TY - CHAP A1 - Dost, Florian A1 - Wilken, Robert ED - Enke, Margit ED - Geigenmüller, Anja ED - Leischnig, Alexander T1 - A Behavioral Approach to Pricing in Commodity Markets: Dual Processing of Prices within and around Willingness-to-Pay Ranges T2 - Commodity Marketing - Strategies, Concepts, and Cases N2 - Many behavioral heuristics are fast and frugal ways for buyers to cope with uncertainty. Therefore, the extent to which pricing can rely on heuristic consumer reactions depends on the specifics and the extent of uncertainties in the market. In markets with a high degree of commoditization, uncertainties about the product are by definition low. However, as buyers are also uncertain about their own preferences, behavioral approaches can explain consumer reactions to price, even in a context of commoditization. In this chapter, we propose and test a dual process framework for pricing around buyers’ reservation prices. Such a dual process includes both rational and heuristic modes of choice. According to our framework, reactions to prices are more or less influenced by heuristic choice, depending on their relation to a buyers’ reservation price. This framework can explain the interrelations between rational and heuristic modes of choice, willingness-to-pay ranges, and latitudes of price acceptance. We also base a set of pricing and combined price-and-communication strategies on this framework. One interesting implication is that these pricing strategies are specific to target groups among buyers: as preferences and reservation prices are heterogeneous even in commodity markets, there are different potential buyers with a more rational or more heuristic mode of choice, for every price imaginable. KW - WTP range KW - dual process pricing Y1 - 2022 SN - 978-3-030-90656-6 SN - 978-3-030-90657-3 U6 - https://doi.org/10.1007/978-3-030-90657-3_6 SN - 2192-8096 SN - 2192-810X SP - 105 EP - 117 PB - Springer CY - Wiesbaden ET - 1st edition ER - TY - GEN A1 - Pannhorst, Matthias A1 - Dost, Florian T1 - A Life-Course View on Ageing Consumers: Old-Age Trajectories and Gender Differences T2 - Applied Research in Quality of Life N2 - This study presents a dynamic, model-based view of consumers’ ageing developments, focused on gender differences, to uncover the pathways and socioeconomic transitions that female and male consumers take through old age. The analysis of longitudinal survey data spanning 15 years uses a latent Markov dynamic cluster model with transitions over time. The resulting life courses allow an exploration of lifestyle-related changes in multiple consumer well-being variables beyond age 50. Substantial well-being differences appear in the ageing paths of men and women. In both cases, a dominant chronological sequence through old age is complemented by less common transitions, rarely associated with advanced age. Although the model does not use chronological age as an independent variable, it outperforms purely agebased, or age- cohort-, and period-based models in predicting old-age consumer wellbeing. These results highlight the importance of considering within-cohort diversity when modelling the accompaniments of old age: while some older consumers enjoy active lifestyles, others of similar age succumb to depression and loneliness, rendering age an insufficient predictor of well-being states. In the future, the presented model could be matched with other, even cross-sectional, consumer survey data to help predict various dynamics in the ageing consumer population. KW - Ageing consumers KW - Consumer well-being Y1 - 2022 UR - https://doi-org.manchester.idm.oclc.org/10.1007/s11482-021-09934-6 U6 - https://doi.org/10.1007/s11482-021-09934-6 SN - 1871-2576 SN - 1871-2584 VL - 17 IS - 2 SP - 1157 EP - 1180 ER - TY - GEN A1 - Schmidt, Lennard A1 - Maier, Erik T1 - Interactive Ad Avoidance on Mobile Phones T2 - Journal of Advertising N2 - 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). Y1 - 2022 UR - https://doi.org/10.1080/00913367.2022.2077266 VL - 51 IS - 4 SP - 440 EP - 449 ER - TY - GEN A1 - Schmidt, Lennard A1 - Maier, Erik A1 - Dost, Florian T1 - Location-Based Interactions with Geographically Targeted Advertising T2 - Proceedings of the European Marketing Academy, Budapest, May 24 - 27, 2022 Y1 - 2022 UR - http://proceedings.emac-online.org/index.cfm?abstractid=A2022-107729&Location-Based%20Interactions%20with%20Geographically%20Ta PB - European Marketing Academy ER - TY - GEN A1 - Rennie, Nicola A1 - Cleophas, Catherine A1 - Sykulski, Adam A1 - Dost, Florian T1 - Analysing and visualising bike sharing demand with outliers T2 - arXiv N2 - Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of terminal locations and inventory, to balance expected demand and capacity. However, external factors such as extreme weather or glitches in public transport, can cause demand to deviate from baseline levels. Identifying such outliers keeps historic data reliable and improves forecasts. In this paper we show how outliers can be identified by clustering terminals and applying a functional depth analysis. We apply our analysis techniques to the Washington D.C. Capital Bikeshare data set as the running example throughout the paper, but our methodology is general by design. Furthermore, we offer an array of meaningful visualisations to communicate findings and highlight patterns in demand. Last but not least, we formulate managerial recommendations on how to use both the demand forecast and the identified outliers in the bike-sharing planning process. Y1 - 2022 U6 - https://doi.org/10.48550/arXiv.2204.06112 SP - 1 EP - 45 ER - TY - GEN A1 - Dost, Florian A1 - Maier, Erik A1 - Bijmolt, Tammo T1 - Empirical Dynamic Modelling For Exploring Complex Time Series T2 - 2022 ISMS Marketing Science Conference Proceedings N2 - Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). We introduce empirical dynamic models (EDM) to management science and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying relationships between variables (i.e. in the Granger sense), predicting variable evolution (i.e. in the Machine Learning sense), and attributing marginal effects from one variable on another (i.e. in the Neyman-Rubin sense). We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, we confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels. Y1 - 2022 UR - https://www.researchgate.net/publication/379237908 ER - TY - GEN A1 - Dost, Florian A1 - Maier, Erik A1 - Bijmolt, Tammo T1 - Empirical Dynamic Modelling for Exploring Complex Time Series in Management and Marketing Research T2 - SSRN eLibrary N2 - Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). The authors introduce empirical dynamic models (EDM) to management and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying, predicting and attributing relationships between variables. We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, the authors confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels. KW - Empirical dynamic models KW - nonlinear time series models KW - omnichannel system KW - customer journey Y1 - 2022 U6 - https://doi.org/10.2139/ssrn.4036834 SN - 1556-5068 SP - 1 EP - 49 ER - TY - GEN A1 - Brusch, Ines T1 - Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities T2 - Information & Management N2 - “A picture is worth a thousand words”: Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images’ diversity either a SVM or a CNN provides the best basis for preference prediction. KW - Image analysis KW - online travel community KW - convolutional neural networks (CNNs) KW - preferences Y1 - 2022 U6 - https://doi.org/10.1016/j.im.2022.103682 SN - 0378-7206 VL - 59 IS - 6 ER -