Trademark Rights: The Impact of Joining the Madrid Protocol on International Marketing Alliances

Abstract

This research examines how trademark rights impact international marketing alliances. Drawing on the transaction cost theory, the authors examine how the strengthening of trademark rights influences the formation of international marketing alliances by reducing uncertainty in the relationships between alliance partners. Using a difference-in-difference approach and data on 29,858 alliances from 45 countries, the authors find that the likelihood of initiating international marketing alliances increases after a country strengthens its trademark rights by joining the Madrid Protocol. This study contributes to the research on the role of institutional environments, specifically those related to intellectual property rights, in the formation of international strategic alliances.

Keywords

trademark Madrid protocol intellectual property rights strategic alliance transaction costs theory

Strategic alliances (SAs), formal agreements between firms to create value and access resources, are popular in modern international business strategy (Lavie and Miller 2008). Our focus is on a specific and vital subset, international marketing alliances, which are collaborative ventures where firms pool brand assets and marketing expertise across national borders. A prominent example is the global coffee alliance between Nestlé and Starbucks, where Nestlé leverages its global distribution network to market Starbucks’s packaged coffee products—a partnership reliant on robust, cross-jurisdictional protection of both firms’ valuable trademarks.

While such alliances can enhance performance (Swaminathan and Moorman 2009) and reduce firm risk (Thomaz and Swaminathan 2015), their formation is often hindered by the institutional environment of the host country (Balachandran and Hernandez 2019). Firms are likely hesitant to commit valuable intangible assets to a partnership in a foreign nation where the legal framework for protecting those assets is weak or uncertain, raising concerns about value appropriation. A key factor in the institutional environment that affects these concerns is the strength of intellectual property rights (IPR).

IPR encompasses patents, copyrights, and trademarks. This study focuses specifically on trademarks, as the regulations governing their protection are particularly relevant to marketing scholars, practitioners, and policymakers (Krasnikov et al. 2009; Krasnikov and Jayachandran 2022). While prior research has shown that stronger patent rights can increase the formation of international alliances (Balachandran and Hernandez 2019), it is not clear that this effect extends to stronger trademark rights and international marketing alliances, where the primary appropriability concerns revolve around intangible brand assets, not patentable inventions. This manuscript aims to fill that gap by empirically investigating whether strengthening trademark rights in a country influences the formation of international marketing alliances.

To do so, we examine a significant structural change in a country's trademark protection regime: its accession to the Madrid Protocol. The Madrid Protocol is an international agreement administered by the World Intellectual Property Organization (WIPO). The protocol provides an efficient way to register trademarks and obtain protection in more than 130 countries. More than a mere procedural convenience, a country's accession to the Madrid Protocol acts as a powerful signal to the international business community. It signals a credible commitment to defending globally recognized, robust standards for IPR protection. For potential foreign partners, this signal reduces uncertainty and the perceived risk of opportunism that could damage their brand assets. From a transaction cost economics perspective, this reduction in risk directly lowers the anticipated costs of negotiating, monitoring, and enforcing alliance agreements, making international marketing alliances a more attractive and viable strategic option.

Using a difference-in-difference (DID) analysis of 29,858 alliances across 45 countries, we find that when a country adopts the Madrid Protocol, the rate of international marketing alliance formation increases. Furthermore, we find an increase in alliances that jointly pursue both R&D and marketing, suggesting that stronger trademark protection reduces risk in the entire innovation-to-commercialization life cycle.

This study contributes to theory by giving insights into a key institutional factor—trademark rights—that supports the formation of international marketing alliances. The rationale for the increased use of such alliances is the firms’ greater confidence that they can appropriate the value from the alliance without compromising their brands, which now receive better protection. The findings also offer practical guidance for managers on assessing country risk for brand-centric collaborations and for policymakers on leveraging IPR treaties as a strategic tool for economic development.

Theory

We use transaction cost theory (TCT) to understand how trademark rights influence the propensity to form marketing alliances. Transaction costs are the costs of coordinating transactions, including negotiating, monitoring, and enforcing agreements. Due to bounded rationality, contracts are inherently incomplete, thereby enhancing the likelihood that partners will behave opportunistically. TCT posits that firms design governance mechanisms ranging from open markets to full internalization within a firm with SAs as an intermediate form to minimize these transaction costs. When the risk of opportunism and uncertainty is high, firms may prefer to internalize activities rather than risk collaboration (Williamson 1985). A robust institutional framework for protecting IPR ensures that contractual terms are enforced, thereby lowering transaction costs. When these costs are lowered, firms are more likely to engage in intermediate forms of governance like alliances. Therefore, the institutional environment, which comprises laws and regulations that lower uncertainty and risk for alliances, assumes significance in alliance governance (Oxley 1999).

Trademark Rights and Alliances

Brands are critical intangible assets and trademarks are the legal mechanism used to protect them from misappropriation (Krasnikov and Jayachandran 2022). Trademarks are likely the most widely used form of IP protection (Hsu et al. 2022). By protecting the core identifiers that help consumers recognize and remember a brand, trademarks serve as a useful proxy for brand equity. (Dinlersoz et al. 2018). Trademarks distinguish a product's source and differentiate it from rivals (Krasnikov and Jayachandran 2022); consequently, their registration has been positively linked to firm performance (Hsu et al. 2022).

Trademark rights can be established in two primary ways: through formal registration with a government body like the U.S. Patent and Trademark Office or through “first use” in commerce. While both methods are valid, registration provides the significant advantage of nationwide priority, offering broad protection against misuse (Krasnikov and Jayachandran 2022). However, these rights are geographically constrained by the territoriality principle. This principle means protection is determined by the laws of each individual country and does not automatically extend across borders (Calboli and Lee 2014; Pinkham and Peng 2017; Yan et al. 2022).

The country-by-country nature of trademark rights creates major obstacles for global companies (Gillespie et al. 2002; Jain 1996). To address this, the Madrid Protocol offers a centralized system for international trademark protection. Administered by WIPO, the treaty lets a trademark holder file one application to seek registration in up to 132 member nations (as of March 2026). This process not only simplifies registration but also permits firms to claim priority and prosecute infringements within member countries, making global brand management more efficient (Samuels and Samuels 2004).

The risk of trademark misuse in marketing alliances is especially high in territories with weak IPR. When a country joins the Madrid Protocol, it does more than just legally strengthen these rights; it credibly signals its commitment to global IPR standards. For foreign firms, this signal lowers the perceived risk of partner opportunism and reduces uncertainty. This, in turn, lowers the anticipated transaction costs, directly encouraging the formation of international marketing alliances.

H_1a: A country's accession to the Madrid Protocol is positively associated with the likelihood of formation of international marketing alliances by firms in that country.

Stronger trademark rights also encourage the formation of alliances that integrate both R&D and marketing. Firms strategically manage a portfolio of patents and trademarks to capture maximum value from innovation. This dynamic is explained by the “value articulation” framework, which describes how firms engage in “value transference” by strategically migrating the competitive advantage secured by a limited-life asset (a patent) to an indefinite-life asset (a trademark) through coordinated marketing and branding (Conley et al. 2013). Patents protect the technological advantage in a product's early life, while trademarks protect the associated goodwill and reputation long after the patent expires. The strategic importance of this is underscored by evidence showing that pairing a patent with a trademark can enhance the patent's value (Bird and Orozco 2014; Conley et al. 2013). By strengthening and simplifying cross-border trademark protection, a country's accession to the Madrid Protocol makes this entire innovation-to-commercialization value chain more secure and less costly for international partners. It reduces risk not only in the marketing phase but in the entire life cycle of the innovation. This increased security incentivizes firms to form more integrated alliances that span both the creation of the initial IP (R&D) and the crucial process of building long-term brand value from it (marketing).

H_1b: A country's accession to the Madrid Protocol is positively associated with the likelihood of formation of international alliances that jointly feature R&D and marketing activities by firms in that country.

Data and Method

We extracted alliance data for the 2000–2022 period from the Securities Data Company (SDC) Platinum Joint Ventures and Strategic Alliances database (Tower et al. 2021). Next, we recorded the date of a country's accession to the Madrid Protocol from WIPO’s website (WIPO.int). During this period, 45 nations with firms joining SAs signed the Madrid Protocol (e.g., Australia acceded to the protocol in 2001, Kazakhstan in 2006, and Brazil in 2019; a complete list of countries is reported in Web Appendix A). We used World Bank data to estimate the country-level controls.

Following prior research (e.g., Swaminathan and Moorman 2009), we created our sample of 29,858 SAs in four steps (Table 1). First, we identified all SAs formed during 2000–2022. The majority of alliances were concentrated in the business services sector including information technology (28%), followed by management consulting (9%) and R&D activities (6%). Next, we selected SAs formed by firms in a nation within ±2 years of when it joined the Madrid Protocol (e.g., Brazilian firms formed 93 and 128 SAs two years before and after signing the protocol, respectively; see Web Appendix B for the examples of alliances announcements). Then, SAs formed during the same ±2 years by firms from countries that had not yet adopted the Madrid Protocol were used to create a control group. Finally, we used propensity score matching to match SAs from the treatment and control groups.

Table 1.
Descriptive Statistics and Correlations (N = 29,858).

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. International strategic alliance (INTL) .500 .500 1.000

2. International marketing strategic alliance (MKT) .174 .379 −.044 1.000

3. International R&D strategic alliance (RD) .096 .295 .065 .023 1.000

4. International marketing and R&D strategic alliance (MKT_RD) .019 .138 .055 .306 .430 1.000

5. Experimental group (EG) .500 .500 −.016 .062 .030 .027 1.000

6. Control group (CG) .500 .500 .016 −.062 −.030 −.027 −1.000 1.000

7. After signing the Madrid Protocol (AFTER) .575 .494 .071 −.012 .039 .041 .000 .000 1.000

8. Prior alliance experience (EXPER) 5.117 24.669 .093 −.013 −.006 −.005 −.026 .026 .027 1.000

9. No. of participants in strategic alliance (N) 2.073 .417 .061 −.024 .016 −.004 −.018 .018 −.007 −.007 1.000

10. Patent applications per thousand population (PA) .795 .734 −.444 .017 −.037 −.031 .123 −.123 .014 .014 .007 1.000

11. Trademark applications per thousand population (TMA) .884 .933 −.099 −.044 −.074 −.055 .063 −.063 −.002 .052 −.004 .433 1.000

12. New businesses started (NEWB) 1.099 1.203 −.295 .002 −.069 −.051 .011 −.011 −.012 .014 −.042 .359 .469 1.000

13. Population (POP) 11.140 1.536 −.551 .033 −.084 −.062 .060 −.060 −.086 −.020 −.042 .627 .049 .339 1.000

14. Individualism (IND) 63.969 30.032 −.574 .026 −.098 −.072 .103 −.103 −.132 −.011 −.064 .695 .482 .558 .622 1.000

15. Economic development (GDP) .034 .032 .051 .003 −.009 −.002 .107 −.107 .100 .084 −.016 −.017 .021 .044 .013 −.027

Notes: Correlations with absolute values above .015 are statistically significant at p < .05.

Dependent Variables

The outcomes of interest are binary variables for the alliance type. We use alliance activity codes provided by the SDC Platinum database to classify alliances. Alliances with codes corresponding to marketing, advertising, or distribution were coded as “marketing,” while those with codes for research and development were coded as “R&D.” Our focus on these alliance types is theoretically driven, as they represent collaborations where intangible assets like brands and technological know-how, the very assets protected by trademarks and patents, are most salient and vulnerable. From SDC Platinum, we also recorded the nations of the partners. The alliance was deemed international if partners belonged to different countries (INTL = 1). We then created binary variables for international marketing alliances (MKT), international R&D alliances (RD), and alliances featuring both (MKT_RD).

Independent Variables

Our identification relies on two sources of variation: the timing of a country signing the Madrid Protocol and the cross-section of countries signing versus not signing. Our first variable, EG, is a dummy variable that is 1 if a partner's country had signed the Madrid Protocol at the time of the alliance announcement. The second, CG, is a dummy that is 1 if a partner's country had not signed the protocol at the time of the alliance announcement. Finally, the variable AFTER is a dummy for the period after accession to the Madrid Protocol (AFTER = 1) versus before (AFTER = 0).

Control Variables

We included several variables to tease out potential confounding effects. First, we controlled for a set of alliance-level characteristics that might be related to the SA type. We measured prior alliance experience (EXPER) by estimating the average number of alliances formed by partners in the previous five years (Swaminathan and Moorman 2009). Next, we included the number of partners in SA (N) that might affect the alliances’ activity scope through ownership and resource configurations in SA. In addition, previous studies suggested that property rights regimes may influence the licensing and creation of intellectual property in SA (Balachandran and Hernandez 2019). Thus, we included metrics that capture the importance of IP in the alliance's home countries. Using World Bank data, we calculated the number of trademark (TMA) and patent (PA) applications per thousand people in these countries. Third, we controlled for the economic development of the countries the alliance members were in using annual growth of the gross domestic product (GDP), the logarithm of country's population (POP), and the number of new businesses started per 1,000 capita (NEWB). Fourth, to account for the effects of national culture, we used the individualism cultural dimension, which has been shown to affect alliance partners’ opportunistic behavior (e.g., Steensma et al. 2000). All control variables were standardized. Furthermore, we included annual and industry (based on Standard Industrial Classification [SIC] codes) fixed effects.

Descriptive Statistics and Model-Free Evidence

Table 1 reports descriptive statistics. Most correlations between predictive variables have absolute values below .2, suggesting that multicollinearity is not a concern. We performed a model-free analysis to see the impact of the Madrid Protocol on SA types (Table 2). Table 2 reports descriptive statistics for the four outcome variables measured in the window [−2 years, +2 years] centered on the protocol's accession. The first column reports the proportion of the international alliances (INTL = 1) formed by partners two years before their native countries signed the Madrid Protocol. The second column provides the proportion two years after the signing. The difference in values in the two columns and the accompanying significance levels are presented in the next column. Similarly, the proportions for ±2 years and their differences are presented in the subsequent columns corresponding to outcomes MKT, RD, and MKT_RD (Table 2). The model-free estimates highlight a noticeable increase in the outcome variables after the partner's home countries joined the protocol. As such, it provides initial evidence that partners alter their behaviors in response to changes in property rights. In the next section, we put these conjectures from the raw data to the test using the DID model.

Table 2.
Model-Free Analysis.

Statistics International Alliance (INTL) International Marketing Alliance (MKT) International R&D Alliance (RD) International Marketing and R&D Alliance (MKT_RD)

Two Years Before Two Years After Difference Two Years Before Two Years After Difference Two Years Before Two Years After Difference Two Years Before Two Years After Difference

(1) (2) (2) − (1) (3) (4) (4) − (3) (5) (6) (6) − (5) (7) (8) (8) − (7)

Mean .473 .507 .034 .157 .192 .035 .094 .139 .045 .014 .043 .029

SD .499 .500 .500 .364 .394 .382 .292 .346 .325 .116 .203 .173

p-Value .000 .000 .000 .000

No. of alliances 6,342 8,587 3,001 4,350 3,001 4,350 3,001 4,350

Notes: The p-value corresponds to the probability of failing to reject the hypothesis that the difference between the means equals 0.

Empirical Model

Because our dependent variables are binary, we use a probit model. Following prior research on nonlinear DID models (e.g., Puhani 2012), we specify the model as shown in Equation 1. In nonlinear models like probit or logit, the coefficient on an interaction term does not represent the marginal effect (Ai and Norton 2003). In a nonlinear DID model, the true treatment effect is related to the incremental effect of the interaction term (Puhani 2012). The specification we use, which includes separate interaction terms for the experimental group (EG × AFTER) and the control group (CG × AFTER), is a valid method for recovering the average treatment effect on the treated (ATT). The difference between the coefficients of these two terms (β₁ − β₂) correctly calculates the treatment effect in this nonlinear framework, a technique employed in prior research such as Puri et al. (2011).

A key assumption of the DID model is that the treatment and control groups would have followed parallel trends in the absence of treatment. To strengthen this assumption and enhance the comparability of our groups, we use propensity score matching at the alliance level. The goal is not to match countries, but to ensure that the specific alliances in our treatment group are similar on observable pretreatment characteristics (e.g., prior alliance experience, industry) to the alliances in our control group. This process creates a more balanced sample, akin to a randomized experiment, thereby producing a more precise estimate of the treatment effect (Imbens and Rubin 2015). The covariate balance table is provided in Web Appendix C.

Alliances formed by the firms from a country within ±2 years of when the government adopted the protocol formed the experimental group (EG = 1), and alliances established by firms in countries that had not joined the protocol at that time formed the control group (i.e., CG = 1). We calculated propensity scores using logistic regression where the log odds ratio of belonging to the treatment group (i.e., EG = 1) is modeled as a function of the following covariates: alliance experience, size, industry (captured by SIC), whether an alliance involved a transfer of technology or licensing, country's property rights protection index, numbers of new businesses and SAs formed in a country, the country's population, and individualism cultural dimension. We matched using the nearest-neighbor approach without replacement based on the logits of the propensity scores. For a match, the difference in the logits of the propensity scores for pairs of companies from the two groups had to be within .25 times the pooled estimate of the common standard deviation of the logits. Candidates for the control group that matched outside the range of scores for treated companies were excluded. We checked the balance between the two groups using two metrics: the standardized difference between the two groups and the variance ratio. The absolute standardized difference was smaller than .25, and the variance ratio was between .5 and 2, which indicates good variable balance.

Next, we employ the probit model, in which the treatment effect is estimated as the cross difference between the conditional expectation of the observed outcome in the treatment group and that in the control group (Ai and Norton 2003). The DID model has the following form:
${DV}_{i, t} = β_{0} + β_{1} (EG \times AFTER) + β_{2} (CG \times AFTER) + δ X_{i, t} + Ω + ε_{i, t},$
(1)

where dependent variable (DV) represents one of four probabilities: international alliance (INTL), international marketing alliance (MKT), international R&D alliance (RD), and marketing activity of the international R&D alliance (MKT_RD). X_i,t are control variables that capture prior alliance experience (EXPER), alliance size (N), trademarking (TMA), patenting (PA), new businesses started in country (NEWB), population (POP), individualism (IND), and countries’ economic development (GDP). Ω represents annual and industry fixed effects. Finally, ε_i,t is an error term. The key variables of interest are cross effects EG × AFTER and CG × AFTER. We are interested in the difference between these two variables to see whether changing property rights affect the propensity to form different types of international SAs by partners originating from participating versus nonparticipating jurisdictions. Our inference is thus based on the comparison of coefficients β₁ and β₂. If the difference is positive (negative), strengthening trademark rights increases (decreases) probabilities associated with different SA types.

Results

Table 3 reports the parameter estimates for Model 1 for the four outcome variables. To assess model fit, we include the log-likelihood ratio. Table 3 reports the parameter estimates for the four probit models, DID estimates derived from the difference between β₁ and β₂, and the p-value of the Wald test under the null hypothesis that the difference is equal to zero (Allison 1999). Section 1, Table 3 indicates that the likelihood of forming international alliances in the experimental group increased by 25.1% after joining the protocol (β₁ = .251, p < .001). Furthermore, the estimate for the difference (β₁ − β₂) was positive and statistically significant, equal to .384 (p < .001), suggesting that accession to the Madrid Protocol increased the probability of forming an international alliance by 38.4% compared to the control group. Moreover, joining the protocol increased the likelihood of creating an international marketing alliance by 20.3% (DID = .203, p = .006) compared with the control group (Section 2, Table 3). Similar evidence emerges from the analysis of DID parameters for RD (DID = .195, p = .040) and MKT_RD (DID = .380, p = .002), suggesting that the likelihoods of forming R&D and joint MKT-RD alliance have increased compared with the control groups by 19.5% and 38.0%, respectively (Sections 3 and 4, Table 3). Overall, these results confirm the positive impact of the Madrid Protocol on the probabilities of forming international marketing alliances and joint international marketing and R&D alliances, supporting H_1a and H_1b. We find that joining the protocol increases the likelihood of forming international SAs in general and international R&D alliances, although we did not hypothesize these effects.

Table 3.
Estimation of the Difference-in-Difference Models.

International Alliance (INTL) International Marketing Alliance (MKT) International R&D Alliance (RD) International Marketing and R&D Alliance (MKT_RD)

(1) (2) (3) (4)

Beta SE p Beta SE p Beta SE p Beta SE p

Experimental group × after signing Madrid Protocol (EG × AFTER) (β₁) .251 .033 .000 .202 .054 .000 .107 .070 .083 .564 .087 .000

Control group × after signing Madrid Protocol (CG × AFTER) (β₂) −.133 028 .000 −.001 .050 .992 −.088 .064 .165 .184 .089 .039

β₁ – β₂ .384 .044 .000 .203 .074 .006 .195 .095 .040 .380 .124 .002

Prior alliance experience (EXPER) .007 .001 .000 .000 .001 .723 .000 .001 .870 −.001 .002 .664

No. of participants in strategic alliance (N) .149 .025 .000 −.068 .039 .084 .032 .047 .494 −.114 .079 .159

Trademark applications per thousand population (TMA) .275 .018 .000 −.007 .024 .775 −.054 .032 .091 −.052 .050 .294

Patent applications per thousand population (PA) −.226 .018 .000 .040 .032 .204 .091 .042 .031 .168 .053 .002

Economic development (GDP) 1.155 .683 .091 1.619 1.166 .165 .518 1.476 .726 .809 2.206 .714

New businesses started (NEWB) −.040 .009 .000 −.014 .017 .404 .004 .021 .846 .014 .035 .684

Population (POP) −.136 .011 .000 −.009 .020 .674 −.064 .028 .022 −.074 .046 .104

Individualism (IND) −.023 .001 .000 −.001 .001 .573 −.004 .002 .030 −.005 .003 .038

Intercept, annual, and industry fixed effects + + + +

Log-likelihood ratio 13,181.30 3,774.27 2,196.26 1,257.22

No. of alliances 29,858 14,940 14,940 14,940

Notes: Beta = unstandardized coefficient.

Furthermore, we performed additional analyses using Equation 1 for the top and bottom 25% of countries in our sample, based on the World Bank's property rights and regulatory quality ratings. Then, we compared the DID effects between these two groups and found a stronger impact of the accession to the Protocol in the group with the higher levels of regulatory quality (Web Appendix D). These findings provide evidence that strengthening trademarking rights has a more substantial effect in environments with lower transaction costs for enforcing property rights.

Discussion

Our empirical analysis offers strong evidence that enhancing trademark rights through the adoption of the Madrid Protocol has a significant and positive influence on the formation of international marketing alliances. The results indicate that after a country joins the Madrid Protocol, firms within that country are more likely to form international marketing alliances and alliances that jointly pursue R&D and marketing objectives. This increase can be attributed to firms’ enhanced confidence in protecting their intangible brand assets, which stems from a reduction in the transaction costs and risks associated with opportunistic behavior by partners.

Theoretical Implications

From a theoretical perspective, our study contributes to understanding how institutional environments, specifically IPR, impact SA formation. By applying TCT, we argue that more substantial trademark rights reduce transaction costs and uncertainty, making international marketing alliances a more attractive governance mechanism. This finding stresses the critical role of property rights in facilitating cross-border economic exchange by minimizing the costs associated with negotiating, monitoring, and enforcing agreements. Our research extends the application of TCT to the specific context of international marketing, showing how a supranational legal framework for trademarks can mitigate the risks of cross-border collaboration (Hartmann et al. 2022). We contribute to the prior research on how IPR may mitigate the risks of misappropriation of intellectual property in interfirm alliances (Cui et al. 2022). The adoption of the Madrid Protocol serves as a commitment to stronger, globally acceptable standards for trademark protection, thereby fostering a conducive environment for alliance formation.

Practical Implications

The findings offer practical, actionable insights for managers. When considering market entry into a foreign country, its status as a Madrid Protocol signatory should be a key variable in the entry mode calculus. In signatory countries, managers can more confidently pursue alliance strategies that leverage brand assets, as opposed to capital-intensive foreign direct investment, knowing that a robust international framework for IP protection is in place. Furthermore, managers of R&D-intensive firms should actively assess opportunities to integrate marketing components into their international R&D alliances in these countries to maximize and protect the long-term returns on innovation.

The results also have significant policy implications. Policymakers should recognize the importance of strong trademark protection in supporting international business collaboration. By acceding to international agreements like the Madrid Protocol, countries can create a more stable and predictable institutional environment, which in turn attracts foreign firms and encourages the formation of SAs that drive economic growth and innovation. Advocating for the adoption of such agreements can enhance global standards for trademark protection, facilitating smoother and more secure international business transactions.

Limitations and Future Research

While our study delivers valuable insights, it is not without limitations. The analysis is based on data from countries that adopted the Madrid Protocol between 2000 and 2022. Future research can explore the impact of other forms of IPR, such as patents and copyrights, on alliance formation. Additionally, further studies can examine the long-term effects by studying interfirm alliances formed in the countries that have not joined the system to overcome the potential limitation that the sample included countries that eventually acceded to the Madrid Protocol. Investigating the role of other factors such as cultural differences and economic conditions in influencing the heterogeneity in alliance formation could provide a more comprehensive understanding of the underlying dynamics.

Supplemental Material

sj-pdf-1-jig-10.1177_1069031X261434589 - Supplemental material for Trademark Rights: The Impact of Joining the Madrid Protocol on International Marketing Alliances

Supplemental material, sj-pdf-1-jig-10.1177_1069031X261434589 for Trademark Rights: The Impact of Joining the Madrid Protocol on International Marketing Alliances by Satish Jayachandran and Alexander Krasnikov in Journal of International Marketing

Variable	Mean	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. International strategic alliance (INTL)	.500	.500	1.000
2. International marketing strategic alliance (MKT)	.174	.379	−.044	1.000
3. International R&D strategic alliance (RD)	.096	.295	.065	.023	1.000
4. International marketing and R&D strategic alliance (MKT_RD)	.019	.138	.055	.306	.430	1.000
5. Experimental group (EG)	.500	.500	−.016	.062	.030	.027	1.000
6. Control group (CG)	.500	.500	.016	−.062	−.030	−.027	−1.000	1.000
7. After signing the Madrid Protocol (AFTER)	.575	.494	.071	−.012	.039	.041	.000	.000	1.000
8. Prior alliance experience (EXPER)	5.117	24.669	.093	−.013	−.006	−.005	−.026	.026	.027	1.000
9. No. of participants in strategic alliance (N)	2.073	.417	.061	−.024	.016	−.004	−.018	.018	−.007	−.007	1.000
10. Patent applications per thousand population (PA)	.795	.734	−.444	.017	−.037	−.031	.123	−.123	.014	.014	.007	1.000
11. Trademark applications per thousand population (TMA)	.884	.933	−.099	−.044	−.074	−.055	.063	−.063	−.002	.052	−.004	.433	1.000
12. New businesses started (NEWB)	1.099	1.203	−.295	.002	−.069	−.051	.011	−.011	−.012	.014	−.042	.359	.469	1.000
13. Population (POP)	11.140	1.536	−.551	.033	−.084	−.062	.060	−.060	−.086	−.020	−.042	.627	.049	.339	1.000
14. Individualism (IND)	63.969	30.032	−.574	.026	−.098	−.072	.103	−.103	−.132	−.011	−.064	.695	.482	.558	.622	1.000
15. Economic development (GDP)	.034	.032	.051	.003	−.009	−.002	.107	−.107	.100	.084	−.016	−.017	.021	.044	.013	−.027

Statistics	International Alliance (INTL)	International Marketing Alliance (MKT)	International R&D Alliance (RD)	International Marketing and R&D Alliance (MKT_RD)
Mean	.473	.507	.034	.157	.192	.035	.094	.139	.045	.014	.043	.029
SD	.499	.500	.500	.364	.394	.382	.292	.346	.325	.116	.203	.173
p-Value			.000			.000			.000			.000
No. of alliances	6,342	8,587		3,001	4,350		3,001	4,350		3,001	4,350

	International Alliance (INTL)	International Marketing Alliance (MKT)	International R&D Alliance (RD)	International Marketing and R&D Alliance (MKT_RD)
Experimental group × after signing Madrid Protocol (EG × AFTER) (β₁)	.251	.033	.000	.202	.054	.000	.107	.070	.083	.564	.087	.000
Control group × after signing Madrid Protocol (CG × AFTER) (β₂)	−.133	028	.000	−.001	.050	.992	−.088	.064	.165	.184	.089	.039

β₁ – β₂	.384	.044	.000	.203	.074	.006	.195	.095	.040	.380	.124	.002

Prior alliance experience (EXPER)	.007	.001	.000	.000	.001	.723	.000	.001	.870	−.001	.002	.664
No. of participants in strategic alliance (N)	.149	.025	.000	−.068	.039	.084	.032	.047	.494	−.114	.079	.159
Trademark applications per thousand population (TMA)	.275	.018	.000	−.007	.024	.775	−.054	.032	.091	−.052	.050	.294
Patent applications per thousand population (PA)	−.226	.018	.000	.040	.032	.204	.091	.042	.031	.168	.053	.002
Economic development (GDP)	1.155	.683	.091	1.619	1.166	.165	.518	1.476	.726	.809	2.206	.714
New businesses started (NEWB)	−.040	.009	.000	−.014	.017	.404	.004	.021	.846	.014	.035	.684
Population (POP)	−.136	.011	.000	−.009	.020	.674	−.064	.028	.022	−.074	.046	.104
Individualism (IND)	−.023	.001	.000	−.001	.001	.573	−.004	.002	.030	−.005	.003	.038

Intercept, annual, and industry fixed effects			+			+			+			+

Log-likelihood ratio	13,181.30	3,774.27	2,196.26	1,257.22
No. of alliances	29,858	14,940	14,940	14,940

Footnotes

Editor

Ayşegül Özsomer

Associate Editor

Timo Mandler

Author Contributions

Both authors share equal authorship.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Nazarbayev University under the faculty-development competitive research grants program (040225FD4709, PI-Alexander Krasnikov).

ORCID iD

Satish Jayachandran

Data Availability

The data that support the findings of this article are not publicly available due to licensing agreements and copyright.

References

Chunrong

Norton

Edward C.

(2003), “Interaction Terms in Logit and Probit Models,” Economics Letters, 80 (1), 123–29.

Allison

Paul D.

(1999), “Comparing Logit and Probit Coefficients Across Groups,” Sociological Methods & Research, 28 (2), 186–208.

Balachandran

Sarath

Hernandez

Exequiel

(2019), “Do Institutional Reforms Perpetuate or Mitigate the Matthew Effect? Intellectual Property Rights and Access to International Alliances,” Strategy Science, 4 (2), 151–74.

Bird

Robert C.

Orozco

David

(2014), “Finding the Right Corporate Legal Strategy,” MIT Sloan Management Review, 56 (1), 81–89.

Calboli

Irene

Lee

Edward

(2014), Trademark Protection and Territoriality Challenges in a Global Economy. Edward Elgar Publishing.

Conley

James G.

Bican

Peter M.

Ernst

Holger

(2013), “Value Articulation: A Framework for the Strategic Management of Intellectual Property,” California Management Review, 55 (4), 102–20.

Cui

Victor

Narula

Rajneesh

Minbaeva

Dana

Vertinsky

Ilan

(2022), “Towards Integrating Country- and Firm-Level Perspectives on Intellectual Property Rights,” Journal of International Business Studies, 53 (9), 1880–94.

Dinlersoz

Emin

Goldschlag

Nathan

Myers

Amanda

Zolas

Nikolas

(2018), “An Anatomy of US Firms Seeking Trademark Registration,” in Measuring and Accounting for Innovation in the 21st Century, Corrado

Carol

Haskel

Jonathan

Miranda

Javier

Sichel

Daniel

, eds. University of Chicago Press, 183–228.

Gillespie

Kate

Krishna

Kishore

Jarvis

Susan

(2002), “Protecting Global Brands: Toward a Global Norm,” Journal of International Marketing, 10 (2), 99–112.

10.

Hartmann

Simon

Lindner

Thomas

Müllner

Jakob

Puck

Jonas

(2022), “Beyond the Nation-State: Anchoring Supranational Institutions in International Business Research,” Journal of International Business Studies, 53 (6), 1282–1306.

11.

Hsu

Po-Hsuan

Dongmei

Qin

Teoh

Siew Hong

Tseng

Kevin

(2022), “Valuation of New Trademarks,” Management Science, 68 (1), 257–79.

12.

Imbens

Guido W.

Rubin

Donald B.

(2015), Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.

13.

Jain

Subhash C.

(1996), “Problems in International Protection of Intellectual Property Rights,” Journal of International Marketing, 4 (1), 9–32.

14.

Krasnikov

Alexander

Jayachandran

Satish

(2022), “Building Brand Assets: The Role of Trademark Rights,” Journal of Marketing Research, 59 (5), 1059–82.

15.

Krasnikov

Alexander

Mishra

Saurabh

Orozco

David

(2009), “Evaluating the Financial Impact of Branding Using Trademarks: A Framework and Empirical Evidence,” Journal of Marketing, 73 (6), 154–66.

16.

Lavie

Dovev

Miller

Stewart R.

(2008), “Alliance Portfolio Internationalization and Firm Performance,” Organization Science, 19 (4), 623–46.

17.

Oxley

Joanne E.

(1999), “Institutional Environment and the Mechanisms of Governance: The Impact of Intellectual Property Protection on the Structure of Inter-Firm Alliances,” Journal of Economic Behavior & Organization, 38 (3), 283–309.

18.

Pinkham

Brian C.

Peng

Mike W.

(2017), “Overcoming Institutional Voids via Arbitration,” Journal of International Business Studies, 48 (3), 344–59.

19.

Puhani

Patrick A.

(2012), “The Treatment Effect, the Cross Difference, and the Interaction Term in Nonlinear ‘Difference-in-Differences’ Models,” Economics Letters, 115 (1), 85–87.

20.

Puri

Manju

Rocholl

Jörg

Steffen

Sascha

(2011), “Global Retail Lending in the Aftermath of the US Financial Crisis: Distinguishing Between Supply and Demand Effects,” Journal of Financial Economics, 100 (3), 556–78.

21.

Samuels

Jeffrey M.

Samuels

Linda B.

(2004), “International Trademark Prosecution Streamlined: The Madrid Protocol Comes into Force in the United States,” Journal of Intellectual Property Law, 12 (1), 151–61.

22.

Steensma

H. Kevin

Marino

Louis

Weaver

K. Mark

Dickson

Pat H.

(2000), “The Influence of National Culture on the Formation of Technology Alliances by Entrepreneurial Firms,” Academy of Management Journal, 43 (5), 951–73.

23.

Swaminathan

Vanitha

Moorman

Christine

(2009), “Marketing Alliances, Firm Networks, and Firm Value Creation,” Journal of Marketing, 73 (5), 52–69.

24.

Thomaz

Felipe

Swaminathan

Vanitha

(2015), “What Goes Around Comes Around: The Impact of Marketing Alliances on Firm Risk and the Moderating Role of Network Density,” Journal of Marketing, 79 (5), 63–79.

25.

Tower

Annette P.

Hewett

Kelly

Saboo

Alok

(2021), “Reexamining the Tradeoff Between Value Creation and Value Appropriation: The Role of Internal Organizational Resources vs. External Strategic International Alliances,” Journal of Business Research, 123, 302–12.

26.

Williamson

Oliver E.

(1985), “Assessing Contract,” Journal of Law, Economics, and Organization, 1 (1), 177–208.

27.

Yan

Jiatao

Zhang

Jingjing

(2022), “Protecting Intellectual Property in Foreign Subsidiaries: An Internal Network Defense Perspective,” Journal of International Business Studies, 53 (9), 1924–44.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.23 MB

Statistics	International Alliance (INTL)			International Marketing Alliance (MKT)			International R&D Alliance (RD)			International Marketing and R&D Alliance (MKT_RD)
	Two Years Before	Two Years After	Difference	Two Years Before	Two Years After	Difference	Two Years Before	Two Years After	Difference	Two Years Before	Two Years After	Difference
	(1)	(2)	(2) − (1)	(3)	(4)	(4) − (3)	(5)	(6)	(6) − (5)	(7)	(8)	(8) − (7)
Mean	.473	.507	.034	.157	.192	.035	.094	.139	.045	.014	.043	.029
SD	.499	.500	.500	.364	.394	.382	.292	.346	.325	.116	.203	.173
p-Value			.000			.000			.000			.000
No. of alliances	6,342	8,587		3,001	4,350		3,001	4,350		3,001	4,350

	International Alliance (INTL)			International Marketing Alliance (MKT)			International R&D Alliance (RD)			International Marketing and R&D Alliance (MKT_RD)
	(1)			(2)			(3)			(4)
	Beta	SE	p	Beta	SE	p	Beta	SE	p	Beta	SE	p
Experimental group × after signing Madrid Protocol (EG × AFTER) (β₁)	.251	.033	.000	.202	.054	.000	.107	.070	.083	.564	.087	.000
Control group × after signing Madrid Protocol (CG × AFTER) (β₂)	−.133	028	.000	−.001	.050	.992	−.088	.064	.165	.184	.089	.039

β₁ – β₂	.384	.044	.000	.203	.074	.006	.195	.095	.040	.380	.124	.002

Prior alliance experience (EXPER)	.007	.001	.000	.000	.001	.723	.000	.001	.870	−.001	.002	.664
No. of participants in strategic alliance (N)	.149	.025	.000	−.068	.039	.084	.032	.047	.494	−.114	.079	.159
Trademark applications per thousand population (TMA)	.275	.018	.000	−.007	.024	.775	−.054	.032	.091	−.052	.050	.294
Patent applications per thousand population (PA)	−.226	.018	.000	.040	.032	.204	.091	.042	.031	.168	.053	.002
Economic development (GDP)	1.155	.683	.091	1.619	1.166	.165	.518	1.476	.726	.809	2.206	.714
New businesses started (NEWB)	−.040	.009	.000	−.014	.017	.404	.004	.021	.846	.014	.035	.684
Population (POP)	−.136	.011	.000	−.009	.020	.674	−.064	.028	.022	−.074	.046	.104
Individualism (IND)	−.023	.001	.000	−.001	.001	.573	−.004	.002	.030	−.005	.003	.038

Intercept, annual, and industry fixed effects			+			+			+			+

Log-likelihood ratio	13,181.30			3,774.27			2,196.26			1,257.22
No. of alliances	29,858			14,940			14,940			14,940