Abstract
Multinational corporations (MNCs) frequently use their foreign subsidiaries to identify new opportunities to access external knowledge. This article builds on the attention-based view to examine how selective attention – the focus on certain issues or answers at the exclusion of others – works in the global knowledge-sourcing process in MNCs. The results reveal an intriguing paradox: while MNCs may establish foreign subsidiaries far from headquarters to identify diverse, novel knowledge, and overcome local search, headquarters’ decision makers tend to favor opportunities that are market proven and simply confirm what the MNC already knows. Subsidiary managers’ pre-selling and selling efforts, however, can play a pivotal role in overcoming that bias. This study combines detailed qualitative data with access to a proprietary database on 137 external knowledge-sourcing opportunities in one of the world’s largest MNCs in the telecommunications sector.
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Notes
ET is a pseudonym to protect company privacy.
As described in detail below, the empirical models presented here control for a number of alternative explanations for why an opportunity may be screened out, including, but not limited to, the level of technological relatedness and attractiveness of the external opportunity, the cultural and geographic distances, as well as the age and size of the source of the external technology.
Jonas et al. (2001: 557) clarify that empirical studies on biased information search in decision making have been predominantly carried out within the framework of dissonance theory (Festinger, 1957) which calls “selective exposure to information” the effect that once committed to an alternative people prefer consonant information vis-à-vis dissonant information. Given, however, that predominantly seeking dissonant information would also be a kind of selectivity, “confirmation bias” would be a more adequate term to describe this effect.
The quotation marks here are important to indicate that the words “irritants” and “antibodies” which are entirely consistent with the corporate immune system metaphor suggested by Birkinshaw and Ridderstråle (1999), were used, without prompting, by my informants.
Knowledge dissonance, however, is qualitatively different from knowledge relevance (Schulz, 2003). Schulz (2003: 442) studied the connection between knowledge inflows and knowledge relevance – the degree to which external knowledge has the potential to connect to local knowledge. Although one may be tempted to believe that knowledge relevance could be equated to knowledge dissonance, in a later part of his paper, Schulz (2003: 444) ends up conflating knowledge that confirms with knowledge that contradicts in the same construct: he affirmed that relevance increases “with the extent to which extra-unit knowledge elaborates, contradicts or strengths local-knowledge.”
In addition to including external opportunities from many different countries, my qualitative data included interviews with all scouters who were responsible for covering Asia.
Five entries during this period not included in my database were excluded by ET before I was afforded access to the database. These entries, according to the company, revealed internal information that was still sensitive at the time of the study. There was no prima facie indication that the entries were, along the dimensions examined in this study, qualitatively different from the rest of the sample.
As reported below, I tested for inter-rater reliability by randomly selecting 30 entries (approximately 20% of the sample) and asking the second (as well as the lead) contact to complete the survey. The correlation between the answers of the lead and second contacts ranged from 0.71 to 0.91.
This was possible because all entries in the Rock database contained the name of the source of the external technology.
Results are robust if the cases which were ignored by the recipient units are excluded.
Using both internal and external sources, I did several cross-checks to make sure that whenever a record indicated that an opportunity was screened out it actually was not pursued by other unit within ET and this was not captured by the subsidiary’s records. There were no such cases and this may be due to the fact that keeping an updated track of the outcome of all opportunities generated by subsidiary managers was a critical performance indicator for the scouting.
It was plausible, though not probable, that subsidiary managers had an incentive to inflate the number of entries that were followed up. In other words, the subsidiary records to which I had access might conceivably have exaggerated the number of opportunities which were attended by corporate decision makers. To check the reliability of these records, I randomly selected 15 entries (roughly one-third of the total successful cases) and contacted the recipient unit directly by email or phone. In all cases, the scouting units’ records were confirmed by the corresponding recipient units. Given this high reliability and the fact that scouters had no incentive to under-report the number of successful cases, I did not systematically contact lines of business for a subsample of cases in which the records indicated an unsuccessful outcome. I used my interviews with the line of business managers to advantage, however, to discuss memos received but not followed up. Again, scouting unit records were consonant with line of business’ accounts. These results reinforce the objective nature of this measure. It should also be acknowledged that an alternative dependent variable could be the amount of attention (e.g., measured by time spent) that decision makers dedicated to each opportunity to transfer an external technology. That information, however, was not available. The dichotomous variable used above, notwithstanding its limitations, was objective and reliable, ruling out possible concerns about respondent bias.
The ideal respondent for the question would have been the recipient unit manager to whom the transfer memo was sent. Unfortunately, it was not possible to gain access to all of the managers in my sample who received transfer memos. I did manage, however, to obtain recipient units’ scores for 38 entries involving respondents from all recipient units and constituting a very good representation of the total sample. The correlation between scouters’ and recipient units’ scores was high (0.82). In addition, I was able to obtain for 30 entries the ratings from the second contacts. For this item (i.e., knowledge dissonance), the correlation between lead contacts’ and second contacts’ scores was also good (0.81).
As described above, I pre-tested the survey instrument extensively during my pilot study and respondents did not report any difficulty in rating external technologies according to this 5-item scale.
Results presented below are robust to an alternative operationalization of knowledge dissonance that does not include technological standards.
Because the company kept detailed records of how scouts spent their time in internal and external activities; that same manager headed the units for the entire period of this study; and that our qualitative interviews revealed internal search and internal selling were clearly distinct categories for the scouts (e.g., scouts called internal selling “downstreaming” as a contrast to “internal search”), we were confident that the measures of effort were a good proxy for the actual effort spent by scouts in each activity. Nevertheless, as a robustness check, following Reuer, Arino, and Mellewigt (2006), I asked the scouts to indicate on a scale of 1–5 (1=insignificant amount of time, 3=reasonable amount of time, 5=a lot of time) how much time was spent on “internal search” and on “internal selling.” The correlations between the estimates of the units’ head and the scouters’ responses were 0.72 and 0.70, respectively. The results presented below are robust to these alternative operationalizations of external search, internal search, and internal selling effort.
Results are robust (and even stronger) if the raw measures of internal search and internal selling efforts are included.
Results are robust to different specifications of cultural distance. For instance, results are similar if cultural distance is measured as the distance from the home country of the company’s headquarters (instead of from the host country of the scouting unit). A similar pattern of results is found when I use a more recent operationalization of cultural distance as suggested in a paper by Berry, Guillén, and Zhou (2010).
Results presented below are robust to several different operationalizations of this variable using shorter and longer (e.g., 1 week, 2 weeks, 2 months, 6 months) time windows.
Patent-based measures are probably the most widely used method of controlling for technological relatedness. Although I use those measures in my robustness checks (and the results are robust to these alternative specifications), using patent-based measures drastically reduces the number of observations in my models, as only about 70% of the sources of the external technologies in my sample held patents (or had submitted patent applications) at the time the transfer memo was written. I performed robustness checks using eight alternative types of patent-based relatedness measures, namely: (1) dummy variable=1 if ET and external source have any patenting in the same technology class, and 0 otherwise; (2) number of external source patents with same classes as ET’s; (3) number of technology classes in common between ET and external source/total number of classes in which the external source has patenting activity; (4) dummy variable=1 if ET and external source have any patenting in the same technology sub-class, and 0 otherwise; (5) number of external source patents with the same sub-classes as ET’s; (6) number of technology sub-classes in common between ET and external source/total number of sub-classes in which the external source has patenting activity; (7) number of ET patents in the same classes as the external source; and (8) number of ET patents in the same sub-classes as the external source.
Although this is, to a large extent, an objective measure, it would be desirable to obtain recipient units’ assessments of the attractiveness of external technologies. As a robustness check, I obtained the recipient units’ scores for 25 entries, which included answers from all recipient units in the sample and constituted a good representation of the total sample. The correlation between scouters’ and recipient units’ scores was quite high (0.9). In addition, I was able to obtain for 30 entries the ratings from the second contacts. The correlation between lead contacts’ and second contacts’ scores was even higher (0.91). These extremely high correlations seem to corroborate the existence of a common understanding/agreement within ET about the formula to be used to calculate potential innovation dividends.
VIF values ranged from 1.2 to 4.2.
Mfx command in Stata. The same levels of significance are obtained when I compute, using Stata’s margeff command, the average of the individual marginal effect values at each observation.
Probability changes are calculated using the spost ado file developed by Long and Freese (2006) for Stata.
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Acknowledgements
The author would like to thank Tina Ambos, Julian Birkinshaw, Laurence Capron, Aya Chacar, Yves Doz, Javier Gimeno, Adam Grant, Mauro Guillen, Martine Haas, Sarah Kaplan, Michael Mol, Phanish Puranam, Subi Rangan, Evan Rawley, Andrew Shipilov, Gabriel Szulanski, and Freek Vermeulen for their comments and suggestions on earlier versions of this manuscript. This article has benefited greatly from the comments and suggestions made by JIBS Area Editor Jaeyong Song and three anonymous reviewers. Kaitlin Meiss provided excellent research support. The author is also grateful to the many managers at ET who generously provided him with their time, insights, and ample access to their internal databases. Funding for this project was provided by the Mack Institute for Innovation Management at the Wharton School. Any errors are the author’s.
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Accepted by Jaeyong Song, Area Editor, 9 December 2014. This article has been with the author for two revisions.
APPENDIX
APPENDIX
Preliminary Field Research
I conducted multiple interviews in Europe, China, Japan, and in the United States. I interviewed all senior managers working in ET’s scouting units, several corporate managers and was able to make several field observations in Silicon Valley. I adopted a semi-structured interview approach (e.g., Glaser & Strauss, 2009) for all interviews. After a brief description of the research project, interviewees were asked to describe their views and company experiences on the relevant set of issues. In this first round of interviews, I did several interviews not only with ET managers (both at headquarters and scouting units) as well as with a number of start-up companies and venture capitalists. The main goal of this initial phase was to obtain more background on how the global knowledge-sourcing process in a knowledge cluster like Silicon Valley typically unveiled.
In a second round of interviews, I focused on the scouters working for ET’s scouting units. These interviews were 60–150 min and followed an interview guide that varied according to the manager’s role. By asking respondents to walk me through real examples, I tried to structure the interviews in such a way that they would provide me with some concrete insights on how and whether certain global knowledge-sourcing opportunities at ET were initiated or not. Interviewees were typically asked questions along the following lines:
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Could you describe the main global knowledge-sourcing activities performed by your unit? How would you rank those activities in terms of time spent by you and your colleagues?
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What exactly does your unit transfer to the business units? How is this “transfer” materialized?
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How do the business units typically react when you introduce an opportunity to transfer an external technology?
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What makes a decision maker in the business units more or less likely to act on an opportunity you introduce to him/her?
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Could you walk me through some real examples?
I found it was often necessary to probe for additional comments, illustrations, and insights. Most of the interviews were recorded with the permission of the interviewee and transcribed. I also took copious notes during the interviews and in many cases the interviewees provided me with diagrams and charts on a white board to explain concepts around which clarifying questions were asked. More importantly, they showed me slides, emails, and other documents to illustrate real cases of how the global knowledge-sourcing process took place at ET.
A few months later, once I had access to the Rock database with all the transfer memos, I engaged in a third round of interviews to pilot and refine the survey instrument. I developed pilot designs that were pre-tested in hour-long, face-to-face interviews with one former scouter, one manager in one line of business, and one R&D manager. On the basis of these tests, I modified questions that did not make sense in ET’s setting, or were ambiguous or in other ways misconstrued. I sought from some of these managers responses to the modified questions, which we then discussed on the phone.
In sum, this preliminary field study provided me with critical background information and relevant quotes, informed my hypotheses development and allowed me to more effectively design and pre-test the survey instrument which data was used for hypotheses testing.
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Monteiro, L. Selective attention and the initiation of the global knowledge-sourcing process in multinational corporations. J Int Bus Stud 46, 505–527 (2015). https://doi.org/10.1057/jibs.2014.72
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DOI: https://doi.org/10.1057/jibs.2014.72