literature review for electronic commerce

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.

Design/methodology/approach

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.

Originality/value

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. https://doi.org/10.1108/IJPDLM-02-2017-0081

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 http://creativecommons.org/licences/by/4.0/legalcode

Introduction

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. Bonobos.com, 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.

Conclusions

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.

Classification of literature review on LML models

Journal pool for reviewed papers

LML design framework

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M&P compensation review 2023/24

Compensation review and implementation is currently underway for the Management and Professional group.

UBC has recently completed a market review to evaluate the competitiveness of its compensation for Management & Professional (M&P) Staff. We are pleased to have finalized the implementation plan for the M&P Compensation Review, and to be responsive to the principles in the AAPS Agreement. It is important to the University and to AAPS that M&P compensation remains competitive, given its fundamental nature in retaining and recruiting outstanding staff.

The University’s bargained agreement with AAPS (the Association of Administrative and Professional Staff) requires the University to conduct salary surveys of the representative comparator market at regular intervals, and this has been done every three to four years since 2004. The results inform targeted changes to the AAPS salary grid that result in the University paying at the “50th percentile” of the comparator market. 

Changes from the M&P Compensation Review are separate from compensation increases that are negotiated during collective bargaining, which tend to come in the form of General Wage Increases (GWIs) to keep up with inflation.

Implementation of Results

It is important to note that this compensation review will not result in a general wage increase (GWI) for all M&P staff. Adjustments are targeted at those job family levels that have fallen behind the 50th percentile of the relevant comparator market.

It is also important to note that the changes that are going to be implemented will not result in any lifting of the overall pay structure, but rather will result in rearrangement of the classification of targeted levels within job families. Separate negotiated GWIs result in across-the-board lifts to the overall pay structure and salaries. 

For your reference, a list of M&P job family levels that will be changed is available at the end of this article. You may also reference UBC's salary scales .

Implementation Rules

Even if a job family level is adjusted, this does not mean that every staff member within that level will receive a salary adjustment. Much depends upon where the M&P staff member’s salary is within the pay grade. View the implementation rules below:

• If the staff member’s salary fell below the minimum of the "new pay grade", then it will be increased to the minimum of the "new pay grade". The effective date is January 1, 2024.
• If the staff member’s salary was at or above the midpoint of the "old pay grade", but fell below the midpoint of the "new pay grade", then it will be increased to the midpoint of the "new pay grade".  The effective date is January 1, 2024.
• Staff members who were above the midpoint of the "old pay grade", and who remain above the midpoint of the "new pay grade" will not receive a salary increase.
• Staff members who were between the minimum and midpoint of the "old pay grade" and who remain between the minimum and midpoint of the "new pay grade" will not receive a salary increase.

Implementation Date

Planning for these changes has recently begun, and it is anticipated that they will be implemented by June 2024.  

Department Administrators will receive a spreadsheet from Human Resources with changes denoted for each eligible M&P staff member in the faculty/department. The spreadsheet will be sent prior to implementation. In addition, staff members whose salaries are impacted will receive an email directly from Human Resources advising them of their new job family level and salary as a result of the changes.

Eligibility Notes

Former staff members who are no longer employed by UBC as of the implementation date are not entitled to any retroactive pay. Staff members on salary continuance as of the effective dates who are eligible for a salary adjustment, will receive the respective pay adjustment. Note that excluded M&P staff not represented by AAPS are not impacted by this implementation.

Summary of effect on Job Families

Note:  Only Job Family Levels that are affected are indicated below. Job Family Levels that are not indicated in the table below will not be changed.

Queries may be directed to [email protected] at the Vancouver Campus, Angela McLean at the Okanagan Campus, or your Human Resources Advisor within your department or unit.

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Artificial intelligence in E-Commerce: a bibliometric study and literature review

• Research Paper
• Published: 18 March 2022
• Volume 32 , pages 297–338, ( 2022 )

Cite this article

• Ransome Epie Bawack 1 ,
• Samuel Fosso Wamba 2 ,
• Kevin Daniel André Carillo 2 &
• Shahriar Akter 3  

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47 Citations

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This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

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Introduction

Electronic commerce (e-commerce) can be defined as activities or services related to buying and selling products or services over the internet (Holsapple & Singh, 2000 ; Kalakota & Whinston, 1997 ). Firms increasingly indulge in e-commerce because of customers' rising demand for online services and its ability to create a competitive advantage (Gielens & Steenkamp, 2019 ; Hamad et al., 2018 ; Tan et al., 2019 ). However, firms struggle with this e-business practice due to its integration with rapidly evolving, easily adopted, and highly affordable information technology (IT). This forces firms to constantly adapt their business models to changing customer needs (Gielens & Steenkamp, 2019 ; Klaus & Changchit, 2019 ; Tan et al., 2007 ). Artificial intelligence (AI) is the latest of such technologies. It is transforming e-commerce through its ability to “correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019 . p. 15). Depending on the context, AI could be a system, a tool, a technique, or an algorithm (Akter et al., 2021 ; Bawack et al., 2021 ; Benbya et al., 2021 ). It creates opportunities for firms to gain a competitive advantage by using big data to uniquely meet their customers' needs through personalised services (Deng et al., 2019 ; Kumar, Rajan, et al., 2019 ; Kumar, Venugopal, et al., 2019 ).

AI in e-commerce can be defined as using AI techniques, systems, tools, or algorithms to support activities related to buying and selling products or services over the internet. Research on AI in e-commerce has been going on for the past three decades. About 4000 academic research articles have been published on the topic across multiple disciplines, both at the consumer (de Bellis & Venkataramani Johar, 2020 ; Sohn & Kwon, 2020 ) and organisational levels (Campbell et al., 2020 ; Kietzmann et al., 2018 ; Vanneschi et al., 2018 ). However, knowledge on the topic has not been synthesised despite its rapid growth and dispersion. This lack of synthesis makes it difficult for researchers to determine how much the extant literature covers concepts of interest or addresses relevant research gaps. Synthesising research on AI in e-commerce is an essential condition for advancing knowledge by providing the background needed to describe, understand, or explain phenomena, to develop/test new theories, and to develop teaching orientations in this research area (Cram et al., 2020 ; Paré et al., 2015 ). Thus, this study aims to synthesise research on AI in e-commerce and propose directions for future research in the IS discipline. The innovative approach of combining bibliometric analysis with an extensive literature review is used to answer two specific research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commence in general, and within information systems (IS) research in particular?

This study's findings show that AI in e-commerce primarily focuses on recommender systems and the main research themes are sentiment analysis, optimisation, trust, and personalisation. This study makes timely contributions to ongoing debates on the connections between business strategy and the use of AI technologies (Borges et al., 2020 ; Dwivedi et al., 2019 , 2020 ). It also contributes to research on how firms can address challenges regarding the use of AI-related benefits and opportunities for new product or service developments and productivity improvements (Makridakis, 2017 ). Furthermore, no study currently synthesises AI in e-commerce research despite its rapid evolution in the last decade triggered by big data, advanced machine learning (ML) algorithms, and cloud computing. Using well-established e-commerce classification frameworks (Ngai & Wat, 2002 ; Wareham et al., 2005 ), this study classifies information systems (IS) literature on AI in e-commerce. These classifications make it easier for researchers and managers to identify relevant literature based on the topic area, research style, and research theme. A future research agenda is proposed based on the gaps revealed during the classification to guide researchers on making meaningful contributions to AI knowledge in e-commerce.

Research method

Bibliometric analysis.

Bibliometric analysis has been increasingly used in academic research in general and in IS research to evaluate the quality, impact, and influence of authors, journals, and institutions in a specific research area (Hassan & Loebbecke, 2017 ; Lowry et al., 2004 , 2013 ). It has also been used extensively to understand AI research on specific fields or topics (Hinojo-Lucena et al., 2019 ; Tran et al., 2019 ; Zhao, Dai, et al., 2020 ; Zhao, Lou, et al., 2020 ). In this study, a bibliometric analysis was conducted to understand research on AI in e-commerce using the approach Aria and Cuccurullo ( 2017 ) proposed. This methodology involves three main phases: data collection, data analysis, and data visualisation & reporting. The data collection phase involves querying, selecting, and exporting data from selected databases. This study's data sample was obtained by querying the Web of Science (WoS) core databases for publications from 1975 to 2020. This database was chosen over others like Google Scholar or Scopus because WoS provides better quality bibliometric information due to its lower rate of duplicate records (Aria et al., 2020 ) and greater coverage of high-impact journals (Aghaei Chadegani et al., 2013 ). The following search string was used to query the title, keywords, and abstracts of all documents in the WoS collection:

(‘‘Electronic Commerce’’ OR ‘‘Electronic business’’ OR ‘‘Internet Commerce’’ OR “e-business” OR “ebusiness” OR "e-commerce” OR “ecommerce” OR “online shopping” OR “online purchase” OR “internet shopping” OR “e-purchase” OR “online store” OR “electronic shopping”). AND (“Artificial intelligence” OR “Artificial neural network” OR “case-based reasoning” OR “cognitive computing” OR “cognitive science” OR “computer vision” OR “data mining” OR “data science” OR “deep learning” OR “expert system” OR “fuzzy linguistic modelling” OR “fuzzy logic” OR “genetic algorithm” OR “image recognition” OR “k-means” OR “knowledge-based system” OR “logic programming” OR “machine learning” OR “machine vision” OR “natural language processing” OR “neural network” OR “pattern recognition” OR “recommendation system” OR “recommender system” OR “semantic network” OR “speech recognition” OR “support vector machine” OR “SVM” OR “text mining”).

This search string led to 4414 documents that made up the initial dataset of this study. For quality reasons, only document types tagged as articles, reviews, and proceeding papers were selected for this study because they are most likely to have undergone a rigorous peer-review process before publication (Milian et al., 2019 ). Thus, editorial material, letters, news items, meeting abstracts, and retracted publications were removed from the dataset, leaving 4335 documents that made up the final dataset used for bibliometric analysis. Figure  1 summarises the data collection phase.

Summary of the data collection phase

Table 1 summarises the main information about the dataset regarding the timespan, document sources, document types, document contents, authors, and author collaborations. The dataset consists of documents from 2599 sources, published by 8663 authors and 84,474 references.

Bibliometrix Footnote 1 is the R package used to conduct bibliometric analysis (Aria & Cuccurullo, 2017 ). This package has been extensively used to conduct bibliometric studies published in top-tier journals. It incorporates the most renowned bibliometric tools for citation analysis (Esfahani et al., 2019 ; Fosso Wamba, 2020 ; Pourkhani et al., 2019 ). It was specifically used to analyse the sources, documents, conceptual, and intellectual structure of AI in e-commerce research. Publication sources and their source impacts were analysed based on their h-index quality factors (Hirsch, 2010 ). The most significant, impactful, prestigious, influential, and quality publication sources, affiliations, and countries regarding research on AI in e-commerce were identified. This contributed to the identification of the most relevant disciplines in this area of research. Documents were analysed using total citations to identify the most cited documents in the dataset. Through content analysis, the most relevant topics/concepts, AI technologies/techniques, research methods, and application domains were identified.

Furthermore, citation analysis and reference publication year spectroscopy (RPYS) were used to identify research contributions that form the foundations of research on AI in e-commerce (Marx et al., 2014 ; Rhaiem & Bornmann, 2018 ). These techniques were also used to identify the most significant changes in the research area. Co-word network analysis on author-provided keywords using the Louvain clustering algorithm was used to understand the research area's conceptual structure. This algorithm is a greedy optimisation method used to identify communities in large networks by comparing the density of links inside communities with links between communities (Blondel et al., 2008 ). This study used it to identify key research themes by analysing author-provided keywords. Co-citation network analysis using the Louvain clustering algorithm was also used to analyse publication sources through which journals communities were identified. It further contributed to identifying the most relevant disciplines in this research area by revealing journal clusters.

The bibliometric analysis results were reported from functionalist, normative, and interpretive perspectives (Hassan & Loebbecke, 2017 ). The functionalist perspective presents the results of the key concepts and topics investigated in this research area. The normative perspective focuses on the foundations and norms of the research area. The interpretive perspective emphasises the main themes that drive AI in e-commerce research.

• Literature review

An extensive review and classification of IS literature on AI in e-commerce complemented the bibliometric analysis. It provides more details on how research in this area is conducted in the IS discipline. The review was delimited to the most impactful and influential management information systems (MIS) journals identified during the bibliometric analysis and completed by other well-established MIS journals known for their contributions to e-commerce research (Ngai & Wat, 2002 ; Wareham et al., 2005 ). Thus, 20 journals were selected for this review: Decision sciences, Decision support systems, Electronic commerce research and applications, Electronic markets, E-service journal, European journal of information systems, Information and management, Information sciences, Information systems research, International journal of electronic commerce, International journal of information management, Journal of information systems, Journal of information technology, Journal of management information systems, Journal of organisational computing and electronic commerce, Journal of strategic information systems, Journal of the association for information systems, Knowledge-based systems, Management science, MIS Quarterly .

The literature review was conducted in three stages (Templier & Paré, 2015 ; Webster & Watson, 2002 ): (i) identify and analyse all relevant articles from the targeted journals found in the bibliometric dataset (ii) use the keyword string to search for other relevant articles found on the official publication platforms of the targeted journals, and (iii) identify relevant articles from the references of the articles identified in stages one and two found within the target journals. All articles with content that did not focus on AI in e-commerce were eliminated. This process led to a final dataset of 229 research articles on AI in e-commerce. The articles were classified into three main categories: by topic area (Ngai & Wat, 2002 ), by research style (Wareham et al., 2005 ), and by research themes (from bibliometric analysis).

Classification by topic area involved classifying relevant literature into four broad categories: (i) applications, (ii) technological issues, (iii) support and implementation, and (iv) others. Applications refer to the specific domain in which the research was conducted (marketing, advertising, retailing…). Technological issues contain e-commerce research by AI technologies, systems, algorithms, or methodologies that support or enhance e-commerce applications. Support and Implementation include articles that discuss how AI supports public policy and corporate strategy. Others contain all other studies that do not fall into any of the above categories. It includes articles on foundational concepts, adoption, and usage. Classification by research style involved organizing the relevant literature by type of AI studied, the research approach, and the research method used in the studies. The research themes identified in the bibliometric analysis stage were used to classify the relevant IS literature by research theme.

Results of the bibliometric analysis

Scientific publications on AI in e-commerce began in 1991 with an annual publication growth rate of 10.45%. Figure  2 presents the number of publications per year. Observe the steady increase in the number of publications since 2013.

Number of publications on AI in e-commerce per year

Institutions in Asia, especially China, are leading this research area. The leading institution is Beijing University of Posts and Telecommunications, with 88 articles, followed by Hong Kong Polytechnic University with 84 articles. Table 2 presents the top 20 institutions publishing on AI in e-commerce.

As expected, China-based affiliations appear most frequently in publications (4261 times). They have over 2.5 times as many appearances as US-based affiliations (1481 times). Interestingly, publications with US-based affiliations attract more citations than those in China. Table 3 presents the number of times authors from a given country feature in publications and the corresponding total number of citations.

Functional perspective

Analysing the most globally cited documents Footnote 2 in the dataset (those with 100 citations) reveals that recommender systems are the main topic of interest in this research area (Appendix Table 10 ). Recommender systems are software agents that make recommendations for consumers by implicitly or explicitly evoking their interests or preferences (Bo et al., 2007 ). The topic has been investigated in many flavours, including hybrid recommender systems (Burke, 2002 ), personalised recommender systems (Cho et al., 2002 ), collaborative recommender systems (Lin et al., 2002 ) and social recommender systems (Li et al., 2013 ). The central concept of interest is personalisation, specifically leveraging recommender systems to offer more personalised product/service recommendations to customers using e-commerce platforms. Thus, designing recommender systems that surpass existing ones is the leading orientation of AI in e-commerce research. Researchers have mostly adopted experimental rather than theory-driven research designs to meet this overarching research objective. Research efforts focus more on improving the performance of recommendations using advanced AI algorithms than on understanding and modelling the interests and preferences of individual consumers. Nevertheless, the advanced AI algorithms developed are trained primarily using customer product reviews.

Interpretive perspective

Four themes characterise research on AI in e-commerce: sentiment analysis, trust & personalisation, optimisation, AI concepts, and related technologies. The keyword clusters that led to the identification of these themes are presented in Table 4 . The sentiment analysis theme represents the stream of research focused on interpreting and classifying emotions and opinions within text data in e-commerce using AI techniques like ML and natural language processing (NLP). The trust and personalisation theme represents research that focuses on establishing trust and making personalised recommendations for consumers in e-commerce using AI techniques like collaborative filtering, case-based reasoning, and clustering algorithms. The optimisation theme represents research that focuses on using AI algorithms like genetic algorithms to solve optimisation problems in e-commerce. Finally, the AI concepts and related technologies theme represent research that focuses on using different techniques and concepts used in the research area.

Normative perspective

Research on AI in e-commerce is published in two main journal subject areas: computer science & AI and business & management. This result confirms the multidisciplinary nature of this research area, which has both business and technical orientations. Table 5 presents the most active publication outlets in each subject area. The outlets listed in the table could help researchers from different disciplines to select the proper outlet for their research results. It could also help researchers identify the outlets wherein they are most likely to find relevant information for their research on AI in e-commerce.

However, some disciplines set the foundations and standards of research on AI in e-commerce through the impact of their contributions to its body of knowledge. Analysing document references shows that the most cited contributions come from journals in the IS, computer science, AI, management science, and operations research disciplines (Table 6 ). It shows the importance of these disciplines to AI's foundations and standards in e-commerce research and their major publication outlets.

The IS discipline is a significant contributor to AI in e-commerce research, given that 24 out of the 40 top publications in the area can be assimilated to IS sources. Table 7 also shows that 7 out of the top 10 most impactful publication sources are assimilated to the IS discipline. The leading paper from the IS field reviews approaches to automatic schema matching (Rahm & Bernstein, 2001 ) and it is the second most globally cited paper in the research area. Meanwhile, the leading paper from the MIS subfield reviews recommender system application developments (Lu et al., 2015 ).

Collaborative filtering, recommender systems, social information filtering, latent Dirichlet allocation, and matrix factoring techniques are the foundational topics in research on AI in e-commerce (Table 8 ). They were identified by analysing the most cited references in the dataset. These references were mostly literature reviews and documents that discussed the basic ideas and concepts behind specific technologies or techniques used in recommender systems.

Furthermore, the specific documents that set the foundations of research on AI in e-commerce and present the most significant historical contributions and turning points in the field were identified using RPYS (Appendix Table 11 ). 2001, 2005, 2007, 2011, and 2015 are the years with the highest number of documents referenced by the documents in the sample. The most cited studies published in 2001 focused on recommendation algorithms, especially item-based collaborative filtering, random forest, gradient boosting machine, and data mining. The main concept of interest was how to personalise product recommendations. In 2005, the most referenced documents focused on enhancing recommendation systems using hybrid collaborative filtering, advanced machine learning tools and techniques, and topic diversification. That year also contributed a solid foundation for research on trust in recommender systems. In 2007, significant contributions continued on enhanced collaborative filtering techniques for recommender systems. Meanwhile, Bo & Benbasat ( 2007 ) set the basis for research on recommender systems' characteristics, use, and impact, shifting from traditional studies focused on underlying algorithms towards a more consumer-centric approach. In 2011, major contributions were made to enhance recommender systems, like developing a new library for support vector machines (Chang & Lin, 2011 ) and the Scikit-learn package for machine learning in Python (Pedregosa et al., 2011 ). In 2015, the most critical contributions primarily focused on deep learning algorithms, especially with an essential contribution to using them in recommender systems (Wang et al., 2015 ).

Results of the literature review study

Classification by topic area.

Most articles on AI in e-commerce focus on technological issues (107 articles, 47%), followed by applications (87 articles, 38%), support and implementation (20 articles, 9%), then others (15 articles, 6%). Specifically, most articles focus on AI algorithms, models, and methodologies that support or improve e-commerce applications (76 articles, 33.2%) or emphasise the applications of AI in marketing, advertising, and sales-related issues (38 articles, 16.6%). Figure  3 presents the distribution of articles, while Appendix Table 12 presents the articles in each topic area.

Classification of MIS literature on AI in e-commerce by topic area

Classification by research style

Most authors discuss AI algorithms, models, computational approaches, or methodologies (168 articles, 73%). Specifically, current research focuses on how AI algorithms like ML, deep learning (DL), NLP, and related techniques could be used to model and understand phenomena in the e-commerce environment. It also focuses on studies that involve designing intelligent agent algorithms that support learning processes in e-commerce systems. Many studies also focus on AI as systems (31 articles, 14%), especially on recommender systems and expert systems that leverage AI algorithms in the back end. The “others” category harboured all articles that did not clearly refer to AI as either an algorithm or as a system (30 articles, 13%) (see Fig.  4 and Appendix Table 13 ).

Classification of MIS literature on AI in e-commerce by type of AI

Furthermore, most publications use the design science research approach (198 articles, 86%). Researchers prefer this approach because it allows them to develop their algorithms and models or improve existing ones, thereby creating a new IS artefact (see Fig.  5 and Appendix Table 14 ).

Classification of MIS literature on AI in e-commerce by research approach

Also, authors adopt experimental methods in their papers (157 articles, 69%), especially those who adopted a design science research approach. They mostly use experiments to test their algorithms or prove their concepts (see Fig.  6 and Appendix Table 15 ).

Classification of MIS literature on AI in e-commerce by research method

Classification by research theme

Based on the main research themes on AI in e-commerce identified during the bibliometric analysis, most authors published on optimisation (63 articles, 27%). They mostly focused on optimising recommender accuracy (25 articles), prediction accuracy (29 articles), and other optimisation aspects (9 articles) like storage optimisation. This trend was followed by publications on trust & personalisation (31 articles, 14%), wherein more articles were published on personalisation (17 articles) than on trust (14 articles). Twenty-nine articles focused on sentiment analysis (13%). The rest of the papers focus on AI design, tools and techniques (46 articles), decision support (30 articles), customer behaviour (13 articles), AI concepts (9 articles), and intelligent agents (8 articles) (see Fig.  7 and Appendix Table 16 ).

Classification of MIS literature on AI in e-commerce by current research themes

This study's overall objective was to synthesise research on AI in e-commerce and propose avenues for future research. Thus, it sought to answer two research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commerce in general and within IS research in particular? This section summarises the findings of the bibliometric analysis and literature review. It highlights some key insights from the results, starting with the leading role of China and the USA in this research area. This highlight is followed by discussions on the focus of current research on recommender systems, the extensive use of design science and experiments in this research area, and a limited focus on modelling consumer behaviour. This section also discusses the little research found on some research themes and the limited number of publications from some research areas. Implications for research and practice are discussed at the end of this section.

Need for more research from other countries

Research on AI in e-commerce has been rising steadily since 2013. Overall, these results indicate a growing interest in the applications of AI in e-commerce. China-based institutions lead this research area, although US-based affiliations attract more citations. Tables 2 and 3 indicate that China is in the leading position regarding research on AI in e-commerce. Observe that Amazon Inc. (USA), JD.com (China), Alibaba Group Holding Ltd. (China), Suning.com (China), Meituan (China), Wayfair (USA), eBay (USA), and Groupon (USA) are referenced among the largest e-commerce companies in the world (in terms of market capitalisation, revenue, and the number of employees). Footnote 3 These companies are primarily from China and the USA. These findings correlate with Table 3 , which could indicate that China and the USA are investing more in the research and development of AI applications in e-commerce (especially China, based on Table 2 ) because of the positions they occupy in the industry. This logic would imply that companies seeking to penetrate the e-commerce industry and remain competitive should also consider investing more in the research and development of AI applications in the area. The list of universities provided could become partner universities for countries with institutions that have less experience in the research area. Especially with the COVID-19 pandemic, e-commerce has become a global practice. Thus, other countries need to contribute more research on the realities of e-commerce in their respective contexts to develop more globally acceptable AI solutions in e-commerce practices. It is essential because different countries approach e-commerce differently. For example, although Amazon’s marketplace is well-developed in continents like Europe, Asia, and North America, it has difficulty penetrating Africa because the context is very different (culturally and infrastructurally). While mobile wallet payment systems are fully developed on the African continent, Amazon’s marketplace does not accommodate this payment method. Therefore, it would be impossible for many Africans to use Amazon’s Alexa to purchase products online. What does this mean for research on digital inclusion? Are there any other cross-cultural differences between countries that affect the adoption and use of AI in e-commerce? Are there any legal boundaries that affect the implementation and internationalisation of AI in e-commerce? Such questions highlight the need for more country-specific research on AI in e-commerce to ensure more inclusion.

Focus on recommender systems

AI in e-commerce research is essentially focused on recommender systems in the past years. The results indicate that in the last 20 years, AI in e-commerce research has primarily focused on using AI algorithms to enhance recommender systems. This trend is understandable because recommender systems have become an integral part of almost every e-commerce platform nowadays (Dokyun Lee & Hosanagar, 2021 ; Stöckli & Khobzi, 2021 ). As years go by, observe how novel AI algorithms have been proposed, the most recent being deep learning (Chaudhuri et al., 2021 ; Liu et al., 2020 ; Xiong et al., 2021 ; Zhang et al., 2021 ). Thus, researchers are increasingly interested in how advanced AI algorithms can enable recommender systems in e-commerce platforms to correctly interpret external data, learn from such data, and use those learnings to improve the quality of user recommendations through flexible adaptation. With the advent of AI-powered chatbots and voice assistants, firms increasingly include these technologies in their e-commerce platforms (Ngai et al., 2021 ). Thus, researchers are increasingly interested in conversational recommender systems (De Carolis et al., 2017 ; Jannach et al., 2021 ; Viswanathan et al., 2020 ). These systems can play the role of recommender systems and interact with the user through natural language (Iovine et al., 2020 ). Thus, conversational recommender systems is an up-and-coming research area for AI-powered recommender systems, especially given the ubiquitous presence of voice assistants in society today. Therefore, researchers may want to investigate how conversational recommender systems can be designed effectively and the factors that influence their adoption.

Limited research themes

The main research themes in AI in e-commerce are sentiment analysis, trust, personalisation, and optimisation. Researchers have focused on these themes to provide more personalised recommendations to recommendation system users. Personalising recommendations based on users’ sentiment and trust circle has been significantly researched. Extensive research has also been conducted on how to optimise the algorithmic performance of recommender systems. ML, DL, NLP are the leading AI algorithms and techniques currently researched in this area. The foundational topics for applying these algorithms include collaborative filtering, latent Dirichlet allocation, matrix factoring techniques, and social information filtering.

Current research shows how using AI for personalisation would enable firms to deliver high-quality customer experiences through precise personalisation based on real-time information (Huang & Rust, 2018 , 2020 ). It is highly effective in data-rich environments and can help firms to significantly improve customer satisfaction, acquisition, and retention rates, thereby ideal for service personalisation (Huang & Rust, 2018 ). AI could enable firms to personalise products based on preferences, personalise prices based on willingness to pay, personalise frontline interactions, and personalise promotional content in real-time (Huang & Rust, 2021 ).

Research also shows how AI could help firms optimise product prices by channel and customer (Huang & Rust, 2021 ; Huang & Rust, 2020 ) and develop accurate and personalised recommendations for customers. It is beneficial when the firm lacks initial data on customers that it can use to make recommendations (cold start problem) (Guan et al., 2019 ; Wang, Feng, et al., 2018 ; Wang, Jhou, et al., 2018 ; Wang, Li, et al., 2018 ; Wang, Lu, et al., 2018 ). It also gives firms the ability to automatically estimate optimal prices for their products/services and define dynamic pricing strategies that increase profits and revenue (Bauer & Jannach, 2018 ; Greenstein-Messica & Rokach, 2018 ). It also gives firms the ability to predict consumer behaviours like customer churn (Bose & Chen, 2009 ), preferences based on their personalities (Buettner, 2017 ), engagement (Ayvaz et al., 2021 ; Sung et al., 2021 ; Yim et al., 2021 ), and customer payment default (Vanneschi et al., 2018 ). AI also gives firms the ability to predict product, service, or feature demand and sales (Cardoso & Gomide, 2007 ; Castillo et al., 2017 ; Ryoba et al., 2021 ), thereby giving firms the ability to anticipate and dynamically adjust their advertising and sales strategies (Chen et al., 2014 ; Greenstein-Messica & Rokach, 2020 ). Even further, it gives firms the ability to predict the success or failure of these strategies (Chen & Chung, 2015 ).

Researchers have shown that using AI to build trust-based recommender systems can help e-commerce firms increase user acceptance of the recommendations made by e-commerce platforms (Bedi & Vashisth, 2014 ). This trust is created by accurately measuring the level of trust customers have in the recommendations made by the firm’s e-commerce platforms (Fang et al., 2018 ) or by making recommendations based on the recommendations of people the customers’ trust in their social sphere (Guo et al., 2014 ; Zhang et al., 2017 ).

Sentiment analytics using AI could give e-commerce firms the ability to provide accurate and personalised recommendations to customers by assessing their opinions expressed online such as through customer reviews (Al-Natour & Turetken, 2020 ; Qiu et al., 2018 ). It has also proven effective in helping brands better understand their customers over time and predict their behaviours (Das & Chen, 2007 ; Ghiassi et al., 2016 ; Pengnate & Riggins, 2020 ). For example, it helps firms better understand customer requirements for product improvements (Ou et al., 2018 ; Qi et al., 2016 ) and predict product sales based on customer sentiments (Li, Wang, et al., 2019 ; Li, Wu, et al., 2019 ; Li, Zhang, et al., 2019 ). Thus, firms can accurately guide their customers towards discovering desirable products (Liang & Wang, 2019 ) and predict the prices they would be willing to pay for products based on their sentiments (Tseng et al., 2018 ). Thus, firms that use AI-powered sentiment analytics would have the ability to constantly adapt their product development, sales, and pricing strategies while improving the quality of their e-commerce services and personalised recommendations for their customers.

While the current research themes are exciting and remain relevant in today’s context, it highlights the need for researchers to explore other research themes. For example, privacy, explainable, and ethical AI are trendy research themes in AI research nowadays. These themes are relevant to research on AI in e-commerce as well. Thus, developing these research themes would make significant contributions to research on AI in e-commerce. In the IS discipline, marketing & advertising is where AI applications in e-commerce have been researched the most. This finding complements Davenport et al. ( 2020 )’s argument, suggesting that marketing functions have the most to gain from AI. Most publications focus on technological issues like algorithms, support systems, and security. Very few studies investigated privacy, and none was found on topics like ethical, explainable, or sustainable AI. Therefore, future research should pay more attention to other relevant application domains like education & training, auctions, electronic payment systems, inter-organisational e-commerce, travel, hospitality, and leisure (Blöcher & Alt, 2021 ; Manthiou et al., 2021 ; Neuhofer et al., 2021 ). To this end, questions that may interest researchers include, what are the privacy challenges caused by using AI in e-commerce? How can AI improve e-commerce services in education and training? How can AI improve e-commerce services in healthcare? How can AI bring about sustainable e-commerce practices?

Furthermore, research on AI in e-commerce is published in two main journal categories: computer science & AI and business & management. Most citations come from the information systems, computer science, artificial intelligence, management science, and operations research disciplines. Thus, researchers interested in research on AI in e-commerce are most likely to find relevant information in such journals (see Tables 5 and 6 ). Researchers seeking to publish their research on AI in e-commerce can also target such journals. However, researchers are encouraged to publish their work in other equally important journal categories. For example, law and government-oriented journals would greatly benefit from research on AI in e-commerce. International laws and government policies could affect how AI is used in e-commerce. For example, due to the General Data Protection Regulation (GDPR), how firms use AI algorithms and applications to analyse user data in Europe may differ from how they would in the US. Such factors may have profound performance implications given that AI systems are as good as the volume and quality of data they can access for analysis. Thus, future research in categories other than those currently researched would benefit the research community.

More experiment than theory-driven research

Most of the research done on AI in e-commerce have adopted experimental approaches. Very few adopted theory-driven designs. This trend is also observed in IS research, where 69% of the studies used experimental research methods and 86% adopted a design science research approach instead of the positivist research approach often adopted in general e-commerce research (Wareham et al., 2005 ). However, this study's findings complement a recent review that shows that laboratory experiments and secondary data analysis were becoming increasingly popular in e-commerce research. Given that recommender systems support customer decision-making, this study also complements recent studies that show the rising use of design science research methods in decision support systems research (Arnott & Pervan, 2014 ) and in IS research in general (Jeyaraj & Zadeh, 2020 ). This finding could be explained by the fact that researchers primarily focused on enhancing the performance of AI algorithms used in recommender systems. Therefore, to test the performance of their algorithms in the real world, the researchers have to build a prototype and test it in real-life contexts. Using performance accuracy scores, the researchers would then tell the extent to which their proposed algorithm is performant. However, ML has been highlighted as a powerful tool that can help advance theory in behavioural IS research (Abdel-Karim et al., 2021 ). Therefore, key research questions on AI in e-commerce could be approached using ML as a tool for theory testing in behavioural studies. Researchers could consider going beyond using AI algorithms for optimising recommender systems to understand its users' behaviour. In Fig.  4 , observe that 73% of IS researcher papers reviewed approached AI as an algorithm or methodology to solve problems in e-commerce. Only 14% approached AI as a system. Researchers can adopt both approaches in the same study in the sense that they can leverage ML algorithms to understand human interactions with AI systems, not just for optimisation. This approach could provide users with insights by answering questions regarding the adoption and use of AI systems.

Furthermore, only 6% of the studies focus on consumer behaviour. Thus, most researchers on AI in e-commerce this far have focused more on algorithm performance than on modelling the behaviour of consumers who use AI systems. It is also clear that behavioural aspects of using recommender systems are often overlooked (Adomavicius et al., 2013 ). There is relatively limited research on the adoption, use, characteristics, and impact of AI algorithms or systems on its users. This issue was raised as a fundamental problem in this research area (Bo et al., 2007 ) and seems to remain the case today. However, understanding consumer behaviour could help improve the accuracy of AI algorithms. Thus, behavioural science researchers need to conduct more research on modelling consumer behaviours regarding consumers' acceptance, adoption, use, and post-adoption behaviours targeted by AI applications in e-commerce. As AI algorithms, systems, and use cases multiply in e-commerce, studies explaining their unique characteristics, adoption, use, and impact at different levels (individual, organisational, and societal) should also increase. It implies adopting a more theory-driven approach to research on AI in e-commerce. Therefore, behavioural science researchers should be looking into questions on the behavioural factors that affect the adoption of AI in e-commerce.

Implications for research

This study contributes to research by innovatively synthesising the literature on AI in e-commerce. Despite the recent evolution of AI and the steady rise of research on how it could affect e-commerce environments, no review has been conducted to understand this research area's state and evolution. Yet, a recent study shows that e-commerce and AI are currently key research topics and themes in the IS discipline (Jeyaraj & Zadeh, 2020 ). This paper has attempted to fill this research gap by providing researchers with a global view of AI research in e-commerce. It offers a multidimensional view of the knowledge structure and citation behaviour in this research area by presenting the study's findings from functional, normative, and interpretive perspectives. Specifically, it reveals the most relevant topics, concepts, and themes on AI in e-commerce from a multidisciplinary perspective.

This contribution could help researchers evaluate the value and contributions of their research topics in the research area with respect to other disciplines and choose the best publication outlets for their research projects. This study also reveals the importance of AI in designing recommender systems and shows the foundational literature on which this research area is built. Thus, researchers could use this study to design the content of AI or e-commerce courses in universities and higher education institutions. Its content provides future researchers and practitioners with the foundational knowledge required to build quality recommender systems. Researchers could also use this study to inform their fields on the relevance of their research topics and the specific gaps to fill therein. For example, this study reveals the extent to which the IS discipline has appropriated research on AI in e-commerce. It also shows contributions of the IS discipline to the current research themes, making it easier for IS researchers to identify research gaps as well as gaps between IS theory and practice.

Implications for practice

This study shows that AI in e-commerce primarily focuses on recommender systems. It highlights sentiment analysis, optimisation, trust, and personalisation as the core themes in the research area. Thus, managers could tap into these resources to improve the quality of their recommender systems. Specifically, it could help them understand how to develop optimised, personalised, trust-based and sentiment-based analytics supported by uniquely designed AI algorithms. This knowledge would make imitating or replicating the quality of recommendations rendered through e-commerce platforms practically impossible for competitors. Firms willing to use AI in e-commerce would need unique access and ownership of customer data, AI algorithms, and expertise in analytics (De Smedt et al., 2021 ; Kandula et al., 2021 ; Shi et al., 2020 ). The competition cannot imitate these resources because they are unique to the firm, especially if patented (Pantano & Pizzi, 2020 ). Also, this research paper classifies IS literature on AI in e-commerce by topic area, research style, and research theme. Thus, IS practitioners interested in implementing AI in e-commerce platforms would easily find the research papers that best meet their needs. It saves them the time to search for articles themselves, which may not always be relevant and reliable.

Limitations

This study has some limitations. It was challenging to select a category for each article in the sample dataset. Most of those articles could be rightfully placed in several categories of the classification frameworks. However, assigning articles to a single category in each framework simplifies the research area's conceptualisation and understanding (Wareham et al., 2005 ). Thus, categories were assigned to each article based on the most apparent orientation from the papers' titles, keywords, and abstracts. Another challenge was whether or not to include a research paper in the review. For example, although some studies on recommender systems featured in the keyword search results, the authors did not specify if the system's underlying algorithms were AI algorithms. Consequently, such articles were not classified to ensure that those included in this review certainly had an AI orientation. Despite our efforts, we humbly acknowledge that this study may have missed some publications, and others may have been published since this paper started the review process. Thus, in no way does this study claim to be exhaustive but rather extensive. Nonetheless, the findings from our rigorous literature review process strongly match the bibliometric analysis findings and those from similar studies we referenced. Therefore, we believe our contributions to IS research on AI in e-commerce remain relevant.

Future research

In addition to recommendations for future research discussed in the previous sections, the findings of this study are critically analysed through the lens of recent AI research published in leading IS journals. The aim is to identify other potential gaps for future research on AI in e-commerce that could interest the IS community.

One of the fundamental issues with AI research in IS today is understanding the AI concept (Ågerfalk, 2020 ). Our findings show that researchers have mostly considered algorithms and techniques like ML, DL, and NLP AI in their e-commerce research. Are these algorithms and techniques AI? Does the fact that an algorithm helps to analyse data and make predictions about e-commerce activities mean that the algorithm is AI? It is crucial for researchers to clearly explain what they mean by AI and differentiate between different types of AI used in their studies to avoid ambiguity. This explanation would help prevent confusion between AI and business intelligence & analytics in e-commerce. It would also help distinguish between AI as a social actor and AI as a technology with the computational capability to perform cognitive functions.

A second fundamental issue with AI research in IS is context (Ågerfalk, 2020 ). Using the same data, an AI system would/should be able to interpret the message communicated or sought by the user based on context. Context gives meaning to the data, making the AI system’s output relevant in the real world. Research on AI in e-commerce did not show much importance to context. Many authors used existing datasets to test their algorithms without connecting them to a social context. Thus, it is difficult to assess whether the performance of the proposed algorithms is relevant in every social context. Future research should consider using AI algorithms to analyse behavioural data alongside ‘hard’ data (facts) to identify patterns and draw conclusions in specific contexts. It implies answering the crucial question, what type of AI best suits which e-commerce context? Thus, researchers would need to collaborate with practitioners to better understand and delineate contexts (Ågerfalk, 2020 ) of investigation rather than make general claims on fraud detection or product prices, for example.

The IS community is also interested in understanding ethical choices and challenges organisations face when adopting AI systems and algorithms. What ethical decisions do e-commerce firms need to make when implementing AI solutions? What are the ethical challenges e-commerce firms face when implementing AI solutions? Following a sociotechnical approach, firms seeking to implement AI systems need to make ethical choices. These include transparent vs black-boxed algorithms, slow & careful vs expedited & timely designs, passive vs active implementation approach, obscure vs open system implementation, compliance vs risk-taking, and contextualised vs standardised use of AI systems (Marabelli et al., 2021 ). Thus, future research on AI in e-commerce should investigate how e-commerce firms address these ethical choices when implementing their AI solutions and the challenges they face in the process.

AI and the future of work is another primary source of controversy in the IS community (Huysman, 2020 ; Willcocks, 2020a , b ). Several researchers are investigating how AI is transforming the work configurations of organisations. Workplace technology platforms are increasingly observed to integrate office applications, social media features and AI-driven self-learning capabilities (Baptista et al., 2020 ; Grønsund & Aanestad, 2020 ; Lyytinen et al., 2020 ). Is this emergent digital/human work configuration also happening in e-commerce firms? How is this changing the future of work in the e-commerce industry?

IS researchers have increasingly called for research on how AI transforms decision making. For example, they are interested in understanding how AI could help augment mental processing, change managerial mindsets and actions, and affect the rationality of economic agents (Brynjolfsson et al., 2021 ). A recent study also makes several research propositions for IS researchers regarding conceptual and theoretical development, AI-human interaction, and AI implementation in the context of decision making (Duan et al., 2019 ). This study shows that decision-making is not a fundamental research theme as it accounts for only 13% of the research papers reviewed. Thus, future research on AI in e-commerce should contribute to developing this AI research theme in the e-commerce context. It involves proposing answers to questions like how AI affects managerial mindsets and actions in e-commerce? How is AI affecting the rationality of consumers who use e-commerce platforms?

This study shows that relatively few research papers on AI in e-commerce are theory-driven. Most adopted experimental research methods and design science research approaches wherein they use AI algorithms to explain phenomena. The IS community is increasingly interested in developing theories using AI algorithms (Abdel-Karim et al., 2021 ). Contrary to traditional theory development approaches, such theories developed based on AI algorithms like ML are called to be focused, context-specific, and as transparent as possible (Chiarini Tremblay et al., 2021 ). Thus, rather than altogether abandoning the algorithm-oriented approach used for AI in e-commerce research, researchers who master it should develop skills to use it as a basis for theorising.

Last but not least, more research is needed on the role of AI-powered voice-based AI in e-commerce. It is becoming common for consumers to use intelligent personal assistants like Google’s Google Assistant, Amazon’s Alexa, and Apple’s Siri for shopping activities since many retail organisations are making them an integral part of their e-commerce platforms (de Barcelos Silva et al., 2020 ). Given the rising adoption of smart speakers by consumers, research on voice commerce should become a priority for researchers on AI in e-commerce. Yet, this study shows that researchers are still mostly focused on web-based, social networking (social commerce), and mobile (m-commerce) platforms. Therefore, research on the factors that affect the adoption and use of voice assistants in e-commerce and the impact on consumers and e-commerce firms would make valuable contributions to e-commerce research. Table 9 summarises the main research directions recommended in this paper.

Conclusions

AI has emerged as a technology that can differentiate between two competing firms in e-commerce environments. This study presents the state of research of AI in e-commerce based on bibliometric analysis and a literature review of IS research. The bibliometric analysis highlights China and the USA as leaders in this research area. Recommender systems are the most investigated technology. The main research themes in this area of research are optimisation, trust & personalisation, sentiment analysis, and AI concepts & related technologies. Most research papers on AI in e-commerce are published in computer science, AI, business, and management outlets. Researchers in the IS discipline has focused on AI applications and technology-related issues like algorithm performance. Their focus has been more on AI algorithms and methodologies than AI systems. Also, most studies have adopted a design science research approach and experiment-style research methods. In addition to the core research themes of the area, IS researchers have also focused their research on AI design, tools and techniques, decision support, consumer behaviour, AI concepts, and intelligent agents. The paper discusses opportunities for future research revealed directly by analysing the results of this study. It also discusses future research directions based on current debates on AI research in the IS community. Thus, we hope that this paper will help inform future research on AI in e-commerce.

Download the bibliometrix R package and read more here: https://www.bibliometrix.org/index.html

Global citation refers to the total number of times the document has been cited in other documents in general and local citations refer to the total number of times a document has been cited by other documents in our dataset.

https://axiomq.com/blog/8-largest-e-commerce-companies-in-the-world/

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Bawack, R.E., Wamba, S.F., Carillo, K.D.A. et al. Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electron Markets 32 , 297–338 (2022). https://doi.org/10.1007/s12525-022-00537-z

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Received : 11 September 2021

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Published : 18 March 2022

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DOI : https://doi.org/10.1007/s12525-022-00537-z

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