@misc{ReichsteinBruschDostetal., author = {Reichstein, Thomas and Brusch, Ines and Dost, Florian and Brusch, Michael}, title = {Captivate Your Audience: Suggestions for Enhancing Social Media Engagement Through Captions}, series = {AMA Winter Academic Conference Proceedings, St. Pete Beach, Florida, on February 23-25, 2024}, journal = {AMA Winter Academic Conference Proceedings, St. Pete Beach, Florida, on February 23-25, 2024}, editor = {Cross, Samantha and Saboo, Alok}, publisher = {American Marketing Association}, address = {Chicago, IL}, isbn = {978-0-87757-018-9}, pages = {570 -- 572}, abstract = {Engagement is important for the success of social media offers. Accordingly, the goal of responsible social media managers is to generate the maximum engagement with their content. Extant research has already addressed the factors that are related to the creation of compelling content. However, very little attention has been paid to the caption, and thus to a part of the social media post that can be easily changed. Our aim is to measure the impact of caption characteristics on engagement. We use, text length, number of emojis, and hashtags as caption variables, as well as comments, likes, and shares as engagement variables. In two different datasets, one from a merchandise online store (Facebook, Study 1) and another from a travel influencer (Instagram, Study 2) we find a similar robust result: the analyses reveal s a non-linear relationship between caption text length and engagement that follows an inverted U-shape. It follows that, contrary to previous assumptions, longer captions of around 600 characters should be used to in¬crease engagement. Estimations show that an increase in engagement of about 20\% is possible.}, language = {en} } @misc{PengDostLukasetal., author = {Peng, Yingying and Dost, Florian and Lukas, Bryan and Homburg, Christian}, title = {Four Types of Information Interplay in Product Reviews and their Sales Effects}, series = {2024 AMA Winter Academic Conference Proceedings}, journal = {2024 AMA Winter Academic Conference Proceedings}, editor = {Cross, Samantha and Saboo, Alok}, isbn = {978-0-87757-018-9}, pages = {604 -- 606}, abstract = {Consumer decision-making is influenced by user-generated information in reviews of products and services. We predict that sales levels are associated with two forms, respectively, of information inconsistency (confliction and competition) and consistency (confirmation and complementary) often found in product and service reviews. For our investigation, we classified information contained in tablet computer reviews as conflicting, competing, confirming, or complementing using BERT (Bidirectional Encoder Representations from Transformers) and word2vec (Word to Vector) techniques. Estimating an aggregated-level sales rank model over a 24-week period on reviews and sales data obtained from Amazon, we show that variations in tablet sales can be partially explained with our information classification. These findings provide new insights for user-generated content research.}, language = {en} } @misc{PhielerDost, author = {Phieler, Ulrike and Dost, Florian}, title = {Does It Pay to Increase Public Messages in Nano-Influencer Campaigns? Investigating the Optimal Public Message Share for Sales and Return-On-Investment}, series = {2023 ISMS Marketing Science Conference Proceedings}, journal = {2023 ISMS Marketing Science Conference Proceedings}, pages = {3}, abstract = {Online influencer marketing has matured, and many managers now seek better performance with nano-influencers in seeding campaigns. Compared to macro-influencers, nano-influencers create more private (direct online or offline) brand messages that supposedly deliver more "persuasive punch". But such private messages lack the usual online tracking per message, forcing managers to ask nano-influencers for self-reports, or to estimate sales at a campaign level if they want to assess their own decisions. Therefore, little is known about how to set up effective nano-influencer campaigns: does it pay to increase the public message share in nano-influencer activity, in order to benefit from private and public influencer activity? Or does it defeat its purpose, diminishing nano-influencers' persuasiveness and total campaign results? This study analyses a unique campaign-level data set of various seeded marketing campaigns comprising the activity of 700,000 nano-influencers. We model the impact of nano-influencers' activity on incremental sales and return-on-investment (ROI) at the campaign level. To answer our questions, we include effects of public message share and optimal public message share and their interaction with activity; instrumenting both for identification, and controlling for campaign, marketing, and brand context. We find conditional optimal public message shares for both sales and ROI. The average campaign benefits most when increasing influencer activity (through design or incentives) at public message shares between 4\% and 5\%. This shows the importance of striking the right balance between nano-influencers' online and offline activity, and suggests a similar balance for combining nano- and macro-influencers in future campaign formats.}, language = {en} } @misc{ReichsteinBruschDostetal., author = {Reichstein, Thomas and Brusch, Ines and Dost, Florian and Brusch, Michael}, title = {Maximizing Engagement Through Caption Optimization: Insights from Social Media Field Data}, series = {2023 ISMS Marketing Science Conference Proceedings}, journal = {2023 ISMS Marketing Science Conference Proceedings}, abstract = {Social media managers are constantly seeking ways to maximize engagement with their content. While prior research has shed light on strategies for creating engaging content, the impact of captions on engagement has been under-explored. Our study focuses specifically on the relationship between caption length and engagement in social media posts. Contrary to previous literature, we find that the relationship between caption length and engagement follows an inverted U-shape. Using field data from an omnichannel retailer (Facebook) and a travel influencer (Instagram), we determine the optimal caption length for maximizing engagement and present surprising results. Our results are robust for different model and identification specifications, indicating that captions are often too short, rather than too long, and that optimizing caption length in our case can increase engagement by up to 25\%. These findings have important implications for social media managers and influencers seeking to enhance the impact of their posts.}, language = {en} } @misc{DostMaierBijmolt, author = {Dost, Florian and Maier, Erik and Bijmolt, Tammo}, title = {Empirical Dynamic Modelling For Exploring Complex Time Series}, series = {2022 ISMS Marketing Science Conference Proceedings}, journal = {2022 ISMS Marketing Science Conference Proceedings}, abstract = {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.}, language = {en} } @misc{DostMaierBijmolt, author = {Dost, Florian and Maier, Erik and Bijmolt, Tammo}, title = {Empirical Dynamic Modelling for Exploring Complex Time Series in Management and Marketing Research}, series = {SSRN eLibrary}, journal = {SSRN eLibrary}, issn = {1556-5068}, doi = {10.2139/ssrn.4036834}, pages = {1 -- 49}, abstract = {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.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Detecting outlying demand in multi-leg bookings for transportation networks}, series = {31st European Conference on Operational Research (EURO) Proceedings}, volume = {31}, journal = {31st European Conference on Operational Research (EURO) Proceedings}, pages = {28}, abstract = {Network effects complicate transport demand forecasting in general and outlier detection in particular. For example, a sudden increase in demand for a specific destination will not only affect the legs arriving at that destination, but also connected legs nearby in the network. Network effects are particularly strong when service providers, such as railway or coach companies, offer many multi-leg itineraries. In such situations, automated alerts can help analysts to adjust demand forecasts and enable reliable planning. In this presentation, we outline a novel two-step method for automatically detecting outlying demand from transportation network book� ings. The first step clusters network legs according to the observed booking patterns. The second step identifies outliers within each cluster to create a ranked alert list of affected legs. We illustrate the method using empirical data obtained from Deutsche Bahn. In addition, we present a detailed simulation study that quantifies the improvement from the clustering step and implications of ranking to measure the criticality of the outliers. Our results show that the proposed approach outperforms independently analysing each leg, especially in highly connected networks where most passengers book multi-leg itineraries.}, language = {en} } @misc{SchmidtMaierDost, author = {Schmidt, Lennard and Maier, Erik and Dost, Florian}, title = {Retail Location Assessment with Urban Scaling}, series = {SSRN eLibrary}, journal = {SSRN eLibrary}, issn = {1556-5068}, doi = {10.2139/ssrn.3738003}, pages = {1 -- 70}, abstract = {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).}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Detecting outlier demand in railway networks}, series = {5th AIRO Young Workshop at Universit{\`a} degli Studi di Napoli Federico II, Italy}, volume = {5}, journal = {5th AIRO Young Workshop at Universit{\`a} degli Studi di Napoli Federico II, Italy}, pages = {31}, abstract = {Transport service providers, such as airlines and railways, often use revenue manage-ment to control offers and demand in mobility networks. Such systems rely on accu-rate demand forecasts as input for the underlying optimisation models. When changes in the market place cause demand to deviate from the expected values, revenue man-agement controls no longer fit for the resulting outliers. Analysts can intervene if they deem the demand forecast to be inaccurate. However, existing research on judgemen-tal forecasting highlights fallibility and bias when human decision makers are not sys-tematically supported in such tasks. This motivates the need for automated alerts to highlight outliers and thereby support analysts. Network effects complicate the problem of detecting outlier demand in practice. Pas-sengers often book travel itineraries that stretch across multiple legs of a network. Thereby, they requests products that require multiple resources - seats on several legs. In the rail and long-distance coach industries, the large number of possible itiner-aries makes it likely for outlier demand to affect multiple legs. At the same time, outli-er demand from a single itinerary may be difficult to recognise as it mixes with regular demand on individual legs. To support outlier detection in transport networks, we pre-sent a method to aggregates outlier detection across highly correlated legs. We propose to use the results of this analysis to construct a ranked alert list that can support ana-lyst decision making. We show that by aggregating we are able to improve detection performance compared to considering each leg in isolation.}, language = {en} } @misc{Dost, author = {Dost, Florian}, title = {Bidirectional Links between Aggregate Advertising and Goods Consumption at the National Level}, series = {46th Eurasia Business and Economics Society Conference Book}, journal = {46th Eurasia Business and Economics Society Conference Book}, edition = {1}, publisher = {EBES}, address = {Istanbul - Turkey}, isbn = {978-605-71739-6-6}, pages = {99}, abstract = {A longstanding question in marketing and economics is whether all of advertising affects aggregate consumption. If it does not, the advertising-consumption system would be a zero-sum game; if it does, advertising can grow markets and represents an economic force of interest for marketing, economics, and public policy. Ashley, Granger, and Schmalensee (1980) test advertising's potential causal impact on aggregate goods consumption. Challenging prior empirical studies that suggested advertising drives aggregate consumption (Taylor \& Weiserbs, 1972), Ashley et al. show that advertising does not influence consumption; rather, consumption causes advertising in a landmark methodological study of the Granger causality test. Yet recent conceptualizations suggests this empirical result and especially the linear autoregression-based Granger method to be inconclusive. Wilkie and Moore (1999) propose a complex aggregate systems view to discussing macro impacts of marketing, and in such systems the Granger tests typically fails (Sugihara et al. 2012). The present research extends prior studies with a new more appropriate nonlinear method (convergence cross-mapping) to test the causal links between television or digital advertising and three types of aggregate consumption—durables, non-durables, and service consumption. The novel test, unlike the Granger causality test, confirms the existence of a bidirectional causal link between advertising and aggregate goods consumption. Specifically, and similar to the brand level, advertising effects on consumption are stronger for durable than for non-durable goods.}, language = {en} } @misc{UrbigDostGeiger, author = {Urbig, Diemo and Dost, Florian and Geiger, Ingmar}, title = {Entrepreneurs misdiagnosing their ventures' innovativeness: The roles of causation and effectuation}, series = {26th Annual Interdisciplinary Conference on Entrepreneurship, Innovation and SMEs, 2023}, journal = {26th Annual Interdisciplinary Conference on Entrepreneurship, Innovation and SMEs, 2023}, abstract = {This text discusses the concept of misdiagnosis in entrepreneurial ventures, where managers' unrealistic optimism can lead to misaligned strategies and late pivots. The study explores the connection between misdiagnosis and decision logics (effectuation and causation) through motivated reasoning and beliefs. The researchers conducted a study with 266 ventures from innovative industries and measured misdiagnosis by comparing managers' judgments with external experts' assessments of venture innovativeness. The results show that causation logic positively influences misdiagnosis, but this effect is reduced when coupled with flexibility. The study highlights the importance of objective performance measures and suggests that motivated reasoning and beliefs explain the link between causation logic and misdiagnosis. Additionally, older and larger ventures with a prevalence of causation logic may experience more misdiagnosis and potential delays in innovation efforts. Flexibility is crucial for older ventures to remain innovative.}, language = {en} }