Oman has been experiencing rapid socio-cultural changes since the past few years, mainly due to the accelerating economy, which allowed people from the country to travel to western countries for tourism and education, thus, exposing them to western food and culture [1]. The market study revealed a lot of opportunities for major tourism initiatives. Oman is moving toward becoming a world-class global tourism hub, backed by competitive advantages that attract international travelers looking for new experiences. Thus, this creates a huge opportunity for pizza restaurants in the country. As westernization is influencing consumers’ dietary habits, Oman population is increasingly opting for pizza from international food chains, like Domino’s and Pizza Hut. As a result, international food chains are continuously expanding their stores in the country. The growth of the online food delivery market is significantly driving the pizza restaurants market growth. This is due to the rising young population, enhanced purchasing power, internet connectivity and modern and changing lifestyles. Omani pizza restaurants market is competitive and fragmented and comprises regional and international competitors. The United States-based fast-food chains continue to dominate the pizza restaurant sector in this region [1]. Omani restaurants market is anticipated during the forecast period (2021-2026):

• The increasing demand for home delivery of pizza, aided by the increasing internet connectivity and consumer spending, is projected to extensively drive the market growth. Moreover, the strong influence of western culture, especially in the food sector, further assists the growth of pizza consumption in Oman

• However, the rising burger chains and other fast-food chains with more economical and satiating food offerings entice the larger share of consumers

Product Quality 

Food quality is defined as one of the most critical constituents of a dining performance and productivity, [2,3]. Food quality is main factor affecting on customer loyalty, expressing customer's perspective; a primary determinant for    appealing    customers    and   retaining   them repeatedly, as well as a core predictor of loyal level from customers, [4]. When it is compared with other standards as service quality or environment, food quality is the most crucial component of customer satisfaction, [3]. There is a positive relationship between food quality and customer satisfaction when measuring the impact of this key term on satisfaction and behavioural intention [5]. Product quality is stated to comprise three main factors for evaluation quality as "safety, appeal and dietary acceptability", [3] and it is always the most important part impacting on customer satisfaction which is equivalent with other factors in restaurants as physical environment and service quality. Singh and Sirdeshmukh [6] found that food quality is one the most important attributes that leads to customer satisfaction. Other studies have also focused on food quality, identifying three key components; taste, appropriate temperature and presentation of the food served [2,5,7-9]. These studies found that food quality has a high impact on customer satisfaction and brand loyalty. The relationship between food quality and customer satisfaction in the fast food industry is proven to be one key to success [10]. Several components of food quality exist, including, (1) the freshness of the ingredients, (2) taste, (3) quality consistency and (4) appearance [5,11].

Statement of the problem

Like in other Middle East countries, culture in Oman is not just about shopping but also about going out to eat [12]. While majority of the GCC produce less food, Oman food production has witnessed ample growth during the last ten years. This rise is driven by high demand, better efficiency and the need for better nutrition and work very closely with local food producers to enhance fortification programs across the food value chain. The food consumption is expected to grow at a rate of 4.6% annually. Oman is likely to witness the highest growth at 4.6% in terms of food consumption which is anticipated to grow from 3.2 million MT in 2018 to 4.0 million MT in 2023. Food quality is a major driving force that motivates consumers towards pizza.

Significance of the Study

Omani population is influenced by the western dietary habits. Oman population is increasingly opting for pizza from international food chains. The study aims to identify the product quality attributes of pizza and its impact on customer satisfaction and repurchase (loyalty). This study will be very helpful for the Pizza brand marketers to establish and survive in the existing markets in Oman. Furthermore, they can expand their markets and enter into new markets with more profits from the existing markets.

Objectives of the Study

• To study the correlation of Quality attributes of Pizza

• To identify the major attributes that contributes to product quality

• To categorize the component factors of pizza quality based on factor analysis

Literature Review

Shinde et al. [13] studied the components of customer satisfaction at Rolls Mania a fast food service restaurant at Kolhapur City, India among 154 customers. Dining experience; accessibility, convenience and affordability and product test are the important components of customer satisfaction. There is a significant relationship between customer satisfaction and customer loyalty. Abdullah et al. [14] conducted a study among 250 customers to understand the relationships between customer loyalty and factors of loyalty that contribute to loyalty towards fast food restaurants in Malaysia such as KFC, McDonalds and Dominos and Nando’s chicken in Kuala Lumpur, Malaysia. Customer loyalty is associated with the quality of product, restaurants environment, convenience and price and value towards fast food businesses. Convenience and quality of food has the most influence on customer loyalty. Restaurant environment and price have no impact on customer loyalty. Manikandan et al. [15] focused on the customers of Domino’s pizza outlets in Chennai. They studied the customer satisfaction in Domino’s pizza outlets, customer perception and customers’ preference about Dominos products. Customers' perceived value, brand trust; customers' satisfaction, repeat purchase behavior and commitment are found to be the key influencing factors of brand loyalty. Bhatnagar and Dharmendra [16] examined the factors influencing the liking and satisfaction of customers towards Dominos’ Pizza in Gwalior, India and found that all the factors showed liking and satisfaction level in favour of Domino’s Pizza. Ossama and Sonia [17] examined the relationship between service design and product design with customer satisfaction and customer’s repurchasing behaviour among 300 consumers in Pakistan. There is a positive impact of product and process on consumer repurchase behaviour. Buying behaviour is highly influenced by product attributes and it positively influence to shape repurchase behavior of consumers. Phan and Nguyen [18], investigated the customers' evaluation and perception about determinants influencing on customer satisfaction at Vietnamese fast-food restaurants. Respondents are likely satisfied and interested in the decoration and design style in fast-food restaurants in Vietnam because it almost generates the comfortable and good feelings in order to enjoy and eat their meals. Service quality and food quality, price and environment elements have impact on customer satisfaction as well as loyalty in the Vietnam fast-food restaurant. Usman and Suresh [19] explored the factors influencing consumer’s satisfaction towards branded fast food outlets in Chennai city in India. The factors include variety of product, quality of product, accuracy of service, delivery time, store location, staff courtesy, ambient conditions and overall satisfaction. 253 consumers were contacted from Chennai city who are customers of KFC, Dominos, Subway, Pizza Hut and Mc Donald’s. Quality of product is more important and there is an association between branded retail outlets and overall satisfaction. Aymar and Joseph [20] determined the drivers of customer satisfaction and brand loyalty at McDonald’s restaurants in Morocco from 150 participants. Price, food quality, service quality and physical environment affects customer satisfaction and brand loyalty. Price fairness at McDonald’s increases both customer satisfaction and brand loyalty. Food quality, such as taste, appropriate temperature and food presentation, are significant attributes leading to enhanced customer satisfaction, as well as brand loyalty. A research focused factors for fast food industry in region of Peshawar, Pakistan from 150 customers of four fast food restaurants; KFC, Chef, Arabian Chick and Pizza Hut showed that service quality and brand are the key factors for satisfaction in fast food industry in Peshawar, Pakistan. Brand, Service quality, physical environment and promotion are the key determinant of customer satisfaction in the purchase of fast food industry. There is a significant positive relationship between the promotion, service quality, customer expectations, brand, physical environment, price and taste of the product to customer satisfaction [21].

It is a descriptive study as it covers the attributes of product quality of pizza, customer satisfaction and repurchase. The study area selected for this research is Nizwa. The period of study is from October 2020 to December 2020. According to the National Center for Statistics and Information, Statistical Year Book, the population of Omanis in Nizwa in 2018 is 86,031. This is taken as the sampling frame. The sample size is determined as 384 respondents. The sampling method adopted is simple random sampling method, where in the customers are contacted on a random basis. Primary data is collected with the help of a structured questionnaire. Pilot study is to be conducted among 15 respondents to identify the validity of the questions. The data is collected online using Google form in both English and Arabic. Factor analysis is used for extraction using SPSS 21 (Table 1).

Table 1: Construct used for Product Quality Attributes of Pizza

Table 1 shows the descriptive analysis of the product quality attributes of Pizza. The mean value is high for the factor ‘appealing’ (3.92) and the standard deviation is 1.14. The low average for the variable ‘healthy dish’ is 3.18 with a standard deviation of 1.28. The mean for the attribute ‘menu varieties is 3.86 and the standard deviation of ‘menu varieties’ is 1.02 which is low. This shows that the data points tend to be close to the mean.

SPSS output shows the abridged version of R-matrix. The top half of Table 2 contains the Pearson correlation coefficient between all pairs of questions whereas the bottom half contains the one-tailed significance of these coefficients. By scanning the significance values it is clear that majority of the values are less than 0.05 and no values are greater than 0.9. Hence, there is no need to eliminate any variable. There is no problem of multicollinearity. Thus all the questions correlate fairly well and none of the correlation coefficients are particularly large. The determinant of correlation matrix value is 0.006 which is greater than 0.00001 also supports the correlation matrix that can be used for factor analysis. Therefore, there is no need to consider eliminating any questions at this stage.

Table 2: Correlation Matrix^a

a. Determinant = 0.006

The Kaiser-Meyer-Olkin measure the sampling adequacy The KMO statistic varies between 0 and 1. A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser, recommends accepting values greater than 0.5 as acceptable. Values between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and values above 0.9 are superb. For this data the value is 0.909, which falls in the range of superb. Therefore, factor analysis is appropriate for these data. Bartlett’s measure tests the null hypothesis that the original correlation is an identity matrix. For factor analysis to work some relationships between variables and if the R-matric were an identity matrix then all correlation coefficients would be zero. For the test to be significant, the significance value should be less than 0.05. For these data, Bartlett’s test is highly significant (p<0.001) and therefore factor analysis is appropriate (Table 3).

Table 3: KMO and Bartlett's Test

The Table 4 shows the table of communalities before and after extraction. Principal component analysis works on the initial assumption that all variance is common. Therefore, before extraction the communalities are all 1. The communalities in the column labeled extraction reflect   the   common   variance   in   the   data  structure.

Table 4: Communalities

Extraction Method: Principal Component Analysis

About 70.3% of the variance is associated with the size and shape of Pizza which is common or shared variance. Similarly, 67.4% of the variance is associated with the first factor (Healthy dish) is common or shared variance. Another way to look at these communalities is in terms of the proportion of variance explained by the underlying factors. After extraction some of the factors are discarded. The amount of variance in each variable that can be explained by the retained factors is represented by the communalities after extraction.

The Table 5 total variance lists the eigenvalues associated with each linear component or factor before extraction and after extraction. The eigenvalues associated with each factor represent the variance explained by that particular linear component and SPSS also displays the eigenvalues in terms of the percentage of variance explained. Factor 1 (Healthy dish) explains 46.15% of total variance. The first two attributes explain relatively large amounts of variance, especially factor 1, whereas subsequent factors explain only small amounts of variance. SPSS then extracts all factors with eigenvalues greater than 1, which leaves with ten factors. The eigenvalues associated with these factors are again displayed and the percentage of variance explained in the Extractions Sum of Squared Loadings. The values in the column are the same as the values before extraction. The values for the discarded factors are ignored and the table is blank after the second attribute. Similarly factor 2 ‘Delicious and tasty’ accounts for 11.40% of variance. Thus 57.56% of the cumulative variance is contributed by the first two variables and remaining factors contribute 42.44% of cumulative variance. In the final part of the table labelled Rotation Sums of Squared Loadings the Eigenvalues after rotation are displayed. Rotation has the effect of optimizing the factor structure and the relative importance of these factors are equalized. Before rotation factor ‘size and shape’ of pizza accounted for considerably more variance (70.3%) than the remaining eleven quality factors. However, after extraction the first factor ‘Healthy dish’ accounts for only 46.15% of variance as compared to other factors. 

Table 5: Total Variance Explained

Extraction Method: Principal Component Analysis, ^aWhen components are correlated, sums of squared loadings cannot be added to obtain a total variance

A scree plot visualizes the Eigenvalues in the diagram. The first 2 components have Eigenvalues over 1 and they are considered as “strong factors”. From the Scree plot it is clear that after the second factor the curve begins to tail off. Therefore, it is probably justifiable to two factors. This output shows the rotated component matrix (also called as the rotated factor matrix) which is a matrix of factor loadings for each variable onto each factor. This matrix contains the same information as the component matrix except that it is calculated after rotation. Factor loadings less than 0.3 are not loaded as it is suppressed. The variables are listed in the size of their factor loadings. Before rotation, most variables loaded highly on to the first factor. After rotation the factor structure has clarified. There are twelve factors and the variables are loaded equally. The suppression of factors less than 0.3 and ordering variables by loading size also makes interpretation easier.

The component matrix is shown in Table 6 before rotation. This matrix contains the loadings of each factor into each component. All the loadings less than 0.3 are suppressed in the output so there are blank spaces for many of the loadings.

Table 6: Component Matrix^a

Extraction Method: Principal Component Analysis, a. 2 components extracted

The above Figure 1 shows the loadings (extracted values of each item under 2 variables) of 12 variables on the 2 factors extracted. The higher the absolute value of the loading, the more the factor contributes to variable. Two variables are extracted wherein the 12 items are divided into 2 variables according to the most important items with similar response in component 1 and component 2. At this stage SPSS has extracted two factors. By Kaiser’s criterion, two factors are extracted and is proved accurate. The criteria is accurate when communalities after extraction is greater than 0.7. The second ground for accuracy is when the average communalities is greater than 0.6. The average of the communalities is found by adding the communalities divided by the number of factors (8.1/12 = 0.675). Thus on one ground Kaiser’s rule is accurate. A model that is a good fit will have less than 50% of the non-redundant residuals with absolute values that are greater than 0.05 which is true for our example. Since there are cross loadings, the factors loadings are redistributed by rotation. A cross loading is when an item loads at 0.32 or higher on two or more factors. Oblique rotation is when the factors are not rotated 90° from each other and the factors are considered to be correlated. Oblique rotation produces a pattern matrix that contains the factor or item loadings and factor correlation matrix that includes the correlations between the factors.

Figure 1: Factor Loadings of 12 Variables on two Extracted Components before Rotation

The pattern matrix gives coefficients that describe the unique relationship between each item and each factor (controlling for the other factors). The Pattern Matrix shows the factor loadings for the rotated solution

Factor loadings are similar to regression weights (or slopes) and indicate the strength of the association between the variables and the factors. The solution has been rotated to achieve an interpretable structure. After rotation, the eight product quality items all hang together on the first factor and the last four items all hang together on the second factor. All of these coefficients are above the 0.30 level to suggest a “salient” loading. This is a “clean” solution as there are no complex items and the factor loadings for each item onto its primary   factor  is   above  (Table 7)  the  salient   threshold.

Table 7: Pattern Matrix^a

Extraction Method: Principal Component Analysis, Rotation Method: Oblimin with Kaiser Normalization, a. Rotation converged in 5 iterations

From the pattern matrix, it is revealed that the product quality attributes of Pizza, i.e. size and shape, dough quality, freshness of ingredient, delicious and tasty, appealing, aroma, menu varieties and consistency are loaded heavily on component 1, based on the loading size of coefficients. Thus component 1 can be termed as “Pizza size and ingredients”. The quality attributes that are heavily loaded on component 2 are healthy dish, nutritional value, dietary acceptability and safe. Therefore, component 2 factors are renamed as “Nutritional factors”.

The structure matrix yields the correlation of each item with each factor. It is a good idea to inspect it to see whether the interpretation from the pattern matrix also holds for the structure matrix. We see that these two matrices yield similar conclusions (Table 8).

Table 8: Structure Matrix

Extraction Method: Principal Component Analysis, Rotation Method: Oblimin with Kaiser Normalization

The correlation between “Pizza size and ingredients” and “Nutritional factors” is 0.467 which is a good score and shows a positive correlation between Pizza size and ingredients and nutritional factors (Table 9). 

Table 9: Component Correlation Matrix

Extraction Method: Principal Component Analysis, Rotation Method: Oblimin with Kaiser Normalization

Findings of the Study

• The correlation value R is highest for Nutritional value (0.515) and dietary acceptability (0.441)

• Factor 1 (Healthy dish) explains 46.15% of total variance. Factor 2 ‘Delicious and tasty’ accounts for 11.40% of variance. Thus 57.56% of the cumulative variance is contributed by the two product quality attributes, ‘healthy dish’ and ‘delicious and tasty’

• After the rotation, the pattern matrix shows that the highest loading is on size and shape of pizza (0.885) and dough quality (0828) of pizza

• After the factor loadings and factor rotations there are two components derived which are termed as “Pizza size and ingredients” and “Nutritional factors”. It is also proved that there is a positive correlation between these two derived factors (0.467)

In this paper the product quality attributes of pizza were analyzed using factor analysis. In all aspects, the data was appropriate to conduct factor analysis and hence sound conclusions could be drawn from this analysis. A principal component analysis has been carried out with oblique rotation. This resulted into two correlated factors, constituting the quality attributes of pizza i.e. “Pizza size and ingredients” and “Nutritional factors”. It turned out that the measurements of the two components loaded on different factors, which could indicate that different kind’s factors are needed for Product Quality of pizza. This can be important for pizza marketers, as it gives opportunities to improve their marketing effectiveness.

Recommendations

• Marketers of Pizza should mention the nutritional value on the packaging

• They should communicate the healthy benefits of pizza through promotion

• The ingredients used for each type of pizza should be communicated on the package of pizza

• The pizza marketers should introduce more menu and varieties of pizza for the consumers

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