Human contribution to service quality in West Bengal’s luxury hotels

Abstract

This article substantiates employees’ contribution to customer satisfaction of hotel guests. The reported study’s purposefulness has been to evaluate the relative importance of the personnel component in achieving service quality and customer satisfaction in luxury hotels. Data were collected from 500 guests at 24 luxury hotels West Bengal, India. Seven common factor measurement models are deemed robustly reliable in determining hotel performance. The results are validated using Structural Equation Modelling (SEM) applied to hotel-guest perceptions that pertain to service quality and customer satisfaction.

Keywords

Personnel service quality customer satisfaction structural equation modeling hotel

Ms. Dipa Banerjee, has over 15 years of rich experience in academics, training and development. She is imparting lecturers in the area of Functional Management and Organizational Behaviour in Private Management Colleges. She is currently perusing her doctoral research in the area of Strategic Management and Human Resource Management. She has to her credit several published research articles at national and international conferences and journals.

Dr. Debasish Biswas, presently serving as an Assistant Professor in the Department of Business Administration, Vidyasagar University. Dr. Biswas qualified UGC-NET in the field of Human Resource Management. He was awarded M.Phil in Commerce. Dr. Biswas served as a Full Time Lecturer in the Department of Management, University of North Bengal, West Bengal. His areas of specializations are Accounting and Financial Management, Human Resource Management, Industrial Relations and Quantitative Techniques. Dr. Debasish Biswas is prolific researcher. He has contributed 65 research articles in different interdisciplinary areas of management in different National Seminars and Conferences. He has authored 30 research articles in National and International journals of repute. He acted as Resource person and Chairperson in different National and International Seminars in different parts of India. He is a life member of Indian Commerce Association, International Business Studies Academia (IBSA, Durgapur) and All India Accounting Association, Kolkata. He has authored five books. He received Best Business Academic of the Year Award -2016 (Gold Medal) at the 69th All India Commerce Conference, organized by Lucknow University.

1 Importance of the study

The main focus of the study is to evaluate the relative importance of personnel components through service quality, which leads to customer satisfaction. Using Structural Equation Modeling (SEM), we found significant positive relations among the personnel components, service quality and customer satisfaction. Also the results of this study showed that service quality has a relationship with customer satisfaction.

2 Review of literature

At the individual level, SEM is particularly appropriate for tourism research because the factors influencing tourism demand are linked to personal determinants of consumer behaviour [14]. This may be due to the complexity of the SEM method and the lack of user-friendly computer manuals [12].

By using SEM correctly, researchers can avoid missteps that could compromise the validity of results; are restrained from inferring incorrect conclusions; and can develop accurate knowledge about causal relationships among variables [5].

Presently the applications of SEM (Structural Equation Modelling), is limited in tourism demand modelling compared to other methods [8].

Mai and Ness [10] in their article analyzed customer satisfaction in terms of eight satisfaction attributes, The initial analysis considered analysis of individual scale items. However unlike traditional multivariate regression models, SEM uses simultaneous equation models in which variables (both observed and latent) may influence one another reciprocally. This makes SEM a very suitable method for analyzing tourism demand [13].

Assaker Guy et al. [1] critically documented that how SEM has been applied from a technical perspective. The article focused on how SEM has been used in published articles and provides guidance for future users. The article then evaluates the methodological quality of applications by assessing how they conform to formal statistical assumptions required for the valid use of these techniques while identifying problem areas and suggesting avenues for improvement. Finally, the article concludes by summarizing the findings and results and providing a checklist of technical issues to consider when using SEM methodology in tourism demand modelling.

Al Muala [11] This research article examines the antecedents of tourist satisfaction among international tourists using SEM. Jordan image is found to be positively and significantly related to tourist satisfaction. The result also shows that the Generating Model (GM) is the best model to explain the international tourists’ satisfaction as compared to the hypothesized models. In this study, five-common factor measurement model was found to be valid and reliable to be used in determining performance of the airline providers. Out of these five factors, three factors (tangibility, reliability, assurance) resulted in strong significance.

Mandal Kaushik et al. [9] The authors have identified four important strategic-facets namely Intelligence, Big Bossing, Contract Orientation and Expert for Indian distributions. Result indicates deviation from the global research outcome. The authors have developed a valid and reliable scale (instrument) that would able to measure the ‘mechanism of channel control’ by the channel leaders for various distribution.

Abdullah Kalthom et al. [2] This research article validated the model of performance of the airline services from the perspectives of Malaysian passengers.

El-garaihy [6] The main objective of this research article is to construct a model of hospitality service quality, within the borders of eastern province in the Saudi Arabia. The research initially examines the literature review, and then adapts the features of hotel industry. Empathy, credibility, responsiveness, security, tangibles, courtesy and competence are the seven dimensions assessing hospitality service quality. Structural Equation Modeling used in order to study conceptual model of hospitality service quality.

3 Research gaps

From the literature reviewed it is evident that in the study location there have been less studies conducted in the hotel sector. Most of the research articles are focused on the importance of customer satisfaction but not focused on the personnel components to achieve customer satisfaction.

4 Purposefulness of the study

To find out the relationship among personnel, service quality and customer satisfaction.

To find out the essential components of personnel, which are basically responsible to achieve the customer satisfaction.

5 Hypotheses

H₁₁: Tangibility positively influences the service quality of hotel industry in West Bengal.

H₁₂: Reliability positively influences the service quality of hotel industry in West Bengal.

H₁₃: Responsiveness positively influences the service quality of hotel industry in West Bengal.

H₁₄: Assurance positively influences the service quality of hotel industry in West Bengal.

H₁₅: Empathy positively influences the service quality of hotel industry in West Bengal.

H₁₆: Service quality positively influences the customer satisfaction of hotel industry in West Bengal.

6 Research methodology

6.1 Sources of data

Secondary data: Secondary data have been collected from the various sources like textbooks and hotel websites and extensive literature review using electronic library databases, Primary Data: The primary data collection have been done through the questionnaire filled by the guest of the hotels.

6.2 Sample size

In our study, a sample of 500 guests from the selected hotels was drawn for this research. The respondents who have fully completed their questionnaires were considered as the sample. A sample size is consisted with the responses of 500.

6.3 Variable measurements

Independent variables: This measure is based on the personnel variables such as Knowledge, Skills, Attitude, Behaviour, Training, Education, Ability and Experience. Dependent Variables: Customer satisfaction and service quality.

6.4 Questionnaire constructs

Phase 1: Construction of raw questionnaire is based on basic information, which were collected in the theoretical models and related to previous studies.

Phase 2: Questionnaire’s assessment done through testing on the clarity and comprehension. The evaluation is done by sending directly to close colleagues to evaluate how they answer and decipher each question.

Phase 3: Questions revision and survey.

Phase 4: Data collection and filtration finalization.

6.5 Scale selection

The investigation includes ‘measurement of attitude’ of the hotel employees using 5-point Liker scale ranging from 1 to 5, where, 1 = ‘Strongly disagree’, 2 = ‘Disagree’, 3 = “Moderately agree, 4 = ‘Agree’, 5 = ‘Strongly agree.

6.6 Analysis of moment structure (AMOS)

This is very powerful multivariate technique to express interdependence between the variables through a path diagram. Path diagram is a flow chart which shows the interconnections of the variables in a logical way. We have conducted Confirmatory Factor Analysis (CFA) on the basis of the result of exploratory factor analysis by using software called analysis of moment structure (AMOS). We have used CFA to determine the goodness of fit between hypothesized model and sample data. Furthermore, SEM estimates the parameters that best reproduce the sample covariance matrix, and the covariance matrix subsequently assumes linearity between variables. SEM further assumes linear relationships between indicator and latent variables and between latent variables themselves. Applying SEM is still limited in hotel sector, although at least two features make SEM applicable for analyzing tourism data. First, SEM allows the researcher to assess latent constructs explicitly and correct for unreliable measures, provided multiple indicators of each construct are available. Second, SEM makes it possible to investigate, using a simple approach, comprehensive theoretical frameworks in which the effects of constructs are propagated across multiple layers of variables via direct and indirect paths of influence. These advantages, coupled with developing more Sophisticated, yet user-friendly computer programs to estimate and test such models, make SEM a solid approach for widespread use in studying customer satisfaction through proper deployment of personnel components.

6.6.1 Confirmatory factor analysis (CFA)

We conducted Confirmatory factor analysis (CFA) on the result of exploratory factor analysis by using software called analysis of moment structure (AMOS version 7). Generally we use CFA to confirm the exploratory factor model. CFA is same as a structural equation modeling (SEM) technique. We use CFA to determine the goodness of fit between hypothesized model and sample data. For goodness of fit statistics, we focus on three models. The three models are hypothesized model (our test model), saturated model and independence model or null model. The null model means where the correlation among the variables are zero i.e. all variables are independent. In case of saturated models the number of estimated parameters equals the number of data points (i.e. variance and covariance of the observed variables).Focusing on the exploratory factor analysis using AMOS, we get the first set of fit statistics From the result it is clear that CMIN which represents the discrepancy between the unrestricted sample covariance matrix S and the restricted covariance matrix Σ (θ). In large sample, CMIN is distributed as a central X² with degrees of freedom equal to 1/2 p (p+1)– t, where p is the number of observed variables and t is the number of parameters to be estimated. In general the test statistic is H₀: Σ=Σ (θ) is equivalent to the hypothesis that Σ – Σ (θ) = 0, follows a central X²with p (p+1) – t degree of freedom. If H₀ is accepted i.e. higher the probability associated with X², the closer fit between the hypothesized model (under H₀) and the perfect fit. We can add a path to a factor model based on the combined comprehension of theoretical, logical and empirical knowledge. Modification indices guide us for effective path addition to the model. Using AMOS we get idea for addition of path to improve the goodness fit of the proposed factor model.

6.6.2 Maximum likelihood estimation (MLE)

Data must also meet the assumed distribution of the estimation approach used. Maximum Likelihood Estimation (MLE) is the dominant approach for estimating SEM; it assumes that indicator variables have multivariate normal distributions.

7 Data analysis and interpretation

In this Research Work, we have applied statistical tools that may provide us a reasonable accuracy in the quantitative aspect vis a vis, contributed to satisfy the result with the concept and conventions of Strategic Human Resource Management. Application of Quantitative tools in the Qualitative Research Work may put always extra millage Our Research is consisted with the applications of the following tools in the domain of Human Resource. Further the model was assessed based on the following indices: the chi-square test, the comparative fit index (CFI), and the root mean square of approximation (RMSEA), as per the suggestions of many scholars [3, 7].

7.1 C MIN/DF

The relative chi square is an index of modification towards the model evaluation. It may help to modify the path to obtain a better result on the model fit. As per our analysis shown in Table 1 it is 1.610, which is acceptable.

Table 1
Fit Indices of CFA for structural model

Fit Index Acceptable Threshold Levels Structural Model Values

χ2/d.f (Chi-square/degree of freedom) Values less than 3 1.610

RMSEA (Root mean-square error of approximation) Values less than 0.06 0.035

GFI (Goodness of fit index) Values greater than 0.90 0.996

AGFI (Adjusted goodness of fit index) Values greater than 0.90 0.974

NFI (Normed fit index) Values greater than 0.90 0.992

CFI (Comparative fit index) Values greater than 0.90 0.997

Fit Index	Acceptable Threshold Levels	Structural Model Values
χ2/d.f (Chi-square/degree of freedom)	Values less than 3	1.610
RMSEA (Root mean-square error of approximation)	Values less than 0.06	0.035
GFI (Goodness of fit index)	Values greater than 0.90	0.996
AGFI (Adjusted goodness of fit index)	Values greater than 0.90	0.974
NFI (Normed fit index)	Values greater than 0.90	0.992
CFI (Comparative fit index)	Values greater than 0.90	0.997

7.2 Goodness of fit index (GFI) and Adjusted goodness of fit (AGFI)

These are chi square based calculations independent of degree of freedom. It varies from 0 (poor fit) to 1 (perfect fit). It indicates the proportion of variance in the sample variance co variance matrix and that is accounted for by the model. In this study, we obtained the GFI value 0.996 as shown in Table 1. So, our model is good and it matches the criteria of goodness of fit.

7.3 Adjusted goodness of fit (AGFI)

It should be ≥0.90 for goodness of model fit. In this analysis, we obtained the AGFI of 0.974 as shown in Table 1, which also matched the criteria of goodness of fit.

7.4 Root mean square error of approximation (RMSEA)

It is based on predicted versus observed co-variance but penalizing for black of parsimony (or simplicity), in assessing a model’s amount of error. It is popular because it does not require comparison with a null mode. In our study as shown in Table 1, we got RMSEA of 0.035, which indicates the excellent fitness of the model and it is estimated at 90% upper end. RMSEA <0.08, acceptable, <0.05 excellent [4].

7.5 Normed fit index (NFI) and Comparative fit index (CFI)

It should be ≥0.90 for goodness of model fit. In this analysis, we obtained the NFI of 0.992 and CFI of 0.997as shown in Table 1, which also matched the criteria of goodness of fit. The result of path analysis of structural model is shown below. It clearly indicates that all the hypotheses framed in our study are fully supported with significant ‘p’ values.

From the path analysis of structural model (shown in Table 2) it is clearly observed that ‘reliability’ is the strongest determinant (t value = +13.000) which influences positively on the quality of service in hotel industry. It is further shown that ‘service quality’ (t value = +12.495) positively influences the customer satisfaction. It is also seen that ‘tangibility’ (t value = +12.487), ‘assurance’ (t value = +5.206), ‘responsiveness’ (t value = +2.664) and ‘empathy’ (t value = +2.094) have also direct positive impact on service quality.

Table 2
Path analysis of structural model

Measurement Path Hypothesis Regression Estimate t- Value (C.R.) P- Value Assessment

QUANOW ← TNOW H₁ 0.518 12.487 ^* Supported

QUANOW ← RELNOW H₂ 0.430 13.000 ^* Supported

QUANOW ← RESNOW H₃ 0.079 2.644 0.008 Supported

QUANOW ← ANOW H₄ 0.180 5.206 ^* Supported

QUANOW ← ENOW H₅ 0.072 2.094 0.036 Supported

SATNOW ← QUANOW H₆ 0.757 12.495 ^* Supported

SATNOW ← RELNOW New (Created) 0.138 2.561 0.010 Supported

Measurement Path	Hypothesis	Regression Estimate	t- Value (C.R.)	P- Value	Assessment
QUANOW	←	TNOW	H₁	0.518	12.487	^***	Supported
QUANOW	←	RELNOW	H₂	0.430	13.000	^***	Supported
QUANOW	←	RESNOW	H₃	0.079	2.644	0.008	Supported
QUANOW	←	ANOW	H₄	0.180	5.206	^***	Supported
QUANOW	←	ENOW	H₅	0.072	2.094	0.036	Supported
SATNOW	←	QUANOW	H₆	0.757	12.495	^***	Supported
SATNOW	←	RELNOW	New (Created)	0.138	2.561	0.010	Supported

Significant Regression co-efficient (P < 0.01 and 0.05).

Through AMOS, the model shown in Fig. 2 has been suggested which is modified in comparison to our hypothesized model based on the hypotheses in our study.

Fig.1

Hypothesized research model. Source: The Authors.

Fig.2

Outcome of hypothesized structural model.

Construct Reliabilities (CR) values, which are more than 0.7 supports internal consistency among the items of underlying factors. The convergent validity of measurement model supports in this study where all Average variance extracted (AVE) is more than 0.5 and Hair et al., [7] The conditions (CR >AVE, MSV <AVE and ASV <AVE) under Measurement model also support convergent and discriminant validity.

Table 3

Measurement model result

Constructs/Factors	Construct Reliability (CR)	Average variance extracted (AVE)	Maximum Shared Variance (MSV)	Average Shared Variance (ASV)
Service quality	0.872	0.765	0.259	0.159
Tangibility	0.932	0.768	0.374	0.237
Reliability	0.874	0.704	0.322	0.178
Responsiveness	0.954	0.876	0.231	0.144
Assurance	0.933	0.753	0.373	0.175
Empathy	0.961	0.864	0.216	0.213
Customer satisfaction	0.922	0.881	0.234	0.065

Source: Result obtained from IBM SPSS Software.

8 Findings of study

Though hotel sector is one of the fast growing service sectors in India, researches do not provide clear idea on personnel components, which will give special emphasis on service quality. Our empirical result showed that the components of personnel will positively influence service quality, which enhances the customer satisfaction.

Based on the above, clearly the proposed model has a very good high degree of conformity. Accordingly, the model is acceptable with a high degree to be used in measuring the quality of services provided by the hotels, which leads to customer satisfaction.

It is also established from the constructs of the proposed framework that reliability factor of service quality, which consists of skill and ability of personnel components have a direct impact upon the customer satisfaction. The use of Structural Equation Modelling (SEM) in validating the model is also a valuable contribution to our study.

Our empirical study confirmed that service quality was a significant determinant of customer satisfaction in the luxury hotels in West Bengal, India.

9 Conclusions

This analytical study focussed on the customer satisfaction in respect of personnel components with special reference to luxury hotels located in West Bengal. Studies revealed that the customer satisfaction was highly dependent upon the employee’s attitude and the service quality offered to the customer. It was also found significant positive relations among the personnel components, service quality and customer satisfaction.

So, in a nutshell, it is asserted that managing personnel appropriately is one of the permanent solutions to ensure the employee’s performance. Specifically, the organization has to pay more attention to components of personnel, which trends to have a greater impact on employee’s performance and ultimately leads to the customer satisfaction.

10 Limitations and future scope

Our Research work also confined to some limitations otherwise it could have derived reasonably effective results. This study was conducted on the responses from guests of hotels, which may vary with other sectors. Data collection from the hotels is one of the limiting factors. The present article only validated the major components of personnel without investigating its impact on any other construct. We have studied the customer satisfaction among luxury hotels in West Bengal only. In future, the study may explore in other hotels in India.

11 Recommendations and Suggestions

In our article, it is empirically proved that reliability having the direct impact on customer satisfaction. Therefore, we should give more emphasis to reliability factor of service quality which are mainly consist of skill and ability of personnel components.

It is also recommended that service quality is directly related to customer satisfaction. Therefore, all the hotel owners should give special emphasis to enhance the service quality, which will ultimately lead to customer satisfaction.

It is further suggested that the major components of personnel have to be given equal importance to improve the service quality.

This research has been conducted mainly in the hotels that of various districts of West Bengal. The same model used for this study may be tested in other cities as well as separately in different categories of hotels in other locations. The results of such studies could yield interesting results and help in better comprehension of the various facets of personnel components in the luxury hotels in West Bengal. In future, this study may explore in other hotels in India. This study focused on some major components of personnel. However, there are other components of personnel, which should be included in the future research.

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