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The purpose of this paper is to provide insightful advice that can improve the practice of using consumers’ pre-launch awareness and preference (AP) changes to predict the sales of new movies.

This paper applies a new movie box-office revenue forecasting model based on consumers’ weekly AP measures, to take advantage of the industry’s practice of using weekly survey data containing the AP measures of upcoming new movies. Specifically, a sales forecasting model is developed on the basis of the theory that the combination of the nature of new product preference (positive vs negative) and the timing of new product awareness (early vs recent) influences entertainment product sales.

This paper shows that early awareness consumers are as important as late awareness consumers in determining new product sales, suggesting that more marketing resources need to be allocated earlier than currently practiced. This paper also shows that when negative preferences dominate positive preferences well ahead of a product’s release, marketing efforts cannot overcome the negative sentiment of the market. Finally, the empirical application illustrates that three consumer segments varying in product expertise and consumption frequency reveal different AP patterns among high-, medium- and low-performance products.

This paper is intended to provide insightful advice that can improve the AP-based approach in entertainment industries. Toward that end, the authors emphasize two major aspects in association with new entertainment product sales: rethinking survey-based AP measures and examining heterogeneous consumer segments’ differential AP patterns.

New industries are created from the pioneering activities of a few firms. These firms generally face great uncertainty and risk, but also stand to benefit from early mover advantages due to the preemption of resources. Based on an empirical analysis of a diverse set of consumer and industrial innovations introduced in the U.S. over the past 100 years, we find that entrants during the pre-firm take-off stage (termed Creators) have higher survival rates than later entrants that enter between the firm and sales take-off (termed Anticipators), and both of these entrant types have higher survival rates than firms that enter after the sales take-off (termed Followers). Notably, survival rates for Creators and Anticipators do not depend on entry time within the cohort group, i.e. what matters is whether an entrant enters before or after the take-off, not whether it entered first in its cohort. Our results indicate that there is no real option value in waiting when one considers survival as a performance measure, which bodes well for firms interested in creating new industries.

Investigates approaches to product launch and elimination decisions in samples of UK and US companies, which is a gap which was identified in the literature. Identifies different approaches to tackling each of the decisions, and different approaches within each sample. There are also differences between the countries. These results provide new insights, as well as support for some previous empirical results. Discusses directions for further research.

Time is the top priority. We now live in real time. It's no longer life in the fast lane because every lane is fast. The computer has changed the way we view time. We expect everything to occur at Pentium speed! A time lag causes stress since it is viewed as an unnecessary waste. This is not a matter of immediate gratification; rather delays—such as standing in line—are viewed as something being wrong with the system, and the company that allows it to happen is perceived as not being up to speed! (Graham, 1996, p. 4).

In the era of Big Data, larger volumes of data arrive in various forms at an increasing pace but of questionable quality and value. The abundant information (that emanates from these 5Vs – volume, variety, velocity, veracity, and value) taxes the bounded capacity of managers. This chapter introduces a taxonomy of approaches available for strategic decision making in an information-rich environment, several of which showcase that automation can help to augment (not supplant) managerial decision making. This taxonomy is then applied to an innovation context. Mapping a stylized version of the phases of the innovation process (i.e., front-end innovation, new product development, commercialization) onto the four decision-making approaches yields an organizing framework for understanding strategic decision making in the realm of innovation. The chapter concludes by identifying promising areas for future research.

This conceptual chapter: (1) explicates the foundational terminology regarding strategic decision making in a marketing context; (2) provides a primer on the era of Big Data and making strategic decisions in an information-rich environment; (3) introduces a taxonomy, which features approaches to decision making in an information-rich environment; and (4) applies the taxonomy in an innovation context to yield an organizing framework.

This chapter focuses on the nascent field that is emerging at the intersection of innovation, marketing strategy, and information-rich environments, and breaks new ground by exploring automation available to aid managerial decision making in this realm.

The main practical implication is to elucidate that managers can apply different approaches to decision making in today’s information-rich environment. Tables 2–4 provide to managers 12 examples of the types of decision making in an innovation context.

This chapter introduces a new taxonomy to classify four approaches for making strategic decisions in an information-rich environment, and extends that framework to the innovation realm. This framework aims to prompt researchers to explore important topics that exist at the intersection of innovation, marketing strategy, and managerial decision making in an information-rich environment.