Keywords

Brand Positioning, Brand Promising, Compensation Management, Domestic Workers, Urban Company.

Introduction

In 2023, the global maid services market was valued at $1883 million and is expected to reach $3846.1 million by 2033, with a CAGR of 7.6%. Residential sectors are the predominant users, representing 70% of this market. According to e-Shram Portal (July 2023), there are over 28.9 million (2.89 crore) domestic workers registered, with 95.8% being women. However, many workers, especially live-in ones, may not have registered on the portal. Thus, there could be more numbers to add. The market is highly unorganized and dominated by offline practice with no proper framework. It usually comes with no or little employment proof, no social protection, render in the employer’s home, no proper remuneration standard, social bonding between employers and workers, etc Armstrong, (2017).

Given this, people find many difficulties in finding the right domestic worker or maid for house hold tasks or chores. To address the market problem, many startups like bookmybai, broomees, helper4u.in, hire-maids.com, etc. are started offering on-demand maid online booking service. Companies are promoting hassle free process, affordable rates, quick booking service. Maids are verified by the platforms. Customers can choose the service, for instance, one-hour work, one-day work, monthly basis, etc. as per their demand or requirement.

Online on-demand home services segment, focusing on digital platforms, is expected to reach a projected revenue of $1092.5 million by 2030, with 22.4% CAGR from 2023 to 2030. The online on-demand home services market in India is expected to reach a projected revenue of $1092.5 million by 2030. A compound annual growth rate of 22.4% is expected of India online on-demand home services market from 2023 to 2030.

However, the quick commerce model, which is started with groceries, fashion, electronics, etc., has become a trend in India, and is now reaching to a new level by offering domestic service. Urban Company, a popular online platform for local service, in March 2025, entered into quick commerce, by launching ‘Insta maid’ service where it promises maids within 15 minutes at lowest price i.e. ₹49/- per hour (Introductory price). It promises mopping, utensil cleaning, and even cooking at customer’s doorstep in 15 minutes Figure 1.

Urban Company positioned its ‘Insta maids’ service as easy, instant and low-cost reliable domestic service provider. However, there was a mix of positive and negative reaction from netizens to Urban Company’s Insta maid service. While this appeals to urban consumers, but many people raised concerns about labor dignity, stereotype branding, wage fairness, security of customer and labor. Many welcomed the service and many backlashed the brand for using the term ‘maid’, which reflects gender-oriented stereotype term. In response, Urban Company changed its brand from ‘Insta maid’ to ‘Insta help’. It further said the prices will get adjusted once the service scales to ensure sustainable earnings for its partners (maids). It also ensures to additional benefits to partners (maids) like free health insurance and accidental and on-the-job life insurance. The relevance of the study emerges from here. This study aims to explore how such a service impacts brand trust and worker compensation satisfaction.

Literature Review

• Brand Promising: According to Aaker (1996), brand promises are pivotal in shaping customer expectations and These promises must align with actual service delivery to maintain brand integrity. Misalignments can lead to trust erosion and brand dilut ion (Keller, 2003). Consumers who are promotion-focussed take less time and less informa t ion to form expectations (Krishnamurthy, et al, 2015).

• Compensation Management: Milkovich and Newman (2013) emphasize that fair compensation is a cornerstone of human resource strategy, directly affecting employee motivation, retention, and In the gig economy, this becomes more critical due to the prevalence of non-standard contracts. Customers’ perception on working conditio ns and service quality of gig workers influence customers’ behavioral intentions, and customers of social conscious consumption have higher influence (Belanche, D., Casaló, L.V., Flavián, C. et al, 2021).

• Service Quality Perception: The SERVQUAL model by Parasuraman et (1988); Poorva (2025) outlines five dimensions-tangibles, reliability, responsiveness, assurance, and empathy-t hat influence customer satisfaction. These dimensions are essential in services where the brand promise involves speed and reliability. Services consisting of tangible, empathy, and reliability have a positive and significant influence on customer satisfaction Shipra and Harshil (2025).

• Work Conditions and Job Satisfaction in gig workers: Herzberg’s Two-Factor Theory identifies hygiene factors like pay and working conditions as essential to employee satisfaction. Inconsistent pay and workloads, common in gig-based service delivery, can reduce motivation and job In India, the proliferation of platforms, particula r ly gig-based, has introduced more concerns around income unpredictability, digital labor rights, and job work dignity (Mehrotra & Sinha, 2021).

Hypotheses

• H₁: There is a significant positive relationship between perceived compensation fairness and brand trust among

• H₂: Customers’ awareness of service worker compensation influences their willingness to reuse the platform.

• H₃: Perceived service quality significantly predicts brand

• H₄: For workers, compensation consistency, work conditions, and perceived value are positively associated with overall job

Research Objectives

1. To measure customer perception of Urban Company's brand promise in the context of fast domestic

2. To evaluate the satisfaction of service workers with the compensation

3. To examine how service quality and work conditions influence brand trust and worker

4. To identify latent variables underlying customer perception and worker satisfaction using factor analysis.

5. To identify gaps between brand communication and internal compensation

Methodology

• Sample: 100 customers and 50 service workers from Mumbai who have used or delivered 'Insta Help' services. Snowball sampling technique is used in the study.

• Instrument: Structured questionnaire with 5-point Likert scale and binary (Yes/No) Questions were grouped under customer perception, compensation awareness, service experience, and worker satisfaction.

• Data Collection: Online and face-to-face surveys conducted in May Anonymity and informed consent were ensured.

• Statistical Tools: Descriptive statistics were used to profile the Regression analysis tested relationships between fairness perception and brand trust. Chi-square tests examined

associations between categorical variables like awareness and reuse intent. ANOVA assessed variation in trust across service quality levels. Exploratory Factor Analysis (EFA) with varimax rotation was used to extract key dimensions underlying perception and satisfaction. Reliability tests (Cronbach's Alpha) ensured internal consistency of scale items.

Data Findings

• Customer Side

1. 74% of respondents found the 15-minute delivery promise

2. 63% were unaware of actual worker pay; 49% believed the service might underpay

3. 73% rated the overall service quality as good to

4. Brand trust correlated strongly with perceived fairness (R = 64, p < 0.01) Tables 1-9.

Table 1 Customer Perception Metrics
Metric	Percentage
Delivery promise credible	74%
Aware of worker pay	37%
Believe service underpays workers	49%
Rate service quality good or better	73%
Trust Urban Company to treat fairly	68%

Table 2 Worker Satisfaction Metrics
Metric	Percentage
Realize advertised hourly wage	34%
Prefer monthly salary	84%
Aware of insurance benefits	48%
Consistent work hours/workload	43%
Feel valued/motivated in work	42%

Table 3 Correlation Analysis
Variable Pair	Pearson Correlation Coefficient	Interpretation
Perceived Compensation Fairness vs. Brand Trust	0.846	Strong positive correlation; statistically significant (p < 0.001)

Table 4 Regression Analysis
Model	Slope	Intercept	R-squared	p-value	Interpretation
Brand Trust = 0.293 + 0.892 × Fairness Rating	0.892	0.293	0.715	<0.0001	Strong and significant relationship; fairness explains ~71.5% of variation in trust.

Table 5 Chi-Square Test
Tested Relationship	Chi-square statistic	p-value	Interpretation
Awareness of Worker Pay vs. Willingness to Reuse the Service	0.18	0.6698	No significant association between awareness of compensation and reuse intention.

Table 6 Anova Test
Tested Factors	F-statistic	p-value	Interpretation
Service quality level vs. brand trust	5.40	0.0052	Statistically significant variation in brand trust across perceived quality levels.

Table 7 Factor Analysis
Component 1	Component 2	Component 3	Interpretation
Operational experience (46.7%)	Brand value alignment (26%)	Work quality perception (24.6%)	These three latent constructs together explain approximately 97.3% of total variance.

Table 8 Component Breakdown (Item Loadings)
Variable	Component 1 (Operational Experience)	Component 2 (Brand Value Alignment)	Component 3 (Work Quality Perception)
Delivery promise	-0.03	+0.50	−0.86
Brand trust	−0.71	+0.14	+0.58
Service quality	-0.05	+0.83	+0.50
Fairness rating	−0.69	+0.21	+0.07

Table 9 Summary
Test Type	Statistic	p- value	Interpretation
Correlation	r = 0.846	<0.001	Strong positive, statistically significant
Regression	b = 0.892	<0.001	Significant predictor of trust
Chi-Square	Chi2 = 0.18	0.6698	No significant association
ANOVA	F = 5.40	0.0052	Significant variation in trust across quality
Factor Analysis	3 factors	-	Explained 97.3% of variance

Worker Side

1. 66% felt the ₹49/hour claim was not consistently

2. 84% preferred monthly salaried models over hourly

3. 52% were unaware of their insurance

4. 57% reported inconsistent workload and job

5. 42% felt motivated and valued at

Analysis

Regression Analysis

Chi-Square Test

Anova

Factor Analysis

These were derived using exploratory factor analysis (EFA) and supported with variance and reliability metrics. EFA is performed using principal component extraction and varimax rotation conducted on 12 perception and satisfaction items. Three latent factors emerged:

Component Breakdown (Item Loadings)

1. Component 1 – Operational experience: Captures trust and fairness, highlight ing customer beliefs in ethical operations. High loadings on brand trust (−0.71) and fairness rating (−0.69), indicating this factor captures trust and ethical perception of

2. Component 2 – Brand Value Alignment: Reflects the match between service delivery and brand promises. High loadings on service quality (+0.83) and delivery promise (+0.50), reflecting performance matching brand expectations.

3. Component 3 – Work Quality Perception: Contrasts customer perception of delivery execution with service Strong loading on delivery promise (−0.86) and moderate on service quality (+0.50), showing customer evaluation of delivery vs. quality.

Discussion

The findings of this study shed light on critical dimensions influencing customer trust and loyalty in the quick commerce domestic help platforms. While brand-promised speed and convenience are important, users assign even more weight to ethical perceptions - especially regarding how fairly service providers (maids) are compensated. The strong correlation between perceived fairness and brand trust emphasizes the need for brands to transparently communicate their compensation strategies. The lack of significant association between awareness of worker pay and reuse intentions suggests that while fairness influences trust, it may not directly impact repeat behavior unless bundled with high service quality.

Conclusion

This research concludes that in service models like Urban Company's 'Insta Help,' compensation management and perceived fairness significantly drive customer trust. Transparent and fair pay practices, when perceived as such by customers, are crucial to sustaining a positive brand image. Service quality amplifies these effects, reinforcing the multifaceted nature of customer perceptions.

Recommendations

• Transparent Compensation Disclosure: Urban Company and similar platforms should include easy-to-understand summaries of compensation breakdowns within the app to enhance perceived

• Customer Education Campaigns: Efforts to educate users about fair wages and working conditions can improve brand alignment and operational

• Enhance Service Delivery Consistency: As trust also varies significantly with perceived service quality, platforms must ensure consistent quality through training and quality

• Personalize Brand Messaging: Tailoring communications to highlight how brand values match with customer expectations (Component 2) may strengthen emotiona l

• Feedback Integration Loop: Collect continuous feedback on both fairness perceptions and service quality to update operational practices in real

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Received: 05-Jul-2025, Manuscript No. AMSJ-25-16050; Editor assigned: 06-Jul-2025, PreQC No. AMSJ-25-16050(PQ); Reviewed: 15- Jul-2025, QC No. AMSJ-25-16050; Revised: 29-Jul-2025, Manuscript No. AMSJ-25-16050(R); Published: 06-Aug-2025