Abstract
We investigate the role of social-media-triggered public pressure on corporate social responsibility (CSR) that includes expectations of transparency and accountability on the firm’s part, and participative/evaluative inputs on the public’s part. Using the date when S&P 500 firms established corporate Twitter accounts, we investigate the impact of corporate social media exposure on CSR outcomes. Results from baseline regressions indicate that firms with Twitter accounts significantly outperform industry peers in CSR rating, after controlling for firm and industry characteristics. To test potential reverse causality, we use a dynamic treatment effect model to explore within firm changes of CSR, and find that firms’ CSR rating improves in year one- and year two after establishing a Twitter account, while no difference emerged in similar comparisons with peers prior to the Twitter account establishment. Additionally, Granger causality tests results show that corporate Twitter presence improves CSR performance; it is also shown that CSR outcomes do not motivate corporations to establish a social media presence on Twitter. Finally, firms with more experience (number of years) on Twitter, and those with more followers, tweets or Twitter accounts are associated with improved CSR outcomes. We draw on two theories and qualitative interviews with senior CSR executives to explain why social media influences CSR firm behaviors.
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Notes
Consider the example of Starbucks. On October 15th, 2017, the company posted a tweet about its donation to Northern California wildfire. A user named Iris Chrysopteron replied “Please do the same for Puerto Rico” due to the damage caused by the hurricane. Starbucks responded to state that they had donated $125,000 to a fund to aid recovery efforts in Puerto Rico. Similarly, on October 10th, 2017, Walmart tweeted about their donation to Puerto Rico on Jimmy Fallon’s Tonight show. A user named Cathy Peace posted a picture depicting a Walmart store in Puerto Rico with inadequate supplies and mentioned that “People aren’t getting the donations and your aisles are empty. They need supplies whether they buy them or are given to them through aid efforts.” At the same time, a Facebook post by Walmart claimed that people in the United States could buy groceries online for family and friends in Puerto Rico and have them use the grocery pick-up option at their local stores. In fact, the service did not work as advertised. This news went viral on social media prompting Walmart to release a statement correcting their erroneous post on Facebook.
A survey by Conference Board and the Rock Center for Corporate Governance at Stanford University suggests that 59% of companies in the survey use social media to interact with customers, 49% to advertise, and 35% to research customers. Approximately 30% use social media to research competitors, research new products and services, or communicate with employees and other stakeholders (Larcker et al. 2012).
Because of data limitations, the time period for number of followers is between 2012 and 2014.
Industry is defined using SIC code information shown in Panel B of Table 1.
We compare the two groups from 2007 because there is only one company in our database with a Twitter account in 2006.
In order to capture the dynamic effect, we extend our basic sample by 1 year.
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Appendices
Appendix 1: Unpacked KLD measure analysis
Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|
Innovative giving | Clean energy | Cash profit sharing | Retirement benefit concern | Promotion | Product quality | |
Twittert−1 | 0.088*** | 0.060*** | 0.005* | − 0.033*** | 0.005* | 0.052*** |
(8.571) | (5.113) | (1.842) | (− 2.910) | (1.652) | (11.857) | |
Assetst−1 | 0.065*** | 0.090*** | 0.010*** | − 0.010* | 0.027*** | 0.014*** |
(9.580) | (14.996) | (4.544) | (− 1.727) | (7.064) | (8.332) | |
ROAt−1 | 0.130** | − 0.254*** | 0.002 | − 0.133*** | − 0.005 | 0.136*** |
(2.092) | (− 3.184) | (0.083) | (− 3.658) | (− 0.267) | (6.009) | |
Book-to-markett−1 | 0.015 | 0.007 | 0.007 | 0.038*** | − 0.011* | − 0.016*** |
(0.977) | (0.407) | (1.607) | (3.366) | (− 1.909) | (− 3.300) | |
Casht−1 | 0.135*** | 0.222*** | 0.097*** | − 0.065** | 0.011 | − 0.099*** |
(3.063) | (4.392) | (6.745) | (− 2.060) | (0.694) | (− 8.445) | |
Dividendst−1 | 0.460* | 1.342*** | 0.135* | − 0.076 | 0.143* | 0.478*** |
(1.698) | (4.586) | (1.819) | (− 0.580) | (1.824) | (4.704) | |
Debtt−1 | 0.080** | − 0.140*** | − 0.021* | 0.079** | − 0.022 | 0.116*** |
(2.002) | (− 2.931) | (− 1.690) | (2.284) | (− 1.525) | (11.503) | |
Constant | − 0.553*** | − 0.732*** | − 0.112*** | 0.731*** | − 0.244*** | 0.022 |
(− 3.010) | (− 2.936) | (− 5.054) | (3.386) | (− 6.632) | (0.128) | |
Observations | 3190 | 3565 | 3310 | 1733 | 2487 | 2861 |
Industry dummy | Y | Y | Y | Y | Y | Y |
Year dummy | Y | Y | Y | Y | Y | Y |
Appendix 2: Factor analysis
Panel A: Eigenvalue of three factors
Factor | Eigenvalue | Difference | Proportion | Cumulative |
---|---|---|---|---|
Factor1 | 1.10007 | 1.22791 | 1.4347 | 1.4347 |
Factor2 | − 0.12784 | 0.07761 | − 0.1667 | 1.2679 |
Factor3 | − 0.20545 | − 0.2679 | 1 |
Panel B: Factor loadings
Variable | Factor1 |
---|---|
Log followers | 0.6276 |
Log tweets | 0.6424 |
Year experience | 0.5417 |
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Balasubramanian, S.K., Fang, Y. & Yang, Z. Twitter Presence and Experience Improve Corporate Social Responsibility Outcomes. J Bus Ethics 173, 737–757 (2021). https://doi.org/10.1007/s10551-020-04537-x
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DOI: https://doi.org/10.1007/s10551-020-04537-x