The Different Phases of the Omnichannel Consumer Buying Journey: A Systematic Literature Review and Future Research Directions

Abstract

In recent decades, retail has been faced with a challenging scenario, resulting from the digital transformation driven by advances on the internet that has transformed retail business models, especially in commercial transactions, giving consumers a new shopping experience. However, it has been a challenge for retailers to maintain the same shopping experience in different marketing channel formats, mainly with regard to understanding the consumption habits of consumers and what can influence their purchase decisions. As far as is known, the buying process is not only about the act of buying. There is an entire buying journey that must be studied to ensure customer satisfaction from the first contact to the after-sales experience. In this context, this article identifies and analyzes the stages of the omnichannel retail purchase journey from the consumer’s perspective. To achieve the proposed objective, this study was conducted through a systematic literature review in accordance with the SPAR-4-SLR protocol. The results present several analyses that demonstrate the complexity involving the consumer’s perspective in the purchase decision process. The insights show how complex it can be to for companies to manage the purchase journey due to the individuality of each consumer. The main finding shows that most marketing studies do not address the omnichannel consumer journey and, when they do, they focus on specific parts to the detriment of a more holistic view of the buying process. The originality of this article lies in the fact that few studies on omnichannel retail have focused on the integration of all touchpoints using an empirical longitudinal evaluation.

1. Introduction

The literature on electronic commerce transactions highlights that the digital transformation has increased the complexity of research on retail by bringing to light a purchase journey that is neither fixed nor sequential [1]. The challenges are even greater because consumers respond differently at each stage of the purchase process [2], and due to the emergence of different types of consumers, retailers are faced with the integration of multiple channels, as in omnichannel retail [3].

In the current context of e-commerce, given the emphasis on today’s digital environment, consumers can begin the purchase process in one channel, continue the purchase process using another and use a third one to complete the purchase. Retail companies have become increasingly interested in understanding this process [4]. Therefore, an omnichannel strategy has become a way to offer a complete experience [5,6] in the integration of marketing channels by introducing a unique shopping experience [7].

The growth of omnichannel retail (OCR) is causing consumers to change their buying habits and behavior [8]. Thus, omnichannel retail has become important, with retailers providing the same experience throughout the shopping process across all channels [9]. Additionally, consumer behavior changes in terms of appeal and impact according to the stage of purchase. Therefore, it is imperative to gather evidence by examining omnichannel integration services at a disaggregated level [10].

The different purchase stages are represented in the consumer journey, which is a sequence of steps that consumers must follow to purchase a product or service [11]. Recognition of the consumer journey as a means of enhancing and managing the consumer experience has driven the recent growth of consumer journey literature [12]. Research that portrays the consumer journey has made broad conceptual interpretations, citing at least seventeen different alternative terms in the marketing and retail literature [13]. There is also a lack of consensus regarding the framing of the consumer journey stages [13] and the link between them [2] through isolated and limited insights into the purchase process in integrated marketing channels. In this article, the consumer journey represents consumer engagement with all touchpoints used by retail involving all decision-making steps in the purchase process, which encompasses search (pre-purchase), purchase and post-purchase [13,14]. The aim of the consumer journey concept is to gain a better understanding of consumers’ perspective during the buying process and insight into their experiences and how past experiences can influence future buying decisions [11,14].

The (i) pre-purchase stage involves the first stage of consumer decision making when the consumer decides to visit any marketing channel available [1], which can occur for several reasons and represents a decision moderated by intrinsic consumer heterogeneity [15]. This step involves aspects such as how the consumer seeks the information necessary for the purchase, the barriers to omnichannel adoption and the specific behavior that shapes the purchase intention. The (ii) purchase stage represents the stage where consumers decide to convert a physical or online visit into a purchase [1,15]. This step includes information about transactions or products, choosing the marketing channel, handling orders or providing services and segmenting consumers based on their buying patterns [2]. Finally, the (iii) post-purchase stage involves everything that occurs after the actual purchase, including the behavioral consequences related to the quality of the service provided, such as recommendations about a company to others, complaints and failures during the purchase and repeat purchase intention, which represent components of customer satisfaction and loyalty [16].

The originality of this article lies in the fact that most marketing research does not address the omnichannel consumer journey and, when it does, it focuses on specific aspects to the detriment of a more holistic view of the purchase process [13]. In view of a previous analysis to identify a gap in the literature to justify the need for this study, it was found that few studies on omnichannel retail have focused on the integration of all touchpoints using an empirical longitudinal evaluation. Unlike previous research on omnichannel consumer behavior [9], this article presents a Systematic Literature Review (SLR) to assess the OCR purchase journey, recognizing the multidimensional and holistic nature of the process with different points of contact throughout the pre-purchase, purchase, and post-purchase stages [17,18].

In recent years, research conducted in the retail sector has shown that the omnichannel theme has been a hot topic [6,19]. Despite the recent increase in the number of studies on omnichannel retail, research conducted in the consumer-centric perspective remains limited and sporadic [20]. Given the strategic importance of the topic and the need for a more comprehensive understanding of the subject [9], the primary purpose of this article is to evaluate the stages of the purchase journey in OCR from the consumer’s perspective by conducting an SLR involving empirical work published up to the end of 2021.

2. Materials and Methods

This article applied the SPAR-4-SLR (Scientific Procedures and Rationales for Systematic Literature Reviews) protocol to conduct the research in three sequential stages [21], as detailed below. The research was carried out without a time limitation up to 2021. Given the criteria for inclusion and exclusion of articles, as shown in Figure 1, the first study was only identified in 2016, indicating the contemporary nature of the topic. To aid the interpretation of the evolution of the topic, the study was divided into two triennia: the first from 2016–2018 and the second from 2019–2021. This division allowed a detailed analysis of the textual corpus to find insights into the purchase stages in recent years.

Figure 1 presents the flowchart with the research stages, highlighting the selection and eligibility criteria of the articles that are part of the textual body. The first step consisted of the search string, where 475 articles were identified in the Scopus and WoS databases. The exclusion criteria for duplicated articles excluded 120 (2x) papers from both databases. Articles not published in English, without DOI, from journals without SJR quartiles, and not published until the end of 2021 were also excluded. Given the importance of highlighting the consumers’ perspective in the study, a few other exclusion criteria were applied to obtain the textual corpus of 43 articles.

2.1. Assembling

The first stage of the research is called Assembling, which covered the identification of previous empirical research on the omnichannel retail domain from the consumer’s perspective, a field of broad academic and managerial interest that has become sufficiently mature to warrant a review. This article focuses on scientific papers published in journals listed in Scopus and Web of Science (WoS) databases, given the complementarity between them, guaranteeing comprehensive and exhaustive coverage of the research domain [22,23]. Three groups of keywords correlated with the Boolean operator “and” were used. The (i) first group of keywords involved variations of the term omnichannel, the (ii) second group was related to retail and, finally, the last (iii) related to consumer behavior, touchpoints during the consumer journey and consumer experience. Figure 1 shows all the selected keyword groups. The textual corpus was selected based on articles published in journals listed in the Scimago Journal; Country Rank database (Q1–Q4), a measure of the scientific influence of academic journals widely used in the literature despite recognized limitations [24,25]. This work did not involve a search for articles in specific journals in the field, recognizing that the diversity in the selection associated with academic rigor can generate significant advances in theory and practice, given the multidisciplinarity of the subject in question [26].

2.2. Arranging

The second step is called Arranging, which covers the organization and selection of articles through the establishment of eligibility criteria. The inclusion criteria covered only articles published in academic journals, thereby excluding conference proceeding papers, consultancy reports, textbooks, master’s and doctoral dissertations, government reports and non-peer-reviewed works. Other inclusion criteria required the presence of a Digital Object Identifier (DOI) and publication in English up to the end of 2021 in journals listed in the SJR ranking, regardless of the ranking quartile. Furthermore, the abstract and, when necessary, the full text of each article were evaluated considering four additional inclusion criteria: (i) articles with empirical evaluation and not exclusively theoretical or including expert opinions; (ii) investigation involving omnichannel retail to the detriment of research that exclusively used other types of marketing channels; (iii) correlation between the keywords used in the search; and (iv) studies conducted from the perspective of the consumer rather than the retailer. The selection of articles implied the involvement of all the authors, one of them actively participating in the tie-breaking of divergent opinions in the first round of evaluation. Details on the application of eligibility criteria to arrive at the final list of 43 works are presented in Appendix A and Figure 1. A full list of the 43 collected papers is presented in Appendix B.

2.3. Assessing

The third and last step of the research is called Assessing and involves the evaluation and reporting of the articles selected in the previous stage. This study adopted a hybrid approach by combining a structured review approach with a bibliometric review to achieve the proposed research agenda.

The structured review was conducted to assess how the path-to-purchase of omnichannel consumer evolved over time, including the assessment of the development of the theme in the theoretical, empirical and methodological fields [21]. In the structured review, a review protocol guided the content analysis of the selected articles, as detailed in Appendix A. The bibliometric review followed a scientometric approach [24] complementing the insights of the structured review focused on holistic investigation, associating documents, researchers, journals and keywords [27]. In addition to identifying, tabulating and presenting descriptive information regarding the publication (authors, journal, year of publication, general objective, dependent variables, geographical area of the research data, and retail segment under study), the applied methodology (methodology of analysis and data collection), the theory underlying the empirical work and the main findings were also evaluated. The selected works were also classified according to the purchase stage directly related to the main theme of the investigation: (i) evaluation of the pre-purchase stage (12 articles), (ii) evaluation of the purchase stage (16 articles) and (iii) evaluation of the post-purchase stage in omnichannel retail (15 articles) [17,18]. The classification followed key criteria and included the participation of all the authors to ensure reliability and consensus in the process. To this end, the following software was used: Bibliometrix, Iramuteq, VOSviewer and Voyant Tools. The review was conducted considering all the articles in the textual corpus and the clusters based on the stages of the purchase journey.

3. Results

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

3.1. Structured Review

3.1.1. Publication Trends

Although an initial time limitation filter was not applied for the publication date of the articles, the review captured articles published only from 2016 onwards, which demonstrates a gap and emergence of the research topic. Considering the two triennia that were evaluated (2016–2018 and 2019–2021), the growing interest in the field can be seen in Figure 2, as 72.09% of the articles were published in the latter three-year period, with over 10 publications per year. The analysis of the number of publications according to the omnichannel consumer’s purchase stage shows that there was greater academic interest in research carried out in the purchase stage, which was 4.33 times greater in the last three years when the two periods were compared. Moreover, the growth trend in the number of published articles related to after-sales research on omnichannel retail is highlighted in Figure 2.

The second triennium saw significant advances in the areas of technology through the influence of augmented reality on the purchase process [28] and the consumer experience and its influence on purchase intention [20,29]. Given these conceptual foundations, the literature on the pre-purchase stage is no longer the most frequently studied, as it had been in the first triennium, while studies on post purchase showed a considerable increase of approximately 175% compared with the first triennium. Several issues in omnichannel retail, such as what information online retailers should disclose to minimize the gap between consumers’ expectations and their perceptions when comparing online and offline channels [30], the impact of flexibility in channel choice and the logistics service quality (LSQ) in the perception of customer satisfaction [31] stood out in post-purchase studies.

The articles were mostly based on retail, in general, but over the years, some retail sectors stood out in terms of the number of papers published. As shown in Figure 3, the clothing sector (fashion) has the largest number of articles published at each purchase stage of the consumer journey (n: 13). Health and lifestyle retail only has articles referring to the purchase stage (n: 2), which highlights the possibility of specific studies on the pre-purchase and post-purchase stages. Department stores were involved in two articles regarding the purchase stage and another for the post-purchase stage (n: 3), enabling the study of the pre-purchase stage to understand the variables that influence the purchase intention of products in this sector. Articles that address food companies have much to explore in the pre-purchase and purchase stages, which can influence the purchase intention of a meal to eat at the point of sale or to take home, revealing a gap in the literature on that specific sector. The articles classified in the general category (n: 24) were studies that did not use a specific retail sector in their research to analyze and contribute to the theme or were articles that studied more than one sector at the same time. In this way, the results achieved can be analyzed in depth in each sector to gauge whether the information can be corroborated in specific retail sectors.

Regarding the quality of the publications according to the SJR ranking, the 43 selected articles are from journals in the 1–3 quartile, involving 32 different journals, which demonstrates, once again, the coverage of the selected literature.

Table 1. Major publishing journals.

Journal	Total Publication Count	Share of Publications (%)	Total Citation Count
Sustainability	4	9.30%	206
International Journal of Retail and Distribution Management	3	6.98%	130
Spanish Journal of Marketing—ESIC	3	6.98%	53
Journal of Theoretical and Applied Electronic Commerce Research	2	4.65%	6
Journal of Services Marketing	2	4.65%	30
Journal of Retailing and Consumer Services	2	4.65%	53
Asia Pacific Journal of Marketing and Logistics	2	4.65%	122

presents the journals with the highest number of publications in the corpus.

Table 1 shows that the journal with the highest number of publications on the subject is Sustainability, responsible for the publication of 4 (9.30%) out of 43 selected articles. The Sustainability journal makes contributions to the topic to enable a better understanding of the impact of channel integration in the selection of shopping methods in omnichannel retail [7]. The other two journals emerged as two highly sought-after means of publishing papers and obtaining knowledge of academic research in marketing (SJM) and business, consulting, and management (IJRDM). The articles published in the IJRDM contribute to the study of satisfaction in omnichannel retail [31] and purchase intention in omnichannel retail [32]. Four other journals follow these two as the most frequent means of publication of research on the consumer journey in omnichannel retail. In contrast, journals like the International Journal of Research in Marketing, the Journal of Business Research and the Journal of Marketing Channels include only one relevant paper each, possibly due to the subject being an emerging topic in marketing studies.

3.1.2. Correlated Theories

This section presents the prominent theories used in studies related to the consumer purchase journey in OCR and the various academic fields to which they belong, providing an opportunity to understand the conceptual domains that respond to new managerial challenges [26]. Table 2 presents the main theories that underlie some selected articles. Many of the selected empirical articles (39.53%—17/43 articles) present an underlying theory, many of them using more than one theory to explain the managerial reality (52.94%—9/17 articles) and many theories cited in only one work (68.75%—11/16 theories), which suggests that the topic requires different theoretical foundations. Over time, the academic literature has witnessed an incremental use of theories that underpin empirical work. When comparing the first triennium, in which only 5 works were published with an underlying theory, the second triennium presents a 140% increase in empirical works that at least mention one of the theories.

The theory of planned behavior (TPB) [33,34] appears as the dominant theoretical lens, followed by commitment-trust theory (CTT) [35], innovation diffusion theory (IDT) [36], unified theory of acceptance and use of technology [37,38], and theory of reasoned action [39,40].

Table 2. Major theoretical foundations.

Theoretical Foundation	Theory	Pre-Purchase	Purchase	Post-Purchase	Total	References
Psychology; Sociology	Theory of planned behaviour (TPB)	2	2	1	5	[7,41,42,43,44]
Marketing	Commitment-trust theory	1	2	0	3	[7,29,44]
Psychology; Sociology	Innovation diffusion theory (IDT)	3	0	0	3	[20,45,46]
Psychology; Sociology	Theory of reasoned action	2	0	0	2	[42,43]
Business; Management	Unified Theory of Acceptance and Use of Technology (UTAUT)	2	0	0	2	[32,47]
Psychology; Sociology	Categorization theory	0	0	1	1	[48]
Psychology; Sociology	Construal level theory (CLT)	0	1	0	1	[49]
Business; Management	Dynamic capability theory (DCT)	0	0	1	1	[50]
Psychology; Sociology	Expectation-confirmation theory (ECT)	0	0	1	1	[30]
Psychology; Sociology	Experiential value theory	1	0	0	1	[29]
Psychology; Sociology	Flow theory	1	0	0	1	[29]
Business; Management	Information theory	0	0	1	1	[30]
Marketing	Means-end theory	0	1	0	1	[7]
Psychology; Sociology	Prospect theory	1	0	0	1	[46]
Psychology; Sociology	Social identity theory	0	0	1	1	[51]
Business; Management	Technology acceptance model (TAM)	0	0	1	1	[41]

Table 2. Major theoretical foundations.

Theoretical Foundation	Theory	Pre-Purchase	Purchase	Post-Purchase	Total	References
Psychology; Sociology	Theory of planned behaviour (TPB)	2	2	1	5	[7,41,42,43,44]
Marketing	Commitment-trust theory	1	2	0	3	[7,29,44]
Psychology; Sociology	Innovation diffusion theory (IDT)	3	0	0	3	[20,45,46]
Psychology; Sociology	Theory of reasoned action	2	0	0	2	[42,43]
Business; Management	Unified Theory of Acceptance and Use of Technology (UTAUT)	2	0	0	2	[32,47]
Psychology; Sociology	Categorization theory	0	0	1	1	[48]
Psychology; Sociology	Construal level theory (CLT)	0	1	0	1	[49]
Business; Management	Dynamic capability theory (DCT)	0	0	1	1	[50]
Psychology; Sociology	Expectation-confirmation theory (ECT)	0	0	1	1	[30]
Psychology; Sociology	Experiential value theory	1	0	0	1	[29]
Psychology; Sociology	Flow theory	1	0	0	1	[29]
Business; Management	Information theory	0	0	1	1	[30]
Marketing	Means-end theory	0	1	0	1	[7]
Psychology; Sociology	Prospect theory	1	0	0	1	[46]
Psychology; Sociology	Social identity theory	0	0	1	1	[51]
Business; Management	Technology acceptance model (TAM)	0	0	1	1	[41]

In general, most theories are mentioned in articles related to the pre-purchase stage of omnichannel retail (66.67%). The results indicate that this is because the theories are related to the consumer’s intentions and behavior, in most cases, before the purchase decision. As well as innovation and technology, they also act as influencers in the decision-making process of whether or not to make a purchase, either through the perceived value during webrooming [46] or through flexibility, customization and integration between channels [20].

The dominant theory (TPB) is widely used in marketing research and has provided the theoretical basis for understanding consumer behavior, suggesting that people are more likely to define their intentions and adopt behaviors related to these intentions when they feel they can achieve the expected result [33] and when they feel they have control over the situation given the transparency of information in the purchase journey [34].

Five articles from the textual corpus use the theoretical foundation of TPB [7,41,42,43,44], but only two of them use this theoretical basis for the development of hypotheses and to understand emerging managerial challenges, such as the service associated with buy-online-pickup-in-store (BOPS) [41] and perceived behavioral control in the selection of the marketing channel [7].

The main conceptual domains captured by this work, including TPB, involve theories and meta-theories that demonstrate the relevance of information science for marketing, as they explain the behavior involved in seeking information and adopting technology [52].

Such theories are considered critical for the development of omnichannel retail and address an important challenge for management literature, since the perceived connectivity of touchpoints, a most neglected dimension in the integration of the omnichannel consumer journey, can have a more significant influence on the consumer experience than the consistency of retail elements [53].

The categorization of theories according to their origin [54] demonstrates the portion of theoretical contribution that disciplines not directly related to marketing have brought to omnichannel retail.

3.1.3. Main Findings

This section shows the main insights obtained for each purchase stage in the consumer journey in omnichannel retail.

Pre-Purchase

Two main themes compose this cluster: adoption [42,43,55] and purchase intent in omnichannel retail (OCR) [20,28,29,32,45,46,47,56,57]. Generally, research has shown that marketing communication strategies regarding the attributes and benefits of OCR require customization to accelerate the adoption of this new approach by different groups of consumers [42,43]. Additionally, consumers do not adopt the innovative characteristics underlying the omnichannel approach, but variables related to the adoption of technological innovations explain the behavioral intention to adopt OCR, such as perceptions of utility, ease of use, compatibility with lifestyle, values, personality traits and previous consumer experience [42,43].

Consumers who tend to avoid cognitive effort consider the need for information integration across channels as a critical barrier to OCR adoption intent [42], while among high-frequency (self-reported) users, the expansion of OCR adoption depends on their perceived ease of use [43]. Among young Indians, price inconsistency is the dominant barrier to the omnichannel approach but given the interrelationship between the various barriers to OCR adoption, treating multiple critical issues with caution can drive the adoption process, which has limitations at different structural hierarchical levels [55].

Most studies that address purchase intention in OCR focus on consumers’ experiences, their preferences, and expectations regarding the advancement of information and communication technologies [20,47], some using the theoretical basis of the Unified Theory of Acceptance and Use of Technology (UTAUT2) with additional elements [32,56]. One of these additional elements, the construct called personal innovation, has proved to be a strong predictor of purchase intent in the omnichannel context [20,56] since consumers are more innovative in what concerns new technologies, which have pronounced purchase intent using different channels and devices in an omnichannel environment. Another additional element to UTAUT2 is perceived safety or perceived risk, which involves uncertainties and potential adversity that are incurred during the purchase process in relation to the lack of transparency, non-uniformity between channels, insecurity of information and performance failure [46]. The results are contradictory and demonstrate that perceived risks may be negatively predicted [32,46] or do not affect omnichannel purchase intention [47,56]. However, the consistency and personalization of the omnichannel experience [20], as well as the opportunity to attract conservative consumers to a more interactive scenario through direct contact in the physical store [56] can reduce the risk perceived by the omnishopper.

Two elements proposed by UTAUT2, effort expectation and performance expectation, proved to be determinants in the OCR purchase intention [32,47,56], while the social construct, which is also a variable in the UTAUT2 model, presented contradictory results. Some studies have shown that OCR is an emerging approach, and the use of technology is not conditioned by the opinion of others [32,56], which contrasts with other research results that demonstrate the social influence on purchase intention in an omnichannel environment [29,47]. Other variables of the UTAUT2 model, such as facilitating conditions, hedonic motivation, habit and price value have also shown non-generalizable results on purchase intention in OCR [32,47,56].

Finally, surveys that assess purchase intent also support the importance of operational issues to serve the omnichannel consumer such as standardization of product codes across all channels, real-time order tracking, and location-based promotions [45], as well as the intention to use different technologies and digital practices in the physical store belonging to the OCR [57]. Satisfaction with the augmented reality experience, for example, showed a positive effect only on mobile purchase intentions. However, the brand advocacy formed after the technology experience showed a mediating effect on the relationship between satisfaction with the technology experience and purchase intention was extended to mobile and offline channels [28]. The flow in omnichannel consumer experiences plays a mediating role in both physical and online purchase intentions to create a seamless experience for omnishoppers [29].

Retailers should define not only which technologies they will invest in, but also how they will encourage their acceptance, as technology adoption is an important predictor of purchase intent [29,56], and the virtual environment must consider interface design, trust, privacy, consumer peer interaction, relationship commitment and personalization [29]. It is also important to note that consumers’ purchase preferences and expectations change along with technology enhancements, and the perceived compatibility when changing purchase channels is a dynamic variable [20,46]. Furthermore, despite the emergence of online and mobile technologies as part of the shopping experience, consumers are still aware of and affected by the physical retail environment that surrounds them, so that diversity, aesthetics, and convenience in physical stores remain on the marketing manager’s agenda [29].

Purchase

Two main themes compose this cluster: channel selection [7,10,44,58,59,60,61,62,63] and omnichannel consumer segmentation [49,64,65,66,67,68,69]. Consumer perspective research has shown that multiple factors are at play in channel selection, such as the type of product [58], intrinsic characteristics of the countries of origin of the research [60] and some demographic and behavioral variables related to the omnichannel consumer [7,59,68]. While consumers tend to favor OCR for purchases of durable products that are difficult to compare online [58], retailers need to be alert to respond to pressure for distinct local responsiveness, as building loyalty to the brand is critical among Japanese consumers [60]; Korean consumers place more value on the functional aspects of omnichannel service [60]; and U.S. consumers with higher education levels or lower incomes have a high perceived risk in relation to OCR [7].

Among the factors empirically evaluated in an OCR environment, perceived risk has been recognized as a prominent driver of channel preference. Generally, perceived risk has a negative impact on consumer channel selection intention [44,59], but this choice does not generally occur in OCR. For example, perceived risk is not significantly associated with buy-online-pickup-in-store (BOPS) and buy-online-curbside-pickup (BOCP), but it negatively influences the choice of buy-in-store-home-delivery (BIHD) [7]. Additionally, the likelihood of adverse consequences from anticipated service failures is even acceptable to the consumer, but the severity of the end results and post-purchase recovery time are likely to shape the omnishopper’s channel preference [10]. Perceived risk can be reduced in the OCR environment due to the ease with which products can be returned to physical stores [58], transparency and uniformity between channels [44], as well as the safety and positive experience regarding payment and delivery methods [59].

In addition to perceived risk, other mediating effects that influence channel choice in an omnichannel approach have been studied. Perceived hedonic value plays a significant role in the selection of BOPS, BOCP and BIHD purchase methods [7], while channel transparency, convenience, and uniformity positively influence consumer perceived behavioral control [44], helping improve the 4Cs (certainties, confidence, conformability and controllability) of purchase during the search, purchase and service processes in the omnichannel environment [44]. Alternatively, the perceived vulnerability of COVID-19 [7], as well as issues related to sustainable logistics [61], are insignificant in channel selection. Potentially, fast or reliable delivery is the most critical omnichannel service attribute for achieving value [60,61] in channel selection among omnishoppers. Likewise, fast, effortless, and flexible transactions constitute some of the main drivers of consumers’ channel preferences [10].

Another important element in the channel selection decision is the product price, as the OCR consumer is particularly price sensitive. In this way, the omnichannel retailer can modify channel selection intent based on its price advantage strategy [44,59]. For example, there are significant positive effects of BOPS use on the frequency of offline purchases and the value of online purchases [62], and the consumer’s willingness to pay more depends on the type of product, its shelf life and the retail channel [58]. Moreover, a higher product price increases the influence of channel transparency, convenience and uniformity in reducing consumers’ perceived risk [44]. Conversely, a lower price or free delivery/free return of the product makes the consumer more willing to pick up their orders in the store or to wait longer for their orders to be delivered [61].

Extensive research has shown the value of omnichannel integration services as opposed to mere online channel enhancement for multichannel strategies [10]. Critically, the research demonstrates the importance of companies leveraging transition strategies for gradual integration and for discerning omnichannel integration services that are worth investing in. Promotional strategies related to the creation of pop-up stores by purely online retailers have the potential to increase foot traffic in the temporary physical store and in turn increase consumer spending at online stores on subsequent purchases [63]. Other marketing strategies can be easily replicated by market competitors, but the presence of omnichannel integration services can change competitive dynamics and become a sustainable competitive advantage in the digital market [10], despite offering the best purchase experience for the consumer, regardless of the channel they may choose.

The second theme that makes up this cluster is consumer segmentation, which is done through a comprehensive database analysis that enables in-depth knowledge of the consumer profile based on observed purchase behavior. Overall, consumers use multiple channels, but they tend to be omnichannel in the offline world or the online world, not both [66,69]. A rich variety of retail segments are prone to omnichannel business [69] with different degrees of OCR adoption. This may be related to opportunity factors [64,69], such as access to the product in physical stores or online. It may also be related to ability factors [64,69], such as online shopping experience, as well as factors of motivation [64,69], such as the option to return goods that are considered unsatisfactory. Distinct opportunities heavily influence the differences between retail segments, followed by differences in skills and motivation [69].

Regarding omnichannel behavior, research has used multiple criteria to segment consumers into three groups: (i) those who visit and buy products from only one channel, either online or offline; (ii) those who use one channel to buy products, but both channels to obtain services; and (iii) those who use both channels to purchase products and services [64]. On the other hand, they may be segmented into four groups: (i) those who use only the online channel; (ii) those who only use the offline channel; (iii) those who join the OCR via the online channel and use the on/offline channels; and (iv) those who join the OCR via the offline channel and use the on/off channels offline [65]. Overall, the omnishopper’s positive experience in the purchase journey represents the essential element to transform omnichannel users into omnichannel buyers [64].

Demographic aspects such as gender, age, education, children, income, smartphone ownership and employment also play a differentiating role in consumer segments [69]. Omnichannel buyers are more innovative in using alternative distribution channels, in addition to being younger and having higher incomes [64]. On the other hand, in some segmentations, demographics did not show significant differences between consumer profiles, which suggests that omnichannel behavior can also be determined by situational factors such as type of product, specific need [66] and nondemographic personal characteristics that influence omnichannel consumer behavior. For example, impulsive consumers tend to make greater use of mobile devices, while individuals with a high need for touch are more likely to use online devices in OCR, with subsequent purchases visiting the physical store to examine and evaluate the product [68].

Similarly, volume [69] and purchase value [65] also play a significant role in differentiating consumer segments. Based on empirical data, the number of purchases, the average purchase value per transaction, and the total purchase value of the omnichannel consumer who makes cross-use of online and offline channels are higher [65]. In terms of sustainability, omnichannel consumers can be classified based on their CO₂ footprint into six purchasing behavior profiles in OCR: two types of single-channel buyers (“the online shopper” and “the traditional shopper”); two types of omnichannel shoppers who shop in-store (“the research shopper” and “the ship-from-store shopper”); and two kinds of omnichannel shoppers who shop online (“the showroomer” and “the click-and-collect shopper”) [66]. “Online shoppers” make the least environmental impact, while “research shoppers” make separate trips for pre-purchase and purchase activities with higher CO₂ production [66].

Finally, consumer segmentation allows companies to prepare assertive promotions to the right consumers through the appropriate channels [69], which can generate additional and sustainable profits with the increase in the number of omnishoppers [49]. In this respect, omnishoppers can be segmented based on the result of integrated omnichannel campaigns [49,67]. The use of omnicoupons showed that most consumers prefer to buy in channels congruent with the channel through which they received their coupons, which suggests that consumers use their accumulated experience in the purchase channel and that specific touchpoints create preferences and choices for specific channels [67]. Retailers who want to increase cross-buying should prioritize catalog coupons, while those who want to increase purchase value should prioritize digital coupons [67]. It is also important to identify the group of consumers that are most responsive to omnichannel-targeted promotions. Overall, consumers with high offline usage behavior are those who make additional purchases when visiting the offline channel during an offline promotion, while online consumers are more likely to simply redeem the gift and leave the physical store without additional purchases [49]. Offline promotions are most effective for individuals with no recent history of offline purchases with the company, but these positive effects are not sustained among exclusively offline consumers [49]. As the successful implementation of omnichannel has become a new norm for strategic marketing in the retail sector, greater attention should be paid to effective consumer segmentation and promotional strategies that maximize omnishopper shopping experiences.

Post-Purchase

Three main themes compose this cluster: OCR perceived quality [30,70,71]; satisfaction [31,41,50,51,72,73]; and omnichannel customer loyalty [48,74,75,76,77,78].

Information on the omnichannel consumer shopping experience should be used for OCR improvements, given that if the perceived experience falls below expectations, customer satisfaction will be reduced, which negatively impacts revisit and repurchase intentions [30,41,72,77,78]. The possibility of omnishoppers influencing the purchase intention of other consumers with recommendations [70] or complaints [71] regarding digital channels exacerbates this situation. Online recommendations from consumers who are familiar with the product affect the webrooming shopping experience in different ways, depending on when it occurs during the purchase journey [70]. For example, online recommendations read before physical interaction can help consumers to bolster their trust in the product, which influences their preferences and choices in the physical store [70]. However, online complaints pose risks because they can negatively influence other consumers, as well as tarnish the brand’s reputation with the potential to go viral [71]. The most prevalent complaints recorded on Facebook by omnishoppers, including those unrelated to actual transactions, involved in-store shopping experience, delivery, marketing activities including communications and pricing, product quality, and customer service [71]. Service recovery may involve a four-dimensional justice framework [71], as well as using artificial intelligence to mitigate perceived quality risk in the commercialization of O2O omnichannel operations [30].

The second theme of this cluster refers to omnichannel customer satisfaction. The development of a successful omnichannel system leads to many challenges, mainly in understanding what consumers perceive as quality and how OCR can increase omnishoppers’ satisfaction in their shopping experiences [72]. The development of a scale to assess perceived quality at the pick-up (PU) stage of the BOPS service resulted in four dimensions that indicate that BOPS consumers are more concerned with functional attributes than with experiential attributes of OCR [41]. The service effectiveness dimension is considered the strongest influencer on BOPS-PU quality perceptions, followed by problem handling, ease of access, and item quality [41]. Certainly, this result cannot be generalized, given that consumer segmentation plays a mediating role in the relationship between consumer experience and attitude [73], and post-purchase omnichannel consumer behavior varies according to the retail segment [31].

Among the functional attributes, it is important to highlight omnichannel capability, a construct composed of three elements: channel consistency, cross-channel, and social media [50]. Research has demonstrated the positive impact of operational logistics service quality (LSQ) on omnichannel capability [31,50]. Alternatively, the development of omnichannel capability may not be enough to achieve customer satisfaction, as this relationship is achieved through the dual mediation composed of flexibility and operational LSQ [31]. In other words, OCR will achieve customer satisfaction with a seamless shopping experience only through full channel integration (i.e., omnichannel capability and flexibility) and good quality of service (i.e., operational LSQ), which theoretically justifies the return on investment to achieve efficiency and effectiveness of the retailers’ logistics operations [31,50,78]. From a management perspective, the first step toward OCR excellence should be to focus on performance across all channels (online and physical) and then on a seamless shopping experience where consumers can switch between channels at any stage of the purchase transaction without negatively affecting their satisfaction with the process [50]. It is important to bear in mind that consumer experience (hedonic, economic, symbolic, and relational experiences) proved to be an important factor in determining customer satisfaction and attitude toward the store [73].

Other omnichannel features that generate customer satisfaction are integrated promotion and integrated information access, which is controlled by perceived convenience, i.e., comparing prices and products in different stores (physical and online) efficiently plays a vital role in customer satisfaction [72]. In contrast, from a sustainability perspective, there is no evidence of a channel integration effect on brand identification and, more importantly, no effect of brand identification on willingness to participate in clothing recycling plans [51]. Individual factors mainly determine consumers’ intention to participate in the clothing recycling process, such as environmental attitudes and satisfaction, while organizational arrangements have a smaller effect [51].

The third and final theme of this cluster refers to omnishoppers’ loyalty intention, which is directly related to the satisfaction construct but requires additional effort from the retailer in offering the consumer a perfect purchase journey [75]. To this end, research has been dedicated to the development of scales that validate brand experience [74] and perceived quality [78], as well as their impacts on customer satisfaction and loyalty in the context of OCR. Both scales are composed of eight dimensions. Although all the dimensions are significant in shaping the retail brand experience scale, intellectual, affective and behavioral aspects play a more prominent role in omnishopper satisfaction and loyalty [74]. On the perceived quality scale, integration is considered the most important factor, despite all the dimensions having a significant positive impact on customer satisfaction and loyalty to omnichannel retailers [78].

The level of channel integration affects omnichannel customer loyalty [75], through LSQ in an OCR environment [76] and the effects of personalization and hedonic motivation during purchase experience [77]. Increasing the omnichannel level of integration positively influences customer satisfaction and loyalty intentions, an effect mediated by the shopping experience and with greater impact among consumers who perceive channels as complementary and among those with a well oriented purchase [75]. In relation to the base case, channels must achieve medium to high levels of integration to increase customer satisfaction and substantially high levels to increase loyalty intent.

The influence of the three LSQ dimensions (condition, availability, and timeliness) on omnishopper satisfaction and loyalty shows different impacts for different channel options. In the BOPS channel, customer satisfaction has a partial mediating effect on the relationship between condition and loyalty and a total mediating effect on the relationship between timeliness and loyalty [76]. In the buy-in-store-ship-direct (BSSD) channel, customer satisfaction has only a partial mediating effect on the relationship between timeliness and loyalty [76]. Although relational LSQ does not affect omnichannel capability, there is a positive correlation between operational and relational LSQ [50], which helps to justify how OCR employee attitudes and behaviors can impact customer satisfaction and loyalty [78].

It is also important to highlight that hedonic motivation represents a key element in creating a cognitive and emotional experience for consumers. Creating personalized offers and ads improves the consumer experience in OCR, which leads to positive effects on loyalty through increased word of mouth and repeated purchase intention [77]. Finally, research has also demonstrated the effect of reciprocity between key purchase channel images and its effects on overall retailer loyalty. Generally, a positive offline channel image improves the online channel image and vice versa, but reciprocal effects of offline (vs. online) images are stronger in general retailer, offline channel and online channel loyalty [48].

3.2. Bibliometric Review

3.2.1. Descriptive Analysis

All the papers comprising the textual corpus were analyzed, published in 32 journals and with 116 authors and co-authors from the WoS and Scopus databases, as shown in Figure 4. According to Figure 4, there was an average of 22.33 citations per published document, an average of 5.39 citations per year per published document, in addition to an average of 0.37 published documents per author and a total of 2.70 authors per published document.

Regarding the degree of cooperation between the authors, there are only 3 papers of single authorship, while 113 authors share authorship, generating a collaboration index of 2.83, in addition to a total of 2.93 co-authors per document. We can also highlight that there are 169 Keywords Plus (ID) from the researched bases and 181 keywords defined by the authors.

Qualitative research represents 39.53% of the corpus, whereas quantitative research accounts for 48.83%. The remaining studies characterize the use of both methods and represent 11.64%. In the period of 2019–2021, there was an increase in the number of studies that used qualitative methods, while quantitative studies decreased (Figure 5). An analysis of the data collection methods (Figure 6) shows that most studies use surveys (65.12%) to obtain information about consumers and to understand better what is important to consumers during the purchase journey. Two other methods are frequently used, such as semi-structured interviews (4.65%) and secondary data (13.95%). Some research uses secondary data as a complementary method to a primary data collection method, such as surveys. As for the data analysis methods (Figure 7), the high variety of methodologies stood out, the main ones being partial least squares structural equation modeling (PSL-SEM) (34.88%), factor analysis (16.28%) and descriptive statistics (9.30%), which are also used as a complementary method to other data analysis methods.

The leading countries that contribute to the evolution of the theme are the USA, Korea, Spain, and China in descending order of the number of articles published (Figure 8). In this scenario, it is possible to observe publishing partnerships between countries and therefore it is possible to affirm that the scientific base on the subject is maturing. Countries such as the United Kingdom, Italy and Greece have a strong collaborative relationship between the authors. However, articles by Spanish and Chinese authors do not show any cross-country collaboration (Figure 8). It is possible that this occurs because it is a topic that is still on the rise in terms of academic interest or it has yet to result in academic/scientific links between the main researchers of the theme around the world. This can be considered a negative aspect, since it can delay scientific progress on the subject, because when there is cooperation between different researchers of different nationalities, it is possible to generate complementary ideas and build a scientific base that is richer in insights, information, and content.

3.2.2. Word Cloud Analysis

A word cloud is the graphic grouping of words according to how frequently they appear in the corpus, enabling a rapid identification of the highlighted lexical content. Figure 9 shows the word cloud developed from a simple lexical analysis of the words of the corpus abstracts subdivided into pre-purchase, purchase, and post-purchase clusters.

At the pre-purchase stage, 1487 occurrence frequencies of the evaluated words were obtained. The three (3) words with the highest occurrence frequency correspond to 6.12% of the recorded occurrences. The word “intention” corresponded to 2.22% of the total occurrences, followed by the word “shop” with 2.02% of the occurrences and “purchase” with 1.88% of the occurrences. At the purchase stage, 1483 occurrence frequencies of the evaluated words were obtained. The three (3) words with the highest frequency of occurrence correspond to 6.88% of the recorded occurrences. Therefore, the word “online” corresponded to 2.90% of the total occurrences, followed by the word “purchase” with 2.23% occurrences, and “offline” with 1.75% occurrences. At the post-purchase stage, 1260 occurrence frequencies of the evaluated words were obtained. The three (3) words with the highest frequency of occurrence correspond to 6.11% of the recorded occurrences. The word “satisfaction” corresponded to 2.30% of the total occurrences, followed by the word “online” with 2.22% occurrences and “loyalty” with 1.59% occurrences. It is important to highlight that the larger and more centralized a given word is in the cloud, the more frequently it appears in the text [79]. Finally, the word cloud analysis introduces the central words of each cluster, helping to identify similarities between the texts of the corpus in each cluster.

3.2.3. Typology Analysis

The typology analysis of a text allows the interpretation of the most relevant words associated with the theme, being complementary to the word cloud. The extraction was performed in Iramuteq Software using the Descending Hierarchical Classification (DHC) proposed by Reinert [80]. The analysis was performed for each cluster defined for the pre-purchase stage, which resulted in 86 text segments, of which 81 were correctly classified (94.19%). Retention must comprise at least 70% of segments for the DHC analysis to be considered statistically significant [79]. Regarding the classification, a total of two classes were obtained for the articles. Words that have a chi-squared (Chi²) statistic greater than 3.80 (χ² > 3.80) or, alternatively, a probability value less than 0.1% (p-value < 0.001) were considered in the analysis, with the rest of the words that did not meet these criteria being eliminated. Notably, the chi-square statistic greater than 3.80 (χ² > 3.80) and the probability value less than 0.1% (p-value < 0.001) portray the level of significance in terms of word association with their respective classes, proving by how much certainty the observed values can be accepted as real by the DHC analysis [80]. The words that met these criteria composed the two classes, as presented in

Table 3. Typology analysis: pre-purchase stage.

Class 1: Purchase Intention			Class 2: Adoption Barriers
Classification (%): 51.85%			Classification (%): 48.15%
Word	Chi2	p-value	Word	Chi2	p-value
intention	24.68	0.0000	channel	10.94	0.0000
shop	13.02	0.0000	need for cognition	9.56	0.0000
model	12.72	0.0000	retailer	9.32	0.0000
behavior	11.82	0.0000	consumer	8.92	0.0000
risk	6.02	0.0001	information	8.00	0.0000
perception	6.02	0.0001	offline	6.98	0.0001
mall	4.95	0.0003	barrier	6.98	0.0001
encounter	4.95	0.0003	online	6.98	0.0001
utaut2	4.95	0.0003	adoption barriers	5.74	0.0002
habit	4.95	0.0003	communication	5.74	0.0002

.

Table 3 highlights that the highest concentration of articles is in the purchase intention class (51.85%). The two classes represent subgroups in the pre-purchase stage, one referring to purchase intention [20,28,29,32,45,46,47,56,57] and the other representing omnichannel adoption barriers [42,43,55]. Extensive cluster analysis was presented in Pre-Purchase Section.

The purchase stage typology analysis resulted in 89 text segments, of which 71 were correctly classified (79.78%). A total of four classes were obtained for the articles. Words that met the chi-squared and p-value criteria comprised the classes presented in

Table 4. Typology analysis: purchase stage.

Class 1: Channel Selection			Class 2: Channel Selection			Class 3: Consumer Segmentation			Class 4: Business Perspective
Classification (%): 39.4%			Classification (%): 21.1%			Classification (%): 21.1%			Classification (%): 18.3%
Word	Chi2	p-value	Word	Chi2	p-value	Word	Chi2	p-value	Word	Chi2	p-value
online	17.90	0.0000	intention	20.08	0.0000	consumer	17.89	0.0000	result	19.37	0.0000
product	14.44	0.0000	decision	15.82	0.0000	seamless	11.69	0.0000	supply	18.91	0.0000
offline	12.84	0.0000	integration	12.83	0.0000	model	11.19	0.0000	chain	18.91	0.0000
store	10.55	0.0000	perceive	11.79	0.0000	behavior	8.72	0.0000	grow	18.91	0.0000
find	8.72	0.0000	selection	11.69	0.0000	preference	7.38	0.0001	management	13.98	0.0000
promotion	6.51	0.0001	evaluation	11.69	0.0000	increasingly	7.38	0.0001	company	9.11	0.0000
high	6.51	0.0001	channel	7.86	0.0001	retailer	5.15	0.0002	increase	4.90	0.0003
difference	6.51	0.0001	retail	5.82	0.0002	offer	3.90	0.0005	opportunity	4.90	0.0003
purchase	6.11	0.0001	apply	3.90	0.0005	analyze	3.90	0.0005	market	4.90	0.0003
price	5.29	0.0002	behavioral	3.90	0.0005	travel	3.90	0.0005	combine	4.90	0.0003

.

Table 4 shows that the highest concentration of articles is in the two classes referring to channel selection (60.05%). The first three classes represent the subgroups presented in the structured review at the purchase stage, one referring to channel selection [7,10,44,58,59,60,61,62,63] and one referring to consumer segmentation [49,64,65,66,67,68,69]. Alternatively, the fourth class presents terms related to the business perspective, as there is no way to describe the purchase of a product or service without thinking about the consumer-company duality. An extensive cluster analysis is presented in Purchase Section.

The post-purchase typology analysis resulted in 71 text segments, all of which were correctly classified (100%). A total of five classes were obtained for the articles. Words that met the chi-squared and p-value criteria comprised the classes presented in

Table 5. Typology analysis: post-purchase stage.

Class 1: Satisfaction and Loyalty			Class 2: Perceived Quality			Class 3: Perceived Quality			Class 4: Failures and Complaints			Class 5: Omnichannel Perspective
Classification (%): 28.2%			Classification (%): 15.5%			Classification (%): 11.3%			Classification (%): 8.4%			Classification (%): 36.6%
Word	Chi2	p-value	Word	Chi2	p-value	Word	Chi2	p-value	Word	Chi2	p-value	Word	Chi2	p-value
loyalty	14.75	0.0000	capability	56.55	0.0000	BOPS	33.38	0.0000	failure	71.00	0.0000	channel	17.73	0.0000
relationship	12.71	0.0000	operational	56.22	0.0000	quality	27.65	0.0000	recovery	58.27	0.0000	online	17.67	0.0000
survey	9.77	0.0000	logistics service quality	48.60	0.0000	item	24.67	0.0000	facebook	58.27	0.0000	offline	10.06	0.0002
customer experience	7.99	0.0000	flexibility	35.75	0.0000	service	17.41	0.0000	complaint	45.92	0.0000	level	9.31	0.0002
timeliness	7.99	0.0000	mediation	17.09	0.0000	dimension	12.78	0.0000	service	16.75	0.0000	increase	9.31	0.0002
personalization	7.99	0.0000	strategy	6.27	0.0001	practitioner	9.62	0.0002	include	14.62	0.0000	consumer	7.87	0.0005
satisfaction	6.56	0.0001	role	6.27	0.0001	guidance	9.62	0.0002	context	9.83	0.0002	integration	7.52	0.0006
support	4.80	0.0003	omnichannel	4.42	0.0004	domain	9.62	0.0002	customer	9.49	0.0002	product	7.34	0.0007

.

Class 1 grouped two important constructs that are related to each other, because to achieve loyalty [48,74,75,76,77,78] customers must be satisfied with their purchase [31,41,50,51,72,73]. Classes 2, 3 and 4 are related because they deal the omnichannel consumer’s perception of quality [30,70,71]. Customers’ complaints about defective products or purchases received that were not in the expected condition directly impact consumers’ perception of quality [71]. Class 5 grouped the main terms used in omnichannel retail, contextualizing the rest of the classes. An extensive cluster analysis is presented in Post-Purchase Section.

3.2.4. Bibliographic Coupling Analysis

The goal of bibliographic coupling is to measure the similarity degree between references using the number of citations shared between each pair of authors in the corpus. Therefore, the greater the number of references in common, the greater the connection strength between them, which demonstrates that the articles have a relevant convergence on the subject, considered more contemporary and capable of providing insights to aid the conducting of future research on the most prominent topics [81]. For this, a coupling network was prepared, using VOSviewer software. The network distribution was performed using the “Association strength” method. Figure 10 presents the coupled document network for all stages: pre-purchase, purchase, and post-purchase, respectively, distributed in 2 clusters, 2 clusters and 3 clusters where each node represents an article that makes up the textual corpus.

Regarding the citation quantity (Figure 10), the article by Juaneda-Ayensa et al. [56] has 140 citations, representing 38% of the citations received in pre-purchase articles. At the pre-purchase stage, the articles that stood out were those that address the impact of the use of technology on purchase intention [56] and refer to the influence of consumer experience on purchase intention [20]. Both articles showed high total link strengths of 99 and 91, respectively, which shows their relevance and theoretical proximity to the other articles in the cluster. Regarding the purchase stage, the most outstanding research addresses the integration and selection of channels in omnichannel retail [10] and consumer segmentation according to consumer behavior [66]. Finally, for the post-purchase stage, the articles that stood out were those that delve into the concept of logistics service quality and the impact on customer satisfaction and loyalty [50,76] and on creating consumer experiences [75,77].

4. Discussion

The consumer journey has become an increasingly important concept for understanding complex consumer behavior and obtaining insights into their experiences, although the term has been used in many disciplines since the 1990s and understanding of the topic continues to evolve [12]. The analysis of the number of publications on the subject increased in the second triennium, but the studies on the purchase stage of the omnichannel consumer showed the highest growth in the order of 4.33 from one triennium to the next. The review of the theoretical foundations shows that although the marketing discipline provides the existing dominant literature, the theories of psychology, sociology and business management also contribute significantly to the researched topic. This implies that the scope of the consumer buying journey literature goes beyond a single theory or discipline.

The descriptive analysis identified the main methods used, mostly quantitative, but with a growing number of qualitative methods in post-purchase articles. Qualitative studies have become important with regard to demonstrating aspects that can be interpreted subjectively, resulting in an intellectual bias that enriches the research outcomes. The data obtained from the corpus articles were mostly primary (86.04%). Concerning the collection methods that were employed, most surveys seek to obtain information about consumers and help to gain a better understanding of what is important to consumers during the purchase journey. Some authors used other collection methods to assist in the presentation of their survey results, such as focus groups [20,43,45] and semi-structured interviews [66,77]. Some research used secondary data as a complementary method to a primary data collection method. Regarding the data analysis methods in the first three years (2016–2018), a small variety of methods were used (5), most of them being partial least squares structural equation modeling (PSL-SEM) and factor analysis. In the second triennium (2019–2021), 15 different methods were used, an increase of 200% in the diversity of methods, but the aforementioned methods continued to be the most frequently used.

The typology obtained in the results supported the division of the subgroups of each stage of the purchase journey (1) pre-purchase: purchase intention and adoption barriers; (2) purchase: channel selection and consumer segmentation; (3) post-purchase: perceived quality, satisfaction, and loyalty. The bibliometric coupling analysis supports the authors who make the most prominent contributions to the future related to the topic, with important themes that may be highlighted in the coming years [20,56,68,76].

This study identified that the field has evolved over the years and the pandemic has accelerated a process that began with the digital transformation due to increasing consumer access to e-commerce. This new reality shows the importance of studies on consumer behavior and analyses that go beyond the traditional horizons in retail to improve the consumer’s experience.

From the consumer’s perspective, the importance of understanding what impacts consumers’ purchase intention and the barriers that exist for the use of technology behind omnichannel so that managers can prepare to overcome these barriers was evident. After consumers demonstrate their purchase intention, various possibilities remain for channels to be chosen, and this selection is related to the profile and habits of consumers. Therefore, it should be highlighted that in the final stage of the purchase, it is imperative to interpret whether the consumer’s experience was positive when considering the perceived quality, the satisfaction of desires, and needs. A positive shopping experience that can retain customers, may make them a brand or company advocate.

5. Conclusions

The primary objective of this article was to evaluate the stages of the purchase journey in OCR from the consumer’s perspective through an SLR. Additionally, the textual corpus analysis a review through the lenses of various theories in the field of marketing, specifically those related to the consumer’s purchase journey, involving the phases of (i) pre-purchase, (ii) purchase, and (iii) post-purchase in omnichannel retail. By presenting an SLR, this research produced findings on the consumer’s journey and insights that can guide assertive strategies to assist managers in decision-making in OCR.

The main finding of this article shows that most marketing studies do not address the omnichannel consumer journey and when they do, they focus on specific aspects to the detriment of a more holistic view of the buying process. Few studies on omnichannel retail have focused on the integration of all touchpoints using an empirical longitudinal assessment. Unlike previous research on omnichannel consumer behavior, this SLR evaluated the omnichannel retail purchase journey, recognizing the multidimensional and holistic nature of the process with different touchpoints across pre-purchase, purchase, and post-purchase situations. At stage (i), two clusters related to purchase intention and omnichannel adoption barriers were discovered. At stage (ii), the discovered clusters refer to channel selection and consumer segmentation. At the last stage (iii) related to post-purchase, three main clusters were identified, referring to the omnichannel consumer’s perception of quality, satisfaction and loyalty. Finally, this work focuses on a consumer-centric perspective. The directions for future research and research limitations are presented below.

5.1. Directions for Future Research

Regarding the particularities of this literature review, this article presents some more in-depth considerations about this research, based on the authors’ insights. The purpose is to recommend the development of future research on the various aspects of consumer behavior in the consumer journey in omnichannel retail. Following the guidelines of previous studies [9], the recommendations for future research are discussed in the subsequent sections using the Theory, Methodology and Context framework.

5.1.1. Theory

The omnichannel retail literature from the consumer’s perspective thrived in the last three years that were analyzed. However, there is still room for future improvement. For example, the behavioral theories that underlie the corpus studies could be used in a wider range of works, as there is a lack of awareness on the part of researchers regarding the importance of grounding theories in obtaining insights, and this aspect can be intensified. Therefore, future research is necessary to explore existing theories to understand and address issues related to the consumer perspective in omnichannel retail in a more assertive and complete way.

Two important clusters of study were identified in the pre-purchase stage: to purchase intention [20,28,29,32,45,46,47,56,57] and omnichannel adoption barriers [42,43,55]. Future studies should examine the relationship between these variables and the degree of importance in designing omnichannel retail strategies. The purchase intention of consumers changes along with technology development [20,46]. This technological development is fundamental for the growth and diffusion of OCR as part of the purchase experience, although the physical retail environment affects some consumers more than others [29,63]. It would be important to analyze in depth how much physical retail influences the purchase intention on online channels or vice versa.

Two main clusters were identified in the purchase stage, one referring to channel selection [7,10,44,58,59,60,61,62,63] and another regarding consumer segmentation [49,64,65,66,67,68,69]. All consumers have their personal preferences and, as a result, they present heterogeneous buying behavior, and this behavior undergoes changes throughout their lives as they embrace new experiences and use new technologies. Therefore, the challenge for researchers and managers is to identify how to anticipate consumer needs to provide a channel that meets and exceeds the expectations of each client. Future studies could categorize the variables that have the greatest influence on channel selection for different consumer segments in order of importance.

Another subject that was less explored in the corpus concerns the impact of sustainability on the purchasing decision process. Consumers show a preference for channels that provide convenience, comfort and ease without considering how they will receive the product and the environmental impact of this choice [66]. Researchers could conduct in-depth investigations of the impact of sustainability actions on omnichannel retail and the perception of consumers regarding the selection of channels and their benefits to the environment.

Concerning the post-purchase stage, three main clusters were identified, one referring to the omnichannel consumer’s quality perception [30,70,71], satisfaction [31,41,50,51,72,73] and loyalty [48,74,75,76,77,78]. The three variables are interconnected, and it would be interesting to develop a study that groups all the dependent variables behind perceived quality, satisfaction and loyalty and the relationship between them clearly. Regarding the BOPS strategy, few studies have attempted to quantify the impact of BOPS use on subsequent purchase behavior or examine the critical roles of offline stores in generating value in the consumer experience [62]. Future research could expand the scope of study beyond the BOPS strategy and explore the nuances of the offline channel as an experiential shopping venue to drive a subsequent purchase in an online channel or vice versa.

5.1.2. Methodology

The study revealed that most empirical studies used surveys as the main research collection method (see Figure 6), which raises doubts about the possibility of generalizing the results and conclusions and may not capture the real behavior experienced by consumers [82,83]. Future studies should aim to measure the variables with evaluation instruments that can generate a less subjective perception. Qualitative techniques began to be used more in the second triennium, showing the importance of bringing a complementary context to the collection of primary data. Quantitative methods, in addition to the survey, are relatively less representative in the context of omnichannel retail and can be further explored, such as modeling techniques [64,66,67]. Therefore, future studies could diversify existing data collection methods to discover distinct empirical evidence from the consumer perspective in OCR.

5.1.3. Context

The contextual analysis serves to highlight several factors that may influence the research results in omnichannel retail from the consumer’s perspective. Most existing studies were conducted in developed or developing economies (see Figure 8). Future research should try to understand the similarities and differences in consumer buying behavior in developed, developing and underdeveloped countries. This would illustrate the influence of socio-demographic and cultural characteristics on consumers’ purchase intentions in an omnichannel environment. In some countries, the technology may not be accessible as easily as in other countries. Another important point to be discussed refers to studies that involve data collected in several sectors (see Figure 3), which generate results that can be more generalizable compared to studies that are concentrated in a single sector. However, it would be interesting to highlight the origin of the sector in these studies so that it would be easier to identify possible biases in each sector. Therefore, researchers should highlight the main retail sector in their studies to shed light on what lies behind the insights obtained. In addition, future studies could analyze the main aspects of each purchase stage defined in the work for a wide range of sectors, such as examining the relationship between customer satisfaction and subsequent purchase intention in different sets of consumers in different segments and compare their results to analyze the relevant similarities and peculiarities of each sample. They could also simply present specific contributions to a particular sector that cannot be generalized to others, such as the purchase of perishable food.

5.1.4. Limitations

In complementing the TMC framework, it is important to highlight the limitations detected in the development of this study. Although this work presents a systematic literature review on the subject, there are some limitations in relation to the research findings: (i) the corpus composition was limited to articles from the Scopus and WoS databases; (ii) only articles published in English were considered in the corpus; and (iii) only articles published in journals classified by the SJR were considered, regardless of quartile. Therefore, there may be relevant research published in other sources, in other formats and in other languages. Furthermore, the analysis was limited to retail studies that enable a better understanding of the purchase process across different channels. Future studies should therefore not be limited to retail but should also explore insights into the consumer buying journey in other contexts. Finally, it is expected that the suggestions for future directions presented here will aid the conducting of new studies that help to advance the theme.