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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

Journal	Number of articles	%
	9	19.15
	7	14.89
	4	8.51
	4	8.51
	4	8.51
	4	8.51
	3	6.38
	3	6.38
	2	4.26
	2	4.26
	1	2.13
	1	2.13
	1	2.13
	1	2.13
	0	0.00
	0	0.00
	1	2.13
Total	47	100

Two articles from special issue on retail logistics (2016); one article from special issue on post crisis in Europe (2016); Europe- and US-based researchers (based on the first author’s affiliation) contributed 44.68 per cent (21 articles) and 38.30 per cent (18 articles) of the papers, respectively. The remaining articles were published by researchers based in China, Canada, Turkey, Australia, Brazil, and Singapore

Level 1 classification	Level 2 classification	References
Push-centric system	Manufacturer-based pick (manufacturer storage)	, (2006), , (2006), (2008), (2008), (2008), (2016), (2017)
	Distribution centre (DC)-based pick (distributor storage)	(2001), , , , , , , , , (2009), (2010), , (2013), (2014), , (2014), , , (2016), (2017), Yang and Strauss (2017)
	Local brick-and-mortar (B&M) store-based pick (retailer storage)	, , , , , , (2006), (2008), (2008), (2009), (2010), , (2016), (2017)
Pull-centric system	Local B&M store	, , , (2009), , , , (2015)
	Information store (dematerialisation)
Hybrid system	Attended collection delivery point (CDP-A) (click-and-collect/pick-up centre/clicks-and-mortar/GRP)	(2001), , , , , (2006), , , (2010), (2014), , (2014), , , (2016), , (2017)
	Unattended collection delivery point (CDP-U) (RB/ULB)	, (2001), (2001), , , , , (2010), (2014), ,

GRP, goods reception point; RB, reception box (independent/with docking mechanism/shared); ULB, urban logistics boxes

Variable	Commentary and discussion on contingency variables
Consumer geographical density	Consumer geographical density is closely related to modes of delivery. When consumer geographical density is high, a merchant’s own fleet can be effectively utilised; when consumer geographical density is low, companies use 3PLs to consolidate orders to increase fleet utilisation
Consumer physical convenience	Consumer physical convenience is higher for LML models using direct-to-home delivery since almost no consumer physical movement is needed. By contrast, consumer physical convenience is lower for models featuring consumer-pickup, since these models require consumers to physically travel to the pickup sites. However, physical inconvenience for order pickup may be offset under situations where a consumer’s commute route overlaps with the pickup route
Consumer time convenience	Consumer-pickup models are most suited for consumers with higher availability of time for order pickup; with direct-to-home delivery preferred for time-poor consumers; models offering longer time windows for AHD are suitable for consumers with time flexibility
Demand volume	High demand products are typically sourced locally and in closer proximity to the consumer to reduce transport costs and fast response times. By contrast, low demand products are stored centrally to gain benefits of aggregation and concomitantly, to allow for extension of geographical coverage. It is possible for low demand and high margin products to be sourced locally, since the benefit gained through higher margins may potentially offset the loss incurred through localised storage structures
Order response time	Short response time can be achieved by reducing transport time by localising operations. Furthermore, response time increases as the number of additional nodes in an LML network increases. As products are being processed at a node, their movement discontinues. Models based on in-transit merge lead to a longer response time in comparison with, for example, a model that features uninterrupted drop-shipping
Order visibility	Visibility is easiest when an LML model is simplified as merely two connected nodes, with one being the product source point and the other being the final consignee’s preferred destination point. For instance, an LML model featured with distributor source and 3PL delivery can be simplified as a two-node network comprised of a distributor and a consumer’s home. This two-node network has the potential to provide the highest order visibility since it is necessary for information systems of all nodes to be integrated. In a multiple-node with more complex network, order visibility decreases. Moreover, for LML models with consumer-pickup to be operational, high visibility via advance consumer notification must be provided
Product availability and product variety	These variables share a single variation pattern. A manufacturer holds an inventory of greater variety and quantity than a distributor does, since the former occupies a more central position in an LML network and serves a wider consumer base. Correspondingly, a distributor has a greater variety of inventory than a retailer. Therefore, manufacturers, distributors, and retailers, respectively, provide product variety and product availability in descending order. Moreover, LML models based on 3PL delivery provide greater variety given the specialist role of 3PL, which is capable of carrying more differentiated products
Product customisability	Because customisation processes typically involve physical alteration of product and/or product packaging, the product manufacturer, by default, has the highest control over customisation; with the distributor and retailer having decreasing amounts of customisation control (e.g. Dell’s ability to customise-to-order)
Product freshness	This variable is governed by two factors: first, the shipping distance separating the manufacturer and consumer’s home, and second, the shipping “directness” (whether the product is shipped to the destination point directly or through one or more “stops” for additional processing). The longer the shipping distance or the higher the shipping indirectness, the lower the level of product freshness
Product margin	LML models with a central product sourcing point, such as the in-transit merge and drop-shipping models, are more compatible with higher margin products because the benefits of inventory aggregation are high and consumers are more willing to tolerate a longer response time. By comparison, LML models with local product sourcing points are more compatible with higher revenue products, offsetting the lower aggregation benefits. These products are either associated with high margin and low demand volume, or with low margin and high demand volume. Additionally, consumer-pickup models are most suitable for higher margin products
Product returnability	Costs of processing returns increases as a function of the distance separating a product source point and the end consumer; more distance and higher cost result in low returnability. Typically, a manufacturer is located the farthest from a consumer segment, followed by the distance of a distributor and a retailer. LML models that source products from retailers, distributors, and manufacturers, respectively, provide returnability in descending order. Additionally, self-delivery and consumer-pickup models typically provide higher returnability since consumers can return products directly
Service capacity	Service capacity varies by delivery type; 3PL delivery is more likely to be susceptible to certain external disruptions such as seasonal bottlenecks compared to self-delivery. Self-delivery models are thus more likely to provide higher service capacity than 3PL delivery

LML design framework

	Structural variable
	Product source			Geographical scope
Contingency variable	Manufacturer	Distributor	Retailer	Locally based	Centrally based
Consumer geographical density	n/a	n/a	n/a	n/a	n/a
Consumer physical convenience	n/a	n/a	n/a	n/a	n/a
Consumer time convenience	n/a	n/a	n/a	n/a	n/a
Demand volume	Low	Medium	High/low	High/low	Low
Order response time	n/a	n/a	n/a	Short	Long
Order visibility	n/a	n/a	n/a	n/a	n/a
Product availability	High	Medium	Low	n/a	n/a
Product variety	High	Medium	Low	n/a	n/a
Product customisability	High	Medium	Low	n/a	n/a
Product freshness	n/a	n/a	n/a	High	Low
Product margin	High	Medium	Low/high	Low/high	High
Product returnability	n/a	n/a	n/a	High	Low
Service capacity	n/a	n/a	n/a	n/a	n/a
	Structural variable
	Mode of distribution			Number of nodes
Contingency variable	Self-delivery	Consumer-pickup	3PL delivery	Two-node	Multiple-node
Consumer geographical density	High	n/a	Low	n/a	n/a
Consumer physical convenience	High	Low	High	n/a	n/a
Consumer time convenience	High	Low	High	n/a	n/a
Demand volume	n/a	n/a	–	n/a	n/a
Order response time	n/a	n/a	–	Short	Long
Order visibility	Low	High	Low	High	Low
Product availability	n/a	n/a	–	n/a	n/a
Product variety	Low	n/a	High	n/a	n/a
Product customisability	n/a	n/a	–	n/a	n/a
Product freshness	n/a	n/a	–	High	Low
Product margin	Low	High	Low	n/a	n/a
Product returnability	High	High	Low	n/a	n/a
Service capacity	Full	n/a	Fluctuated	n/a	n/a
Consumer time convenience	High	Low	High	n/a	n/a

These four cells are interrelated. The two combinations, namely “high margin and low volume” and “low margin and high volume”, can both lead to sufficient incentives for a localised operation and a retailer-sourcing strategy. Scale of rating – fast, medium, slow; low, medium, high; and full, fluctuated

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Systematic literature review of e-Commerce implementation studies

• D. Paris , M. Bahari , +1 author W. Ismail
• Published 31 July 2016
• Business, Computer Science
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ORIGINAL RESEARCH article

Analysis of the driving path of e-commerce to high-quality agricultural development in china: empirical evidence from mediating effect models.

• 1 School of Economics, Shandong University of Finance and Economics, Jinan, China
• 2 School of Foreign Languages Education, Shandong University of Finance and Economics, Jinan, China
• 3 Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

Purpose: This study investigates the impact of e-commerce on high-quality agricultural development (HQAD) in China. As the agricultural sector transitions towards higher quality production in the digital era, understanding the influence pathways and mechanisms of e-commerce becomes crucial. We aim to quantify this influence through a hierarchical approach.

Methods: Utilizing provincial panel data from 2000 to 2021, we construct a comprehensive HQAD evaluation system using the entropy method. Parallel mediating effect models are employed to empirically assess the multi-level effects of e-commerce on HQAD.

Results: Benchmark regression analyzes reveal a significant positive effect of e-commerce on HQAD, indicating its role as a key driver in China’s agricultural advancement. Mechanism tests identify several intermediary pathways through which e-commerce indirectly promotes HQAD, including market expansion, agricultural value chain optimization, enhanced social services, and improved infrastructure. Notably, market expansion and value chain optimization demonstrate the most substantial mediation effects, accounting for 43.27 and 14.18% of the total effect, respectively.

Discussion: This research contributes to the literature by establishing a comprehensive HQAD evaluation framework, providing a theoretical foundation for future studies. By incorporating circulation factors into the production system, we elucidate the complex influence mechanisms of e-commerce on agricultural production, addressing a significant research gap. Furthermore, we propose a novel “demand-driven supply optimization” paradigm, offering valuable insights for policy formulation aimed at fostering HQAD in China.

1 Introduction

China, a developing nation with a population of 1.4 billion, heavily relies on its agricultural sector to ensure food safety and ecological stability. The agricultural sector, a cornerstone of the national economy, has undergone significant transformations driven by industrialization. Grain production has increased dramatically from 304.77 million tons in 1978 to 686.53 million tons in 2022, successfully meeting the nutritional needs of the population. However, this progress has been accompanied by environmental degradation, food safety concerns, climate change impacts, and biological invasions. As living standards improve, consumer preferences are shifting towards healthier and safer food products. Consequently, enhancing agricultural quality has become crucial for ensuring global food security, promoting human well-being, and supporting robust economic development ( Garibaldi et al., 2011 ).

The concept of high-quality development, introduced by the Chinese government in October 2017, has emerged as a new imperative for the agricultural sector ( Wang D. et al., 2022 ; Wang G. et al., 2022 ). While HQAD has garnered global attention, it remains in its nascent stages. Traditional approaches to promoting HQAD have focused on input factors such as land, labor, capital, fertilizer, and irrigation, as well as their allocation efficiency ( Chi et al., 2022 ). However, in the absence of technological advancements, the law of diminishing returns limits the sustainability of this approach. Recent scholarly attention has shifted towards digital and biotechnologies ( Tang and Chen, 2022 ), yet these predominantly address production efficiency through altered input combinations, often neglecting the crucial aspect of market circulation – a significant obstacle to HQAD, given the perishable nature of most agricultural products ( Ashokkumar et al., 2019 ).

The emergence of “Internet +” as a new economic paradigm since the 1990s has given rise to e-commerce, an advanced business model leveraging computer networks. E-commerce, underpinned by internet infrastructure and information technology, offers advantages such as high efficiency and strong profitability ( Gai, 2023 ). Its integration with agriculture encompasses various aspects, including industrial layout, value chain development, logistics optimization, product sales and service, and brand management. This convergence applies modern information technology and management methods to agricultural product sales, revolutionizing both production methods ( Tang, 2022 ) and consumption patterns. The rise of online retail, booking, purchasing, and payment reflects a shift towards a “customer-centric” approach ( Zhang and Berghäll, 2021 ), transforming the traditional supply-driven agricultural development model into a demand-driven paradigm ( Nosratabadi et al., 2020 ). At the micro level, agricultural producers’ organizations are deepening vertical cooperation within the industry through increased consolidation to access larger markets and higher economic profits. The application of internet-based remote planning and monitoring facilitates the optimization of procedures and decision-making, ultimately improving agricultural product quality ( Nosratabadi et al., 2020 ). At the macro level, the reduction in fertilizer use, improvements in technical efficiency, increased farmland utilization, and enhanced farmer welfare ( Twumasi et al., 2021 ; Zhang and Berghäll, 2021 ; Zhu et al., 2021 ) reflect the intrinsic demands of high-quality agriculture. The deep integration of e-commerce and agriculture provides new avenues for agricultural transformation and upgrading.

This study investigates the driving pathways through which e-commerce contributes to HQAD. The paper is structured as follows: Section 2 presents a theoretical discussion on the action mechanism, elucidating the internal processes by which e-commerce promotes HQAD. Section 3 employs the entropy method to establish an evaluation system for HQAD and quantifies China’s development index based on provincial data. Section 4 decomposes and measures the driving impact of e-commerce through an empirical analysis of mediating effects. Finally, Section 5 offers conclusions and policy recommendations.

2 Literature review and research hypostudy

2.1 literature review.

The evolution of modern agriculture has witnessed a paradigm shift from “efficiency pursuit” to “green production” and, more recently, to “high-quality development” ( Wang D. et al., 2022 ; Wang Z. et al., 2022 ). Contemporary scholarly discourse on HQAD primarily focuses on four key aspects: conceptual definition, characteristic identification, measurement methodologies, and influencing factors.

2.1.1 Definition of the connotation of HQAD

HQAD emerges as a product of China’s economic development trajectory, signifying a transition from a quantitative “having or not” paradigm to a qualitative “good or bad” evaluation of agricultural production. As a nascent concept, recent scholarly endeavors have explored its multifaceted connotations, broadly categorized into three perspectives. (1) Development concept-based approach. In contrast to traditional agriculture, HQAD is guided by principles of “innovation, coordination, green, openness, and sharing” ( Cui et al., 2022 ), reflecting the extent to which these five concepts are operationalized in the production process. (2) Production efficiency perspective. The crux of HQAD lies in the adoption of resource-efficient technologies, optimal allocation of production factors, and enhancement of total factor productivity, epitomizing an intensive mode of production ( Baráth et al., 2020 ). This approach emphasizes not only the scale and speed of agricultural product supply but also quality and efficiency ( Tang and Chen, 2022 ). (3) Agricultural function-based view. The evolutionary trajectory of agriculture has seen an expansion from its primary food provision function to encompassing industrial raw material supply, labor provision, and more recently, services such as leisure tourism, cultural preservation and innovation, and environmental stewardship ( Pang et al., 2016 ). Consequently, HQAD emphasizes the synergy between economic functions and environmental protection, as well as human social development ( Chi et al., 2022 ). HQAD thus emerges as a multidimensional construct necessitating a comprehensive examination of its conceptual, production, and social systems. Current scholarly efforts, while valuable, remain fragmented, lacking a cohesive theoretical framework.

2.1.2 Research on the measurement of HQAD

While the HQAD concept originated in Chinese academia, parallel global discourses on sustainable and smart agriculture reflect similar aspirations. Measurement methodologies have evolved from singular indicators to comprehensive evaluation systems ( Bao et al., 2021 ), primarily focusing on: (1) Agricultural production efficiency. Scholars employing Data Envelopment Analysis (DEA) have identified that non-agricultural employment negatively impacts agricultural production efficiency ( Chang et al., 2022 ). Conversely, enhanced land tenure rights both directly and indirectly improve efficiency through increased agricultural investment ( Zhang and Chen, 2022 ). (2) Green ecological efficiency. The approach to measuring green ecological efficiency in agriculture has developed along two main streams. The first stream focuses on constructing comprehensive green agriculture evaluation systems. For instance, Bergius et al. (2018) assessed green agriculture development in Tanzania’s Southern Agricultural Growth Corridor (SAGCOT) across dimensions such as resource utilization efficiency and ecological stability. Similarly, Yao and Sun (2023) employed indicators including agricultural resource input, total agricultural output value, and carbon emissions to gauge green agriculture development in China. The second stream concentrates on measuring agricultural green total factor productivity. Scholars in this area have employed various methods, including Stochastic Frontier Analysis (SFA) ( Orea and Wall, 2017 ), Data Envelopment Analysis (DEA) ( Suzigan et al., 2020 ), and Undesirable Slacks-Based Measure (SBM) ( Guo et al., 2015 ) to quantify and analyze the agricultural green total factor productivity. (3) Quality evaluation systems. Recent scholarship has reconstructed HQAD evaluation systems based on the five development concepts: green, innovation, opening, coordination, and sharing ( Liu et al., 2020 ; Wang H. et al., 2022 ). Findings suggest that in China, the green dimension exhibits the most rapid development, followed by innovation and sharing ( Cui et al., 2022 ). Scholars have also developed evaluation systems for green agriculture, high-quality agriculture, and brand agriculture ( Lu et al., 2022 ). Prevalent methodologies include the entropy method ( Wang S. et al., 2021 ; Wang G. et al., 2022 ; Yang et al., 2023 ), TOPSIS ( Li et al., 2023 ), and fuzzy analytic hierarchy process. Despite these advancements, research on HQAD measurement remains limited, with significant variations in results due to disparities in indicator selection and calculation methodologies.

2.1.3 Research on the influencing factors of HQAD

HQAD is a complex, multifaceted endeavor influenced by numerous factors. Current scholarly discourse primarily analyzes these influencing factors through the lenses of agricultural industrial agglomeration, technological innovation, and market conditions. (1) Industrial agglomeration. The concentration of agricultural industries has facilitated economies of scale and improved infrastructure utilization ( Guo et al., 2020 ). This agglomeration fosters resource sharing and technology diffusion, attracting key factors such as scientific personnel and green mechanization, thereby enhancing green total factor productivity (GTFP) ( Li et al., 2017 ). However, excessive agglomeration can lead to crowding effects, potentially impeding agricultural development ( Yin and Wu, 2021 ). Xu et al. (2022) employed Moran’s I index and the spatial Durbin model (SDM), revealing a U-shaped relationship between the degree of agricultural industrial agglomeration and green development. (2) Technological innovation. Innovation plays a pivotal role in the evolution of traditional agriculture in China. Advancements in cultivation techniques, efficient resource utilization, and pollution mitigation are key drivers of progress ( Chandio et al., 2023 ). HQAD is characterized by digitalization and informatization, propelled by technologies such as robotic systems and smart machines (RSSM), and farm management information systems ( Schwering et al., 2022 ). Precision and smart agriculture represent the primary developmental trajectories. Higgins et al. (2017) posit that the adoption of precision agriculture technologies has reduced production and operational costs while augmenting the value of the agricultural industry chain. In China, a 1% increase in research and development investment correlates with a 1.79% improvement in HQAD levels ( Yang et al., 2023 ). (3) Market conditions. The degree of marketization, encompassing factors such as the business environment and agricultural openness, exerts a long-term positive influence on agricultural sector in China ( Wang D. et al., 2022 ; Wang H. et al., 2022 ). Enhanced market conditions facilitate the innovative application of new technologies and the modernization of agricultural operations, guiding the optimization of the agricultural industry chain ( Soegoto and Faridh, 2020 ; Molina et al., 2024 ). Within the market sphere, financial product innovations, such as digital inclusive finance, have provided accessible financial services ( Li et al., 2023 ).

2.1.4 Research on the impact of e-commerce on agriculture

Among the myriad factors influencing agricultural development, scholars have increasingly recognized the significance of e-commerce. The research in this domain can be categorized into the following aspects. (1) Impact on green agricultural development. Studies have focused on whether e-commerce mitigates agricultural pollution. Evidence suggests that e-commerce promotes green agriculture by catalyzing industrial restructuring and facilitating green technology innovation ( Han et al., 2023 ). (2) Influence on consumer behavior. Bai et al. (2024) investigated emerging e-commerce modalities, finding that live-streaming e-commerce significantly enhances consumer purchase intention due to its real-time experiential advantages. However, purchase intention and satisfaction are influenced by multiple factors, including consumer expectations, product quality, brand image, e-commerce platform characteristics, and logistics efficiency ( Liu and Kao, 2022 ). Maintaining consumer satisfaction has emerged as a critical challenge for agricultural product suppliers ( Zaghloul et al., 2024 ). (3) Impact on agricultural product sales efficiency. E-commerce connects global internet platforms, generating powerful resource integration effects and information resource advantages. This integration encompasses previously dispersed supply chain resources and information and communication technology infrastructure ( Farid and Riaz, 2023 ). Additionally, vast amounts of agricultural data are collected and utilized in information dissemination systems ( Kinsey and Buhr, 2003 ). To leverage the positive impact of e-commerce on agriculture, website functionalities and communication strategies should be tailored to different farmer typologies, such as professional operators, online hesitators, offline loyalists, and online enthusiasts, based on their distinct characteristics. While these studies do not directly examine the impact of e-commerce on HQAD, they collectively indicate that e-commerce is an essential factor promoting agricultural modernization from various perspectives.

Extant literature has made substantial contributions to our understanding of agricultural development. Scholars have elucidated various factors influencing agricultural productivity and sustainability (e.g., Guo et al., 2020 ; Wang Z. et al., 2022 ). Nevertheless, several limitations persist in the current body of research. First, studies predominantly emphasize supply-side factors, often neglecting demand-side drivers such as e-commerce ( Li et al., 2023 ). Second, there is a dearth of comprehensive frameworks that simultaneously explore multiple pathways of influence, limiting our understanding of the complex interplay between e-commerce and various aspects of agricultural development ( Zhang and Berghäll, 2021 ). Third, existing evaluation systems for HQAD frequently omit the social contribution dimension, resulting in an incomplete assessment of agriculture’s multifaceted impact ( Chi et al., 2022 ). These gaps in the literature underscore the necessity for a more holistic approach to studying HQAD, one that incorporates demand-side factors, examines multiple pathways, and encompasses a broader spectrum of evaluation criteria.

This study makes several significant contributions to the existing literature on HQAD. Firstly, unlike previous research focusing on supply-side factors ( Guo et al., 2020 ; Chandio et al., 2023 ), we identify e-commerce as a key driver from the demand side, offering fresh insights into agricultural advancement mechanisms. Secondly, we propose and empirically test multiple pathways through which e-commerce influences HQAD, including market expansion, agricultural industry chain optimization, social service improvement, and infrastructure supply enhancement. This comprehensive framework provides a more nuanced understanding of e-commerce’s role. Lastly, we improve upon existing assessment models by incorporating agricultural social contribution as a new dimension, alongside agricultural quality and efficiency, innovation capability, and green development. This enhanced evaluation system offers a more holistic view of agricultural development’s impact.

2.2 Research hypostudy

HQAD represents an innovative approach to sustainable development, with e-commerce serving as a crucial component within the “Internet+” framework. The integration of e-commerce signifies market convergence and a customer-centric paradigm ( Schwering et al., 2023 ), offering substantial benefits in resource integration and information accessibility. By leveraging the “power source,” “supply chain,” and “guarantee chain,” e-commerce facilitates a seamless connection between circulation and production, thereby fostering HQAD. These impact mechanisms are illustrated in Figure 1 .

Figure 1 . The mechanism of e-commerce affecting the HQAD.

2.2.1 E-commerce, market expansion, and HQAD

HQAD epitomizes the optimization of production methods under market forces, with sustained market demand serving as the “power source.” Agricultural producer organizations, as profit-driven entities, aim to maximize economic returns ( Pendyala et al., 2022 ). Their investment decisions and production practices are influenced by market price signals. However, the fragmentation of agricultural product markets across regions disrupts the efficient transmission of price signals, resulting in reduced income. Traditional offline distribution of agricultural products heavily relies on intermediaries, and information asymmetry limits farmers’ ability to influence market prices, leading to lower farmgate prices ( Ashokkumar et al., 2019 ). Consequently, intermediaries capture a significant portion of agricultural profits. Compared to non-agricultural sectors, agricultural production is characterized by high risks, labor intensity, and low returns. This economic disparity has led to substantial rural–urban migration and impeded the qualitative improvement of agricultural practices. The critical challenge lies in eliminating information barriers between farmers and broader markets.

Concurrently, the disintermediation effect of e-commerce provides farmers with alternative marketing channels and access to a wider consumer base, thereby enhancing distribution efficiency. Farmers can directly engage with larger markets and increase their share of the final product value ( Kumar et al., 2023 ). Recently, established e-commerce platforms such as Taobao and JD.com have introduced innovative “live+e-commerce” models. The integration of technologies like Virtual Reality (VR) and Augmented Reality (AR) offers consumers an immersive 3D shopping experience that stimulates multiple senses, including visual, auditory, and tactile, thus enhancing consumer engagement ( Ricci et al., 2023 ). These technologies not only excel in product visualization ( Chen et al., 2024 ) but also enable more robust interactions, increased transparency, higher conversion rates, and stronger customer loyalty. The emergence of new market segments such as contract farming, agritourism, and rural tourism has propelled agriculture from low value-added activities to medium and high-end market tiers. E-commerce model innovations have enhanced information accessibility and logistics efficiency, expanded the market reach of agricultural products, and generated a significant market discovery effect. Simultaneously, e-commerce demonstrates a substantial market integration effect. Agricultural e-commerce platforms like Taobao, JD.com , and Pinduoduo not only extend online markets but also integrate offline markets. Information disclosure, product standardization, logistics, and services in agricultural markets nationwide are increasingly harmonized. In 2021, rural online retail sales in China reached 2.17 trillion yuan, accounting for 14.8% of total agricultural product sales ( Feng, 2023 ), with the market size of live e-commerce surpassing 1.2 trillion yuan by 2022. Based on these observations, we propose the following hypothesis:

H1: E-commerce facilitates market expansion both online and offline, thereby providing a stronger “power source” for HQAD.

2.2.2 E-commerce, agricultural industrial chain and HQAD

Traditional small-scale agriculture is characterized by decentralization and limited scope. In the context of large-scale and high-end agricultural development trends, small-scale production faces challenges such as insufficient scientific and technological expertise and inadequate investment capital ( Lin and Wang, 2014 ). The lag in organizational structure remains a primary barrier to HQAD. The agricultural industry chain comprises various organizational elements, including upstream (agriculture), midstream (agricultural product processing enterprises), and downstream (service industry) sectors. To achieve HQAD, it is crucial to vertically integrate resources across the entire industrial chain and align with advanced organizational modes and management practices. E-commerce, as an innovative technology leveraging the Internet for trade, offers key functionalities in vertical integration and efficient supply chain management. The synergy between e-commerce and agriculture presents promising prospects, attracting leading e-commerce enterprises, network anchors, and social capital. The traditional “farmers + wholesalers + retailers” supply chain model has evolved into a multi-agent chain involving “e-commerce enterprises + production bases + professional cooperatives + social capital.” E-commerce platforms facilitate the transformation of agricultural products into standardized logistics systems, establishing various quality control measures such as product standards, supply assurance, organic traceability, and premium brand development. Within the virtual supply chain, order-based and contract-based systems assist farmers in adopting market-oriented mindsets. The vertical integration of the supply chain system shifts the mode of operation from individual independence to cooperative multi-agent approaches.

Customer retention is a critical factor in the development of e-commerce and HQAD. E-commerce enterprises can leverage big data analytics to comprehensively understand consumer demand fluctuations and user feedback. The application of mathematical models enables effective differentiation of user interests, optimizing system recommendations and marketing strategies ( Ma and Wang, 2024 ). Concurrently, agricultural product quality plays a pivotal role in influencing consumers’ purchasing decisions and satisfaction levels. Contemporary consumers increasingly demand refined and high-end agricultural products. While common products struggle to find markets, premium and environmentally friendly products are in high demand, highlighting a misalignment in the supply structure of agricultural products ( Zhou et al., 2019 ). E-commerce facilitates the networking of information flow, capital flow, and logistics, emphasizing “wireless connectivity” and “information sharing.” The traditional “price-centered” market approach is being supplanted by a new “data-centric” model. Market demands and data signals are driving the need for branding, sustainability, and optimization of agricultural supply chains. Online platforms showcase the entire process of agricultural production and processing, allowing consumers to easily trace product origins through QR code scanning, thereby enhancing user loyalty. By 2021, 24.7% of Chinese agricultural products achieved quality and safety traceability. Data mining studies have demonstrated that the prediction accuracy of attribute analysis tools is 20.36% higher than that of general analysis tools ( Yang, 2022 ). Based on these observations, we propose the following hypothesis:

H2: The vertical synergy effect of e-commerce drives the industrialization of agricultural organizations. Through the mediation of the “industrial chain,” e-commerce promotes HQAD.

2.2.3 E-commerce, social service, and HQAD

HQAD relies not only on technological innovation in production but also on the support of various social services, including operational management, financial insurance, information consulting, brand operation, and marketing circulation ( Chen et al., 2021 ). As agricultural technology advances, the level of division of labor and specialization in agriculture deepens, creating opportunities for market-oriented e-commerce operations in the service sector ( Zhong et al., 2022 ). Currently, e-commerce companies are evolving into service platforms, offering farmers modern service “tools” by integrating online and offline agricultural technologies, resources, financial services, and logistics solutions. International examples demonstrate the positive impact of e-commerce on agricultural development. For instance, e-wallet technology has enabled more than 50% of Nigerian farmers to access improved seeds and fertilizers ( Adebo, 2014 ). E-commerce has supported Bangladeshi farmers in enhancing their technologies and modernizing agriculture by facilitating access to funds from credit institutions ( Khandker and Koolwal, 2016 ). In Pakistan, crop yields have seen significant growth following the provision of agricultural consulting and financial services to farmers ( Elahi et al., 2018 ). In China, comprehensive e-commerce providers such as “JD Agricultural Materials” and “Rural Taobao” treat agricultural inputs like seeds, feed, fertilizers, pesticides, and machinery as tradable commodities. They offer farmers online purchasing and machinery operation services, aiding in cost reduction and shortened procurement cycles. Service-oriented agricultural e-commerce providers such as “Nong Yi Sheng” and “Yi Nong Bao” promptly update agricultural technology and product information to offer farmers online consultation and diagnostic services, enhancing their knowledge and production skills ( Du et al., 2023 ). Vertical agricultural e-commerce platforms like “Feng Shou Xia” assist farmers in acquiring, storing, and transporting agricultural products through offline service stations and social networks, enabling them to effectively market their goods. Since its inception in 2018, “Feng Shou Xia” has served over 100,000 farmers, significantly boosting their income. Based on these observations, we propose the following hypothesis:

H3: E-commerce leads the modernization of agricultural social services. Through the mediation of social services, e-commerce strengthens the soft “security chain” for HQAD.

2.2.4 E-commerce, infrastructure, and HQAD

HQAD requires the implementation of a comprehensive quality control system that prioritizes green, organic, and pollution-free production methods to ensure the cultivation of nutritious, safe, and reliable agricultural products. This agricultural transformation is contingent upon the availability of critical infrastructure, including robust power supply networks, extensive telecommunications coverage, and efficient logistics systems, with a primary focus on modern facilities and advanced technological investments ( Usman et al., 2024 ). Since the early 21st century, the Chinese government has significantly increased investments in rural infrastructure to support the expansion of e-commerce into agrarian regions. A notable initiative is the “E-commerce into Village” project, launched in 2014, which aims to enhance rural e-commerce service points, establish comprehensive logistics networks, develop robust supply chains and regional brands, and improve farmer training programs ( Xin, 2021 ). By 2021, broadband coverage in China’s administrative villages had achieved full penetration, with optical fiber and 4G network coverage exceeding 99%. Furthermore, by the end of 2022, 5G network deployment had been completed in county and urban areas. The elimination of infrastructural barriers is crucial in enabling rural regions to establish self-sustaining e-commerce ecosystems ( Leong et al., 2016 ).

Concurrently, vendors on major e-commerce platforms such as Amazon, JD.com , and Suning are evolving beyond their traditional roles as intermediaries. These entities are increasingly establishing self-operated stores, engaging in logistics and warehousing operations, and providing specialized services such as cold chain logistics for agricultural products ( Han et al., 2023 ). This transformation has been conceptualized by scholars like Qin et al. (2021) as the “market-platform” model. According to Tosza (2021) , e-commerce can be categorized into rule-setter and infrastructure providers. In the current market landscape, the provision of efficient logistics services has become a critical determinant of e-commerce platforms’ competitive advantage. The infrastructural impact of e-commerce advancement extends to agricultural production methods. Modern logistics facilities, coupled with advanced equipment and technologies such as automated production management control systems and integrated water and fertilizer management, are being extensively utilized to facilitate the automation of farming, animal husbandry, and aquaculture practices. This technological integration addresses the “hardware” deficiencies in agricultural transformation and enhancement, fostering sustainable production practices such as ecological farming and circular agriculture ( Zheng and Zhou, 2023 ). Based on these observations, we propose the following hypothesis:

H4: The proliferation of e-commerce has catalyzed increased investment in rural infrastructure by both enterprises and government entities. Through the modernization of logistics and other critical infrastructure, e-commerce provides the hardware for HQAD.

3 Construction of index system and its research methods

3.1 construction of index system of hqad.

In recent years, HQAD has emerged as a significant focus in academic research, with numerous scholars assessing its developmental level. Baráth et al. (2020) utilized total factor productivity as a proxy variable to evaluate the level of HQAD in Slovenia. Concurrently, Chinese scholars have explored the construction of multidimensional index systems. For instance, Liu et al. (2020) established a comprehensive evaluation framework based on five new development concepts: innovation, coordination, green development, opening up, and sharing. Li et al. (2023) formulated an evaluation index system encompassing aspects such as farmer income, industrial efficiency, technological support, production efficiency, labor quality, and green production. Building upon existing research, this study has developed a comprehensive index system for HQAD, focusing on agricultural quality and efficiency enhancement, agricultural innovation capability, agricultural green development, and agricultural social contribution.

1. Agricultural quality and efficiency enhancement. The HQAD framework necessitates consistent productivity growth while emphasizing qualitative and efficiency improvements. This study incorporates five key indicators to comprehensively assess the enhancement of agricultural quality and efficiency: agricultural labor productivity, agricultural land yield, agricultural economic development, structural composition of the agricultural sector, and agricultural export competitiveness.

2. Agricultural innovation capability. Technological advancement is fundamental to HQAD. The integration of cutting-edge technologies and agricultural mechanization significantly influences production efficiency and product quality. Drawing from extant literature, this study employs indicators such as the degree of agricultural mechanization, agricultural financial allocation, research and development (R&D) investment intensity, proportion of R&D personnel, and shifts in the agricultural industrial structure to evaluate innovation capability.

3. Green agricultural development. HQAD prioritizes sustainable resource utilization, energy efficiency, and environmental stewardship throughout the production process. To gauge the level of green development, this study utilizes key indicators including the effective irrigation coefficient, agricultural fertilizer application intensity, pesticide utilization rate, and agricultural carbon emissions.

4. Agricultural social contribution. The primary objective of agricultural development is to meet societal needs. HQAD emphasizes the social benefits of agriculture and its active contribution to societal progress. Consequently, this study incorporates indicators such as the agricultural labor employment ratio, rural resident income levels, rural residents’ Engel coefficient, agricultural GDP contribution rate, and urbanization rate to assess the social impact of the agricultural sector.

3.2 Data sources

This study examines different provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2000 to 2021 as case studies. The data utilized primarily came from the China Statistical Yearbook, China Rural Statistical Yearbook, China Financial Statistical Yearbook, China Science and Technology Statistical Yearbook, China Environmental Statistical Yearbook, China Population and Employment Statistical Yearbook, and the statistical yearbook of each province.

3.3 Description of research methods

This study employs the entropy method to evaluate the target layer and each criterion layer by analyzing data index fluctuations. The methodological approach comprises three main stages. First, the extreme value method is applied to normalize the original data and compute the relative weights of individual indices. Second, the entropy, difference coefficient, and weight of each index are calculated to assess the information content and discriminatory power of the indicators. Third, the indices for the four dimensions are computed, and subsequently, a comprehensive development index is derived using an objective linear weighting function. The detailed procedural steps for this analysis are as follows.

1. Data standardization processing. For positive indicators, the data are standardized using Equation (1) as shown below:

For negative indicators, Equation (2) is applied for standardization:

where, x i j represents the original data of j indicator of i region, min x i j is the minimum value of the indicator, max x i j is the maximum value of the indicator, and x i j ∗ is the result after standardization of the indicator x i j .

1. Normalize the indicators and calculate the proportion of the J th indicator in the region i , as shown in Equation (3) :

1. Calculate the information entropy of the index e j , as indicated by Equation (4) :

where e j is the information entropy of the J th index, n is the number of provinces to be evaluated, n  = 31.

1. Calculate the difference coefficient g j and weight w j of the J th index using Equations (5 , 6) , respectively, as follows:

where g j is the difference coefficient of the J th index, and the larger the coefficient, the greater the effect of this index on the object of study, and the more important this economic index.

1. According to the weights calculated in (4), the standardized indicators in (1) is weighted and summarized to obtain the comprehensive indicator of HQAD.

2. Repeat steps (1) to (5) by different years. The construction of evaluation system of HQAD index is shown in Table 1 .

Table 1 . Construction of evaluation system of HQAD index.

3.4 Construction of e-commerce development index

The assessment of e-commerce development employs various methodologies, including the e-commerce readiness framework developed by the Asia-Pacific Economic Cooperation (APEC) and the life cycle model proposed by the Organization for Economic Co-operation and Development (OECD). In the Chinese context, the China Internet Research and Development Center (CIRC) has formulated a comprehensive e-commerce index comprising 32 indicators, tailored to the nation’s specific circumstances. This study builds upon existing research by synthesizing multiple sources to construct a robust index system. Primary references include the E-commerce Development Index in China published by the Chinese Academy of Social Sciences and the index system released by the CIRC. The selection of indicators was further refined based on the availability of relevant data across various provinces. The index system is presented in Table 2 .

Table 2 . Construction of index system of e-commerce development level.

4 Empirical measurement of e-commerce driving HQAD in China

4.1 basic model setting.

Drawing upon the aforementioned theoretical framework, this study proposes a basic econometric model to empirically examine the impact of e-commerce on HQAD. The model is specified as follows.

where quality it represents the HQAD index provinces i in the year t ; e c it stands for the e-commerce development index; X stands for the other possible control variable and ε it is the error perturbation term. If α 1 is significantly positive, that means the e-commerce has boosted HQAD.

4.2 Variable selection

To enhance the robustness of our analysis on the impact of e-commerce, this study incorporates several potential confounding factors as control variables. (1) Crop disaster rate, measured as the ratio of disaster-affected crop area to total planted area in each region. (2) Foreign direct investment (FDI) level, calculated as the ratio of FDI (converted to Chinese yuan using the annual average exchange rate) to GDP. (3) Financial development level, represented by the ratio of outstanding financial credits to total regional output. (4) Macroeconomic environment, indicated by the proportion of secondary and tertiary industry output to total regional output. (5) Whether to cancel agricultural tax or not. Considering the policy factor that China completely abolished agricultural tax on January 1, 2006, a dummy variable is set for the cancellation of agricultural tax, with a value of 0 before 2006 and 1 after 2006. (6) The density of rural roads. Following Tian and Xiong (2023) , the density of rural roads is calculated as (regional highway mileage – regional first-class highway mileage – regional second-class highway mileage + out-of-region highway mileage) / (regional area – regional construction area). The descriptive analysis of the relevant variables is presented in Table 3 .

Table 3 . Descriptive analysis of relevant variables.

4.3 Benchmark regression and result analysis

The benchmark regression analysis, aimed at evaluating the impact of e-commerce on HQAD, yielded a statistically significant coefficient of 0.147. This finding underscores the substantial influence of e-commerce on agricultural sector in China. The proliferation of e-commerce platforms in recent years has revolutionized the distribution channels for agricultural products in China, offering a more efficient and accessible marketplace. By streamlining processes across the agricultural supply chain, including procurement, storage, and delivery, e-commerce has effectively mitigated costs and enhanced operational efficiency. These improvements have, in turn, contributed significantly to the advancement of China’s HQAD. Table 4 presents a comprehensive breakdown of our regression results, providing further insights into the magnitude and nature of this relationship.

Table 4 . Benchmark regression of e-commerce impact effects.

4.3.1 Robustness test

To further validate the robustness of our findings regarding the impact of e-commerce, we employed several alternative methodologies, with results presented in Table 5 . Firstly, we conducted new our explanatory and dependent variables, utilizing principal component analysis to reconstruct both the e-commerce and HQAD indices for regression analysis. As evidenced in column (1) of Table 5 , the newly constructed e-commerce development index continues to exhibit a significant positive effect on the revised HQAD index. Secondly, we incorporated a joint fixed effect model, accounting for both control area and year, which reaffirmed the conclusions drawn from our baseline regression. Lastly, to mitigate the potential influence of outliers, we applied a winsorization technique at the 1% level for all variables. The results of this analysis, presented in column (3), demonstrate a high degree of consistency with our benchmark regression outcomes. These comprehensive robustness checks collectively reinforce the stability and reliability of our primary findings, providing strong support for the significant impact of e-commerce on HQAD.

Table 5 . Robustness test results.

4.3.2 Endogenous processing

To address potential endogeneity issues in our regression analysis, we employed a series of instrumental variables (IVs). Our IVs included the lagged e-commerce index, an interaction term between a historical variable and the 1987 time trend of telephone ownership per 100 residents ( Huang et al., 2019 ), and the mean e-commerce development level of neighboring provinces. The validity of these IVs was rigorously tested. Wald statistic results rejected the null hypothesis of “no correlation with endogenous variables” at the 10% significance level, indicating our IVs are not weak. Additionally, the overidentification test failed to reject the null hypothesis of “exogenous instrumental variables” at the 10% level, satisfying the exogeneity condition. Upon addressing potential bidirectional causality through our IV approach, we observed a substantial increase in the e-commerce impact coefficient. The results of this instrumental variable regression analysis, presented in columns 1, 2, and 3 of Table 6 , provide robust evidence for the causal relationship between e-commerce and our dependent variable.

Table 6 . Regression results of endogenous processing.

4.3.3 Heterogeneity analysis

The spatial heterogeneity of agricultural development, intrinsically linked to geographical location and natural resource endowments, suggests potential regional variations in the impact of e-commerce on HQAD across China’s diverse landscape. Our econometric analysis, presented in columns (1)–(4) of Table 7 , provides empirical evidence of these regional disparities. The results indicate that the Central region of China experiences the most pronounced effect of e-commerce on HQAD, with the largest coefficient of 1.064, significant at the 1% level. Similarly, the Northeastern and Western regions demonstrate substantial impacts, with coefficients of 0.466 and 0.153, significant at the 1 and 5% levels, respectively. In contrast, the Eastern region exhibits a comparatively smaller, albeit still significant, impact with a coefficient of 0.024. These regional disparities may be attributed to the varying degrees of constraints faced by different areas in terms of geographical conditions, infrastructure development, and access to consumer markets. The Central, Western, and Northeastern regions, which traditionally face more significant barriers in these aspects compared to the more developed Eastern region, appear to benefit more substantially from e-commerce. This technology effectively mitigates geographical limitations by facilitating cross-regional flows of agricultural products and production factors, consequently emerging as a particularly potent driver of HQAD in these regions. This finding corroborates the recent work of Yang et al. (2023) , who similarly observed differential impacts of e-commerce across China’s diverse geographical landscape.

Table 7 . Regional heterogeneity in the impact of e-commerce on HQAD.

4.4 Mechanism analysis

Drawing upon our theoretical framework, we developed four mediating indicators to investigate the potential pathways through which e-commerce influences HQAD. (1) Online trading volume of agricultural products (TVAP), derived by multiplying the scale of annual agricultural product transactions by the provincial ratio of e-commerce transactions to the national total, with logarithmic transformation applied to mitigate heteroscedasticity; (2) Modernization degree of agricultural industry chain (MDAIC), constructed using a five-dimensional indicator system encompassing integrated development, innovation and upgrading, global cohesion, policy coordination, and stable operation; (3) Socialized service level (SSL), computed from the input index of the service industry in agriculture, forestry, animal husbandry, and fishery as reported in provincial input–output tables, with linear interpolation employed to address missing data; and (4) Supply degree of agricultural infrastructure (SDAI), utilizing the length of long-distance fiber optic cable lines in rural and urban areas as a proxy for digital infrastructure supply, given its critical role in e-commerce development and fundamental importance to HQAD.

To empirically examine the hypothesized mediating pathways, we employed a parallel mediation effect model framework. This approach extends the baseline model ( Equation 7 ) to incorporate the potential mediating variables, allowing for a more nuanced analysis of the mechanisms through which e-commerce influences HQAD. The expanded model is specified as follows.

Equations 8–11 measure the relationship between explanatory variables and mediating variables. Equation 12 examines the relationship among explanatory variables, mediating variables, and the explained variable.

Adopting Hayes (2013) approach, this study utilized 5,000 bootstrap samples and the PROCESS macro model 4 in SPSS, with a 95% bias-corrected confidence interval (CI) to analyze the parallel mediating effects of e-commerce on HQAD. Bootstrap sampling, not relying on assumptions about the sampling distribution, is the most effective method for assessing mediating effects. A statistically significant indirect effect is indicated if the CI range excludes zero, suggesting the presence of a mediating effect ( Wang, 2024 ). Table 8 presents the coefficients of multiple parallel mediating regressions, examining the direct and indirect effects of e-commerce on HQAD through various mediators. The results indicate a significant direct positive effect of e-commerce on HQAD, with a coefficient of 0.2173 at the 1% significance level, suggesting that e-commerce contributes to HQAD even when considering mediating factors. Furthermore, we found there exist significant positive relationships between e-commerce and four mediating variables at the 1% significance level. This implies that e-commerce development enhances these aspects of the agricultural sector. Additionally, all four mediating variables demonstrate positive effects on HQAD. These findings suggest that each mediator plays a role in transmitting e-commerce’s effects on HQAD. Table 9 reveals the parallel mediating effects of e-commerce on HQAD, with all variables showing significant indirect effects. Market expansion (TVAP) demonstrates the strongest mediating effect, accounting for 43.27% of the total effect, highlighting its crucial role in fostering HQAD through e-commerce. The modernization of the agricultural industry chain (MDAIC) follows, contributing 14.18% to the total effect, emphasizing the importance of industry chain upgrades. The socialized service level (SSL) also plays a notable role, accounting for 3.31% of the total effect, underscoring the significance of improved agricultural services. The supply of agricultural infrastructure (SDAI) shows the smallest mediating effect, suggesting that while important, it may have less immediate impact on HQAD compared to other factors. Significant differences are observed between the mediating effect of TVAP and those of SSL, MDAIC, and SDAI, providing a nuanced understanding of the complex relationships within this e-commerce-driven developmental process.

Table 8 . Regression coefficients of E-commerce and mediating variables on HQAD.

Table 9 . Parallel mediating effects of e-commerce on HQAD.

5 Discussion

This study’s key findings reveal that e-commerce significantly promotes HQAD in China through four pathways: market expansion, agricultural industry chain modernization, social service optimization, and infrastructure supply. Notably, market expansion exhibits the strongest mediating effect (43.27%), followed by industry chain modernization (14.18%). These results extend previous research by Han et al. (2023) on e-commerce’s role in green agriculture and Bai et al. (2024) on consumer behavior, while challenging traditional supply-side dominant perspectives by emphasizing demand-side factors in driving HQAD. Theoretically, this study advances existing frameworks by proposing a “demand-driven supply adjustment” model, integrating both demand and supply-side factors, and employing a multi-pathway analysis to uncover the complex mechanisms of e-commerce’s impact on HQAD. This approach not only enriches agricultural economics theory but also provides a new paradigm for studying digital transformation in other economic sectors. Practically, these findings offer valuable insights for agricultural policy-making and e-commerce development strategies. They suggest the need for policies supporting rural digital infrastructure, e-commerce training, and the optimization of industry chain integration and social services. Furthermore, the comprehensive framework developed for assessing agricultural development quality can aid governments and relevant institutions in more accurately monitoring and evaluating agricultural progress, thereby informing more targeted development strategies.

6 Conclusions and policy implications

This study constructs a comprehensive evaluation system for HQAD in China and investigates the impact of e-commerce on HQAD using provincial panel data from 2000 to 2021. By employing mediating effect models, we examine the multiple pathways through which e-commerce influences HQAD. Our findings contribute to the existing literature and offer important policy implications. The main conclusions are as follows: Firstly, e-commerce significantly promotes HQAD in China, with a positive impact coefficient of 0.147. This finding aligns with Chen et al. (2022) , who reported that e-commerce adoption enhances technical efficiency in wheat production. However, our study extends beyond a single crop to encompass overall agricultural development. Secondly, the impact of e-commerce on HQAD varies across regions. The Central region of China experiences the largest impact (coefficient: 1.064), followed by the Northeastern (0.466) and Western (0.153) regions, while the Eastern region shows the smallest impact (0.024). This regional heterogeneity adds nuance to the findings of Yang et al. (2023) , who focused on the national-level impact of agricultural R&D on HQAD. Lastly, e-commerce drives HQAD through four main pathways: market expansion (43.27% of total effect), modernization of the agricultural industry chain (14.18%), optimization of agricultural social services (3.31%), and supply of agricultural infrastructure (1.50%). This multi-pathway analysis provides a more comprehensive understanding compared to previous studies like Zhong et al. (2022) , which primarily focused on the digital economy’s impact on agricultural technological progress. The market expansion effect of e-commerce is the most prominent, highlighting the crucial role of demand-side factors in driving HQAD. This finding challenges the traditional supply-side dominant perspectives in agricultural development research.

Based on these findings, several policy implications are derived. As a developing country with a large agricultural sector, (1) Given that the impact of e-commerce on HQAD shows significant heterogeneity across different regions, policymakers should formulate differentiated e-commerce development strategies based on the specific conditions and characteristics of each region. This approach aims to fully leverage the potential of e-commerce in promoting HQAD and ensure that each region can maximize the benefits brought by e-commerce according to its own circumstances. (2) The study indicates that market expansion is the primary pathway through which e-commerce promotes HQAD. Therefore, policymakers should prioritize supporting the construction and optimization of agricultural e-commerce platforms, encourage the development of diverse e-commerce models such as live-streaming sales and community marketing. Simultaneously, the rural logistics system should be improved to reduce agricultural product circulation costs. Furthermore, policies should support cross-regional e-commerce cooperation, breaking geographical limitations to achieve nationwide and even international sales of agricultural products. (3) This research finds that agricultural industry chain modernization is the second most significant pathway through which e-commerce impacts HQAD. Therefore, policies should encourage vertical integration of the agricultural industry chain, support the development of agricultural cooperatives, and promote the application of digital technologies throughout the supply chain. Specific measures may include providing tax incentives to support agricultural industry chain integration, establishing special funds to support agricultural cooperatives in developing e-commerce, and organizing training programs to enhance farmers’ digital skills. (4) Although this study shows that agricultural socialized services and infrastructure demonstrated smaller mediating effects, they are crucial for long-term agricultural development. Therefore, policies should focus on improving rural digital infrastructure, such as accelerating 5G network coverage in rural areas and providing inclusive internet access services. Concurrently, e-commerce training for farmers should be strengthened, and the digitalization level of agricultural extension services should be enhanced. Additionally, policies should support the development of smart agriculture, such as promoting the application of Internet of Things (IoT) and big data technologies in agricultural production.

Through mediation analysis, this study contributes to establishing a comprehensive “demand-driven supply adjustment” framework, offering valuable insights for future researchers. However, the research has some limitations. Firstly, due to data constraints, the HQAD evaluation system may not fully capture all dimensions, suggesting that future studies could incorporate more comprehensive indicators. Secondly, this paper extensively explores the impact of e-commerce development, with future research delving into new transformations brought by next-generation digital technologies like the Internet of Things and blockchain, deepening the understanding of e-commerce impact. Lastly, the mediating effect model may not encompass all influencing paths of e-commerce. Subsequent research should integrate theoretical analysis with China’s specific conditions, considering regional development disparities to further investigate other potential mechanisms and various adaptation scenarios.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: the datasets used during the current study are available from the first author on reasonable request. Requests to access these datasets should be directed to YK, [email protected] .

Author contributions

YK: Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing. XZ: Formal analysis, Investigation, Resources, Writing – review & editing. XL: Data curation, Methodology, Writing – original draft. PP: Funding acquisition, Methodology, Resources, Supervision, Visualization, Writing – review & editing, Writing – original draft. JL: Resources, Validation, Writing – review & editing. CF: Investigation, Validation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is supported by the Social Science Planning Project of Shandong Province (grant number 21DTJJ01) and the Key Soft Science Research Project of Shandong Province (grant number 2023RZB06050).

Acknowledgments

This work was assisted by the Faculty of Economics and the Centre of Excellence in Econometrics at Chiang Mai University, the China–ASEAN High-Quality Development Research Center and International Exchange and Cooperation Office at Shandong University of Finance and Economics.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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Keywords: e-commerce, high-quality agricultural development, mediation effect, agricultural economic growth, green agricultural development

Citation: Kong Y, Zhang X, Li X, Pastpipatkul P, Liu J and Feng C (2024) Analysis of the driving path of e-commerce to high-quality agricultural development in China: empirical evidence from mediating effect models. Front. Sustain. Food Syst . 8:1371254. doi: 10.3389/fsufs.2024.1371254

Received: 16 January 2024; Accepted: 04 July 2024; Published: 26 July 2024.

Reviewed by:

Copyright © 2024 Kong, Zhang, Li, Pastpipatkul, Liu and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Pathairat Pastpipatkul, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Ultrasound and intrapleural enzymatic therapy for complicated pleural effusion: a case series with a literature review.

1. Introduction

2. case series, 2.1. chest us examination technique and surgical procedure, 2.2. case report 1, 2.3. case report 2, 2.4. case report 3, 2.5. case report 4, 2.6. case report 5, 3. materials and methods, search strategy, 4. pleural effusion: the role of chest ultrasound, 5. intrapleural enzyme therapy: state of the art, 6. discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Inchingolo, R.; Ielo, S.; Barone, R.; Whalen, M.B.; Carriera, L.; Smargiassi, A.; Sorino, C.; Lococo, F.; Feller-Kopman, D. Ultrasound and Intrapleural Enzymatic Therapy for Complicated Pleural Effusion: A Case Series with a Literature Review. J. Clin. Med. 2024 , 13 , 4346. https://doi.org/10.3390/jcm13154346

Inchingolo R, Ielo S, Barone R, Whalen MB, Carriera L, Smargiassi A, Sorino C, Lococo F, Feller-Kopman D. Ultrasound and Intrapleural Enzymatic Therapy for Complicated Pleural Effusion: A Case Series with a Literature Review. Journal of Clinical Medicine . 2024; 13(15):4346. https://doi.org/10.3390/jcm13154346

Inchingolo, Riccardo, Simone Ielo, Roberto Barone, Matteo Bernard Whalen, Lorenzo Carriera, Andrea Smargiassi, Claudio Sorino, Filippo Lococo, and David Feller-Kopman. 2024. "Ultrasound and Intrapleural Enzymatic Therapy for Complicated Pleural Effusion: A Case Series with a Literature Review" Journal of Clinical Medicine 13, no. 15: 4346. https://doi.org/10.3390/jcm13154346

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