Tenure trumps title: Assessing the logic of informant selection in inter-organizational network research

Researchers collecting survey data on inter-organizational networks typically choose a single informant with the most senior job title in each organization from whom to obtain a report of the organization’s inter-organizational ties. This approach to informant selection is based on the logic that gr...

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Bibliographic Details
Published inNetwork science (Cambridge University Press) Vol. 7; no. 3; pp. 402 - 421
Main Authors Beacom, Amanda M., Valente, Thomas W.
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.09.2019
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Summary:Researchers collecting survey data on inter-organizational networks typically choose a single informant with the most senior job title in each organization from whom to obtain a report of the organization’s inter-organizational ties. This approach to informant selection is based on the logic that greater seniority confers greater knowledge of inter-organizational relationships. The present study investigated the wisdom of this logic, using data in which multiple informants’ reports of inter-organizational network ties were collected for each organization. We calculated the degree of agreement in network reports between the informant with the most senior job title and a second informant in the organization. To determine if alternative criteria to seniority serve as better approaches to informant selection, we assessed other potential predictors of agreement in informants’ reports. Results indicated that (1) informants’ perceptions of the network differed significantly according to job title, suggesting little agreement between senior informants and their more junior colleagues; and (2) greater informant tenure in the network and industry were associated with greater agreement among informants. These results call a common data collection practice into question and suggest that tenure may trump title as a criterion for informant selection in inter-organizational network research.
ISSN:2050-1242
2050-1250
DOI:10.1017/nws.2019.26