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We examine the role of financial and nonfinancial performance measures in managing revenues derived from life cycles of a type of intellectual property products − motion pictures.

Our study focuses on the first two markets in which audiences can watch a motion picture – the upstream theatrical market and the downstream home video market. We combine data collected from numerous public and proprietary sources and form a final sample of 654 motion pictures. Then we perform regression analysis on the data.

First, three measures of a movie’s performance in the theatrical market, opening box office revenue, peak rank, and weeks at the peak rank, have positive effects on subsequent revenues in the home video market. Second, the same set of performance measures also predicts the motion picture’s life span in the theatrical market. Third, when the actual life span of a motion picture in the theatrical market deviates from its predicted value, the total return on investment in the motion picture decreases.

We do not have data on other downstream markets related to motion pictures, such as pay-per-view and online video streaming.

This study suggests that the public and proprietary data can be used to inform managerial decisions regarding intellectual property product life cycles.

This is the first accounting study that directly examines life cycle revenues of intellectual property products. We also extend literature on revenue driver and revenue management research to the product level.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

Crowdsourcing – a form of collaboration across organizational boundaries – provides access to knowledge beyond an organization’s local knowledge base. Integrating work on organization theory and innovation, the authors first develop a framework that characterizes crowdsourcing into a main sequential process, through which organizations (1) define the task they wish to have completed; (2) broadcast to a pool of potential contributors; (3) attract a crowd of contributors; and (4) select among the inputs they receive. For each of these phases, the authors identify the key decisions organizations make, provide a basic explanation for each decision, discuss the trade-offs organizations face when choosing among decision alternatives, and explore how organizations may resolve these trade-offs. Using this decision-centric approach, the authors continue by showing that there are fundamental interdependencies in the process that makes the coordination of crowdsourcing challenging.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

If you knew one of your child’s friends smoked pot with her mom, would that worry you? If you knew another one of your child’s friends spoke in tongues, would that worry you more or less?

As a response to the COVID-19 pandemic, both educators and students adapted to course delivery modes no longer centered on in-person interactions. Resiliency and self-regulation are key to success in online contexts, but the rapid transition to remote learning left many students without the necessary support to develop these skills. Much of the existing literature on self-regulation and resiliency focuses on cognitive processes and strategies such as goal orientation, time management, and mindset. However, the added stress and trauma of learning in the context of a global pandemic highlighted the many other factors relevant to students’ development of these skills. Drawing from the literature, the authors explore evidence-informed teaching practices to foster self-regulation and resiliency, highlight the power and privilege of being able to be resilient, advocate for the development of pedagogies of kindness, and emphasize the “how” of implementing techniques to best support students. The authors provide evidence-informed suggestions with the goal of assisting instructors and students during times of high stress, while acknowledging their limitations in addressing structural inequalities highlighted by the COVID-19 pandemic. Nonetheless, the authors argue that evidence-informed techniques and compassionate pedagogies adopted during a period of upheaval remain applicable to future in-person and online pedagogies.

Although two decades have passed since the publication of Walsh and Ungson’s (1991) seminal article on organizational memory, there has been only limited theoretical elaboration and application of this critical aspect of cognition in the strategic management literature. We remedy this gap by advancing the construct of competitive memory, which we define as a firm’s dynamic capability consisting of stored information from its past competitive interaction with a given rival that can be brought to bear on present or future competitive actions. We theorize that competitive memory is composed of both procedural and declarative elements and can be accessed automatically and deliberatively. Additionally, we suggest that competitive memory is relational: As rivals within a competitive set interact in the market, competitive memory drives not only their strategic actions, but also their expectations about their competitors. Last, competitive memory is also dynamic, which can be constructed and reconstructed over time by an organization’s enactment of its internal and external environments and by purposive memory trials with its competitive set.