Frenkel Ter Hofstede

Image Reinforcement or Impairment: The Effects of Co-Branding on Attribute Uncertainty

Co-branding is often used by companies to reinforce the image of their brands. In this paper, we investigate the conditions under which a brands image is reinforced or impaired as a result of co-branding, and the characteristics of a good partner for a firm considering co-branding for image reinforcement. We address these issues by conceptualizing attribute beliefs as two-dimensional constructs: The first dimension reflects the expected value of the attribute, while the second dimension reflects the degree of certainty about the attribute. We argue that these parameters are updated after consumers are exposed to a co-branding activity, and we develop an analytical model that incorporates these notions. An analysis of the model leads to several propositions, which we test in an experiment. Our findings indicate that it is not necessarily in a brands best interest to choose an alliance partner that is of the highest performance possible. Moreover, we find that, while expected values of the brand attributes may improve as a result of co-branding, under certain conditions the uncertainty associated with the brands increases through the alliance. Implications for co-branding researchers and practitioners are discussed.

A Monte Carlo study of time aggregation in continuous-time and discrete-time parametric hazard models

This study investigates the effects of time aggregation in discrete and continuous-time hazard models. A Monte Carlo study is conducted in which data are generated according to various continuous and discrete-time processes, and aggregated into daily, weekly and monthly intervals. These data are analyzed with flexible continuous-time and discrete-time parametric hazard models. The estimates of the structural parameter and baseline hazard seem robust to the form of the distribution of the data generation process when the time-aggregation window is small. Both estimates of continuous-time models and of discrete-time models suffer from time aggregation, but the estimates of the discrete-time model are more sensitive to aggregation.

How to Separate the Wheat from the Chaff: Improved Variable Selection for New Customer Acquisition

Steady customer losses create pressure for firms to acquire new accounts, a task that is both costly and risky. Lacking knowledge about their prospects, firms often use a large array of predictors obtained from list vendors, which in turn rapidly creates massive high-dimensional data problems. Selecting the appropriate variables and their functional relationships with acquisition probabilities is therefore a substantial challenge. This study proposes a Bayesian variable selection approach to optimally select targets for new customer acquisition. Data from an insurance company reveal that this approach outperforms nonselection methods and selection methods based on expert judgment as well as benchmarks based on principal component analysis and bootstrap aggregation of classification trees. Notably, the optimal results show that the Bayesian approach selects panel-based metrics as predictors, detects several nonlinear relationships, selects very large numbers of addresses, and generates profits. In a series of post hoc analyses, the authors consider prospects’ response behaviors and cross-selling potential and systematically vary the number of predictors and the estimated profit per response. The results reveal that more predictors and higher response rates do not necessarily lead to higher profits.

The Implications of Offering Free Versions for the Performance of Paid Mobile Apps

The mobile application (app) industry has grown tremendously over the past ten years, primarily fueled by small app development businesses. Lacking advertising budgets, these small and relatively unknown businesses often offer free versions of their paid apps to be noticed in the crowded app industry and to reduce customer uncertainty about app quality and fit. The authors build on the existing marketing and information systems literature on sampling and versioning to investigate the implications of offering free versions for the adoption speed of paid apps. Using a unique data set of 7.7 million observations from 12,315 paid apps, and accounting for endogeneity, the authors find that although the practice of offering free versions of paid apps is popular, it is negatively associated with paid app adoption speed. They also find that this negative association between free version presence and paid app adoption speed is stronger both for hedonic apps and in the later life stages of paid apps. The authors hope that the studys results will encourage app developers to reevaluate their current strategy of offering free versions of paid apps and prompt academics to produce more work focusing on this industry.

Cross-Platform Spillover Effects in Consumption of Rich Digital Media

To inform product release and distribution strategies, research has analyzed cross-market spillovers in new product adoption. However, models that examine these effects for digital and viral media are still evolving. Given resistance to advertising, firms often seek to promote their own viral content to boost brand awareness. However, a key shortcoming of virality is its ephemeral nature. To gain insight into sustaining virality, we develop a quasi-experimental approach that estimates the backward spillover onto a focal platform by introducing a piece of content onto a new platform. We posit that introducing content to the audience of a new platform can generate word-of-mouth (WOM), which may affect its consumption within an earlier platform. We estimate these spillovers using data on 381 viral videos on 26 platforms (e.g., YouTube, Vimeo), and observe how consumption of videos on an initial “lead�? platform is affected by their subsequent introduction onto “lag�? platforms. This spillover is estimated as follows: for each multi-platform video, we compare its view growth after being introduced onto a new platform to that of a synthetic control based on similar single-platform videos. Analysis of 275 such spillover scenarios reveals that introducing a video onto a lag platform roughly doubles its subsequent view growth in the lead platform. This positive cross-platform spillover is persistent, bursty, and strongest in the first 42 days. We find that spillover is boosted when the video is consumed more in the lag platform, when the consumption rate peaks earlier in the lag platform, and when the lag platform targets a foreign market. Delaying a video’s introduction onto a lag platform affects spillover concavely, while its introduction onto additional platforms shows diminishing returns. We find further support for positive spillover through a small-scale randomized field experiment. Implications are discussed for platforms, content creators, and policy makers.