Job Satisfaction and Churn Intention of App-Based Delivery Partners in Bengaluru under the New Gig-Worker Welfare Laws

Bengaluru runs on delivery partners. Groceries in ten minutes, dinner in thirty, parcels by evening: each order depends on a rider whose relationship with the platform is legally not employment. Riders join quickly and leave quickly, and the fleet partners who recruit and manage them for platforms spend heavily on constant replacement. This MBA-HR dissertation asks which aspects of the work drive a delivery partner's satisfaction, and which push them to quit the platform.

The study is carried out with a fictional fleet-management firm, Namma Last-Mile Fleet Services, which onboards and supervises about 1,800 riders for food, quick-commerce and parcel platforms across four Bengaluru zones. It combines a survey of about 240 active partners with interviews of 15 partners and 4 hub supervisors.

Satisfaction is measured on six dimensions: earnings and incentive design, algorithmic control (ratings, auto-assignment, ID blocking), physical working conditions (heat, rain, rest points), safety and accident cover, platform support, and awareness of social-security entitlements. The policy setting is new: the Code on Social Security, 2020 came into force on 21 November 2025 and recognises gig and platform workers, and Karnataka's Platform-based Gig Workers (Social Security and Welfare) Act, 2025 created a welfare fee on platforms. A logistic regression in R estimates how each dimension changes the odds of intending to leave within six months, and the interviews explain the numbers. The report follows Bangalore University's dissertation format with a body of at least 120 pages in APA 7.

Abstract

This study examines job satisfaction and churn intention among app-based delivery partners in Bengaluru, using a mixed-methods design with a survey of about 240 partners and 19 interviews. Six satisfaction dimensions are measured and related to the intention to leave the platform within six months through binary logistic regression. The study also measures partners' awareness of the Code on Social Security, 2020 and the Karnataka gig-worker welfare law, and recommends retention measures that a fleet partner can implement.

Introduction

Gig and platform work has become one of the largest sources of new urban jobs in India. NITI Aayog's 2022 report on the gig and platform economy estimated about 77 lakh gig workers in 2020–21 and projected 2.35 crore by 2029–30. Unlike employees, platform workers are paid per task, rated by customers and managed largely by algorithms. For HR, this raises a classic question in an unusual setting: what produces satisfaction and retention when there is no employment contract, no manager in the usual sense and no fixed salary?

Literature gap

Job-satisfaction research in India is extensive for IT, banking and manufacturing employees, usually using Herzberg's two-factor theory or the Job Descriptive Index. Gig-work studies by labour researchers document earnings and working conditions, but they rarely use validated satisfaction scales or model quitting intention statistically. Very few studies were done after the labour codes came into force, so awareness of the new entitlements is unknown.

Proposed study

• Adapt and validate a six-dimension satisfaction scale for delivery work.
• Estimate the effect of each dimension on churn intention.
• Compare zones, vehicle ownership (own vs rented EV) and full-time vs part-time partners.
• Measure awareness of e-Shram registration and state and central welfare provisions.
• Recommend retention measures within the fleet partner's control.

Feasibility

• Operational: the host gives access to hubs and dark-store waiting points in four zones during the six-week organisational project.
• Technical: R and Excel are taught in the programme; Tableau Public is free.
• Economic: travel within Bengaluru and a small token of appreciation (tea and water) at hubs.
• Ethical: consent in the partner's language; no partner IDs, phone numbers, Aadhaar or licence numbers recorded.

The host firm replaces a large share of its rider fleet every quarter. Each replacement costs onboarding time, a safety briefing, a bag and uniform, and lost orders while the new rider learns the area. The firm's managers have opinions about why riders leave, such as low incentives, rain, police fines and rating penalties, but no data. Meanwhile the legal environment has changed: the Code on Social Security, 2020 is now in force, Karnataka has notified a welfare fee payable by platforms, and riders may or may not know what they are entitled to. The firm needs to know which dissatisfiers actually predict quitting, which groups of riders are most at risk, and which interventions within its own control (hub facilities, shift planning, insurance help-desks, transparent incentive explanation) could reduce churn. The study addresses this by measuring satisfaction across six dimensions and modelling the intention to leave the platform within six months.

The study covers active delivery partners managed by the host across four Bengaluru zones (central, east, south, north) working for food, quick-commerce and parcel platforms. It covers two-wheeler and bicycle riders, not car or truck drivers. It studies intention to leave, not actual exits, and it does not audit any platform's algorithm or earnings data. Legal provisions are described for awareness measurement only; the study does not give legal advice.

Research design

Explanatory sequential mixed methods: a quantitative survey first, followed by interviews to explain the strongest and most surprising results. Secondary data comes from the Code on Social Security, 2020, the Karnataka Act and Rules, the e-Shram portal, NITI Aayog's 2022 report and the host's anonymised monthly headcount.

Sampling

• Population: about 1,800 active partners managed by the host.
• Sample size: Cochran for a proportion (p = 0.5, e = 0.06, 95%) gives 267; with the finite population correction for N = 1,800 this falls to about 233, rounded to 240.
• Method: time-location sampling at 12 hubs and waiting points (3 per zone), visited in morning, afternoon and late-evening slots on weekdays and weekends, with every 3rd arriving partner invited. This reaches part-time and night riders that a hub roster would miss.
• Interviews: 15 partners selected purposively for variety (new and long-serving, EV renters, women riders, students) and 4 hub supervisors.

Instrument

Five-point Likert scales, adapted from the Job Satisfaction Survey (Spector) and gig-work studies; translated into Kannada and Hindi. Pilot n = 25; Cronbach's alpha ≥ 0.70.

Analysis

1. Descriptives and satisfaction index by dimension.
2. Reliability (psych::alpha) and exploratory factor analysis.
3. Chi-square and Mann-Whitney / Kruskal-Wallis tests for group differences.
4. Binary logistic regression (glm, family = binomial): churn intention on six dimensions plus controls; odds ratios, Hosmer-Lemeshow, Nagelkerke R², ROC-AUC.
5. Thematic analysis of interviews (Braun and Clarke six steps), integrated with the quantitative results in a joint display.

Timeline (16 weeks)