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Information environment and interfirm alliance

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Abstract

Interfirm alliances require effective mechanisms that facilitate the integration of pertinent knowledge between independent firms. This paper examines whether a firm’s information environment helps improve collaborative outcomes. Using a large panel of firms engaging in global research and development (R&D) collaboration, we find that firms with better information environments are associated with a higher level of collaborative outputs and a broader scope of collaborative networks. The improvements in collaborative outcomes are more pronounced when alliance partners have a greater reliance on informal relationships instead of formal contracts. Further analysis shows that a better information environment helps foster a more durable relationship between partners. Lastly, a path analysis shows that facilitating interfirm collaboration is one of the main channels through which transparency boosts a firm’s innovative productivity. Overall, our findings suggest that the quality of a firm’s information environment plays a crucial role in sustaining efficient relational contracting in interfirm alliances.

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Notes

  1. The number of interfirm alliances has been growing 25% per year since 1985. Alliances have consistently produced a return on investment of close to 17% among the top 2,000 companies in the world for a decade. That’s 50% more than the average return on investment for those same companies.

  2. Another example is Qualcomm and Daimler's joint development of new technologies to enable wireless charging of electric vehicles as well as in-car wireless charging of mobile devices (Forbes 2015). Other examples of interfirm collaboration include Samsung selling displays to Apple; General Motors and Ford sharing transmission technologies, and Google placing advertisements for Yahoo (Brandenburger and Nalebuff 2021).

  3. Relational contracting theories suggest that cooperative and efficient behaviors can be sustained through reciprocal interactions in a long-term relationship, even though formal agreements are not fully enforceable through third parties. For more examples in relational contracting theories, see (Bernheim and Whinston 1998; Bull 1987; Halac 2012; Levin 2003; MacLeod and Malcomson 1989).

  4. The alliance between General Motors (GM) and Toyota is a classic example. GM was keen to learn some of Toyota’s manufacturing management practices, whereas Toyota wanted to learn how to manage labor and how to run a manufacturing plant in the United States. However, both partners needed to prevent leakage of some of their core proprietary skills, i.e., Toyota’s small car design and supplier management and GM’s management of its dealerships.

  5. Although public firms could file confidential treatment (CT) requests with the SEC to protect some information from public disclosure for a time, there are two limitations. First, it is uncertain whether a request would be approved. Second, such requests can only be justified if the information is immaterial to investors but is proprietary in nature.

  6. This differs from financial contracts where the lenders usually assume oversight authority and impose restrictive covenants upon violation.

  7. These contracts are necessarily self-enforcing. The partners must jointly understand the implicit relational knowledge, including “actions that constitute cooperation,” as well as “the payoff to cooperation for each party, of each party’s ability and incentive to defect, and of the actions and payoffs that constitute punishment” (Gibbons and Henderson 2012).

  8. While firms can privately communicate with collaborative partners, unverifiable and distorted information can also lead to mistrust given the private interests of each party. Instead, verified public information and information from analysts can help minimize information asymmetries among partners, allowing them to make reliable inferences about the prospects of collaboration.

  9. The alliance between General Motors and Toyota is a classic example of such a dilemma. General Motors was keen to learn some of Toyota's manufacturing management practices through the alliance, whereas Toyota wanted to learn how to manage U.S. labor and how to run a manufacturing plant in the United States from GM. However, both partners were also keen to prevent leakage of some of their core proprietary skills to the other. Toyota was keen to protect its skills of small car design, effective supplier management, and GM's ability to manage dealerships in the United States.

  10. Consistent with the literature (Griliches et al. 1987), we compute joint patent counts based on the patent’s application year, instead of its grant year, as the application year better captures the actual effective time of R&D. We also exclude those co-owned patents filed by individual inventors.

  11. As an untabulated sensitivity test, we also implement a lead-lag analysis in examining the effect of the information environment on subsequent collaboration. Our results are qualitatively unchanged.

  12. Using USPTO patent data to measure cross-country R&D performance has been widely adopted in international studies (Acharya and Subramanian 2009; Griffith et al. 2006; Hsu et al. 2014).

  13. We conduct several additional analyses on sample selection. First, we restrict our sample to successful collaborative firms with nonzero co-owned patents during sample period and find that our results are stronger. Second, to mitigate the concern that firm choice to engage in collaboration may not be random in a pooled sample, we construct a propensity-score-matched sample to eliminate the pre-treatment systematic differences between collaborating and noncollaborating firms that may be correlated with firm collaboration choice. Our inferences remain unchanged.

  14. To be sure, we also repeat our analysis using only the U.S. sample and find consistent results. Results are available upon request.

  15. As a robustness check, we also control for total R&D inputs by scaling our outcome variable by total R&D expenditure and report the results in Table 10. As shown, our results remain inferentially unchanged.

  16. The average number of joint patents filed is 0.022 (Table 2). An interquartile increase in IE translates into an increase in JOINT_PATENT of 0.014 * 0.220 = 0.003, i.e., from 0.022 to 0.025. The percentage change in the number of patent filings is 14%.

  17. The average number of partners is 0.049 (Table 2). An interquartile increase in IE translates into an increase in NUM_PARTNER of 0.022 * 0.220 = 0.005, i.e., from 0.049 to 0.054. The percentage change in the number of patent filings is 10%.

  18. Research suggests that formal contracts are less enforceable in countries with weak legal environments and thus the signaling role of accounting information in a contractual relationship may be more valuable (Bova and Pereira 2012; Chen et al. 2011; Chen et al. 2010a; Chen et al. 2010b; Fan and Wong 2002; Hope et al. 2011; Kaplan 1994a; Kaplan 1994b).

  19. The data is obtained from the World Bank database, with a coverage of 213 economies over the period 1996–2009. It is available for download at http://databank.worldbank.org/data/databases/rule-of-law. For a detailed description, see http://info.worldbank.org/governance/wgi/pdf/rl.pdf.

  20. The data is available for download at http://scholar.harvard.edu/nunn/pages/data-0.

  21. As an untabulated analysis, we further examine whether transparency-duration relation varies with collaborative output. Supportive of a relational contracting perspective, we find that the positive effect of IE on DURATION is more pronounced when collaborative performance is low and partners have greater incentive to renege.

  22. Similar to Hirshleifer et al. (2013), we set missing R&D to zero in computing RDC. The RDC variable represents the R&D assets that would exist if firms capitalized annual R&D expenditures, assuming a straight-line method of depreciation and an annual depreciation rate of 20% over a five-year useful life (Chan et al. 2001; Lev et al. 2005).

  23. The coefficient estimate on IE alone in column (2) remains statistically significant, suggesting that transparency may also enhance R&D productivity through other channels, e.g., the reduction of agency cost (Zhong 2018).

  24. As shown by Bhattacharya and Daouk (2002), it is the first prosecution, rather than the introduction, of these laws that matters for capital market participants to update their priors. Note that we do not stipulate that insider trading enforcement per se leads to an improvement in the information environment but rather that these events proxy for changes in the disclosure and reporting policies of some firms around the time the enforcement happened. As an untabulated sensitivity analysis, we also use mandatory IFRS adoption as another economic shock to firm transparency and find consistent results.

  25. Insider trading can contribute to the timely incorporation of new information into stock prices. Fernandes and Ferreira (2008) find that, in their global sample of firms, tightening insider trading laws improves the information environment via either more informative stock prices or increased public information collection.

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Acknowledgements

We thank Divya Anantharaman, Peter Demerjian, Ellen Engel, Atif Ellahie, Inder K. Khurana, Cheng-Few Lee (editor), Valeri Nikolaev, Raynolde Pereira, Bharat Sarath, two anonymous reviewers, and the workshop participants at the Annual Accounting Association meeting, International Accounting Section Midyear Meeting, Rutgers Business School, and University of Illinois at Chicago for their helpful comments and suggestions.

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Authors and Affiliations

  1. Department of Accounting, Rutgers Business School-Newark and New Brunswick, New Brunswick, USA

    Feng Gao

  2. Department of Accounting, Liautaud Graduate School of Business, University of Illinois at Chicago, Chicago, USA

    Rong Zhong

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Appendix A. Variable description

Appendix A. Variable description

Variables

Description

Source

Dependent variable

R&D collaboration

 JOINT_PATENT

= Natural log of one plus total number of joint patents

USPTO

 NUM_PARTNER

= Natural log of one plus total number of a firm's co-authorship for all joint patents applied in a given year

USPTO

 DURATION

= Number of years between the application year of first joint patent and that of last joint patent

USPTO

 LOG_DURATION

= Natural log of one plus DURATION

USPTO

R&D productivity

 PATENT/RDC

= Natural log of one plus total number of patents applied by a firm in a given year scaled by R&D capital (RDC). RDC is calculated as RDEXPt + 0.8*RDEXPt-1 + 0.6*RDEXPt-2 + 0.4*RDEXPt-3 + 0.2*RDEXPt-4, where RDEXP equals annual R&D expense

USPTO, Worldscope

 CITATION/RDC

= Natural log of one plus total number of citations summed across all patents applied by a firm in a given year scaled by R&D capital (RDC). RDC is calculated as RDEXPt + 0.8*RDEXPt-1 + 0.6RDEXPt-2 + 0.4*RDEXPt-3 + 0.2*RDEXPt-4, where RDEXP equals annual R&D expense

USPTO, Worldscope

Test variable

 DISC_ACCR

= Absolute value of discretionary accruals calculated based on Jones's model modified by Kothari et al. (2005). Discretionary accruals are estimated based yearly regressions across all firms within each country and industry (2-digit SIC code)

Worldscope

 INT_GAAP

= An indicator variable equals to one if the firm reports under IFRS or U.S. GAAP during the year and zero otherwise

Worldscope

 ANALYST

= Total number of analysts making a forecast for year t's earnings. Higher values indicate greater transparency

Worldscope

 ACCURACY

= Product of (-1) times the absolute value of the forecast error scaled by beginning stock price, where the forecast error is analysts' mean annual earnings forecast less the actual earnings as reported by I/B/E/S. Higher values indicate greater transparency

Worldscope

 IE

= A composite measure of information environment, calculated as the average of the scaled percentile rank of four variables: the inverse measure of DISC_ACCR, INT_GAAP, ANALYST, ACCURACY. If ACCURACY is unavailable, IE captures the average percentile rank of the remaining three variables

Worldscope

Control variable

 R&D

= Total research and development expenditure scaled by total assets

Worldscope

 PATENT

= Natural log of one plus total number of patents applied by firm in a given year

USPTO

 SALES

= Natural log of sales (in thousands of US$)

Worldscope

 EMPLOYMENT

= Natural log of one plus total number of employees (in thousands)

Worldscope

 ROA

= Net income before extrordinary items scaled by total assets

Worldscope

 SALES_GROWTH

= Annual change in net sales scaled by beginning total sales

Worldscope

 FIRM_AGE

= Natural log of one plus the number of years listed in Worldscope

Worldscope

 STOCK_ISSUE

= The sum of a firm's net equity issues (scaled by total assets) over a rolling five-year window ending in the current fiscal year. Higher values indicate greater access to external financing

Worldscope

 LEV

= Total liabilities scaled by total assets

Worldscope

 CFO

= Cash flows from operations scaled by total assets

Worldscope

 K/L

= Ratio computed as net property, plant, and equipment scaled by total number of employees

Worldscope

 CLOSE%

= Total number of shares held by insiders as a percentage of the total number of shares outstanding

Worldscope

 MTB

= Market value of equity divided by book value of equity

Worldscope

 HERFINDAHL

= Industry Herindahl index based on all firms within each country, where industries are defined by three-digit SIC code

Worldscope

 IND_GROWTH

= Average industry sales growth for firms within each country and industry, where industries are defined by three-digit SIC code

Worldscope

 INTRODUCTION

= An indicator variable capturing the introduction stage in a firm's life cycle, which equals one if CFO < 0, CFI < 0 and CFF > 0, and zero otherwise

Worldscope

 GROWTH

= An indicator variable capturing the growth stage in a firm's life cycle, which equals one if CFO > 0, CFI < 0, and CFF > 0, and zero otherwise

Worldscope

 MATURE

= An indicator variable capturing the mature stage in a firm's life cycle, which equals one if CFO > 0, CFI < 0, and CFF < 0, and zero otherwise

Worldscope

 DECLINE

= An indicator variable capturing the decline stage in a firm's life cycle, which equals one if CFO < 0, CFI > 0, and CFF ≤ or ≥ 0

Worldscope

 IMPORT

= the logarithm of the level of imports that the country has with the US in each year at each three-digit ISIC industry level

Nicita and Olarreaga (2007)

 EXPORT

= the logarithm of the level of exports that the country has with the US in each year at each 3-digit ISIC industry level

Nicita and Olarreaga (2007)

 REL_VALUE

= Ratio of value added in a three-digit ISIC industry in a particular year to the total value added by that country in that year

Nicita and Olarreaga (2007)

Conditioning variable

 WEAK

= An indicator variable capturing the strength of a country's legal environment, which equals one if a country (1) has civil law origin or (2) the value of rule of law index is below the sample median and zero otherwise

La Porta et al. (1998)

 RELATION_SPEC

An index measuring the degree to which firms in an industry rely on relationship-specific investments

Nunn (2007)

 CROSS_BORDER

= An indicator variable that equals to one if the co-owned patent in year t is generated based on cross-border (international) collaborations, and zero otherwise

USPTO

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Gao, F., Zhong, R. Information environment and interfirm alliance. Rev Quant Finan Acc 60, 643–677 (2023). https://doi.org/10.1007/s11156-022-01105-4

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  • DOI: https://doi.org/10.1007/s11156-022-01105-4

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