Disruptive Factors and Customer Satisfaction at Chain Stores in Karachi, Pakistan

Abstract

Purpose: This study aims to determine the relationship between disruptive factors and customer satisfaction at chain stores. Survey-based questionnaires were designed in the distribution technique to measure the findings in this study. Research design, data, and methodology: In terms of the sampling technique, the researchers adopted the simple random sampling technique with a total of 200 sample sizes. For the statistical method, the researchers applied multiple linear regression analysis to determine the potential factors that affect customer satisfaction at chain stores. The analysis of this study measured how product quality, pricing policies of chain stores, design and layout, responsiveness, and location of chain stores impart their roles in customer satisfaction. Results: This study found a significant relationship between the product quality and location of chain stores on customer satisfaction. In addition, the responsiveness, pricing policy, and physical design of chain stores impart an insignificant role in customer satisfaction. However, it is proven that the location of chain stores and product quality positively impact customer satisfaction. Conclusions: The study is geographically limited to the region of Karachi, Pakistan. Therefore, the findings may differ in the context of study implications in the other areas.

1. Introduction

Retail is viewed as one of the most important sectors in the world. The global retail industry is mature and highly competitive in the industrialized countries of Europe and North America. On the other hand, the growing economies of Asia-Pacific, the Middle East, and Latin America have been critical in driving market expansion. However, the retail industry has seen various shifts with shifting economic ituations worldwide. The global economy, which slowed in 2019, crashed in 2020 because of the COVID-19 pandemic, making forecasting difficult for the retail business.

Consumer spending, which accounts for over two-thirds of GDP, has been a crucial measure of retail market health. Furthermore, the growing popularity of online shopping has been a significant rival to the physical retail industry (particularly during the COVID-19 crisis). Aside from that, global smartphone penetration is boosting the e-commerce sector. Furthermore, disruptive technologies such as the Internet of Things (IoT), augmented reality, and others are revolutionizing the retail industry. Price disparities between online and brick-and-mortar businesses may hinder retail sector growth.

The enhancement of online and traditional shopping by physical stores forces retailers to give an excellent experience to their customers. Thus, retailers' knowledge of their customers has become a competitive edge for retailers (Shi et al., 2020). To obtain a long-term and sustainable competitive advantage in the global market, retailers must create differentiation in their stores. With this approach, they can boost client happiness by providing them with an experience that meets or exceeds their expectations. In Pakistan, numerous retail chains are now growing in number over time. However, they face issues like lack of communication and unorganized setups that hamper their growth (Ahmad et al., 2013).

Previously, researchers investigated the impact of customer experience on satisfaction levels, either in physical stores or online. At the same time, this study integrates chain retailers (online and physical) and is centered on past literature research. Researchers like Otto et al. (2020) used the customer-delivered value theory in their study. They revealed that customer satisfaction has been influenced by these six factors: non-currency cost, service value, product value, personnel value, image value, and currency cost. Söderlund and Sagfossen (2017) investigated the influence of the efforts of suppliers and consumers on the level of customer satisfaction through different experiments. Gvili and Levy (2019) conducted a study in the context of e-commerce. They discovered that the product and value heavily influence consumer satisfaction and the service supplied to them online, while security and other information harvesting aspects have the least negligible impact. The existing literature on several shopping situations concentrates on the factors influencing customer satisfaction in one case or some general factors (Nisar & Prabhakar, 2017). These studies did not focus on the differences among the factors influencing customer satisfaction in retail chains in Pakistan. However, the present research aimed to analyze customer satisfaction by studying these factors: product quality, responsiveness, price, store location, physical design, and appearance.

The study will be significant for sales and marketing managers who can apply these concepts and results in their retail chain stores to increase their marketing strategies. The significance of this study is based on consumer perception to understand better the actual needs and preferences of customers who purchase at retail chains in Karachi. With these findings, sales and marketing managers can improve their sales and marketing strategies. They will be able to identify and implement the needed strategies to enhance customer satisfaction and increase retail chains' sales, resulting in high revenues. Furthermore, the management can efficiently manage all five factors; product quality, responsiveness, price, store location, physical design, and appearance, by adjusting their strategies to match customer demand.

1.1. Issues Relating to Chain Stores

The superstore or retail market has become one of the most competitive markets in Pakistan, and it is highly profitable for anyone if they manage it right (Sumbal et al., 2019). In this fast-paced world, individuals' lives have become so busy that they do not have much time for shopping, so they prefer those stores where they can conveniently get their essential products. That is one of the reasons for the remarkable expansion of the retail chain in Pakistan. In Karachi, the roads are bustling, and it becomes very tough for people to move from one place to another for shopping, so they prefer to shop at one department store where they find all the essential items. Moreover, consumers prefer stores that provide quality products and the best customer service (Darzi & Bhat, 2018).

Moreover, little research in the Pakistani context has investigated the relationships between customer satisfaction and disruptive factors that impact it. Therefore, the gap is still there in this field. Consequently, the present study aims to fill this gap in the literature.

1.2. Purpose of Research

Customer satisfaction is the fundamental indicator of the sustainable competitiveness of retailers. The primary purpose of this research is to investigate the relationships among the variables from customers' perspectives, customer satisfaction levels, and the factors influencing the satisfaction levels (product quality, responsiveness, price, store location, physical design, and appearance). Moreover, the research aims to make retailers aware of how they can enhance customer satisfaction and competitiveness.

The main objective of this research is to determine the impact of distinct factors that impact customer satisfaction in retail chains in Karachi, Pakistan. Product quality (PQ), responsiveness (RSP), pricing policy (PP), store location (SL), and physical design (PD) are the distinct factors. Therefore, five sub-objectives were developed for this study; (1)-To determine the effect of product quality (PQ) on customer satisfaction among retail chains; (2)-To determine the effect of responsiveness (RSP) on customer satisfaction among retail chains; (3)-To determine the effect of pricing policy (PP) on customer satisfaction among retail chains; (4) To determine the effect of store location (SL) on customer satisfaction among retail chains; and (5)-To determine the effect of physical design (PD) on customer satisfaction in retail chains.

2. Review of Literature

2.1. Cognitive Dissonance Theory

Cognitive dissonance refers to the uncomfortable feelings experienced by individuals when they hold two conflicting ideas in their minds (Kim, 2011). The Cognitive Theory of Dissonance states that individuals should be motivated to lessen their dissonance, as motivation influences their beliefs, attitudes, and behaviors. The Theory of Cognitive Dissonance, proposed by Festinger in 1957, explains the relations among individual cognitions. According to this theory, people have different propensities to pursue consistency in their cognitions, such as beliefs and behavior. Dissonance occurs when there is something contradictory between attitudes and behaviors. Therefore, a change is needed to remove this dissonance. Attitudes are more likely to change to accommodate specific behavior. Some factors that cause cognitive dissonance are forced compliance behavior, decision making, and effort (McLeod, 2018). In the context of shopping, the discomfort that buyers often experience after making the purchase can be described as dissonance. This situation can be related to the second factor, which is decision making. The customers avoid disharmony by using numerous factors, resulting in satisfaction. However, when they are not satisfied, it can create dissonance. People have several ways to reduce the dissonance aroused by making decisions (Festinger, 1964). Therefore, organizations tend to change their attitude toward customers by providing certain benefits (Sharifi & Esfidani, 2014), hoping that good decisions can avoid dissonance. This study is based on discovering variables that are useful to customers and increase their level of satisfaction. Also, we summarized the main content of this theory in the following table:

Table 1: Main content of the theory

2.2. Customer Satisfaction

Zuniga et al. (2009) stated that customer satisfaction is an overall feeling towards a product supplier. It is an emotional reaction to the customer's expectations about the product and what they get to satisfy their needs. Hoffman and Bateson (2011) stated that satisfaction is the feeling of delight, pleasure, excitement, or disappointment experienced by individuals when comparing the product's actual performance with their expectations. Satisfaction can also be defined as the contentment people experience when their needs, expectations, or desires are met. Furthermore, variable customer satisfaction is measured by customers' satisfaction with retailers' products or services (Angelova & Zekiri, 2011). Afthanorhan et al. (2019) defined customer satisfaction as the levels of quality-of-service performance that meet customer expectations. Business entities get many advantages when their customers are satisfied with their services. Satisfied customers stay loyal to the company and spread positive word of mouth about a brand to their friends and family. According to Zuniga et al. (2009), customer satisfaction is an overall estimation of product or service utilization and performance. Syafarudin (2021) and later cited by Olfa (2022), the superior competitors always tend to have high customer satisfaction.

Hoffman and Bateson (2011) compared the product or service's actual performance with the customer's satisfaction expectations for the product or service. The success of any enterprise can be determined by its customers' satisfaction level, as organizations' primary focus is to serve their customers more efficiently than their competitors (Solimun & Fernandes, 2018). Any business's long-term and sustainable survival do not entirely depend on the revenue from its sales. Still, it also depends on their ability to serve their customers. Thus, companies that serve their customers with superior value are more efficient in satisfying them. Satisfied customers prove the essential asset of any enterprise (Tseng & Wu, 2014).

As per a consensus, customer satisfaction is the response to their emotions. Satisfaction is an attitude of the customers derived from their past experiences and sets positive or negative emotional responses based on these experiences. The satisfaction of customers is an emotional response that their purchases have developed. Orel and Kara (2014) studied customer satisfaction in the service industry and found that satisfaction results from service quality and whether it fulfills customers' expectations. Organizations have established long-term and sustainable relationships with their customers through the tool of customer satisfaction. Very few businesses have succeeded in a competitive market without such sustainable relations. Krystallis and Chrysochou (2014) show that the most crucial factors that influence the well-being of any enterprise are its customers' loyalty, and the satisfaction customers get after purchasing such a brand. Therefore, customer satisfaction can be concluded as the difference between their expectations and the actual value they receive from a purchase.

2.3. Hypothesis Development

2.3.1. Product Quality and Customer Satisfaction

One essential factor that significantly influences customer satisfaction is the product/service quality provided to customers at retail stores. Gilmore (1974) stated that product quality is its capacity to satisfy customers' specific desires. Product quality can be measured by the features and advantages associated with the product (Ishaq et al., 2014). If a product fulfils the customer’s expectations, the customer will be pleased and consider the product acceptable or even high-quality (Jahanshahi et al., 2011). Therefore, product quality has been considered one of the core determinants of customer satisfaction. Thus, the researchers developed the following first hypothesis:

H1: The higher the level of product quality, the more the level of customer satisfaction

2.3.2. Responsiveness and Customer Satisfaction

Responsiveness can be defined as the staff willing to serve their customers better and be helpful and available when they require service. Customers also expect retail shops to satisfy their needs and ensure the availability of essential commodities. Customer satisfaction rises when employees are more attentive to their customer's requirements and respond quickly and courteously (Iberahim et al., 2016). Therefore, the researchers developed the second hypothesis based on the above discussion.

H2: The higher the level of responsiveness, the higher the level of customer satisfaction

2.3.3. Pricing Policy and Customer Satisfaction

The price structure of retail stores also significantly influences customer satisfaction as the price can attract or repel customers. Moreover, the price is also an indicator of product or service quality. Customers mostly expect high value from products or services tagged with soaring prices. Similarly, suppose the prices are lower than expectations. In that case, it raises questions about quality in customers' minds as the competition among the retail stores is remarkably high in Bangladesh, which enables the customers to establish the reference prices. Internal reference prices are those set in the customer's memory of a product or service, and they compare this price with the actual price (Triatmanto et al., 2016). Therefore, retailers and companies must be careful while setting the price so that it may not exceed the one already established in customers' minds to avoid negative deviation. Therefore, the researcher hypothesizes that:

H3: The stores' pricing policy affects customers' satisfaction levels

2.3.4. Store Location and Customer Satisfaction

Previous literature suggested four decision factors for store location: the traffic factor, the land utilization factor, the demographic factor, and the economic factor (So & Hwang, 2012). However, customers think very cautiously when they make any purchase from some retailer. They usually prefer to shop at those places where they get the most convenience, such as car parking, availability of the essential items, and other convenience factors. Rana et al. (2014) conducted a study on a large population in Dhaka. According to the research, individuals prefer the convenience and availability of vital commodities at a single store. People choose to buy things from places that are easily accessible and where they can get their chosen items because the city is notorious for traffic congestion. Thus, the researcher hypothesizes that:

H4: The better the store location, the higher the level of customer satisfaction

2.3.5. Physical Design and Customer Satisfaction

The physical appearance of a retail store and the staff provide a fabulous hint about the store's service quality to its customers. The outlets having eye-catchy and attractive appearances can easily attract the customer's attention. If the outlet's design is unique and novel, then the customers take little time to find such a store and make revisits just because of the physical appearance of the outlet (Sürücü et al., 2019). Therefore, the researcher hypothesizes that:

H5: The better physical design of chain stores increases customer satisfaction

2.4. Research Model

Based on the literature discussed, Figure 1 shows the proposed research model for this study. This research framework shows the relationship between independent and dependent variables.

Figure 1: Proposed model

3. Research Methodology

3.1. Research Design

Researchers use various research methods to conduct their studies, such as descriptive, casual, and exploratory research. However, the descriptive research method has been used in this study to describe the research question in detail. Descriptive analysis collects data and information about a specific situation for interpretation purposes (Kotler & Armstrong, 2006). The significance of this type of research depends on the sample selected by the researcher to collect valuable information about the research objectives. Based on this justification, the researcher perceived that descriptive analysis is one of the most reliable data collection methods for measuring the relationship among study variables. The researcher used the survey to collect primary data and distributed the questionnaires among the respondents. Therefore, the researcher incorporated several factors into questionnaires to determine the customer's satisfaction with shopping.

3.2. Research Approach

The research approach used for analysis is either qualitative, quantitative, or both. In this study, the researcher used the quantitative technique to collect the data from the selected sample (Al-Wareth et al., 2021; Das et al., 2021). The quantitative research method includes the subject's numbers and helps to generalize the results. This approach produces accurate results with greater objectivity. According to Kothari (2004) and Haque et al. (2021), researchers mostly use quantitative methods in their studies to statistically analyse their data. The investigator can quickly evaluate the data gathered from respondents through different statistical techniques. Further, the researcher utilized the questionnaire to collect the information and data from the selected respondents.

3.3. Pre-Test

A pre-test is one of the methods used to verify the questionnaire by experts in the field to ensure the questions provided are suitable before proceeding to the next stage (Zikmund et al., 2013). This study distributed the questionnaire to five retail store managers and two academicians to validate the items. At these stages, respondents commented on the structure of items, grammatical errors, and the wording of the items used in the questionnaire. It is to ensure the respondents comprehend the questions well without further explanation.

3.4. Pilot Test

Before the questionnaire is distributed to the actual survey respondents, it is crucial to conduct a pilot study. Therefore, before the mass distribution of the survey questionnaire, a pilot study was conducted to ensure that the questionnaires were understood, reliable and usable to collect data from a large-scale population. According to Heiman (2000), a pilot study should use respondents with similar characteristics to actual respondents in the study. Therefore, during the pilot study, a sample of 30 respondents was selected randomly from one selected retail store in Karachi in October 2020. Factor analysis was used at this stage, and no items were deleted during the process. The items will be used in the actual survey, as attached in Appendix A.

3.5. Population and Sampling Technique

Data has been gathered from the customers of different retail chains in Karachi, a merchandising retailer whose headquarters is in Pakistan. The population for this research is all the customers of all branches of retail chains. Therefore, it accurately represents the population and is not over or under-represented.

Since this study has no accurate number populations, the researchers distributed the questionnaire using a simple random sampling technique, which involves the customers of retail chains. To choose the respondents, the researchers have set a lower limit of one (1) and an upper limit of twenty (20) in a random number generator before generating the random number. In this case, the number six (6) appeared as a random number. Therefore, for every sixth customer entering the retail chain store, the customers were chosen as the respondents during the questionnaire distribution. Moreover, the researcher used this method to generalize the results accurately (Rashid et al., 2020). Based on this technique, the researcher selected the sample based on randomness.

3.6. Instrument of Data Collection and Sample Size

The researchers collected the data from primary sources. In the nature of the research, primary data collection can be carried out through interviews or surveys. The study's main objective was to collect data from the consumers of retail chains in Karachi to check their satisfaction. As mentioned earlier, the researcher conducted the survey and distributed questionnaires among the 200 shopping mall customers and collected their responses using simple random sampling. The data collection was conducted from 4 pm to 9 pm every day in December 2020 in all retail chains in Karachi. According to Hair et al. (2010), when the research consisted of seven primary constructs and less, a minimum sample size of 150 was acceptable. Meanwhile, Hashmi et al. (2020a) mentioned that a sample of 200 is sufficient to generalize the analysis results. Therefore, with a total of 200 responses, the data was sufficient for further analysis.

3.7. Statistical Technique

To analyse the data that has been obtained from questionnaires, the researcher used SPSS as a statistical tool. Further, the researcher performed inferential statistics to infer the selected sample (Hoffman & Bateson, 2011). The researcher also used the Pearson Correlation matrix to check the correlation among the research factors and test the hypothesis. This parametric technique measures the association and relation between two study variables.

4. Results

The reliability analysis of any given research is considered extremely important as it ensures that each constructor of the variable used in this research, either dependent or independent, holds reliability. The analysis is represented by the value of Cronbach’s alpha, using the number of items each construct has. The construct is reliable if the value is above 0.7 (Hashmi et al., 2020b). The independent variables of this study are product quality, responsiveness, pricing policy, store location, and physical design, while the dependent variable is customer satisfaction. The Cronbach's alpha value for this study is 0.94, which means it was above the minimum acceptable value, 0.70. Various tests have analysed the relationship between independent and dependent variables, including Pearson Correlation. The results of the Pearson Correlation matrix are shown in Table 2, expressing the moderate to a high correlation between each variable, ranging from 0.497 to 0.783 with significant p values. The value means that each variable correlates with the other and validates the developed hypotheses.

Table 2: Correlations

In Table 3, the model summary shows three components discussed to understand the model fit for independent and dependent variables: R, R-square, Adjusted R-Square, and Std Error of Estimate. It is observed that the value of R-square is often used to imply that the independent variance variables impact the dependent variable. At the same time, the adjusted R-square implicates the adjustment of error and external factors and depicts the variance or effects accordingly. Table 2 represents those independent variables (Store Location, Pricing factor, Product Quality, Physical Design, and Responsiveness) that predict the dependent variable (Customer Satisfaction) by 54%. The adjusted R square values are adequate and fulfil the test assumptions.

Table 3: Model Summary

After the regression analysis, Table 4 indicates the results of the ANOVA used to ensure the reliability of the relationship between independent and dependent variables by using the value of F. On the other hand, the value of sig is used to indicate whether the relationship between independent and dependent variables is significant. The results show that the value of F is 47.529, which implies that the relationship between independent variables (Store Location, Pricing Policy, Product Quality, Physical Design, and Responsiveness) and the dependent variable (Customer Satisfaction) is reliable and significant as the p-value is 0.000 < 0.001.

Table 4: ANOVA

a. Dependent Variable: CS

b. Predictors: (Constant), SL, PP, PQ, PD, RSP

Table 5 shows the coefficient values with p values and expressing that PQ (product quality) and SL (store location) has a significant effect (p value < 0.05) on CS (customer satisfaction); whereas PD (product design), PP (Pricing Policy), RSP (Responsiveness) have insignificant effect (p value > 0.05) on CS (Customer Satisfaction).

Table 5: Coefficients

a. Dependent Variable: CS

Table 6 and Figure 2 summarize the hypotheses and express that the results accepted hypotheses H1 and H4 but rejected hypotheses H2, H3, and H5.

Table 6: Hypotheses Assessment Summary

Figure 2: Model with hypotheses results

5. Discussion

5.1. Discussion of Hypotheses Findings

Considering the above hypotheses, the researchers obtained the general framework shown in Figure 2. As indicated in the five hypotheses, five disruptive factors were used in this study to test: product quality, responsiveness, price, store location, and physical design. This study found that meeting customer satisfaction in the retail chain industries during the Covid-19 pandemic has been influenced by two disruptive factors – product quality and store location. However, three disruptive factors – responsiveness, pricing policy, and physical design were rejected. There was no significant relationship between these three factors with customer satisfaction in the retail chain industries in Karachi, Pakistan.

5.1.1. Hypothesis 1: Relationship between Product Quality and Customer Satisfaction

Hypothesis 1 proposed that the higher the level of product quality, the more level of customer satisfaction will be. From the multiple regression analysis, the result shows that the hypothesis was supported. It means that product quality will have a significant relationship with customer satisfaction. Product quality can be measured by the features and advantages associated with the product. Based on the findings, regardless of environmental forces, product quality was a crucial factor that played a pivotal role in the customer satisfaction of the chain stores. Even though the world has faced with Covid-19 pandemic, the study conducted by other researchers (Syafarudin, 2021; Diputra & Yasa, 2021; Rahmawati & Sentana, 2021; Jahanshahi et al., 2011) still shows the significant impact between product quality and customer satisfaction. Further, this finding is also supported by Ishaq et al. (2014), who claimed that product quality plays a vital role in retaining the customer loyalty of chain stores.

5.1.2. Hypothesis 2: Relationship between Responsiveness and Customer Satisfaction

Hypothesis 2 proposed that the higher the pricing policy of the retail chain store, the more customer satisfaction will be. From the multiple regression analysis, the result reveals that the hypothesis was not supported. It means that responsiveness has no significant relationship with customer satisfaction. Although previous research by Munusamy et al. (2010) shows responsiveness has a relationship with customer satisfaction, the finding in this study is contradictory. This finding might be the difference since the study was conducted during the Covid-19 pandemic. This difference might be due to the limited communication and movement between customers and service providers during this pandemic. The change of technology used might also be one of the reasons for the transition between personal interaction and technology used and between the physical store and online store. Therefore, responsiveness might not be essential to meeting customer satisfaction in adapting to the current situation. However, this factor might be relevant after people adapt to the unique environment and the Covid-19 pandemic recovering.

5.1.3. Hypothesis 3: Relationship between Pricing Policy and Customer Satisfaction

Hypothesis 3 proposed that the higher the level of responsiveness affects the customer's satisfaction level. From the multiple regression analysis, the result reveals that the hypothesis was not supported. It means that pricing policy has no significant relationship with customer satisfaction. In discussing this variable, the limited dimensional pricing policy used in this study might lead to no significant relationship between customer satisfaction. Matzler et al. (2006) suggest that when customers are satisfied or dissatisfied with the overall price, they may refer to more specific price dimensions such as price-quality ratio, relative price, price reliability, and others. As a result, low price satisfaction does not always suggest that monetary prices are excessively high. Other pricing dimensions can also influence price satisfaction. Customers may be pleased with one price dimension while being unsatisfied with another. As a result, an overall pricing measure cannot account for these variations.

Additionally, the difference in purchasing power also will reflect this finding. As a result, measuring satisfaction at the level of individual pricing dimensions offers researchers and managers greater specificity and diagnostic value. More specific efforts to boost overall price satisfaction can be adopted if contentment with single pricing characteristics and their relative relevance is measured.

5.1.4. Hypothesis 4: Relationship between Store Location and Customer Satisfaction

Hypothesis 4 proposed that better store locations will have higher customer satisfaction levels. From the multiple regression analysis, the result shows that the hypothesis was supported. It means that store location will have a significant relationship with customer satisfaction. A better store location is another factor that enhances the customer satisfaction of chain stores. The location of chain stores plays a vital role in customers' satisfaction because the purchasing of products is majorly focused on the location of a chain store. Consumers are primarily concerned with the location of chain stores since most customers prefer to shop at more convenient stores, such as those with vehicle parking and other critical features for customer convenience (Nguyen et al., 2022). According to Rana et al., (2014), customers prefer to buy from stores in better locations and away from traffic. Because there are two primary areas for retailers: commercial streets and commercial malls, there is a positive association between the attractiveness of retail activities and the location of the stores. The stores on commercial streets are less convenient for their customers than those at commercial malls. Thus, customers prefer to purchase goods and services from stores in commercial malls and well-reputed places rather than on the street. Customers are more satisfied with the products of stores in commercial malls and well-reputed places than on the road.

5.1.5. Hypothesis 5: Relationship between Physical Design and Customer Satisfaction

Hypothesis 5 proposed that better physical design increases the satisfaction level of customers. From the multiple regression analysis, the result reveals that the hypothesis was not supported. It means that physical design has no significant relationship with customer satisfaction. Although a study by Rana et al. (2014) has shown that physical design has a significant relationship to customer satisfaction, this finding contradicts the current study. This finding might be due to cultural differences, people's perception, and purchasing power in Pakistan compared to other studies, especially during the Covid-19 pandemic. However, the findings might differ if conducted in other states, countries, or regions due to cultural differences and demographic factors. This finding was supported by So and Hwang (2012), which shows that demographic factors influence customer decision in discount stores. The customer satisfaction in supermarket store attributes differed among customers from different regions due to different demographic factors.

5.2. Implications, Limitations, and Recommendations

There are a few limitations present in this study, but the main limitation of this study was the time constraints and the cost. With the sudden spread of pandemic Covid-19, flexibility is needed to adapt to the changes, especially in handling the limitation of time and cost. However, this study was successfully conducted with all stakeholders' support and cooperation.

Further, the study is geographically limited to the region of Karachi, Pakistan. The limited region covered in this study might contribute to the weak supporting findings of the theory due to diverse cultures, people’s perception and purchasing power, especially in the pandemic Covid-19 situation. However, in the other areas, the findings may differ in the context of study implications. It is suggested that a comparative study will be conducted on customer satisfaction before and after pandemic Covid-19.

Lastly, the pandemic and lockdown situations also affected the study as the study was limited to collecting data from a small sample size. The findings might be different if the sample is larger. Therefore, for further study, it is suggested to increase the sample size, and more regions involve in studying the customer satisfaction among Pakistanis in retail chain stores.

Appendix A