Securing Higher-Quality Data from Self-Administered Questionnaires
Will behavioural and ‘big’ data replace or complement survey data (Miller, 2017; Sturgis & Luff, 2020)? Seemingly, the answer is the latter. Recent studies suggest no decline in consumer survey usage (Sturgis & Luff, 2020). Unlike behavioural and ‘big’ data, the enduring benefits of survey methodology include investigating ‘why’ (i.e. tapping into underlying attitudes, beliefs and values), offering patrons a channel for voicing their pleasures and concerns, assessing inherently subjective and thus indirectly observable marketing constructs (e.g. processing fluency, which is better captured by self-reports than proxy measures), and reducing privacy concerns via informed consent (Kostyk et al., 2021; SurveyMonkey, 2021). In addition, survey data can provide a more comprehensive perspective on consumer behaviour than transactional data and online data. In essence, surveys will continue to be a vital data source for academic and industry research (Miller, 2017). Nevertheless, academic and applied marketing researchers will need to adapt their surveys to an evermore challenging respondent environment characterised by low participation rates, shortened attention spans, fraudulent responses, server farms and ‘bot’ respondents (Bohannon, 2016; Couper, 2013; Dennis et al., 2020; Perkel, 2020). Insensitivity to this new data collection reality will produce inaccurate results that increasingly discredit assessments of public sentiment and behavioural intentions. For example, consumer surveys and political polls differ in purpose and scientific rigour, yet recent inaccurate political polling has caused eligible voters to question the accuracy of survey research (National Research Center, 2019). Regardless, survey researchers must strive to minimise reputational damage and optimise survey-data–based decisions by enhancing data quality
Year of publication: |
[2022]
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Authors: | Kostyk, Alena ; Zhou, Wenkai ; Hyman, Michael ; Paas, Leo |
Publisher: |
[S.l.] : SSRN |
Description of contents: | Abstract [papers.ssrn.com] |
Saved in:
Extent: | 1 Online-Ressource |
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Series: | |
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: The University of Auckland Business School Research Paper Series Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments 2021 erstellt Volltext nicht verfügbar |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013293129
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