Analytics for strategic corporate social responsibility

Often organized as disconnected philanthropic activities independent from core corporate strategy, the potential that Corporate Social Responsibility (CSR) can play in gaining competitive advantage for firms have largely been untapped. One of the challenges in unlocking the strategic advantages of CSR lies in measuring results and connecting outcomes to key business priorities. The application of data analytics can support CSR practitioners in overcoming this challenge. By adopting CSR analytics, practitioners can more transparently measure the impact of their activities, identify underlying relationships with business goals, and utilize data to make strategic choices that contribute to corporate strategy. This research focuses on the case of Wayfair, one of the largest e-commerce companies in the world. In support of the firm's corporate goal of improving human resource outcomes, analytics strategies have been developed to describe, predict, and prescribe CSR interventions. These strategies impact the five dimensions of employee engagement: employee satisfaction, employee identification, employee commitment, and employee loyalty. This research study aims to encourage firms to increase focus and investments on the adoption of analytics in CSR. By doing so, firms can achieve business goals and contribute to sustainable development.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Samaniego, Ponce Ernest.

Advisor dc:contributor.advisor

• Jacob Cohen.

• Sloan School of Management.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127073

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127073