Filtering Survey Responses from Crowdsourcing Platforms: Current Heuristics and Alternative Approaches

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

Seeding as Part of the Marketing Mix:Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods

Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%-18% throughout the campaigns

Identifying and Responding to Outlier Demand in Revenue Management

Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. Many passenger transport service providers, such as railways and airlines, control the sale of tickets through revenue management. State-of-the-art systems in these industries rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). So far, little research focuses on automating and evaluating the detection of outlier demand in this context. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with time series extrapolation. We evaluate the approach in a simulation framework, which generates outliers by varying the demand model. The results show that functional outlier detection yields better detection rates than alternative approaches for both online and offline analyses. Depending on the category of outliers, extrapolation further increases online detection performance. We also apply the procedure to a set of empirical data to demonstrate its practical implications. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers. We show that identifying instances of outlier demand and adjusting the forecast in a timely fashion substantially increases revenue compared to what is earned when ignoring outliers

Detecting outlying demand in multi-leg bookings for transportation networks

Network effects complicate demand forecasting in general, and outlier detection in particular. For example, in transportation networks, sudden increases 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 relevant when transport service providers, such as railway or coach companies, offer many multi-leg itineraries. In this paper, we present a novel method for generating automated outlier alerts, to support analysts in adjusting demand forecasts accordingly for reliable planning. To create such alerts, we propose a two-step method for detecting outlying demand from transportation network bookings. The first step clusters network legs to appropriately partition and pool booking patterns. The second step identifies outliers within each cluster to create a ranked alert list of affected legs. We show that this method outperforms analyses that independently consider each leg in a network, especially in highly-connected networks where most passengers book multi-leg itineraries. We illustrate the applicability on empirical data obtained from Deutsche Bahn and with a detailed simulation study. The latter demonstrates the robustness of the approach and quantifies the potential revenue benefits of adjusting for outlying demand in networks

A non-linear causal network of marketing channel system structure

This article takes a systems perspective to study marketing channel system structure dynamics and their interactions with economic system dynamics. A novel, non-linear method from ecology is used to establish a causal network of mostly bi-directional causal forcing between economic variables and marketing channel system structure. This resulting causal network facilitates a comprehensive understanding of a marketing channel system. The study finds a highly endogenous and non-linearly interrelated subsystem encompassing online/offline retail channel structure, retail/wholesale channel structure, the ratio of import to consumption and the competitive dynamics of the economic system. Surprisingly, marketing channel system structure is rather resilient to changes in economic growth. In contrast, changes in retail/wholesale channel structure affect economic growth. The results may help to caution marketing managers changing their marketing channel structures too routinely. Moreover, the identified causal network presents a starting point for further empirical marketing channel system analyses. Implications particularly affect future empirical marketing channel system studies based on linear structural models

Value-based pricing in competitive situations with the help of multi-product price response maps

This article introduces multi-product price response maps for various value pricing applications in competitive situations. The maps are based on the direct elicitation of individual willingness to pay (WTP) as a range for competing products; they reveal an individual's or market's choice probability for a focal product, at its own and competing products' prices. Transforming the price response into profit, revenue, or unit sold maps supports optimal pricing decisions. The maps are also useful for optimizing profit differences from the closest competitor and for portfolio pricing. Managers can use a consumer indecisiveness map, gained from the WTP range data, to devise complementary marketing measures at prices where consumer uncertainty is high. The illustration of this approach uses two empirical examples, featuring two or more competing consumer goods, and demonstrates the predictive and external validity of these proposed maps

The positive effect of contextual image backgrounds on fluency and liking

Abstract In e-commerce websites, products may be presented either deprived of context, in a product image on white background, or with context, in an image with a contextually fitting background. Extant fluency research would suggest preferring context-less to contextual images, because detailed image contexts increase the complexity of the image, possibly decreasing viewers’ fluency perceptions and, in turn, liking. The current research, however, establishes that despite their higher complexity, contextual images can also be perceived more fluently and liked more, because they facilitate the recognition of the product. Three experimental studies show this positive effect of contextual backgrounds in an e-commerce setting (e.g., actual product images from e-commerce). Furthermore, the present investigation shows that the positive effect of contextual backgrounds is amplified for ambiguous products, as they profit more from a facilitation of recognition. Online retailers can thus profit from presenting products in contextual images, particularly if the products are ambiguous or difficult to recognize

Supporting the Adoption Funnel with Differential Effects from Traditional Advertising, Online Displays, and a Micro-Influencer Campaign

Growing a digital platform requires bringing users to the platform and converting them into adopting the online service or community. Growth may be supported with advertising, but knowledge about what type of advertising affects what stage in the adoptio

Proximity Begins with a Smile, But Which One?:Associating Non-duchenne Smiles with Higher Psychological Distance

This study reveals that Duchenne (genuine) and non-Duchenne (non-genuine, polite) smiles are implicitly associated with psychological proximity and distance, respectively. These findings link two extensive research streams from human communication and psychology. Interestingly, extant construal-level theory research suggests the link may work as smiles signaling either a benign situation or politeness, resulting in conflicting predictions for the association between smile type and psychological distance. The current study uses implicit association tests to reveal theoretically and empirically consistent non-Duchenne-smile–distance and Duchenne-smile–proximity associations for all four types of psychological distance: temporal, spatial, social, and hypothetical. Practically, the results suggest several useful applications of non-Duchenne smiles in human communication contexts