Visibility of corporate websites: The role of information prosociality

被引:20
作者
Pant, Gautam [1 ]
Pant, Shagun [2 ]
机构
[1] Univ Iowa, Dept Management Sci, Iowa City, IA 52246 USA
[2] Univ Iowa, Dept Finance, Iowa City, IA 52246 USA
关键词
Online visibility; Social media; Web mining; Explanatory and predictive models; SOCIAL MEDIA; COMPETITIVE ALTRUISM; WEB; MANAGEMENT; IMPACT; POWER; FIRM; DESIGN; BLOG;
D O I
10.1016/j.dss.2017.12.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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. (C) 2017 Elsevier By. All rights reserved.
引用
收藏
页码:119 / 129
页数:11
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