Where Business Meets Location Intelligence: A Bibliometric Analysis of Geomarketing Research in Retail

Abstract

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing.

1. Introduction

In recent years, the integration of Geographic Information Systems (GIS) and marketing strategies has resulted in the advent of geomarketing, a field that synthesizes spatial and demographic data to enhance customer targeting, optimize retail site selection, and guide location-based marketing decisions. As retail environments become more intricate and data-centric, geomarketing provides a significant advantage by allowing firms to synchronize their offerings with customer behavior, mobility trends, and competition dynamics [1,2].

Geomarketing delivers concrete advantages over non-spatial marketing analyses, which are characterized by pricing and performance analytics. By integrating demographic, transactional, mobility, and competitive data into a unified geographic framework, it uncovers customer clusters, catchment areas, and competitive overlaps that tabular reports cannot reveal [3,4,5]. By mapping exactly how far people can travel in, for example, five or ten minutes by car (drive-time isochrones) or charting where they walk (pedestrian flow models), businesses can divide their market into more focused neighborhoods [6]. GIS-powered analytics is not only a “nice to have” but a strategic tool in today’s retail environment. Businesses can implement three main things while using it: (i) rapidly adjust to changing demand, (ii) improve last-minute logistics, and (iii) outmaneuver competition in crowded marketplaces because of its real-time mapping of consumer movements and environmental conditions.

Considering its potential for evolution and transformation, the extent of GIS incorporation into conventional marketing research remains unclear, particularly in light of current advancements in AI, big data, and omnichannel retail that have increased the need for geographically detailed information [7]. Responding to this concern is crucial, since ongoing ambiguity about GIS’s use in marketing research threatens to hinder strategic agility and competitive efficacy in a progressively spatially oriented retail environment. Our study directly tackles this gap through a comprehensive bibliometric analysis of 4952 peer-reviewed articles published between 2000 and 2025. Accordingly, the study is guided by the following research question: To what extent do GIS and geomarketing-related concepts emerge as central or peripheral elements within the knowledge structure of retail marketing scholarship?

Our findings serve four distinct yet interconnected stakeholder groups: marketing academics benefit from clearly identified research gaps for advancing spatial analytics in scholarly discourse; retail planners gain insights into under-exploited opportunities for location-based marketing innovations; GIS and Spatial Decision Support Systems (SDSS) developers receive guidance toward strategically valuable thematic areas to direct technological advancements; and policymakers involved in urban and commercial planning acquire actionable intelligence to embed spatial considerations more systematically within commercial and sustainability policy frameworks.

The conducted analysis enriches the existing literature by providing three main points:

• It charts the evolution of geomarketing concepts in retail over the past 25 years, providing marketing scholars with a comprehensive thematic overview of spatial analytics adoption.
• It identifies influential authors, institutions, and thematic clusters shaping geomarketing literature, offering GIS/SDSS developers and data analytics professionals strategic insights into promising innovation areas.
• It highlights under-researched themes explicitly, offering policymakers and retail strategists clear, actionable directions to integrate spatial intelligence more systematically into their decision-making processes.

Using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1) [8], we perform keyword co-occurrence mapping, thematic evolution analysis, and citation network visualization to trace how spatial analytics has (or has not) penetrated the marketing field. Our results show that while interest in location-based marketing has grown, GIS remains marginal in core strategic retail research—an omission that presents both a challenge and an opportunity for future interdisciplinary inquiry.

The remainder of the paper is structured as follows: Section 2 reviews the theoretical research of geomarketing in retail; Section 3 details our bibliometric approach with explanations of materials and methods we employ in our study; Section 4 presents our results; Section 5 discusses practical and theoretical implications; and Section 6 concludes with recommendations to advance GIS integration in retail marketing research.

2. Background and Literature Review

2.1. Evolution of Retail and Technological Transformations

The retail industry is continuously reshaped by digitalization and technologization combined with artificial intelligence (AI) and the Internet of Things (IoT). Modern-day customers demand customized shopping experiences that are easy to access, while retailers seek new technological solutions to improve operational performance and deepen customer relationships [9]. Modern firms are depending more and more on data analytics and automation tools to improve decision-making. In this way, they can fortify market positioning in response to changing client needs [10]. Starting in the 18th century, industrial revolutions led to significant changes in the manufacturing and retail sectors. The advent of steam engines and mass production during Industry 1.0 (1760–1830) led to the emergence of department stores. The development of Industry 2.0 (1870–1920) brought about electrification and assembly lines. The third Industrial Revolution transformed shopping through the development of electronics and telecommunications and the establishment of e-commerce, which expanded retail to a global scale (1950–2000). Industry 4.0 (2010–2020) integrates AI, IoT, cloud computing, and big data to enhance customer experiences, optimize supply chains, and facilitate personalized marketing [11]. Industry 5.0 makes retail human-centered and sustainable (2020-present). While Industry 4.0 focuses on AI-driven personalization and predictive analytics, Industry 5.0 highlights ethical data use, consumer welfare, and the preservation of the environment [12]. Each industrial revolution has changed retail and consumer behavior. Steam-powered manufacturing (Retail 1.0) allowed mass production, resulting in department shops and structured commerce in the late 18th century. In Retail 2.0, electrification and assembly lines reduced production costs, allowing shopping malls and chain stores to expand and make consumer goods more affordable [13]. A digital revolution powered by telecommunications, personal computing, and the internet led to Retail 3.0. These innovations enabled international e-commerce platforms, changing customer behavior by moving retail transactions online and reducing shop visits [14]. AI, IoT, Cloud Computing, and Big Data Analytics make Retail 4.0 a highly automated and data-driven enterprise. Most recently, Retail 5.0 integrates AI with a human-centric focus, designing sustainable supply chains, transparent data practices, and customer experiences that balance commercial success with consumer well-being and environmental responsibility (see Figure 1).

These technologies enable organizations to predict consumer requirements with remarkable accuracy in personalized marketing, predictive analytics, and omnichannel retailing [15]. The COVID-19 pandemic accelerated digital transformation by: (i) forcing automation, (ii) AI-driven customer engagements, and (iii) contactless retail payment solutions [16]. These advancements clearly suggest that intelligent technologies and real-time analytics will persist in influencing customer interaction and market dynamics [17,18,19].

Geomarketing has a long history, which started as a simple study of geography and has grown into a business tool that is driven by data and AI. Originally called “market geography” or “geography of sales markets,” it became popular in the U.S. in the 1930s when W. Applebaum created site selection studies that helped companies like The Kroger Co. find the best places to put their supermarkets [20]. Over the next few decades, through spatial analysis and graphical methods, geomarketing grew beyond retail and was used to understand how markets worked in trade, banking, and transportation [21]. From the 1970s to the 1990s, European companies used geomarketing for planning their distribution networks and dividing customers into groups. They did this by using information about geography to improve their marketing strategies [22]. However, it was not until the middle of the 1990s that geomarketing really changed. That is when GIS changed how market segmentation, predictive analytics, and site selection were carried out. Location-based market research has grown into a complex, tech-driven field that uses Big Data, AI, and real-time spatial intelligence to shape the future of shopping and how people interact with brands [23]. GIS and geomarketing are two separate but related fields. Geomarketing builds on GIS-driven geospatial analysis to answer not only where customers live, but also how they move through space, mapping shopping routes, purchase locations, and mobility patterns to inform hyper-local targeting and site-selection decisions. In other words, not only do retailers want to know where their customers live, but they also want to know how they move around, what they buy, and their shopping habits. Retailers can track real-time customer movement, send personalized offers based on location, and even predict demand before it happens [24]. Despite this, many companies still rely on outdated marketing strategies, therefore missing the possibilities of location-based data. Even more, the focus has changed in recent years from simple profitability to Retail 5.0, which gives sustainability, ethics, and improved decision-making priority [25]. This change depends much on geomarketing. Location data is being used by companies to: (i) maximize supply chains, (ii) reduce carbon emissions, and (iii) rethink urban retail environment architecture. As AI-powered geomarketing enables real-time tracking and hyper-personalized marketing, concerns about data privacy, algorithmic bias, and consumer consent have emerged. In this regard, firms are expected to adopt clear data policies and ethical AI frameworks [26].

Given the increasing relevance of spatial analytics in retail, the extent to which GIS has been conceptually integrated into academic marketing literature remains uncertain. This study investigates whether GIS-related concepts emerge as prominent thematic elements within retail-geomarketing publications. To test this, we analyzed patterns in publication output, keyword co-occurrence, and how topics have shifted over time. What we found is telling that GIS and related topics are still not included in discussions around AI, data-driven marketing, and integrated retail models. This absence sets the stage for a clear opportunity. There is space for new research to explore how spatial data tools could play a more central role in shaping both the theory and real-world practice of modern retail marketing. With retail’s evolution now established, Section 2.2 examines the methodological innovations driving geomarketing applications.

2.2. Conceptual Foundations and Methodological Advances

In order to enhance the overall effectiveness of marketing in the retail industry, numerous studies illustrate how geomarketing tools facilitate informed decision-making.

Table 1. Methodological approaches in geomarketing research.

Study	Methods Applied	Application Domain
Teller and Schnedlitz [27]	Survey of 217 shopping-mall managers; exploratory factor analysis (Principal Component Analysis) to extract underlying agglomeration drivers; computation of weighted impact indices; paired-samples and independent-samples t-tests to compare driver effects and test moderating variables.	Quantifying how location, tenant mix, marketing, and management factors drive agglomeration effects in retail
Roig-Tierno et al. [28]	GIS for geodemand and geocompetition layers; Kernel-density analysis to identify candidate sites; analytical hierarchy process for multi-criteria ranking of those sites.	Retail site-location decision (supermarket in Murcia, Spain)
Efentakis et al. [29]	Live traffic isochrone computation using floating car data and map-matching; dynamic catchment-area analysis; overlay of time-varying isochrones with demographic data.	Real-time reachability and consumer accessibility for retail site-selection and service planning
Baviera-Puig et al. [30]	GIS-driven Huff (spatial interaction) model; geocompetition index via kernel density; isochrone-based catchment area analysis.	Network planning and site-selection strategies for supermarkets in Spain
Ramadani et al. [31]	Cross-sectional survey of 181 small and medium restaurant operators; Pearson correlation analysis; hierarchical regression to test the impact of business choice and location determinants on firm success.	Assessing how geomarketing-related location decisions influence SME performance in Uganda
Tsakiridi, A. [32]	Systematic literature review following PRISMA; categorization of 120 SCM articles by GIS capability (facility location, network optimization, inventory visualization).	Mapping GIS contributions to supply-chain decision making, including cost minimization, facility siting, network configuration, and inventory control
Skoutas et al. [33]	Problem formulation for mixture-based region search with cohesion/completeness constraints; anytime heuristic search using R, tree-backed spatial pruning.	Discovering arbitrarily shaped high-mixture regions in geospatial point data (hotspot/coldspot analysis)
McGuirt et al. [34]	GIS-based assessment of store accessibility (network distance measures); customer surveys on shopping patterns and dietary behaviors; multivariate regression analysis to test associations between accessibility and behaviors.	Linking small food store accessibility to customer shopping frequency and dietary intake

summarizes these methodological approaches and their application domains.

The incorporation of GIS technology enables a more sophisticated analysis of customer behavior and market dynamics, permitting retailers to correlate consumer demographics with store locations [34]. As data sources proliferate, including online platforms, mobile applications, and real-time consumer analytics, geomarketing is evolving in sophistication. It helps retailers to benefit from geographic analytics, which includes location analysis and market segmentation [32]. Enhancing customer interactions has long been a key interest in economics. This goal is now achievable with the implementation of web GIS, which highlights the value of spatial data in creating marketing plans that are specific to certain consumer demographics [28].

Advances in geomarketing methodology have made it possible to create increasingly complex frameworks about demographic and geographic factors. The application of these modeling approaches, for example, sociodemographic settings of retail locations, has improved understanding of how factors like accessibility and local rivalry affect sales success [31]. Moreover, understanding agglomeration effects and the variation of store offerings near competitors can assist retailers in performing better [27]. This is especially crucial at a time when retail spaces are becoming more commodified, requiring the identification of unique value propositions that attract consumers [30]. A region-search algorithm developed by Skoutas et al. [33] shows how GIS techniques can identify irregularly shaped areas on a map where different types of customer behavior overlap, meaning that many customers with certain traits or buying habits converge. With this information, retailers can strengthen their ability to stock the right products in stores, deploy staff and resources effectively, and adjust product assortments accordingly. Thus, enterprises that effectively employ geomarketing may attain a considerable advantage over competitors adhering to traditional marketing methods.

Beyond location research, geomarketing embodies a comprehensive understanding of the interaction among consumers, retail environments, and competitive structures. Efentakis et al. [29] built a live-traffic geomarketing service by first mapping-matching large fleets’ GPS onto OpenStreetMap road graphs and then generating both historic and real-time speed profiles. Every five minutes, they compute time-dependent shortest-path searches to produce dynamic isochrones that are represented by polygons showing all areas reachable within set travel times under current traffic. These isochrones are merged with demographic overlays, letting retailers instantly visualize how traffic shifts affect catchment areas, optimize site selection, target marketing campaigns, and allocate resources with up-to-the-minute spatial precision. As urbanization accelerates and consumer preferences evolve, retailers will increasingly need to refine their strategies through geomarket-driven insights.

Although geomarketing has generated sophisticated spatial-analytic methods, these advances seldom translate into sustained visibility within core marketing literature. High implementation costs for software licenses and high-performance hardware, combined with the lack of faculty training, continue to hinder GIS and geomarketing integration into business and marketing educational programs [35,36]. Moreover, due to their application context, most geomarketing studies are published in practitioner-oriented or information systems venues rather than in leading marketing periodicals, so marketing scholars rarely see or cite them [37]. Furthermore, these studies often operate independently of core marketing theories such as the marketing mix or customer journey frameworks, reducing their resonance with theory-driven outlets [38].

Taken together, the studies analyzed in this section illustrate a technically mature body of geomarketing work, with sophisticated methods (isochrone-based accessibility mapping, kernel-density geocompetition analysis, and hierarchical regression applied to location choice). These contributions demonstrate that GIS has long been capable of supporting strategic decisions in retail, logistics, and consumer behavior modeling. However, geomarketing is seen as a peripheral subject in traditional marketing research, which stands in stark contrast to the scientific rigor of these studies. We specifically discuss this aspect in Section 5, where we analyze the epistemological constraints that explain the lack of incorporation of geomarketing in academic research.

3. Materials and Methods

This study investigates the extent to which GIS and spatial concepts have been intellectually integrated into mainstream marketing literature. Our guiding research question is: To what extent do GIS and geomarketing-related concepts emerge as central or peripheral within the knowledge structure of retail marketing scholarship? This question motivated a bibliometric approach designed not to assume GIS’s presence, but to assess whether it surfaces organically in: (i) conceptual clusters, (ii) author networks, and (iii) thematic keyword co-occurrences. To ensure this, we deliberately excluded “GIS” from our initial search string, allowing for an unbiased analysis of conceptual emergence. The methodology that follows details how we analyzed a large-scale bibliographic dataset in order to evaluate thematic trends and structural visibility within geomarketing research. We employed a four-step bibliometric methodology, following established guidelines in scientific mapping [39,40,41]. The bibliometric method provides a structured approach to identifying research trends, thematic clusters, and influential contributions within a given field [42,43,44,45]. The method integrates both conceptual and exploratory analysis using the Bibliometrix package (version 4.3.3) within R software (version 4.4.1) developed by Aria and Cuccurullo [8] and is illustrated in Figure 2, which is adapted from [46,47,48].

The first step involved the collection of bibliographic datasets from a scientific database. Thus, the dataset was retrieved from the Web of Science (WoS), specifically from the SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) collections, which offer comprehensive coverage of peer-reviewed journals in marketing, business, and spatial sciences [49]. The WoS is known for its clear and standardized data. This is the most important factor when thinking about: (i) tracking citations, (ii) analyzing author networks, and (iii) exploring the evolution of research over time [50,51]. Compared to databases like Scopus, WoS offers better consistency in cited references and is especially strong in the sciences and social sciences [52].

The search employed a Boolean query to find relevant literature on geomarketing and topics related to retail. The Topic Search (TS) targeted: (i) titles, (ii) abstracts, (iii) author keywords, and (iv) Keywords Plus. The search string applied was: TS = (“geomarketing” OR “geographic marketing” OR “location-based marketing” OR “spatial marketing”) AND TS = (“retail*” OR “consumer behavior” OR “shopping behavior” OR “store location” OR “customer segmentation”). The use of truncation (“retail*”) allowed the retrieval of word variants, ensuring comprehensive coverage. The search was performed on 15 March 2025, within the WoS Core Collection. It was limited to English language journal publications (Article OR Review) released from 2000 to 2025. Further filtering was conducted to restrict results to pertinent subject categories, including Business, Management, Economics, Geography, Regional and Urban Planning, and Computer Science–Information Systems. This process yielded 5159 articles. Due to export limitations in the WoS platform (a maximum of 500 records per export), the dataset was downloaded in multiple batches using the “Full Record + Cited References (CR)” format and subsequently merged into a single text file for analysis.

Although GIS was not explicitly included as a search term, we intended to examine whether geospatial concepts naturally surface in geomarketing research literature. To do this, we specifically tracked the presence and prominence of the following terms in our keyword co-occurrence and thematic analyses: Geographic Information Systems, spatial analysis, spatial analytics, location intelligence, geospatial, isochrone, catchment area, kernel-density, Huff model, map matching, spatial clustering, spatial interaction, spatial decision support systems, geocoding, and spatial econometrics. This curated list enabled us to provide an impartial evaluation of the actual degree to which GIS and associated terms are incorporated in conventional marketing research.

To ensure the quality and relevance of the bibliographic dataset, we implemented a structured pre-processing phase. This included eliminating duplicate records, rejecting incomplete entries, and manually reviewing articles that fell outside our conceptual framework, such as studies solely centered on transportation logistics, spatial epidemiology, or remote sensing, which, despite their spatial characteristics, do not correspond with our research emphasis on retail marketing and business-oriented spatial analysis. The initial search yielded 5159 records from Web of Science; however, the final curated dataset consisted of 4952 pages. This curated corpus, exported in BibTeX format, was utilized for all ensuing bibliometric and network analyses. The complete analytical process was executed utilizing the open-source {Bibliometrix} R-package, which is specifically tailored for extensive science mapping and bibliometric assessment [8].

Going further, the third step implied the use of the Bibliometrix framework to conduct a co-occurrence analysis of keywords that map the intellectual structure of geomarketing research. These analytical methods were employed to identify: (i) high-frequency terms, (ii) keyword networks, and (iii) thematic clusters. Moreover, we applied co-citation and bibliographic coupling techniques in order to reveal influential publications and inter-author collaborations. To better understand how the field has evolved, we visualized thematic changes over time using trend analysis and thematic mapping based on centrality and density. This helped us spot how certain topics have gained traction while others remain on the fringe.

In the final step, we took a closer look at what these patterns’ implications are. We explored not just what has been studied, but what has been missed. A key focus was on whether GIS-related concepts made their way into keyword networks and thematic clusters or if they were absent altogether. That gave us a clear picture of how spatial technologies are or are not being discussed in the context of geomarketing. These findings laid the groundwork for identifying research gaps, particularly in the newer landscapes of Retail 4.0 and 5.0. While AI, IoT, and Big Data are gaining attention, GIS still seems to be sitting on the sidelines. Through this structured, transparent bibliometric approach, our goal is to map the research terrain and point out where GIS might contribute more meaningfully to future marketing papers.

4. Results

The final dataset we completed our analysis on included 4952 publications spanning the last 25 years. The articles were pulled from 938 different journals and academic sources. Research in this area has grown at a steady pace of around 2.9% per year. As the results show, on average, each article was around 8.5 years old and has been cited nearly 38 times, which confirms the relevance and visibility of this field. Authorship is broad and diverse; over 10,000 researchers have contributed, with nearly a third of the studies involving international collaboration. That tells us the topic resonates globally. Most of the documents are peer-reviewed journal articles, but the dataset also includes book chapters and a few conference papers. Keyword-wise, the field is thematically rich, with close to 12,000 author keywords and more than 5700 Keywords Plus. This gives us a wide lens through which we can explore the geomarketing in retail. In short, it is a dynamic body of work, diverse enough to map big-picture trends but specific enough to spot where research still needs to grow.

4.1. Publication Trends

Publishing trends from 2000 to 2025, illustrated in Figure 3, indicate a consistent rise in research output during the early 2000s, succeeded by a brief fluctuation between 2007 and 2009. The global financial crisis of 2007–2008 had consequences on extensive budget reductions in retail and university research, postponing or diminishing new studies [53,54]. Moreover, after 2015, the number of publications continued to grow, and it reached its peak in the early 2020s. This happened due to technological advancements and the availability of data from online sources.

Although the raw count of articles for 2025 appears to decline, this is due to incomplete indexing at the time of data extraction (March 2025). To address this and prevent misinterpretation, we employed a linear forecasting model utilizing publishing data from the most recent five complete years (2020–2024). The model forecasts an annual production of roughly 442 articles in 2025. The projected number, indicated in green in Figure 3, highlights the idea that the research trend in geomarketing continues to follow an upward trajectory.

4.2. Knowledge Structure and Thematic Evolution

4.2.1. Comparison of Author Keywords vs. Keywords Plus

When researching the knowledge structure and theme evolution of geomarketing in retail, we examined both Author Keywords (DE) (Figure 4) and Keywords Plus (ID) (Figure 5) to have a better understanding of how topics are represented in this field. Author Keywords are manually chosen by the paper’s authors and thus reflect the specific focus of each study, whereas Keywords Plus are algorithmically generated by Web of Science from cited references, capturing broader, foundational concepts across the literature. We discovered a fascinating distinction between them. Author Keywords are typically more specific because researchers select them themselves. Terms such as “Retailing,” “Online Retailing,” “e-commerce,” “Pricing,” and “Supply Chain Management” rank highly among the domain keywords in our data, indicating a distinct emphasis on digital commerce and retail operations. Keywords Plus, conversely, are generated by Web of Science using citation patterns, highlighting wider ideas such as “Impact,” “Model,” or “Behavior.” This distinction leads to Author Keywords being more precise and discipline-specific, whereas Keywords Plus highlight more expansive theoretical notions.

This discrepancy can be attributed to three key factors. First, Keywords Plus terms are derived from highly cited references, meaning that they tend to capture foundational theories rather than emerging applications. Consequently, broader terminology such as “Impact” and “Model” prevail, while not being the central focus of current geomarketing research. Secondly, Keywords Plus phrases consolidate concepts from other disciplines, resulting in the predominance of general marketing themes over specific topics in retail or spatial analytics. Third, Author Keywords frequently appear in many fragmented forms (e.g., “Retailing,” “Retail,” “Store Location”), thereby diminishing their visibility in co-occurrence networks, whereas Keywords Plus amalgamate analogous terms via an automated process.

This divergence signals that while researchers are moving toward emerging, technology-driven themes, the indexed knowledge structure remains anchored in legacy paradigms, potentially delaying scholarly recognition of spatial or AI-integrated innovations.

4.2.2. Keyword Co-Occurrence Analysis

Co-occurrence of keywords network analysis is a method utilized to demonstrate the frequency of co-occurrence of distinct terms within the same texts across a dataset. The co-occurrence analysis reveals connections between concepts, assisting scholars in defining the intellectual framework and theme clusters within a discipline [55]. In our case, the network presented in Figure 6 encompasses the results of Keywords Plus and reveals two main research directions in geomarketing and retail. The first cluster, colored in red in Figure 6, is focused on consumer behavior and marketing theory. Terms such as impact, model, and behavior are connected, meaning that researchers focus on understanding consumer shopping patterns in conjunction with how businesses can influence their choices. Terms such as satisfaction, loyalty, and perception regularly emerge, underscoring the emphasis on customer experience. However, the relatively lower presence of technology-driven terms such as information and technology suggests that GIS-based consumer analytics has yet to be widely integrated into mainstream marketing research.

The second cluster, colored in blue, centers around management and business strategy, with strong links between terms like management, competition, price, and supply chain. This points to research dealing with retail operations, logistics, and competitive positioning. Although AI and geospatial analytics are increasingly used in business intelligence, this cluster shows no evidence of GIS-based methods being applied to support strategic decisions. This highlights a research gap, where integrating location intelligence and spatial modeling could strengthen areas like retail site selection, market positioning, and predictive analysis.

Figure 7 illustrates the results of the co-occurrence network based on Author Keywords (DE), and the most important takeaways are represented by how researchers frame their studies. “Retailing” is clearly the central theme, serving as the most frequent and connected keyword. This result was expected since we explicitly searched for papers that were related to retail; thus, this result confirms the dominant role of retail in the field’s discourse. The following terms, such as “consumer behavior,” “pricing,” “supply chain management,” and “competition,” reflect a strong interest in understanding how marketing strategies intersect with shopper preferences, logistics, and economic performance. We can also see an important emphasis on e-commerce and digital environments, with keywords like “electronic commerce,” “online retailing,” “internet retailing,” and “omnichannel retailing” closely linked to consumer behavior and retailing. This indicates a shift in focus toward online and hybrid retail models. Emerging topics such as “sustainability,” “machine learning,” and “forecasting” suggest that researchers are beginning to integrate advanced technologies and ethical concerns into retail analytics, though their presence is still peripheral. Surprisingly, GIS or spatial terms are absent from this map, which supports the observation that geospatial analysis is underrepresented in author-driven keyword framing, highlighting a potential gap in integrating location intelligence into current research discussions.

4.2.3. Thematic Evolution

Figure 8 shows how key topics in geomarketing and retail have evolved over time, based on Keywords Plus. The horizontal lines indicate the period during which a term was actively used, while the size of the blue bubbles reflects how often the term appeared; the bigger the bubble, the more frequently the term was used. We included in the trend topic visualization only keywords occurring at least 30 times in the dataset, ensuring the analysis focuses on thematically relevant and frequently used terms. Even from the beginning, we can say that terms like “governance,” “firms,” “power,” and “market” dominated the early years. This reflects a more traditional emphasis on business structures and the market’s predominant characteristics.

The mid-phase, encompassing 2014 to 2018, was a shift toward more consumer-oriented and performance-related themes, and terms like “consumer,” “performance,” “model,” “satisfaction,” and “behavior” began appearing more often. This indicates growing academic interest in measuring outcomes and modeling consumer dynamics. However, recent years (2019–2023) are most notable for the growth coming from tech-related and strategic keywords such as “online,” “impact,” “strategies,” “technology,” “future,” and “marketplace.” These show a clear move toward digital transformation, strategic analytics, and future-forward retail models. “Strategic analysis” and “technology” suggest a growing advantage for how digital tools shape decision-making. Even in the most recent years, terms like “GIS” or “location intelligence” do not show up, reinforcing our claim that spatial methods are underrepresented in current keyword trends.

Figure 9 provides a clear illustration of the temporal evolution of author-selected keywords within the geospatial marketing and retail literature. In contrast to Keywords Plus, which are generated automatically, Author Keywords represent what scholars deem essential to their studies, serving as a robust indicator of deliberate academic emphasis.

The earliest terms to emerge from 2004 to 2014 include “internet”, “internet retailing”, “supermarkets”, and “electronic commerce”. These point to the initial academic interest in the digitalization of retail and the rise of online shopping platforms. Going further in the mid-phase of our analysis, 2014–2018, we see the growth of more specific and operational concepts like “inventory management”, “consumer behavior”, “service quality”, and “customer satisfaction”. This shift suggests that researchers became more concerned with how consumers interact with retail systems and how to measure those interactions effectively. However, in recent years, 2019–2023, topics such as “omnichannel retailing”, “online retailing”, “logistics”, “retail operations”, and “sustainability” have surged in popularity. These reflect a new wave of research that combines digital transformation with broader operational and ethical concerns. Worth mentioning, “customer experience” and “supply chain management” have also gained traction, indicating an increased emphasis on end-to-end retail strategy. The appearance of “game theory”, “trust”, and “customer loyalty” as emerging terms implies an interest in predictive models and behavioral analysis, addressing the groundwork for AI-enhanced strategies. Despite the focus on logistics and channels, there is still no strong presence of spatial or GIS-related terms, which confirms once again that geospatial thinking is rarely framed explicitly by authors in this domain.

Figure 10 shows the thematic landscape of geomarketing research in retail, segmented in a two-dimensional matrix, with the degree of development (density) on the vertical axis and the degree of relevance (centrality) on the horizontal axis. As such, the terms are segmented into four quadrants: niche themes (high density, low centrality), motor themes (high density, high centrality), emerging or declining themes (low density, low centrality), and basic themes (low density, high centrality).

In Figure 10A, which is based on Keywords Plus, we see that the most important themes of the field revolve around foundational concepts like “model,” “impact,” “management,” “information,” and “online.” These appear in the basic themes quadrant, suggesting they are central to the literature but not deeply developed within specific subfields. Meanwhile, topics like “satisfaction,” “behavior,” “quality,” and “perceptions” are placed in the niche themes area, being well-developed but not widely connected to other clusters, indicating specialized discussions.

By contrast, Figure 10B, built from Author Keywords, shows a slightly different result. Here, “retail,” “quality,” and “satisfaction” emerge as motor themes, meaning they are both central and well-developed. This reflects the practitioners’ and researchers’ emphasis on these topics as both theoretically important and methodologically advanced. Interestingly, “online retailing,” “price,” and “electronic commerce” remain in the basic theme quadrant, highly relevant but still needing more structured theoretical frameworks.

Another notable point is that, while augmented reality and e-retailing are visible in the niche themes quadrant in Figure 10B, these terms are absent in Figure 10A, pointing to their emergence in author-driven language rather than citation-derived metadata. In particular, the implication of augmented reality in marketing is no longer just an emerging theme, as numerous retailers have started using such techniques, either to pioneer methods of marketing or shopping [56] or as an improvement to traditional marketing methods.

4.3. Research Influence and Academic Contributions

4.3.1. Core Journals (Bradford’s Law)

The bibliometric analysis of journal contributions presented in Figure 11, based on Bradford’s Law, introduced in 1934, gives us a clear view of how knowledge in the field is concentrated [57]. As we move along the curve, we can see a sharp drop in the number of articles published per journal, which means that just a handful of journals produce most content when it comes to publishing research on geomarketing in retail. In the shaded area, we find these core journals: the Journal of Retailing, the International Journal of Retail and Distribution Management, and the European Journal of Business Research. These are the most important sources for foundational work and new ideas in the field. The rest of the journals contribute too, although far less frequently, supporting Bradford’s idea that a small group of sources will always dominate the output in any scientific field.

4.3.2. Most Prolific Authors in Geomarketing in Retail Research

Table 2. The top 10 most productive authors based on publication count.

Rank	Author	Articles Published
1	Grewal D	29
2	Pantano E	26
3	Richards TJ	25
4	Sarkar B	21
5	Hübner A	20
6	Wood S	19
7	Kuhn H	17
8	Liu Y	17
9	Park J	17
10	Rabinovich E	17

Source: Authors’ representation using the Bibliometrix package in R software.

presents the top 10 active contributors to geomarketing and retail research. Grewal D. leads with 29 publications and is widely known for his work on omnichannel retailing, pricing strategies, and customer experience in digital environments [58,59,60]. Pantano E., close behind with 26 articles, explores the integration of emerging technologies, like AI and augmented reality, into retail spaces [61,62,63]. Richards T.J., with 25 contributions, focuses more on quantitative modeling, particularly on pricing and food retail [64]. Hübner A., and Kuhn H., have made consistent contributions to logistics and retail operations, while authors like Wood S., and Rabinovich E., often examine consumer behavior and store performance [65,66,67,68]. Together, this group of researchers shapes much of the contemporary discourse on how marketing, technology, and operations interact in retail.

4.3.3. Most Influential Research Institutions

Regarding the most prolific institutions that lead the research outcome (

Table 3. The top 10 institutions leading research output.

Rank	Institution
1	University of North Carolina (USA)
2	MIT Sloan School of Management (USA)
3	University of Mannheim (Germany)
4	University of Melbourne (Australia)
5	Tsinghua University (China)
6	University of Oxford (UK)
7	HEC Paris (France)
8	National University of Singapore (NUS)
9	University of Toronto (Canada)
10	University of Barcelona (Spain)

Source: Authors’ representation using the Bibliometrix package in R software.

), the USA and Europe dominate the field, with the University of North Carolina (UNC), MIT Sloan School of Management, University of Mannheim, and University of Oxford leading in retail and geomarketing research. Tsinghua University (China) and the National University of Singapore (NUS) (Singapore) show strong Asian research contributions. France (HEC Paris) and Spain (Barcelona) are also key contributors to retail consumer behavior studies.

4.3.4. Global Research Collaboration and Country Contributions

Figure 12 and

Table 4. Corresponding Authors’ Countries.

Rank	Country	Articles	Global Collaboration (%)
1	USA	1186	24.3%
2	CHINA	840	30.2%
3	UK	503	26.6%
4	GERMANY	240	28.3%
5	AUSTRALIA	182	44.5%
6	INDIA	177	24.3%
7	SPAIN	175	21.1%
8	FRANCE	162	36.4%
9	CANADA	129	44.2%
10	SOUTH KOREA	123	55.3%

Source: Authors’ representation using the Bibliometrix package in R software.

illustrate the global network of scholarly collaboration in geomarketing and retail research, highlighting both publication volume and international partnerships. Unsurprisingly, the leading contributor is the United States, with 1186 articles, though its international collaboration rate (24.3%) is modest in comparison to others. China follows with 840 publications and a slightly higher collaboration rate (30.2%), while the UK and Germany also demonstrate strong output and meaningful cross-border partnerships, with 26.6% and 28.3% international collaboration, respectively.

Australia and Canada, while producing fewer papers overall, stand out with high collaboration rates, 44.5% and 44.2%, indicating their research is often developed through international cooperation. South Korea tops the list in terms of cross-country collaboration, with over half (55.3%) of its publications co-authored with international partners, suggesting a deeply connected research approach. France and India contribute significantly as well, though with differing collaboration profiles.

4.4. Most Cited Papers and Foundational Research

Table 5 presents the foundational literature that has shaped the field of geomarketing and retail research. Intuitively, the most dominant journal is the Journal of Retailing, hosting six of the ten most-cited articles, with the other highly cited papers coming from journals that tackle similar topics, such as the Journal of Marketing. The top-ranked paper by Wetzels et al. [69] published in MIS Quarterly illustrates how advanced statistical techniques, particularly Partial Least Squares (PLS) modeling, are used to explain complex constructions in marketing research. It is not just a methodological paper; its widespread citation shows a broader need in the field for robust analytic frameworks. Sirdeshmukh et al. [70] and Srinivasan et al. [71] have both made lasting contributions by exploring how trust and loyalty shape customer behavior, especially in online environments.

This topic remains just as relevant today, as retail keeps shifting toward hybrid and omnichannel experiences. Their research shows how things like confidence in staff or ease of use on a website can directly influence whether people stick with a brand. Verhoef, et al. [72] offer a more recent perspective, arguing that retail is moving toward a unified customer journey that combines digital and physical touchpoints. It is a viewpoint that resonates strongly today, as companies lean heavily on AI, big data, and geolocation to personalize the shopping experience. MacKenzie and Podsakoff [73], on the other hand, bring us back to the basics. They show that even the best theories can fall apart without good research design, and that small flaws in how we ask questions can lead to big misinterpretations.

Table 5. Most Cited Papers.

Rank	Paper	Year	Citations (TC)	Avg. Citations/Year
1	Wetzels M, 2009, Mis Quart [69]	2009	2937	172.8
2	Sirdeshmukh D, 2002, J Marketing [70]	2002	2161	90.0
3	Mackenzie SB, 2012, J Retailing [73]	2012	1848	132.0
4	Verhoef PC, 2015, J Retailing [72]	2015	1322	120.2
5	Srinivasan SS, 2002, J Retailing [71]	2002	1217	50.7
6	Arnold MJ, 2003, J Retailing [74]	2003	1187	51.6

Source: Authors’ representation using the Bibliometrix package in R software.

Table 5. Most Cited Papers.

Rank	Paper	Year	Citations (TC)	Avg. Citations/Year
1	Wetzels M, 2009, Mis Quart [69]	2009	2937	172.8
2	Sirdeshmukh D, 2002, J Marketing [70]	2002	2161	90.0
3	Mackenzie SB, 2012, J Retailing [73]	2012	1848	132.0
4	Verhoef PC, 2015, J Retailing [72]	2015	1322	120.2
5	Srinivasan SS, 2002, J Retailing [71]	2002	1217	50.7
6	Arnold MJ, 2003, J Retailing [74]	2003	1187	51.6

Source: Authors’ representation using the Bibliometrix package in R software.

Taken together, these influential papers introduced new methods, captured timeless truths about consumer trust, and foresaw the digital transformation of retail. We believe this is the reason they have become go-to references for anyone trying to understand or expand the field of geomarketing in retail.

5. Discussion

This study sets out to evaluate the integration of GIS concepts into retail marketing through a bibliometric analysis of 4952 peer-reviewed articles published between 2000 and 2025. The results reveal a paradox: despite the increasing availability of spatial data and the expanding digitalization of retail environments [75,76], the academic incorporation of geomarketing—especially GIS-driven methodologies—remains limited and fragmented.

5.1. The Inconsistent Use of GIS in Retail Research

Both author-generated and system-generated keyword networks show a negligible presence of GIS or spatial-intelligence concepts, which supports our inquiry into the uncertain role of GIS within marketing research. Instead, topics like pricing [58], customer behavior [70,74], and omnichannel retailing [62,72] dominate the intellectual structure of the field. We deliberately excluded ‘GIS’ from our search to test whether spThank you for your observation regarding the reference numbering in Table 5. We would like to clarify that the numbering is not strictly consecutive because the table presents the papers ranked by total citations, while the references in the main text are introduced and discussed based on thematic relevance and interpretive flow, not by citation rank.

As a result, the reference numbers in Table 5 (starting from [69] onward) are intentionally non-consecutive to reflect their original position in the main text, where they have already been cited. We have ensured that all references remain consistent and properly linked throughout the manuscript.atial concepts would emerge organically, and for the most part, they did not. This suggests a missed opportunity to integrate spatial perspectives into foundational constructs such as customer satisfaction, loyalty, and market competition.

While scholars such as Wandosell et al. [77] and Manoharan and Sathesh [78] have shown how GIS can improve retail site selection and customer engagement, these contributions remain isolated. Additionally, the potential of spatial intelligence in influencing network planning and competitive strategy was highlighted in seminal geomarketing work by Libório et al. [38] and Banwo et al. [79]. Nevertheless, retail marketing theory has not yet fully utilized these findings. This suggests a methodological lag, in which research designs do not completely include the instruments that are available.

To further illustrate this disconnect, we conducted a systematic text mining procedure across all 4952 records in our dataset to determine how frequently the term “geomarketing“ is explicitly used in the literature. Using regular expression-based searches applied to the Title, Abstract, Author Keywords, and Keywords Plus fields, we identified only 22 documents that directly mention geomarketing or its variants (e.g., “geo-marketing”). This remarkably low count, i.e., less than 0.5% of the corpus, confirms the marginal conceptual visibility of geomarketing as a distinct research theme. Despite the growing operational use of spatial tools in retail, the academic vocabulary has not kept pace, and spatial terminology has yet to coalesce into a widely recognized domain within marketing scholarship.

5.2. Interdisciplinary Integration Opportunities

Our mapping revealed clear thematic blind spots. Despite Retail 4.0/5.0′s data-driven imperatives [80], location-aware consumer decision models remain underdeveloped. The underrepresentation of GIS concepts may reflect multiple barriers: high implementation costs, lack of faculty training, publication in practitioner or IS outlets, and weak alignment with core marketing theory. Yet these gaps also highlight fertile ground: AI-driven personalization, omnichannel operations, and sustainability strategies could all be significantly enhanced through embedded spatial decision support.

Geographic information systems significantly improve analytics in areas such as store location optimization, spatial customer segmentation, urban retail strategy, and hyper-personalized marketing—fields where the principles of Retail 4.0 and 5.0 are becoming increasingly relevant [81,82]. The viability of integrating geographical data infrastructures with predictive analytics has been evidenced in related fields such as policy forecasting and sustainability evaluation [83], highlighting the methodological capabilities of spatial intelligence. Nevertheless, spatial analytics is predominantly seen as an operational toolset, rather than a theoretical framework that might influence decision-making structures in marketing. According to [84,85], geographical decision support systems offer a structured methodology for integrating geographical data into decision-making processes. However, their implementation is still constrained in strategic marketing and retail analytics, where spatial thinking has not yet established itself as a fundamental analytical feature. This signifies a distinct necessity to integrate SDSS frameworks into marketing models to facilitate dynamic, real-time, location-aware retail strategies. Researchers, including [86,87], underscore the incorporation of developing technologies, including AI and big data, into retail systems, while predominantly neglecting the spatial aspect. The current findings underscore this disparity and advocate for the acknowledgment of GIS as a fundamental enabling technology rather than an ancillary analytical instrument.

Figure 13 illustrates a conceptual synthesis of the findings explored in this section. This highlights the potential to integrate GIS and SDSS into new retail marketing trends—specifically AI and predictive analytics, omnichannel strategy, and sustainability. Even though these innovation areas are prominently featured in recent literature, they have not yet fully integrated spatial intelligence as a fundamental analytical element. We thus propose emphasizing GIS/SDSS as a connecting tool for upcoming research and practical applications.

Supplementary Analysis: Targeted Keyword Occurrence of Spatial Concepts

To further substantiate this observed gap, we conducted a targeted keyword tracking analysis during the bibliometric mapping process. This supplementary procedure systematically identified the occurrence of sixteen core GIS-related terms presented in

Table 6. Occurrence of Spatial Concepts in Author and Index Keywords.

Keyword	Total Occurrences
GIS	277
spatial analysis	8
Geographic Information Systems	5
spatial interaction	5
Huff model	1
geospatial	1
spatial econometrics	1
catchment area	0
geocoding	0
isochrone	0
kernel-density	0
location intelligence	0
map matching	0
spatial analytics	0
spatial clustering	0
spatial decision support systems	0

across both author-generated and indexer-assigned keywords.

The results were striking, while the term GIS appeared 277 times, nearly all other terms, including catchment area, spatial clustering, and isochrone, occurred fewer than five times, or not at all. These findings, summarized in Table 6, confirm that geospatial concepts, although widely used in practice, remain peripheral in mainstream marketing research. Rather than contradicting our hypothesis, this sharp absence of spatial keywords reinforces it, underscoring the conceptual isolation of spatial thinking in the marketing knowledge structure.

This supplementary analysis reinforces our central argument: spatial analytics have not yet achieved intellectual integration within the marketing research ecosystem, even as their practical relevance increases.

5.3. Consequences for Research and Practice

The absence of geospatial orientation in prevailing academic discourse has tangible consequences. Retail models that neglect spatial behavior are likely to become obsolete in the context of mobile tracking, location-based personalization, and smart city logistics [90]. The integration of GIS facilitates the creation of contextually aware marketing strategies, incorporating consumer flows, accessibility, and urban infrastructure into the decision-making framework [91].

Furthermore, the ethical and sustainability emphasis of Retail 5.0 [92,93] inherently corresponds with geographic data applications. Location intelligence helps mitigate carbon footprints by enhancing supply chains and pinpointing underrepresented markets. Nonetheless, this potential has not been extensively investigated within marketing literature, a conclusion supported by Skoutas et al. [33] in their spatial optimization research.

This study validates and expands upon current apprehensions that GIS is not only underutilized but also conceptually under-integrated within the marketing research framework. The future necessitates more multidisciplinary collaboration among marketing scientists, geographers, and data analysts, utilizing the complete potential of spatial intelligence to transform both theory and practice.

5.4. Managerial and Policy Implications

The restricted conceptual significance of GIS in academic marketing discussions reflects overlooked potential in practice. Marketers and urban planners might utilize location knowledge to enhance hyperlocal promotions, refine sustainable zoning, and optimize last-mile logistics [10,94,95]. Instead of concluding marginality, our findings emphasize the necessity for intentional efforts, both theoretical and practical, to integrate GIS more systematically into the marketing and retail strategy framework. Marketers can utilize location information to categorize consumers by demographics and mobility patterns, uncovering unexploited potential for location-based advertising and improved delivery routes.

Policymakers and urban planners, on the other hand, can benefit from deeper retailer collaboration by aligning commercial zoning, public transportation development, and sustainability goals. An excellent example would be the fact that businesses can openly provide municipalities with aggregated foot traffic or store visitation data so that they may plan better infrastructure improvements, thus benefiting both public services and local commerce.

Furthermore, the underutilization of spatial analytics in academic research, as indicated by the infrequent occurrence of GIS-related keywords and the sparse recognition of geomarketing as a distinct theme, constitutes a significant oversight for retail strategies, especially in a time when physical and digital channels are becoming increasingly integrated. This underscores the considerable potential of SDSS-based frameworks in guiding sustainable retail zoning, urban infrastructure development, and regulatory advancement. Integrating GIS into theoretical frameworks and practical implementations enables scholars and practitioners to acquire more comprehensive and flexible marketing insights.

6. Study Strengths and Limitations

This study’s main strength is its rigorous application of bibliometric methods to examine the thematic and structural development of geomarketing literature over the last 25 years. The data retrieved from the Web of Science Core Collection offered a high-caliber dataset for analyzing trends in keywords, citations, and authorship networks. The Bibliometrix R tool enabled the extraction of visual and statistical insights, allowing for a comprehensive analysis of intellectual structure and thematic trends. This research enriches the literature by correlating geomarketing patterns with significant retail advancements, including the advent of AI, omnichannel strategies, and ethical consumer engagement. Through the contextualization of keywords, co-occurrence, and thematic mapping alongside an auxiliary keyword-tracking analysis, we illustrate that spatial intelligence has not yet been naturally integrated into marketing research, despite its proven benefit in retail operations.

However, the study has several limitations. The omission of significant articles from alternative databases, such as Scopus, or high-impact works from conference proceedings, may have stemmed from an exclusive reliance on Web of Science. The restriction to English-language publications also limits the inclusion of geomarketing studies from non-English speaking regions, particularly in rapidly urbanizing markets where spatial data is becoming increasingly essential. This analysis utilized metadata rather than the whole content of publications. As a result, it may not capture the complexities or actual implementation of GIS methodologies in specific case studies. Subsequent studies may strengthen this foundation by integrating bibliometric analysis with full-text mining and expert interviews to investigate the practical application of GIS across various industries.

Despite these constraints, the study offers a reproducible and transparent methodology in order to investigate the extent to which geomarketing is manifested in marketing research. It also prompts a reevaluation of bibliographic indexing processes and keyword rules to guarantee that emerging interdisciplinary methodologies, such as GIS, receive the academic recognition they merit.

7. Conclusions

This study originated from the observation that, despite the increasing importance of digital technologies in retail strategy, the degree to which GIS has been conceptually incorporated into mainstream marketing literature is unclear and warrants rigorous examination. Understanding the historical development of this field and recognizing current deficiencies can enhance the amalgamation of geographical sciences and marketing research. By applying a bibliometric approach to 4952 peer-reviewed documents published between 2000 and 2025, we explored how spatial intelligence has (or has not) been embedded in the intellectual structure of retail marketing research.

Although prior literature [28,31] has emphasized the value of GIS in applications like market segmentation, consumer profiling, and site selection, the function of spatial intelligence has not yet been systematically incorporated into marketing theory. This study indicates that these concepts are marginal within the wider marketing discourse. GIS-related terminology seldom features in author keywords, Keywords Plus, or thematic clusters, corroborating our prediction about their peripheral conceptual significance. Conversely, existing research paradigms remain concentrated on price, consumer satisfaction, digital transformation, and performance analytics, indicating that spatial methodologies have not yet impacted the theoretical foundation of marketing research.

Since 2015, there has been a progressive theme shift, marked by an increasing focus on artificial intelligence, real-time data, and sustainability. These trends indicate a willingness to adopt more multidimensional methodologies, facilitating a reinvigorated interaction with GIS, not merely as a mapping instrument, but as an integral component of a comprehensive analytical framework that encompasses geographic, economic, and behavioral aspects, despite such applications being conceptually underrepresented in existing literature.

The auxiliary keyword tracking analysis further reinforces this point. Of the 4952 articles reviewed, only 277 explicitly referenced “GIS,” and related spatial concepts such as “spatial analytics,” “geocoding,” or “location intelligence” were either absent or marginal. This absence is not an error, but an empirical insight into the conceptual blind spots within retail marketing scholarship.

Future geomarketing research can benefit from enhanced interdisciplinary integration, especially by incorporating GIS capabilities into marketing models that focus on real-time customer behavior, hyperlocal strategies, and urban sustainability. By strengthening connections between geography, data science, and marketing, GIS might transition from a functional tool to a fundamental theoretical foundation of Retail 4.0 and 5.0.

Current findings not only identify the existence of this conceptual gap but also emphasize the unrealized potential of geomarketing to act as a cohesive catalyst for innovation within the retail sector.