The recent advent of remote sensing, mobile technologies, novel transaction systems, and highperformance computing offers opportunities to understand trends, behaviors, and actions in a manner that has not been previously possible. Researchers can thus leverage "big data" that are generated from a plurality of sources including mobile transactions, wearable technologies, social media, ambient networks, and business transactions. An earlier Academy of Management Journal (AMJ) editorial explored the potential implications for data science in management research and highlighted questions for management scholarship as well as the attendant challenges of data sharing and privacy (George, Haas, & Pentland, 2014). This nascent field is evolving rapidly and at a speed that leaves scholars and practitioners alike attempting to make sense of the emergent opportunities that big data hold. With the promise of big data come questions about the analytical value and thus relevance of these data for theory development-including concerns over the context-specific relevance, its reliability and its validity.To address this challenge, data science is emerging as an interdisciplinary field that combines statistics, data mining, machine learning, and analytics to understand and explain how we can generate analytical insights and prediction models from structured and unstructured big data. Data science emphasizes the systematic study of the organization, properties, and analysis of data and their role in inference, including our confidence in the inference (Dhar, 2013). Whereas both big data and data science terms are often used interchangeably, "big data" refer to large and varied data that can be collected and managed, whereas "data science" develops models that capture, visualize, and analyze the underlying patterns in the data. In this editorial, we address both the collection and handling of big data and the analytical tools provided by data science for management scholars.At the current time, practitioners suggest that data science applications tackle the three core elements of big data: volume, velocity, and variety (McAfee & Brynjolfsson, 2012;Zikopoulos & Eaton, 2011). "Volume" represents the sheer size of the dataset due to the aggregation of a large number of variables and an even larger set of observations for each variable. "Velocity" reflects the speed at which these data are collected and analyzed, whether in real time or near real time from sensors, sales transactions, social media posts, and sentiment data for breaking news and social trends. "Variety" in big data comes from the plurality of structured and unstructured data sources such as text, videos, networks, and graphics among others. The combinations of volume, velocity, and variety reveal the complex task of generating knowledge from big data, which often runs into millions of observations, and deriving theoretical contributions from such data. In this editorial, we provide a primer or a "starter kit" for potential data science applications in management resear...

Marketing managers increasingly recognize the need to measure and communicate the impact of their actions on shareholder returns. This study focuses on the shareholder value effects of pharmaceutical direct-to-consumer advertising (DTCA) and direct-to-physician (DTP) marketing efforts. Although DTCA has moderate effects on brand sales and market share, companies invest vast amounts of money in it. Relying on Kalman filtering, the authors develop a methodology to assess the effects from DTCA and DTP on three components of shareholder value: stock return, systematic risk, and idiosyncratic risk. Investors value DTCA positively because it leads to higher stock returns and lower systematic risk. Furthermore, DTCA increases idiosyncratic risk, which does not affect investors who maintain well-diversified portfolios. In contrast, DTP marketing has modest positive effects on stock returns and idiosyncratic risk. The outcomes indicate that evaluations of marketing expenditures should include a consideration of the effects of marketing on multiple stakeholders, not just the sales effects on consumers.

Are persistent marketing effects most likely to appear right after the introduction of a product? The authors give an affirmative answer to this question by developing a model that explicitly reports how persistent and transient marketing effects evolve over time. The proposed model provides managers with a valuable tool to evaluate their allocation of marketing expenditures over time. An application of the model to many pharmaceutical products, estimated through (exact initial) Kalman filtering, indicates that both persistent and transient effects occur predominantly immediately after a brand's introduction. Subsequently, the size of the effects declines. The authors theoretically and empirically compare their methodology with methodology based on unit root testing and demonstrate that the need for unit root tests creates difficulties in applying conventional persistence modeling. The authors recommend that marketing models should either accommodate persistent effects that change over time or be applied to mature brands or limited time windows only.

Marketing managers increasingly recognize the need to measure and communicate the impact of their actions on shareholder returns. This study focuses on the shareholder value effects of pharmaceutical direct-to-consumer advertising (DTCA) and direct-to-physician (DTP) marketing efforts. Although DTCA has moderate effects on brand sales and market share, companies invest vast amounts of money in it. Relying on Kalman filtering, the authors develop a methodology to assess the effects from DTCA and DTP on three components of shareholder value: stock return, systematic risk, and idiosyncratic risk. Investors value DTCA positively because it leads to higher stock returns and lower systematic risk. Furthermore, DTCA increases idiosyncratic risk, which does not affect investors who maintain well-diversified portfolios. In contrast, DTP marketing has modest positive effects on stock returns and idiosyncratic risk. The outcomes indicate that evaluations of marketing expenditures should include a consideration of the effects of marketing on multiple stakeholders, not just the sales effects on consumers. Recent research has demonstrated that a firm's advertising affects stock returns, beyond the effect of advertising on revenues and profits (e.g., Joshi and Hanssens 2010). Similarly, communicating the added value created by product innovation yields greater firm value effects (e.g., Srinivasan et al. 2009). As McAlister, Srinivasan, and Kim (2007) report, a firm's advertising also lowers its systematic market risk. Consequently, to evaluate marketing expenditures properly and improve marketing resource allocations, models that consider the effects of marketing expenditures on multiple stakeholders, such as customers and investors, are necessary (e.g., Luo 2007). KeywordsIn this study, we examine the influence of advertising expenditures on investors and thus (1) the levels of stock returns and the risk associated with these returns, distinguishing (2) systematic (market risk factor) and (3) idiosyncratic (firm-specific) risks. Our framework also moves beyond the theories and variables used in previous studies to determine whether firms should invest in marketing actions with limited sales response. In doing so, this study offers new contributions over previous research (e.g., Joshi and Hanssens 2010;McAlister, Srinivasan, and Kim 2007). In particular, we simultaneously estimate the effects of marketing on all three components of shareholder value using Kalman filtering. Building on the four-factor model that Carhart (1997) proposes, we develop a dynamic model to relate pharmaceutical direct-to-consumer advertising (DTCA) and direct-to-physician (DTP) marketing expenditures to stock returns and volatilities while controlling for financial performance.Pharmaceutical DTCA is a relatively new, and heavily debated, phenomenon: The Food and Drug Administration (FDA) relaxed regulations on DTCA only in 1997. Recent research suggests limited short-and long-term effects of DTCA on sales, which places pharmaceutical marketers, simil...

Many firms are allocating increasing parts of their advertising budgets to banner advertising. Yet, for firms that predominantly sell offline, existing research provides little guidance on online advertising decisions. In this study, the authors analyze the impact of banner advertising on consumers’ online and offline behavior across multiple distinct campaigns for one focal firm, which predominantly sells through the offline channel. Results suggest that banner and TV advertising increase website visit incidence for consumers who have not visited the focal firm's website in the previous four weeks (nonrecent online consumers). For these consumers, banner and TV advertisements indirectly increase offline sales through website visits. For consumers who have visited the firm's website in the previous four weeks (recent online consumers), the authors find evidence for a cross-campaign, brand-building effect of banner advertising, and TV ads also directly affect offline purchases. Overall, the findings indicate that for firms that predominantly (or even exclusively) sell offline, banner advertising is most suitable to generate awareness for a firm's new products among nonrecent online consumers, and to build their brand(s) among recent online consumers.