Abstract
This study investigates whether the personalities of Chief Marketing Officers (CMOs) of technology-based new ventures affect how the increasing maturity of new ventures translates into web traffic. Drawing on upper echelon theory and the interactionist theory of job performance, we explain how certain personality traits from the five-factor model are relevant to the job demands a CMO faces in technology-based new ventures. We build a multi-source dataset on 627 new ventures and use a novel approach to measuring personality that is based on computer text analysis—specifically, the LIWC application—which we apply to the CMOs’ Twitter accounts. Our findings indicate that a CMO’s extraversion positively moderates the relationship between a new venture’s maturity and web traffic, while a CMO’s conscientiousness is a negative moderator of this relationship. These results have useful theoretical and practical implications for the role of the CMO and for marketing new ventures in general.
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Notes
In line with the extant CMO research (e.g., Nath and Mahajan 2008), the term CMO refers to the top marketing executive in a TMT, regardless of the actual title.
This notion suggests that a personality dimension can also cover traits that are not relevant to the specific situation of new ventures that we investigate but might cause a detrimental response for other outcomes. This concept is rooted in the trait activation concept from Tett and Burnett (2003), which is part of interactionist model from Judge and Zapata (2015) that we employ. The trait activation concept argues that personality traits are expressed as responses to cues, such that, if a situation does not “wake up” a specific trait by means of cues, it is not relevant to the situation. Since we embed the CMO’s personality in the situation of new ventures whose maturity is increasing, we seek traits in the context of this specific situation and discuss (in the section on future research) scenarios in which a personality dimension that we derive as being favorable for CMOs for our purposes (i.e., transforming increasing maturity into web traffic) may be detrimental in other situations.
The ten most frequent Crunchbase categories in the sample are: software, mobile, enterprise software, software as a service, internet, analytics, advertising, e-commerce, social media, and apps.
While one must account for variations in the data that stem from factors that affect all companies of a certain type, we caution against interpreting these variables as “classic” industry measures. Our sample consists of technology-based new ventures, which are often characterized as combining digital technologies with an industry specialization. For example, Uber (not in our sample) has the SIC code 4111—“local and suburban transit”—but it is also affected by trends in the “computer-related services” industry (SIC code 7370). Not surprisingly, the 2-digit SIC code 73 is the most frequent code in our sample.
Web Appendix D provides a more detailed explanation of the application of the between effects model and the random effects model.
These results are based on the negative binomial model, since prediction of logged dependent variables is not straightforward (Wooldridge 2013, pp. 212–213). Predictions were calculated using the Stata command margins, keeping all other variables at their mean value.
When interpreting the economic magnitude of the effect, it is important to keep in mind that web traffic is not measured in absolute numbers but rather in page views per one million website visits (as provided by Alexa to measure the relevance of a website on a given day, relative to the traffic volume on that day; see also Zhang et al. (2011) and Edelman and Brandi (2015)). To illustrate, two page views per million for a given URL can be interpreted as follows: If one randomly selects 1000,000 website visits, two of those visits would be directed to that particular URL. To put this in perspective, in the United States, 77% of the adult population uses the Internet at least once a day (Perrin and Jiang 2018), and most of these users likely visit multiple webpages each day. This implies that the absolute number of page views is much higher. Hence, when interpreting the effect, the focus should instead lie on the increase or decrease in web traffic from low to high values of the moderator.
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The authors are thankful for the insightful comments provided by the editor, John Hulland, the associate editor, and by three anonymous reviewers. The authors gratefully acknowledge the helpful comments on earlier versions of this paper provided by Martin Klarmann.
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Winkler, HJ., Rieger, V. & Engelen, A. Does the CMO’s personality matter for web traffic? Evidence from technology-based new ventures. J. of the Acad. Mark. Sci. 48, 308–330 (2020). https://doi.org/10.1007/s11747-019-00671-9
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DOI: https://doi.org/10.1007/s11747-019-00671-9