## 3 Methodology
### Sample Characteristics and Setting  
The study was conducted in the context of a regional fuel service station located in a semi-rural district within a developing country. This setting is representative of digitally emerging environments, where the adoption of omnichannel strategies is still in progress and is shaped by infrastructural, economic, and cultural constraints. The station offers basic fuel services but has also initiated efforts to modernize customer interactions through the partial implementation of digital information platforms, mobile payment systems, and logistics integration, including inventory visibility and flexible pickup options. This context was selected for two main reasons. First, it provides an ideal case for observing the early-stage implementation of omnichannel service strategies outside of metropolitan or digitally mature zones [42]. Second, it allows for an examination of real user perceptions in a market where traditional and digital service models coexist, often in tension. The station serves both digitally literate users--typically younger clients familiar with mobile technologies--and those with minimal exposure to digital systems, such as older adults and rural residents who rely on face-to-face interaction. The target population consisted of approximately 150 regular customers, defined as individuals who had engaged with the station at least three times in the past month. These customers were chosen based on their recent and repeated interactions with the business, ensuring that their perceptions were grounded in direct and relevant experience. The population included users who had engaged with digital components of the service, such as mobile payment, stock inquiries through messaging platforms, or digital loyalty tracking, as well as those who had used only physical channels. The sampling procedure was revised to more accurately reflect a non-probabilistic approach. Although the term \"probabilistic\" was initially used, participants were, in fact, selected without randomization from a comprehensive sampling frame based on their availability and willingness during their visits to the service station. Accordingly, the method is now described as non-probabilistic, and its limitations concerning external validity and generalizability have been explicitly addressed. From this population, a sample of 108 customers was selected using a non-probabilistic sampling method, which takes into account a confidence level of 95%, a margin of error of 5%, and a response distribution of 50% (the most conservative assumption for variability in perception-based studies):  
\[n=\frac{Z^{2}pq\cdot N}{E^{2}(N-1)+Z^{2}\cdot pq} \tag{1}\]  
where  
\(n=\) sample size;  
\(p=0.5\);  
\(q=1-p=0.5\);  
\(N=\) population size = 150;  
\(E=\) error level (5% for 95% confidence) = 5%;  
\(Z=\) Z-value for 95% confidence interval = 1.96;  
Replacing these values into Equation (1),  
\[n=\frac{1.96^{2}(0.5)(0.5)\cdot 150}{(0.05)^{2}(150-1)+(1.96)^{2}\cdot(0.5)(0.5)} \tag{2}\]  
\[n=108.08\ \approx 108\]This yielded a statistically representative sample size capable of supporting inferential statistical analysis [43]. Participants were approached in person at the service station, during peak and off-peak hours, and were invited to participate in the study voluntarily. Before responding to the survey, each participant received a brief verbal explanation of the study's goals and the importance of their responses for service improvement. To be included in the sample, participants had to meet the following inclusion criteria: (i) be at least 18 years of age, (ii) have used the station's services a minimum of three times in the past month, and (iii) have interacted with at least one digital or hybrid channel (e.g., mobile payments, WhatsApp ordering, or online promotions).