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|a As firms rely increasingly on “big data” to segment and target current and potential customers, the challenge of data falsification—individuals providing incorrect personal data in response to requests—is becoming a significant problem. Based on public opinion surveys, within some demographic groups, over three-quarters of individuals confirm that they have given inaccurate information in response to data requests. Obviously, firms that embrace a covert assumption of honesty in online data disclosures are deluding themselves and are likely falling into the trap of “garbage in, garbage out” in their segmenting and targeting. Despite the frequency and importance of falsification, however, it has received scant attention in the privacy research stream. Most researchers focus on the act of disclosure (and its counter-construct, withholding of data)and overlook that many of the data elements being disclosed may in fact be falsified. To address this weakness in the literature stream, we develop a nomological model that predicts both falsification and withholding behavior, and we test it using a sample collected with the assistance of an online panel provider. We find strong support for the model and show how context could play a significant role in moderating some of the proposed relationships. We then discuss important implications for practice and research. © 2018 Elsevier B.V.
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