Visibility of corporate websites: The role of information prosociality

作者:

Highlights:

• Community-engaging pages are positively associated with firm website visibility.

• Study covers 2–3 thousand firms, millions of page downloads, and 5 years.

• Results observed consistently over time despite a wide variety of controls.

• Strong utility in predicting future website visibility with machine learning models

摘要

With an ever expanding content and user base, the Web presents information discovery and consumption challenges for both consumers and producers of information. Producers of information strive for visibility among consumers who have limited attention. Corporate websites are a primary digital marketing channel for firms through which they seek to gain a bigger share of their stakeholders' (i.e., customers, investors, communities) attention. Using observations spanning several years we study the website visibility, as measured by user traffic, of more than 2500 public firms and its association with properties of corporate websites and the corresponding firms. One property that is of particular interest to us is the availability of “community-engaging” pages, i.e., pages that support blogs or forums on the website or provide links to external social media platforms such as Facebook. These community-engaging pages signify online prosocial services provided by firms. We find that websites with larger number of community-engaging pages are associated with higher visibility. This provides a novel empirical support for the promotion and use of social media content and tools on websites of firms. We also find that websites with more specific content are associated with lower visibility while providing more out-links is associated with higher visibility. We observe these results consistently over time. These associations are observed while controlling for the size of the firms, types of their industries, the magnitude of media attention and other firm-level heterogeneity. Finally, machine learning models derived from our empirical analysis provide strong predictive utility for out-of-sample data.

论文关键词:Online visibility,Social media,Web mining,Explanatory and predictive models

论文评审过程:Received 25 July 2017, Revised 7 November 2017, Accepted 12 December 2017, Available online 14 December 2017, Version of Record 12 January 2018.

论文官网地址:https://doi.org/10.1016/j.dss.2017.12.006