ).

Examples for support programs by online marketplaces are Amazon’s Seller Central ( ; Accessed 14–03-2022), eBay’s Seller Tools ( ; Accessed 14–03-2022) or Idealo’s Partner Program ( ; Accessed 14–03-2022).

Cumulatively, these values exceed 100% as some articles discussed more than one kind of market structure. Applied by economists to simplify real markets as the foundation of price theory, perfect competition relates to a market structure which is controlled entirely by market forces and not by individual firms. Instead, individual firms only act as price takers and cannot earn any economic profit. The conditions for a perfect competition, such as full information, homogeneous products, fully rational buyers, no scale, network or externality effects, no entry barriers, and no transaction costs, are rarely attainable in practical settings (Stigler 1957 ). If not all conditions for perfect competition are fulfilled, the market structure is imperfect which applies to most practical settings. Besides a monopoly with only one seller on the market, three market structures with competing firms exist: Monopolistic, duopolistic, and oligopolistic competition. An oligopoly is characterized by a small number of firms in which the behavior of one firm drives the actions of other firms. A duopoly is a particular case of an oligopoly in which two firms control the market. An extreme case of imperfect competition is (quasi) monopolistic competition in which products are differentiated and firms maintain a certain spare capacity giving them a certain degree of pricing power to maximize their (short-term) profits. In consequence, prices can be higher than corresponding the competitive (Bertrand) price (Vives 2001 ).

For an overview of the top 15 online shops in the United States in 2021, see Davidkhanian ( 2021 ).

Abhishek, Vibhanshu, Kinshuk Jerath, and Z. John Zhang. 2016. Agency selling or reselling? Channel structures in electronic retailing. Management Science 62 (8): 2259–2280. .

Article   Google Scholar  

Adida, Elodie, and Georgia Perakis. 2010. Dynamic pricing and inventory control: Uncertainty and competition. Operations Research 58 (2): 289–302. .

Afeche, Philipp, Hu Ming, and Yang Li. 2011. Reorder flexibility and price competition for differentiated seasonal products with market size uncertainty. SSRN Electronic Journal . .

Aggarwal, Praveen, and Taihoon Cha. 1998. Asymmetric price competition and store vs. national brand choice. Journal of Product & Brand Management 7 (3): 244–253. .

Aksoy-Pierson, Margaret, Gad Allon, and Awi Federgruen. 2013. Price competition under mixed multinomial logit demand functions. Management Science 59 (8): 1817–1835. .

Allen, Beth, and Martin Hellwig. 1986. Bertrand-Edgeworth oligopoly in large markets. Review of Economic Studies 53 (2): 175–204. .

Anand, Krishnan S., and Karan Girotra. 2007. The strategic perils of delayed differentiation. Management Science 53 (5): 697–712. .

Anderson, Eric T., and Nanda Kumar. 2007. Price competition with repeat, loyal buyers. Quantitative Marketing and Economics 5 (4): 333–359. .

Anderson, Chris K., Henning Rasmussen, and Leo MacDonald. 2005. Competitive pricing with dynamic asymmetric price effects. International Transactions in Operational Research 12 (5): 509–525. .

Anton, James J., Gary Biglaiser, and Nikolaos Vettas. 2014. Dynamic price competition with capacity constraints and a strategic buyer. International Economic Review 55 (3): 943–958. .

Anufriev, Mikhail, Dávid Kopányi, and Jan Tuinstra. 2013. Learning cycles in bertrand competition with differentiated commodities and competing learning rules. Journal of Economic Dynamics and Control 37 (12): 2562–2581. .

Armstrong, J. Scott, and Kesten C. Green. 2007. Competitor-oriented objectives: Myth of market share. International Journal of Business 12 (1): 117–136.

Google Scholar  

Ba, Sulin, Jan Stallaert, and Zhongju Zhang. 2012. Research note—Online price dispersion: A game-theoretic perspective and empirical evidence. Information Systems Research 23 (2): 575–592. .

Babaioff, Moshe, Renato Paes Leme, and Balasubramanian Sivan. 2015. Price competition, fluctuations, and welfare guarantees. In EC '15: Proceedings of the Sixteenth ACM Conference on Economics and Computation, Portland, USA. 15-Jun-15 - 19-Jun-15.

Balakrishnan, Anantaram, Shankar Sundaresan, and Bo. Zhang. 2014. Browse-and-switch: Retail-online competition under value uncertainty. Production and Operations Management 23 (7): 1129–1145. .

Balasubramanian, Sridhar. 1998. Mail versus mall: A strategic analysis of competition between direct marketers and conventional retailers. Marketing Science 17 (3): 181–195. .

Baloglu, Seyhmus, and Lisa Marie Assante. 1999. A content analysis of subject areas and research methods used in five hospitality management journals. Journal of Hospitality & Tourism Research 23 (1): 53–70. .

Bernstein, Fernando, and Awi Federgruen. 2004. A general equilibrium model for industries with price and service competition. Operations Research 52 (6): 868–886. .

Bernstein, Fernando, and Awi Federgruen. 2005. Decentralized supply chains with competing retailers under demand uncertainty. Management Science 51 (1): 18–29. .

Bernstein, Fernando, Jing-Sheng Song, and Xiaona Zheng. 2008. “Bricks-and-mortar” vs. “clicks-and-mortar”: An equilibrium analysis. European Journal of Operational Research 187 (3): 671–690. .

Bertrand, Joseph Louis François. 1883. Thèorie mathèmatique de la richesse sociale. Journal des Savants 499–508.

Bertsimas, Dimitris, and Georgia Perakis. 2006. Dynamic pricing: A learning approach. In Mathematical and computational models for congestion charging , ed. Siriphong Lawphongpanich, Donald W. Hearn, and Michael J. Smith, 45–79. Boston: Kluwer Academic Publishers.

Chapter   Google Scholar  

Besbes, Omar, and Denis Sauré. 2016. Product assortment and price competition under multinomial logit demand. Production and Operations Management 25 (1): 114–127. .

Bischi, Gian-Italo, Carl Chiarella, and Michael Kopel. 2004. The long run outcomes and global dynamics of a duopoly game with misspecified demand functions. International Game Theory Review 6 (3): 343–379. .

Blattberg, Robert C., and Kenneth J. Wisniewski. 1989. Price-induced patterns of competition. Marketing Science 8 (4): 291–309. .

Boardman, Rosy, and Helen McCormick. 2018. Shopping channel preference and usage motivations. Journal of Fashion Marketing and Management: An International Journal 22 (2): 270–284. .

Boccard, Nicolas, and Xavier Wauthy. 2000. Bertrand competition and Cournot outcomes: Further results. Economics Letters 68 (3): 279–285. .

Borenstein, Severin, and L. Rose Nancy. 1994. Competition and price dispersion in the U.S. airline industry. Journal of Political Economy 102 (4): 653–683. .

Borsenberger, Claire. 2015. The concentration phenomenon in e-commerce. In Postal and delivery innovation in the digital economy , ed. Michael A. Crew and Timothy J. Brennan, 31–41. Cham: Springer International Publishing.

Boyd, E. Andrew, and Ioana C. Bilegan. 2003. Revenue management and e-commerce. Management Science 49 (10): 1363–1386. .

Bresnahan, Timothy F., and Peter C. Reiss. 1991. Entry and competition in concentrated markets. Journal of Political Economy 99 (5): 977–1009. .

Brown, Jeffrey R., and Austan Goolsbee. 2002. Does the internet make markets more competitive? Evidence from the life insurance industry. Journal of Political Economy 110 (3): 481–507. .

Bryant, John. 1980. Competitive equilibrium with price setting firms and stochastic demand. International Economic Review 21 (3): 619–626. .

Brynjolfsson, Erik, and Michael D. Smith. 2000. Frictionless commerce? A comparison of internet and conventional retailers. Management Science 46 (4): 563–585. .

Cachon, Gérard P., and Pnina Feldman. 2010. Dynamic versus static pricing in the presence of strategic consumers. The Wharton School, University of Pennsylvania (Working Paper).

Cachon, Gérard. P., and Patrick T. Harker. 2002. Competition and outsourcing with scale economies. Management Science 48 (10): 1314–1333. .

Caillaud, Bernard, and Romain de Nijs. 2014. Strategic loyalty reward in dynamic price discrimination. Marketing Science 33 (5): 725–742. .

Calvano, Emilio, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello. 2020. Artificial intelligence, algorithmic pricing, and collusion. American Economic Review 110 (10): 3267–3297. .

Campbell, Colin, Gautam Ray, and Waleed A. Muhanna. 2005. Search and collusion in electronic markets. Management Science 51 (3): 497–507. .

Cao, Yong, and Thomas S. Gruca. 2003. The effect of stock market dynamics on internet price competition. Journal of Service Research 6 (1): 24–36. .

Caplin, Andrew, and Barry Nalebuff. 1991. Aggregation and imperfect competition: On the existence of equilibrium. Econometrica 59 (1): 25–59. .

Caro, Felipe. 2012. Zara: Staying fast and fresh. The European Case Clearing House. ECCH Case study 612-006-1.

Caro, Felipe, and Jérémie. Gallien. 2012. Clearance pricing optimization for a fast-fashion retailer. Operations Research 60 (6): 1404–1422. .

Cattani, Kyle, Olga Perdikaki, and Ann Marucheck. 2007. The perishability of online grocers. Decision Sciences 38 (2): 329–355. .

Cavallo, Alberto. 2018. More Amazon effects: online competition and pricing behaviors. National Bureau of Economic Research (Working Papers 25138).

Cebollada, Javier, Yanlai Chu, and Zhiying Jiang. 2019. Online category pricing at a multichannel grocery retailer. Journal of Interactive Marketing 46 (1): 52–69. .

Chen, Yongmin. 1997. Paying customers to switch. Journal of Economics Management Strategy 6 (4): 877–897. .

Chen, Ming, and Zhi-Long. Chen. 2015. Recent developments in dynamic pricing research: Multiple products, competition, and limited demand information. Production and Operations Management 24 (5): 704–731. .

Chen, Yongmin, and Michael H. Riordan. 2008. Price-increasing competition. RAND Journal of Economics 39 (4): 1042–1058. .

Chen, Ying-Ju, Yves Zenou, and Junjie Zhou. 2018. Competitive pricing strategies in social networks. RAND Journal of Economics 49 (3): 672–705. .

Cheng, Kuangnen. 2017. Competitive dynamics across strategic groups: A literature review and validation by quantitative evidence of operation data. International Journal of Business Environment 9 (4): 301–323. .

Chiang, Wen Chyuan, Jason C.H. Chen, and Xu Xiaojing. 2007. An overview of research on revenue management: Current issues and future research. International Journal of Revenue Management 1 (1): 97–128. .

Chintagunta, Pradeep K., Junhong Chu, and Javier Cebollada. 2012. Quantifying transaction costs in online/offline grocery channel choice. Marketing Science 31 (1): 96–114. .

Chioveanu, Ioana. 2012. Price and quality competition. Journal of Economics 107 (1): 23–44. .

Chioveanu, Ioana, and Jidong Zhou. 2013. Price competition with consumer confusion. Management Science 59 (11): 2450–2469. .

Choi, S. Chan. 1996. Price competition in a duopoly common retailer channel. Journal of Retailing 72 (2): 117–134. .

Christen, Markus. 2005. Research note: Cost uncertainty is bliss: The effect of competition on the acquisition of cost information for pricing new products. Management Science 51 (4): 668–676. .

Chu, Junhong, Pradeep Chintagunta, and Javier Cebollada. 2008. Research note: A comparison of within-household price sensitivity across online and offline channels. Marketing Science 27 (2): 283–299. .

Chung, Byung Sun, Jiahan Li, Tao Yao, Changhyun Kwon, and Terry L. Friesz. 2012. Demand learning and dynamic pricing under competition in a state-space framework. IEEE Transactions on Engineering Management 59 (2): 240–249.

Clemons, Eric K., Il-Horn Hann, and Lorin M. Hitt. 2002. Price dispersion and differentiation in online travel: An empirical investigation. Management Science 48 (4): 534–549. .

Cooper, William L., Tito Homem-de-Mello, and Anton J. Kleywegt. 2015. Learning and pricing with models that do not explicitly incorporate competition. Operations Research 63 (1): 86–103. .

Currie, Christine S. M., Russell C. H. Cheng, and Honora K. Smith. 2008. Dynamic pricing of airline tickets with competition. Journal of the Operational Research Society 59 (8): 1026–1037. .

Dana, James D., and Nicholas C. Petruzzi. 2001. Note: The newsvendor model with endogenous demand. Management Science 47 (11): 1488–1497. .

Dana, James D., and Kevin R. Williams. 2022. Intertemporal price discrimination in sequential quantity-price games. Marketing Science . .

Dasci, Abdullah, and Mustafa Karakul. 2009. Two-period dynamic versus fixed-ratio pricing in a capacity constrained duopoly. European Journal of Operational Research 197 (3): 945–968. .

Dautzenberg, Kirsti, Constanze Gaßmann, and Britta Groß. 2018. Online-Handel: Das Spiel mit dem dynamischen Preis. . Accessed 14 March 2022.

Davidkhanian, Suzy. 2021. US retail spending jumped nearly 16% this year despite inflation, supply chain woes. . Accessed 14 March 2022.

de Toni, Deonir, Gabriel Sperandio Milan, Evandro Busata Saciloto, and Fabiano Larentis. 2017. Pricing strategies and levels and their impact on corporate profitability. Revista de Administração 52 (2): 120–133. .

Deck, Cary, and Gu Jingping. 2012. Price increasing competition? Experimental evidence. Journal of Economic Behavior & Organization 84 (3): 730–740. .

den Boer, Arnoud V. 2015a. Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys in Operations Research and Management Science 20 (1): 1–18. .

den Boer, Arnoud V. 2015b. Tracking the market: Dynamic pricing and learning in a changing environment. European Journal of Operational Research 247 (3): 914–927.

DeSarbo, Wayne S., and Rajdeep Grewal. 2007. An alternative efficient representation of demand-based competitive asymmetry. Strategic Management Journal 28 (7): 755–766. .

Devaraj, Sarv, Ming Fan, and Rajiv Kohli. 2002. Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics. Information Systems Research 13 (3): 316–333. .

Diamond, Peter A. 1971. A model of price adjustment. Journal of Economic Theory 3 (2): 156–168. .

Dickson, Peter R., and Joel E. Urbany. 1994. Retailer reactions to competitive price changes. Journal of Retailing 70 (1): 1–21. .

Dinerstein, Michael, Liran Einav, Jonathan Levin, and Neel Sundaresan. 2018. Consumer price search and platform design in internet commerce. American Economic Review 108 (7): 1820–1859. .

Dong, James, A. Serdar Simsek, and Huseyin Topaloglu. 2019. Pricing problems under the Markov chain choice model. Production and Operations Management 28 (1): 157–175. .

Dudey, Marc. 1992. Dynamic Edgeworth-Bertrand competition. Quarterly Journal of Economics 107 (4): 1461–1477. .

Dzyabura, Daria, Srikanth Jagabathula, and Eitan Muller. 2019. Accounting for discrepancies between online and offline product evaluations. Marketing Science 38 (1): 88–106. .

Elmaghraby, Wedad, and Pınar Keskinocak. 2003. Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science 49 (10): 1287–1309. .

Farias, Vivek, Denis Saure, and Gabriel Y. Weintraub. 2012. An approximate dynamic programming approach to solving dynamic oligopoly models. RAND Journal of Economics 43 (2): 253–282. .

Fay, Scott. 2008. Selling an opaque product through an intermediary: The case of disguising one’s product. Journal of Retailing 84 (1): 59–75. .

Federgruen, Awi, and Hu Ming. 2015. Multi-product price and assortment competition. Operations Research 63 (3): 572–584. .

Ferreira, Kris Johnson, Bin Hong Alex. Lee, and David Simchi-Levi. 2016. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Operations Management 18 (1): 69–88. .

Fisher, Marshall, Santiago Gallino, and Jun Li. 2018. Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Science 64 (6): 2473–2972. .

Frambach, Ruud T., Henk C.A. Roest, and Trichy V. Krishnan. 2007. The impact of consumer internet experience on channel preference and usage intentions across the different stages of the buying process. Journal of Interactive Marketing 21 (2): 26–41. .

Gallego, Guillermo, and Hu Ming. 2014. Dynamic pricing of perishable assets under competition. Management Science 60 (5): 1241–1259. .

Gallego, Guillermo, Woonghee Tim Huh, Wanmo Kang, and Robert Phillips. 2006. Price competition with the attraction demand model: Existence of unique equilibrium and its stability. Manufacturing & Service Operations Management 8 (4): 359–375. .

Gallego, Guillermo, and Garrett J. van Ryzin. 1997. A multiproduct dynamic pricing problem and its applications to network yield management. Operations Research 45 (1): 24–41. .

Gallego, Guillermo, and Ruxian Wang. 2014. Multi-product optimization and competition under the nested logit model with product-differentiated price sensitivities. Operations Research 62 (2): 450–461. .

Gao, Fei, and Su Xuanming. 2018. Omnichannel service operations with online and offline self-order technologies. Management Science 64 (8): 3595–3608. .

Geng, Qin, and Suman Mallik. 2007. Inventory competition and allocation in a multi-channel distribution system. European Journal of Operational Research 182 (2): 704–729. .

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. 2009. Dynamic pricing - State-of-the-art. Journal of Business Economics 3 (2): 1–40.

Gupta, Varun, Dmitry Ivanov, and Tsan-Ming Choi. 2021. Competitive pricing of substitute products under supply disruption. Omega . .

Hagiu, Andrei. 2007. Merchant or two-sided platform? Review of Network Economics 6 (2): 115–133. .

Hall, Joseph M., Praveen K. Kopalle, and David F. Pyke. 2009. Static and dynamic pricing of excess capacity in a make-to-order environment. Production and Operations Management 18 (4): 411–425. .

Hamilton, Jonathan H., and Steven M. Slutsky. 1990. Endogenous timing in duopoly games: Stackelberg or cournot equilibria. Games and Economic Behavior 2 (1): 29–46. .

Harsha, Pavithra, Shivaram Subramanian, and Joline Uichanco. 2019. Dynamic pricing of omnichannel inventories. Manufacturing & Service Operations Management 21 (1): 47–65. .

Heese, H. Sebastian, and Victor Martínez-de-Albéniz. 2018. Effects of assortment breadth announcements on manufacturer competition. Manufacturing and Service Operations Management 20 (2): 302–316. .

Hinterhuber, Andreas. 2008. Customer value-based pricing strategies: Why companies resist. Journal of Business Strategy 29 (4): 41–50. .

Isler, Karl, and Henrik Imhof. 2008. A game theoretic model for airline revenue management and competitive pricing. Journal of Revenue and Pricing Management 7 (4): 384–396. .

Israeli, Ayelet, Fiona Scott-Morton, Jorge Silva-Risso, and Florian Zettelmeyer. 2022. How market power affects dynamic pricing: Evidence from inventory fluctuations at car dealerships. Management Science 68 (2): 895–916. .

Ittoo, Ashwin, and Nicolas Petit. 2017. Algorithmic pricing agents and tacit collusion: A technological perspective. SSRN Electronic Journal . .

Janssen, Maarten C. W., and José Luis Moraga-González. 2004. Strategic pricing, consumer search and the number of firms. Review of Economic Studies 71 (4): 1089–1118. .

Jerath, Kinshuk, Serguei Netessine, and Senthil K. Veeraraghavan. 2010. Revenue management with strategic customers: Last-minute selling and opaque selling. Management Science 56 (3): 430–448. .

Kachani, Shmatov, Soulaymane Kachani, and Kyrylo Shmatov. 2010. Competitive pricing in a multi-product multi-attribute environment. Production and Operations Management 20 (5): 668–680. .

Kastius, Alexander, and Rainer Schlosser. 2022. Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management 21 (1): 50–63. .

Keskin, N. Bora, and Assaf Zeevi. 2017. Chasing demand: Learning and earning in a changing environment. Mathematics of Operations Research 42 (2): 277–307. .

Kim, Jun B., Paulo Albuquerque, and Bart J. Bronnenberg. 2011. Mapping online consumer search. Journal of Marketing Research 48 (1): 13–27. .

Koças, Cenk. 2005. A model of internet pricing under price-comparison shopping. International Journal of Electronic Commerce 10 (1): 111–134. .

Könönen, Ville. 2006. Dynamic pricing based on asymmetric multiagent reinforcement learning. International Journal of Intelligent Systems 21 (1): 73–98. .

Kopalle, Praveen, Dipayan Biswas, Pradeep K. Chintagunta, Jia Fan, Koen Pauwels, Brian T. Ratchford, and James A. Sills. 2009. Retailer pricing and competitive effects. Journal of Retailing 85 (1): 56–70. .

Kutschinski, Erich, Thomas Uthmann, and Daniel Polani. 2003. Learning competitive pricing strategies by multi-agent reinforcement learning. Journal of Economic Dynamics and Control 27 (11): 2207–2218. .

Lal, Rajiv, and Ram Rao. 1997. Supermarket competition: The case of every day low pricing. Marketing Science 16 (1): 60–80. .

Lal, Rajiv, and Miklos Sarvary. 1999. When and how is the internet likely to decrease price competition? Marketing Science 18 (4): 485–503. .

Lancaster, Kelvin. 1979. Variety, equity, and efficiency: Product variety in an industrial society . New York: Columbia University Press.

Book   Google Scholar  

Larson, Ronald B. 2019. Promoting demand-based pricing. Journal of Revenue and Pricing Management 18 (1): 42–51. .

Le Chen, Alan Mislove, and Christo Wilson. 2016. An empirical analysis of algorithmic pricing on Amazon Marketplace. In Proceedings of the 25th International Conference on World Wide Web - WWW '16 , 1339–1349, Montreal. 11-Apr-16 - 15-Apr-16. New York: ACM Press.

Lebow, Sara. 2019. Worldwide ecommerce continues double-digit growth following pandemic push to online. . Accessed 14 March 2022.

Lee, Khai Sheang, and Soo Jiuan Tan. 2003. E-retailing versus physical retailing. A theoretical model and empirical test of consumer choice. Journal of Business Research 56 (11): 877–885. .

Lee, Thomas Y., and Eric T. Bradlow. 2011. Automated marketing research using online customer reviews. Journal of Marketing Research 48 (5): 881–894. .

Levin, Yuri, Jeff McGill, and Mikhail Nediak. 2008. Risk in revenue management and dynamic pricing. Operations Research 56 (2): 326–343. .

Levin, Yuri, Jeff McGill, and Mikhail Nediak. 2009. Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Management Science 55: 32–46. .

Li, Jun, Serguei Netessine, and Sergei Koulayev. 2017. Price to compete… with many: How to identify price competition in high-dimensional space. Management Science 64 (9): 4118–4136. .

Li, Michael Z. F., Anming Zhang, and Yimin Zhang. 2008. Airline seat allocation competition. International Transactions in Operational Research 15 (4): 439–459. .

Lin, Yen-Ting, Ali K. Parlaktürk, and Jayashankar M. Swaminathan. 2014. Vertical integration under competition: Forward, backward, or no integration? Production and Operations Management 23 (1): 19–35. .

Lin, Kyle Y., and Soheil Y. Sibdari. 2009. Dynamic price competition with discrete customer choices. European Journal of Operational Research 197 (3): 969–980. .

Liozu, Stephan. 2015. The pricing journey: The organizational transformation toward pricing excellence . Stanford: Stanford Business Books.

Lippman, Steven A., and Kevin F. McCardle. 1997. The competitive newsboy. Operations Research 45 (1): 54–65. .

Liu, Qian, and Dan Zhang. 2013. Dynamic pricing competition with strategic customers under vertical product differentiation. Management Science 59 (1): 84–101. .

Loginova, Oksana. 2021. Price competition online: Platforms versus branded websites. Journal of Economics and Management Strategy . .

Mantin, Benny, Daniel Granot, and Frieda Granot. 2011. Dynamic pricing under first order Markovian competition. Naval Research Logistics 58 (6): 608–617. .

Martínez-de-Albéniz, Victor, and Kalyan Talluri. 2011. Dynamic price competition with fixed capacities. Management Science 57 (6): 1078–1093. .

Maskin, Eric, and Jean Tirole. 1988. A theory of dynamic oligopoly, II: Price competition, kinked demand curves, and edgeworth cycles. Econometrica 56 (3): 571–599. .

Matsubayashi, Nobuo, and Yoshiyasu Yamada. 2008. A note on price and quality competition between asymmetric firms. European Journal of Operational Research 187 (2): 571–581. .

McGill, Jeffrey I., and Garrett J. van Ryzin. 1999. Revenue management: Research overview and prospects. Transportation Science 33 (2): 233–256. .

Miklós-Thal, Jeanine, and Catherine Tucker. 2019. Collusion by algorithm: Does better demand prediction facilitate coordination between sellers? Management Science 65 (4): 1552–1561. .

Mitra, Subrata. 2021. Economic models of price competition between traditional and online retailing under showrooming. Decision . .

Mizuno, Toshihide. 2003. On the existence of a unique price equilibrium for models of product differentiation. International Journal of Industrial Organization 21 (6): 761–793. .

Mookherjee, Reetabrata, and Terry L. Friesz. 2008. Pricing, allocation, and overbooking in dynamic service network competition when demand is uncertain. Production and Operations Management 17 (4): 455–474. .

Moorthy, K. Sridhar. 1988. Product and price competition in a duopoly. Marketing Science 7 (2): 141–168. .

Motta, Massimo. 1993. Endogenous quality choice: Price vs. quantity competition. The Journal of Industrial Economics 41 (2): 113–131. .

Nalca, Arcan, Tamer Boyaci, and Saibal Ray. 2010. Competitive price-matching guarantees under imperfect store availability. Quantitative Marketing and Economics 8 (3): 275–300. .

Netessine, Serguei, and Robert A. Shumsky. 2005. Revenue management games: Horizontal and vertical competition. Management Science 51 (5): 813–831. .

Netzer, Oded, Ronen Feldman, Jacob Goldenberg, and Moshe Fresko. 2012. Mine your own business: Market-structure surveillance through text mining. Marketing Science 31 (3): 521–543. .

Nip, Kameng, Changjun Wang, and Zizhuo Wang. 2020. Competitive and cooperative assortment games under Markov chain choice model. SSRN Electronic Journal . .

Noel, Michael D. 2007. Edgeworth price cycles, cost-based pricing, and sticky pricing in retail gasoline markets. Review of Economics and Statistics 89 (2): 324–334. .

Obermeyer, Andy, Christos Evangelinos, and Ronny Püschel. 2013. Price dispersion and competition in European airline markets. Journal of Air Transport Management 26 (1): 31–34. .

Olivares, Marcelo, and Gérard. P. Cachon. 2009. Competing retailers and inventory: An empirical investigation of General Motors’ dealerships in isolated U.S. markets. Management Science 55 (9): 1586–1604. .

Parlar, Mahmut. 1988. Game theoretic analysis of the substitutable product inventory problem with random demands. Naval Research Logistics 35 (3): 397–409.;2-Z .

Penz, Elfriede, and Margaret K. Hogg. 2011. The role of mixed emotions in consumer behaviour. European Journal of Marketing 45 (1): 104–132. .

Perakis, Georgia, and Anshul Sood. 2006. Competitive multi-period pricing for perishable products: A robust optimization approach. Mathematical Programming 107 (1–2): 295–335. .

Perloff, Jeffrey M., and Steven C. Salop. 1985. Equilibrium with product differentiation. Review of Economic Studies 52 (1): 107. .

Phillips, Robert L. 2021. Pricing and revenue optimization , 2nd ed. Stanford: Stanford Business Books.

Pigou, Arthur Cecil. 1920. The economics of welfare . London: Macmillan & co.

Popescu, Dana. 2015. Repricing algorithms in e-commerce. Technology and Operations Management (Working Paper No. 2015/75/TOM). .

Putsis, William, and Ravi Dhar. 1998. The many faces of competition. Marketing Letters 9 (3): 269–284. .

Ratchford, Brian T. 2009. Online pricing: Review and directions for research. Journal of Interactive Marketing 23 (1): 82–90. .

Richards, Timothy J., and Stephen F. Hamilton. 2006. Rivalry in price and variety among supermarket retailers. American Journal of Agricultural Economics 88 (3): 710–726. .

Ringel, Daniel M., and Bernd Skiera. 2016. Visualizing asymmetric competition among more than 1,000 products using big search data. Marketing Science 35 (3): 511–534. .

Ryan, Jennifer K., Daewon Sun, and Xuying Zhao. 2012. Competition and coordination in online marketplaces. Production and Operations Management 21 (6): 997–1014. .

Salop, Steven C. 1976. Information and monopolistic competition. American Economic Review 66 (2): 240–245.

Sarkar, Amit, and Brojeswar Pal. 2021. Competitive pricing strategies of multi channel supply chain under direct servicing by the manufacturer. RAIRO Operations Research 55 (1): 1849–1873. .

Scarpi, Daniele, Gabriele Pizzi, and Marco Visentin. 2014. Shopping for fun or shopping to buy: Is it different online and offline? Journal of Retailing and Consumer Services 21 (3): 258–267. .

Schinkel, Maarten Pieter, Jan Tuinstra, and Dries Vermeulen. 2002. Convergence of Bayesian learning to general equilibrium in mis-specified models. Journal of Mathematical Economics 38 (4): 483–508. .

Schlereth, Christian, Bernd Skiera, and Fabian Schulz. 2018. Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans. European Journal of Operational Research 269 (3): 1165–1179. .

Schlosser, Rainer, and Martin Boissier. 2018. Dealing with the dimensionality curse in dynamic pricing competition: Using frequent repricing to compensate imperfect market anticipations. Computers and Operations Research 100: 26–42. .

Schlosser, Rainer, Martin Boissier, Andre Schober, and Matthias Uflacker. 2016. How to survive dynamic pricing competition in e-commerce. In Proceedings of the 10th ACM conference on recommender systems, Boston. 15-Sept-16 to 19-Sept-16. New York: Association for Computing Machinery.

Schlosser, Rainer, and Keven Richly. 2019. Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects. Journal of Revenue and Pricing Management 18: 451–464. .

Serth, Sebastian, Nikolai Podlesney, Marvin Bornstein, Jan Lindemann, Johanna Lattt, Jan Selke, Rainer Schlosser, Martin Boissier, and Matthias Uflacker. 2017. An interactive platform to simulate dynamic pricing competition on online marketplaces. In IEEE 21st International Enterprise Distributed Object Computing Conference , 61–66, Quebec City, Canada. 10-Oct-17 to 13-Oct-17. .

Shugan, Steven M. 2002. Editorial: Marketing science, models, monopoly models, and why we need them. Marketing Science 21 (3): 223–228. .

Siegert, Caspar, and Robert Ulbricht. 2020. Dynamic oligopoly pricing: Evidence from the airline industry. International Journal of Industrial Organization 71: 102639. .

Simon, Hermann. 1979. Dynamics of price elasticity and brand life cycles: An empirical study. Journal of Marketing Research 16 (4): 439–452. .

Smith, Michael D., and Erik Brynjolfsson. 2001. Consumer decision-making at an internet shopbot: Brand still matters. The Journal of Industrial Economics 49 (4): 541–558. .

Stigler, George J. 1957. Perfect competition, historically contemplated. Journal of Political Economy 65 (1): 1–17. .

Stiglitz, Joseph E. 1979. Equilibrium in product markets with imperfect information. American Economic Review 69 (2): 339–345.

Sun, Haoying, and Stephen M. Gilbert. 2019. Retail price competition with product fit uncertainty and assortment selection. Production and Operations Management 28 (7): 1658–1673. .

Talluri, Kalyan T., and Garrett J. van Ryzin. 2004. The theory and practice of revenue management . Boston: Springer.

Thomadsen, Raphael. 2007. Product positioning and competition: The role of location in the fast food industry. Marketing Science 26 (6): 792–804. .

Tranfield, David, David Denyer, and Palminder Smart. 2003. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management 14 (3): 207–222. .

Tuinstra. 2004. A price adjustment process in a model of monopolistic competition. International Game Theory Review 6 (3): 417–442. .

van de Geer, Ruben, Arnoud V. den Boer, Christopher Bayliss, Christine S. M. Currie, Andria Ellina, Malte Esders, Alwin Haensel, Xiao Lei, Kyle D. S. Maclean, Antonio Martinez-Sykora, Asbjørn Nilsen Riseth, Fredrik Ødegaard, and Simos Zachariades. 2019. Dynamic pricing and learning with competition: Insights from the dynamic pricing challenge at the 2017 INFORMS RM and pricing conference. Journal of Revenue and Pricing Management 18 (3): 185–203. .

van Mieghem, Jan A., and Maqbool Dada. 1999. Price versus production postponement: Capacity and competition. Management Science 45 (12): 1639–1649. .

Villas-Boas, J. Miguel. 1999. Dynamic competition with customer recognition. RAND Journal of Economics 30 (4): 604–631. .

Villas-Boas, J. Miguel. 2004. Consumer learning, brand loyalty, and competition. Marketing Science 23 (1): 134–145. .

Villas-Boas, J. Miguel. 2006. Dynamic competition with experience goods. Journal of Economics and Management Strategy 15 (1): 37–66. .

Villas-Boas, J. Miguel, and Russell S. Winer. 1999. Endogeneity in brand choice models. Management Science 45 (10): 1324–1338. .

Vinod, Balakrishna. 2020. Advances in revenue management: The last frontier. Journal of Revenue and Pricing Management 20: 15–20. .

Viswanathan, Siva. 2005. Competing across technology-differentiated channels: The impact of network externalities and switching costs. Management Science 51 (3): 483–496. .

Vives, Xavier. 2001. Oligopoly pricing: Old ideas and new tools . London: MIT Press.

von Stackelberg, Heinrich. 2011. Market structure and equilibrium . Berlin: Springer.

Wang, Zizhuo, and Hu Ming. 2014. Committed versus contingent pricing under competition. Production and Operations Management 23 (11): 1919–1936. .

Wang, Sujuan, Hu Qiying, and Liu Weiqi. 2017. Price and quality-based competition and channel structure with consumer loyalty. European Journal of Operational Research 262 (2): 563–574. .

Wang, Yongzhao, and Xiaojie Sun. 2019. Dynamic vs. static wholesale pricing strategies in a dual-channel green supply chain. Complexity 13: 1–14. .

Wang, Ningning, Ting Zhang, Xiaowei Zhu, and Peimiao Li. 2020. Online-offline competitive pricing with reference price effect. Journal of the Operational Research Society 72 (3): 642–653. .

Wang, Wenche, Fan Li, and Yujia Zhang. 2021. Price discount and price dispersion in online market: Do more firms still lead to more competition? Journal of Theoretical and Applied Electronic Commerce Research 16 (2): 164–178. .

Weatherford, Lawrence R., and Samuel E. Bodily. 1992. A taxonomy and research overview of perishable-asset revenue management: Yield management, overbooking, and pricing. Operations Research 40 (5): 831–844. .

Weintraub, Gabriel Y., C. Lanier Benkard, and Benjamin van Roy. 2008. Markov perfect industry dynamics with many firms. Econometrica 76 (6): 1375–1411. .

Wenzelburger, J. 2004. Learning to play best response in duopoly games. International Game Theory Review 6 (3): 443–459. .

Won, Eugene J. S., Oh. Yun Kyung, and Joon Yeon Choeh. 2022. Analyzing competitive market structures based on online consumer-generated content and sales data. Asia Pacific Journal of Marketing and Logistics Advance Online Publication . .

Wu, Lin-Liang Bill, and Desheng Wu. 2016. Dynamic pricing and risk analytics under competition and stochastic reference price effects. IEEE Transactions on Industrial Informatics 12 (3): 1282–1293. .

Xu, Xiaowei, and Wallace J. Hopp. 2006. A monopolistic and oligopolistic stochastic flow revenue management model. Operations Research 54 (6): 1098–1109. .

Yang, Jian, and Yusen Xia. 2013. A nonatomic-game approach to dynamic pricing under competition. Production and Operations Management 22 (1): 88–103. .

Yang, Yongge, Yu-Ching Lee, and Po-An Chen. 2020. Competitive demand learning: a data-driven pricing algorithm. Cornell University (Working Paper). .

Yano, Makoto, and Takashi Komatsubara. 2006. Endogenous price leadership and technological differences. International Journal of Economic Theory 2 (3–4): 365–383. .

Yano, Makoto, and Takashi Komatsubara. 2018. Price competition or price leadership. Economic Theory 66 (4): 1023–1057. .

Yao, Dong-Qing, and John J. Liu. 2005. Competitive pricing of mixed retail and e-tail distribution channels. Omega 33 (3): 235–247. .

Young, Jessica. 2022. US ecommerce grows 14.2% in 2021. . Accessed 14 March 2022.

Zhang, Jianxiong, Liyan Lei, Shichen Zhang, and Lijun Song. 2017. Dynamic vs. static pricing in a supply chain with advertising. Computers & Industrial Engineering 109 (1): 266–279. .

Zhang, Ting, Ling Ge, Qinglong Gou, and Li-Wen Chen. 2018a. Consumer showrooming, the sunk cost effect and online-offline competition. Journal of Electronic Commerce Research 19 (1): 55–74.

Zhang, Zhichao, Qing Zhang, Zhi Liu, and Xiaoxue Zheng. 2018b. Static and dynamic pricing strategies in a closed-loop supply chain with reference quality effects. Sustainability 10 (1): 157. .

Zhao, Xuan, Derek Atkins, Hu Ming, and Wensi Zhang. 2017. Revenue management under joint pricing and capacity allocation competition. European Journal of Operational Research 257 (3): 957–970. .

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Gerpott, T.J., Berends, J. Competitive pricing on online markets: a literature review. J Revenue Pricing Manag 21 , 596–622 (2022).

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An literature review examples on e-commerce literature reviews is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject.

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