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Online firestorms pose severe threats to online brand communities. Any negative electronic word of mouth (eWOM) has the potential to become an online firestorm, yet not every post does, so finding ways to detect and respond to negative eWOM constitutes a critical managerial priority. The authors develop a comprehensive framework that integrates different drivers of negative eWOM and the response approaches that firms use to engage in and disengage from online conversations with complaining customers. A text-mining study of negative eWOM demonstrates distinct impacts of high- and low-arousal emotions, structural tie strength, and linguistic style match (between sender and brand community) on firestorm potential. The firm’s response must be tailored to the intensity of arousal in the negative eWOM to limit the virality of potential online firestorms. The impact of initiated firestorms can be mitigated by distinct firm responses over time, and the effectiveness of different disengagement approaches also varies with their timing. For managers, these insights provide guidance on how to detect and reduce the virality of online firestorms.

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Strategic flexibility (SF) is a concept that has evolved from strategy through other disciplines, including management, marketing, innovation, entrepreneurship and operations. However, despite attempts to consolidate the domain of SF, there remain theoretical and empirical tensions underlying its antecedents, the consequences and contingencies. Based on 106 independent samples reported in 98 different studies (n = 26,940 firms), we provide a meta-analytical examination of these tensions. We highlight and resolve several disagreements regarding the enablers, inhibitors and triggers of SF, and we reveal an adjusted mean performance effect of 0.24. We further find that the measurement of SF, as well as some, but not all, dimensions of the environment, moderate the performance effect. Finally, an explorative analysis reveals that innovation outcomes and market outcomes mediate the positive relationship between SF and financial outcomes, in addition to a negative direct effect. These insights provide a comprehensive and coherent understanding of the nomological network of SF and a stronger basis for further theorizing and conducting empirical research. Moreover, our findings help firms to refine their strategy by implementing the right enablers that drive SF and to understand how and when their investment in SF pays off.

Investigating the ambidextrous effects of its proactive and responsive dimension offers a fresh perspective on market orientation. Drawing upon the ambidexterity literature, the author derives hypotheses on the joint effects of combining and balancing proactive and responsive market orientation. He examines his hypotheses with two-wave panel survey data from 167 strategic business units. Using time-lagged performance data and polynomial regression with response surface analysis to overcome limitations of previous studies of ambidexterity, the author finds that the balance between proactive and responsive market orientation has an incremental positive effect on performance beyond their combined effect; that performance will decline less sharply when proactive is higher than responsive market orientation; and that as the level of balance increases, performance will first decrease and then increase. Given resource scarcity, an important and counterintuitive implication of the present study is that balancing proactive and responsive market orientation is as important as their combination.

Big data technologies and analytics enable new digital services and are often associated with superior performance. However, firms investing in big data often fail to attain those advantages. To answer the questions of how and when big data pay off, marketing scholars need new theoretical approaches and empirical tools that account for the digitized world. Building on affordance theory, the authors develop a novel, conceptually rigorous, and practice-oriented framework of the impact of big data investments on service innovation and performance. Affordances represent action possibilities, namely what individuals or organizations with certain goals and capabilities can do with a technology. The authors conceptualize and operationalize three important big data marketing affordances: customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. The empirical analysis establishes construct validity and offers a preliminary nomological test of direct, indirect, and conditional effects of big data marketing affordances on perceived big data performance. Keywords Big data technologies and analytics. Affordance theory. Marketing affordances. Service innovation. Big data performance. Industry digitalization "There is nothing so practical as a good theory (Kurt Lewin)." More and better customer data have long been marketers' holy grail when such information was scarce. Firms are now investing significant resources into big data technologies and analytics (BDTA), following the assumption that they may drive superior performance (Lambrecht and Tucker 2015), enable business transformation (Davenport and Bean 2019), and facilitate disruptive business model innovations (Sorescu 2017). This is particularly evident in the service industry, where BDTA are changing the nature of the customer-firm connection, thereby disrupting existing value propositions (Huang and Rust 2017). Yet, the accelerating rate of big data investments is not always matched by an increased quality and effectiveness of marketing decisions (Shah et al. 2012), and senior managers report mixed perceptions of the extent to which big data contribute to a firm's performance (Bean and Davenport 2019). 1 Hence, developing a better understanding 1 According to a recent survey of senior executives in Fortune 1000 and industry-leading US firms, 91.6% of companies are accelerating the pace of big data investments. However, only 62.2% report measurable results from these investments; 59.5% declare to drive innovation with data, 47.6% claim to be competing on data and analytics, and only 31% perceive themselves as a data-driven organization (Davenport and Bean 2019).

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