Factors of customers’ channel choice in an omnichannel environment: a systematic literature review

Wolf, Lukas; Steul-Fischer, Martina Article — Published Version Factors of customers’ channel choice in an omnichannel environment: a systematic literature review Management Review Quarterly Provided in Cooperation with: Springer Nature Suggested Citation: Wolf, Lukas; Steul-Fischer, Martina (2022) : Factors of customers’ channel choice in an omnichannel environment: a systematic literature review, Management Review Quarterly, ISSN 2198-1639, Springer International Publishing, Cham, Vol. 73, Iss. 4, pp. 1579-1630, https://doi.org/10.1007/s11301-022-00281-w This Version is available at: https://hdl.handle.net/10419/312473 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Management Review Quarterly (2023) 73:1579–1630 https://doi.org/10.1007/s11301-022-00281-w 1 3 Factors ofcustomers’ channel choice inanomnichannel environment: asystematic literature review LukasWolf1 · MartinaSteul‑Fischer1 Received: 1 January 2022 / Accepted: 16 June 2022 / Published online: 21 July 2022 © The Author(s) 2022 Abstract The proliferation of mobile devices and the continuous development of online technologies has led to an increasing variety of channels, leaving customers with a choice of channel alongside the choice of product, service, or retailer. Any attempt to optimize customer experience and engage in successful omnichannel management will require a complete, multifaceted understanding of the processes around channel choice of customers. To date, the many existing studies around multiand omnichannel research have failed to yield an integrated, comprehensive synthesis of factors involved in customers´ channel choice. Our study conducted a systematic literature review to the end of identifying the factors involved in channel choice which appear in the scientific literature on this topic over the last two decades. We retrieved 128 papers from three bibliographic databases (EBSCO Host, Scopus, and Web of Science) and carried out descriptive analysis on them. Qualitative thematic analysis inductively extracted 66 different factors of channel choice, each assignable to five broader categories, from the studies included in the review. The findings indicate that perceived channel characteristics, customer needs and situational or contextual factors influence customers´ channel choice directly, and customer characteristics and characteristics of products or services influence it indirectly. Alongside its presentation of an integrated conceptual framework comprising these relationships, our study details a comprehensive research agenda with regards to theories, contexts, and methods and, in particular, with regards to factors influencing customers´ channel choice. Our findings advance the academic understanding of channel choice behavior and provide researchers and practitioners in this area with information on important implications for omnichannel management. Keywords Channel choice· Omnichannel management· Multichannel management· Consumer behavior· Customer behavior· Systematic literature review * Lukas Wolf [email protected] Extended author information available on the last page of the article 1580 L.Wolf, M.Steul-Fischer 1 3 JEL Classification M310· M370· O330 1 Introduction The proliferation of mobile devices and the ongoing development of online technologies has led to a constantly increasing variety of channels, such as mobile apps and social media (Li etal. 2017). This leaves today’s customers with the choice of channel as an additional factor alongside their choice of product, service, and retailer (Xu and Jackson 2019a). Understanding the factors involved in channel choice from a customer perspective has always been an important part of channel management (Neslin etal. 2006). The recent move toward synergetic management of multiple channels in the context of omnichannel management places customer orientation still higher on the agenda (Lemon and Verhoef 2016; Verhoef etal. 2015). Numerous studies (for example Saghiri etal. 2017; Wagner etal. 2020) have emphasized the importance of understanding the factors driving channel choice to the enhancement of customer experience and the consequent improvement of omnichannel management. The multitude of studies around multiand omnichannel research driven by this exigency (see, for example, Barwitz and Maas 2018; Gensler etal. 2012), have yet failed to yield an integrated, comprehensive synthesis of factors in customers´ channel choice, despite some initial attempts at summaries (e.g. Li etal. 2017; Neslin etal. 2006). This paper will therefore seek to advance our understanding of channel choice behavior in an omnichannel environment by identifying factors of channel choice and synthesizing the existing body of knowledge via a systematic literature review (SLR). The key principles of an omnichannel environment include: (1.) the offering of merchandise and services through a multitude of available channels, (2.) integration of channels into a unified system from a retailer´s point of view (i.e. enabling the same task-fulfillment on every channel), and (3.) seamless inter-channel interaction from a customer perspective (i.e. allowing for easy switching between channels or devices along the customer journey) (Beck and Rygl 2015; Verhoef etal. 2015). This means that, successful omnichannel strategies require an understanding of both the retailer´s and the customer´s perspective. This notwithstanding, most conceptual studies and SLRs in this area to date address multior omnichannel related topics solely from a retailer or management perspective (cf. Appendix A). For instance, Gao et al. (2020) performed a literature review to the end of systematically summarizing studies on multichannel integration along the customer journey. Hossain et al. (2019) examined multichannel integration quality within service delivery channels with the aid of a SLR and qualitative interviews. Gerea et al. (2021) summarized and synthesized the existing body of research on omnichannel customer experience management highlighting the importance to omnichannel businesses of pursuing a customer-centered approach. Wang etal. (2021b) reviewed current studies from the research fields of information systems, operations and marketing, presenting a multidisciplinary view of omnichannel retailing. Finally, Cai and Lo (2020), Lopes etal. (2021), and Salvietti etal. (2021) made use of bibliometric approaches such as bibliographic 1581 1 3 Factors ofcustomers’ channel choice inanomnichannel… modeling via citation analysis to aggregate findings on omnichannel management and to propose research fields in this domain. The only SLR to date that takes a customer perspective is Mishra etal. (2021), which explored the cognitive, affective, and conative dimensions of general consumer decision-making in omnichannel retailing. This review´s authors outlined the rapidly evolving research within this field and emphasized the importance of investigating customers´ channel choice behaviors in a separate study (Mishra etal. 2021).1 The research presented here differs from the SLR by Mishra et al. (2021) in its intent of establishing factors of customers’ channel choice in a multiand omnichannel environment, rather than setting out a general examination of customer behavior in omnichannel environments. The criteria for the selection of literature for the review, including keywords, databases, and time span, and the descriptive and thematic analysis conducted diverge from those in Mishra etal. (2021). The existing body of research on channel choice in multiand omnichannel environments encompasses a diverse range of heterogeneous studies, conceptual papers, and other types of scientific work; we can therefore consider this topic mature in research terms, which means it is eminently suitable for a thorough SLR that both synthesizes and expands it (Paul etal. 2021; Webster and Watson 2002). However, the plethora of SLRs to date on multior omnichannel related topics have been unable to draw conclusions on customers’ behaviors as regards channel choice. The present study is therefore one of the first reviews on multiand omnichannel management to proceed from a customer perspective and the first attempt to systemically summarize and conceptualize factors of customers´ channel choice in this context. It therefore contributes to the literature on multiand omnichannel environments in multiple ways: First, in synthesizing and categorizing factors of channel choice, the review advances our understanding of customer behavior, which is vital to successful omnichannel management (Mishra etal. 2021; Verhoef etal. 2015). The inductive approach taken by the thematic content analysis described in this article yields a conceptual framework for use by researchers and practitioners as an aid to their comprehensive understanding of customers’ channel choice. Second, the article, drawing on a descriptive and thematic analysis of relevant published work, sets out an extensive research agenda encompassing contexts, theories, methods, and particularly factors of channel choice for research in this area. Finally, it delineates a number of implications for channel management. The findings of our literature review indicate that customers´ channel choice behavior is a highly complex process influenced by a variety of factors, including channel characteristics, customer needs, and situational or contextual factors. While some indirectly influential factors of channel choice, such as customers´ age are frequently the subjects of academic study, the current literature in the area of multiand omnichannel business lacks understanding of the directly influential factors. Further, 1 In addition to the SLRs discussed, there exist several literature reviews on supply chain and logistics aspects of multiand omnichannel retailing. They include Lafkihi etal. (2019); Melacini etal. (2018); Taylor etal. (2019). For a comprehensive overview on earlier literature reviews on omnichannel retailing, including logistics and supply chain management, see Mishra etal. (2021). 1582 L.Wolf, M.Steul-Fischer 1 3 extant channel choice research concerns itself with a limited number of industries or products and countries, and its findings require validation via the introduction of other methods (e.g. longitudinal studies) and research designs and via the use of theoretical foundations and models. The review is structured as follows. After discussing the methodology used for the SLR, the paper provides a descriptive overview of the articles retrieved for the literature review and then proceeds to analyze factors of channel choice as represented in the literature identified. Following this, a section proposing potential areas for future research on channel choice behaviors sets out a comprehensive research agenda. The concluding section points to implications and limitations of this study. 2 Methodology The relevance of SLRs to research in the area of business is currently higher than it has ever been (Snyder 2019). In collating and evaluating findings from numerous empirical studies, SLRs provide an overview of heterogeneous and interdisciplinary research domains and help prevent bias by bringing together evidence at a metalevel (Snyder 2019). They can also act as foundations or springboards for future research or apply a particular perspective to it (Paul and Criado 2020). The basis of a SLR may be either a research domain, a theory or a research methodology (Paul etal. 2021; Paul and Criado 2020). The present review falls into the domain-based category; more specifically, it is a structured theme-based review (Paul etal. 2021) conducted to the end of providing an overview and synthesis of the determining factors of channel choice, from a customer perspective, identified within the existing literature on multiand omnichannel retail. This approach was most conducive to our objectives, as it enabled us to develop a thorough understanding of the literature in the area of channel choice and identify relevant gaps and, ultimately, a future research agenda. Many guidelines on conducting SLRs recommend the development of a detailed systematic review protocol to ensure transparency and reproducibility of the review process (Kitchenham and Charters 2007; Okoli 2015; Paul et al. 2021; Snyder 2019). We accordingly determined our search strategy prior to carrying out the SLR; this entailed the selection of search terms, relevant databases, and inclusion criteria (Fisch and Block 2018; Paul etal. 2021). Search terms and relevant databases: A search string requires the use of words and phrases (i.e. search terms) directly related to the research question (Snyder 2019). This presented us with a challenge, as academic publications do not demonstrate a consistently uniform understanding or use of the terms “multi- “, “cross- “, or “omni-channel” (Beck and Rygl 2015). Nor is there a universally accepted definition of “channel choice” as a concept; other terms used to describe it include “interaction choice” (Barwitz and Maas 2018), “channel preference” (Becker etal. 2017; Boardman and McCormick 2018), “adoption of channels” (Bilgicer etal. 2015), or “channel use/usage” (Frasquet etal. 2015, 2019). We therefore sought to prevent exclusion of relevant results by testing a number of alternative terms, synonyms, and abbreviations of “choice” and “omnichannel”. The final search string, constructed 1583 1 3 Factors ofcustomers’ channel choice inanomnichannel… with the aid of Boolean operators and truncations, included the terms: (choice OR choose* OR select* OR use OR usage OR utili* OR adopt* OR prefer*) AND ("omni channel" OR "multi channel" OR "cross channel" OR "dual channel" OR omnichannel OR multichannel OR cross-channel OR dualchannel). To the end of comprehensively covering the relevant literature, we searched within three electronic literature databases: EBSCO Host, Scopus, and Web of Science (Kuckertz and Block 2021; Wanyama etal. 2021). These databases are considered the most important and widely used within research in business and the social sciences and consequently find frequent use for SLRs within the business field (examples are Eckert and Hüsig 2022; Lu etal. 2018b; Neuhaus etal. 2021; Tueanrat etal. 2021). Inclusion criteria: The inclusion criteria we defined for our review related to language, year of publication, source type, domain, journal quality, and an assessment of the content and research design of the article in question (Kitchenham and Charters 2007; Okoli 2015; Paul etal. 2021; Paul and Criado 2020; Snyder 2019). To meet the criteria for inclusion, papers had to be written in English and published after 2000 in peer-reviewed journals from the business discipline with an impact factor greater than 1 (Paul etal. 2021).2 We set the beginning of the time frame for the search at the year 2000 due to the rarity of commercialized online channels and therefore of research on multichannel or omnichannel management prior to the turn of the millennium (Gao etal. 2020; Hossain etal. 2019). For the content assessment, two researchers independently read the abstracts of all articles after the initial identification (n = 924) and judged whether they addressed channel choice from a customer perspective. There was agreement between the researchers on suitability or non-suitability in this regard for 94.48% of the articles (n = 873). To further ensure validity and reliability, a third researcher conducted an independent assessment of the articles upon which the initial two researchers did not agree (n = 51). The papers selected at this stage (n = 134) were read in detail and assessed for content and method eligibility. This process resulted in the exclusion of thirteen studies due to inapposite content, mainly because the articles examined the consequences of channel choice rather than the reasons behind it (one example is Herhausen etal. 2019). We also removed eleven non-empirical studies, such as introductions to special issues of journals (e.g. Thaichon etal. 2022). Cross-referencing yielded 18 further articles that were eligible for inclusion (see Appendix B). The final selection of published research on channel choice comprised 128 papers. Figure1, developed following the PRISMA guidelines (Moher etal. 2009), provides an 2 All three electronic databases chosen permit the ex-ante filtering of journals by language (= English) and year of publication (= after 2000). We searched the Business Source Complete Collection in EBSCO Host and limited the results to peer reviewed articles available as full text that appeared in academic journals (= source type). In the Scopus database, we searched title, abstract and keywords and limited the results to articles appearing in journals (= source type) and in the subject area business, management and accounting. Finally, we searched the Core Collection in the Web of Science database for title, abstract, author keywords and keywords plus and limited the results to the subject categories of business and management. The literature search took place on May 4, 2022. For ex-post filtering by impact factors, we used the Journal Citation Report (JCR) by Clarivate Analytics 2021 (Journal Citation Reports 2021). 1584 L.Wolf, M.Steul-Fischer 1 3 overview of the complete search process. Appendix B lists the articles included in the final review. Extraction of data and analysis:The articles ultimately selected for the review were synthesized and structured with the aid of a concept matrix (Webster and Watson 2002). First, we extracted descriptive information from each article, such as year of publication, research focus (omnichannel, multichannel, or both), channels considered, research contexts (country, industry), customer journey stage investigated (i.e. pre-purchase, purchase or post-purchase) and methodology employed (Snyder 2019). We used Microsoft Excel for the concept matrix and the subsequent descriptive analysis. For the thematic analysis, we followed the widely-used qualitative process proposed by Braun and Clarke (2006). This process entails familiarization with the data (i.e. the articles retrieved), generating initial codes with regards to interesting features, searching for themes across these codes, reviewing and refining these themes with the aid of a thematic map and, finally, defining and naming the themes and producing the report. In line with this process, we inductively coded factors of Fig. 1 The search process based on PRISMA guidelines (Moher etal. 2009) 1585 1 3 Factors ofcustomers’ channel choice inanomnichannel… channel choice using the MAXQDA software and generated five categories/themes on the basis of the literature review, including 14 sub-categories/sub-themes and 66 factors/codes of channel choice. 3 Profile oftheliterature onchannel choice Exploring channel choice from a customer perspective has always been an integral part of channel research (Neslin etal. 2006). However, with the proliferation of mobile devices and new technologies, and the increasing prevalence of omnichannel management, the understanding of channel choice has become all the more important in recent years (Barwitz and Maas 2018). This trend is confirmed by our review. As Fig.2 illustrates, the number of articles published that met our selection criteria has more than doubled in the past six years. Of the 128 papers retrieved, 77 (~ 60%) were published between 2016 and May 2022; Fig. 2 Distribution of channel choice articles across years of publication and research fields. Note: The list of channels is not complete, as we included only the ten channels most frequently mentioned in the literature. Among the channels omitted here are agencies (e.g. Hosseini etal. 2018) and internet-enabled TVs (e.g. Wagner etal. 2020). The “internet” channel includes all online channels investigated in the studies which were not specified further (examples include websites, price comparison sites, online shops). “Mobile” channels are cited as having occurred in articles which considered the online channel for mobile devices separately from the static online channel (e.g. Sands et al. 2016). As most studies on channel choice investigate two or more different channels, the totals exceed the number of articles retrieved for the review 1586 L.Wolf, M.Steul-Fischer 1 3 of these, 35 (~ 45%) explicitly address omnichannel management. Contrastingly, all articles selected that appeared prior to 2016 focus exclusively on channel choice in a multichannel environment. Most studies consider the traditional internet channel or physical stores in their investigation of channel choice (91% and 80% respectively of the articles retrieved). In recent years, emerging online channels such as mobile channels (i.e. the accessing of online channels via mobile devices), social media, and search engines have gained a greater share of attention, and, in general, the variety of channels considered has diversified in the last years in line with the move towards an omnichannel environment. Only a few channels, such as physical catalogs, have received less attention in the course of time. This finding is consistent with Konuş etal. (2014). We identified 58 journals that published papers related to channel choice within multior omnichannel environments in the last two decades. The highest number of articles were published by the Journal of Retailing and Consumer Services (n = 23), followed by the International Journal of Retail and Distribution Management (n = 12) and the Journal of Business Research (n = 8). Most of the studies do not indicate the application of specific theories to explain customers´ channel choice. However, some studies are explicitly grounded in popular theories of consumer and technology research, such as the theory of planned behavior (e.g. Pookulangara etal. 2011a), the theory of reasoned action (e.g. Pookulangara etal. 2011b) and the diffusions of innovations theory (e.g. Bilgicer etal. 2015). Table1 shows the distribution of the articles across research contexts. Of the 128 articles retrieved, 114 explicitly state the country in which the research was conducted and 105 the relevant industrial context, while 118 indicated the stage of the customer journey that the work observed. Over a quarter of the studies (n = 34) examined channel choice in the U.S. The distribution across sectors is more diverse. The most frequently studied industrial context is fashion and beauty (n = 36), including clothing (e.g. Lu and Rucker 2006) and cosmetics (e.g. Chiou et al. 2017); consumer electronics follow in second place (n = 20) and financial services and insurances in third place (n = 17). In terms of the customer journey stage investigated, most studies (n = 49) focused on channel choice for a combination of the prepurchase stage (i.e. recognition of needs, consideration, search) and the purchase stage (i.e. choice, ordering, payment). Several studies examined channel switching between these stages (phenomena such as “showrooming” for switching from offline to online – an example is Daunt and Harris 2017 – and “webrooming” for switching from online to offline, e.g. Santos and Gonçalves 2019). Seven studies analyzed the post-purchase period (i.e. consumption, usage, engagement, service requests) separately, mostly in terms of the choice of channel for making a complaint (e.g. MiquelRomero etal. 2020). For the most part, the studies are quantitative (n = 110, ~ 86%), with surveys and company database analysis the most commonly used methods (Table2). Qualitative methods such as interviews and focus groups make relatively rare appearances, as do mixed methods approaches, used by only five articles (for example Mahrous and Hassan 2017). 1593 1 3 Factors ofcustomers’ channel choice inanomnichannel… and Huang 2014). Other customers may discount some channels from the outset due to not having the required facilities, such as credit cards for online shopping (Lu and Rucker 2006). Channel choice and cross-channel switching along the customer journey may therefore not always constitute intentional or planned behavior (Maggioni etal. 2020). The proven influence of marketing campaigns and a customer’s social setting on channel choice is indicative of its potential dependence on context (Bilgicer etal. 2015). Situational and contextual factors, including time, place, communication and channel availability (see Table5), impact the opportunity and ability to use various channels, influencing customers’ choice of channel both directly and indirectly. One situational factor identified in the channel choice literature is time, for instance with regard to the time of day, week or year. Park and Lee (2017) found use of mobile channels throughout the day due to their ubiquity and portability, but a concentration of desktop device use between typical working hours of 9 a.m. and 6 p.m. (Park and Lee 2017). Time of day also has a direct impact on access to many brick-and-mortar stores due to opening hours (Chocarro etal. 2013). Other work has noted a preference for the internet over other channels during the months of November and December (Bilgicer etal. 2015; Kalyanam etal. 2017), which Kalyanam etal. (2017) attribute to the advantages of the online channel (no standing in line and no waiting time) during the holiday shopping season. A further dimension of time’s impact on channel choice relates to the requirements and motivations of customers who have little time available; these consumers will focus on the speed of the process they can achieve by using a particular channel (Cheng and Huang 2014; Oppewal etal. 2013) and place greater emphasis on utilitarian needs such as convenience-seeking (Barwitz and Maas 2018). Unsurprisingly, a lack of time limits the number of channels utilized along the customer journey (Oppewal etal. 2013) and seems to promote the use of mobile devices (Wagner etal. 2020). Table 5 Factors of channel choice investigated in the articles reviewed – 3. Situational and contextual factors 1594 L.Wolf, M.Steul-Fischer 1 3 Regarding place and social surrounding, it is shown that customers tend to adopt the same channels as their neighbors (local contagion) and as socio-demographically similar customers (homophily) (Bilgicer et al. 2015). This effect, however, diminishes over time and is consequently particularly strong among new customers (Bilgicer et al. 2015).4 Physical distance between a customer’s location and offline channels manifests to the customer as a transaction or access cost (Soysal and Krishnamurthi 2016), while proximity to brick-and-mortar stores will prompt a customer to use them (Bilgicer etal. 2015; Soysal and Krishnamurthi 2016). Location may also impact the use of online channels due to regionally poor internet coverage or broadband connectivity (Fornari etal. 2016). Communication, especially in the form of corporate marketing campaigns, is an important explanatory factor in channel choice behavior (Bilgicer etal. 2015). Promotion of a specific channel via discounts, incentives, or similar may boost positive attitudes toward the channel’s use (Srisuwan and Barnes 2008) and accelerate channel adoption (Bilgicer etal. 2015; Sun etal., 2019; Venkatesan etal. 2007). Many studies indicate congruency between marketing campaigns and channel choice; a campaign conducted via emails, for instance, may engender increased customer willingness to choose an online channel as they already use a digital device to access the mail (Ansari etal. 2008; Bilgicer etal. 2015; Kalyanam etal. 2017; Mark etal. 2019; Polo and Sese 2016). Finally, and evidently, the availability of channels directly influences channel choice. Channel awareness refers to the customer’s knowledge of channels’ existence and those within her consideration set. The availability of infrastructure and ownership of required equipment, such as a PC, smartphone, and/or credit card, act as key enablers for most digital channels (Boulay etal. 2014; Lu and Rucker 2006; Madden etal. 2017). 4.1.4 Customer characteristics Customer characteristics have an indirect influence on channel choice (see Table 6). In terms of the frequently studied variable of customer age, younger customers are more likely to use online channels along the customer journey than their older counterparts (see, for example, Brand etal. 2020; Keyser etal. 2015), and the pattern re-emerges in relation to mobile and other relatively innovative channels such as social media (examples are in Dorie and Loranger 2020; Singh and Jang 2022; Singh and Swait 2017). However, no customer chooses digital channels just because she is young. Instead, younger generations that have grown up with digital technologies have greater experience and confidence in their use (Lipowski and Bondos 2018); they also tend to perceive fewer risks in online channels (Li etal., 2019). As a group, younger customers are more price-conscious and place less emphasis on privacy issues (Madden etal. 2017), which factors further reduce their focus on perceived online channel risks and create emphasis on needs such as cost avoidance. Very young customers, however 4 For a comprehensive overview of the social influence on consumer behavior and, in particular, technology adoption see Graf-Vlachy etal. (2018). 1595 1 3 Factors ofcustomers’ channel choice inanomnichannel… – children aged 6 to 12years – may view online channels critically and prefer offline channels for shopping (Boulay etal. 2014); this may be due to the greater importance of other needs, such as enjoyment-seeking, among this group, or to varying perceptions of channel attributes (for example, children may find physical stores easier to navigate than online channels), or to situational aspects which inhibit the use of certain channels, such as a lack of access to online payment options. Similarly, Alt et al. (2021) found an inverse U-shaped relationship between age and choice of digital channels for very complex products such as life insurance. This can be explained by the lack of financial literacy and the resulting need for information among the youngest insurance customers and the lack of experience with digital channels and the resulting perception of channel risks by the oldest customers (Alt etal. 2021). These findings lend further support to the argument that customer characteristics influence channel choice only indirectly due to the diversity of needs, perceptions, situations, and contexts that characterize the approach of individual customers to a channel. Beside customer age, socio-demographic factors identified in the channel choice literature include gender, income and education level, country and residence, household size and occupation. A customer’s country and residence influences channel choice in several ways, including the potential effect of cultural dimensions on customer needs and consequently on channel selection (Ali etal. 2021; Hofstede etal. 2010; Lu etal. 2018a; Park and Kim 2018), physical distance from offline channels (Lim etal. 2021; Sousa etal. 2015; Soysal and Krishnamurthi 2016), and availability or otherwise of channels due to infrastructural conditions such as internet coverage or broadband connectivity (Fornari etal. 2016). Table 6 Factors of channel choice investigated in the articles reviewed – 4. Customer characteristics 1596 L.Wolf, M.Steul-Fischer 1 3 Psychographic factors such as customer price consciousness, openness to innovation, impulsiveness, and risk aversion serve in numerous studies to segment various types of multichannel and omnichannel customers (see, for example, Brand et al. 2020; Konuş etal. 2008; Maggioni etal. 2020; Sands etal. 2016; Sebald and Jacob 2020). Hallikainen et al. (2019), for instance, demonstrated that “digital channel enthusiasts” show high levels of innovativeness and low levels of technology-related insecurities. Keyser etal. (2015), by contrast, found innovativeness not to be a significant covariate for the segmentation of multichannel customers. Similarly, the literature points to various differing effects of price consciousness, impulsiveness, and risk aversion on channel choice. Consumers also diverge with respect to their relationship with the channel and with the vendor, both of which evolve and change over the customer’s lifetime and play an important role in channel choice. The level of customers’ experience with a channel (“channel experience”) receives the most mentions in the literature as a factor in channel choice, occurring in almost 40% of the articles reviewed (n = 47). The extent of existing channel experience lessens emphasis on the channel’s perceived disadvantages, such as perceived risks (Xu and Jackson 2019b) and perceived access, transaction and search costs (Kalyanam et al. 2017; Konuş et al. 2014), as well as enhancing its perceived benefits, which might include convenience (Blázquez 2014) and media richness (Lipowski and Bondos 2018), and boosting customers’ confidence and sense of self-efficacy in their use of the channel (Chiu etal. 2011; van Dijk etal. 2007). Channel experience, then, can act as a facilitator or a barrier, particularly with regard to digital channels (Eckl and Lingenfelder 2021; Hallikainen etal. 2019; Sousa etal. 2015). Generally speaking, customers tend to use the channels with which they are experienced (Albesa 2007; Hu and Tracogna 2020; Polo and Sese 2016). The level of channel experience a customer has can thus explain channel inertia (i.e. use of the same channel throughout the customer journey and lifetime) and the slow adoption of digital channels in relation to some sectors (Frambach etal. 2007; Sousa and Voss 2012). For instance, Filotto etal. (2021) found that a lack of online experience among customers may be the most critical barrier to the adoption of internet banking. All this notwithstanding, in certain situations which may represent disruptions of the shopping process, such as the switching of retailers, customers may re-evaluate the benefits and costs of channels, and renounce previous habits (Li etal. 2017). 4.1.5 Characteristics oftheproduct orservice inquestion Another category with indirect influence on channel choice is the characteristics of the product or service of interest (see Table7). Customer needs, and the importance of perceived channel characteristics, vary in accordance with the pricing, involvement, risk level, and complexity of the specific product or service in question (Guo etal. 2021; Kondo and Okubo 2022). Like customer characteristics, then, the type of product or service affects channel choice indirectly. Expensive products, for instance, motivate a higher need for information and risk reduction (Kakalejcík etal. 2019; Xu and Jackson 2019a). The influence of involvement (Chocarro etal. 2013), 1597 1 3 Factors ofcustomers’ channel choice inanomnichannel… risk level (Heitz-Spahn 2013), and complexity (Keyser etal. 2015; Kim etal. 2019), each factors closely related to price, is similar. Customers interested in purchasing high-priced, risky or complex types of products or services, may frequently avoid channels associated with an inherent perception of risk, such as the mobile channel (Park and Lee 2017; Sun etal. 2019). Such products may also be associated with showrooming as customers try to satisfy their need for information and service by visiting brick-and mortar stores at the pre-purchase stage, but switch to online channels for the actual purchase (Daunt and Harris 2017; Guo etal. 2021; van Nguyen etal. 2022). The literature on channel choice makes two sets of distinctions regarding types of products and services: they may be either “search” or “experience” goods (see, for example, Goraya etal. 2022), and additionally, along lines similar to those identifying divergent customer needs, either utilitarian or hedonic goods (cf. Eckl and Lingenfelder 2021; Singh and Swait 2017). Researchers (Lee and Jung 2020; Park and Lee 2017; Verhagen etal. 2019) find relatively greater needs for touch, information, and customer service in relation to experience goods, which are those containing significant proportions of qualities that elude determination prior to their use and only emerge in the course of that use (Nelson 1970). Search goods – products whose major qualities are more amenable to ascertainment prior to use – appear to evoke fewer needs of this type. This said, the literature shows no clear effect of these qualities on channel choice, variously reporting a tendency for experience goods to be purchased online (Park and Lee 2017) and to cause webrooming (purchase offline; Lee and Jung 2020), while other authors note no significant trends in this respect (Chocarro etal. 2013; Voorveld etal. 2016). Similarly, no significant difference emerges between channel choices made for utilitarian and hedonic product purchases (Pookulangara etal. 2011a; Singh and Swait 2017). Other product categories may be linked to customers´ channel choice. Chintagunta etal. (2012), for instance, found more frequent use of online channels for heavy or bulky items due to convenience-seeking and a tendency to visit brick-and-mortar stores for perishable goods, probably due to a high need for touch and necessity of immediate possession. Table 7 Factors of channel choice investigated in the articles reviewed – 5. Product or service characteristics 1598 L.Wolf, M.Steul-Fischer 1 3 4.1.6 Stage ofcustomer journey Several studies (e.g. Ali etal. 2021; Barwitz and Maas 2018; Frambach etal. 2007; Frasquet etal. 2015; Gensler etal. 2012) note strong variations in customer needs, and consequently in the importance of perceived channel characteristics, by the stage of the customer journey in question. High needs for information at the pre-purchase stage give way to an emphasis on cost and risk avoidance when it comes to purchase (Kollmann etal. 2012; Polo and Sese 2016; van Dijk etal. 2007), and factors such as redress-seeking may gain ascendance in the after-sales phase (Miquel-Romero etal. 2020). These variations may motivate differences in channel choice. The types of channel switching along the customer journey which receive most attention in the articles reviewed are webrooming (cf., for example, Kim et al., 2019) and showrooming (e.g. Schneider and Zielke 2020). Webrooming appears more prevalent (Frasquet etal. 2015; Guo etal. 2021; Schröder and Zaharia 2008; Zhai etal. 2017), in line with the typical characteristics of each channel; the pre-purchase stage may feature use of online channels for convenient and easy information-gathering, and a later switch to offline channels for making the purchase may be motivated by perceived risk reduction and a desire to make use of the sensory and customer care benefits of brick-and-mortar stores (Herrero-Crespo etal. 2022). However, channelswitching along the customer journey is not the typical case, especially in relation to non-complex and non-expensive types of product, with several studies finding use of the same channel for information-gathering and the subsequent purchase (examples are Cao 2012; Gensler etal. 2012; Noble etal. 2005; Oppewal etal. 2013). Even in an omnichannel environment, customers tend to be either onlineor offline-focused throughout the pre-purchase and purchase stages (Acquila-Natale and Iglesias-Pradas 2021; Valentini etal. 2020). This inertia appears to fall away at the after-sales stage (Frasquet et al. 2019; Keyser etal. 2015; Miquel-Romero etal. 2020), perhaps due to a reduced importance of channel convenience and a greater emphasis on other channel attributes such as personal contact and interactivity in this phase (Miquel-Romero etal. 2020). This would also explain the frequent choice of physical stores and social media channels for complaints (Dalla Pozza 2014; Frasquet et al. 2019; Miquel-Romero et al. 2020). Interestingly, when customers choose a complaint channel for a second complaint after an unsatisfactory first attempt, they tend to switch channels again; probably to take advantage of synergy effects between the channels (Frasquet etal. 2021). 4.2 An integrated framework ofchannel choice In an omnichannel environment, customers can choose the most efficient channel in accordance with its perceived utility in any situation (Gensler etal. 2012; Hosseini etal. 2018). The ultimate choice of channel depends on a number of directly and indirectly influential factors that often occur together and relate closely to and reciprocally impact one another; this calls for an integrated framework for the mapping of factors in channel choice (Gensler etal. 2012; Miquel-Romero etal. 2020). The conceptual framework we present here is fundamentally a Venn diagram illustrating 1599 1 3 Factors ofcustomers’ channel choice inanomnichannel… that customers´ channel choice is directly influenced by an interplay of perceived channel characteristics, customer needs and situational or contextual factors, and indirectly influenced by customer characteristics and product or service characteristics (Fig.3): Customers tend to choose channels whose characteristics give them the perceived ability to meet their needs (intersection between customer needs and perceived channel characteristics; Cervellon etal. 2015; Noble etal. 2005). However, there are situations and contexts that limit or inhibit the selection of channels – such as time pressure and a lack of required devices—or highlight new channels – an example might be marketing campaigns (Chocarro etal. 2013). Characteristics of customers and of products or services have a decisive influence on situational and contextual factors and on customers’ assessment of channel characteristics and of their needs. 5 Suggestions forfuture research The literature on channel choice is driven by the need to understand customer behavior in a constantly evolving channel environment. Its underlying objective is to derive implications for businesses that offer more than one channel. To this end, several studies segment customers, to provide paths to appropriate adaptation of channel management and marketing strategies (see, for example, Cheng and Huang 2014; Keyser etal. 2015; Lee and Jung 2020). Others investigate the adoption of digital and mobile channels by customers for the purpose of understanding crosschannel effects and switching of channels along the customer journey (e.g. Singh and Jang 2022; Yang etal. 2013). Research in this area now finds itself confronted with an ever-increasing array of possible channels and a proliferation of mobile Fig. 3 The conceptual framework 1600 L.Wolf, M.Steul-Fischer 1 3 devices that are driving the evolution of the multichannel environment into an omnichannel environment which remains under-researched in terms of the scene it sets for channel choice (Lemon and Verhoef 2016; Saghiri etal. 2017). Unanswered questions remain regarding factors of channel choice in this new environment and the management and marketing tactics arising therefrom (see Tables3–7). Further, extant channel choice research focuses on a limited number of industries and countries, and its findings require validation via the introduction of other methods and research designs. In addition, the studies lack theoretical foundations and frameworks. Against this backdrop, this section discusses the research agenda for factors of channel choice and potential areas for new research in this field. 5.1 Research agenda forfactors ofchannel choice Channel characteristics: We identified quality, convenience, risk, and cost as the principal characteristics of a channel in the channel choice literature. However, most studies investigate these characteristics separately (e.g. Arora etal. 2017; Oppewal etal. 2013), risking the omission or overlooking of possible interrelationships and spillover effects. A broader, more holistic view of channel characteristics would be desirable (Dennis etal. 2016; Rodríguez-Torrico etal. 2017; van Dijk etal. 2007). We are in need of further research classifying channels according to their similarities or dissimilarities in terms of characteristics; this would improve the effectiveness of channel management, enabling, for example, the discontinuation of channels that customers perceive as very similar to others and therefore as superfluous (Hosseini etal. 2018). Numerous studies highlight both positive (cross-channel synergies; see, for example, Fornari etal. 2016; Kumar etal. 2019; Verhagen and van Dolen 2009; Yang etal. 2014) and negative (cross-channel competition or cannibalization; e.g. Bilgicer etal. 2015; Li etal. 2017; Lim etal. 2021) cross-channel effects. A holistic understanding of these effects along the customer journey could be of significant benefit to channel management (Bilgicer etal. 2015; Dorie and Loranger 2020; Trenz etal. 2020; Yang etal. 2014). Channel characteristics are constantly evolving alongside emerging new technologies and digital devices (Sands etal. 2016). Further research could, for example, examine the impact of virtual and augmented reality technologies such as virtual dressing rooms on perceptions of channel characteristics (Eckl and Lingenfelder 2021; Kim etal. 2021). We also note the changing role of some channels in the omnichannel environment; brick-and-mortar stores, for instance, are gaining in significance for the preand post-purchase stages, but may be taking on a more complementary role for the actual purchase (Fornari etal. 2016; Miquel-Romero etal. 2020).5 In light of these shifts, the question emerges as to how retailers might adapt their assortment, prices and communications to channel-specific characteristics and the channels’ new roles while implementing effective omnichannel management in 5 This appears to be the case notwithstanding the findings on webrooming cited above; webrooming is a phenomenon associated with channel switching, which remains relatively rare. 1601 1 3 Factors ofcustomers’ channel choice inanomnichannel… the sense of providing a seamless and integrated multichannel experience (Blázquez 2014; Singh and Jang 2022; Valentini etal. 2020). Customer needs: The optimization of customer experience is an integral part of omnichannel management (Verhoef etal. 2015). Multiand omnichannel businesses require an understanding of both utilitarian and hedonic needs. Some traditional benefits of specific channels, such as human contact and immediate quality evaluation in physical stores, are becoming less clearly defined and less unambiguously attributable (Hallikainen etal. 2019); social media, for instance, provide interactivity and human contact and thus meet customers’ need for comfort (Dalla Pozza 2014). Future research should investigate the influence of emerging channels and technologies on the fulfillment of customer needs (Dalla Pozza 2014; Lee and Jung 2020), and specifically the influence of perceived benefits of the online channel (including vast ranges, customer-centeredness, ease of use) on customer needs in general, as perceptions of online channels’ benefits may conceivably spill over to customers’ needs and expectations when using other channels (Santos and Gonçalves 2019; Verhagen etal. 2019). While utilitarian needs tend to have a greater influence on channel choice than hedonic needs, the investigation of the latter may gain significance as the proportion of product and service types offered through a variety of channels rises (Lee and Jung 2020). Future research should therefore seek to ascertain any differences in the impact of hedonic needs on channel choice among industries or countries (Cervellon etal. 2015; Koistinen and Järvinen 2009; Noble etal. 2005). In our time of climate crisis and heightened ecological consciousness, needs such as sustainability orientation are likely to acquire prominence and require corresponding attention from researchers (Cervellon etal. 2015). Situational or contextual factors: Situational factors are difficult for businesses to influence, which makes it all the more important that they understand their potential impact on channel selection so they can adapt their channel management accordingly (Chocarro etal. 2013). One example in this regard might be the finding by Koistinen and Järvinen (2009) that customers preferred specific channels (such as marketplaces) for their grocery purchases on weekends due to their hedonic characteristics. Whether the day of the week or the season of the year influence channel choice in general, however, remains unclear (Bussière 2011). Future research could examine such potential differences more closely and generalize the findings for other sectors. A further notable research gap relates to communication, more specifically to the impact of (electronic) word of mouth on channel selection (Bilgicer et al. 2015; Kim etal. 2019). The ever-evolving infrastructure, accessibility, and affordability of digital devices may also be driving change in channel choice behavior (Frasquet etal. 2015; Singh and Swait 2017; Wagner etal. 2020). We encourage researchers to identify the sectors in which digital devices complement established channels along the customer journey and those in which the former act more as substitutes for the latter (Singh and Swait 2017; Voorveld etal. 2016). The current COVID-19 pandemic and the associated measures have exercised a major influence on channel choice behavior and served as an object lesson in the power of situational factors; social distancing and lockdowns, for instance, push up the access costs of brick-and-mortar stores (Wang etal. 2021a). There is clearly much research to be 1602 L.Wolf, M.Steul-Fischer 1 3 done into the pandemic’s shortand long-term impact on channel choice (Filotto etal. 2021; Frasquet etal. 2021; Kondo and Okubo 2022). Customer characteristics: Socio-demographic and psychographic characteristics of customers find entry into most studies on channel choice, as either independent or control variables. However, the studies do not speak with one voice on the impact of gender, education level, income level, household size, openness to innovation, and impulsiveness on channel choice. It would be of benefit to the field for future research to empirically identify any sectors within which these factors have a significant influence on channel choice and may therefore help inform customer segmentation (Boardman and McCormick 2018; Dalla Pozza etal. 2018; Sebald and Jacob 2020). In terms of customer location, the question remains as to whether global design or local adaptation of channel strategies is preferable in the light of differences in customer needs (Korgaonkar and Karson 2007; Lu etal. 2018a; Sebald and Jacob 2020). Customer characteristics also involve the customer’s relationship with channels and with the vendor. We perceive a need for research into ways to enhance the extent of customers´ experience with a channel as one of the major factors of channel choice (Hu and Tracogna 2020; Xu and Jackson 2019b) and for analysis of crosschannel experience spillovers (Gensler etal. 2012). While some studies investigated how channel choice evolves over time (see, for example, Valentini etal. 2011), we are as yet without an updated and omnichannel perspective (Hu and Tracogna 2020). Additionally, with regard to relationship with the vendor, the role of brands in channel choice behavior remains unclear despite its potential importance (Boardman and McCormick 2018; Cervellon etal. 2015; Frasquet etal. 2015; Korgaonkar and Karson 2007). Practitioners would further benefit from studies on the differential impact of marketing campaigns on new or existing customers in terms of their susceptibility to persuasion to adopt a particular channel (Valentini etal. 2011). Product or service characteristics: The distinctions between types of product or service that are examined in some work on channel choice (i.e. “search” versus “experience” goods, utilitarian versus hedonic goods) do not result in clear evidence. The result is a need for future research to examine possibilities for higherquality segmentation criteria with regard to channel choice for a range of types of product and service (Chang etal. 2017; Frasquet etal. 2015; Gensler etal. 2012; Heitz-Spahn 2013; Kondo and Okubo 2022; Pookulangara etal. 2011a); such work has the potential to inform businesses’ omnichannel management. One criterion for segmentation might be the level of customer´s involvement with the product (Arora and Sahney 2018; Gallant and Arcand 2017; Konuş etal. 2008), although the number of studies investigating the impact of involvement level on channel choice is low (they include Chocarro etal. 2013; Frasquet etal. 2015; Voorveld etal. 2016). 5.2 Research agenda fortheories, contexts, andmethods This section is based on the TCM (i.e. theories, contexts, and methodology) framework introduced by Paul etal. (2017). It first details the research agenda for omnichannel management with regard to theories, before proceeding to examine 1609 1 3 Factors ofcustomers’ channel choice inanomnichannel… Table 8 (continued) Author(s) Title Objective Perspective Category Type of reviewaMethodologybSample Time span Databases Mishra etal. (2021) Consumer decisionmaking in omnichannel retailing: Literature review and future research agenda Categorize and analyze key findings regarding consumer behavior and decisionmaking within omnichannel retailing Customer Omnichannel Structured Descriptive content & thematic analysis 131 articles 2011–2020 (April) Web of Science Salvietti etal. (2021) An Overview on omnichannel research: Intellectual foundations and implications for research Map the omnichannel research landscape and identify main research streams, theoretical foundations and future research agenda Management Omnichannel Bibliometric Bibliographic & topic modeling (co-citation cluster analysis) 314 articles 1985–2020 Web of Science [Core collection] 1610 L.Wolf, M.Steul-Fischer 1 3 Table 8 (continued) Author(s) Title Objective Perspective Category Type of reviewaMethodologybSample Time span Databases Wang etal. (2021b) Satisfying consumers all around: a multidisciplinary view of omnichannel retail Review current studies on omnichannel retail in information systems, operations and marketing research Management Omnichannel Narrative type Thematic analysis (narrative conceptualization) 33 articles 2014–2020 (June) n.a This paper Factors of Channel Choice from a Customers´ Perspective in an Omnichannel Environment: A Systematic Literature Review Summarize and analyze determining factors of channel choice in multiand omnichannel retailing Customer Multiand Omnichannel Structured Descriptive content & thematic analysis 128 articles 2000–2022 (May) EBSCO Host [Business Source Complete], Scopus, Web of Science [Core Collection] a According to Paul and Criado 2020; b According to Paul etal. 2021 1611 1 3 Factors ofcustomers’ channel choice inanomnichannel… Table 9 Final selection of articles on channel choice Author (Year) Title Journal Acquila-Natale and Iglesias-Pradas (2021) A matter of value? Predicting channel preference and multichannel behaviors in retail Technological Forecasting and Social Change Albesa (2007) Interaction channel choice in a multichannel environment, an empirical study International Journal of Bank Marketing Ali etal. (2021) Investigating the Situated Culture of Multi-Channel Customer Management: A Case Study in Egypt Journal of Global Information Management Alt etal. (2021) Digital touchpoints and multichannel segmentation approach in the life insurance industry International Journal of Retail and Distribution Management Ansari etal. (2008) Customer Channel Migration Journal of Marketing Research Arora etal. (2017)aUnderstanding consumer’s showrooming behaviour Asia Pacific Journal of Marketing and Logistics Barwitz and Maas (2018) Understanding the Omnichannel Customer Journey: Determinants of Interaction Choice Journal of Interactive Marketing Becker etal. (2017) Cross-Industrial User Channel Preferences on the Path to Online Purchase: Homogeneous, Heterogeneous, or Mixed? Journal of Advertising Bezes (2016)aComparing online and in-store risks in multichannel shopping International Journal of Retail and Distribution Management Bilgicer etal. (2015) Social Contagion and Customer Adoption of New Sales Channels Journal of Retailing Blázquez (2014) Fashion Shopping in Multichannel Retail: The Role of Technology in Enhancing the Customer Experience International Journal of Electronic Commerce Boardman and McCormick (2018) Shopping channel preference and usage motivations: Exploring differences amongst a 50-year age span Journal of Fashion Marketing and Management Appendix B See Table9. 1612 L.Wolf, M.Steul-Fischer 1 3 Table 9 (continued) Author (Year) Title Journal Boulay etal. (2014) When children express their preferences regarding sales channels: Online or offline or online and offline? International Journal of Retail and Distribution Management Brand etal. (2020) ‘Online Omnivores’ or ‘Willing but struggling’? Identifying online grocery shopping behavior segments using attitude theory Journal of Retailing and Consumer Services Cao (2012)aThe relationships between e-shopping and store shopping in the shopping process of search goods Transportation Research Part A: Policy and Practice Cervellon etal. (2015) Shopping orientations as antecedents to channel choice in the French grocery multichannel landscape Journal of Retailing and Consumer Services Chang etal. (2017) Applying push–pull-mooring to investigate channel switching behaviors: M-shopping self-efficacy and switching costs as moderators Electronic Commerce Research and Applications Cheng and Huang (2014)aHigh speed rail passenger segmentation and ticketing channel preference Transportation Research Part A: Policy and Practice Chintagunta etal. (2012)aQuantifying Transaction Costs in Online/Off-line Grocery Channel Choice Marketing Science Chiou etal. (2017) Consumer choice of multichannel shopping: The effects of relationship investment and online store preference Internet Research Chiu etal. (2011)aThe challenge for multichannel services: Cross-channel free-riding behavior Electronic Commerce Research and Applications Cho and Workman (2011) Gender, fashion innovativeness and opinion leadership, and need for touch: Effects on multi-channel choice and touch/non-touch preference in clothing shopping Journal of Fashion Marketing and Management Chocarro etal. (2013)aSituational variables in online versus offline channel choice Electronic Commerce Research and Applications Choi and Park (2006) Multichannel retailing in Korea: Effects of shopping orientations and information seeking patterns on channel choice behavior International Journal of Retail and Distribution Management 1613 1 3 Factors ofcustomers’ channel choice inanomnichannel… Table 9 (continued) Author (Year) Title Journal Dalla Pozza (2014) Multichannel management gets “social” European Journal of Marketing Dalla Pozza etal. (2018) Multichannel segmentation in the after-sales stage in the insurance industry International Journal of Bank Marketing Daunt and Harris (2017) Consumer showrooming: Value co-destruction Journal of Retailing and Consumer Services Dennis etal. (2016) Does social exclusion influence multiple channel use? The interconnections with community, happiness, and well-being Journal of Business Research Dholakia etal. (2005) Multichannel retailing: A case study of early experiences Journal of Interactive Marketing Dorie and Loranger (2020) The multi-generation: Generational differences in channel activity International Journal of Retail and Distribution Management Eckl and Lingenfelder (2021)aDeterminants of Consumers’ Purchase Channel Preference in Omni-Channel Retailing Marketing: ZFP – Journal of Research and Management Eriksson and Nilsson (2007) Determinants of the continued use of self-service technology: The case of Internet banking Technovation Falk etal. (2007) Identifying cross-channel dissynergies for multichannel service providers Journal of Service Research Fiestas and Tuzovic (2021) Mobile-assisted showroomers: Understanding their purchase journey and personalities Journal of Retailing and Consumer Services Filotto etal. (2021) Shaping the digital transformation of the retail banking industry. Empirical evidence from Italy European Management Journal Fornari etal. (2016) Adding store to web: migration and synergy effects in multi-channel retailing International Journal of Retail and Distribution Management Frambach etal. (2007)aThe impact of consumer Internet experience on channel preference and usage intentions across the different stages of the buying process Journal of Interactive Marketing Frasquet etal. (2015) Identifying patterns in channel usage across the search, purchase and post-sales stages of shopping Electronic Commerce Research and Applications 1614 L.Wolf, M.Steul-Fischer 1 3 Table 9 (continued) Author (Year) Title Journal Frasquet etal. (2019) Understanding complaint channel usage in multichannel retailing Journal of Retailing and Consumer Services Frasquet etal. (2021) Complaint behaviour in multichannel retailing: a crossstage approach International Journal of Retail and Distribution Management Gallant and Arcand (2017)aConsumer characteristics as drivers of online information searches Journal of Research in Interactive Marketing Gensler etal. (2012) Understanding consumers’ multichannel choices across the different stages of the buying process Marketing Letters Goraya etal. (2022) The impact of channel integration on consumers’ channel preferences: Do showrooming and webrooming behaviors matter? Journal of Retailing and Consumer Services Guo etal. (2021) Webrooming or showrooming? The moderating effect of product attributes Journal of Research in Interactive Marketing Hallikainen etal. (2019) Individual preferences of digital touchpoints: A latent class analysis Journal of Retailing and Consumer Services Harris etal. (2017) Online and store patronage: a typology of grocery shoppers International Journal of Retail and Distribution Management Heitz-Spahn (2013) Cross-channel free-riding consumer behavior in a multichannel environment: An investigation of shopping motives, sociodemographics and product categories Journal of Retailing and Consumer Services Herrero-Crespo etal. (2022) Webrooming or showrooming, that is the question: explaining omnichannel behavioural intention through the technology acceptance model and exploratory behaviour Journal of Fashion Marketing and Management Hosseini etal. (2018) Mindfully going omni-channel: An economic decision model for evaluating omni-channel strategies Decision Support Systems Hu and Tracogna (2020) Multichannel customer journeys and their determinants: Evidence from motor insurance Journal of Retailing and Consumer Services 1615 1 3 Factors ofcustomers’ channel choice inanomnichannel… Table 9 (continued) Author (Year) Title Journal Huang etal. (2017)aInvestigation of Chinese students’ O2O shopping through multiple devices Computers in Human Behavior Hussein and Kais (2021) Multichannel behaviour in the retail industry: evidence from an emerging market International Journal of Logistics Research and Applications Jebarajakirthy etal. (2021) Deciphering in-store-online switching in multi-channel retailing context: Role of affective commitment to purchase situation Journal of Retailing and Consumer Services Jerath etal. (2015) An Information Stock Model of Customer Behavior in Multichannel Customer Support Services Manufacturing and Service Operations Management Jo etal. (2021) Who are the multichannel shoppers and how can retailers use them? Evidence from the French apparel industry Asia Pacific Journal of Marketing and Logistics Kakalejcík etal. (2019)aDifferences in Buyer Journey between Highand LowValue Customers of E-Commerce Business Journal of Theoretical and Applied Electronic Commerce Research Kalyanam etal. (2017) Basket Composition and Choice Among Direct Channels: A Latent State Model of Shopping Costs Journal of Interactive Marketing Kazancoglu and Aydin (2018) An investigation of consumers’ purchase intentions towards omni-channel shopping: A qualitative exploratory study International Journal of Retail and Distribution Management Keyser etal. (2015) Multichannel customer segmentation: Does the after-sales channel matter? A replication and extension International Journal of Research in Marketing Kim etal. (2019) Understanding shopping routes of offline purchasers: selection of search-channels (online vs. offline) and search-platforms (mobile vs. PC) based on product types Service Business Kim etal. (2021) Channel stickiness in the shopping journey for electronics: Evidence from China and South Korea Journal of Business Research Koistinen and Järvinen (2009) Consumer observations on channel choices: Competitive strategies in Finnish grocery retailing Journal of Retailing and Consumer Services 1616 L.Wolf, M.Steul-Fischer 1 3 Table 9 (continued) Author (Year) Title Journal Kollmann etal. (2012) Cannibalization or synergy? Consumers’ channel selection in online-offline multichannel systems Journal of Retailing and Consumer Services Kondo and Okubo (2022) Understanding multi-channel consumer behavior: A comparison between segmentations of multi-channel purchases by product category and overall products Journal of Retailing and Consumer Services Konuş etal. (2014) The effect of search channel elimination on purchase incidence, order size and channel choice International Journal of Research in Marketing Konuş etal. (2008) Multichannel Shopper Segments and Their Covariates Journal of Retailing Korgaonkar and Karson (2007) The influence of perceived product risk on consumers’ e-tailer shopping preference Journal of Business and Psychology Kukar-Kinney and Close (2010) The determinants of consumers’ online shopping cart abandonment Journal of the Academy of Marketing Science Kumar etal. (2019) Why Do Stores Drive Online Sales? Evidence of Underlying Mechanisms from a Multichannel Retailer Information Systems Research Lee and Kim (2009) Gift shopping behavior in a multichannel retail environment: The role of personal purchase experiences International Journal of Retail and Distribution Management Lee and Jung (2020) Fashion consumers’ channel-hopping profiles by psychographics and demographics International Journal of Market Research Li etal. (2019) Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China Journal of Advanced Transportation Li etal. (2017) Customer Channel Migration and Firm Choice: The Effects of Cross-Channel Competition International Journal of Electronic Commerce Lim etal. (2021) The impact of mobile app adoption on physical and online channels Journal of Retailing Lipowski and Bondos (2018) The influence of perceived media richness of marketing channels on online channel usage: Intergenerational differences Baltic Journal of Management 1617 1 3 Factors ofcustomers’ channel choice inanomnichannel… Table 9 (continued) Author (Year) Title Journal Lu etal. (2018a) Cross-national variation in consumers’ retail channel selection in a multichannel environment: Evidence from Asia–Pacific countries Journal of Business Research Lu and Rucker (2006) Apparel acquisition via single vs. multiple channels: College students’ perspectives in the US and China Journal of Retailing and Consumer Services Madden etal. (2017) E-commerce transactions, the installed base of credit cards, and the potential mobile E-commerce adoption Applied Economics Maggioni etal. (2020) Consumer cross-channel behaviour: is it always planned? International Journal of Retail and Distribution Management Mahrous and Hassan (2017) Achieving Superior Customer Experience: An Investigation of Multichannel Choices in the Travel and Tourism Industry of an Emerging Market Journal of Travel Research Mark etal. 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(2013) Experimental analysis of consumer channel-mix use Journal of Business Research Park and Kim (2018) A new approach to segmenting multichannel shoppers in Korea and the U.S Journal of Retailing and Consumer Services Park and Lee (2017)aAn empirical study on consumer online shopping channel choice behavior in omni-channel environment Telematics and Informatics 1618 L.Wolf, M.Steul-Fischer 1 3 Table 9 (continued) Author (Year) Title Journal Polo and Sese (2016) Does the Nature of the Interaction Matter? Understanding Customer Channel Choice for Purchases and Communications Journal of Service Research Pookulangara etal. (2011a) Explaining consumers’ channel-switching behavior using the theory of planned behavior Journal of Retailing and Consumer Services Pookulangara etal. (2011b) Explaining multi-channel consumer’s channel-migration intention using theory of reasoned action International Journal of Retail and Distribution Management Rodríguez-Torrico etal. 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Transportation 44:885–904. https:// doi. org/ 10. 1007/ s111160169683-9 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Authors and Affiliations LukasWolf1 · MartinaSteul‑Fischer1 Martina Steul-Fischer [email protected] 1 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Lange Gasse 20, 90403Nuremberg, Germany