Reassessing the digital transformation of multinational enterprises: The role of cross-country information distance

2.1. Real options theory and MNEs’ decision-making

Real options theory has gained growing attention in management and international business research and is often described as an important lens to study firm decisions under conditions of uncertainty (McGrath, 1999; Trigeorgis and Miller, 2025; Verdu et al., 2012). The core concept of a real option is to invest in flexibility to make future business decisions, considering the potential variability in expected losses and returns under conditions of uncertainty (Trigeorgis and Reuer, 2017). In an international context, firms’ internationalization decisions are a complex process largely impacted by foreign market uncertainties regarding the different institutional, social, and monetary systems (Chi et al., 2019; Sahaym et al., 2012). Building on this, researchers increasingly emphasize the value of real options for studying MNEs’ decision-making across diverse environments, as it accounts for uncertainties arising from changing market conditions and cross-country effects (Chi et al., 2019). A recent study by Trigeorgis and Miller (2025) further emphasizes that the real options perspective “can complement other MNE theories” (p. 4), offering a suitable framework for understanding the portfolio of international decisions and performance of MNEs.

In fast-changing environments, MNEs will face more pressure to modify their business models and create new products or processes (Verdu et al., 2012), as changes are necessary when operating in high levels of uncertainty. When facing cross-country differences, MNEs can gather new information and have more options to make investments based on assessments of the demand trend in a foreign market (Chi et al., 2019). Multinationality is a key consideration in the exploration or acquisition of the real option life-cycle stage (Trigeorgis and Reuer, 2017), when firms acquire or create options through actions such as foreign direct investment (FDI) in subsidiaries abroad. In the following stages of development and exploitation of real options, organizational realities such as information structures and strategies, for example, market entry (Belderbos et al., 2020) or switching, play key roles in managing and exercising real options toward strategic change (e.g. digital transformation in subsidiaries), continuation/extension, or discontinuation (Trigeorgis and Reuer, 2017). Real options awareness and exercising of real options toward their exploitation can also lead to better value and performance (Ioulianou et al., 2021).

2.2. Information distance across countries and digitalization of MNEs

Despite widespread research on the types of cross-country distance and the related challenges and opportunities for MNEs (Berry et al., 2010; Li et al., 2020; Messner, 2023; Wu & Fan, 2023), less is known about whether uncertainties created by countries’ different paces in informational globalization (i.e. information distance) affect MNEs’ performance, especially those in industries that rely heavily on digital infrastructures for operating overseas. In an international business context, information distance comprises the distance across two dimensions of informational globalization, according to Gygli et al. (2019). First, de facto informational globalization captures the actual cross-border flows of ideas, knowledge, and technology, including indicators such as foreign patent applications and high-technology exports (Gygli et al., 2019; Haelg, 2022). Second, de jure informational globalization reflects a country’s capacity to share information internationally, assessed through Internet access, television availability, and press freedom, indicating the structural conditions that enable information exchange (Gygli et al., 2019). Our conceptualization of information distance is distinct from a related conceptualization by Lindner and Puck (2024), who stress the role of institutional conditions and socio-political risks, operationalizing their construct using permutation entropy of financial returns in lead indices.

In IT-intensive sectors, it is important to consider informational globalization along with digitalization, which, as a symbol of the fourth industrial revolution, is increasingly changing how firms are organized and how they operate internationally (Luo and Zahra, 2023; Meyer et al., 2023; Teece, 2025). Digitalization involves utilizing information including big data and new information-processing techniques and algorithms to increase production efficiency and economic welfare (Autio et al., 2021; Bhandari et al., 2023). Research indicates that country-level informational globalization is becoming increasingly relevant and influential in today’s interconnected digital economy (Ramzan et al., 2023). Due to the frequent changes in digital technologies, research has also started to examine global digital interconnectivity and interdependence risk due to the linkages between suppliers, distributors, customers, MNEs, and their many stakeholders in different countries (Luo, 2022).

The pace of change in informational globalization across countries can impact the digital transformation in MNEs and their subsidiaries (Autio et al., 2021; Grimpe et al., 2023; Lee et al., 2023). Considering the disparities in digital literacy, technological development, and other aspects of informational globalization between countries (Ciulli and Kolk, 2023; Han and Lee, 2022; Lee and Lee, 2021), new challenges and opportunities may arise due to the home–host country information distance. Significantly, the international activities of MNEs from IT-intensive sectors, which span the areas of data processing and cybersecurity, are more sensitive to information distance compared with traditional MNEs (Meyer et al., 2023).

Luo (2022) suggests that MNEs and industries with higher data intensity are more affected by digital risk across countries and governments regulating digitalization, with implications for their subsidiaries’ digital transformation and performance. The common features of MNEs from “digitally savvy” IT-intensive sectors include their high degree of information and knowledge intensity, identifiable online presence, and variations in digital transformation (Monaghan et al., 2020; Nambisan and Luo, 2022). According to real options theory, unpredicted uncertainties in the global informational context can affect the potential gains from MNEs’ international activities, reshaping their strategic orientation in digital transformation and leading to greater variance in financial performance. As the evolution of informational environments in different countries is largely virtual (Luo, 2022), it is critical to examine the linkages between country-level information distance and its impact on MNEs’ digitalization and performance.

2.3. The impact of information distance on MNEs’ digital transformation and performance

Greater information distance across countries can hinder MNE subsidiaries’ ability to sustain digital transformation and achieve stronger financial performance. Existing research suggests that high levels of information asymmetry in foreign markets exacerbate MNEs’ risk exposure (Lindner and Puck, 2024), creating a negative sign for MNE transformation due to increased difficulties in understanding the “rule of the game” in the host country. From a real options perspective, a greater information distance also implies increased environmental uncertainty and downside risk (Trigeorgis and Miller, 2025). MNEs are likely to reduce their commitment to the host country and focus on cost-cutting strategies. Also, with a significant gap in the flow of ideas, knowledge, and data between home and host countries, MNE subsidiaries are less likely to implement transformation initiatives effectively and successfully (Murphree et al., 2022).

Differences in key aspects of information sharing, such as digital media access and press freedom, can present formidable barriers to subsidiaries’ initiative and their digital transformation (Figueira et al., 2023; Li et al., 2021). Conversely, a smaller information distance has advantages for the digital transformation of MNE subsidiaries. It reduces uncertainty for both headquarters and subsidiaries and stimulates knowledge sharing so subsidiaries can become vehicles of Industry 4.0 (Jankowska et al., 2021). For example, Ingršt and Zámborský (2021) have shown that knowledge flows between European subsidiaries of Australian and New Zealand multinationals are negatively affected by aspects of cross-border distance linked to the quality of communication channels for organizational information exchange (Gaur et al., 2019), and that this negatively impacts subsidiaries’ innovation performance (e.g. their digital transformation). Taken together, we predict:

The growing complexity of internationalization due to entering substantially different informational environments will lead to MNEs facing additional costs to maintain their operation and legitimacy in the host country. If firms choose a proactive adaptation option (Verdu et al., 2012), they need to adapt to multiple technological standards different from the home country’s practices (Kim et al., 2016), thereby directing their attention to modifying their business models to fit the local technological and informational environment. Although real options theory predicts that firms can explore alternative options under uncertainty (Nanda and Rhodes-Kropf, 2017; Wang and Hwang, 2007), there is a trade-off between reducing market adaptation costs and maximizing option values from engaging in innovation activities, especially in digitalization. Fitting the different standards and meeting the local market demand for technological products require MNEs to invest in substantial switching costs (Kim et al., 2016), resulting in these firms’ deteriorating profit margins in the host-country subsidiaries.

The real options view highlights that firms hedge or diversify risks by expanding into other potential business areas or finding alternative markets (Sahaym et al., 2012). In line with this logic, MNEs will either invest in non-digital activities that are less likely to violate changing digital standards or reduce their commitments to the host country and enter other markets with lower information distance. As a result, these firms are prone to slowing down the pace of digitalization and risk performance damage in the host country. Under conditions of unpredictable uncertainty, another option is risk and uncertainty management (Clarke and Liesch, 2017; Zámborský et al., 2022), which requires MNEs to wait for new solutions to emerge for overcoming information distance and digital risk. While the waiting option allows companies to better manage digitization risks in international business (Luo, 2022), it does so in the form of time-consuming and costly modifications (Clarke and Liesch, 2017). However, maintaining the competitive advantage of firms in IT-intensive industries requires technological change or innovation (Sahaym et al., 2012), and such options will be detrimental to MNE subsidiaries’ financial performance. We propose:

2.4. The moderating effects of intra-firm features

To increase strategic options under information distance, real options theory also highlights the importance of increasing strategic and operational flexibility (Verdu et al., 2012). Intra-firm features, especially the firms’ financial liquidity and operational flexibility, affect MNEs and their subsidiaries’ vulnerability to information distance.

2.4.2. Operational flexibility

Another critical factor is operational flexibility, a firm’s ability to respond profitably to environmental fluctuations by shifting factors of production within a multinational network of subsidiaries (Tang and Tikoo, 1999). It can assist MNEs in coping with information distance to improve digitalization outcomes. Studies find that MNEs maintaining subsidiaries’ flexibility in operating in foreign markets tend to perform better in value creation and innovations (Beugelsdijk and Jindra, 2018; Ingršt and Zámborský, 2021), including those underpinning digital transformation. For instance, MNE subsidiaries with more operational flexibility can better embed in host-country networks (Ambos et al., 2011; Fisch and Zschoche, 2012), helping them learn about digital differences and making their digital transformation more legitimate (Jankowska et al., 2021). Flexibility also enables knowledge transfer across the subunits of firms (Keupp et al., 2011), which allows firms to utilize available resources to cope with country-level information distances that hinder their digitalization. In another aspect, high flexibility allows MNE subsidiaries to pursue strategically valuable real options and act independently instead of following the home country’s technological requirements (Beugelsdijk and Jindra, 2018), particularly under conditions of high information distance. Flexibility also provides switch/growth options for MNEs, which are critical in firms’ digital transformation under information distance. Hence, we propose:

H4a. Operational flexibility positively moderates the relationship between home–host country information distance and MNE subsidiaries’ digital transformation.

Following real options theory, flexibility provides switch or abandon options for firms (McGrath, 1999), enabling them to be more flexible in cutting profit-losing projects when operating in the host country with a large information distance. First, local responsiveness enables MNEs to adapt to local informational conditions, thereby achieving better subsidiary financial performance. When firms can remain responsive to the changing needs of host countries, their resource integration configurations are likely to become more efficient (Geleilate et al., 2020; Keupp et al., 2011). Second, research shows that flexibility is needed when MNEs operate in an environment with a greater level of uncertainty and a variety of challenges and opportunities (Beugelsdijk and Jindra, 2018). Operational flexibility implies internal economies of scale (Beugelsdijk and Jindra, 2018), which can help MNEs and their subsidiaries cope with market pressures and uncertainty under the conditions of information distance. As a result, the negative impact of information distance on subsidiary performance can be reduced. Hence, we predict:

H4b. Operational flexibility positively moderates the relationship between home–host country information distance and MNE subsidiaries’ financial performance.

The research framework of this study is depicted in Figure 1.

OPEN IN VIEWER

Figure 1.

Research framework.

3.1. Methods

This study uses the GLS (generalized least squares) regression approach, as it is often used for examining time series panel data. Based on the results of the Hausman test, the random-effects GLS model is more efficient than the fixed-effects models (Jiang et al., 2010; Madsen and Rodgers, 2015). In testing the proposed hypotheses, the following equations are employed:

D i g i t a l i z a t i o n i t = ∅ + ∂ * d ( f p , f q ) i t − 1 + C V i t − 1 + ∈ i t − 1

(1)

S u b s i d i a r y p e r f o r m a n c e i t = ∅ + ∂ * d ( f p , f q ) i t − 1 + C V i t − 1 + ∈ i t − 1

(2)

When moderators are included, the following equations are employed:

D i g i t a l i z a t i o n i t = ∅ + ∂ 1 * d ( f p , f q ) i t − 1 + ∂ 2 * d ( f p , f q ) i t − 1 * M o d e r a t o r i t − 1 + ∂ 3 * M o d e r a t o r i t − 1 + C V i t − 1 + ∈ i t − 1

(3)

S u b s i d i a r y p e r f o r m a n c e i t = ∅ + ∂ 1 * d ( f p , f q ) i t − 1 + ∂ 2 * d ( f p , f q ) i t − 1 * M o d e r a t o r i t − 1 + ∂ 3 * M o d e r a t o r i t − 1 + C V i t − 1 + ∈ i t − 1

(4)

Equations (1) and (3) focus on testing the impact of explanatory variables on digital transformation, while equations (2) and (4) examine that impact on the subsidiaries’ financial performance. d ( f p , f q ) i t − 1 refers to the independent variable (i.e. information distance), and its measurement is outlined in the next section about variables. ∈ i t − 1 is the error term, and ∂ i is the regression coefficient. C V i t − 1 is a vector that includes all control variables.

3.2. Dependent variables

The two dependent variables (DVs) in this study include the extent of digital transformation of MNE subsidiaries and the financial performance of subsidiaries, which are measured using data collected from Dow Jones Newswires and Osiris. Digital transformation is captured through a CATA method that relies on the news reports collected from Dow Jones Newswires (N = 20,573) and uses the Linguistic Inquiry and Word Count (LIWC) software. The CATA method enables firm activities to be measured in a standardized manner (Haselhuhn et al., 2022; McKenny et al., 2018). It offers greater generalizability and can capture firms’ digitalization efforts, which are often not fully released in archival data. Researchers have developed novel dictionaries that can help better analyze the texts collected and construct variables related to firm digitalization. This study used the Digital Orientation Dimensions Dictionary offered by Kindermann et al. (2021), which enables the firm’s digital technology scope to be analyzed using keywords such as Internet-of-Things, blockchain, and cloud-based. Unlike traditional measurements such as using R&D expenditures or patent counts to capture firms’ technological activities, the text analysis approach allows for the capture of various dimensions of firms’ digital activities using qualitative evidence (Escoz Barragan and Becker, 2025).

This approach has gained increasing popularity as a measure of firm digital transformation (e.g. Escoz Barragan and Becker, 2025; Zhu and Dong, 2025) owing to its ability to assess how MNEs align their structures, processes, and strategies with the integration and utilization of digital technologies, offering a more holistic view of transformation that goes beyond just the technological perspective. In this study, the score of the scope of digital technologies generated by CATA reflects the extent to which firms can create or incorporate new digital tools into their operations (Kindermann et al., 2021), which is used to proxy the firms’ digital transformation.

Subsidiary performance is measured using the gross profit divided by operating revenue (i.e. gross profit margin), which reflects the subsidiary’s earnings or ability to generate profits and its bargaining power in the host country (Moatti et al., 2015; Tang and Tikoo, 1999). The DVs are captured in year t, and all other explanatory and control variables are measured in year t − 1 to establish causal relationships.

3.3. Independent variable

The independent variable, information distance, is measured using the informational globalization indexes collected from KOF, which is a relatively new and comprehensive dataset that calculates a range of country-level globalization indexes including trade, financial, interpersonal, informational, cultural, and political aspects (1970–2021) (Dreher, 2006; Potrafke, 2015). The informational aspect covers six subindices, including a country’s Internet bandwidth, international patents, and high-technology exports—the de facto measures; and television access, Internet access, and press freedom—the de jure measures (Dreher, 2006). The six subindices allow us to consider a country’s informational environment and capacity for digital globalization. To construct the independent variable, the indices for each country are condensed into one dimension using factor analysis, as the de facto and de jure measures are highly correlated. That is, a comprehensive dimension is created for the home-country list (Cronbach’s alpha = 0.871) and the host-country list (Cronbach’s alpha = 0.894). With the two informational dimensions (one based on home countries and the other based on host countries), information distance is calculated based on the Euclidean distance approach as follows:

d ( f p , f q ) = ∑ i = 1 n ( f p i − f q i ) 2

(5)

where f p is the condensed dimension for the host country, f q is that for the home country; and d () in equation (5) indicates the two points’ distance in Euclidean n-space (Danielsson, 1980). The overall informational globalization index for each country, measured using the average value of the subindices (Dreher, 2006), is used as an alternative measure of the independent variable in the robustness check. That is, the alternative measure of information distance is calculated using the Euclidean approach based on the average scores of the indices (for the home countries and the host countries of MNEs).

3.4. Moderators

Following previous studies, financial liquidity is measured using the current assets stocks divided by current liabilities (Chang and Singh, 1999; DeSarbo et al., 2009). The prominent source of control power by the headquarters of MNEs is reflected by equity ownership (Ambos et al., 2011). When an MNE subsidiary is fully controlled by its headquarters, its operational flexibility in foreign countries may be restricted (Ambos et al., 2011; Geleilate et al., 2020). Hence, this study created a dummy variable to capture a subsidiary’s operational flexibility, which equals 1 if it is not fully controlled by the headquarters (not 100% ownership stake) and 0 otherwise.

3.5. Control variables

We included a set of control variables at the subsidiary, headquarters, and country levels, which may influence the main effects. At the subsidiary level, firm age is controlled, as younger firms may undertake riskier innovation activities (Coad et al., 2016), resulting in them reacting differently to information distance. Firm age is measured by taking the natural logarithm of the total number of years since the firm was established. Firm size, measured by the total assets of firms in each year (natural logarithm-transformed), is also related to scale economy effects and affects firm performance (Randøy and Goel, 2003). Intangible resources are crucial for competitive advantage and innovation (Randøy and Goel, 2003); hence, we controlled for the intangible asset ratio by dividing intangible assets by total assets. The gearing ratio, measured using total debts divided by total assets (Randøy and Goel, 2003), reflects the firm’s financial burden and is included as a control variable.

Given that the strategic orientation of subsidiaries is often shaped by their headquarters (Dahms et al., 2023; Scott-Kennel and Giroud, 2015), we controlled the headquarters’ scope of internationalization and intangibility, as the global presence and strategic focus on technological expertise can influence the subsidiary’s digitalization decisions. We included a dummy variable, scope of internationalization (equals 1 if the firm has multiple target countries, and 0 otherwise), to capture whether the MNEs have multiple foreign locations. We controlled the intangible asset ratio at the headquarters level to account for its impact on subsidiaries’ digitalization. We also controlled for news intensity using total word count.

At the country level, previous studies show that the institutional environment of an MNE’s home country impacts its ability to deal with difficult conditions when operating in a foreign country (Cuervo-Cazurra and Genc, 2008). Voice and accountability and political stability are controlled, as these are important institutional dimensions that can reflect the home governments’ influence. Geographic distance between countries can impact the performance of MNEs; hence, it is controlled using the distance (per 1000 km) between the capital cities of the home and host countries. Considering that using shared language in a foreign country enables firms to better understand the specific challenges, such as information distance, a dummy variable was created that equals 1 if the two countries use a common language.

In addition, the host country’s characteristics, such as macroeconomic uncertainty, IP (intellectual property) protection, and trade openness, are controlled. First, in countries that lack strong protections for IP rights, firms face potential appropriation risk and may lack incentives to engage in innovative activities. Host-country IP protection is controlled using data collected from the Heritage Research database. Second, the standard deviation of inflation rates is controlled, as it reflects macroeconomic uncertainty in the host country (Bennett et al., 2023). Third, trade openness reflects the degree of trade liberalization of a foreign country, which may affect MNEs’ investment (Bertrand, 2011). Host-country trade openness is controlled using the total exports as a percentage of gross domestic product (GDP) (collected from the World Bank database).

4.1. Main findings

In presenting the regression results, the first two models only included control variables (for the two DVs). In Table 2, Model 1 examines the impact of information distance on firms’ digital transformation, and Model 2 is based on subsidiary performance as the DV. Models 1 and 2 aim to test H1 and H2, respectively. Table 2 shows that the regression coefficient of information distance in Model 1 is negative and significant (β = −0.115, p < 0.050), providing support to H1 that information distance between home and host countries leads to firms facing more difficulties to improve their digitalization. In Model 2, the impact of information distance on firms’ subsidiary performance is also significantly negative (β = −0.094, p < 0.050), confirming our prediction in H2 about the negative influence of country-level information distance.

OPEN IN VIEWER

Table 2.

The impact of home–host country information distance on firm-level outcomes.

DV	Digital transformation		Subsidiary performance		Digital transformation		Subsidiary performance
Models	(All controls)		(All controls)		Model 1		Model 2
	β	p	β	p	β	p	β	p
Information distance					-0.115 (0.058)	0.048	-0.094 (0.046)	0.040
Firm age	0.064(0.055)	0.246	0.127(0.049)	0.010	0.065(0.055)	0.240	0.124(0.049)	0.012
Firm size	-0.049(0.055)	0.366	-0.090(0.044)	0.041	-0.057(0.055)	0.293	-0.096(0.044)	0.029
Intangible ratio	0.068(0.041)	0.098	-0.002(0.030)	0.952	0.069(0.041)	0.095	-0.001(0.030)	0.972
Gearing ratio	-0.073(0.035)	0.039	-0.015(0.025)	0.530	-0.073(0.035)	0.039	-0.016(0.025)	0.508
Headquarters’ scope of internationalization	0.208(0.163)	0.201	0.157(0.170)	0.355	0.187(0.162)	0.248	0.142(0.170)	0.404
Headquarters’ intangibility	0.044(0.045)	0.327	0.168(0.033)	0.000	0.043(0.045)	0.336	0.168(0.033)	0.000
News intensity	-0.026(0.029)	0.373	-0.008(0.020)	0.700	-0.029(0.029)	0.317	-0.011(0.020)	0.595
Voice and accountability	-0.028(0.060)	0.644	0.019(0.050)	0.711	-0.050(0.061)	0.409	0.007(0.050)	0.882
Political stability	-0.041(0.056)	0.466	0.004(0.044)	0.925	-0.072(0.058)	0.216	-0.016(0.045)	0.725
Geographical distance	-0.116(0.077)	0.130	-0.039(0.081)	0.627	-0.113(0.076)	0.137	-0.035(0.081)	0.665
Common language	0.142(0.153)	0.353	0.030(0.166)	0.855	0.096(0.153)	0.533	-0.007(0.167)	0.968
IP protection	-0.048(0.046)	0.303	0.004(0.034)	0.909	-0.062(0.047)	0.184	-0.005(0.035)	0.890
Macroeconomic uncertainty	-0.023(0.039)	0.552	0.006(0.028)	0.840	-0.010(0.039)	0.791	0.018(0.028)	0.518
Trade openness	-0.049(0.069)	0.481	-0.090(0.067)	0.181	-0.054(0.069)	0.434	-0.092(0.067)	0.169
Industry effects	Y		Y		Y		Y
Year effects	Y		Y		Y		Y
R-squared	0.106		0.125		0.123		0.120

Note: N = 927; standard errors are in parentheses; p-values are italicized.

In the regression tables, bold formatting is used solely to identify the key independent variables tested in our hypotheses.

In Table 3, Models 3a and 3b examine the moderating effect of financial liquidity on the relationship between information distance and digitalization and the relationship between information distance and subsidiary performance, respectively. The results show that financial liquidity can reduce the negative impact of information distance on firms’ digitalization but not on subsidiary performance. The coefficient of the interaction term “information distance × financial liquidity” is significantly positive in Model 3a (β = 0.155, p < 0.010) and significantly negative in Model 3b (β = −0.147, p < 0.001). Such a moderating effect is illustrated in Figure 2.

OPEN IN VIEWER

Table 3.

Results of the moderation analysis.

DV	Digital transformation		Subsidiary performance		Digital transformation		Subsidiary performance		Digital transformation		Subsidiary performance
Models	Model 3a		Model 3b		Model 4a		Model 4b		Model 5a		Model 5b
	β	p	β	p	β	p	β	p	β	p	β	p
Information distance	-0.124 (0.058)	0.033	-0.079 (0.046)	0.083	-0.464 (0.222)	0.037	-0.477 (0.163)	0.003	-0.450 (0.222)	0.042	-0.498 (0.161)	0.002
Information distance × financial liquidity	0.155 (0.054)	0.004	-0.147 (0.037)	0.000					0.152 (0.054)	0.005	-0.151 (0.036)	0.000
Information distance × operational flexibility					0.377 (0.230)	0.102	0.421 (0.170)	0.013	0.353 (0.230)	0.125	0.461 (0.168)	0.006
Financial liquidity	0.022(0.036)	0.540	-0.107(0.025)	0.000					0.020(0.036)	0.578	-0.109(0.025)	0.000
Operational flexibility					-0.019(0.214)	0.930	-0.281(0.229)	0.220	-0.027(0.216)	0.901	-0.256(0.230)	0.266
Firm age	0.072(0.055)	0.191	0.112(0.049)	0.023	0.069(0.055)	0.211	0.126(0.049)	0.010	0.077(0.056)	0.170	0.114(0.049)	0.020
Firm size	-0.059(0.055)	0.283	-0.095(0.044)	0.029	-0.061(0.055)	0.265	-0.104(0.044)	0.018	-0.063(0.055)	0.256	-0.103(0.043)	0.017
Intangible ratio	0.059(0.041)	0.156	-0.004(0.030)	0.897	0.064(0.041)	0.123	-0.007(0.030)	0.824	0.054(0.041)	0.194	-0.009(0.030)	0.751
Gearing ratio	-0.072(0.035)	0.041	-0.019(0.024)	0.422	-0.072(0.035)	0.040	-0.015(0.024)	0.531	-0.071(0.035)	0.042	-0.019(0.024)	0.441
Headquarters’ scope of internationalization	0.188(0.163)	0.249	0.155(0.171)	0.362	0.198(0.163)	0.224	0.152(0.171)	0.375	0.199(0.164)	0.226	0.167(0.171)	0.329
Headquarters’ intangibility	0.040(0.045)	0.367	0.174(0.033)	0.000	0.045(0.045)	0.318	0.170(0.033)	0.000	0.042(0.045)	0.347	0.177(0.032)	0.000
News intensity	-0.029(0.029)	0.321	-0.012(0.019)	0.546	-0.032(0.029)	0.277	-0.014(0.020)	0.463	-0.031(0.029)	0.281	-0.016(0.019)	0.411
Voice and accountability	-0.037(0.061)	0.539	-0.013(0.050)	0.800	-0.052(0.061)	0.389	0.008(0.050)	0.871	-0.040(0.061)	0.516	-0.013(0.050)	0.797
Political stability	-0.086(0.058)	0.137	-0.006(0.045)	0.902	-0.069(0.058)	0.238	-0.013(0.045)	0.781	-0.084(0.058)	0.152	-0.001(0.045)	0.987
Geographical distance	-0.114(0.077)	0.138	-0.046(0.081)	0.575	-0.135(0.079)	0.086	-0.077(0.083)	0.355	-0.135(0.080)	0.089	-0.088(0.083)	0.291
Common language	0.072(0.155)	0.642	0.012(0.168)	0.945	0.106(0.158)	0.502	0.049(0.172)	0.775	0.083(0.159)	0.601	0.065(0.172)	0.706
IP protection	-0.054(0.047)	0.253	-0.009(0.034)	0.798	-0.059(0.047)	0.209	-0.003(0.035)	0.923	-0.051(0.047)	0.280	-0.007(0.034)	0.828
Macroeconomic uncertainty	-0.020(0.039)	0.616	0.030(0.028)	0.293	-0.017(0.040)	0.673	0.013(0.029)	0.661	-0.025(0.040)	0.525	0.023(0.028)	0.415
Trade openness	-0.052(0.069)	0.457	-0.093(0.067)	0.163	-0.069(0.071)	0.327	-0.126(0.068)	0.065	-0.067(0.071)	0.349	-0.128(0.068)	0.061
Industry effects	Y		Y		Y		Y		Y		Y
Year effects	Y		Y		Y		Y		Y		Y
R-squared	0.122		0.126		0.121		0.131		0.121		0.139

Note: N = 927; standard errors are in parentheses; p-values are italicized.

OPEN IN VIEWER

Figure 2.

The moderating effect of financial liquidity.

The different moderating effects show that financial slack is important for MNEs’ prompt digitalization when facing large information distance between home and host countries, as they can better cover unexpected expenditure or production changes in digitalization (e.g. Lang et al., 2012; Ren et al., 2023). However, maintaining slack resources cannot help these firms enhance their subsidiary performance due to the lower rate of expected return (e.g. Amihud and Mendelson, 2008; Brockman and Chung, 2003). Hence, H3a and 3b can be accepted.

Model 4a of Table 3 shows that the moderating effect of operational flexibility on the relationship between information distance and firms’ digitalization is positive but insignificant (β = 0.377, p > 0.100). Yet, in Model 4b, the coefficient of the interaction term is positive and significant (β = 0.421, p < 0.050). This shows that maintaining operational flexibility is critical for MNE subsidiaries to gain more profits from information distance, as they can be more embedded in the host country to better understand the different digital trends and deploy resource configurations (Geleilate et al., 2020). However, a higher degree of subsidiaries’ operational flexibility cannot help firms leverage the different digital trends in countries to develop broader sets of digital technologies, which implies greater complexity of the digital transformation of firms (e.g. Kronblad, 2020). Therefore, H4b is accepted, while H4a is not. The moderating effect of subsidiaries’ operational flexibility is illustrated in Figure 3.

OPEN IN VIEWER

Figure 3.

The moderating effect of operational flexibility.

4.2. Endogeneity concerns and robustness checks

4.2.1. Endogeneity

Although our results show low partial correlations between the main variables, and we controlled multiple variables at different levels to account for potential factors influencing the main relationships, the risk of omitted variable bias remains (Lindner et al., 2020), which could undermine the validity of our findings. To address concerns of endogeneity, we employed an instrumental variable (IV) approach and used two-stage least squares regression (2SLS) (Bascle, 2008). In selecting appropriate IVs, we focused on variables that affect the information distance between countries (the endogenous explanatory variable) but do not directly impact the DVs.

First, we used the trade flow difference, measured by the Euclidean distance approach, between home and host countries. Trade flow differences can impact country relationships and information transmission (Mansfield and Pevehouse, 2000; Wu et al., 2012), but they do not directly affect firms’ digitalization or performance, making them a suitable instrument for analysis. Second, we considered the economic classification of home and host countries (i.e. whether both are developed or developing), which affects information distance through market sophistication and information exchange. However, it does not directly affect MNE subsidiaries’ digitalization and strategic decisions. Third, we included a 2-year lagged explanatory variable, following previous practices (Cui et al., 2018; Nguyen et al., 2019), to capture historical differences in information exchange between countries that could influence current cross-country information flows but are unlikely to be directly correlated with future firm decisions or performance.

We tested the strength and validity of the IVs by conducting the first-stage regression and overidentification tests. The results showed that the F-statistic for all three IVs was greater than 10 in the first-stage regression, and the Sargan test supported the validity of the instruments (Nguyen et al., 2019). The IV estimation results, presented in Table 4, indicate that the regression coefficient for information distance remained significantly negative across different IVs. The Hausman test of endogeneity showed no significant evidence of endogeneity, further addressing the potential concerns.

OPEN IN VIEWER

Table 4.

Results of the instrumental variables (IV) estimations.

DV	Digital transformation		Subsidiary performance		Digital transformation
Models	IV = Trade flow difference		IV = Economic classification		IV = Lagged information distance
	β	p	β	p	β	p
Information distance (instrumented)	-0.366(0.113)	0.001	-0.413(0.117)	0.000	-0.199(0.048)	0.000
Firm age	0.021(0.038)	0.577	0.026(0.039)	0.499	-0.003(0.047)	0.948
Firm size	-0.107(0.037)	0.004	-0.108(0.037)	0.004	-0.142(0.042)	0.001
Intangible ratio	0.048(0.036)	0.182	0.046(0.036)	0.204	0.070(0.040)	0.081
Gearing ratio	-0.045(0.033)	0.181	-0.044(0.034)	0.197	-0.064(0.037)	0.086
Headquarters’ scope of internationalization	0.207(0.079)	0.009	0.195(0.080)	0.015	0.315(0.083)	0.000
Headquarters’ intangibility	0.038(0.038)	0.310	0.042(0.038)	0.267	0.043(0.042)	0.299
News intensity	0.005(0.036)	0.882	0.004(0.037)	0.912	0.017(0.038)	0.653
Voice and accountability	-0.078(0.050)	0.119	-0.089(0.051)	0.081	-0.045(0.051)	0.371
Political stability	-0.129(0.051)	0.012	-0.141(0.052)	0.007	-0.113(0.050)	0.023
Geographical distance	-0.077(0.039)	0.050	-0.075(0.040)	0.060	-0.085(0.044)	0.054
Common language	-0.033(0.087)	0.706	-0.054(0.088)	0.540	0.032(0.081)	0.693
IP protection	-0.173(0.050)	0.001	-0.186(0.051)	0.000	-0.138(0.047)	0.003
Macroeconomic uncertainty	0.032(0.038)	0.397	0.034(0.038)	0.370	0.053(0.043)	0.213
Trade openness	-0.137(0.038)	0.000	-0.137(0.038)	0.000	-0.107(0.043)	0.013
Industry effects	Y		Y		Y
Year effects	Y		Y		Y
Observations	927		927		735
R-squared	0.123		0.109		0.190

Note: N = 927; standard errors are in parentheses; p-values are italicized.

5.1. Contribution to research

The contribution of this study is reflected in several ways. First, this study enriches the growing stream of literature on digitalization by connecting it to the concept of information distance. While studies in the international business field often discuss how cross-country distance impacts MNEs’ activities and performance (Li et al., 2020; Messner, 2023; Zámborský and Yan, 2022), the discussions focus mainly on the geographical, cultural, and institutional aspects of distance that are less likely to change or may change slowly over time. The impact of information distance, which emphasizes informational efficiency between countries (Lindner and Puck, 2024), on MNEs remains largely underexplored.

This study fills the research gap by conceptualizing and testing the role of a new type of distance that is becoming increasingly important in the literature on international business in the digital age (Lindner and Puck, 2024; Meyer et al., 2023). Our construct of information distance is valuable as it goes beyond the existing conceptualizations that assume that financial markets are sufficiently efficient interpreters of information stemming from the underlying national context (Lindner and Puck, 2024). We contribute to the literature by stressing a broader conceptualization of information distance based on informational globalization (Gygli et al., 2019), which acknowledges the actual flows of ideas, knowledge, and images, and the ability to share information across countries. By doing so, our study enriches research on cross-country distance and MNE digital transformation.

Second, existing studies often discuss digitalization at the firm level (Ciulli and Kolk, 2023; Kindermann et al., 2021; Kronblad, 2020; Yi et al., 2023), yet less is known about the linkages between country-level information distance and subsidiary-level digital transformation. This study enriches the literature on MNE subsidiaries and digitalization (e.g. Grimpe et al., 2023; Lee et al., 2023; Murphree et al., 2022) by recognizing both country-level information distance and subsidiary-level digital transformation while exploring the boundary conditions that impact the country–firm (subsidiary) linkages. By applying the real options perspective to examine the role of intra-firm features, our study contributes to the literature on MNE strategic changes, particularly digital transformation, in an era of increasing global uncertainty (Trigeorgis and Miller, 2025).

Our study has important implications for research on international business in the digital age (Meyer et al., 2023). Research on digital firms and their various types such as born-digital firms (e.g. Stallkamp et al., 2023) and digital platform firms (e.g. Stallkamp and Schotter, 2021) emphasizes the need for FDI—for example, MNE subsidiaries—and analyzes the roles of cultural and geographic distance, business models, and industry differences. Our study implies that the relationship between national contextual factors (information distance) and digital transformation and performance of subsidiaries can be explained by incorporating a real options perspective, as financial liquidity and operational flexibility alter options availability under market and digital uncertainty.

5.2. Contribution to practice

This study offers important practical implications for government officials and managers of MNEs. The impact of information distance on MNEs shows that government officials should support MNEs’ digital transformation by providing clear technological standards and assisting firms in learning about taxation, digital regulations, and potential risks when entering different countries. Countries lagging behind their technological competition could see their firms facing additional pressures in internationalization due to information distance. Hence, governments need to understand the importance of digital infrastructure development and formulate solid policies to encourage informational globalization.

Managers of MNEs, especially those in information-intensive sectors, need to pay more attention not only to adapting to geographical, cultural, and institutional distance between home and host countries but also to learning about information distance. By utilizing firm-specific capabilities and proactively embedding them into the host-country knowledge networks, MNEs can convert challenges of information distance into new opportunities and investment options. Managers need to enhance the internal governance of firms by keeping sufficient liquidity to cope with unexpected digital disruptions. More importantly, maintaining operational flexibility can help firms better leverage informational and technological gaps between countries and promote digitalization.

5.3. Limitations and future research directions

The limitations of this study leave room for future improvement. One concern is our reliance on content extracted from news reports to capture firm-level digital transformation. While this approach enables large-scale analysis, it may not fully reflect firms’ actual investments or the true intensity and scope of their digital initiatives. We therefore encourage future research to incorporate more direct measures to enhance construct validity. Also, our study chose MNEs from IT-intensive sectors as the research subject, as these firms are seeking digitalization and are more affected by the changing informational environment. However, other types of firms may also embrace the potential for digitalization, such as those in transportation, construction, or energy industries. Future research could explore the impact of information distance on both digital and non-digital firms to broaden the study’s scope and applicability. While this study examined multiple aspects of a country’s informational globalization, other areas, such as blockchain usage and artificial intelligence (AI) applications, could also be explored (Ciulli and Kolk, 2023). Future studies could also explore the different dimensions of digitalization and improve the measurement of information distance in this study. The digitalization process of MNE subsidiaries is also likely influenced by their parent companies, not solely by information distance (Murphree et al., 2022). Due to data limitations, the role of MNE parent companies was not emphasized in our study, presenting opportunities for further research.