TY - GEN A1 - Schmidt, Lennard A1 - Maier, Erik T1 - The interaction effect of mobile phone screen and product orientation on perceived product size T2 - Psychology & Marketing N2 - Perceived product size is a key concern in online retail, particularly in fashion and grocery. The screen on which consumers view a product (e.g., desktop or mobile) might constitute a frame that biases size perception, on the basis of assimilation and contrast effects (pool and store theory). The rise of mobile commerce exacerbates this issue, as framing effects might be stronger versus desktop settings as screens are smaller. Further, as mobile phone's screen orientation varies situationally (vertical vs. horizontal), the perceived product size might vary, depending on the interaction of screen and product orientation. By introducing the framing ratio as a means to predict extent, dimensionality and symmetry of size biases, we generalize specific findings from extant research. Empirically, four experimental studies demonstrate that contextual frames (i.e., vertical vs. horizontal screens) and product orientation (e.g., jeans vs. shoes) interact to bias the size perception, in that sizes are overestimated on the dimension that approaches the frame (high framing ratio), compared with conditions where the frame is distant (low framing ratio). If product size is misperceived, willingness to pay might be affected (e.g., for groceries). Thus, size perceptions have a direct impact on managerially relevant variables. KW - framing effects KW - framing ratio KW - horizontal-vertical illusion (HVI) KW - mobile commerce KW - screen KW - screen orientation KW - size perception Y1 - 2019 U6 - https://doi.org/10.1002/mar.21214 VL - 36 IS - 9 SP - 817 EP - 830 ER - TY - GEN A1 - Schmidt, Lennard A1 - Dost, Florian A1 - Maier, Erik T1 - Filtering Survey Responses from Crowdsourcing Platforms: Current Heuristics and Alternative Approaches T2 - International Conference on Information Systems N2 - Information Systems research continues to rely on survey participants from crowdsourcing platforms (e.g., Amazon MTurk). Satisficing behavior of these survey participants may reduce attention and threaten validity. To address this, the current research paradigm mandates excluding participants through filtering heuristics (e.g., time, instructional manipulation checks). Yet, both the selection of the filter and the filtering threshold are not standardized. This flexibility may lead to suboptimal filtering and potentially “p-hacking”, as researchers can pick the most “successful” filter. This research is the first to tests a comprehensive set of established and new filters against key metrics (validity, reliability, effect size, power). Additionally, we introduce a multivariate machine learning approach to identify inattentive participants. We find that while filtering heuristics require high filter levels (33% or 66% of participants), machine learning filters are often superior, especially at lower filter levels. Their “black box” character may also help prevent strategic filtering. KW - Survey Research KW - Filtering KW - Amazon MTurk KW - Reliability KW - Validity KW - Effect Size Y1 - 2019 UR - https://aisel.aisnet.org/icis2019/research_methods/research_methods/8 ER -