Keywords

Quick commerce, M-commerce, E-commerce, Mobile App, Consumer Behaviour, Dark stores.

Introduction

Quick commerce, or Q-commerce, is a delivery model in online retail that emphasizes the rapid delivery of goods, often within an hour of order placement. This approach focuses on delivering small quantities of everyday essentials like groceries, personal care items, and pharmaceuticals, catering to consumers' increasing demand for speed and convenience. The democratisation of mobile internet and omni-channel distribution has ultimately led to a boost of the Q-commerce industry. The COVID19 pandemic and the subsequent control measures like lockdowns and social distancing led to disruptions in the e-commerce industry (Modgil. et al., 2021). This has also influenced consumer behaviour and led to an increase in the online purchase of perishables and groceries by customers. A part of the shift can be attributed to the diversion of spending from clothing and lifestyle to household essentials (Nahata, 2022). Further, COVID19 led to an expansion in online grocery purchases by 80% in 2020 to USD 2.66 billion (Patil et al., 2021). The demand was accompanied by instant delivery expectations, leading to the emergence of the quick commerce business. It is estimated that the industry will grow at a CAGR of 27.9% between FY 2022 and FY 2027 (Pratik and Arora, 2022).

Today’s technologies combine logistics and hyperlocal delivery networks, as well as AI-based systems, due to which this model has become operationally feasible and efficient. These elements combine to form a trifecta of shifting consumer expectation, enabling technology, and altering lifestyle, allowing a near perfect setting for quick commerce.

The movement from physical storefronts to online purchasing, and then more recently, the concept of Q-commerce, shows an ongoing push for faster and seamless shopping experiences. E-commerce brought about a notable change in consumer behaviour whereby consumers could shop from home with relative ease and browse a vast range of products. To reach a wider audience, marketing tactics changed with technology, shifting from conventional printed brochures to online advertising and social media marketing. Furthermore, the convenience of online buying was enhanced by the growth of m-commerce, or mobile commerce, which allowed customers to use smartphones and other mobile devices to make purchases. The shift to mobile platforms and applications improved accessibility and fuelled e-commerce's rise in popularity.

The next stage of this technical development is Q-commerce, which focuses on lightning-fast delivery times. This service of delivering products to customers in below thirty minutes seems to cater specifically to the time-starved urban consumer in urban cities like Bangalore, Delhi, or Mumbai. The fast-paced population, mainly Gen-Z, who appreciate efficiency and instant gratification in their day-to-day life, have taken to Q-commerce apps as the preferred platform.

The Evolution of E-Commerce and the Rise of Quick Commerce – Global Scenario

The development of e-commerce clearly is characterized by rapid pace of development. In the 1960s, Electronic Data Interchange (EDI) had already put business transactions online, while during the 1990s online marketplace sites such as Amazon and eBay enabled web-based businesses. The growth of the internet in the early 2000 made it easy to shop online. The mid-2000s marked the birth of mobile commerce (m-commerce) with the advent of smartphones that made app-based shopping a real possibility.

In 2010, one can see surge in the companies like Amazon and Alibaba and the rise of social commerce dominating the e-commerce landscape. The most recent iteration: Q-commerce, a type of trade where items are delivered super-fast, typically within 30 minutes, thanks to the use of logistics and inventories managed by AI.

The U.S. quick commerce market is valued between $20 billion to $25 billion and has been led by quick grocery and convenience item delivery companies. Founded in 2013, Gopuff runs many of the warehouses around the country to make fast delivery possible. Intacart, which first debuted in 2012 as a grocery delivery service, has entered the quick-commerce space and stated that its gross transaction value for the third quarter of 2024 rose 11% year over year to $8.30 billion (Reuters 2025). Europe's quick commerce industry has seen rapid growth, meanwhile an estimated 45% of consumers in Britain, Germany and France say they would use such a service even more. The potential market in grocery retail alone will reach roughly €13 billion by 2030. Getir, Gorillas and Flink are some of the leading quick commerce platforms in Europe. Getir, founded in Turkey,  was downloaded 7.7 million times in 2021, the most downloaded quick commerce app at the time. Gorillas, a Germany-based outfit, was the next most popular with 950,000 downloads. Flink, Germany’s other successful start-up, reached a 40% usage in 2024 to be the most used quick commerce provider in Germany (Business of Apps, 2025).

Quick Commerce – Indian Scenario

Quick commerce is gaining traction as a disruptive trend in India’s e-commerce sector, reshaping the narrative of convenience with ultra-speedy delivery of consumable goods which usually takes anywhere between 10 to 30 minutes. The model fits urban India’s demand of instant gratification, rising digital adoption, evolving consumer lifestyles and efficient logistics. In contrast to classic e-commerce’s scheduled delivery, quick commerce utilises hyperlocal fulfilment centres and sophisticated technology to ensure the last mile delivery is as efficient as possible. As the Indian market readies for substantial growth, quick commerce is transforming customer behaviour and retail strategies. However, the sector faces challenges such as high operational costs, the need for sustainable unit economics, and intense competition among players. Given their increasing need to stand out amidst ferocious competition, as companies are working towards finding their sweet spot between speed, scale and revenue; quick commerce is going to be instrumental in India’s retail future (IBEF 2024). One can say that, the Indian Q-commerce industry will indefinitely continue its fast growth, due to more users going online and shopping through smartphones, increasing disposable income for consumers, and the increasing preference for online shopping. The Q-commerce market in India has witnessed unprecedented growth from $100 million in 2020 to $6 billion in 2024 and estimated to be valued at $9.95 billion by 2029. The segment initially targeted at customers seeking only the necessities, it is slowly moving into other categories while consolidating its models around profitability and sustainability. The influx of digital payments and rapidly growing smartphone penetration has set up Q-commerce to disrupt the future of retail by addressing consumers’ expectations for speed and convenience. Mentioning about q-commerce players in India, Blinkit, formerly known as Grofer is India’s biggest q-commerce companies with 40% to 60% market share. BigBasket is India’s first online grocery delivery company,founded in 2011 currently owned by Tata Digital. there are many players like Zepto, Dunzo, Swiggy Instamart, Amazon fresh, M-Now and Slikk who are in quick commerce keeping up with the dynamic change in consumer needs in this fast-paced world (App Supply Management 2025).

Rationale of the Study

With competition intensifying among various q-commerce players, businesses must continuously refine their strategies to enhance customer satisfaction and build long-term loyalty. By analysing consumer behaviour through a data-driven approach, this research offers insights that can help Q-commerce companies optimize pricing models, improve operational efficiency, and expand service offerings. This study investigates into factors affecting the usage of Q-commerce; it employs multiple linear regression analysis to identify the primary drivers of Q-commerce usage. These findings will not only aid businesses in staying competitive but also provide valuable insights for policymakers and industry leaders on the evolving role of quick commerce in modern retail.

Literature Review and Hypotheses Formulation

The COVID-19 epidemic disrupted the e-commerce business, giving rise to a new catchphrase known as 'Quick commerce' (Patil et al, 2021). Quick commerce modernized the grocery business by meeting consumer demand for daily items such as groceries, veggies, and fruits through fast deliveries. According to Stojanov (2022), rapid commerce refers to e-commerce that delivers things to customers' doorsteps promptly. Instant deliveries have evolved beyond ready-made meals to include groceries, pharmaceuticals, cosmetics, and electronics. Consumer behaviour is heavily influenced by convenience, urbanization, and busy lifestyles, leading enterprises to prioritize shorter delivery times.

The initial search on online databases for research papers from the year 2021 until now on Quick Commerce yielded approximately 20.5 lakh (2.05 million) results. To refine the focus, the search was narrowed to Quick Commerce in India, which came to 4.43 lakh (443,000) results. Further specifying the search to Quick Commerce Apps in India reduced the results to 52,800 articles, while looking into Quick Commerce Apps for Groceries Dailies and Essentials in India brought it down to 19300 articles. A more targeted search on Quick Commerce Apps Usage in Mumbai resulted in 17,400 results, and when focusing on Factors Affecting Quick Commerce Apps Usage in Mumbai, the number remained the same. Later, searches on Factors Influencing Quick Commerce Adoption in Mumbai and Factors Influencing Quick Commerce Apps Adoption in Mumbai generated 2,570 and 2,240 results, respectively. To further refine these, researchers went ahead with the timeline starting from 2022 onwards, which bought the count to 775 articles. This systematic refinement helped to narrow down the vast pool of literature to the most relevant studies, allowing extracting the key insights on consumer behaviour, adoption factors, and market dynamics of Quick Commerce in Mumbai. The review examines various studies on consumer behaviour, exploring aspects such as the impact of demographic factors, psychological factors, and external influences on consumer decision-making. The findings of the review provide insights into the complex and multifaceted nature of consumer behaviour, highlighting the need for businesses to deeply understand their target consumers to develop effective marketing strategies. Few relevant papers which were published before the above timeline were also reviewed and were considered in hypotheses formation.

The literature review gave the necessary insights to derive important variables that emerged as independent factors influential in the adoption and usage of quick commerce apps. These include overall experience rating, the impact of Q-commerce app’s feasibility on its adoption, Q-commerce vs. E-commerce pricing, delivery speed and likelihood to recommend the Q-commerce platforms. Based on these insights, the hypotheses were formulated to explore the relationship between these independent factors and quick commerce adoption and usage being the independent factor.

Overall Experience Rating

According to Kircova & Kurt, 2023, the relationship between the Overall Experience Rating and consumer adoption in quick commerce is established by different interrelated factors, such as customer satisfaction, service experience, and app usability. Customer satisfaction acts as a central mediator. The higher the level of customer satisfaction, the stronger is the loyalty of the customers. Further, an individual's awareness of service experience often leads them to generalize their overall service experiences and that would, in turn, enhance the role of customer satisfaction on loyalty (Kircova & Kurt, 2023). Apart from customer satisfaction, the different dimensions of quick commerce such as - app design, ease of navigation, and security assurance, positively affect customer retention and repeat usage. In fact, trust emerged as a significant factor of service experience and loyalty, which means that only if a customer perceives a Q-commerce platform as trustworthy, secure, and efficient would he continue using it (Kapoor et al., 2023). Beyond service experience elements, the overall quality of a quick commerce app—including perceived usefulness, transaction security, and system efficiency—significantly influences its experience rating. An intuitive and responsive interface enhances navigation, fostering brand trust and habitual usage, which ultimately boosts customer retention and loyalty (Azizi et al., 2020; Magdalena et al., 2024). Given the highly competitive nature of the quick commerce industry, ensuring enhanced customer experience through efficient service delivery, streamlined platform usability, and customer-centric technological improvements is crucial. The ability to consistently meet or exceed customer expectations in terms of speed, reliability, and security significantly influences whether users remain loyal to a platform or switch to competitors offering superior service. Therefore, quick commerce providers must continuously refine their digital infrastructure, optimize app functionalities, and personalize service offerings to foster strong customer retention and long-term engagement (Suganda & Priadi, 2023).

H0: There is no significant relationship between overall customer rating and the frequency of Q-commerce usage.

Impact of Q-Commerce App Feasibility on its Usage

The usability of Q-commerce platforms is influenced by multiple interdependent factors, each of which contributes to enhanced user interaction, satisfaction, and long-term engagement. Among these, the most critical elements are learnability, efficiency, accuracy, and utility, all of which shape the overall user experience. Collectively, these components streamline interactions, making it easier for consumers to complete transactions while reinforcing positive user perceptions of the platform (Hutahaean et al., 2024). Among the most important factors in the adoption of Q-commerce platforms - is the perceived ease of use, which directly affects a person's willingness to work with a platform. Users will be more inclined to adopt and continue using a platform they find easy to operate. Furthermore, trustworthiness, security, and transparency build transactional trust and are important in affecting consumer purchasing intentions and long-term loyalty toward the platform. (Peiris et al., 2015). Apart from ease to use and trust, factors such as content quality, functional quality, and privacy protection mechanisms directly affect user satisfaction and their continuous use intention. Therefore, a high quality, well-structured interface improves the involvement of customers (Xu & Huang, 2023). Ensuring a well-designed, efficient, and user-friendly interface can significantly increase engagement levels, driving higher adoption rates and long-term consumer retention. At the same time, platforms must address challenges related to infrastructure limitations such as limited warehousing space, high real estate costs, traffic congestion, delivery radius constraints, and rider shortages, competition, and evolving user demands to maintain their market position and achieve sustained growth in the industry (Singh, 2024). Ultimately, effective app feasibility is not just about facilitating smooth transactions but also about creating a holistic and rewarding customer experience.

Thus, the hypothesis framed here is:

H₀: The difficulties encountered while using Q-commerce apps do not affect how frequently customers use them.

Q-Commerce vs. E-Commerce Pricing

The formation of consumer price expectations through historical pricing patterns and perceived value among consumers has been one of the important determinants of Q-commerce pricing strategies. When consumers remember that the price of a product was higher in the past, they are likely to create a pessimistic expectation about the price, which in turn reduces their search efforts and purchase intentions. In response to that, sellers must use appropriate discretion in changing prices (Yuan & Han, 2011). Consumer expectations and behaviours are critical in determining prices in quick commerce (Q-commerce) as opposed to traditional commerce. Q-commerce, highlighted by its focus on speed and convenience, serves to further enhance these consumer expectations regarding the service quality and fulfilment speed. Such high demand for instant access to goods, forces sellers into dynamic pricing to cope with competition and respond to changing consumer behaviour. (Singh, 2024). Consumers rely on heuristics to render about price, so their perception becomes greatly exaggerated for even a small price change. Price-sensitive consumers tend to exhibit greater reactivity to increases than decreases of the same magnitude, thus making it difficult for sellers to unchanging price policy across different platforms. This effect adds to the difficulties that businesses face in managing the cost perceptions of their consumers while at the same time keeping profitability intact (Lowe et al., 2012). In addition to consumer behaviour, the overall competitive environment of Q-commerce creates competitive pricing pressures. In an extremely dynamic and fast-paced environment, a company must weigh the trade-off between affordability in its operation with the actual demand of the pricing strategies in satisfaction with quality-of-service provision. This aspect of pricing provides Q-commerce providers the benefit to adjust their pricing models in real time; thereby improving their competitiveness in providing consumer satisfaction based on real-time demand insights. (Norby et al., 2016). From this following hypothesis is framed:

H0: Differences in perceived pricing between Q-commerce and E-commerce do not have significant influence Q-commerce usage.

Importance of Delivery Speed

Goyal, 2024 in his paper states that the increasing emphasis on speed and immediacy in quick commerce radically affects consumer behaviour and purchasing decisions-especially among the younger consumers such as millennials and Gen Z. The increasing pace of urbanization makes consumers expect faster delivery services more than ever as they take advantage of the technological advancements and new business models, including dark store fulfilment. A dark store islike a warehouse, solely for online order fulfilment, including inventory handling, picking, packing, and delivering of goods. This model of fulfilment attempts to address the demand for instant delivery while meeting any other consumer expectation that the products should be available with minimal waiting time. In addition, consumers were said to prefer easy and quick shopping transactions on these platforms (Singh, 2024). Advanced logistics and predictive stocking methods greatly enhance the attractiveness of quick commerce in terms of product availability and service level maintenance. Hence, an important variable affecting repurchase behaviour is delivery time, which assumes an even greater significance in cases whereby goods are delivered later than perceived. Late deliveries have a far more negative influence than deliveries ahead of time, rendering a need for operational efficiency and reliable delivery on the part of the companies involved (Harter et al., 2024). In most cases, consumers who suffer delays will see the service as unreliable, thus reducing their chances of return to the platform for future purchases. In contrast, platforms that meet or exceed delivery expectations earn loyalty and repeat transactions. In order to survive competition, firms within the quick commerce sector must keep upgrading their delivery operations and customer experience. The positive correlation between timely deliveries and repeat purchases highlights the need for logistics optimization, improved real-time tracking capabilities, and reduced fulfilment errors. (Pratiwi & Ananda, 2024). As fast commerce keeps transforming the modern consumer behaviour, retail businesses are expected to be agile and employ data-driven strategies to adhere to changes in customer expectations and maintain engagement for the long haul. Therefore the hypothesis here is:

H₀: The perceived importance of delivery speed does not have significant impact on Q-commerce usage.

Likelihood to Recommend

Kapoor et al. (2023) states that service experience and operational efficiencies in Q-commerce highly affects customer retention. It revolutionizes customer expectations with its rapid turnaround capability so that he or she is more satisfied and loyal due to app design and security assurance positively affecting the likelihood to recommend the app to another. Also, improving the delivery and return handling speeds helps reduce the rates of return of the products as well as enhance the satisfaction of the customers, which in turn increases the chances of retaining customers (Cao et al., 2023). Furthermore, factors such as website usability, product offerings, and customer service quality also play critical roles in fostering customer loyalty in the e-commerce landscape as is also mentioned in the above points (Rahman 2023). Collectively, these elements underscore the importance of q-commerce in driving customer retention through improved service quality and operational effectiveness, which in turn leads to them recommending the platform to another. The hypothesis here is:

H0: The customers’ willingness to recommend a Q-commerce platform does not affect their own usage frequency.

Methodology

A quantitative research design was used in this study. The survey-based data collection approach set out to procure data from 304 respondents in Mumbai about their Q-commerce usage pattern, preferences, and influencing factors, out of which 300 responses were considered appropriate. The impact of variables was assessed using a Multiple Linear Regression (MLR) model. This statistical approach allowed a comprehensive study of factors impacting Q-commerce usage and provided a data-backed approach to understanding the core factors of adoption as well as usage frequency. The significance of each factor was determined using p-values (<0.05 considered significant) and R² values (indicating explanatory power). Factors with low p-values showed a strong impact on usage, while high p-values suggested no significant effect. This method helped quantify the relationship between customer experience, challenges, and other variables in quick commerce.

Data Analysis, Findings and Results

Q-Commerce Usage Patterns, Ordering Preferences, and Customer Satisfaction

Survey data collected was processed using Microsoft Excel. Percentage calculations were performed to determine the distribution of responses across various categories Tables 1-6.

Table 1 Product Category Preferences
Category	Percentage (%)
Snacks and Beverages	62.79%
Groceries & Other Essentials	16.61%
Groceries Alone	10.63%
Other (Mixed item combinations)	9.97%

Table2 Frequency of Q-Commerce Usage
Frequency of Usage	Percentage (%)
1–2 times per week	73.42%
3–5 times per week	25.35%
Daily	1.23%

Table 3 Customer Service Ratings
Rating (out of 5)	Percentage (%)
5/5	28.9%
4/5	68.11%
3/5	2.66%
2/5	0.33%
1/5	None
0/5	None

Table 4 Platform Preferences
Platform	Product Type	Percentage (%)
Q-commerce	Convenience Goods	90%
E-commerce	Shopping Goods	68%
Retail Stores	Specialty Goods	75.67%

Table 5 Payment Method Preferences
Payment Method	Percentage (%)
Digital Payment Platforms	77.41%
Cash on Delivery (COD)	22.59%

Table 6 Table of Coefficient
Factor	Coefficient	Standard Error	P-Value (Significance)	R2 (Explanatory Power)	Meaning
Overall Customer Rating	0.35029	0.02988	2.4363×10−26	0.3163	Directly influences usage frequency
App Feasibility	-0.08056	0.01515	2.0765×10−7	0.08692	Challenges reduce Q-commerce usage
Pricing Insights (Q-commerce vs. E-commerce)	-0.04805	0.03464	0.1665	0.006434	People don’t care much about price differences
Importance of Speed & Convenience	-0.02072	0.07301	0.7768	0.0002710	Fast delivery doesn’t drive higher usage
Likelihood to Recommend Q-commerce	-0.01672	0.05898	0.7770	0.0002705	Recommending doesn’t mean more personal usage

One can realize that majority of quick commerce orders are for Snacks and Beverages, accounting for 62.79% of total purchases, making them the most popular category. Groceries & Other Essentials follow at 16.61%, while Groceries alone make up 10.63% of orders; remaining responses included a combination of items.

A dominant share of respondents (73.42%) uses Q-commerce 1-2 times per week, indicating that most consumers rely on these platforms for occasional or emergency purchases. A smaller yet significant portion (25.33%) uses Q-commerce more frequently, around 3-5 times per week. Only 1.23% of respondents report daily usage, highlighting that while Q-commerce is convenient, it is not yet a primary shopping method for most users.

Most respondents (68.11%) rated the customer service of Q-commerce platforms available to them as 4 out of 5, indicating a high level of satisfaction. Additionally, 28.9% gave a perfect 5/5 rating, reflecting exceptional service experiences. A smaller portion (2.66%) rated it 3/5, suggesting moderate satisfaction, while only 0.33% of respondents gave a 2/5 rating, indicating minimal dissatisfaction. Overall, customer service in Q-commerce is well received, with most users expressing positive feedback. None of the respondents gave 1or zero rating.

Consumer preferences vary across different shopping channels based on the nature of the products. 90% of respondents use Q-commerce for convenience goods, probably because these items are needed frequently and require quick delivery. 68% prefer E-commerce for shopping goods due to the availability of a wide range of options, price comparisons, and the convenience of home delivery. Additionally, 75.67% rely on retail stores for specialty goods, as these products often require personal inspection, brand trust, or expert guidance before purchase. This pattern reflects how different shopping modes cater to specific consumer needs efficiently. This breakdown shows that Q-commerce leads in immediate, everyday needs, while planned and high-value purchases lean toward E-commerce and physical retail.

Most of the respondents 77.41% prefer and use digital platforms when using q-commerce while 22.59% of the respondents prefer cash on delivery. The availability of digital payment options is likely to encourage higher engagement and repeat usage. It can be noted that frictionless payment system enhances the q-commerce usage.

Overall Customer Rating

Result: Highly Significant (p=2.43633×10⁻²⁶, moderate-strong impact, directly influences usage frequency).

Hence, we fail to accept H0 - There is no significant relationship between overall customer rating and the frequency of Q-commerce usage.

Impact of Q-commerce App Feasibility on its Adoption

Result: Significant (p = 2.0765×10⁻⁷, low-moderate impact, challenges reduce usage).

Hence, we fail to accept H0 - The difficulties encountered while using Q-commerce apps do not affect how frequently customers use them.

Q-Commerce vs. E-Commerce Pricing

Result: Not Significant (p = 0.1665, very low impact, price differences are not a major concern).

Hence, we accept H0 - Differences in perceived pricing between Q-commerce and E-commerce do not have significant influence Q-commerce usage.

Importance of Delivery Speed

Result: Not Significant (p = 0.7768, very low impact, fast delivery is not a major driver of usage).

Hence, we accept H0 - The perceived importance of delivery speed does not have significant impact on Q-commerce usage.

Likelihood to Recommend

Result: Not Significant (p = 0.7770, very low impact, recommending does not translate to increased personal usage).

Hence, we accept H0 - The customers’ willingness to recommend a Q-commerce platform does not affect their own usage frequency.

Findings

This study outlines the critical drivers of Q-commerce adoption and the frequency of use of Q-Commerce apps. Overall customer rating and feasibility in using the app have been identified as the most important drivers with customer satisfaction, app design, security, and order fulfilment coming into play for engagement efforts. In contrast, usability barriers would further worsen usage, which indicates that users would be put off by poor navigation and inefficient search functions, emphasizing the need for robust digital experiences.

Surprisingly, price perception and delivery speed do not significantly influence Q-commerce usage, suggesting that consumers prioritize convenience and reliability over affordability and ultra-fast delivery. Additionally, likelihood to recommend a platform does not necessarily translate to increased usage, meaning frequent users may rely on Q-commerce out of necessity rather than preference.

The findings of this study also indicate that frequent engagement was that of digital payment users as compared with cash on delivery (COD) users, thus the idea that frictionless payment experiences generate routine purchase is confirmed. Other than this, snack, beverage, and grocery items make up most orders in this regard, supporting convenience and impulse as the primary motives for Q-commerce usage. One can conclude that with investments in technological improvement, personalized engagement, and reliable services, Q-commerce can strengthen its consumer retention in the long run.

Limitations

The study is limited to the respondents residing in Mumbai, thus insights may not be fully applicable to cities with different consumer behaviours, infrastructure, and economic conditions. Study analyses variable such as overall customer rating, the impact of Q-commerce app feasibility, Q-commerce vs. E-commerce pricing, delivery speed, and likelihood to recommend, there can be other factors influencing adoption of Q-commerce, which are not considered here. The data interpretation is based on quantitative analysis, limiting a deeper understanding of why customers prefer certain platforms or how they perceive specific features beyond numerical data. A qualitative approach, such as interviews or focus groups, could provide richer insights. In the survey responses, there is a possibility of response bias, where participants may overstate or understate their preferences, satisfaction levels, or purchasing behaviours.

Suggestions

To enhance platform usability and improve the overall customer experience, optimizing app navigation, search functionality, and checkout processes is essential to minimize friction. Additionally, integrating AI-driven personalized recommendations can further engage users by offering tailored product suggestions, making the shopping experience seamless and efficient.

To diversify product offerings and expand market reach, introducing premium, organic, or international product selections can cater to specific consumer preferences. Additionally, collaborating with local businesses and brands to offer exclusive products can enhance differentiation and attract a broader customer base.

To improve customer engagement and retention, implementing a gamified rewards system can incentivize frequent purchases by offering points for referrals and repeat orders. Incentivising subscription-based delivery plans for essential items can provide both convenience and cost savings, encouraging long-term customer commitment. Additionally, conducting regular customer feedback surveys and offering real-time support can help identify areas for improvement and enhance platform performance. By adopting these strategies, Q-commerce platforms can boost customer satisfaction, streamline operations, and foster long-term consumer loyalty in a highly competitive market. Regulatory framework plays a crucial role to address the issues such as data protection, consumer grievances, q-commerce players to be responsible for the correctness of the statements made relating to the goods and services provided by them, cyber laws to protect the cyber-crimes specially related to digital payment methods.

Examining eco-friendly delivery solutions, such as electric vehicles and sustainable packaging, to minimize the environmental impact of rapid deliveries, which can help in achieving sustainability goals. Future scope of study calls for research, which expands the investigation beyond Mumbai, i.e. conducting a comparative study across different cities in India to understand regional variations in Q-commerce adoption and other challenges.

References

App Supply Management. (2025). Quick Commerce companies in India, https://www.aajenterprises.com/quick-commerce-companies-in-india/

Azizi, F. F. N., Aknuranda, I., & Tolle, H. (2020). From e-Commerce Overall Quality to e-Loyalty: A Purchase-centred Framework Journal of Information Technology and Computer Science, 5(1), 86-103.

Indexed at, Google Scholar, Cross Ref

Cao, Y., et al. (2023) A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

Indexed at, Google Scholar, Cross Ref

Devi, K., & Indoria, D. (2021). Digital Payment service in India: A review on Unified Payment Interface.

Goyal, A. K. (2024). Key Factors Driving the Rapid Growth of Quick Commerce in Urban Areas of India. International Journal for Multidisciplinary Research, 6(6).

Cross Ref

Harter, A., Stich, L., & Spann, M. (2024). The Effect of Delivery Time on Repurchase Behavior in Quick Commerce. Journal of Service Research.

Indexed at, Google Scholar, Cross Ref

Hutahaean, H. A., Ruwaida, A., Adnyani, N. L. S. S., Govindaraju, R., & Sudirman, I. (2024). Incorporating Functional Quality into Usability Model of E-Commerce Application. TEM Journal, 1889–1904.

IBEF. (2024), Economic Survey 2023-24, https://www.ibef.org/economy/economic-survey-2023-24

Kapoor, A., Sindwani, R., & Goel, M. (2023). Exploring quick commerce service experience: a moderated mediated investigation using SEM and fsQCA. Total Quality Management & Business Excellence, 1–24.

Kircova, I., & Kazemzadehazad KURT, U. (2023). Experience and Loyalty Relationship in Online Quick Grocery Shopping by the Mediating Role of Customer Satisfaction and Service Experience Consciousness. Isletme, 4(2), 190–210.

Indexed at, Google Scholar, Cross Ref

Lowe, B., Lowe, J., & Lynch, D. (2012). Behavioral aspects of pricing (pp. 349–369). Routledge.

Cross Ref

Magdalena, N. N., Princes, E., & Hidayat, D. (2024). Unlocking Customer Loyalty: A Study of User Experience in Tokopedia. IEEE, 1–6.

Modgil, S., Dwivedi, Y. K., Rana, N. P., Gupta, S., & Kamble, S. (2021). Has Covid-19 accelerated opportunities for digital entrepreneurship? An Indian perspective. Technological Forecasting and Social Change, 175, 121415.

Nahata, K. (2022). How Quick Commerce is changing the E-commerce Landscape in India. Entrepreneur India. https://www.entrepreneur.com/en-in/news-and-trends/how-quick-commerce-is-changing-the-e -commerce-landscape-in/429915

Norby, V., Mintun, W., Raub, P., & Mann, N. (2016). Consumer behavior-based dynamic product pricing targeting.

Google Scholar

Patil, Uma, Sandoval, Lazaro and Sawant, Arundhati. (2021). COVID-19 Accelerates the Growth of India's Online Grocery Market.

Google Scholar

Peiris, P., Kulkarni, D., Mawatha, C. (2015). An Empirical Study of Customer Adoption of E-Commerce: A Customer Trust Model to Support the Adoption of E-Commerce among Small- and Medium-Sized Enterprises in Sri Lanka. International Journal of Business and Information, 10(4), 491.

Indexed at, Google Scholar, Cross Ref

Pratik, Kshitiz and Arora, Pallavi. (2022). India Quick Commerce Market Outlook to FY’27F - Driven by Increasing demand for Faster Delivery & Convenience and Shifting Customer behavior towards Unplanned Purchases. Ken Research. Retrieved from https://www.kenresearch.com/consumer-products-and-retail/wholesale-and-retail/india-quickcommerce-market-outlook-to-2027/561418-95.html#details

Pratiwi, E. R., & Ananda, S. D. (2024). Consumer Behavior in Making Purchasing Decisions on the Shopee Program On-Time Guarantee. International Journal Administration, Business & Organization, 5(5), 24–35.

Indexed at, Google Scholar, Cross Ref

Rahman, M. (2023). 2023's Top ecommerce sites in Bangladesh [By User Behavior]. SEO Agency. https://seoaudit.agency/ecommerce-site-bangladesh/

Singh, R. (2024). The Impact of Quick Commerce on Consumer Behavior and Economic Trends in India: A Systematic Review. International Journal of Social Science and Economic Research, 09(08), 2859–2874.

Cross Ref

Stojanov, Michal. (2022). Q-COMMERCE – the next generation e-commerce.

Google Scholar

Suganda, U. K., & Priadi, Y. (2023). Improving customer loyalty through customer experience, price attractiveness, and customer relationship management. Journal Manajemen Industri Dan Logistik, 7(1), 195–204.

Indexed at, Google Scholar, Cross Ref

Xu, Y., & Huang, F. (2023). E-commerce Platform Quality, User satisfaction and Continuous Use Intention under Data-driven marketing.

Indexed at, Google Scholar, Cross Ref

Yuan, H., & Han, S. (2011). The Effects of Consumers’ Price Expectations on Sellers’ Dynamic Pricing Strategies. Journal of Marketing Research, 48(1), 48–61.

Indexed at, Google Scholar, Cross Ref

Received: 16-May-2025, Manuscript No. AMSJ-25-15936; Editor assigned: 17-May-2025, PreQC No. AMSJ-25-15936(PQ); Reviewed: 31-May-2025, QC No. AMSJ-25-15936; Revised: 15-Jun-2025, Manuscript No. AMSJ-25-15936(R); Published: 25-Jun-2025