Abstract
How does language friction affect alliance formation? Language friction is a form of cultural friction arising from structural differences in the respective languages used by potential partners to reason and solve problems together. A little language friction may prompt partners to rethink solutions, thereby enhancing collaboration, but excessive friction may impede collaboration. We develop a Language Friction Index (LFI) to quantify relative differences in linguistic structure for any language pair. Utilizing a unique data set of semiconductor design activities (1988–2001), our empirical analysis finds an inverted U-shaped relationship between partners’ LFI and the likelihood of cross-border research and development (R&D) alliance formation. This relationship is further moderated by prior ties and technological distance. Our findings have several important implications, including: (1) language differences are a measurable and discernible source of cultural friction; (2) the effects of language friction are economically significant and strategically consequential; (3) certain aspects of language friction occur independent of language proficiency and persist despite the use of lingua franca to reduce language barriers; (4) linguistic diversity is an indirect marker of cognitive diversity, which is useful in boosting creativity, especially in first-time collaborations; (5) beyond R&D alliances, language friction may also influence other types of strategic interactions and organizational processes.
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Notes
English is spoken by an estimated 1.75 billion people worldwide, including approximately 385 million native speakers, 1 billion non-native speakers, and 565 million Internet users (Neeley, 2012).
VHDL, the VHSIC Hardware Description Language, is an artificially constructed language used to specify chip designs (VHSIC is an acronym for Very High Speed Integrated Circuit). The US Department of Defense established the standard in the 1980s to improve interoperability among chips developed by contractors for use in military projects (Dewey & de Geus, 1992).
Identifying which languages have official status is important because native speakers are often afforded legal protections and rights to use these languages in government proceedings and business transactions. In theory, granting official status to a language may reduce discrimination against its speakers in areas such as employment, although in reality the enforcement of such laws and the implementation of such practices may differ widely across countries (Ferner, Almond, & Colling, 2005).
In the United States, linguistic diversity index=0.334, slightly above the Netherlands (0.301) and slightly below Germany (0.378) meaning that the likelihood of two randomly selected people speaking different native languages is similar in each country.
We exclude Masculinity Distance from our regression analyses. This dimension captures country differences due to differential emphasis on performance (masculinity) and relationships (femininity) (Shenkar, 2001). Since the implicit assumption in choosing alliance partners is to create a relationship and improve performance for both potential collaborators, we excluded this variable. Our approach of using selected and disaggregated dimensions of cultural distance is consistent with prior work (Shenkar, 2012, 2001). Our results are robust to the inclusion of the variable Masculinity Distance and are available upon request.
For an example of the construction and application of a sub-index using WALS data in an empirical study, see the Gender Intensity Index (Santacreu-Vasut et al., 2014).
See Summer Institute of Linguistics, Inc. for a comprehensive living glossary of linguistic terms (http://www-01.sil.org/linguistics/GlossaryOflinguisticTerms/). See Ethnologue for a definitive guide to 7105 known living languages (http://www.ethnologue.com/).
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Acknowledgements
This research has benefited from data support by the Global Semiconductor Alliance (GSA) and is funded in part by a grant from UNC’s CIBER. We thank seminar participants at the University of Hawaii at Manoa, three anonymous referees, and Area Editor Jaeyong Song for their insightful comments and helpful feedback. A previous version of this article was awarded Runner-Up for the 2012 AIB Best Conference Paper Prize.
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Accepted by Jaeyong Song, Area Editor, 3 September 2014. This article has been with the authors for two revisions.
APPENDIX
APPENDIX
Definitions of Key Linguistics Terms7
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1
A language is a formalized system for communicating information.
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2
A language consists of a lexicon (a set of words) and a grammar (a set of rules).
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3
A word is a spoken or written element that conveys meaning.
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a)
Spoken words may be verbally represented by sounds or utterances, or non-verbally by signs or gestures.
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b)
Written words may be textually represented by characters (letters of an alphabet or script), or symbols (notation, ideograms, or pictographs).
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c)
Thinking maps or links words to ideas.
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a)
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4
An idea is an individual thought or mental representation. A concept is a collection or combination of ideas.
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5
Words are bundles of linguistic features. A feature is a marker or an identifiable structural property of a language.
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6
All languages have features which may be classified as grammatical, syntactical, phonological, morphological, lexical, semantic, or other types of linguistic features.
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a)
Examples of grammatical features include number (singular or plural), tense (past, present, or future), gender (masculine, feminine, or neuter).
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b)
Examples of syntactic features include the function (noun, verb, subject, or object) and the arrangement order of words in a phrase or sentence.
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c)
Phonological features pertain to how sounds are used and organized in a language.
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d)
Morphological features pertain to the content and function of words and sub-units such as a roots, prefixes, and suffixes.
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e)
Lexical features describe the characteristics of a language’s lexicon, while semantic features categorize the various elements of the meaning of a word.
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a)
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7
Lexical similarity is the overlap between the lexicons or word sets of a pair of languages.
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8
Semantic similarity is the overlap in the meanings of words in a corpus (a large, structured set of texts) that is written in one or more languages.
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9
When comparing language pairs, lexical and semantic similarity each capture different aspects of mutual intelligibility, or how easily speakers of one language are able to understand speakers of the other language in the pair.
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M Joshi, A., Lahiri, N. Language friction and partner selection in cross-border R&D alliance formation. J Int Bus Stud 46, 123–152 (2015). https://doi.org/10.1057/jibs.2014.56
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DOI: https://doi.org/10.1057/jibs.2014.56