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Short- and long-term market returns of international codevelopment alliances of new products

  • Original Empirical Research
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Abstract

Strategic alliances entail process-oriented decisions, in which information about outcomes is unveiled over time. Therefore, it is difficult for investors to gauge the value of such decisions in the short term; longitudinal analysis is necessary. Accordingly, the authors apply latent growth modeling to a data set of 270 international codevelopment alliances announced over an 18-year period. The results demonstrate that investors reward firms for their international codevelopment alliances in the short term but punish them in the long term. Initially, exchange conditions have positive effects, but these effects decrease over time. However, the decrease slows when firms’ market updates contain positive news. Although investors view sharing of innovation resources as a competitive advantage in the short term, they perceive exchange conditions as transaction hazards in the long term. The results also show that long-term decreases in market returns are greater when codevelopment activities are conducted offshore rather than onshore.

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Notes

  1. Our conceptualization of international codevelopment alliances is similar to international marketing alliances defined by Bello et al. (2010), because both entail cooperative arrangements in which autonomous firms based in different countries pool resources for the joint accomplishment of individual corporate goals. However, compared with these authors’ focus on equity and non-equity joint ventures, our study is specific to non-equity codevelopment alliances in which the parties implement activities together. We examine alliances that have the highest levels of not only adaptation opportunities but also opportunistic misappropriation risks.

  2. The market efficiency hypothesis suggests that the promises of an event are immediately reflected in stock prices (Fama and French 2015; McWilliams and Siegel 1997; Srinivasan and Bharadwaj 2004), given that the event is “unanticipated” by the market and there are no other confounding events around the event date.

  3. For example, CUSTBASE= \( {\sum}_{k=1}^{k=t}{\delta}^{t-k}{\mathrm{SALES}}_{\mathrm{k},\kern0.5em } \) where t = 1, 2, …, 10 years.

  4. We also coded two variables to measure the location of the codevelopment activity: (1) differentiating North American locations (e.g., Canada) from all other locations and (2) contrasting Anglo-Saxon countries and others. The results remained consistent when we employed these variables in our model estimation.

  5. We operationalized domain of the alliance activity as the relatedness of the technology in the alliance to the firm. Consistent with the “relatedness of the investment” (e.g., Koh and Venkatraman 1991) and “resource diversity” (e.g., Cui 2013) measures, we coded a binary variable as 1 if the business description and the first two digits of the SIC industry code of the alliance function were the same as those of the focal firm and 0 if different, with information obtained from the SDC Platinum Database.

  6. Ganesan et al. (2005) note that geographic distance influences a firm’s mode of communication and therefore the firm’s ability to monitor its partner. Given that the focal firms were U.S.-based, we measured geographic distance using a standardized distance score between the capitals of the countries-of-origin of the alliance firms (obtained from http://www.wcrl.ars.usda.gov/cec/java/lat-long.htm).

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Acknowledgements

The authors would like to acknowledge the Marie Curie Actions Research Fellowship Program, Product Development and Management Association and Academy of Marketing Science for their support. In addition, the authors would like to thank Steven H. Seggie, Stefan Wuyts and the attendees of the Ozyegin University Interorganizational Marketing Research Camp and the 2015 Theory and Practice in Marketing Conference for their comments on prior versions of this manuscript.

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Authors and Affiliations

  1. College of Administrative Sciences & Economics, Koc University, Rumelifeneri yolu, Istanbul, 34450, Turkey

    Nukhet Harmancioglu

  2. Mays Business School, Texas A&M University, 4112 TAMU, College Station, TX, 77843-4112, USA

    David A. Griffith

  3. Department of Marketing, BI Norwegian School, Nydalsveien 37, 0484, Oslo, Norway

    Tuba Yılmaz

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Appendices

Appendix 1: Sample international codevelopment alliance announcements

“Toshiba Corp (TC) and Sandisk Corp (SD) planned to form a strategic alliance to provide research and development services for 90 nanometer process technology that was to lead to overall increase of supply and improvement in competitiveness of NAND flash memory in Japan. TC’s advanced expertise in NAND flash process technology and the multi-level cell technology pioneered by SD was to accelerate the joint development of 90nm process technology and contribute to the early launch of 2Gb and 4Gb MLC NAND flash memory. These chips were to be produced for TC and SD in TC’s advanced fabrication production facility at Yokkaichi, Japan under the supervision of FlashVision, a joint venture between TC and SD.” [proprietary technology (specific technology name: 1) (licensed technology: 0) (radical innovation: 1)]; [alliance scope (partner involvement: 1/5) (multiple products: 1)] [location (offshore: 1)].

“Abbott Laboratories (AL) and Domantis Ltd. (DL) planned to form a strategic alliance to provide research and development of multiple therapeutic products in the United States and United Kingdom. DL and AL were to collaborate on the identification and optimization of DL’s antibodies to the first two undisclosed AL therapeutic targets. The alliance was to provide AL with non-exclusive access to DL’s Domain Antibody Technology for use with additional therapeutic targets. Under terms of the agreement, DL was to receive funding for collaborative research or AL’s use of the Domain Antibody Technology, and DL was to also receive license fees and development milestones as well as royalties on commercial sales.” [proprietary technology (specific technology name: 0) (licensed technology: 1) (radical innovation: 0)]; [alliance scope (partner involvement: 2/5) (multiple products: 1)] [location (offshore; 0)].

Appendix 2: Robustness checks for the analysis of abnormal stock returns

We conducted several robustness checks. We tested for the presence of outliers, heteroskedasticity, autocorrelation, and multicollinearity. We checked for multivariate outliers by analyzing the Mahalanobis distance and Cook’s distance coefficients, the leverage statistic, and studentized residuals. We considered an observation an outlier if the corresponding Mahalanobis distance was more than 31.26 (at 0.001 alpha level, df = 11, where df is the number of independent variables) and/ or Cook’s distance was greater than 0.029 (i.e., 4/ [n - k – 1]), where n is the number of cases and k is the number of independent variables. Furthermore, if a case had a leverage statistic over 0.5, we determined that the case had undue leverage and we therefore identified it as an outlier. Outliers were observations with ± 3.3 standardized residuals (corresponds to 0.001 alpha level). When we repeated the regression after removing observations with large residuals (outliers with potentially undue influence and/or high leverage on the results), the results did not change materially. The White test for heteroskedasticity was not significant (at the 0.05 alpha-level) after we removed outliers, indicating lack of potential heteroskedasticity of residuals. We also examined the plots of the residuals versus fitted values for any patterns of increasing residuals and found no such patterns. We tested for the presence of autocorrelation of errors using the Durbin-Watson statistic and failed to reject the null hypothesis of no autocorrelation in the errors.

Appendix 3: Checks for endogeneity, unobserved heterogeneity and self-selection bias

To test for potential endogeneity, we followed the procedure outlined by Raassens et al. (2012). The location of the codevelopment activity reflects the degree of control the focal firm relinquished to its alliance partner. It is possible that firms will design their codevelopment alliances and determine the location of the activity according to the characteristics of the market environment, the partner, or the firm itself. Therefore, we tested for potential endogeneity of this variable. In a first-stage model, we regressed the potentially endogenous variable (i.e., location of the codevelopment activity) on the other variables in our model (i.e., proprietary technology, alliance scope, capabilities asymmetry, prior partner experience, technological uncertainty, cultural distance, alliance concentration, firm resources, type of partner, and type of innovation). As instrumental variables, we used domain of the codevelopment activity,Footnote 5 equity participation of partners, geographic distanceFootnote 6 between the partners, and labor costs in the country in which codevelopment takes place. We then assessed the instruments’ relevance. We found support for the validity of our instruments using Sargan’s (1958) test (χ2 = 3.881, χ2 = .227, both ps > .05; respectively for the short-term and long-term returns equations). Durbin-Wu-Hausman tests indicated that endogeneity of the location variable is not an issue in our study (χ2 = 1.139, χ2 = .314, both ps > .05).

Furthermore, we checked for potential unobserved heterogeneity and self-selection bias (as well as endogeneity) using the Heckman two-step estimation approach (Chen et al. 2009). We collected an additional sample of firm announcements in which the division of labor is clear (i.e., one partner is responsible for a set of activities and the other partner is accountable for a complementary set of activities) (Reuer et al. 2002). It is possible that forward-looking firms organize their international codevelopment alliances and decide on the division of the activities, depending on the characteristics of their firms and the country-of-origin of partner firms, for the sake of future returns. In the first stage of the Heckman estimation approach, we estimated a probit model on the choice of division of labor. The inverse Mills ratio λ in the Heckman model serves as the self-selection correction parameter and its significance is indicative of the existence of self-selection bias. We found a non-significant inverse Mills ratio for both the short-term and long-term returns equations (λ = −.265 and λ = −139.1, both ps > .05; respectively), indicating that self-selection bias is not a concern. The second stage includes ordinary least squares (OLS) regressions of abnormal returns on the explanatory variables and λ. In the model estimation, we included geographic distance and the labor costs in the partner’s country, because these factors may influence a firm’s decision to jointly (versus separately) implement development activities. The only variable that influenced the firm’s (co)development decision was geographic distance; it exerted a negative effect. This suggests that if partners are remotely located, they choose to conduct development activities independently (as opposed to jointly) by making divisions of labor clear. It is particularly interesting that labor cost (hourly wage compensation) is not a significant determinant of (co)development choice. Therefore, based on our overall Heckman results, we did not find any other unobserved heterogeneity that is not accounted for in our model. Our model yields an unbiased estimate of the effect of international codevelopment on firm value.

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Harmancioglu, N., Griffith, D.A. & Yılmaz, T. Short- and long-term market returns of international codevelopment alliances of new products. J. of the Acad. Mark. Sci. 47, 939–959 (2019). https://doi.org/10.1007/s11747-018-00622-w

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  • DOI: https://doi.org/10.1007/s11747-018-00622-w

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