Determinants of Wholly Owned Foreign Direct Investments in E-Commerce Firms: A Hierarchical Country- and Firm-Level Analysis

Mueller, Marius; Swoboda, Bernhard Article — Published Version Determinants of Wholly Owned Foreign Direct Investments in E-Commerce Firms: A Hierarchical Countryand Firm-Level Analysis Management International Review Provided in Cooperation with: Springer Nature Suggested Citation: Mueller, Marius; Swoboda, Bernhard (2025) : Determinants of Wholly Owned Foreign Direct Investments in E-Commerce Firms: A Hierarchical Countryand Firm-Level Analysis, Management International Review, ISSN 1861-8901, Springer, Berlin, Heidelberg, Vol. 65, Iss. 4, pp. 699-736, https://doi.org/10.1007/s11575-025-00575-7 This Version is available at: https://hdl.handle.net/10419/330650 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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 International Review (2025) 65:699–736 https://doi.org/10.1007/s11575-025-00575-7 RESEARCH ARTICLE Determinants ofWholly Owned Foreign Direct Investments inE‑Commerce Firms: AHierarchical Country‑ andFirm‑Level Analysis MariusMueller1· BernhardSwoboda1 Received: 6 August 2024 / Revised: 11 February 2025 / Accepted: 14 March 2025 / Published online: 15 May 2025 © The Author(s) 2025 Abstract Despite their digital nature, leading and globally expanding e-commerce firms establish a physical presence in select countries over time to implement full local operations and management. However, we know surprisingly little about the drivers of such wholly owned foreign direct investment decisions in certain countries, while other countries are served virtually. This study proposes a framework for the specific, business model-related firmand host-country antecedents of e-commerce firms’ wholly owned foreign direct investment. We employ novel data concerning 1,726 operation modes chosen by the 241 leading e-commerce firms in Europe from 2010–2020 and apply multilevel modeling. In contrast to manufacturing firms, the insights reveal how e-commerce firms leverage previously unknown online experience capabilities to inform foreign direct investment decisions, challenging the currently questioned role of experience in international business research on digital firms. Moreover, business model-related country-level antecedents, such as host country logistics performance, the internet user population, or the rule of law, also explain such decisions differently. These findings contribute to the emerging research on digital firms’ internationalization and have direct implications for e-commerce managers interested in the drivers of foreign direct investment but also for manufacturers. The latter increasingly use e-commerce firms for their own international expansion, whereas policymakers aim to attract growing e-commerce firms to establish their own presence in host countries. Keywords E-commerce firms· Foreign direct investment· Logistics performance· Internet use· Rule of law· International online experience· Multilevel modeling The authors thank three reviewers for their very helpful andconstructive comments. A preliminary version of the manuscript was awarded the best conference paper in the Internationalization Process of SMEs and International Entrepreneurship Track at the Annual Conference of the European International Business Academy, 2023 in Lisbon. Extended author information available on the last page of the article 700 M.Mueller, B.Swoboda 1 Introduction Dynamic digitalization has sparked a debate on its impact on IB research and theory (e.g., Stallkamp etal., 2023; Yang et al., 2025). While most firms are affected, virtual expansion strategies are particularly evident among e-commerce firms (ECFs), i.e., firms selling physical goods via own online shops or marketplaces to consumers (reaching 23% of global retail sales in 2027; ITA, 2024). They mostly internationalize virtually, have immediate access to markets at low cost, and also offer new digital expansion opportunities for manufacturers (e.g., Gong etal., 2024). However, leading ECFs add selective physical presences over time. For example, serving 150 countries virtually, Amazon (2024), is present in 21 countries with wholly owned foreign direct investments (FDI, i.e., websites with a local domain, language, currency, and investments in own physical assets, such as warehouses and subsidiaries). Alibaba (2024) has FDI in 11 countries; Rakuten (2024) has FDI in four countries. Such selective choices offer ECFs local advantages such as penetration of markets through adapted offers and same-daydelivery services, local images, or management (e.g., Batsakis etal., 2023a; Hua & Wu, 2024). IB research assumes that digitalization reduces the importance of firms’ experience or country barriers and facilitates rapid virtual internationalization (e.g., Schu etal., 2016). However, this nascent research hashardly addressed ECFs’ FDI, which is also important to digital firms. This study provides a first theory-based understanding of ECFs’ FDI and helps establish an empirical baseline for research on digital firms’ internationalization (Stallkamp et al., 2023; Verbeke etal., 2018). ECFs’ specific digital and physical business model affects ECFs’ FDI differently than the production-based FDI of manufacturers does (e.g., Brouthers etal., 2022). Thus, we seek novel insights by linking ECFs’ virtual and physical business model elements, i.e., yet unknown specific online learning capabilities and host country barriers in FDI. Extending refined internationalization process models, we explore why ECFs pursue FDI despite virtual options or low country barriers and whether virtual presence provides sufficient learning to make FDI decisions (e.g., Swoboda & Sinning, 2022). Empirical studies have mainly addressed FDI among manufacturers (e.g., meta-analyses by Cuervo‐Cazurra etal., 2023; Wan etal., 2023) and service or IT firms. The latter, for example, highlight traditional antecedents of FDI, such as institutional distance (Picot-Coupey etal., 2014; Sánchez Peinado & Pla Barber, 2006), host country or FDI experience (Evans etal., 2008; Pla-Barber etal., 2014), and firm size or market attractiveness (Czinkota etal., 2009; Ekeledo & Sivakumar, 2004a). For digital firms, especially ECFs, only a few studies exist on various decisions. Examples include the roles of distances or capabilities in ECFs’ internationalization speed (Luo etal., 2005; Schu etal., 2016) or of distances in market selection (Schu & Morschett, 2017). For entry choice, distances have been shown to be relevant (Lee etal., 2023; Rothaermel etal., 2006), alongside reputation for geographic scope (Kotha etal., 2001). Country-level antecedents were addressed by three studies on speed (e.g., legal factors, internet use; Luo etal., 2005), market selection (e.g., rule of law (ROL), market attractiveness; 701 Determinants ofWholly Owned Foreign Direct Investments in… Schu & Morschett, 2017), and market entry (culture; Rothaermel etal., 2006). Virtual presence, i.e., dot-com vs. local domains, was shown to be influenced by the entrepreneurial orientation of small exporting manufacturers only (Ipsmiller etal., 2022); FDI vs. other modes were linked to the institutional distances of digital firms only (Stallkamp etal., 2023). No study on ECFs has considered virtual vs. physical presence. Conceptual papers refer to new theoretical antecedents and virtual modes (Brouthers etal., 2022), whereas other papers question the novelty of both for ECFs (Hennart, 2022). Despite this controversy, a surprising research gap exists in the literature on digital firms’ FDI. While earlier studies expected the internet to replace physical presence (e.g., Sinkovics etal., 2013), scholars have recently argued that ECFs and platforms can obviate market-seeking FDI (e.g.,Stallkamp etal., 2023; Yang etal., 2025). However, practical evidence reveals that ECFs establish selected physical presences over time, which fewer scholars have noted, leading to calls for closer examination of ECFs’ local asset decisions owing to their growing relevance and specific hybrid (digital and physical) business model (e.g., Batsakis etal., 2023a; Swoboda & Sinning, 2022). We address this gap by analyzing an important research question: Whether and how strongly do specific firmand host country-level factors drive ECFs’ choice of wholly owned FDI? In theorizing respective effects, we add to mode choice research of digital firms and offer two contributions to literature. First, in light of ECFs’ specific business model, we expand theoretical discussions of physical market presence in the emerging research on digital firms. Their marketseeking FDI differs from manufacturers’ FDI focus on wholly owned production subsidiaries abroad or the online presence of small manufacturers often studied in IB (e.g.,Ipsmiller etal., 2022; Tolstoy etal., 2023), as those lacking online knowledge or commerce skills also turn to ECFs abroad (Gong etal., 2024). Although ECFs can ship from abroad, only a physically present business can facilitate locally adapted goods, prices or services, management, or relationships with customers, authorities, or suppliers (Batsakis et al., 2023a; Mueller & Swoboda, 2025). As ECFs physically move goods, they also differ from IT firms such as Google or Oracle and i-business firms such as Netflix or eBay (Lee etal., 2023; Paul & Gupta, 2014). Such firms’ offers and customer interactions are usually digital with different FDI requirements that support a scaling of platforms via data centers or cloud infrastructure (Brouthers etal., 2022). ECFs’ specific hybrid business model requires an analysis of specific FDI decisions. This calls for developing theoretical rationales on the antecedents of ECFs’ decisions to add a physical presence in a country or not (Meyer etal., 2023). Second, we contribute novel insights to the literature on digital internationalization concerning the joint role of firmand country-level antecedents of FDI (which is generally rarely done in FDI research, e.g., Wan etal., 2023). We address research calls to clarify to what extent ECFs’ FDI depends on specific online capabilities and external barriers (e.g., Meyer etal., 2023; Tolstoy etal., 2021). In particular, at the firm level, we know that distances affect various decisions of ECFs, whereas the role of experience in digital firms’ internationalization has been debated (e.g.,Gabrielsson etal., 2022; Pezderka & Sinkovics, 2011). We challenge skeptics by highlighting ECFs’ specific online capabilities and path-dependent virtual learning over time 702 M.Mueller, B.Swoboda (e.g., in general and in host countries; Swoboda & Sinning, 2022). We newly theorize that, unlike offline firms, ECFs gain experience digitally from successful offers, promotions, or local online interactions with consumers that allow them to exploit FDI opportunities, for example. We provide a first understanding of such unknown online learning mechanisms for a physical presence, as research on this topic has been limited, despite the growth of digital businesses (Gong etal., 2024). At the country level, specific antecedents of ECFs’ business model abroad seem obvious: host countries’ logistics, internet users, or ROL. We theorize that these match the logic of specific barriers or location advantages in digital markets for ECFs’ hybrid business model (e.g., Dunning & Wymbs, 2001; in difference to those most often studied for manufacturers, Pezderka & Sinkovics, 2011). Only macroeconomic studies have discussed specific logistics or internet barriers, with contradictory findings for export vs. FDI flows. Finally, we also respond to calls for adopting hierarchical modeling when analyzing firmand country-level drivers of digital firms’ internationalization (e.g., Surana etal., 2024). The remainder of this study proceeds as follows. Drawing on conceptualizations and theory, we derive and test hypotheses based on 1,726 mode decisions over 11 years corresponding to 241 ECFs originating in 22 home countries. After the results are presented, we provide implications and directions for further research. 2 Conceptualization, Theory, andHypotheses 2.1 Operation Modes ofECFs Operation modes represent governance structures that allow firms to implement their business model in host countries differently (e.g., Brouthers etal., 2022). We conceptualize ECFs’ decisions regarding virtual vs. physical presence for several reasons that are specific to ECFs. The business model and internationalization of ECFs are based on a virtual presence with digital learning but require physical distribution, such as “last-mile” logistics and locally adapted offers (e.g., Meyer etal., 2023). While IB research has discussed many drivers of manufacturers’ FDI, we rethink this decision for digital firms and advance theory on how ECFs’ operation mode decisions are made. ECFs may simplify (mode) decisions to practical decisions (Pan & Tse, 2000). The latter, we argue, ultimately focus on whether a physical presence should be added and define whether the business model can be fully transferred abroad. Virtual presence among international ECFs refers to country-specific websites (e.g., dot-uk domain, language, currency) where goods are shipped from abroad (whereas dot-com domains are not host country specific; Hua & Wu, 2024; Ipsmiller etal., 2022; Swoboda & Sinning, 2022). This also applies to ECFs’ cooperation with contractual or local service providers that are partly strategically controlled (e.g., networks), which remain export-based (Hennart, 2022). Joint ventures with shared operational control are rare and atypical (e.g., Brouthers et al., 2022; Mueller & Swoboda, 2025, and in our data). Physical presence refers to wholly owned FDI that is specific to ECFs’ business needs, i.e., local domains and long-term investments 703 Determinants ofWholly Owned Foreign Direct Investments in… in physical assets that facilitate the local replication of the business model (e.g., Stallkamp etal., 2023). Specific examples include wholly owned warehouses that focus on last-mile delivery and marketing subsidiaries that facilitate local customer relationship management and offers (e.g., assortments, prices, customer service, Tolstoy etal., 2021). However, establishing such a physical presence depends on local market knowledge and specific infrastructure (e.g., Ahi etal., 2023). 2.2 Theory andECFs’ Specific Antecedents The only study on digital firms’ FDI theoretically explains the role of distance for FDI on the basis of the transferability of complementary resources abroad (Stallkamp etal., 2023). We extend this view and refer to capability and redefined internationalization process reasoning (major theories in research on the internationalization of digital firms, e.g., Cahen & Borini, 2020; Vahlne & Johanson, 2017; Yang etal., 2025). We theorize that ECFs can scale and transfer online capabilities and leverage digital learning as a path-dependent process in FDI (Schu & Morschett, 2017). ECFs’ experiences from virtual presence, which are distinct from traditional learning through the physical presence of manufacturers, for example (e.g., Coviello etal., 2017), determine their decisions to add physical presence in selected countries over time. We make both theories applicable to explaining ECFs’ FDI decisions, offering novel rationales for facilitating digital capability transfer and opportunity recognition via virtual presence and focused exploitation with FDI (e.g.,Swoboda & Sinning, 2022; Vahlne & Johanson, 2017). In particular, we offer rationales on the relative value and limits of different types of online experience capabilities and clarify the questionable role of experience in digital firms (Pezderka & Sinkovics, 2011). As both theories only implicitly consider country factors, we complementarily theorize business model-specific rationales on barriers and location advantages for ECFs (Dunning & Wymbs, 2001). This supports a joint view of the firmand country-level antecedents of FDI. We argue that a lack of specific logistic and internet infrastructure or ROL in a country may be a barrier to FDI (i.e., force virtual presence, Meyer etal., 2023), whereas their presence offers specific location advantages for ECFs’ business model. In line with capability and process theories, we conceptualize virtual locationand nonlocation-bound online knowledge (e.g.,Cahen & Borini, 2020; Vahlne & Johanson, 2017). Nonlocation-bound online knowledge pertains to ECFs’ learned routines in dealing with foreign customers virtually to exploit FDI opportunities in foreign markets over time (opportunity identification is likely simplified virtually; Tang & Gudergan, 2018). Such knowledge also pertains to prior FDI online experience, which may affect subsequent FDI in other countries (Schwens etal., 2018). It can be transferred as an online capability across foreign markets virtually. Locationbound knowledge pertains to ECFs’ insights into a specific country’s needs from virtual presence over time (not transferable to other markets; He etal., 2013). However, ECFs may rely too much on online capabilities and run the risk of falling into a “virtuality trap” (Sinkovics etal., 2013). We thus identify knowledge based on or 704 M.Mueller, B.Swoboda learning via virtual presence as an important but controversial basis for FDI among digital firms. We conceptualize ECFs’ specific host country infrastructure and ROL as specific barriers to ECFs’ hybrid business model and sources of specific location advantages (Dunning & Wymbs, 2001; Meyer etal., 2023). While many external antecedents affect manufacturing and service firms’ FDI (e.g., Wan etal., 2023), obvious local barriers or advantages for digital firms’ or ECFs’ FDI remain largely unexplored (e.g., Cumming etal., 2023). Countries’ logistics infrastructure, commonly referred to as “logistics performance”, and internet users or infrastructure, for example, have been linked to both FDI and exports by macroeconomic studies only (Halaszovich & Kinra, 2020; Latif etal., 2018). Despite the high logistics costs of ECFs (over 25% of total costs; Reuters, 2023; Janjevic & Winkenbach, 2020) or their reliance on sufficient internet users in a country, the critical role of host country logistics performance or internet users for ECFs needs theorization. Similarly, the critical role of ROL in digital business models has been highlighted (e.g., Schu & Morschett, 2017), but its role in mode choices has not. Addressing this theoretically, we propose that ECFs may face strong local barriers and costs for FDI if specific e-commerce infrastructure is lacking, while its presence could be a specific location advantage. For example, a country’s low logistics performance can be a barrier for ECFs’ ability to ensure quick delivery from warehouses, whereas high performance can make delivery more efficient. A lack of internet users can be a barrier to reaching customers, while the opposite offers location advantages with a latent broad customer base (e.g., Meyer etal., 2023). ECFs may also face local legal barriers and compliancerelated risks or increased coordination costs due to a lack of ROL (e.g., Luo etal., 2005). Additionally, a systematic literature review supports our choices of antecedents (see Figure1).[1] We study experience as an important antecedent according to offline mode research, whereas the role of (online) experience is unknown and questioned for online businesses (e.g., Sinkovics et al., 2013). At the firm level, some antecedents of ECFs’ internationalization (e.g., mainly distances or market capabilities) have been explored, but no types of online experience. At the country level, ECFs’ business model-specific antecedents have mostly not been considered Firm LevelCountr y Level Industry International Experience Host Country Experience FDI Experience Other (see meta-analyses) LogisticsInternetRule of LawOther (see meta-analyses) Offline Manufacturing Dow & Larimo, 2009; He r nández & Nieto, 2015; Tang & Guder - g an, 2018, etc. He et al., 2013; Wan et al., 2023; etc. Cui et al., 2013; Schwens et al., 2018; etc. Beugelsdijk et al., 201 8 Klier et al., 2017;Wan et al., 2023;etc. –– Bailey, 2018; Stoian & Filippaios, 2008, etc. Cuervo‐Cazurra et al. , 2023; Kostova et al., 2020;Morschett et al., 2010; etc. Services/ IT Czinkota et al., 2009; Ekeledo & Sivakumar, 2004a; Evans et al., 2008 Pla-Barber et al., 2014 Picot-Coupey et al., 2014; Sánchez Peinado & Pla Barber, 2006; etc. –––Picot-Coupey et al., 2014; Sánchez Peinado & Pla Barber, 2006; etc. Online Manufacturing – – Ipsmiller et al., 2022 – –– – Services/ IT1– – – Stallkamp et al., 2023 – –– – N ote : Italics =Studies on binary mode choice, e.g., FDI vs. other modes. Etc.=Further studies not listed here.1 Including i-business and ECFs. Fig. 1 Literature review on the antecedents of operation mode decisions 705 Determinants ofWholly Owned Foreign Direct Investments in… empirically. This review highlights the lack of knowledge and joint theoretical consideration of firmand country-level antecedents of digital firms’ mode decisions. Next, we derive hypotheses for each relationship based on theoretical rationales and clarify our current empirical knowledge first. We control and test traditional antecedents in stability checks (e.g., firm size or age and market attractivenessfactors, such as size, growth, population, or development). 2.3 Hypotheses Regarding Online Experience Factors As mentioned, we argue that specific online capabilities emerge from ECFs’ pathdependent virtual learning processes in foreign markets that are used to make decisions on physical presence (e.g., Swoboda & Sinning, 2022). ECFs can transfer general, country-specific, or FDI-related online experience capabilities differently based on virtual learning to enhance their ability to recognize and particularly exploit selected FDI opportunities (e.g., Vahlne & Johanson, 2017). International online experience refers to ECFs’ degree of general nonlocationbound knowledge regarding virtual operations in foreign markets, which is acquired via local domains over time (as an empirically established proxy; e.g., Wan etal., 2023). Empirical studies have often identified international experiences as antecedents of manufacturers’ FDI (e.g., inconclusively, Dow & Larimo, 2009, Hernández & Nieto, 2015, Tang & Gudergan, 2018; see Figure1) and that of service and IT firms (e.g.,Czinkota etal., 2009; Ekeledo & Sivakumar, 2004a). However, we argue that virtually gained international experience differs from the experience of manufacturing or service firms (e.g., Pezderka & Sinkovics, 2011). Online experience pertains to ECFs’ digital capabilities, which enable them to provide offers and services or interact with authorities in different countries over time and can be used to exploit FDI opportunities abroad. While offline experience may be used to promote virtual expansion in other countries (as indicated for local domains of small manufacturing firms; Ipsmiller etal., 2022), it is unclear whether online experience is sufficient for deciding on the physical presence of ECFs (e.g., Coviello etal., 2017), as we argue. We theoretically argue that high international online experience facilitates learning and the recognition of market opportunities for FDI. ECFs accumulate knowledge and transfer their online capabilities abroad in terms of digitally successful offers, services such as products or promotions, or relations to foreign authorities (e.g.,Schu & Morschett, 2017; Vahlne & Johanson, 2017). Over time, ECFs recognize sales opportunities more easily in certain growing e-commerce markets and especially exploit them with FDI and local supply chain processes such as logistics and purchasing (e.g., Swoboda & Sinning, 2022). This may be less risky even if physical processes are not established abroad. For example, with nearly 20 years of international online experience, Amazon recognized opportunities for FDI in Australia and Singapore in 2017. This was based on the firm’s digital capabilities from a long virtual presence abroad and learned potential to establish supply chain processes in these growing markets (Pash, 2017). We thus argue that ECFs with high 706 M.Mueller, B.Swoboda international online experience have a better sense of where to exploit FDI and may be less reluctant to invest in new physical and adapted virtual processes. In the case of a low international online experience, few virtual capabilities to transfer or recognize opportunities abroad exist (e.g., Tolstoy etal., 2021). Firms have less knowledge of how to promote digital offers or which products are successful abroad or how to deal with authorities in different countries. Such ECFs’ ability to recognize investment opportunities is limited. ECFs are especially limited in exploiting market opportunities in a foreign country (e.g., developing new supply chain processes alongside adapted market offers, Stallkamp etal., 2023). Their risks of FDI are greater and missed opportunities may hinder success (e.g., Swoboda & Sinning, 2022). ECFs may even underestimate such experience, which could cause their business models to fail abroad (e.g., Pezderka & Sinkovics, 2011). We propose the following: Hypothesis 1 (H1) International online experience has a positive effect on an ECF’s propensity to choose wholly owned FDI. Host country online experience is acquired through virtual presence over time and pertains to the degree of a firm’s location-bound knowledge regarding virtual operations in a particular foreign market. Empirical studies have often shown the role of host country experience for manufacturers’ FDI (e.g., He etal., 2013; see Figure1) but only once for service firms (Pla-Barber etal., 2014). These studies have argued that firms with such experience identify more local opportunities, overcome local obstacles, and increase their control in countries (e.g., Wan etal., 2023). In contrast, for digital firms, we only know that such market (but not online) knowledge affects market selection (Schu & Morschett, 2017). We argue that ECFs with host country online experience can transfer their capabilities and knowledge of local customers or business practices more easily, especially to exploit FDI opportunities in a country (e.g., Ipsmiller etal., 2022). This location-bound knowledge is likely a major online capability to exploit FDI opportunities in a specific country (e.g., Colton etal., 2010). We argue that high host country online experience provides unique knowledge from ECFs’ country-specific virtual interactions over time. This includes especially knowledge of local customer needs and preferences but also local business practices, competitors, or further conditions. ECFs with such knowledge develop country-specific online capabilities and can more easily recognize local market opportunities and a suitable time to exploit them with FDI in a country. For example, a country may exhibit strong local e-commerce growth but also specific payment or delivery conditions. Exploiting such local opportunities requires adapted virtual offers to customer needs and especially supportive physical investments (e.g., locally adapted delivery, returns, products, prices, or enterprise resource planning systems; Janjevic & Winkenbach, 2020). This may help establish the ECF physically as a local business. For example, Amazon entered Brazil in 2012 but chose wholly owned FDI only in 2019. The firm’s main reasons included different taxation rules across regions (e.g., specific taxation in each federal geographical unit) and complicated 713 Determinants ofWholly Owned Foreign Direct Investments in… the home market of the acquired company was counted (not additional countries). We had to exclude 61 ECFs with investments only prior to 2010, mostly traditional brick-and-mortar food retailers, and fashion verticals entering Eastern European and Asian markets since the 1990s, and for 45 ECFs, we did not find information on the operation mode at all or could not reconstruct firm-level information over time (one year before FDI). The sample included 244 firms with 1,806 operation mode decisions in 169 host countries. To include country-level independent variables (e.g., following Kostova etal., 2020), we had to obtain additional data from the World Bank (2022a, 2022b, 2022c, an established source in IB, e.g., Kostova etal., 2020) and the only one available providing these data over time, which we matched with the time-lagged mode decision years. We had to exclude 80 operation mode decisions in 25 very small host countries for which data on the World Governance Indicators (WGIs) and World Development Indicators or on the Logistics Performance Index were not available (19 or 61 decisions and 4 or 21 countries, respectively). Additional firm-level variables, such as international experience, firm size, or platform business, were obtained from Digital Commerce 360 with a one-year time lag. Table 1 Sample distribution Home countries Number of ECFs Number of entries Austria 1 8 Belgium 1 1 China 7 93 Czech Republic 3 7 Denmark 6 49 Finland 1 7 France 38 225 Germany 38 203 United Kingdom 66 535 Ireland 1 2 Italy 11 270 Korea (Rep.) 1 12 Lithuania 1 1 Netherlands 9 28 Poland 5 18 Romania 1 3 Russian Federation 3 4 Spain 7 62 Sweden 6 19 Switzerland 4 11 Turkey 4 13 United States 28 155 Total 241 1,726 714 M.Mueller, B.Swoboda The final dataset included 1,726 mode decisions across 144 countries from 2010–2020; these decisions were made by 241 ECFs originating in 22 home countries (see Table1). We used multilevel modeling to account for our hierarchically structured data and employed a maximum likelihood estimator with robust standard errors and test statistics. 3.2 Measurement We used different sources for our measures, all of which were matched one year prior to mode implementation, as discussed, because FDI is planned prior to its implementation (e.g., Swoboda etal., 2007). This approach resulted in a time lag for the assessment of internal and external factors (e.g., Li etal. 2021). Dependent Variable. We measured wholly owned FDI (1) vs. non (wholly owned) FDI (0) as a dichotomous variable capturing international ECFs’ operation mode decisions in each host country over time (e.g., He etal., 2013; Stallkamp etal., 2023). The measurement of mode choices as binary decisions has commonly been used in IB (e.g., Kostova etal., 2020, showing often consistent effects to metric measurement). We used a successive data selection procedure involving several data sources and recorded the initial year of an ECF’s country website alongside the local domain, language, and currency (as no exact day/month data were available in 80% of the cases). Wholly owned FDI includes typical forms of long-term investment in a country, such as warehouses and logistics or service/marketing subsidiaries that are wholly owned by an ECF (e.g., Cahen & Borini, 2020). We carefully assessed each case of wholly owned FDI to ensure that it was predominantly related to the ECF’s online activities by verifying its location and functions as an operation mode for online selling documented by the firm (e.g., logistics, fulfillment, online customer service). In contrast, modes without wholly owned FDI (e.g., country-specific domain only, local partnerships, partial ownership) in logistics or services of the online business were classified as non (wholly owned) FDI. Independent Variables. International online experience captured firms’ overall knowledge about foreign market accumulated through virtual presence over time (e.g.,Dow & Larimo, 2009; Wan etal., 2023). We measured the number of years for which an ECF was internationally active online with regard to each mode decision (i.e., one-year lagged), which is the most frequently used proxy for international experience over time (e.g.,Tang & Gudergan, 2018; Wan etal., 2023). We referred to the entered country data provided by Digital Commerce 360 (2021) and collected the initial time of entry and subsequent operation mode decisions through the various abovementioned sources. We tested the alternative measure of the number of countries (which has been used less frequently) in our stability checks (Ipsmiller etal., 2022), while other measures, such as the diversity of experience ratios, were unavailable (e.g., Clarke etal., 2013). Firms’ host country online experience was captured as a time-based indicator reflecting knowledge about a particular foreign market through virtual presence over time (e.g.,Clarke etal., 2013; Wan etal., 2023). This experience was measured by the number of years that an international ECF was active online in a specific country 715 Determinants ofWholly Owned Foreign Direct Investments in… one year before a mode was implemented based on our collection of each operation mode decision through various sources. Alternative measures, such as the number of operations or locations in a country, were not available for most ECFs (e.g., Tang & Gudergan, 2018). ECFs’ FDI online experience is considered a time-based measure of firms’ knowledge about implementing FDI in foreign markets over time (e.g., Cui etal., 2013). We measured this variable as the number of years since an ECF first implemented FDI in a country in addition to a country-specific domain (e.g., Schwens etal., 2018). We referred to our (lagged) data on subsequent FDI over time provided by the above sources. Alternative measures, such as the number of FDI instances in a country or area over time, were not available (e.g.,Barkema & Drogendijk, 2007; Hong & Lee, 2015). We measured logistics performance in a country via the six-dimensional logistics performance index (Arvis etal., 2018, p. 8; Schu & Morschett, 2017). This indicator reflects the quality of trade infrastructure, ease of logistics services and customs, tracking ability, and timely shipments within a country (World Bank, 2022a). The data (one-year lagged) cover eleven years in two-year periods. We used the approximate mean values of two years to measure this variable in the interim years (e.g., 2010 and 2012 for 2011; Blomkvist & Drogendijk, 2016). We measured internet user population within a country as the logarithm of the total population using the internet on the basis of data obtained from World Bank (2022c). This measure was based on a country’s internet penetration and the total population over the observed period of eleven years with a one-year time lag. Internet use is most often captured by internet penetration, which we tested in our stability checks (e.g., Batsakis etal., 2023a). However, this factor uses only the proportion of internet users (rather than the actual number of users) to indicate ECFs’ potential customers. For example, while a very small country characterized by high-level internet penetration may be initially appealing for FDI, the number of potential customers in this country may be very low, and FDI may not be worthwhile (e.g., Mueller & Swoboda, 2025). We measured differences in countries’ ROL regarding the relevant dimension of the WGIs over eleven years with a one-year time lag (World Bank, 2022b). This dimension represents the extent to which agents have confidence in and abide by the rules of society (i.e., the quality of contract enforcement, property rights, police, and courts as well as the likelihood of crime and violence) and is particularly relevant for ECFs (Oxley & Yeung, 2001; Schu & Morschett, 2017). However, regulatory institutions are broader and captured most frequently by an index of the institutional dimension of the Global Competitiveness Report (GCR; Kostova etal., 2020), followed by an index of all six WGIs. As WGIs were shown to be relevant to ECFs’ internationalization speed (Swoboda & Sinning, 2022), we tested both indices in stability checks but assumed them to be less relevant to FDI decisions. Controls. At both levels, important variables were controlled and measured one year prior to FDI. At the firm level, we controlled for firm size as a traditionally important capability for FDI (Pezderka & Sinkovics, 2011). Firm size was measured by the logarithm of firms’ online sales and obtained from Digital Commerce 360. Alternatives (e.g., 716 M.Mueller, B.Swoboda number of employees) were not available to us; ECFs hire fewer employees (Luo etal., 2005). We controlled for firms’ online age, which was measured as the number of years since an ECF started selling online, as not all ECFs founded their businesses online (which thus likely represents a digital capability). This factor may affect wholly owned FDI, as such ECFs may have more time to acquire digital resources to support FDI (e.g., Sinkovics etal., 2013). Data were collected, e.g., from ECFs’ websites. The home market size of an ECF was controlled as it might affect its expansion and wholly owned FDI (Swoboda & Sinning, 2022). A small (vs. large) home market size offers limited domestic growth opportunities and may force international investment. Market size was measured by the country’s logarithmized GDP per capita based on World Bank (2022c) data. We controlled whether ECFs ran an online marketplace (i.e., a hybrid platform with physical delivery) in addition to their core retail business (Meyer etal., 2023). Wholly owned FDI may be required for such ECFs aiming to provide and control fulfillment services in foreign markets to attract local vendors or offer local customer service, not otherwise guaranteed (Brouthers etal., 2022). We measured dichotomously: 1=yes (marketplace) and 0=no (Digital Commerce 360, 2021). Firms’ multichannel strategy was controlled. Firms operating only online have a greater proportion of internet-based activities than do firms operating multichannel (e.g., offline channels, Schu etal., 2016). ECFs with additional offline channels are more flexible and can leverage existing organizational capabilities to make online related FDI and integrate offline and online supply chain activities (e.g., returning in-store purchases to warehouses; Swoboda & Sinning, 2022). We measured this dichotomously: 1=also operating offline stores and 0=not (Digital Commerce 360, 2021). Prior international and host country experience with an offline store was included as a control because firms such as Apple may have gained experience in a country prior to online-related FDI (e.g., via offline stores), which they could use to scale and support further FDI regarding their online operations. This factor was measured dichotomously (1=prior international/host country experience and 0=no such experience). We also collected these data from various sources. We controlled for population density at the country level as one possible indicator of market attractiveness (as a possible barrier or location advantage), particularly relevant for commerce firms, as it may affect their wholly owned FDI, which is often located in densely populated regions with high demand (Chan et al., 2011). We measured this variable as people per sq. km of land area according to data obtained from World Bank (2022c) as these data cover all our host countries over time and avoid potential collinearity issues with the other country-level variables. We conducted additional analyses to test alternative measures of attractiveness in stability checks. Further measures, such as online market sales or numbers of online customers, were available only for certain European countries and only over the last five years. Table 2 shows descriptive statistics and correlations of all variables. Correlations at the firm level did not raise collinearity concerns, and all were assessed 717 Determinants ofWholly Owned Foreign Direct Investments in… Table 2 Descriptive statistics and correlations (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Mean 0.140 3.210 0.210 1.170 18.320 10.120 10.530 0.150 0.670 0.050 0.540 3.463 0.846 16.167 281.998 SD 0.350 4.405 1.153 3.076 2.320 6.143 0.450 0.358 0.470 0.222 0.498 0.520 0.937 1.501 972.473 VIF (max.) – 1.912 1.107 1.709 1.169 1.336 1.097 1.414 2.822 1.080 2.492 1.233 1.093 1.169 1.064 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Wholly owned FDI (1/0) (1) 1 International online experience (2) 0.255*** 1 Host country online experience (3) 0.407*** 0.270*** 1 FDI online experience (4) 0.220*** 0.625*** 0.224*** 1 Firm size (log.) (5) 0.113*** 0.218*** 0.097*** 0.230*** 1 Online age (6) 0.023ns 0.350*** 0.058* 0.268*** 0.269*** 1 Home market size (log.) (7) 0.077** 0.141*** 0.052* 0.126*** − 0.070** 0.139*** 1 Marketplace (1/0) (8) 0.008ns 0.018ns − 0.018ns 0.096*** 0.130*** − 0.105*** − 0.105*** 1 Multichannel (1/0) (9) − 0.077** − 0.052* − 0.064** − 0.047† − 0.039ns 0.134*** 0.151*** − 0.502*** 1 Prior host country experience (1/0) (10) 0.165*** 0.039ns 0.004ns 0.006ns − 0.060* 0.008ns 0.111*** − 0.091*** 0.147*** 1 Prior international experience (1/0) (11) − 0.008ns 0.076** − 0.016ns 0.008ns − 0.022ns − 0.002ns 0.173*** − 0.408*** 0.739*** 0.204*** 1 Logistics performance (12) 0.182*** 0.094*** 0.104*** 0.031ns − 0.022ns 0.029ns 0.063** 0.018ns − 0.186*** 0.099*** − 0.081** 1 718 M.Mueller, B.Swoboda Table 2 (continued) Internet user population (log.) (13) 0.148*** 0.063** 0.061* 0.067** − 0.011ns 0.005ns − 0.024ns 0.073** − 0.178*** 0.087*** − 0.132*** 0.319*** 1 Rule of law (14) 0.156*** 0.071** 0.100*** − 0.005ns − 0.046† 0.010ns 0.085*** − 0.028ns − 0.133*** 0.079** − 0.046† 0.857*** 0.009ns 1 Population density (15) 0.079** 0.059* − 0.002ns 0.048* 0.046† 0.063** 0.004ns 0.029ns 0.010ns 0.068** 0.026ns 0.172*** 0.149*** − 0.069** 1 Ns Not significant, FDI Foreign direct investment, SD Standard deviation, VIF Variance inflation factor * p < 0.05 ** p < 0.01 *** p < 0.01 † p < 0.10 719 Determinants ofWholly Owned Foreign Direct Investments in… individually; however, we detected a high correlation of.857 between logistics performance and ROL at the country level. This may result from logistics performance relying to some degree on the ROL in a host country (e.g., as public investors rely on strong enforceable contracts, construction permits, or the timely implementation of infrastructure projects due to high ROL, e.g., Cumming etal., 2023; Janjevic & Winkenbach, 2020). To avoid collinearity issues, these two variables were used only in separate regressions. We further included theoretically relevant controls and conducted numerous robustness checks (Kalnins, 2022; Lindner etal., 2022) by reference to independent theoretical rationales concerning the important roles played by both factors among ECFs’ FDIs. We observed variance inflation factors below the usual threshold in all models, and the highest score remained below three (O’Brien, 2007). 3.3 Method Hypotheses were tested using multilevel modeling in Mplus 9 to account for nested data considering firmand country-level effects and variance both between and within countries simultaneously (Hox etal., 2018, p. 215). Multilevel modeling accounts for variances pertaining to the dependent variable both within and between groups; for example, effects of the independent variables may differ between groups (i.e., host countries), leading to more accurate estimation across countries (which are not considered in standard regression analysis; e.g., Finch & Bolin, 2017, pp. 33–37). Multilevel modeling accounts for our hierarchically structured data (e.g., experience is tied to a specific firm, while ROL is linked with a specific country, e.g., Hernández & Nieto, 2015) and includes relevant information simultaneously in the analysis. The latter approach reduces the risk of biased results; thus, this method has been recommended in IB research (e.g., Surana etal., 2024). We computed stepwise random intercept and slope models, and the Akaike information criterion (AIC), Bayesian information criterion (BIC), and −2 log likelihood were used to assess model fit (Hox etal., 2018, p. 123, Kaufman, 1996). First, a baseline model with only the controls was calculated. We then added all the country-level independent variables separately, which were included as cluster means to account for variation within clusters over time. Firm-level independent variables were subsequently tested separately before all the variables were entered simultaneously into the model. All the independent variables were grand mean centered and standardized (increasing interpretability of intercepts and ensuring the comparability of coefficient estimates; Hox etal., 2018, pp. 61–63; Stallkamp etal., 2023). The following equation describes the model with only the controls and firm-level independent variables ( ILij) first separately, then jointly: (1) Logit (FDI ij )=𝛽 0j +𝛽 1j( IL ij) +𝛽 controls ILC ij +r j 720 M.Mueller, B.Swoboda with logit FDIij= logarithmized odds of the probability of ECF i choosing wholly owned FDI in country j; IntOnExpij = ECF’s international online experience; HoCoOnExpij= host country online experience; FDIOnExpij= FDI online experience; β0j = first-level intercept; β1j, β2j, β3j = regression scores of firm-level independent variables; ILCij = firm-level controls; CLCj= country-level control; ri j = first-level error term. Equation (3) shows the model with a country-level independent variable ( CLj) only. We then allowed the intercept β values to vary across countries as follows: with γ00 = second-level intercept of the dependent variable; γ10 and γ20 = secondlevel intercepts of the random slopes of the firm-level predictors; u0j = country-level residual variance; u1j, u2j, and u3j = random slopes of the firm-level variables at the country level. Substitution led to the following: where LPj = LP of host country j; INTj and ROLj = country internet user population and ROL; γ 03, γ04, and γ05 = fixed effects coefficients of the country-level predictors; and 𝜀 = residual variances and errors. 3.4 Results The results of the hypothesis tests are presented in Table3. We standardized all the variables prior to estimation to assess the relative effect sizes and magnitudes as well as the reported odds ratios (Exp(B), Kaufman, 1996). Owing to the strong correlation observed in this context, logistics performance and ROL were assessed in separate models. (2) Logit(FDI ij )=𝛽 0j +𝛽 1j (IntOnExp ij )+𝛽 2j (HoCoOnExp ij ) +𝛽3 j (FDIOnExp ij )+ 𝛽 controls ILC ij +𝛽 controls CLC +r ij (3) Logit (FDI ij )=𝛽 0j +𝛾 03( CL j) +𝛾 ILC ILC ij +𝛾 CLC CLC j + 𝜀 (4) 𝛽0j=𝛾00 +u0j (5) 𝛽1j=𝛾10 +u1j (6) 𝛽2j=𝛾20 +u2j (7) 𝛽3j=𝛾30 +u3j (8) Logit (FDIij)=𝛾00 +𝛾10(IntOnExpij)+𝛾20(HoCoOnExpij)+𝛾30(FDIOnExpij ) +𝛾03(LPj)+𝛾04(INTj)+𝛾05(ROLj)+u0j+u1j(IntOnExpij) +u2j(HoCoOnExpij)+u3j(FDIOnExpij) +𝛾 ILC ILC ij +𝛾 CLC CLC j +𝜀 721 Determinants ofWholly Owned Foreign Direct Investments in… For ECFs with more international online experience, the results indicated an increasing likelihood of wholly owned FDI (b = 0.420, p < 0.001, Exp(B) = 1.522), supporting Hypothesis 1. A one-standard-deviation increase in this experience increased the odds of FDI by a factor of 1.522 in Models 9 and 10. The more experience ECFs obtained online internationally, the more likely they were to choose FDI. ECFs’ host country online experience increased their likelihood of choosing wholly owned FDI (bModel 9 = 3.165, p < 0.001, Exp(B) = 23.689; bModel 10 = 3.305, p < 0.001, Exp(B) = 27.250). A one-standard-deviation increase in host country experience increased the odds of FDI by a factor of 23.689 in Model 9 and 27.250 in Model 10. The more experience ECFs have gained in specific foreign online markets, the more likely they are to choose FDI over time. Hypothesis 2 is supported. ECFs’ FDI online experience increased the likelihood of these firms choosing wholly owned FDI when assessed individually (bModel 4 = 0.439, p < 0.001); however, no significant effect was observed when international and host country online experience were included (bModel 9 = 0.125, p > 0.10; bModel 10 = 0.114, p > 0.10). Therefore, Hypothesis 3 was rejected. Logistics performance increased ECFs’ likelihood of choosing wholly owned FDI (b = 0.526, p < 0.001, Exp(B) = 1.692), supporting Hypothesis 4. A one-standard-deviation increase in host country logistics performance increased the odds of ECFs choosing wholly owned FDI by a factor of 1.692. The better a host country’s logistical infrastructure is, the more likely ECFs are to choose FDI, such as through local warehouses or subsidiaries. The internet user population increases the likelihood of ECFs choosing wholly owned FDI (bModel 9 = 0.553, p < 0.001, Exp(B) = 1.738; bModel 10 = 0.379, p < 0.001, Exp(B) = 1.461). A one-standard-deviation increase in the internet user population increases the odds of ECFs choosing FDI by a factor of 1.738 when ROL is accounted for and by 1.461 when logistics performance is accounted for. The larger a country’s internet user population is, the more likely ECFs are to use FDI for expansion. These results support Hypothesis 5. ROL increases the likelihood of ECFs’ wholly owned FDI (b = 0.515, p < 0.001, Exp(B) = 1.674). A one-standard-deviation increase in the host country’s ROL increased the odds of ECFs choosing the FDI mode by a factor of 1.674. The more transparent and effective a country’s regulations are, the more likely ECFs are to opt for FDI. Thus, Hypothesis 6 is supported. As controls, firm size, marginal ECFs’ platform business model, and prior host country experience significantly and consistently affected the likelihood of ECFs choosing FDI. 3.5 Stability Checks To ensure stability, alternative models were tested. First, a random split-half test was conducted. The results of the firmand country-level variables remained stable (see Web Appendix A). Second, we replaced several independent variables with alternatives (see Web Appendix B). 722 M.Mueller, B.Swoboda Table 3 Results Intercept Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 bpbpbpbpbpbpbpbpbp Exp(B) bp Exp(B) − 2.109*** (0.146) − 2.290*** (0.219) − 1.760*** (0.164) − 2.323*** (0.207) − 1.688*** (0.178) − 2.692*** (0.156) − 2.344*** (0.136) − 2.473*** (0.153) − 2.742*** (0.212) − 2.665*** (0.216) Firm Level International Online Experience → Wholly owned FDI (1/0) 0.675*** (0.085) 0.381** (0.111) 0.420*** (0.114) 1.522 0.420*** (0.113) 1.522 Host Country Online Experience → Wholly owned FDI (1/0) 4.274*** (0.770) 4.256*** (0.808) 3.165*** (0.627) 23.689 3.305*** (0.634) 27.250 FDI Online Experience → Wholly owned FDI (1/0) 0.439*** (0.081) 0.120ns (0.088) 0.125ns (0.086) 1.133 0.114ns (0.086) 1.121 Country Level Logistics Performance → Wholly owned FDI (1/0) 0.633*** (0.098) 0.526*** (0.103) 1.692 Internet User Population (log.) → Wholly owned FDI (1/0) 0.505*** (0.101) 0.553*** (0.102) 1.674 0.379*** (0.117) 1.461 Rule of Law → Wholly owned FDI (1/0) 0.466*** (0.091) 0.515*** (0.094) 1.738 Controls (Firm Level) Firm Size (log.) → Wholly owned FDI (1/0) 0.641*** (0.139) 0.524*** (0.129) 0.405** (0.134) 0.466*** (0.126) 0.318** (0.113) 0.627*** (0.133) 0.651*** (0.138) 0.630*** (0.135) 0.347** (0.115) 1.415 0.336** (0.110) 1.399 Marketplace (1/0) → Wholly owned FDI (1/0) − 0.187* (0.076) − 0.172* (0.069) − 0.144† (0.079) − 0.233** (0.081) − 0.165* (0.077) − 0.131† (0.074) − 0.199* (0.080) − 0.132† (0.073) − 0.143† (0.079) 0.867 − 0.151† (0.079) 0.860 Multichannel (1/0) → Wholly owned FDI (1/0) − 0.519*** (0.101) − 0.290* (0.118) − 0.409** (0.118) − 0.464*** (0.109) − 0.280* (0.126) − 0.404*** (0.098) − 0.474*** (0.100) − 0.439*** (0.097) − 0.181ns (0.121) 0.834 − 0.181ns (0.119) 0.834 Home Market Size (log.) → Wholly owned FDI (1/0) 0.236* (0.100) 0.159† (0.095) 0.180ns (0.112) 0.166† (0.091) 0.123ns (0.106) 0.202* (0.098) 0.241*** (0.099) 0.206* (0.098) 0.092ns (0.101) 1.096 0.095ns (0.103) 1.100 729 Determinants ofWholly Owned Foreign Direct Investments in… pressure. Moreover, societies face dynamic changes in IT that require regulation to enable the population and industries to keep pace with these shifts. 5 Limitations andFuture Research This study has limitations that suggest directions for future research. We explore longitudinal data concerning leading ECFs, but an extended database will support further conclusions, e.g., regarding ECFs worldwide, business-to-business ECFs, or regional differences in wholly owned FDI (e.g., Yang etal., 2025). Examining smaller ECFs or the growing platform business within ECFs can provide new insights into FDI (our controls for firm size and the marketplace affect FDI both positively and negatively). The control of previous experience in a host country (beyond online) is also positive, suggesting further research on going vs. born digitals (Batsakis etal., 2023a). With respect to our measurements, we consider FDI but cannot identify acquisitions or cooperative modes (both and esp. ECFs’ various partnerships; e.g., Brouthers et al., 2022). Measuring ECFs’ degree of control, mode switches, or learning from failure over time is challenging but could reveal stepwise decisions affected by antecedents (e.g., Mueller & Swoboda, 2025). As noted, data on more ECF-specific antecedents across nations could become available for future research (e.g., ECFs’ online marketing capabilities, such as social media usage or web traffic in countries). Online market potential, local e-commerce competition, or specific ROL and media barriers that affect ECFs’ internationalization can be studied by reference to future data concerning e-markets (e.g.,Batsakis etal., 2023a; Pezderka & Sinkovics, 2011). In our framework, we capture the local environment with infrastructural factors and the ROL, while normative or cultural institutions may be studied (e.g., Lee etal., 2023). We often refer to opportunities or customer needs, which are critical for ECFs, yet what drives the adaptation of online offers or services is unclear but important (e.g., assortment, payment options; Ipsmiller etal., 2022). It is also unclear how adaptation/standardization decisions affect ECFs’ local or general performance or growth. The same applies to competition or digital capabilities (e.g., Fleury etal., 2024). 6 Conclusions Despite many opportunities of digital internationalization highlighted in literature, no study has investigated whether and how digital and especially ECFs choose between virtual vs. physical presence, i.e., websites with local domains and investments in own warehouses or subsidiaries abroad. Such mode choices can have significant implications. Developing theory about the underlying mechanisms of this decision, we improve our limited knowledge of digital firms’ expansion. In particular, we contribute to theory by clarifying and explaining how operation mode decisions relate to the exploitation of ECFs’ online knowledge and host 730 M.Mueller, B.Swoboda country infrastructure, i.e., how business model-specific factors act as determinants of this choice. We find support for most of our hypotheses; however, the factors we analyzed explain FDI to different degrees. The relevance of elements of ECFs’ virtual and physical business models to this major decision is thus, for the first time, theoretically and empirically clarified and contrasted with i-businesses’ or manufacturers’ digital internationalization. Notes 1. We conducted a systematic literature review in >50 journals following Harzing’s journal quality list on the basis of Gaur and Kumar (2018). We used the keywords ECFs, digital firms, i-businesses, FDI, mode or entry strategy, virtual or physical presence, international or host country experience, logistics and internet infrastructure, ROL or barriers (for studies since 2010 and cross-citations). As indicated in the introduction, empirical studies on “offline firms” (manufacturing, service/IT) have analyzed many antecedents of mode decisions, albeit mostly at the firm level. For example, Dow and Larimo (2009) highlighted the role of international experience, He etal. (2013) that of host country experience, and Cui etal. (2013) that of FDI experience in the binary decision “FDI vs. other modes.” The insights are not always consistent (e.g., non-significant Dow & Larimo, 2009, negative Hernández & Nieto, 2015, positive Tang & Gudergan, 2018). Other antecedents are shown by meta-analyses (e.g., Wan etal., 2023 with institutional distance, R&D, advertising intensity). Significantly fewer studies have focused on mode choices among service/IT firms. Most underline the positive role of international experience on FDI (Czinkota etal., 2009; Ekeledo & Sivakumar, 2004a; Evans etal., 2008), while Pla-Barber etal. (2014) show a positive role of host country and a negative of FDI experience. Other antecedents are also addressed (e.g., distances by Picot-Coupey etal., 2014; strategic motives by Sánchez Peinado & Pla Barber, 2006). Fewer studies address country-level antecedents and to the best of our knowledge, mostly specific to manufacturing or service firms. Some studies have highlighted the relevance of regulatory factors, including ROL, for manufacturers’ FDI (e.g.,Bailey, 2018; Stoian & Filippaios, 2008). Other research, for example, a recent meta-analysis of 224 studies (Cuervo‐Cazurra etal., 2023), has addressed political risks, market attractiveness, uncertainty or legal factors. In an online context, only two studies consider operation mode decisions. Among manufacturing firms with a virtual presence, Ipsmiller etal. (2022) revealed a positive effect of entrepreneurial orientation on the choice of two types of virtual presence. Among online service/IT firms, Stallkamp etal. (2023) revealed positive effects of distance on FDI choice. To our knowledge, no empirical studies have considered country-level antecedents or examined antecedents that pertain specifically to ECFs’ business model and mode decisions. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1157502500575-7. 731 Determinants ofWholly Owned Foreign Direct Investments in… Funding Open Access funding enabled and organized by Projekt DEAL. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations Conflict of interest None. 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