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Case study
Publication date: 16 August 2022

Meena Galliara, Swati Sisodia and Pragya Nagpal

The learning outcomes are as follows: to analyze the driving forces that lead non-government organizations (NGOs) to develop need-based programs; to evaluate the strategy…

Abstract

Learning outcomes

The learning outcomes are as follows: to analyze the driving forces that lead non-government organizations (NGOs) to develop need-based programs; to evaluate the strategy adopted by NGOs in attaining the organization’s mission and creating a social impact, corporate social responsibility, inclusion, labor market, social enterprise, strategy and vocational learning; to apply social business canvas for analyzing the business model deployed by NGOs to develop market linkages; to analyze the challenges in setting and scaling NGO programs and strategies designed to address the same; and to enable students to brainstorm in creating future growth options for scaling up and replicating NGO programs.

Case overview/synopsis

The case describes the journey of Salaam Bombay Foundation (SBF), a national-level NGO registered in 2002 in Mumbai, India. In March 2020, SBF had an annual budget of INR 13.98 crores (US$1.84m). It addresses the challenging environments children from economically constrained families face by engaging them in continuing school education and providing vocational training. Since its inception, SBF has launched and executed many in-school and after-school programs. To successfully transit skilled adolescents and teenagers into the labor market and help them make informed career decisions, SBF launched “DreamLab,” a stipend-based “internship” model, in August 2018. Gaurav Arora, Vice President SBF, was assigned the responsibility to scale up skills@school and DreamLab internship programs. With disruptions caused by the pandemic in March 2020, Arora struggled to operationalize DreamLab as initially planned. The case is at a crucial decision point where clouds of uncertainty have made Arora and his team anxious about their future course of action.

Complexity academic level

The case is intended for students of undergraduate and graduate programs in Business Management, Social Entrepreneurship and Social Work programs. Executives of management development programs can also use the case to analyze the effectiveness and management of the skill development program.

Supplementary materials

Teaching notes are available for educators only.

Subject code

CSS 3: Entrepreneurship.

Details

Emerald Emerging Markets Case Studies, vol. 12 no. 3
Type: Case Study
ISSN: 2045-0621

Keywords

Content available
Article
Publication date: 2 February 2021

Swati Garg, Shuchi Sinha, Arpan Kumar Kar and Mauricio Mani

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource…

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Abstract

Purpose

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

Design/methodology/approach

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

Findings

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Originality/value

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

Details

International Journal of Productivity and Performance Management, vol. 71 no. 5
Type: Research Article
ISSN: 1741-0401

Keywords

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