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Article

Navigating the Digital Shift: How Indian LOOROs Coped Amid COVID-19

by
Anasuya K. Lingappa
1,
Bhaavya Maheshwari
2 and
Asish Oommen Mathew
1,*
1
Department of Humanities & Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
2
Department of Electronics & Communication, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
*
Author to whom correspondence should be addressed.
COVID 2026, 6(1), 12; https://doi.org/10.3390/covid6010012
Submission received: 13 October 2025 / Revised: 6 December 2025 / Accepted: 10 December 2025 / Published: 6 January 2026
(This article belongs to the Section COVID Public Health and Epidemiology)
Browse Figures
Versions Notes

Abstract

Local Owner-Operated Retail Outlets (LOOROs) in India faced unprecedented disruption during the COVID-19 pandemic, with digital transformation emerging as both a challenge and an opportunity. The growing dominance of larger online and offline competitors, who swiftly adopted digital payments, posed a threat to traditional business models of these small neighborhood retailers. This study employs the Stimulus–Organism–Response (S-O-R) framework to examine the antecedents shaping LOORO owners’ attitudes toward digital payment practices and how these attitudes influence their intention and actual adoption. A survey of 175 LOOROs in Navi Mumbai was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that resource availability and customer care significantly influenced adoption, whereas competitor and customer pressure had little effect. Overall, LOORO owners demonstrated a positive outlook toward integrating digital payment systems, indicating their adaptive capacity to navigate the digital shift accelerated by the COVID-19 pandemic.

1. Introduction

Digitalization, a knowledge-based and data-driven activity enabled by technology and communication networks, has transformed lifestyles globally [1]. This transformation has triggered a major shift, as evidenced by the gradual move from cash to digital payments [2]. At the individual level, most services, ranging from utility bills to banking and travel bookings, are now accessible and paid through mobile applications. However, despite India’s vast internet user base of 692 million [3], many still prefer to use cash, with reluctance toward digital adoption [4]. Recent studies have examined this issue through the lens of India’s digital payment system infrastructure and adoption dynamics [5].
The Micro, Small, and Medium Enterprises (MSME) sector is India’s second-largest employment generator [6], with Local Owner-Operated Retail Outlets (LOOROs), also known locally as Kirana stores, being a crucial segment of this sector. Kirana stores are neighborhood retail stores, typically under 500 sq. ft., offering convenience and familiarity [7]. LOOROs have been slow to adopt digital solutions, despite technology offering a solution to regain competitiveness [8]. However, competitive pressures and customer expectations demand digitalization [9,10].
The sudden, unexpected onset of COVID-19 pandemic amplified these pressures. Lockdowns, supply chain disruptions, and reduced foot traffic during the pandemic accelerated the use of the internet [11] and smartphone usage in emerging economies [12]. Simultaneously, digital payment practices in India gained substantial popularity and momentum during this period [13]. In a volatile context like the pandemic, contactless digital payments emerged as an important way out to thwart the spread of the virus [14]. Social distancing and concerns over cash as a potential medium for contracting the virus led to increased digital payment adoption [15,16]. Especially for LOOROs, their small shop sizes make digital payments crucial for managing transactions quickly.
Despite increasing research on digital payments in India, few studies have explored digital adoption from the perspective of small, owner-operated retailers in the setting of the COVID-19 pandemic. Most existing work is customer-focused [17] and neglects informal, small enterprises such as LOOROs. Studies rarely analyze how behavioral factors, such as empathy, customer/competitor pressures, or resource constraints, influence digital payment adoption from the standpoint of LOORO owners. Additionally, the drivers of adoption identified before the pandemic may not fully apply to the disrupted COVID-19 environment. This highlights a significant gap in understanding the unique adoption dynamics within India’s LOORO segment, which remains an understudied yet economically vital part of the MSME sector.
Our study addresses this gap by empirically analyzing data from LOORO owners in Navi Mumbai, a commercial hub with over a million residents. Its unique contribution lies in shifting the focus from customer-centric studies to exploring the perspectives of LOORO owners during the peak of the COVID-19 pandemic. Specifically, this study investigates the impact of Available Resources (AR), Customer Pressure (CP), Perceived Pressure from Competition (PPC), and Empathy (EMP) on Attitude (ATT), Intention to Adopt (ITA), and Current Use of Digital Payments (CU). This study is structured around the following questions:
○
RQ1: How do available resources, customer pressure, perceived pressure from competition, and empathy influence LOORO owners’ attitudes toward adopting digital payments?
○
RQ2: How does attitude influence intention to adopt digital payments?
○
RQ3: Does intention to adopt digital payments translate into current use among LOORO owners?
This study aligns with the United Nations Sustainable Development Goals, specifically SDG 8 and SDG 9. Section 2 reviews the existing literature and theory. Section 3 formulates the hypotheses; Section 4 describes the methodology and results. Section 5 offers a discussion. Finally, Section 6 provides the implications, limitations & future scope.

2. Literature Review

2.1. LOOROs and Digital Payment Scenario in India

According to [8], a retail store can be categorized as a LOORO if it is an independent local shop where the owner manages daily operations. It typically sells FMCG products and operates during regular business hours, at least five days a week. LOOROs suffered greatly during the COVID-19 pandemic owing to their small size and physical nature. In such situations, digital payments were seen as a way to boost consumer traffic and enable LOORO owners to respond to consumers more quickly [17].
LOOROs form a significant part of MSMEs in India, and among the 63 million MSMEs in the country, 99% are micro-enterprises. Only 5 million MSMEs accept digital payments [18]. The retail industry in India accounts for approximately $790 billion, with the unorganized retail sector accounting for $695 billion [19]. This indicates the vast untapped potential of digital payments in this sector. The Indian government has been making focused efforts, such as the Digital India initiative, to promote digital payments, aiming to reduce the country’s cash dependency, thereby reducing corruption and enhancing transparency [20,21]. Many people who previously lacked access to banking facilities were able to open bank accounts with the widespread introduction of the Pradhan Mantri Jan Dhan Yojana. Additionally, the Ministry of Electronics and Information Technology launched the ‘Digi Dhan Abhiyan’ campaign to encourage cashless transactions.

2.2. Stimulus–Organism–Response Model (S-O-R) Model

Several theoretical frameworks have been used in prior studies to investigate the consumers’ adoption of new technology. Based on the critical review conducted by Dahlberg et al. [22] and Pal et al. [23], studies in the domain of digital payments and Fintech are dominated by established theories such as the technology acceptance model (TAM) [24], the unified theory of acceptance and use of technology (UTAUT/UTAUT2) [25,26] and the diffusion of innovation (DOI) theory [27]. TAM is widely accepted [28] because it incorporates key factors such as perceived usefulness and ease of use [29]. Additionally, UTUAT/UTUAT2 was found to be extensively used in studies on consumer behavior in the FinTech domain [30,31]. Other prevalent frameworks include the theory of planned behavior (TPB) [32], theory of reasoned action (TRA) and task–technology fit (TTF).
Although several researchers turned to well-established frameworks, many others introduced new constructs because of circumstantial importance [23]. In the case of digital payments during COVID-19, especially in a developing country like India, context plays a critical role. In times of crisis, digital payments do not merely involve the acceptance of a new technology. For LOORO owners, contextual factors such as lockdowns, fear of contagion, adherence to social distancing rules, financial vulnerability and concern for customer well-being compelled them to look beyond their traditional practice of preferring cash payments. To incorporate these contextual and crisis-driven issues, adoption of a holistic and flexible approach, such as the S-O-R framework, is essential.
This study is based on Mehrabian and Russell’s Stimulus–Organism–Response (S-O-R) model [33]. The S-O-R model was thought to be better suited for our study, as it explicitly includes internal emotional states (organism) triggered by external stimuli, providing an important understanding of psychological responses of LOORO owners during a crisis like the pandemic. This was particularly true for small retailers whose adoption of digital payments during the pandemic may be due to fear of contagion and changed social conditions [28,34]. Although digital payments existed before the onset of the COVID-19 pandemic in India, their rapid adoption may have been accelerated for external reasons. These drivers precede internal cognitive evaluations (attitudes towards digital payments), resulting in behavioral responses (intent to adopt and use). The S-O-R model has been used by prior researchers in various contexts, such as mobile banking [35], mobile applications [36], green marketing and innovation [37], and drone deliveries [38], to name a few. Several studies have also applied the theory to explain digital payment adoption, including P2P payments [39], facial recognition payments [40], digital payments [41] and WhatsApp payments [42]. These examples demonstrate the versatility and adaptability of the S-O-R framework.

2.3. Available Resources

LOOROs, often limited by scarce external and internal resources, frequently struggle to invest in digital tools that could provide them with a competitive edge. Poor physical and digital infrastructure, limited access, unreliability of digital technologies, and high costs hinder the adoption of digital tools by small retailers [10]. A study in Jaipur, India [43], found that about 60% of the sample had not adopted any form of digital payment, highlighting the low adoption rate among small businesses. Another study in Bangalore indicated that only 48% of small merchants accepted digital payments, suggesting that 52% avoided digital platforms [44]. The reasons for low digital payment adoption include reliance on cash transactions, high operating costs, and a lack of digital skills. Challenges such as low cash flow and additional charges associated with digital payments further complicate small merchants’ attitudes towards digital payment systems. Although LOOROs have some control over their internal resources, certain external factors may restrict their ability to adopt digital technologies [45,46]. Consequently, we view available resources as the primary stimulus, reflecting how much a LOORO perceives resource constraints as limiting their capacity to adopt digital payments [47].

2.4. Customer Pressure

Customer pressure is considered a significant predictor of fintech adoption among small retailers, mainly due to the increasing expectation for efficient, convenient and trackable transactions that digital technology can provide [48]. Customer demand hastens the uptake of Industry 4.0 technologies, underscoring the decisive role of customer involvement in digital technology adoption [49,50]. The relationship between customer pressure and digitalization is complex and affects various aspects of business operations and strategies. Given their high levels of customer interaction, LOOROs are particularly influenced by the need to respond to evolving customer expectations and pressures [51,52].

2.5. Perceived Pressure from Competitors

The perceived pressure from competitors is evident in how businesses view their development in relation to others and how they anticipate external pressures to maintain a competitive advantage [45,53]. According to Porter and Millar [54], adopting innovation can enable organizations to shift competition norms. This pressure is evident in many sectors where digital transformation is essential for improved performance. Therefore, increased adoption of digital technologies often occurs in industries, such as LOOROs, where competitiveness is intensifying [55].

2.6. Empathy

Empathy is the ability to share the emotions or experiences of another person by imagining what it would be like to be in their shoes [56]. Empathy is crucial in retail for understanding user needs. LOORO owners need to be able to put themselves in the user’s shoes to grasp their perspective [57]. During the COVID-19 pandemic, when lockdowns were enforced and limited shopping was permitted under strict social distancing guidelines, customer experiences were reshaped by the sensitivity of businesses that demonstrated empathy and showed care and concern [16]. Therefore, the fourth stimulus considered in this study is empathy towards customers.

3. Hypothesis Development

3.1. Influence of Availability of Resources on Attitude

One of the factors influencing the adoption of digital payments by LOOROs is resource availability, which encompasses a business’s ability to invest in digital infrastructure and the knowledge required to operate and perform digital payments [10]. Alamsyah et al. [58] found that adequate infrastructure facilitates the use of digital payment methods. Golitsis et al. [59] identified inadequate payment infrastructure as a significant barrier, suggesting that resource availability may be crucial for the successful implementation of digital payments. Furthermore, the affordability of fees on digital platforms and the merchants’ digital literacy help overcome obstacles to adoption. Therefore, we propose the following hypothesis:
H1. 
The availability of resources has a positive influence on the attitude towards digital payment methods.

3.2. Influence of Customer Pressure on Attitude

Customer pressure relates to the perception of customers’ expectations of digitalization [60]. Customer expectations can influence retail businesses to adopt technology for digitalization [52]. To maintain a competitive position, LOOROs are more likely to adopt payment technologies and innovations when they perceive pressure from their customers [61]. According to Kumar et al. [62], 98.1% of respondents in Bengaluru’s suburban area preferred digital wallet services. As more people opt for digital payment methods, LOOROs are compelled to adopt digital payments. Therefore, we can hypothesize that,
H2. 
Customer pressure towards digitalization has a positive influence on the attitude towards digital payment methods.

3.3. Influence of Perceived Competitiveness Towards Customers on Attitude

During the COVID-19 pandemic, an increasing number of people sought contactless payment options, and digital solutions such as UPI payments became more convenient [63,64]. During the crisis, many businesses had no choice but to switch to digital payments as their competitors offered contactless payment options. However, small stores are generally slower to adopt digital technologies because of low digital literacy and transaction costs [65]. Hence, LOOROs were forced to explore digital payment options to remain competitive [10]. This change is part of a larger trend in which digital payments are no longer optional but necessary. Hence, we hypothesize:
H3. 
Perceived Competitiveness towards digitalization has a positive influence on the attitude towards digital payment methods.

3.4. Influence of Empathy Towards Customers on Attitude

An organization’s ability to provide services tailored to customer needs is linked to empathy [56]. Understanding customers’ expectations, as noted by Bahadur et al. [66], aligns with a shop owner’s empathy, as it helps them anticipate their needs. Previous studies have shown that customer satisfaction increases when frontline service staff demonstrate empathy and care [67]. Conversely, a lack of empathy has been found to affect customer engagement negatively [67,68,69]. Therefore, we propose the following hypothesis.
H4. 
Empathy towards customers has a positive influence on the attitude towards digital payment methods.

3.5. Influence of Positive Attitude Towards Digitalization on the Intention to Use Digital Payment Methods

The four dimensions of the Stimulus, namely available resources, perceived pressure from competitors, customer pressure, and empathy, cause the organism’s (emotional) reactions, influencing the owner’s attitude toward digital payment options [47]. Although attitude does not directly influence actual behavior, it affects usage through the behavioral intention to use digital payments [26,32]. Therefore, the owner’s attitude toward digital payment options shapes their intent to use them [70]. To support this argument, we propose the following hypothesis.
H5. 
Attitude towards digitalization has a positive influence on the intention to adopt digital payment methods.

3.6. Influence of Intention to Use Digital Payment Options on Actual Usage

Prior studies indicate that the intention to use influences actual usage behavior [26,32,71]. For example, a study conducted during the demonetization period in India revealed that behavioral intention significantly influenced the use of digital payments [72]. In line with the S-O-R framework, once the organism is exposed to stimuli, the actual behavior of the organism, that is, the LOORO owner, is affected by the behavioral intentions they form. Therefore, it directly impacts the usage of digital payments [47]. Hence, we hypothesize:
H6. 
The intention to adopt digital payment methods has a positive influence on the current usage of digital payment methods.
The conceptual model developed for this study is shown in Figure 1.

4. Methods

4.1. Research Instrument

A quantitative method was used in this study. Cross-sectional data were collected in person through a questionnaire survey. Structural equation modeling (PLS-SEM) technique was utilized to validate the measurement model and test the hypotheses. Validated questions from prior studies were included in the questionnaire used in this research. Additionally, the content validity of the questionnaire was assessed by experts in the field. The reflective constructs were measured using a survey questionnaire (Table 1) on a five-point Likert scale. Before conducting the primary survey, a pilot test was conducted with 20 LOORO owners to evaluate the clarity, reliability, and understanding of the items. Minor wording changes were made based on their feedback before the final version of the questionnaire was released.

4.2. Data Collection

Data was collected by personally visiting the kirana stores (LOOROs). Convenience sampling was used because there was no formal or usable sampling frame for LOOROs, and mobility restrictions during the COVID-19 period made probability-based sampling impractical. Survey forms were distributed in the areas of Nerul, Seawoods, Sanpada, and Vashi in Navi Mumbai, Maharashtra, India. The criteria used for selecting the shops to be surveyed are that the establishment must be independently owned, with the owner actively involved in day-to-day store operations. The LOORO owner was allowed to respond in either Hindi (the local language) or English. The questionnaire went through a thorough translation and validation process. The original English version was translated into Hindi, following Sperber’s [74] forward–backward translation guidelines, to ensure semantic and conceptual equivalence. Two bilingual experts reviewed the translations, and any discrepancies were resolved through consensus. Content validity was further confirmed by three domain experts who assessed item clarity, relevance, and alignment with construct definitions. The survey was conducted over four months, starting in May 2021, during the aftermath of India’s severe second COVID-19 wave. During this period, mobility restrictions, increased health concerns, and reliance on contactless transactions remained significant, directly influencing the perceptions and adoption of digital payment practices among LOORO owners.
A total of 175 completed responses were analyzed for the study, and 42 shops either refused to participate or did not respond, resulting in a response rate of 80.65%. A mean replacement algorithm was used to handle the missing data. The socio-demographic characteristics of the respondents are shown in Table 2. Ninety-two percent of the respondents were male, and the rest were female. Eighty-four percent of the respondents were between the ages of 18 and 45.

4.3. Statistical Analysis

4.3.1. Measurement Model Analysis

The assessment of indicator reliability depends on outer loadings. Any item with an outer loading below 0.4 should be omitted, while items with outer loadings between 0.4 and 0.7 should only be discarded if doing so improves Cronbach’s alpha and Composite reliability. A Cronbach’s alpha above 0.6 is considered acceptable for explanatory analysis [75], although values between 0.5 and 0.6 indicate moderate reliability [76]. A Composite reliability between 0.6 and 0.7 is acceptable for explanatory analysis, while values between 0.7 and 0.9 are regarded as satisfactory [75]. The outer loading values of AR_4, PPC_2, CP_1, and ATT_5 were below 0.6; therefore, they were removed from the model to ensure good composite reliability and convergent validity. These items showed weak indicator reliability and did not adequately represent their constructs, as maintaining them in a negative state influenced construct reliability. Their removal enhanced the overall measurement quality, leading to higher composite reliability and AVE values for the retained indicators (Table 3).
Composite Reliability (CR) ranged from 0.768 to 0.947, which is well above the threshold value of 0.70. Convergent validity is the extent to which two measures of a construct that are theoretically related are, in fact, related [77]. An average variance extracted (AVE) greater than 0.5 for each construct indicates that the model satisfies convergent validity [78]. The Average Variance Extracted (AVE) for the constructs ranged from 0.526 to 0.856, which is well above the threshold value of 0.50, confirming convergent validity. The results of the measurement model analysis are shown in Table 3.
Discriminant validity assesses whether concepts or measurements that should not be related are indeed unrelated [77]. The questionnaire was tested for divergence using the discriminant validity method, as outlined in the Fornell–Larcker criteria. In the result matrix, the diagonals represent the square root of the AVE values. These diagonal values should exceed any other value in their respective row and column to demonstrate discriminant validity [75] (Table 4).

4.3.2. Structural Model Analysis

The first step in assessing the structural model is to check for collinearity among the constructs. Variance Inflation Factor (VIF) values greater than 3 suggest the possibility of multicollinearity, and values above 5 indicate its presence. All constructs have VIFs below the minimum threshold of 5 and remain under 3, indicating no multicollinearity (Table 5).
Using the bootstrapping algorithm, the path coefficients are calculated from t-statistic values based on 5000 subsamples. Table 6 shows the t-values and path coefficients of the constructs, emphasizing the significant relationships.
The results supported four out of six hypotheses (Figure 2). Available resources (β = 0.412, p < 0.001) and empathy (β = 0.305, p < 0.01) significantly influenced attitude. However, perceived pressure from competitors (β = 0.071, p > 0.05) and customer pressure (β = 0.119, p > 0.05) did not have a significant effect on attitude. Therefore, hypotheses 1 and 4 are supported, while hypotheses 2 and 3 are not. The intention to adopt was significantly affected by attitude (β = 0.758, p < 0.001), supporting Hypothesis 5. Finally, the intention to adopt significantly influenced current usage (β = 0.657, p < 0.001), supporting Hypothesis 6.
The R2 values, also known as the coefficients of determination, indicate the proportion of variance in endogenous constructs that is explained by exogenous constructs [75]. The interpretation of R2 values varies across studies. Values from 0.33 to 0.67 signify moderate influence, while values from 0.19 to 0.33 denote weak influence [79]. Table 7 presents the R2 values obtained. The R2 for Attitude was 0.642, for Intention to Adopt was 0.42, and for Current Usage was 0.627, all suggesting moderate influence on the respective endogenous constructs.

5. Discussion

The study observed a significant influence of available resources on positive attitudes toward digital payment options. These results are consistent with those of Bollweg et al. [80] and Delgado-de Miguel et al. [81]. To accept payments via the Unified Payments Interface (UPI) in India, the most popular e-payment method, LOOROs must have devices capable of running UPI apps, such as Paytm, PhonePe and Google Pay, along with internet connectivity for seamless transactions and scannable QR codes. The results indicate that the availability of such resources affected LOORO’s attitude toward digital payments during the COVID-19 pandemic. This result aligns with prior research, which shows that access to digital infrastructure remains the strongest factor influencing the adoption of digital payment systems [82]. India’s rapid growth in digital payments is closely linked to resource accessibility, driven by policy efforts to expand broadband and digital services for small merchants [83]. This finding also aligns with cross-country studies conducted during the pandemic, which consistently show that resource availability, device readiness, and connectivity significantly impact SME’s digital payment adoption [84,85].
This study found that perceived pressure from competitors and customers did not significantly influence the attitude of LOOROs towards adopting digital payment practices, contradicting Bollweg et al. [80], who found that perceived pressure from competitors and customers was a significant factor. This aligns with Franco et al. [86], who highlight the critical influence of customer pressure in the digitalization of SMEs. Their view finds support in a study conducted by Khan & Ali [53], who found that consumer pressure was a key driver. However, the same study found that competitive pressure had no significant effect on the adoption of digital payment applications. These results must be cautiously viewed in the light of a crisis like the COVID-19 pandemic, where health concerns and convenience dominated adoption decisions rather than social influences or competitive pressure. For example, in China, research conducted during COVID-19 showed that perceived benefits were crucial, suggesting that perceptions were shaped mainly by contexts rather than competitive or social pressure [34]. Similarly, a study in India found that while digital payment adoption during COVID-19 was influenced by factors such as performance expectancy, ease of use, and trust, social factors did not play a role [87]. Bhatia et al. also found that factors such as perceived risk and expectation played more dominant roles in digital payment use during COVID-19, indicating that intrinsic drivers outpaced social pressure drivers [88]. This suggests individuals value risk mitigation and system trust over external social pressure from competitors or customers. Similar mixed or inconclusive results have been reported in other emerging markets, where competition did not consistently promote digital payment adoption among SMEs [89]. These findings could also be attributed to the fact that most respondents have already adopted digital payment options in their businesses; therefore, perceived pressure from competitors and customers does not significantly influence their attitude.
Furthermore, to distinguish themselves from competitors, LOOROs often focus on creating unique customer experiences. Therefore, customer loyalty remains vital, as customers may continue to patronize a specific retail outlet despite the payment options offered by competing LOOROs, prioritizing factors such as familiarity and proximity. Additionally, India’s relationship-based retail culture, where customer loyalty is built on long-term relationships rather than payment methods, diminishes the influence of competitive pressure on the adoption of payment systems.
These contradictory findings may also be related to the Indian cultural context, where customer payment preferences are diverse: younger consumers often expect digital payments, whereas older or less tech-savvy customers prefer cash. Consequently, LOOROs may not face consistent customer-driven pressure to adopt digital payment methods.
Finally, it is also possible that measures for customer and competitive pressures in normal business contexts might not have captured the nuanced pandemic-driven behavioral factors. The pandemic introduced exceptional circumstances like enforced lockdowns and altered consumer priorities, which may have diluted normal pressure effects [90]. Thus, we consider failure to modify measurement scales to include COVID-specific contextual pressures could have led to insignificant findings.
We found that empathy significantly influenced attitudes toward adopting digital payments. During the COVID-19 pandemic, people’s challenges and suffering fostered feelings of empathy for one another. Studies agree that having empathic concern for those most at risk of contracting the virus increases the desire to adopt and uphold social precautions [16,91,92]. They also found that traits such as empathy correlate with the voluntary adoption of social distancing. Since the use of digital payments encourages social distancing, this aligns with the findings of our study. As the pandemic compelled consumers to depend more on contactless payments and e-commerce, local businesses recognized the need to offer digital payment options to their customers, who were concerned about social distancing and safety. Findings from other Asian countries, such as China [84], also suggest that broader prosocial motivations and perceived safety benefits played a role in technology adoption during the COVID-19 pandemic.
The study found positive attitudes towards digitalization, which significantly influenced the intention to adopt digital technologies. This aligns with many previous studies [93,94,95], which have shown that attitude has a strong impact on intent. LOOROs are more likely to implement these technologies because they view digital payments as an effective way to improve customer experience, streamline transactions, and stay competitive. Similar relationships between attitude and intention have been observed among SMEs in other emerging economies that have adopted mobile payments [70].
Furthermore, the results revealed that the intention to provide digital payments has a positive relationship with the current use of such technology. These findings align with those of Bollweg et al. [80]. This is further supported by Chaveesuk et al. [96] and Musyaffi et al. [97]. Comparable evidence from Indonesia confirms that intention is a key determinant of QR-based mobile payment use among MSME merchants [70].

6. Implications, Limitations and Future Scope

6.1. Theoretical Implications

LOOROs in India faced unprecedented challenges due to repeated lockdowns and reduced foot traffic during the COVID-19 outbreak. However, digital payments proved to be a silver lining, helping them manage the crisis as contactless transactions became possible, ensuring safety for both employees and customers. This study captures a critical moment when India adopted disruptive technology, paving the way for transparent and convenient transactions that continued even after the pandemic subsided. Our study offers a unique contribution to the body of knowledge in the context of a densely populated metropolis like Mumbai, capturing the perspectives of LOORO owners during the intense lockdown period and shortly afterwards. It also provides a foundation for researchers to understand the factors from the perspective of a developing economy that supported the acceptance of digital technology amid a crisis.

6.2. Practical Implications

The findings that resource availability significantly influences local LOORO’s adoption of digital payments have important policy implications. First, by educating LOORO owners about the benefits of digital payments, policymakers should focus on initiatives that foster a positive attitude towards digital payments. These initiatives may include awareness campaigns, training sessions, tools, and support needed to improve their knowledge of digital payment technology, which can enhance their chances of adopting digital payments. To build trust and confidence among small businesses and customers alike, governments can establish clear regulatory standards that ensure data privacy and security in digital payment systems. Adoption rates can be significantly increased by offering incentives to firms that use digital payments, such as lower transaction fees or tax benefits.

6.3. Limitations of the Study and Future Scope

Our study has several limitations. First, the study only used data from Navi Mumbai, a single Indian city, which limits the applicability of the findings to other cities or areas where factors influencing digital payment adoption may vary. Second, convenience sampling, although practical during the pandemic, may reduce the sample’s representativeness and introduce regional or socioeconomic bias. While we attempted to keep participants homogeneous and minimize bias, participation primarily depended on proximity and the willingness of LOORO owners. Third, although the sample size was sufficient for PLS-SEM analysis, a larger and more diverse sample could provide more profound insights into the factors influencing digital payment adoption. Additionally, since the questionnaire focused on digital payment practices, social desirability bias may have occurred, with some LOORO owners possibly overstating positive attitudes or behaviors to conform to expected norms. Lack of COVID-contextual scales could have reduced the sensitivity of the measurement and might have contributed to the insignificant results. Finally, most respondents belonged to essential service categories, such as general stores, pharmacies, and food vendors. Owing to travel restrictions, the study was mostly limited to urban neighborhoods.
Developing countries like India are going through rapid digitalization, transforming public mindset towards contactless payments and boosting the national economy. Small businesses such as LOOROs operate within this continuously evolving digital ecosystem. Thus, there is considerable potential for future research. First, there is always a scope to examine new drivers leading to customer intent to adopt digital payments. Some likely drivers include community influence, store loyalty, visibility anxiety, inclusivity and modernity signaling, and word of mouth among consumers, which might impact the adoption of digital payments. Additionally, future studies should consider moderating factors such as education level, digital literacy, and access to technology, which may affect the adoption of digital payment systems. Second, longitudinal studies may be conducted to understand whether the relationship between the factors considered and the adoption patterns observed during COVID-19 persists. Such studies would help us understand whether the crisis-induced findings were temporary reactions or long-term behavioral shifts. Furthermore, researchers could explore the extension of S-O-R theory in other digital payment studies subjected to context-specific stimuli, such as unexpected regulatory or policy changes. As the current study is quantitative, a qualitative/mixed-methods approach could be employed subsequently, involving interviews with LOORO owners to gain a better understanding of their motivation and hesitations in adopting digital payment methods. Understanding the possible discrepancies and associations between constructs in different settings may offer valuable and interesting insights. Hence, comparative review studies between developed and developing countries may be conducted in the future to investigate situation-specific adoption behaviors brought forth by the COVID-19 pandemic.

Author Contributions

Conceptualization, A.K.L. and B.M.; Data curation, B.M. and A.O.M.; Formal analysis, A.K.L., B.M. and A.O.M.; Investigation, A.K.L. and A.O.M.; Methodology, B.M. and A.O.M.; Project administration, A.K.L.; Software, B.M. and A.O.M.; Supervision, A.O.M.; Validation, A.O.M.; Writing—original draft, A.K.L. and B.M.; Writing—review and editing, B.M. and A.O.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was carried out in full compliance with ethical research guidelines. It qualifies for exemption from formal ethical approval as per the institutional circular by the Institutional Ethics Committee (IEC) of Kasturba Medical College and Kasturba Hospital under the Manipal Academy of Higher Education (MAHE) (ECR/146/Inst/KA/2013/RR-19), on 14 December 2021, DHR registration number EC/NEW/INST/2019/374).

Informed Consent Statement

Verbal informed consent was obtained from all survey respondents of the study prior to collecting the data.

Data Availability Statement

Data is available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LOOROLocal Owner-Operated Retail Outlets
S-O-RStimulus–Organism–Response
PLS-SEMPartial Least Squares Structural Equation Modeling
MSMEMicro, Small and Medium Enterprises
ARAvailable Resources
CPCustomer Pressure
PPCPerceived Pressure from Competition
EMPEmpathy
ATTAttitude
ITAIntention to Adopt
CUCurrent Use of Digital Payments
FMCGFast Moving Consumer Goods

References

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Figure 1. Conceptual Model (Author’s own work).
Figure 1. Conceptual Model (Author’s own work).
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Figure 2. Structural Model Results (** p < 0.01, *** p < 0.001).
Figure 2. Structural Model Results (** p < 0.01, *** p < 0.001).
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Table 1. Research Instrument.
Table 1. Research Instrument.
Available Resources—Adapted from [73]
AR1My business is able to invest in digital payment applications.
AR2I have the required knowledge on digital payment applications.
AR3I have sufficient resources for performing digital payment transactions.
AR4I feel that my staff have enough skills to perform digital payment transactions.
AR5My business is equipped to conduct digital payment transactions with the currently available resources.
Perceived Pressure from Competitors—Adapted from [8,73]
PPC1I have to catch up with my competitors when it comes to mobile digital payments.
PPC2I think many of my local competitors are ahead of me in terms of using digital payment options for transactions.
PPC3The growing number of stores adopting digital payments leaves me with no choice but to accept digital payments as well.
PPC4If I don’t offer the option of digital payments, my customers may choose to go to another store that does.
Customer Pressure—Adapted from [73]
CP1I think my customers push me towards adopting digital payment options.
CP2I think society today expects stores to offer digital payment options.
CP3My customers prefer digital payment options when shopping in-store.
CP4My customers explicitly ask me if I have digital payment options.
Empathy—Adapted from [16]
EMP1Post-COVID-19, I care about providing contactless payment.
EMP2Post-COVID-19, I believe that cashless transactions would make my customers feel safer.
EMP3Post-COVID-19, I care about providing a safe shopping experience in my store.
EMP4Post-COVID-19, I understand my customers’ concerns about safer payment options.
Attitude—Adapted from [73]
ATT1I think digital payment options are good.
ATT2I find digital payment applications easy to learn.
ATT3I think that digital payment applications will be widely used in the future.
ATT4I find digital payment options to be advantageous.
ATT5I think using digital payment applications would enhance my effectiveness on the job.
ATT6I think using digital payment applications would make it easier to do my job.
Intention to Adopt—Adapted from [73]
ITA1I plan to use mobile digital payment applications in my business.
ITA2I intend to offer digital payment options in my business.
ITA3I would like to give customers the option to use digital payments.
ITA4I intend to use more mobile transactions in place of cash transactions in future.
Current Usage—Adapted from [73]
CU1I currently use digital payment options in my business.
CU2I currently offer customers the option to pay through mobile applications.
CU3My customers pay me via digital payment applications.
CU4The majority of my transactions are performed using digital payment applications.
Table 2. Socio-demographic characteristics of the respondents.
Table 2. Socio-demographic characteristics of the respondents.
AttributeFrequencyPercentage
Gender
Male16192.00%
Female148.00%

Age
18–251810.30%
26–358045.70%
36–454928.00%
46–551810.30%
Above 55105.70%
Monthly Revenue
Less than Rs. 50,0004224.00%
Between Rs. 50,001–100,0002916.60%
Between Rs. 100,001–200,0002413.70%
Between Rs. 200,001–500,0002313.10%
Between Rs. 500,001–1,000,000105.70%
Between 1,000,001–15,000,00063.40%
Above Rs. 15,000,00021.10%
Prefer not to say3922.30%
Area in sq ft.
Below 1003218.30%
101–2004123.40%
201–3004726.90%
301–4002816.00%
401–500158.60%
Above 500126.90%
Table 3. Measurement model analysis.
Table 3. Measurement model analysis.
Latent VariableIndicatorOuter LoadingsCronbach’s AlphaComposite ReliabilityAverage Variance Extracted (AVE)
Available ResourcesAR10.7730.8360.8900.671
AR20.814
AR30.830
AR50.856
Perceived Pressure from CompetitorsPPC10.7800.5450.7680.526
PPC30.741
PPC40.647
Customer PressureCP20.8340.8120.8880.726
CP30.854
CP40.868
EmpathyEMP10.7710.8280.8860.660
EMP20.839
EMP30.847
EMP40.791
AttitudeATT20.7900.8030.8710.629
ATT30.842
ATT40.791
ATT60.745
Intention to AdoptITA10.8160.8070.8740.635
ITA20.829
ITA30.834
ITA40.701
Current UsageCU10.9220.9160.9470.856
CU20.914
CU30.939
Table 4. Discriminant validity (Fornell–Larcker criterion).
Table 4. Discriminant validity (Fornell–Larcker criterion).
AttitudeAvailable ResourcesCurrent UsageCustomer PressureEmpathyIntention to UsePerceived Pressure from Competitors
Attitude0.793
Available Resources0.7560.819
Current Usage0.6600.7910.925
Customer Pressure0.6480.7070.6230.852
Empathy0.7210.7430.6660.6680.813
Intention to Adopt0.7920.7500.6480.6500.7640.797
Perceived Pressure from Competitors0.4620.4930.4820.4440.4010.4420.725
Table 5. Multicollinearity testing—Inner VIF values.
Table 5. Multicollinearity testing—Inner VIF values.
ConstructVIF Values
Attitude1.667
Available Resources2.888
Customer Pressure2.246
Empathy2.457
Intention to Adopt1.886
Perceived Pressure from Competitors1.354
Table 6. Hypothesis Testing.
Table 6. Hypothesis Testing.
Hypothesized RelationshipPath CoefficientsStd. Errort-Valuesp-ValuesBCI [LL]BCI [UL]Result
Available Resources -> Attitude (H1)0.4120.1203.4200.0010.1740.649Supported
Customer Pressure -> Attitude (H2)0.1190.1350.8850.376−0.1130.414Not Supported
Perceived Competitiveness -> Attitude (H3)0.0710.0651.1000.272−0.0580.196Not Supported
Empathy -> Attitude (H4)0.3050.0973.1430.0020.1100.485Supported
Attitude -> Intention to Adopt (H5)0.7580.04716.0170.0000.6370.833Supported
Intention to Adopt -> Current Usage (H6)0.6570.06210.5670.0000.5220.768Supported
Table 7. R2 Values.
Table 7. R2 Values.
ConstructR-SquareR-Square Adjusted
Attitude0.6420.634
Current Usage0.4200.417
Intention to Adopt0.6270.625
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Lingappa, A.K.; Maheshwari, B.; Mathew, A.O. Navigating the Digital Shift: How Indian LOOROs Coped Amid COVID-19. COVID 2026, 6, 12. https://doi.org/10.3390/covid6010012

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Lingappa AK, Maheshwari B, Mathew AO. Navigating the Digital Shift: How Indian LOOROs Coped Amid COVID-19. COVID. 2026; 6(1):12. https://doi.org/10.3390/covid6010012

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Lingappa, Anasuya K., Bhaavya Maheshwari, and Asish Oommen Mathew. 2026. "Navigating the Digital Shift: How Indian LOOROs Coped Amid COVID-19" COVID 6, no. 1: 12. https://doi.org/10.3390/covid6010012

APA Style

Lingappa, A. K., Maheshwari, B., & Mathew, A. O. (2026). Navigating the Digital Shift: How Indian LOOROs Coped Amid COVID-19. COVID, 6(1), 12. https://doi.org/10.3390/covid6010012

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