Abstract
Purpose
Export market orientation can be broadly divided into intelligence (generation and dissemination) and responsiveness activities. Although previous studies assess intelligence and responsiveness activities, little is known about what type of international channel partner acts as an enabling condition for the impact of these activities on export venture performance. This study aims to examine the extent to which the selection of international channel partners through word-of-mouth referrals versus direct contacts affects the benefits of intelligence and responsiveness activities.
Design/methodology/approach
Data were collected from 246 exporting manufacturers in Japan. To test the hypotheses, we conducted regression analyses using a subjective performance measure at the venture level. We also performed a post hoc analysis using objective performance measure at the function level.
Findings
We find that the extent to which international channel partners are selected through word-of-mouth referrals has a moderating role in the export market-oriented activities–performance linkages. Specifically, it acts as an enabling condition for intelligence activity and a disenabling condition for responsiveness activity.
Originality/value
This study contributes to a better understanding of export market orientation by classifying it into intelligence and responsiveness activities and providing empirical evidence on their different interaction effects with partner selection. It also contributes to the elaboration of agency theory by offering insights into the fit between task characteristics and contract type. Our study is critical for business managers as it suggests guidelines for manufacturing exporters engaging in export market-oriented behaviors and export channel management.
Keywords
Citation
Ishii, R. and Kikumori, M. (2024), "Export market orientation, performance and international partner selection: word-of-mouth referral versus direct contact", International Marketing Review, Vol. 41 No. 7, pp. 117-139. https://doi.org/10.1108/IMR-01-2024-0008
Publisher
:Emerald Publishing Limited
Copyright © 2024, Ryuta Ishii and Mai Kikumori
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Export marketing researchers emphasize the importance of export market orientation (Bıçakcıoğlu-Peynirci and Ipek, 2020; Cadogan et al., 2009; Miocevic et al., 2023). Prior research provides empirical evidence that export market orientation is a critical driver for export performance at both the export function level (e.g. Boso et al., 2012; Cadogan et al., 2006; Lengler et al., 2013) and the export venture level (e.g. He et al., 2018; Ipek and Tanyeri, 2021; Murray et al., 2011). Moreover, studies divide export market orientation into several dimensions and examine each dimension’s effect on export performance (Chung, 2012; Katsikea et al., 2019; Rose and Shoham, 2002). In other words, export marketing researchers adopt a disaggregated approach to export market orientation and examine the benefits of export market orientation in detail. One such way is to broadly divide this orientation into two components: intelligence (generation and dissemination) and responsiveness activities (Özturan et al., 2014). Intelligence activities help exporters understand current customer behaviors and predict future export trends; by contrast, responsiveness activities allow exporters to meet customer needs in an immediate and flexible manner and take defensive actions against competitors.
Although export marketing researchers provide rich insights on export market orientation, most ignore the role of international channel partners in export market-oriented activities (see Table 1). Exceptional studies, such as He et al. (2013, 2018), show that the use of a hierarchical channel (i.e. non-use of international channel partners) as an export channel enables export market-oriented benefits more than a non-hierarchical channel (i.e. use of international channel partners). However, many exporters rely on international channel partners, such as foreign importers, overseas distributors, and local sales agencies, to overcome economic, sociocultural, geographic, and language barriers (Aykol and Leonidou, 2018; Li et al., 2017; Skarmeas et al., 2016), and conduct export market-oriented activities while using them. Thus, it is essential to focus on exporters using non-hierarchical channels (i.e. relying on international channel partners) and examine what type of international channel partner acts as an enabling condition of intelligence and responsiveness activities.
An important classification of international channel partners is whether they are selected by word-of-mouth (WOM) referral or direct contact. Unlike in domestic channel partner selection, firms do not have much information about their international channel partners’ capabilities, motivations, philosophies, and policies (Cavusgil et al., 1995). Thus, some exporting manufacturers rely on WOM referrals from business connections and existing partners when selecting international channel partners (Ishii and Kikumori, 2023; Kikumori and Ishii, 2023; Tóth et al., 2020). Others, conversely, select international channel partners through direct contacts. This results in substantial variation in the extent to which each exporter selects international channel partners through WOM referrals or direct contacts. Between the two extreme cases, in which all international channel partners are selected either through WOM referrals or through direct contacts, diverse firms exist in terms of the extent of their use of WOM referrals (versus direct contacts).
Accordingly, we examine international channel partner type—whether partners are selected through WOM referrals or direct contacts—as an enabling boundary condition of intelligence and responsiveness activities. Why does selecting international channel partners through WOM referrals or direct contacts affect the benefits of intelligence and responsiveness activities? We consider that these activities have different task characteristics and that channel partners selected by WOM referrals or direct contacts are governed by different contract types. The fit between task characteristics and contract types should affect performance outcomes. Agency theory (Bergen et al., 1992; Eisenhardt, 1989) focuses on the relationship between a principal (e.g. a manufacturing exporter) and an agent (e.g. an international channel partner), providing rich insights into task characteristics and contract types. Therefore, we draw on agency theory and examine the fit between export market-oriented activities and channel partner types.
Our study offers key insights for researchers by classifying export market orientation into intelligence and responsiveness activities and providing empirical evidence on their different interaction effects with channel partner types. Specifically, partner selection through WOM referral acts as an enabling condition for intelligence activity and a disenabling condition for responsiveness activity. We also contribute to the elaboration of agency theory by presenting evidence regarding the fit between task characteristics and contract type. From a practical perspective, our study is imperative for business managers because it suggests guidelines for manufacturing exporters engaging in export market-oriented behaviors and export channel management.
Theoretical background
Level of analysis and conceptual model
Figure 1 illustrates our conceptual model. We focus on export venture performance, which refers to the extent to which a firm’s objectives with respect to exporting a product into a foreign market are achieved (Cavusgil and Zou, 1994; Myers, 1999). Export performance can be captured at two different levels (Oliveira and Cadogan, 2018; Oliveira et al., 2012): performance in a single export venture and in the export function (i.e. multiple export ventures as a whole). Since channel partner selection through WOM referral is captured at the export venture level, we use export venture performance as an outcome indicator.
The model shows that intelligence and responsiveness activities—related to information inputs and outputs, respectively—influence export venture performance. These relationships are theoretically grounded in the resource-based theory. Moreover, the impact of these two types of activities on export outcomes depends on the extent to which international channel partners are selected through WOM referrals. Specifically, we posit that international partner selection through WOM referral acts as an enabling condition for the impacts of intelligence activities on export outcomes and a disenabling condition for the impacts of responsiveness activities. Agency theory provides the theoretical foundation, in which export market-oriented activities are viewed as activities delegated by export manufacturing firms (i.e. principals) to their international channel partners (i.e. agents). In the next section, we propose our hypotheses.
The relationship between export market orientation and export performance
Export market orientation refers to an “information processing capability that draws heavily on a market-driven exploitative logic to fuel business success” (Cadogan et al., 2016, p. 5046). It differs from domestic-focused market orientation as it relates to intelligence activity in export markets, which have numerous political, geographic, competitive, sociocultural, and distribution structure differences (Cadogan et al., 2003; Cadogan et al., 2002; Murray et al., 2007). Specifically, export market orientation capabilities emphasize a better understanding of and response to customer preferences, competitor behavior, and institutional pressures in foreign markets (Ipek and Bıçakcıoğlu-Peynirci, 2020; Miocevic et al., 2023).
Export market orientation comprises three dimensions: export market intelligence generation, dissemination, and responsiveness. Most studies adopt an aggregated approach, viewing export market orientation as the sum of these three dimensions, which facilitates the interpretation of their research findings. However, if the three dimensions have different effects on business outcomes, the aggregated approach could lead to erroneous conclusions or incomplete insights; in this case, a disaggregated approach that considers the impact of each dimension separately is advantageous (Cadogan, 2012; Katsikea et al., 2019). Indeed, several prior studies employ a disaggregated approach to provide more detailed findings on export market orientation (e.g. Chung, 2012; Katsikea et al., 2019; Rose and Shoham, 2002) [1]. In this study, we adopt a disaggregated approach to examining export market orientation. Following Özturan et al. (2014), we group export market orientation into two components: intelligence (generation and dissemination) and responsiveness activities. The former are concerned with information input, which plays an important role in accumulating external information within the organization. By contrast, the latter is related to the output of the input information and crucial in implementing specific actions for foreign customers and local competitors.
The resource-based theory (Barney, 1991; Peteraf, 1993) is often used as a theoretical basis for examining the relationships between export market-oriented activities (e.g. intelligence and responsiveness activities) and performance outcomes. It argues that a firm’s valuable and inimitable resources/capabilities determine its business outcomes and survival. Exporting manufacturers with superior intelligence activities better acquire and maintain information about foreign customers and competitors, as well as knowledge of the economic, legal, and cultural changes surrounding their product export. Understanding the overall situation in the export market through intelligence activities allows a firm to gain strategic insights and predict subsequent long-term trends, latent customer needs, and emerging business opportunities (Chung, 2012). Further, internal information dissemination creates an information-sharing platform, resulting in collaborations between top management and employees, either among departments or individuals, and possibly leading to solidarity among individuals and the resolution of inter-departmental conflicts (Katsikea et al., 2019). The accumulation of information about customers and the export environment enhances the ability to provide useful information to business partners, such as importers and local agencies, which increases these partners’ commitment to and cooperation with the exporting firm and enhances business outcomes (Ju et al., 2011; Racela et al., 2007). Thus, the capability to promote intelligence activities can be considered a valuable resource. In addition, this capability cannot be acquired by reading policy manuals or textbooks; because of its socially complex and implicit nature, it is an inimitable resource. Therefore, we propose the following hypothesis:
There is a positive relationship between intelligence activity and export venture performance.
The capability to implement responsiveness activities is also regarded a valuable and inimitable resource. Exporting manufacturers with superior responsiveness can better meet customer needs in a flexible and immediate manner, which leads to loyalty among foreign customers (Miocevic et al., 2023), enhances the likelihood of firm survival by developing appropriate products and modifying promotional methods in response to dynamic changes in the export environment (Cadogan et al., 2002), and maintains a firm’s competitive position by developing flexible defensive actions or taking immediate countermeasures against competitors’ marketing strategies (Chung, 2012). This responsiveness to foreign customers, export environment, and competitors significantly contributes toward achieving venture success. Furthermore, the capability to implement responsiveness activities cannot easily be developed simply by reading textbooks; thus, it is a valuable and inimitable resource. Therefore, we propose the following hypothesis:
There is a positive relationship between responsiveness activity and export venture performance.
Moderating role of international channel partner type
The success of intelligence and responsiveness activities in export markets depends on various export environments. We consider international channel partners in the host country as one such export environment. For example, an exporter actively engaged in intelligence activities ends up making decisions based on low-quality information and obtaining poor export outcomes if its international channel partners provide false or distorted information about the host country (Ishii, 2021). Similarly, an exporter who actively engaged in responsiveness activities does not achieve better customer satisfaction or greater market share if its international channel partners fail to promote the exporter’s new products/brands in the host country (Pyper and Doherty, 2022). From the agency theory perspective, such rigidity or lax attitude on the part of the international channel partner is referred to as moral hazard or “hidden action.” For successful intelligence and responsiveness activities, exporting manufacturers must curb hidden actions among their international channel partners. Agency theory (Bergen et al., 1992; Eisenhardt, 1989) provides considerable insight on this front.
According to agency theory, a principal (e.g. a manufacturing exporter) faces agency problems when delegating a task to an agent (e.g. an international channel partner). Problems arise when information asymmetries and goal incongruence exist between the two parties. One agency problem is moral hazard or the “hidden action” problem (Bergen et al., 1992); that is, an agent acts to maximize their own utility at the expense of a principal’s goals (e.g. providing false information, conducting inappropriate promotions) because the agent is familiar with the task details and does not share the goals with the principal. To prevent this problem, a principal can either use behavior-based contracts and invest in a monitoring system that eliminates information asymmetries, or use outcome-based contracts and invest in an outcome-based incentive system aligned with the goals of both parties (Eisenhardt, 1989).
We consider that the type of contract that tends be used depends on how the international channel partner is selected. On the one hand, channel partners selected through WOM referrals are embedded in a closed network that includes the referrer (Kikumori and Ishii, 2023). Information about their channel partners is easily transferred through the referrer, and it is relatively easy for manufacturers to monitor the behaviors of such channel partners. Consequently, relationships between exporting manufacturers and these channel partners are governed by behavior-based contracts, in which the manufacturers directly control their channel partner’s behaviors. Specifically, the manufacturer imposes strict restrictions on how its products are promoted and on new product introductions and attempts close coordination through providing financial and material support to partners introduced through WOM referrals. On the other hand, relationships established through direct contacts with manufacturers are governed by outcome-based contracts because the cost of monitoring the behaviors of such partners is extremely high for manufacturers, and there is a high degree of goal incongruence with such firms. In the outcome-based contract, exporting manufacturers control channel partners based solely on measurable outcomes, provide no financial or physical support, and have minimal coordination with the partners (Lassar and Kerr, 1996).
From the perspective of agency theory, what are the task characteristics of export market-oriented activities? The task of intelligence activity is characterized by high outcome measurability and low outcome uncertainty. For the former, information about export markets can be obtained in the form of customer sales data and reports on changes in the local export environment, which makes it easy to measure the outcomes of intelligence activities. Moreover, as such data and reports can be generated through the diligent collection and organization of information, the uncertainty of the outcome is also regarded low.
Tasks with these characteristics are more efficiently performed by channel partners governed by outcome-based contracts (Bergen et al., 1992; Eisenhardt, 1989). Such partners, whose goals are aligned with those of exporting manufacturers in gathering a wealth of information, are willing to provide detailed customer data and generate sales reports. By contrast, channel partners referred through WOM and governed by behavior-based contracts cannot provide as rich and high-quality information because their goals are not aligned with those of the exporters in the same way as those governed by outcome-based contracts. Therefore, we propose the following hypothesis [2]:
The more the international channel partners are selected through WOM referrals, the weaker the positive relationship between intelligence activity and export venture performance.
By contrast, responsiveness activities are characterized by low outcome measurability and high outcome uncertainty. For example, promoting new products to meet customer needs and responding to customer complaints are activities that require tacit knowledge and skills; there are no clear criteria for evaluation. Additionally, there is a high degree of uncertainty around outcomes; even if a new product is introduced to meet customer needs, whether it sells depends largely on various factors, such as environmental changes and competitor activities that are unrelated to the overseas channel partner’s efforts.
Such tasks are more efficiently performed by international channel partners referred through WOM and governed by behavior-based contracts (Bergen et al., 1992; Eisenhardt, 1989). Through close coordination and control, manufacturers can tailor their sales activities to foreign customers and promote their products in a manner appropriate to their product characteristics. Conversely, it is not efficient for these tasks to be performed by channel partners governed by performance-based contracts (Bergen et al., 1992; Eisenhardt, 1989). They either do not engage in customer service efforts that are unsure to lead to successful outcomes or they shirk such efforts, utilizing the fact that outcomes are difficult to measure. Consequently, they would, for example, respond inappropriately to customer complaints or promote new products inadequately. Therefore, we propose the following hypothesis [3]:
The more international channel partners are selected through WOM referrals, the stronger the positive relationship between responsiveness activity and export venture performance.
Methods
Research context
Data collection was conducted in Japan. This has two merits. The first is related to WOM referrals. Compared to Western countries, Japan is characterized by high levels of collectivism, uncertainty avoidance, and long-term orientation, as well as a highly contextual social network system; thus, Japanese firms often use WOM referrals to select business partners (Money et al., 1998). This allowed us to collect data not only from exporters that select international channel partners through direct contacts but also from exporters that select them through WOM referrals. The second is related to international channel partners. In the context of a relatively collectivist culture such as Japan, manufacturing exporters seek to build close relationships with international channel partners, prefer exclusive contracts with them, and tend to contract with one partner per country (Ishii, 2024). This tendency was appropriate for the purpose of this study, which examines the role of an international channel partner in a particular country.
Data collection
To test the hypotheses, data were collected from exporting manufacturers in Japan. Based on a directory of exporting firms published by the Japan External Trade Organization, we selected 911 exporting manufacturers in Japan that employed more than 10 people. We first contacted each company by telephone to obtain their agreement to participate in the survey and identify key informant information. We identified 555 eligible firms; the rest were excluded for various reasons, such as being unwilling to participate or no longer exporting.
We mailed a survey packet to the participants. For companies that had not returned the questionnaire within two weeks, a phone call was made to remind them. We received completed questionnaires from 281 companies, of which 246 were usable; 35 responses were unusable owing to either not using an international channel partner or having too many missing values. On average, the 246 sample firms employed 107 people and exported to 12 countries, including China, the United States, South Korea, Taiwan, and Hong Kong. These firms belonged to machinery, electronics, chemicals, and food/beverage industries.
To check key informant competency, we included items related to respondents’ knowledge, experience, and involvement in export sales. Respondents’ knowledge, experience, and involvement levels were, on average, 5.0 (SD = 2.5), 5.2 (SD = 1.7), and 5.9 (SD = 1.3), respectively. The average length of their service was 19.7 years (SD = 11.5), and the average tenure in their department was 12.9 years (SD = 10.4). Most respondents held senior positions, such as president, managing director, department head, or section manager. The figures suggest that respondents had sufficient knowledge, experience, and involvement in export sales to provide information on decision-making at their firms.
Common method bias
During the questionnaire design, we addressed common method bias as follows. First, we identified ambiguous and unclear questionnaire items and modified them based on comments from academic scholars and business managers. Second, we added questions on the dependent and independent variables. Finally, we ensured confidentiality to reduce respondents’ evaluation concerns and social desirability bias.
To check common method bias, we adapted Lindell and Whitney’s (2001) marker variable (MV) method. The MV in this study was measured with the item “Your company aims to develop a product that has few potential customers” (1 = “strongly disagree” and 7 = “strongly agree”). As shown in Table 2, a comparison between the partial correlation coefficients that control for the MV and the uncontrolled correlation coefficients indicated little difference in terms of size and significance. These results suggest that common method bias was not a serious issue in this study.
Measures
Table 2 presents a correlation matrix of all constructs and Table 3 provides a list of these constructs and scale items. Export venture performance was measured with four items based on He et al. (2013) and He et al. (2018). Intelligence activity comprised two dimensions: export intelligence generation and dissemination. Each dimension was measured using three items from Cadogan et al. (1999). Responsiveness activity was also measured with three items from Cadogan et al. (1999). International channel partner selection through WOM referral was captured using three items from Yang et al. (2017).
The questionnaire included important control variables. We controlled for firm size, captured by the number of full-time employees in each exporting manufacturer. To control for export experience, we measured the number of years a firm had been exporting its products (Aulakh and Kotabe, 1997). To control for export scope, we measured the number of export markets the firm had entered (Cadogan et al., 2002). We also controlled for environmental dynamism and diversity, each measured by three items adapted from Eyuboglu et al. (2017). Psychic distance was captured using 12 items from Sousa and Lages (2011).
A confirmatory factor analysis was performed to assess measurement validity. We included all questionnaire items in the model. The results showed a good fit with the data. Chi-square was significant (χ2 = 93.17, d.f. = 48, p < 0.01), but other fit indices were within the recommended criteria (χ2/d.f. = 1.94; root mean square error of approximation (RMSEA) = 0.062; non-normed fit index (NNFI) = 0.95; comparative fit index (CFI) = 0.97, incremental fit index: IFI = 0.97). The average extracted variances (AVE) and the composite reliabilities (CRs) were greater than 0.50 and 0.70, respectively. As shown in Table 3, the square roots of the AVE exceeded the correlation between all possible pairs of variables. Accordingly, the measurement items used in this study indicated appropriate convergent and discriminant validities.
Endogeneity
Two types of export market-oriented activities (i.e. intelligence and responsiveness activities) could be endogenous variables owing to reverse causality where firms with higher export performance are more likely to have excess resources and engage in export market-oriented activities. Thus, we checked endogeneity bias (Jean et al., 2016; Sande and Ghosh, 2018). Specifically, we used two-stage least squares (2SLS) instrumental variable regression. This requires an instrument that is theoretically relevant to an independent variable but uncorrelated with the error term in the model. As instrumental variables, we used three variables related to premium product offerings (see Table 3 for measurement items). Exporting manufacturers offering high-quality products at high prices tend to be export market-oriented (Homburg et al., 2004) but do not necessarily achieve high export outcomes. Moreover, following Sande and Ghosh (2018), we created two instrumental variables using the interaction term of WOM referrals with export market orientation variables predicted by the three premium product offering variables. Consequently, five instruments were used to predict four endogenous variables (i.e. intelligence activity, responsiveness activity, and their interactions with WOM referrals). The first stage F statistics were all significant (p < 0.01), and the Sargan test did not reject the null hypothesis (p > 0.10), suggesting that all instrumental variables fulfilled the requirement of relevance and exogeneity conditions. The Wu–Hausman test did not reject the null hypothesis (p > 0.10) that the constructs related to intelligence and responsiveness activities are exogenous. We, thus, concluded that the instrumental variable approach was unnecessary and used OLS regression to test our hypotheses.
Analysis
To test the hypotheses, we conducted a hierarchical regression analysis using the ordinary least squares technique. Three models were identified for this analysis. Model 1 included only the control variables, Model 2 included all variables except for the interaction terms of intelligence and responsiveness activities with channel partner selection through WOM referral, and Model 3 included these interaction terms. We used mean-centered scores for the interaction terms before entering them into the analysis. As shown in Table 4, the value of adjusted R2 were 0.02, 0.11, and 0.14 in Models 1, 2, and 3, respectively. F-tests associated with the change in R2 showed that the differences between Model 1 and Model 2 (Δ0.09, p < 0.01) and that between Model 2 and Model 3 (Δ0.03, p < 0.01) were statistically significant. Therefore, we adopted Model 3 to test the hypotheses. Since our hypotheses were all directional, we used one-tailed tests for hypothesis testing.
Among these models, the largest variance inflation factor (VIF) was 1.47, which was well below the widely used thresholds of 5.0 or 10.0 (Mason and Perreault Jr, 1991). Thus, multicollinearity was not a serious issue in this study.
Results
Hypotheses testing
H1a, which proposes a positive relationship between intelligence activity and export venture performance, is supported if intelligence activity positively relates to export venture performance. The results show that the coefficient of intelligence activity is positive and significant (β = 0.30, p < 0.01). Thus, H1a is supported.
H1b, which posits a positive relationship between responsiveness activity and export venture performance, is supported if responsiveness activity is positively related to export venture performance. The results indicate that although the coefficient of responsiveness activity is positive (β = 0.07), it is not one-tailed significant at 2.5% (t-value = 1.11). Thus, this result suggests that, on average across all types of channel partner selection modeled, H1b is not supported. However, it still remains the case that the model presented here can identify situations where responsiveness and venture success are positively related. Specifically, H2a and H2b argue that the export market orientation–performance linkages are moderated by international channel partner selection.
H2a, which argues that the benefits of intelligence activity are reduced by international channel partner selection through WOM referral, is supported if the interaction effect between intelligence activity and WOM referral is negative. The results show that the coefficient of interaction term between intelligence activity and WOM referral is negative and significant (β = −0.15, p < 0.05). To interpret this result better, we perform a simple slope analysis and graphically drew the relationship at high (one standard deviation above) and low (one standard deviation below) WOM referral conditions. As shown in Panel A of Figure 2, the coefficient of intelligence activity is positive and significant in the high WOM referral condition (β = 0.66, p < 0.01) but not significant (β = 0.25, n.s.) in the low WOM referral condition. Therefore, H2a is supported.
H2b argues that the benefits of responsiveness activity are strengthened by international channel partners selected via WOM referral; this hypothesis is supported if the interaction effect between responsiveness activity and WOM referral is positive. The results show that the coefficient of the interaction term between responsiveness activity and WOM referral is positive and significant (β = 0.21, p < 0.01). As with H2a, we perform a simple slope analysis. As shown in Panel B of Figure 2, the coefficient of responsiveness activity is positive and significant in the high WOM referral condition (β = 0.27, p < 0.05) but not significant in the low WOM referral condition (β = −0.17, n.s.). Therefore, H2b is supported.
Two control variables are significantly related to export venture performance. First, the larger the firm size, the higher the export venture performance (β = 0.13, p < 0.05). Larger firms have greater human, financial, and material resources and are, therefore, more likely to succeed in their export ventures. Second, export ventures are less likely to succeed under conditions of greater environmental diversity (β = −0.17, p < 0.01). Diverse customers make it difficult for export ventures to successfully adapt, and diverse competitors and intermediaries hinder the actions of export ventures; in such environments, export ventures are less likely to succeed.
Post hoc analysis
The performance outcome of our study is export venture performance. The venture-level performance (i.e. performance in a single venture) and the function-level performance (i.e. performance in all multiple ventures) should be distinguished and are not alternative measures. However, our main independent variable, export market orientation, is a firm-level variable and might affect the function-level export performance. Therefore, we conduct a post hoc analysis with the function-level performance as the dependent variable to check the robustness of our findings. Notably, the moderating variable, the extent to which international channel partners are selected through WOM referrals, is captured as behaviors in the main export venture. Therefore, we conduct the post hoc analysis assuming that WOM referral usage behaviors at the main export venture also occur at the firm-wide level.
We use export sales performance as the dependent variable for this post hoc analysis, proxied by the ratio of export sales to total sales. This is a quantified objective measure, whereas the venture-level performance is measured as subjective satisfaction. Therefore, this post hoc analysis is important in the sense that findings obtained using subjective measures are checked using objective indicators.
As shown in Table 4, F-tests show that adjusted R2 is significantly higher in Model 6 than in Model 4 (Δ0.14, p < 0.01) and Model 5 (Δ0.03, p < 0.01). In Model 3, the coefficient of intelligence activity is positive and significant (β = 0.13, p < 0.05), which is consistent with the findings using venture-level subjective performance. As for the interaction effect, the coefficient of the interaction term between intelligence activity and WOM referral is not significant (β = −0.00, n.s.). These results suggest that intelligence activity is also beneficial at the function level and the degree of its benefits does not depend on the international channel partner type.
The results show that the coefficient of responsiveness activity is positive and significant (β = 0.27, p < 0.01). In addition, the coefficient of the interaction term between responsiveness activity and WOM referrals is positive and significant (β = 0.21, p < 0.01). As shown in Figure 3, export market orientation is more likely to positively affect export function performance in the high (one standard deviation above) WOM referral condition (β = 0.47, p < 0.01) than in the low (one standard deviation below) WOM referral condition (β = 0.08, n.s.). The findings are consistent with those in the analysis at the venture level, in that we find a positive moderating role of WOM referral.
In summary, the extent to which international channel partners are selected through WOM referrals acts as an enabling condition for responsiveness activity in the function-level analysis. Therefore, our arguments regarding international channel partner type may be applicable not only at the venture level but also at the function level. However, in the function-level analysis, partner selection through WOM referral does not act as an enabling condition for intelligence activity, and the main effect of responsiveness activity is positive and significant. This suggests that, compared to the venture level, the benefits of export market orientation do not depend as much on the international channel partner type at the function level.
Discussion
Although prior research on export market orientation examines the benefits of intelligence and responsiveness activities, little is known about the moderating effect of international channel partner type. To address this research gap, we investigate whether the extent to which international channel partners are selected through WOM referrals (as opposed to direct contacts) acts as an enabling condition for the impacts of intelligence and responsiveness activities on export venture performance. Interestingly, the main effect of responsiveness activity is always positive and large at the function level (i.e. at the firm level), but not always at the venture level. This may be because the organizational culture of Japanese firms, which have a strong clan culture (Deshpande and Farley, 2004), emphasizes shaping the long-term and holistic vision of the organization by generating information and reducing inter-departmental conflicts by sharing information within the company. A venture is merely a single product market in the firm’s wider portfolio of product markets. Thus, in Japanese firms with its focus on a long-term and holistic vision, activities emerging from export market-oriented responsiveness are balancing bigger picture firm-level matters and prioritizing them over smaller more micro-level venture-related matters. In addition, we use cross-sectional data at a single point in time, which suggests an examination of the short-term relationship between responsiveness activity and venture success. If we had collected data from Japanese firms over time, we might have found a longer-term effect of responsiveness activity on venture outcomes.
Notably, the non-significance of the main effect of responsiveness activity indicates that the relationship between responsiveness activity and venture success is not significant on average across all channel partner choices. In other words, the responsiveness–performance link is not significant when channel partners are selected either by WOM referral or direct contact. However, in the high WOM referral condition, the relationship between responsiveness activity and venture outcome is positive and significant; this is the condition under which H1b is supported. These findings may be regarded as consistent with previous studies that examine export market orientation and channel issues. He et al. (2018) show that the use of channels that are easier to control and coordinate (i.e. hierarchical channels) enhances the benefits of export market orientation. Similarly, our findings suggest that export market-oriented responsiveness requires an easy-to-coordinate channel established by WOM referrals to lead to venture success.
Our study provides valuable insights for researchers interested in developing export performance theory. By adopting a disaggregated approach to export market orientation, we provide empirical evidence of the different effects of intelligence and responsiveness activities. Although most previous studies lump export market orientation into a single concept, it has two very different aspects: information inputs and information-based outputs. Our evidence indicates that intelligence activities involved in inputs are stronger drivers of export venture-level performance, whereas responsiveness activities are powerful factors of export function-level performance. Consistent with previous studies (Chung, 2012; Katsikea et al., 2019; Rose and Shoham, 2002), our study supports the effectiveness of a disaggregated approach to export market orientation.
Furthermore, our study is relevant to researchers in that it identifies a new enabling condition of export market orientation. Prior research shows that firm characteristics (e.g. organizational structure, strategic orientation) and environmental characteristics (e.g. market competitiveness, institutional distance) act as enabling/disenabling conditions for export market orientation. However, existing research lacks a focus on the role of international channel partner characteristics. By contrast, our study examines how international channel partner type acts as an enabling/disenabling condition for the impacts of export market-oriented activities on performance outcomes. By offering one piece of empirical evidence on this front, our study successfully responds to Ipek and Bıçakcıoğlu-Peynirci’s (2020) call for research on how export channels and inter-organizational relationships facilitate export market orientation.
This study applies agency theory to examine the interplay between export market orientation and channel partner type. Our findings highlight the importance of the fit between the task characteristics of export market orientation and the contract characteristics of international channel partners. We suggest that intelligence and responsiveness activities are distinct tasks in terms of outcome measurability and outcome uncertainty. Although these activities are elements of the same concept of export market orientation, they may have considerably different characteristics in terms of tasks that are delegated to others. In addition, our study suggests that international channel partners contracted through WOM referrals tend to be governed by behavior-based contracts, whereas those contracted through direct contacts tend to be governed by outcome-based contracts. Our evidence indicates that the former group of channel partners is better suited to responsive activities, whereas the latter group is better fitted to intelligence activities. Overall, we contribute to the elaboration of agency theory in that we apply it to the export marketing context and provide empirical evidence that the fit between task and contract characteristics enhances outcomes.
Managerial implications
Our study provides the following practical insights for business managers in export manufacturing firms. As shown in Panel A of Figure 4, export manufacturing firms can adopt one of four patterns, varying in the degree of intelligence activity and the way they select international channel partners. In Figure 4, the arrows indicate the direction that exporters located in each cell should pursue to achieve high performance. Firms with low levels of intelligence activity (cells A and B) should increase their levels of intelligence activity. This will allow exporters to anticipate future markets and improve internal human ties, thereby achieving higher export outcomes. Exporters that engage in a high level of intelligence activities and select international channel partners through WOM referrals (cell C) should actively engage in partner selection through direct contact if they value the benefits of intelligence activities. For exporters (cell D) engaged in a high level of intelligence activities with channel partners selected through direct contacts, maintaining the current strategy will lead to higher outcomes.
Similarly, as shown in Panel B of Figure 4, exporting manufacturing firms can adopt one of four patterns, varying in the degree of responsiveness activity and the way international channel partners are selected. Exporters that select their international channel partners through WOM referrals and have a low level of responsive activity (cell E) should increase their level of responsive activity as soon as possible. Thus, they can improve their performance in their export ventures. Firms that have high levels of responsiveness activity but that select international channel partners through direct contacts (cell H) will not be able to successfully benefit from their responsiveness activities. They should use WOM referral for business partner selection and engage in responsiveness activities with partners with whom they have close relationships. Exporters (cell F) that engage in a low level of responsiveness activity and use direct contact for partner selection have considerable difficulty in increasing the benefits of responsiveness activity as this requires an increase in the level of responsiveness activity themselves and a change in partner selection methods. Exporters engaged in responsiveness activities with WOM partners (cell G) should maintain the status quo because they have already achieved high venture performance.
Limitations and future research directions
This study has four limitations, which offer opportunities for future research. First, we use cross-sectional data collected from one person per firm. The use of such convenience data, though widely employed in the field of export marketing strategy, entails the risk of common method bias. To eliminate such methodological problems, collecting data from multiple respondents in the same firm or use longitudinal data is crucial. Additionally, longitudinal data provide an opportunity to examine the impact of export market orientation on both short-term and long-term venture success. Therefore, researchers are encouraged to make such efforts in future data collection.
Second, this study does not provide evidence on the types of contracts that govern international channel partners referred by WOM or direct contact. Therefore, future research should examine the relationship between the method of selecting international channel partners (WOM referral versus direct contact) and the type of contract (behavior-based versus outcome-based contracts).
Third, this study examines the relationship between export market orientation and export venture performance by dividing market orientation into two dimensions: intelligence and responsiveness activities. However, according to Katsikea et al. (2019, p. 2096), “it is important to consider export market intelligence generation and dissemination as distinct constructs” because these dimensions have different mechanisms to enhance export performance. Researchers are, therefore, encouraged to examine the differences between intelligence generation and dissemination. In addition, there is room for future research to investigate the relationship among the three dimensions of export market orientation, to examine the relationship between these three dimensions and performance outcomes, and to identify enabling/disenabling boundary conditions. These efforts can elaborate a disaggregated approach to export market orientation.
Finally, we show that international channel partner types play a moderating role in the export market-oriented activities–performance linkage. However, it remains unexamined whether other channel factors serve as enabling or disenabling conditions for the benefits of intelligence and responsiveness activities. Potential candidates include strong channel relationships (e.g. Miocevic, 2016; Skarmeas et al., 2016) and export channel type (e.g. Ishii, 2022; Li et al., 2023; Oliveira et al., 2018). Another interesting research question is whether international channel partner types enhance the benefits of other international marketing strategies, such as dynamic capabilities (Ciszewska-Mlinarič et al., 2024; Pfajfar et al., 2024; Vardarsuyu et al., 2024) and international servitization (Agnihotri et al., 2023; Bıçakcıoğlu-Peynirci and Morgan, 2023; Zahoor et al., 2023), which have received more attention in recent years. Future research should examine the interplay between international marketing strategies and channel issues.
Figures
Figure 1
Conceptual model
Figure 2
Moderating role of word-of-mouth referral
Figure 3
Post hoc analysis: moderating role of word-of-mouth referral
Figure 4
Exporters’ strategy pattern and export performance
Selected empirical studies on export market orientation and export venture performance
| Author(s) | Research context | Approach to EMO concept | International channel partner’s role | International channel partner’s type | Key findings |
|---|---|---|---|---|---|
| Murray et al. (2011) | 491 export ventures based in China | Aggregated | – | – | Export market orientation positively affects export venture performance by enhancing product development, pricing, and communication capabilities |
| Chung (2012) | 100 New Zealand export ventures | Disaggregated | – | – | The dimensions of intelligence generation and dissemination positively affect the dimension of responsiveness, leading to high strategic export performance. Business and political ties moderate the relationships among export market orientation dimensions |
| Katsikea et al. (2019) | 168 Greek manufacturing export ventures | Disaggregated | – | – | The dimensions of intelligence generation and dissemination enhance the development of export sales strategy, leading to high export venture performance |
| Ipek and Tanyeri (2021) | 221 Turkish export ventures | Aggregated | – | – | Home country institutional features (i.e. economic, regulatory, and sociocultural environments) determine the degree of export market orientation, which enhances export venture performance |
| He et al. (2013) | 195 Chinese manufacturing export ventures | Aggregated | ✓ | – | The fit between the degree of export market orientation, institutional features, and export channel type is positively related to export venture performance |
| He et al. (2018) | 214 Chinese manufacturing export ventures | Aggregated | ✓ | – | Export channel type moderates the benefits of export market orientation. Export market orientation is likely to enhance export venture performance more when using a hierarchical export channel (i.e. no channel partner mode) than when using other channels (i.e. channel partner cooperation mode) |
| The present study | 246 Japanese manufacturing export ventures | Disaggregated | ✓ | ✓ | The extent to which international channel partners are selected through word-of-mouth referrals has a moderating role in shaping high export performance; it acts as an enabling condition for intelligence activity, as well as a disenabling condition for responsiveness activity |
Note(s): EMO = Export market orientation
Source(s): Author’s own creation
Descriptive statistics and correlations
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Export venture performance | (0.84) | 0.22* | 0.28* | 0.22* | 0.18* | 0.08 | 0.14* | 0.17* | 0.02 | 0.02 | −0.09 | 0.01 |
| 2. Export function performance | 0.22* | – | 0.30* | 0.12 | 0.35* | 0.14* | 0.18* | 0.28* | 0.22* | 0.01 | 0.06 | 0.06 |
| 3. Export intelligence generation | 0.28* | 0.29* | (0.84) | 0.25* | 0.51* | 0.14* | 0.04 | 0.15* | 0.07 | 0.18* | 0.16* | 0.14* |
| 4. Export intelligence dissemination | 0.22* | 0.12 | 0.25* | (0.79) | 0.12 | −0.12 | −0.05 | 0.08 | 0.04 | −0.16* | −0.08 | −0.09 |
| 5. Export intelligence responsiveness | 0.18* | 0.35* | 0.50* | 0.12 | (0.78) | 0.15* | 0.14* | 0.16* | 0.13* | 0.05 | 0.02 | 0.16* |
| 6. Partner selection through WOM referral | 0.08 | 0.14* | 0.14* | −0.12 | 0.15* | (0.73) | 0.00 | 0.11 | 0.06 | 0.14* | 0.14* | 0.09 |
| 7. Firm size (log) | 0.14* | 0.18* | 0.03 | −0.05 | 0.14* | 0.00 | – | 0.23* | 0.23* | 0.02 | −0.10 | 0.01 |
| 8. Export experience (log) | 0.17* | 0.28* | 0.15* | 0.08 | 0.16* | 0.11 | 0.23* | – | 0.49* | −0.07 | 0.03 | −0.02 |
| 9. Export scope (log) | 0.02 | 0.21* | 0.07 | 0.04 | 0.13* | 0.06 | 0.23* | 0.49* | – | −0.10 | −0.02 | 0.02 |
| 10. Environmental dynamism | 0.02 | 0.00 | 0.18* | −0.16* | 0.05 | 0.14* | 0.02 | −0.07 | −0.10 | – | 0.46* | 0.25* |
| 11. Environmental diversity | −0.09 | 0.06 | 0.16* | −0.07 | 0.02 | 0.14* | −0.10 | 0.03 | −0.02 | 0.44* | – | 0.15* |
| 12. Psychic distance | 0.01 | 0.05 | 0.14* | −0.09 | 0.16* | 0.09 | 0.01 | −0.02 | 0.02 | 0.25* | 0.15* | – |
| Mean | 3.44 | 28.00 | 4.05 | 4.64 | 4.62 | 3.72 | 4.19 | 11.16 | 16.15 | 3.43 | 4.47 | 4.59 |
| Standard deviation | 1.46 | 31.74 | 1.27 | 1.12 | 1.34 | 1.45 | 1.47 | 15.68 | 13.10 | 1.30 | 1.23 | 1.05 |
| Max | 7.00 | 100.00 | 7.00 | 7.00 | 7.00 | 7.00 | 10.09 | 120.00 | 80.00 | 7.00 | 7.00 | 7.00 |
| Min | 1.00 | 0.10 | 1.00 | 2.00 | 1.00 | 1.00 | 1.10 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Note(s): *p < 0.05. The diagonal in parentheses shows the square root of the AVE; adjusted correlations for potential common method variance are listed above the diagonal
Source(s): Author’s own creation
Constructs and scale items
| Constructs and scale items | Standardized factor loadings (t-values) |
|---|---|
| Export venture performance (CR = 0.91, AVE = 0.71) | |
| Regarding the major product line in the host country | |
| 1. Your company is satisfied with sales during the past 3 years | 0.87a |
| 2. Your company is satisfied with profitability during the past 3 years | 0.71 (12.62) |
| 3. Your company is satisfied with sales growth during the past 3 years | 0.92 (18.94) |
| 4. Your company is satisfied with financial goals during the past 3 years | 0.86 (17.35) |
| Export function performance | |
| Please indicate the ratio of exports to total sales | |
| Export intelligence generation (CR = 0.90, AVE = 0.75) | |
| 1. We periodically review the likely effect of changes in our export environment (e.g. technology and regulation) | 0.78a |
| 2. We generate a lot of information to understand the forces which influence our overseas customers’ need and performance | 0.86 (10.82) |
| 3. We constantly monitor our level of commitment and orientation to serving export customer needs | 0.95 (12.70) |
| Export intelligence dissemination (CR = 0.83, AVE = 0.63) | |
| 1. Information about our export competitors’ activities often reaches relevant personnel too late to be of any use. (reversed) | 0.70a |
| 2. Important information concerning export market trends (regulatory, technology) is often discarded as it makes its way along the communication chain. (reversed) | 0.94 (10.65) |
| 3. Information regarding the way we serve our export customers takes forever to reach relevant personnel. (reversed) | 0.71 (10.36) |
| Export intelligence responsiveness (CR = 0.82, AVE = 0.61) | |
| 1. If a major competitor were to launch an intensive campaign targeted at our foreign customers, we would implement a response immediately | 0.86a |
| 2. We rapidly respond to competitive actions that threaten us in the major export market | 0.85 (12.89) |
| 3. We give close attention to after sales service in the major export market | 0.60 (9.50) |
| International channel partner selection through WOM referral (CR = 0.77, AVE = 0.55) | |
| 1. Your company often relies on information from other companies in the same industry when selecting foreign sales partners | 0.50a |
| 2. Your company often selects foreign sales partners through business friends | 0.78 (3.69) |
| 3. Your company often selects foreign sales partners based on business partners’ recommendations or referrals | 0.89 (6.32) |
| Environmental dynamism [formative indicator] | |
| 1. In the export country, customers frequently change their product preferences | |
| 2. In the export country, competitors frequently change their strategies | |
| 3. In the export country, international channel partners frequently change their strategies | |
| Environmental diversity [formative indicator] | |
| 1. There are many competitors in the export country | |
| 2. End customers in the export country have different needs from each other | |
| 3. There is a wide variety of international channel partners in the export country | |
| Psychic distance [formative indicator] | |
| To which degree do you perceive the following elements to be different between Japan and the major export country? (Seven-point scale from 1 = “very similar” to 7 = “very different”) | |
| 1. Level of economic and industrial development | |
| 2. Communications infrastructure | |
| 3. Marketing infrastructure | |
| 4. Technical requirements | |
| 5. Market competitiveness | |
| 6. Legal regulations | |
| 7. Purchasing power of customers | |
| 8. Lifestyles | |
| 9. Consumer preferences | |
| 10. Level of literacy and education | |
| 11. Language | |
| 12. Cultural values, beliefs, attitudes, and tradition | |
| Firm size | |
| Please indicate the number of full-time employees in your company | |
| Export experience (years of exporting) | |
| Please indicate the period of time since your company started selling export product line items in export countries | |
| Export scope (number of export markets) | |
| Please indicate in how many countries your company sells the export product line item | |
| Instrumental variable (premium product offering) | |
| How do you rate your product line on the following product characteristics compared with your competitors’ characteristics? | |
| 1. Prestige or image of the brand (1 = “not high” and 7 = “high”) | |
| 2. Price range (1 = “low price range” and 7 = “high price range”) | |
| 3. Product quality and performance (1 = “low end” and 7 = “high end”) |
Note(s): All items were measured using seven-point scales from 1 = “strongly disagree” to 7 = “strongly agree,” unless otherwise indicated. CR = composite reliability. AVE = average variance extracted aFixed to the value of 1.00
Source(s): Author’s own creation
Hierarchical regression analysis results
| Hypotheses testing | Post hoc analysis | |||||
|---|---|---|---|---|---|---|
| Dependent variable: export venture performance | Dependent variable: export function performance | |||||
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
| Independent variables | ||||||
| Intelligence activity | 0.31 (4.69)** | 0.30 (4.47)** | 0.16 (2.40)** | 0.13 (2.09)* | ||
| Responsiveness activity | 0.01 (0.17) | 0.07 (1.11) | 0.24 (3.57)** | 0.27 (4.12)** | ||
| Moderator variables | ||||||
| Partner selection through WOM referral | 0.07 (1.17) | 0.04 (0.65) | 0.09 (1.49) | 0.08 (1.33) | ||
| Interaction terms | ||||||
| Intelligence activity × WOM referral | −0.15 (−2.27)* | −0.00 (−0.04) | ||||
| Responsiveness activity × WOM referral | 0.21 (3.13)** | 0.21 (3.11)** | ||||
| Control variables | ||||||
| Firm size (log) | 0.11 (1.62) | 0.12 (1.90) | 0.13 (2.03)* | 0.15 (2.23)* | 0.13 (2.10)* | 0.14 (2.27)* |
| Export experience (log) | −0.05 (−0.64) | −0.05 (−0.75) | −0.04 (−0.59) | 0.12 (1.58) | 0.11 (1.59) | 0.12 (1.74) |
| Export scope (log) | 0.13 (1.76) | 0.07 (1.01) | 0.08 (1.11) | 0.08 (1.07) | 0.01 (0.12) | 0.00 (0.05) |
| Environmental dynamism | 0.07 (1.01) | 0.06 (0.89) | 0.10 (1.46) | −0.04 (−0.52) | −0.05 (−0.78) | −0.03 (−0.40) |
| Environmental diversity | −0.12 (−1.64) | −0.14 (−1.98)* | −0.17 (−2.44)** | 0.08 (1.13) | 0.07 (1.04) | 0.05 (0.80) |
| Psychic distance | −0.02 (−0.26) | −0.00 (−0.01) | 0.01 (0.17) | −0.05 (0.78) | 0.01 (0.03) | −0.01 (−0.11) |
| R2 | 0.04 | 0.14 | 0.18 | 0.07 | 0.19 | 0.23 |
| Adjusted-R2 | 0.02 | 0.11 | 0.14 | 0.05 | 0.16 | 0.19 |
| F-statistics | 1.73 | 4.43 | 4.73 | 3.05 | 6.11 | 6.31 |
Note(s): All reported betas are standardized (t-values in parentheses). Critical t-values are 1.960 and 2.326 for significance levels of 2.5% and 1% respectively (because all hypotheses are directional, we used one-tailed tests). **p < 0.01, *p < 0.025
Source(s): Author’s own creation
Notes
International marketing researchers adopt a disaggregated approach to market orientation not only in the context of exporting but also in the context of multinational companies (e.g. Dong et al., 2013; Özsomer et al., 2023).
This hypothesis can also be argued from the tie-strength perspective. Channel partners selected through WOM referrals have strong ties with export manufacturers (Granovetter, 1973). Such strong ties have a disadvantage: they hold both homogenous and redundant information (Noordhoff et al., 2011; Rindfleisch and Moorman, 2001), making it difficult for exporting manufacturers to access the wealth and novelty of information. In this case, exporters’ information activities are not likely to lead to greater export outcomes.
This hypothesis can also be argued from the tie-strength perspective. International channel partners selected through WOM referrals have strong ties with exporters, allowing close coordination and flexibility (Stanko et al., 2007). Such partners can react quickly to exporters’ requirements and respond flexibly to policy changes. Thus, exporters’ responsiveness activities are more likely to be successful when they have a large number of cooperative international channel partners selected through WOM referrals.
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Acknowledgements
We would like to thank Professor Robert Morgan for his helpful comments and invaluable feedback at the 2023 KSMS International Conference, which helped expand our ideas and encouraged us to further develop this work. We also would like to thank the anonymous IMR reviewers and the Editor-in-Chief, Professor John Cadogan for their constructive and valuable comments, which helped improve the paper significantly.
Funding: This work was supported by the JSPS KAKENHI Grant Numbers JP23K12572 amd JP23K12573.
Corresponding author
About the authors
Ryuta Ishii is Associate Professor of Marketing at Ritsumeikan University, Japan. His research interests include channel management and international marketing strategy, particularly in international marketing channels, export strategic orientation, organizational capability, and cross-border business relationships. His research has been published in several journals, including International Marketing Review, Industrial Marketing Management, Journal of Business and Industrial Marketing, and Marketing Intelligence and Planning.
Mai Kikumori is Associate Professor of Marketing at Ritsumeikan University, Japan. Her research interests include digital marketing and social interactions, particularly in word-of-mouth, online reviews, and social media usage. Her research has been published in Industrial Marketing Management, Journal of Business and Industrial Marketing, Journal of Global Fashion Marketing, and other journals.