INTRODUCTION:

Tourism in Kashmir plays a significant role in the region's economy. As the demand for quality experiences grows, service quality measurement becomes critical. Two prominent models ServQual and ServPerf used in service quality assessment developed by Parasuraman et al. and Cronin and Taylor respectively^2,11. This study aims to compare the applicability and performance of these models in the unique socio-cultural and geopolitical context of Kashmir.

LITERATURE REVIEW:

ServQual is a model that identifies the disparities between what customers expect and what they perceive across five areas: tangibles, reliability, responsiveness, assurance, and empathy. SERVPERF, on the other hand, measures only the performance perception, arguing that expectations are implicit in performance ratings. Prior studies have shown mixed results regarding their applicability across different service industries and geographic regions.

A study in hospitality industry regarding SERVPERF had superior predictive ability based on satisfaction of customers¹⁰. Similarly, SERVPERF offers greater diagnostic value for managers in service settings¹. Conversely, SERVQUAL remained a useful tool particularly in culturally complex or expectation-sensitive contexts⁷.

The interaction between perceived destination-based service quality and tourist satisfaction proved to be a good predictor of destination loyalty⁹. There is huge gap between customers’ expectations and perceptions towards the company’s services¹². The demographic variables are impacting less the level of service quality experienced by the tourists³. The elements of job resources exert its direct impact on employee perceived service quality and their turnover intentions⁸. Customer’s satisfaction can only be attained by organisations if they are able to provide quality services at affordable prices after matching customer’s expectations⁴.

In the South Asian context, SERVQUAL in Malaysia's tourism sector is effective for cross-cultural service evaluation⁶. However, SERVPERF is more suitable in India's hospitality industry due to its simpler, performance-focused approach⁵. These findings underline the relevance of contextual factors, such as customer expectations and cultural norms, in choosing between the models.

METHODOLOGY:

Sample and Data Collection: The study targeted domestic and international tourists visiting Gulmarg, Pahalgam, and Sonamarg. A total of 120 usable responses were collected using stratified random sampling. Questionnaires were distributed in person at tourist spots.

Instrumentation: Both ServQual and ServPerf questionnaires were used. Five service quality dimensions contained respective 22 items. Among the 7 point and 5-point likert scale, 5-point likert scale was preferred used for responses.

Data Analysis: Reliability testing through the Cronbach's alpha and Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were included in the Statistical analysis. Comparative model fit was assessed using indices such as Chi-square/df, RMSEA, CFI, and TLI.

Table 1: Demographics

Demographic-Variable	Frequency	Percentage
*Gender*
Male	68	56.70%
Female	52	43.30%
*Age Group*
18-30	48	40.0%
31-50	56	46.7%
51+	16	13.3%
*Tourist Type*
Domestic	80	66.7%
International	40	33.3%

RESULTS:

Reliability and Validity: A good internal consistency has been demonstrated by both models as Cronbach's alpha value exceeds 0.70 for all five dimensions. EFA revealed consistent factor structures aligning with the theoretical models.

Table 2: Cronbach's Alpha Values

Model	Tangibles	Reliability	Responsiveness	Assurance	Empathy
SERVQUAL	0.78	0.80	0.82	0.79	0.77
SERVPERF	0.81	0.83	0.84	0.82	0.80

Exploratory Factor Analysis (EFA):

The exploratory factor analysis (EFA) utilized principal component analysis with varimax rotation. In both models, five factors with eigenvalues exceeding 1 were identified, which explained 68.4% of the variance in SERVQUAL and 71.2% in SERVPERF. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was found to be 0.89 for SERVQUAL and 0.91 for SERVPERF, indicating that the data was suitable for factor analysis. Additionally, Bartlett’s Test of Sphericity yielded significant results (p < 0.001), further affirming the appropriateness of the data.

Table 3: EFA Factor Loadings

Dimension	Example Item	SERVQUAL Loading	SERVPERF Loading
Tangibles	"Facilities are visually appealing."	0.74	0.78
Reliability	"Services are delivered as promised."	0.72	0.79
Responsiveness	"Prompt service is provided."	0.77	0.82
Assurance	"Staff instills confidence."	0.75	0.80
Empathy	"Staff gives individual attention."	0.70	0.76

Model Fit Comparison: The findings from the CFA indicated that SERVPERF exhibited a marginally superior model fit (Chi-square/df = 2.1, CFI = 0.94, RMSEA = 0.052, TLI = 0.93) when contrasted with SERVQUAL (Chi-square/df = 2.5, CFI = 0.91, RMSEA = 0.061, TLI = 0.89).

Table 4: CFA Model Fit Indices for SERVQUAL and SERVPERF

Model	Chi-square/df	RMSEA	CFI	TLI
SERVQUAL	2.50	0.061	0.91	0.89
SERVPERF	2.10	0.052	0.94	0.93

Figure 1: CFA Model Fit Indices Comparison

DISCUSSION:

The findings suggest that while both models are useful, SERVPERF's performance-only approach may better reflect the actual experiences of tourists in Kashmir. This might be due to the volatile nature of the region where tourists may not have well-formed expectations prior to arrival.

CONCLUSION:

The research study adds to the existing literature by offering empirical evidence from a conflict-sensitive tourism destination. Stakeholders in Kashmir tourism should consider adopting the SERVPERF model for regular service quality assessments to improve service delivery and tourist satisfaction.

ACKNOWLEDGEMENT:

I am grateful to the Indian Council of Social Science Research (ICSSR) support that facilitated in smooth execution of this research article.

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Received on 22.08.2025 Revised on 06.12.2025

Accepted on 14.02.2026 Published on 17.03.2026

Available online from March 20, 2026

Int. J. Ad. Social Sciences. 2026; 14(1):15-18.

DOI: 10.52711/2454-2679.2026.00005

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