Keywords

Perceived Value, Customer Satisfaction, Customer Loyalty.

Introduction

Quick Service Restaurants (QSR) is different from other industries since it requires careful execution of product, service, and human elements as a part of the service delivery process (Nguyen et al., 2018). Therefore, the perception of value in QSR is not straightforward; at best, it can be perceived as multidimensional (Gallarza et al., 2020). In a similar vein, loyalty in QSR cannot be simplistically quantified as the extent of repeat purchase. Few customers visit the premise only when there is an offer. Few customers exhibit polygamous brand loyalty and are loyal to competing brands at the same time. Few customers are occasional visitors who are never to be seen again (Kanakaratne, et al., 2020). Others who frequent the service premises quickly disappear when a competing store opens or when the social group preference changes. Therefore, loyalty in QSR also qualifies as a multifaceted construct.

The relationship between perceived value and loyalty is long established (Chen & Hu, 2010; El-Adly, 2019). It is said that customers who experience better-perceived value, tend to remain more loyal than others (Keshavarz & Jamshidi, 2018). However, modeling this relationship is a complex task since both perceived value and loyalty are multi-faceted. To orchestrate service value in a manner that customers become monogamous loyal to the brand, it is important to understand that: a) service value is multidimensional, b) customer loyalty is multidimensional (Li, et al., 2020), c) customers experience shift in customer loyalty with change in time and external conditions (Pekovic & Rolland, 2020), d) in such case, for the same customer, when experiencing different loyalty states, different dimensions of perceived value could become important. The objective of the study is to identify which value dimensions are important in transitioning from lower loyalty states to higher loyalty states, to envisage higher repeat customer patronage (Hu et al., 2019) and higher profitability (Kumar & Srivastava, 2021) in long run.

Literature Review

Perceived Value in QSRs

A fundamental basis for conceptualizing perceived value in a service context was developed by Zeithaml (1988). Her study revealed that perceived quality accelerates perceived value, which further induces purchase intentions. Perceived value is also paramount for repurchase intentions (Chang & Wildt, 1994; Petrick et al., 1999; Woodruff, 1997) and loyalty formation (Nguyen & LeBlanc, 1998; Lewis & Soureli, 2006). Clark & Wood (1998) argued that perceived value is of utmost significance in the quick-service industry in determining the overall customer’s meal experience.

Acknowledging the exchange of values as the focal phenomenon in services marketing, Holbrook (1994) elucidated the nature and types of values important for a customer during the purchase experience. He proposed a framework that classified value into three dimensions: Extrinsic value vs. Intrinsic value (functional, utilitarian ability vs. an endin-itself), Self-oriented value vs. Other-oriented value (effect on oneself vs. the effect on others), Active value vs. Reactive value (customer acting on the object vs. the object acting on the customer). Investigating this classification as dichotomies, Holbrook (1998) established a matrix identifying eight types of customer value, namely: efficiency, excellence, status, esteem, play, aesthetics, ethics, and spirituality.

This conceptualization of the nature of customer value in marketing research and practice has been a useful contribution. However, subsequent studies by Sánchez-Fernández et al., (2009) put forward that some dimensions of Holbrook’s typology are closely related and difficult to operationalize separately. The authors combined status and esteem under an overarching category of social value and ethics and spirituality under altruistic value. The present study follows the six customer value types by (Sánchez-Fernández et al., 2009).

Loyalty in QSRs

Empirical studies in services marketing (Bowden, 2009; Lai, 2015; Jin et al., 2016; Kusumawati & Rahayu, 2020) have suggested that behavioral loyalty determines the long-term profitability of the firm (Hyun, 2010). However, few researchers argue that to completely understand customer patronage behavior, it is important to study attitudinal loyalty along with behavioral loyalty (Kaur et al., 2019; Saini & Singh, 2020). A two dimensional concept of brand loyalty postulated by Day (1976) laid the foundation to further this argument. Based upon Day’s framework, Dick and Basu (1994) defined customer loyalty “as the strength of the relationship between a customer’s relative attitude towards brand/store/service/vendor (attitudinal loyalty) and patronage behavior such as the proportion of purchase, and the probability of purchases from the same supplier (behavioral loyalty)” (p.99). The authors proposed a two-dimensional cross-classification of loyalty with relative attitude (attitudinal loyalty) on one axis and repeat patronage (behavioral loyalty) on the other (both at two levels-high and low) to explain brand loyalty. As an outcome, four specific loyalty states were revealed: (i) no loyalty (low relative attitude with low repeat patronage), (ii) spurious loyalty (low relative attitude with high repeat patronage), (iii) latent loyalty (high relative attitude with low repeat patronage) and (iv) true loyalty (high relative attitude with high repeat patronage).

No loyalty could be experienced by first-time customers or customers who are not aware of the brand. Spurious loyalty is characterized by frequent customer visits with no strong attitude formation towards the brand among the visitors (Ngobo, 2017). This could happen due to multiple reasons: absence of competitors (Arslan, 2020), inertia (Cui et al., 2021), consumption because of peer / social influence (Balqiah et al., 2017), or perceiving the product as low involvement (Mathew & Thomas, 2018). Latent loyalty is experienced by customers who view the brand favorably, but unable to accomplish frequent repeat purchase because of a lack of resources such as time/money (Friman et al., 2020), social/social norms (Purani et al., 2019), availability/accessibility (Rahmiati et al., 2020). True loyalty is the most preferred state where the customer exhibits consistent repeat purchases along with a strong positive attitude for the brand in consideration (Mathew & Thomas, 2018).

Transitionary Nature of Customer Loyalty

Extant literature states that customer moves from lower loyalty state to higher loyalty in a series of orderly steps. A typology called the brand loyalty pyramid exists (Aaker, 1991) that aptly illustrates this concept. This pyramid is composed of five tiers. The first tier represents non-loyal customers how don’t perceive any value in the brand name. The second-tier customers are satisfied buyers who stick to the brand because of inertia. The third-tier buyers are satisfied but would not want to switch because of the risk associated with switching costs. The fourth-tier customers are named as friends while the fifth-tier customers are named as committed. Both these customers experience higher states of loyalty and show an increased inclination to stay loyal to the brand. A similar concept called the “loyalty ladder” (Payne, 1994) exists. According to this model, customer successively moves from non-customer to partner. The intermediate stages in the journey in successive order are clients, supporters, advocates. To move up in the hierarchy, customer must perceive better attitudinal loyalty or more behavioral loyalty or combination of both towards the brand depending upon the rung of the ladder. Ozimek (2010) further added two more rungs below the loyalty ladder namely hostile and enemies. A hostile customer is “dissatisfied with an organization's products or services and prepared to spread that dissatisfaction by word of mouth (WOM) among friends and colleagues”. An enemy is even more detrimental than hostile customers as they consciously invest energy and resources to campaign against the brand (Topalovic & Marinkovic, 2020).

Taking a non-linear approach to customer loyalty, Baloglu (2002) also reiterated Dick and Basu’s framework (1994) of classifying the customers into four quadrants based on the relative strength of attitudinal vs behavioral loyalty. This typology also classifies customers into four quadrants namely: non-loyal, spurious loyal, latent loyal, and true loyal like Dick & Basu (1994). These multiple prototypes of loyalty frameworks suggest that: a) loyalty is transitionary, b) multiple phases (states) of loyalty can exist, c) customer can move from one state of loyalty to the other. Considering the simplicity and applicability of Dick & Basu's (1994) framework in the restaurant setting (Tabaku & Kruja, 2019), we adhere to this framework in explaining the states of loyalty.

Research Objectives

While loyalty ladder, loyalty pyramid explicates a clear linear progression from lower rung to higher rung; within Dick & Basu's (1994) / Baloglu (2002) framework, the general route followed by customers who are climbing up in customer loyalty is not known. The first objective of the research is to explore the progression of customer loyalty within Dick and Basu's (1994) / Baloglu (2002) framework specific to the QSR segment.

The extant literature proves the multidimensional nature of perceived value and the transitionary nature of loyalty states. There is a possibility that, to progress from a loyalty state to a subsequent higher state, a customer might judge the value offered by the restaurant on various value dimensions. Based upon this assumption, the first hypothesis is proposed as follows:

H₁: Customer transits from lower loyalty state to higher loyalty state based on evaluation of perceived value dimensions.

If H₁ is found true, then there is no reason to assume that the importance of value dimensions is similar across all loyalty states. To enable the switch to a higher-value state, different value dimensions could be important for customers experiencing different states of customer loyalty.

To pursue the research objectives, we use a host of complex multivariate techniques spanning from factor analysis, confirmatory factor analysis, PERMAP and culminate with multiple logistic regression. Based upon the multivariate analysis of data, we formulate managerial implications that enable customers to move upward from their current loyalty state.

Methods

Sampling and Data Collection

Data were collected through self-administered questionnaires circulated across eight cities in India from different parts of the country with the help of a market research agency. Data was collected from people exiting QSRs at different points of the day. Stratified random sampling based on gender was used to ensure the equitable participation of both genders in the survey. a gender ratio of 52% males and 48% females was achieved in the final sample used for the study. A total of 960 questionnaires were distributed online, of which 580 usable responses were received. Respondents answered the questionnaire concerning their favorite QSR.

Profile of the Sample

The profiles of the sample used for this study are represented in Table 1. Out of the total 580 responses, half of the respondents (50.86%) lie in the age group of 20-29 years. a large part (44.48%) is composed of education with a bachelor’s degree. Employment-wise, there is a major chunk of students (46.55%). Income does not show huge gaps across categories, the highest being 34.66% of people earning more than 0.5 million in Indian National Rupees (INR).

Table 1 Profile Of The Sample
Characteristic	N	Percentage
Age
Below 20years	132	22.76%
20-29 years	295	50.86%
30-39 years	95	16.38%
40-49 years	26	4.48%
50-59 years	20	3.45%
60 years and above	12	2.07%
Gender
Female	278	48%
Male	302	52%
Education
Higher Secondary	94	16.21%
Bachelors	258	44.48%
Masters	186	32.07%
PhD	25	4.31%
Others	17	2.93%
Employment
Private sector	149	25.69%
Public sector	75	12.93%
Student	270	46.55%
Self employed	48	8.28%
Others	37	6.38%
Income (in INR)
Less than 1 lac	96	16.55%
1 lac to 3 lacs	136	23.45%
3 lacs to 5 lacs	147	25.34%
More than 5 lacs	201	34.66%

Measurement Items

The model consisted of six service value dimension constructs with 24 measurement items: efficiency (5 items), quality (4 items), social value (3 items), play (4 items), aesthetics (4 items), altruistic value (4 items) (Sánchez-Fernández et al., 2009). Loyalty was also envisaged as a multidimensional construct with 16 measurement items, four for each construct: cognitive loyalty, affective loyalty, conative loyalty, behavioral loyalty (Han et al., 2008). All items were measured using a 5-point Likert scale.

Data Analysis and Results

Stage 1: Assessing the Psychometric Properties of the Scale

Factor analysis was performed independently for items of perceived value and loyalty. Based upon cross-loading and non-loadings, few items were removed. The final factor structure was subjected to confirmatory factor analysis. Details of the items loading on constructs in CFA are given in Table 2. The chi-square value for this model was 1342.23, with 597 degrees of freedom. The root mean square residual (RMR) occurred to be 0.045 and lay in the acceptable range (<0.100). The root mean squared error of approximation (RMSEA) also lied in an acceptable range (<0.080) as the value was 0.047. The model fit indices indicate a good fit. All path coefficients were found to be positive and significant in the measurement model.

We have estimated the construct reliability of the scales used in the study using Cronbach’s alpha and composite reliability values. According to Hair et al. (2006), all the Cronbach’s alpha and composite reliability (CR) scores are in acceptable ranges (CR>0.8) Table 2. Building further on Hair et al. (2006), the Average Variance Extracted (AVE) values greater than 50%, and the CR values higher than 0.70 imply that the variance explained is greater than the error components of the construct. The values also indicate higher convergent validity. Fornell & Larker (1981) proposed that the AVE should be greater than the variance shared between the constructs in the model, which leads to discriminant validity. In the confirmatory phase, discriminant validity was established with the correlation coefficient between value dimensions less than 1 (Bagozzi & Heatherton, 1994).

Table 2 Reliability And Convergent Validity Test Results
Construct	Item	Item code	AVE	CR	Cronbach’s a
Efficiency	In general, you are happy with the prices of the restaurant	EFF 3	0.585	0.808	0.805
	The prices are good, considering what you have received from the restaurant	EFF 4
	The effort, time, and money spent in the restaurant are right	EFF 5
Quality	The service provided by the restaurant staff was up to the standard	QUAL 1	0.528	0.770	0.763
	Members of the restaurant staff are competent, accessible, and polite	QUAL 2
	Your relationship with the restaurant staff has been adequate	QUAL 3
Social Value	The people and the environment of the restaurant are in accordance with its social level and status	SOV 1	0.482	0.736	0.738
	You feel close to the environment and the people in the restaurant	SOV 2
	In general, your experience in the restaurant is important for your social relationships, your self-esteem, and your status	SOV 3
Play	The environment of the restaurant (music, customers, etc.) has helped you to enjoy your stay	PLAY 1	0.506	0.754	0.754
	Going to the restaurant has served as a way of temporary escape for you	PLAY 2
	The staff of the restaurant has contributed to making your stay more amusing and entertaining	PLAY 3
	You have enjoyed your visit to the restaurant	PLAY 4
Aesthetics	You like the arrangement of the table and the food in the restaurant	AES 1	0.502	0.750	0.751
	The appearance of the staff at the restaurant is appropriate	AES 3
	In your opinion, the restaurant’s taste is fine	AES 4
Altruistic Value	The environmental preservation of the restaurant is coherent with your ethical and moral values	ALTV 2	0.609	0.822	0.811
	You feel attracted by the spiritual atmosphere of the restaurant	ALTV 3
	Going to the restaurant has had an ethical and spiritual value for you	ALTV 4
Cognitive Loyalty	I consider the restaurant my first choice when I need to eat out in the city	COGL 1	0.528	0.770	0.768
	I consider the restaurant my primary restaurant when I eat out in the city	COGL 2
	The service of the restaurant is better than that of other similar restaurants in the city	COGL 3
Affective Loyalty	I like visiting the restaurant very much	AFFL 1	0.557	0.834	0.847
	To me, the restaurant is the one I enjoy the most in the city	AFFL 2
	Compared with other similar restaurants, I prefer this restaurant more	AFFL 3
	This restaurant is the one that I appreciate most in the city	AFFL 4
Conative Loyalty	I intend to visit the restaurant again when I want to eat out in the city	CONL 1	0.605	0.821	0.819
	I intend to recommend the restaurant to others	CONL 2
	I intend to say good things about the restaurant to others	CONL 3
Behavioral Loyalty	When I eat out in the city, I always visit this restaurant	BEHL 1	0.482	0.736	0.785
	Compared with other restaurants in the city, I have visited more at this restaurant	BEHL 3
	Compared with other restaurants in the city, I have used more of the services offered at this restaurant	BEHL 4

Stage 2: Calculating loyalty states for responses

Dick & Basu’s (1994) / Baloglu (2002) framework for calculating the loyalty state was used for each response. The sum of factor scores of cognitive loyalty and affective loyalty was used as a measure for attitudinal loyalty. The sum of factor scores of conative loyalty and behavioral loyalty was used as a measure for behavioral loyalty. Median splits were used on attitudinal loyalty and behavioral loyalty to get high and low categories. Dick & Basu’s (1994) classification was used to determine loyalty levels. The classification scheme is as follows: No Loyalty (low on attitudinal and behavioral loyalty), (ii) Spurious Loyalty (low on attitude loyalty and high on behavioral loyalty), (iii) Latent Loyalty (high on attitudinal loyalty with low on behavioral loyalty) and (iv) True Loyal (high on attitudinal loyalty and behavioral loyalty).

Stage 3: Visualizing the Relationships between Loyalty States and Value Dimensions

PERMAP effectively visualizes multi-dimensional relationships in a low-dimensional space (Heady & Lucas, 1997). The present study employs PERMAP to visually represent the differing importance of value dimensions across different loyalty states and gauge similarity across value dimensions. As input for analysis, the mean factor scores of six value dimensions across different loyalty states were calculated and fed into PERMAP software Table 3. To obtain a perceptual map ratio data type was selected, and Cos (α) distance measure was used. PERMAP uses a metric called objective loss function (OLF) to depict the extent of discrepancy between the data and its depiction on the map. A lower value on the metric, preferably closer to zero, indicates a good fit. For the data used in the analysis, OLF obtained was 0.000039 which indicates a good fit. In the PERMAP visual, the arrows indicate value dimensions whereas circles indicate loyalty states. Projections of the tie-lines on the vectors indicate the actual position of the circle with respect to that vector.

Table 3 Mean Scores Across Loyalty Levels And Value Dimensions
Loyalty Levels	Avg of Aesthetics	Avg of Social	Avg of Play	Avg of Quality	Avg of Efficiency	Avg of Altruistic
Non- Loyal	3.21	3.06	2.91	3.33	3.07	2.66
Spurious Loyal	3.46	3.42	3.21	3.59	3.14	2.79
Latent Loyal	3.84	3.60	3.52	3.80	3.59	2.91
True Loyal	4.08	4.01	3.82	4.16	3.76	3.42

Interpretation of PERMAP is two-pronged. Proximity in the evaluation of value dimensions is represented using the cosine of the angle between the vectors. In the PERMAP visual, vectors close to each other, indicate similar evaluation by customers on those vectors. It could infer single higher-order construct ensuing from those vectors. As observed, all the vectors corresponding to a single higher-order perceived value dimension. The second interpretation relates to interpreting the tie-projection of all circles on the vector. Circles (loyalty states) whose tie-lines are closer to the arrowhead of the vector (perceived value dimension) are to be deemed as getting better scored (evaluated) compared to others Figure 1. It is observable that non-loyal customers have tie lines associated with all the value dimension vectors away from the arrowhead, indicating that non-loyal customers score least on all value dimensions. Similarly, we may observe that the spurious loyal customers better evaluate all value dimensions over non-loyal customers latent loyal customers have a better evaluation of all value dimensions over the spurious loyal customers. Correspondingly, true loyal customers have a better evaluation of all value dimensions over latent loyal customers. These findings indicate an ascending order in loyalty transition starting from non-loyal to true loyal state in the following manner: non-loyal state ? spurious loyal state ? latent loyal state ? true loyal state.

Stage 4: Logistic Regression

Based on the results of the third stage, three logistic regressions were considered. The objective of the logistic regression framework was to explain which value dimensions were responsible for customer transition from a) non-loyal state to spurious loyal state, b) spurious loyal state to latent loyal state, c) latent loyal state to true loyal state. For all the logistic regressions, independent variables were factor scores on value dimensions and the dependent variable was loyalty state. However, the data used for analysis and reference variable coding differed across all three logistic regressions.

The first logistic regression explains the value dimensions responsible for customer transition from non-loyal state to spurious loyal state. Accordingly, customers only in the non-loyal state and spurious loyal state were considered. The non-loyal customer state acted as the reference category for the dependent variable in the regression. The second logistic regression explains the value dimensions responsible for customer transition from spurious loyal state to latent loyal state. Accordingly, customers only in the spurious loyal state and latent loyal state were considered. Spurious customer state acted as a reference category for the dependent variable in the regression. The third logistic regression explains the value dimensions responsible for customer transition from latent loyal state to the true state. Accordingly, customers only in the latent loyal state and true loyal state were considered. The latent customer state acted as a reference category for the dependent variable in the regression. Results are tabulated in Table 4.

Table 4 Parameter Estimates
Logistic Regression 1		B	Std. Error	Wald	Df	Sig.	Exp(B)	95% Confidence Interval for Exp(B)
								Lower Bound	Upper Bound
	Intercept	-5.215	1.215	18.412	1	0
Spurious loyal	Mean AES	0.234	0.245	0.906	1	0.341	1.263	0.781	2.043
	Mean ALTV	-0.156	0.213	0.536	1	0.464	0.855	0.563	1.299
	Mean QUAL	0.311	0.257	1.47	1	0.225	1.365	0.826	2.257
	Mean EFF	0.006	0.217	0.001	1	0.977	1.006	0.657	1.541
	Mean PLAY	0.263	0.243	1.173	1	0.279	1.301	0.808	2.093
	Mean SOV	0.685	0.276	6.166	1	0.013	1.985	1.155	3.409

From the logistic regression results, it may be observed that social value is the only variable that is positively significant in transitioning the customer from a non-loyal state to a spurious loyal state. Similarly, it may be observed that for customers who are in a spurious loyal state, aesthetics, efficiency, and play are positively significant in determining the transition from spurious loyal to the latent loyal state. Finally, it was found that altruistic value, quality, and social value were positively significant in determining the transition from the latent loyal to the true loyal state. In all logistic regressions, at least one value dimension was found significant. This proves the first hypothesis that loyalty switch to a higher state is influenced by perceived value dimensions. Different value dimensions were significant in each of the three logistic regression. This finding proves the second hypothesis of the study Tables 5, 6.

Table 5 Parameter Estimates
Logistic Regression 2		B	Std. Error	Wald	Df	Sig.	Exp(B)	95% Confidence Interval for Exp(B)
								Lower Bound	Upper Bound
Latent loyal	Intercept	-6.441	0.571	16.814	1	0
	Mean AES	0.846	0.323	6.879	1	0.009	2.33	1.238	4.385
	Mean ALTV	-0.368	0.236	2.441	1	0.118	0.692	0.436	1.098
	Mean QUAL	-0.043	0.297	0.021	1	0.884	0.958	0.535	1.714
	Mean EFF	0.933	0.265	12.411	1	0	2.542	1.513	4.272
	Mean PLAY	0.555	0.286	3.767	1	0.052	1.743	0.995	3.054
	Mean SOV	-0.08	0.34	0.055	1	0.814	0.923	0.474	1.796

Table 6 Parameter Estimates
Logistic Regression 3		B	Std. Error	Wald	Df	Sig.	Exp(B)	95% Confidence Interval for Exp(B)
								Lower Bound	Upper Bound
True Loyal	Intercept	-4.269	1.229	12.073	1	0.001
	Mean AES	-0.07	0.279	0.062	1	0.803	0.933	0.54	1.612
	Mean ALTV	0.448	0.175	6.525	1	0.011	1.565	1.11	2.207
	Mean QUAL	0.502	0.232	4.694	1	0.03	1.651	1.049	2.6
	Mean EFF	-0.135	0.19	0.503	1	0.478	0.874	0.602	1.269
	Mean PLAY	0.168	0.234	0.514	1	0.473	1.183	0.748	1.87
	Mean SOV	0.523	0.256	4.169	1	0.041	1.688	1.021	2.789

Discussion of Results

Our study aims to comprehend the ascending order of loyalty states from lower to higher according to Dick & Basu's (1994) / Baloglu (2002) framework and explicate which value dimensions trigger the customer progression from lower loyalty state to successive higher loyalty state in hierarchical order. The study’s focus is limited to the QSR industry. To pursue these objectives, firstly, the value dimensions employed in the questionnaire were psychometrically validated. The exploratory and confirmatory factor analysis reaffirms value dimensions pertinent to quick-service restaurants in the Indian context are aesthetics, social value, play, quality, efficiency, and altruistic value. PERMAP depicts the orderly progress of loyalty states as non-loyal spurious loyal latent loyal true loyal state.

The results of the logistic regression show which value dimensions significantly determine the switch from a lower loyalty state to a higher loyalty state. During a shift from non-loyal to spurious loyal, only social value is significantly important. Similarly, during a shift from spurious loyal to latent loyal, aesthetics, efficiency, and play are significant.

Finally, during the shift from latent loyal to true loyal, the value dimensions quality, social, and altruistic value are significant.

Managerial Implications

Customers start their relationship with QSR in a non-loyal state. To ascend from non-loyal state to spurious loyal state, results point towards social value as the only determinant. Accordingly, for the first-time customers, clues of social value may be orchestrated by: offering group discounts, positioning the QSR as a preferred hangout place for a small gathering, training the service staff to be courteous towards the customers, and building brand imagery and product offerings around target audience profile (Rintamaki et al., 2006). For customers who are already in the spurious loyal stage, values related to aesthetics, efficacy, and play are to be orchestrated to enable the ascent to the latent loyal stage. Ambiance and offerings may be envisaged creatively to suit the target audience's tastes and preferences to clue aesthetic value (Hyun et al., 2018). Innovation in receiving the customers, orchestrating service delivery, and seeking feedback may induce play value effectively (Woolley & Lowe, 2013). The ability to prognosticate customer special requirements and acting proactively, quick delivery of the order can clue efficacy (Susskind, 2000). In a mature relationship where the customer ascends from latent loyalty to true loyalty, cues related to quality, altruistic and social values must be orchestrated. Quality can be orchestrated using clues such as open kitchen, chef/process superiority over other QSRs, evidence of superior ingredients used (Lu & Gursoy, 2017). Social value can be woven into customer service experience by showcasing awards, customer memorials (feedback), photographs of prominent public figures who visited the place in prominent locations within the restaurant premises (Gallus & Frey, 2016). Positive media coverage also enhances the social value of QSR (Morris & Shin, 2002). Altruistic value can be exhibited by donating to charities, lending help in times of adversity, and being tolerant to special requirements of different ethnicities during food preparation when such requests are made (De Groot & Steg, 2008).

Getting acquainted with a customer is a mammoth task. Our study suggests that social value is the only clue that becomes crucial both in the early stages and maturing stages of the relationship. Hence, it has to be given special emphasis both by early QSR startups as well as well-established QSR-based enterprises. Managers can efficiently track the customer's loyalty state by observing past purchases, behaviors from previous encounters such as special requests, and suggestions or by administering the loyalty questionnaire given in this study as part of seeking feedback. Study results suggest that it is desirable on the part of the employees and management that they emphasize cues related to value dimensions differently for customers in different loyalty states. The study outlines the need to segment the customers based on loyalty states and pursue unique marketing strategies such as relationship marketing, personalized selling, and up and cross-selling initiatives differently for customers belonging to different loyalty states.

Conclusion & Future Research Directions

Our study is limited to only one service setting and one geography. Future research in this area should attempt to investigate the validity of study findings in other product and service categories. Specifically, it would be interesting to explore if the results attained here hold for other restaurant categories such as fine-dine restaurants. One interesting area of research is to generate a repertoire of customer behaviors that are exhibited by customers in different loyalty states in dine-in restaurants.

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Received: 30-Nov-2022, Manuscript No. AMSJ-22-12943; Editor assigned: 31-Nov-2022, PreQC No. AMSJ-22-12943(PQ); Reviewed: 23-Dec-2022, QC No. AMSJ-22-12943; Revised: 28-Jan-2023, Manuscript No. AMSJ-22-12943(R); Published: 06-Feb-2023