Can Advertising Investments Counter the Negative Impact of Shareholder Complaints on Firm Value?

507

308

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

Section: Investorsmentioning

confidence: 99%

“…Language used in YouTube ads is correlated with their virality (Tellis et al 2019). Shareholder complaints about nonfinancial concerns and topics that receive high media attention substantially increase firms' advertising investments (Wies et al 2019).…”

Section: Text's Impact On Receiversmentioning

confidence: 99%

Uniting the Tribes: Using Text for Marketing Insight

Berger¹,

Humphreys²,

Ludwig³

et al. 2019

507

308

“…where i and t indicate the firm and the time of the event, respectively; W is a vector of control variables in Table W3 of the Web Appendix; IMR it is the inverse Mills ratio from the first-stage selection model; and e it represents the twodimensional clustered standard errors that account for the clustering across firms and events (Cameron, Gelbach, and Miller 2011;Thompson 2011). All continuous variables are Winsorized at the 1% and 99% levels to reduce the effect of outliers (e.g., Wies et al 2019). Our descriptive findings further indicate that despite the popular definition for sociopolitical activism in recent literature, which limits CSA to "sociopolitical statements" (e.g., Hambrick and Wowak 2019), 40% of the CSA events in our sample are accompanied by a form of action from the firm.…”

Section: Model Specificationmentioning

confidence: 99%

Corporate Sociopolitical Activism and Firm Value

Bhagwat¹,

Warren²,

Beck³

et al. 2020

260

200

Stakeholders have long pressured firms to provide societal benefits in addition to generating shareholder wealth. Such benefits have traditionally come in the form of corporate social responsibility. However, many stakeholders now expect firms to demonstrate their values by expressing public support for or opposition to one side of a partisan sociopolitical issue, a phenomenon the authors call “corporate sociopolitical activism” (CSA). Such activities differ from commonly favored corporate social responsibility and have the potential to both strengthen and sever stakeholder relationships, thus making their impact on firm value uncertain. Using signaling and screening theories, the authors analyze 293 CSA events initiated by 149 firms across 39 industries, and find that, on average, CSA elicits an adverse reaction from investors. Investors evaluate CSA as a signal of a firm’s allocation of resources away from profit-oriented objectives and toward a risky activity with uncertain outcomes. The authors further identify two sets of moderators: (1) CSA’s deviation from key stakeholders’ values and brand image and (2) characteristics of CSA’s resource implementation, which affect investor and customer responses. The findings provide new and important implications for marketing theory and practice.

“…We calculate the extent of firm diversification as one minus the Herfindahl index of the firm's sales across all its business segments (e.g., Kovach et al 2015) and the firm's capital intensity as the ratio of its net plant, property, and equipment to its total assets (e.g., McAlister et al 2016). Consistent with prior research (e.g., Wies et al 2019), we measure the firm's financial leverage as the ratio of its total long-term debt to its total assets. Industry concentration is the four-digit NAICS Herfindahl index of firm sales (e.g., Gruca and Rego 2005), and industry growth is the difference in the natural logarithm of the total sales of all firms within the same four-digit NAICS code at the end of the current year from the end of the preceding year (e.g., Whitler, Krause, and Lehmann 2018).…”

Section: Measures and Datamentioning

confidence: 99%

Customer Satisfaction and Its Impact on the Future Costs of Selling

Lim¹,

Tuli

Grewal

2020

Although scholars have established that customer satisfaction affects different dimensions of firm financial performance, a managerially important but overlooked aspect is its effect on a firm’s future cost of selling (COS), that is, expenditures associated with persuading customers and providing convenience to them. Accordingly, this study presents the first empirical and theoretical examination of the impact of customer satisfaction on future COS. The authors propose that while higher customer satisfaction can lower future COS, the degree to which a firm realizes this benefit depends on its strategy and operating environment. Analyzing almost two decades of data from 128 firms, the authors find that customer satisfaction has a statistically and economically significant negative effect on future COS. While the negative effect of customer satisfaction on future COS is weaker for firms with higher capital intensity and financial leverage, this effect is stronger for more diversified firms and for firms operating in industries with higher growth and labor intensity. The authors also find that these effects may vary across two components of COS, cost of persuasion and convenience.