Human-like communication forms in web surveys |
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Coordinator 1 | Dr Jan Karem Höhne (University of Duisburg-Essen) |
Coordinator 2 | Professor Frederick Conrad (University of Michigan) |
Since the inception of web surveys, researchers have incorporated cutting-edge communication technology to improve data quality and respondent experience. One approach has been to use these technologies to give web surveys a human touch, facilitated by the ubiquity of multimedia-enabled mobile devices and high-speed internet. For example, it is relatively easy to program the web survey so that respondents can self-administer spoken questions by playing audio-recordings on their smartphones or tablets and answer by speaking via the built-in microphones in those devices. This potentially recreates key aspects of daily conversation, which respondents might prefer to clicking and typing in answers to textual questions. It is also possible to answer survey questions by uploading photos and videos, which may result in more accurate information, as it may not rely on respondent’s memory to the same extent as traditional self-reports. Video communication platforms, such as Skype and Zoom, support in-person interviews that are conducted remotely, reducing geographic barriers and interviewer field costs. In addition, advances in AI technology facilitate the use of life-like virtual interviewers that may appeal to survey respondents, potentially decreasing nonresponse and social desirability bias. These technology-driven methodologies expand the existing methodological toolbox for all substantive research fields that rely on web survey data. However, there is only a small body of research on the general feasibility of these approaches and their implications for data quality and participants’ satisfaction. In this session, we therefore invite contributions that report experimental and non-experimental research on human-like technology-mediated communication in web surveys carried out in different settings (e.g., lab or field) and with different study designs (e.g., cross-sectional or longitudinal).