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Ad avoidance (e.g., “blinding out” digital ads) is a substantial problem for advertisers. Avoiding mobile banner ads differs from active ad avoidance in nonmobile (desktop) settings, because mobile phone users interact with ads to avoid them: (1) They classify new content at the bottom of their screens; if they see an ad, they (2) scroll so that it is out of the locus of attention and (3) position it at a peripheral location at the top of the screen while focusing their attention on the (non-ad) content in the screen center. Introducing viewport logging to marketing research, we capture granular ad-viewing patterns from users’ screens (i.e., viewports). While mobile users’ ad-viewing patterns are concave over the viewport (with more time at the periphery than in the screen center), viewing patterns on desktop computers are convex (most time in the screen center). Consequently, we show that the effect of viewing time on recall depends on the position of an ad in interaction with the device. An eye-tracking study and an experiment show that 43% to 46% of embedded mobile banner ads are likely to suffer from ad avoidance, and that ad recall is 6 to 7 percentage points lower on mobile phones (versus desktop).
Recent regulation in the European Union (i.e., the General Data Protection Regulation: GDPR) affects websites’ information privacy practices. This regulation addresses two dimensions: websites must (1) provide visible notice about which private information they collect through cookies and (2) allow consumers the choice to disagree to such tracking. Policy makers need to understand the degree of implementation of their regulation, but also its effect on consumers. We develop a typology of website cookie notices along the dimensions notice visibility and choice. A field study shows that most websites only offer low notice visibility and limited choice over the collection of private information. In addition, four experimental studies in the EU and United States explore the effects of information privacy practices: while offering choice over whether or which data are used increases consumer power, visibility of the notice (vs. no notice) only affects risk perceptions. We establish the novel suggestion that perceived risk is mitigated if consumers have more choice over their data (indirectly through greater power). Power and risk influence consumers’ affect and purchase intent.
Methodik:
▪ Die kartenbasierte Standortanalyse nutzt frei verfügbare Infrastrukturmerkmale einer Stadt, um daraus die Attraktivität eines Standortes relativ zu anderen möglichen Standorten zu ermitteln. Über die Dichte und Zentralität des Wegenetzes (Straßen und Fußwege) wird die Attraktivität bestimmt.
▪ Traditionelle Standortanalysemodelle beruhen hingegen auf einer Vielzahl von gemessenen Variablen, wie etwa Bevölkerung oder Durchschnittseinkommen im Einzugsgebiet. Daten zu diesen Variablen sind oft schwer oder nur teuer zu bekommen und die damit verbundenen multivariaten Modelle häufig komplex.
▪ Der kartenbasierte Ansatz baut auf Forschung zur Skalierung von Städten auf: verschiedene Merkmale einer Stadt (z.B. Bevölkerung, Einkommen, Infrastruktur oder Wirtschaftsleistung) folgen regelmäßigen Wachstumspfaden (sogenannten „Skalierungsgesetzen“). Daher kann man schwer beobachtbare Faktoren (z.B. Bevölkerung an einem Standort oder das Potential eines Ladens) durch leicht beobachtbare ersetzen (z.B. Infrastrukturmerkmale, wie Straßen).
▪ Aus öffentlichen Karten extrahieren wir daher Informationen zum Grad der Skalierung eines Standortes (gemessen durch Dichte oder Zentralität auf Basis des Straßen- oder Fußwegenetzes). Aus dem Skalierungsgrad lässt sich die Attraktivität des Standortes bestimmen und mit anderen Standorten vergleichen. Gleichzeitig können wir die Wettbewerbsintensität an einem Standort nach Branchen abbilden.
▪ Alle Attraktivitäts- und Wettbewerbskarten („Heatmaps“) sind frei zugänglich unter: https://handels.blog/standorteleipzigs/
▪ Die Attraktivitätskarten („Heatmaps“) sind vor allem zur Betrachtung und zum Vergleich verschiedener Standorte aus der Vogelperspektive gedacht. Sie treffen keine absoluten Aussagen („Ich kann X€ Umsatz erwarten.“) und sollten durch jeden Händler um eine genauen Analyse des Mikroumfeldes ergänzt werden.
Recent regulatory changes (i.e., General Data Protection Regulation of the European Union) enforce that seller (e.g., retail and service) and all other websites disclose through cookie notices which data they collect and store. At the same time, websites must allow consumers to disagree to the tracking of their browsing behavior. Despite sellers' concern about the loss of consumer insights—as consumers might disagree to the collection of their browsing data—cookie notices might also have a surprising side-effect: Consumers might accept frequent price changes (from personalized or dynamic pricing) more readily, if they agree through a cookie notice that their behavior can be tracked. Specifically, two experimental studies show that consent to the tracking of browsing behavior increases consumers internal attribution of a price change, as consumers attribute the cause of the change (here: giving up data) to themselves. This increases price fairness perceptions and, in turn, purchase intent. As a result, for online sellers of goods or services the implementation of cookie notice should no longer be thought as a matter to be avoided, but rather a trade-off decision: Loss of a part of consumer insights versus higher acceptance of data-driven marketing mix decisions, such as frequent price changes.
Advertisers have to pay publishers for “viewable” ads, irrespective of whether the users paid active attention. In this paper, we suggest that a granular analysis of users’ viewing patterns can help us to progress beyond mere “viewability” and toward actual differentiation of whether a user has paid attention to an ad or not. To this end, we use individual viewport trajectories, which measures the sequence of locations and times an object (e.g., an ad) is visible on the display of a device (desktop or mobile). To validate our model and benchmark it against the extant models, such as the “viewability” policy (50% threshold) model, we use data from an eye-tracking experiment. Findings confirm the improved model fit, highlight distinct viewing patterns in the data, and inform
information processing on mobile phones. Consequently, implications are relevant to publishers, advertisers, and consumer researchers.
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.
Location is an impactful but irrevocable driver of retail store performance. Unless retailers rely on their gut feelings for finding high potential locations, they have to invest in extensive location research, calibrating performance models on expensive rich data (e.g., income or education of households in each prospective trading area). To prevent “the death of the high street”, also public administrators care for location potentials. This research proposes a parsimonious new model for location potentials, drawing from emerging urban scaling literature outside of marketing. We show that a measure of the local urban scale explains stores’ sales, local competitive intensity, and defining aspects of store lifecycles (managers’ location choice, sales ramp-up to a steady state after opening, store closure). We demonstrate these capabilities of the scaling approach using six datasets, including data from two retail chains (grocery and variety stores), public data, map data, and an experiment with retail managers. Our parsimonious model compares well to more complex multivariate benchmarks and remains more robust across modeling choices. We put forth a scale measure that can be cheaply obtained from map data, offering accessible applications for retail and public policy managers (e.g., “heat maps” across all potential locations in a city) and to marketing research in general (e.g., as input or control variable for geo or mobile marketing).
The interaction effect of mobile phone screen and product orientation on perceived product size
(2019)
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.