Impact of Consumer Demographics on Image Building of Star Category Hotels
Priyadarshini Seth
Senior Lecturer,
IHM Lucknow,
Sector G, Aliganj,Lucknow
Dr. Ranjeeta Tripathi
Assistant Professor,
Amity University,
Uttar Pradesh,
Lucknow Campus
Dr. Amit Kumar
Assistant Professor,
DTHM, Central University of Haryana
Abstract
Branding and building a brand image are important notions for all the industries as positive brand image leads to repetitive business and increased business. In service industry creating a brand image is even more significant because of its intangible nature. The customers of service industry themselves also become one of the factors of image building as they provide feedbacks and reviews about their experiences which may result in formation of positive or negative brand image for prospective customers. This study aims at studying the impact of demographics on star category hotels, where the product is intangible in nature and customer experience is the construct of image building. Also, the study will explore any relationship between purpose of stay and factors of image building. The study area chosen is Delhi and NCR as lot of star category hotels of various brands exists in the region. A structured questionnaire was developed to collect information from the 550 consumers. Results indicated that demographic characteristics like educational qualification, annual income and employment status creates a significant difference on image building but there is no significant difference created by age, gender, marital status, nationality and mode of information about the hotel on image building parameters identified. Further, analysis showed that there exists positive correlation between purpose of stay and image building parameters.
Keywords: Branding, Image Building, Demographics, Customer Experience, Service Industry, Intangible
Introduction
Branding and building a brand image are important notions for all the industries as positive brand image leads to repetitive business and increased business. In service industry creating a brand image is even more significant because of its intangible nature. Image building creates a set of expectation in the minds of the customer, which he/ she can expect to be delivered while availing the facilities/services of that hotel. Image building is the result of consumer behavior. The attitudes, convictions, and actions of anyone with a local, regional, national, or international interest in tourism are referred to as touristic consumer behaviour (Belber, 2009). On the same emotional, socioeconomic, and sociocultural variables that drive consumer behaviour generally also apply to tourist-related consumer behaviour in accommodation businesses. (Rızaoğlu, 2003). Buying behavior could differ depending on myriad of factors and psychological, physical and demographic factors are the reasons for such differences (Türker & TÜRKER, 2013). Image building is becoming essential because of globalization and increasing competition in the market. Vast choices available to customers have forced hotels to build a strong image of their brand by offering superior service quality. As a result, many hotels employ their resources, time, and money in promotional efforts, which may help consumers form a positive perception of the brand. According to Low and Lamb (2000), a brand's image combines practical and symbolic brand beliefs to create the consumer's overall perception of the brand. According to Hsieh et al. (2004), brand image is the result of elicited sentiments, perceptions thoughts, opinions, and beliefs towards a product or service. Image building of a brand is the result of various factors such as past experience, superior service quality, reliability on the brand, responsiveness of employees, trust etc. The customers of service industry themselves also become one of the factors of image building as they provide feedbacks and reviews about their experiences which may result in formation of positive or negative brand image for prospective customers. Few studies have stressed on the fact that customers' perceived levels of satisfaction are crucial to the profitability of hospitality and tourism businesses. This is because customer happiness is important in motivating customers to return, refer friends, and provide great reviews, all of which affect behavioral loyalty of customers (Baka, 2016; Gavilan et al.,2018; Guo et al., 2017; Tan et al., 2018).
As a result, hotel management should carefully assess customer traits and should not undervalue the impact of demographic factors. Individual characteristics like age, gender, and marital status are examples of demographic factors, as are things like income, education, and employment. When compared to other elements influencing customer behavior, demographic considerations are crucial since they are quantitative and significant.
According to UNWTO, the COVID-19 pandemic has caused a 22% fall in international tourist arrivals during the first quarter of 2020, and58% to 78% for the year. The situation is expected to improve by year 2021 wherein domestic demand is expected to recover faster than international demand. The figures indicate higher competition in the market. Consumer will prefer to visit a hotel which has an image of offering safest environment and also the customer has a trust on the hotel. Therefore, pandemic has also increased the importance of image building for star category hotels. Also, it has been researched that travel is a common way for all generations, regardless of age, to spend their disposable income (expedia.com, 2018).
Numerus studies have been done to study the factors which create an impact on the image building of products which are tangible in nature. This study aims at studying the impact of demographics on star category hotels, where the product is intangible in nature and customer experience is the construct of image building. Also, the study will explore any relationship between purpose of stay and factors of image building.
Literature Review
Hotels need to adapt to high competition and changing economic environment in order to sustain in globally challenging business circumstances. Legrand et al. (2013) and Von Bergner & Lohmann (2014) identified sustainability as one the most important challenges being faced by hospitality industry. Consumer profile is changing and becoming demanding continuously. Consumers travel for various reasons and hotel image building is important to meet the needs of the consumer during their specific travel intentions. Sohrabi et al. (2012) describes that the decision making process for selecting a hotel is complicated. Kotler (1991) described an image as the collection of thoughts, feelings, and experiences that a person has about a certain thing. Lamb et al. (2002) studies that socio-demographics, behavioral characteristic, motivation and geographical factors of consumers directly affects their choice of hotel. Authors describe socio demographic dimension as reference groups, family members, friends etc. of the consumers. Based on Kandampully&Suhartanto's research from 2000, increasing customer retention is another way that a hotel's profitability can be increased by cultivating a positive opinion of its corporate image. Berry (2000) argued that product is the primary brand in case of goods purchasing, whereas, in case of purchase of service, the firm and its image becomes the primary brand.
Virvilaite and Daubaraite (2011) describes in their study that image building is a long term process which cannot be cloned, therefore providing long term competitive advantage to the hotel. Balmer (2012) streeses that brand image is also considered as a strategic resource for the organization.Baruca&Civre (2012) mention in their study that hotel product, marketing activities and personal characteristics of the consumers together influences their choice of hotel. Authors also identifies four clusters of guest having impact on choice of hotel and image building, firstly the guests who are representative of self and others’ opinion, secondly the consumers who trusts advertisements, thirdly the guest who verifies every information and lastly the guests who are product orientedIn their study, Manhas & Tukamushaba (2015) note that brand image can even influence customers to distinguish between hotels in the same star category. According to Cho and Fiore (2015), brand image encompasses three different forms of client associations with a brand (cognitive, emotional, and sensory associations), which together make up its dimensions. The personal ideas, opinions, and judgements that consumers have about a brand are referred to as cognitive associations (Keller 2001). The sensations and emotions that consumers have for a brand, like as joy, enthusiasm, excitement, or rage, are referred to as emotional associations (Keller 2001). The physical senses that consumers associate with a brand—sight, sound, smell, taste, and touch—are reflected in the sensory connections (Hulte'n 2011; Schmitt 2011).Tu & Chih (2013) Based on their research results mentioned that corporate brand image significantly affects customer perceived value, customer satisfaction and loyalty, which in turn results in repetitive business and increased profitability. Companies should work on creating a positive brand image among consumers and have a strong sense of identity. According to Huang & Cai (2015), consumers rely on hotel brands to make risk-reducing purchases, and the hotel business uses branding methods to obtain a competitive edge. With this idea in mind, quantifying a brand's worth from the perspective of the customer becomes a crucial duty for hotel marketers. Through their investigation on how to enhance consumer purchase intentions in the hotel business, Lien et al. (2015) provide evidence of the significance of brand image. According to their research, a strong brand image had a considerable impact on a hotel's perceived value and had a beneficial impact on customers' faith in the hotel's goods and services.
According to Lee et al. (2017), hotel brand image has a significant role in how travellers evaluate hospitality businesses. According to Prabowo et al. (2019), brand reputation plays a key role in influencing word-of-mouth, which in turn influences consumers' propensity to make repeat purchases. Price, quality, and contentment are the factors related with the perceptions of building brand image, according to Sürücü et al. (2019). Author also stated in their study that improving customer-based brand equity is a necessary component of managing hotel brands. Understanding how consumers view a hotel's brand when compared to a rival is crucial for success in the hospitality industry.
According to Uca et al. (2017), demographic factors including age, average monthly income, and marital status do have an impact on image building and hotel choice, but gender has no bearing on this. Even though a hotel runs under the same brand, Lee et al. (2017) found that the location affects consumers' impressions of the hotel and its image. According to a study by Bowen and Chen from 2001, choosing brands with high-end goods and services is significantly influenced by money.
Wallace et al. (2004) highlighted the link between socioeconomic status and demographic traits and brand preference. They discovered that consumers' attitudes towards brand choice are influenced by their income levels. While Klopotan et al. (2016) maintained that the degree of wealth is irrelevant in defining the brand selection process, Mohamad et al. (2017) suggested that middle-class families chose luxury brands more frequently than do families with higher earnings.
Age, gender, education, lifestyle, personality, and money are among the demographic aspects that influence a consumer's selection while choosing a hotel, according to Saha et al. (2010). Gonçalves & Sampaio (2012) showed significant brand switching across genders and across all age groups after analysing consumer panel data. According to a 2009 study by Melnyk et al., women have a different psychological temperament than males when it comes to creating an impression of a brand in their minds. This is because it is believed that women place a higher priority on long-lasting relationships, have more sentiments, and base their decisions on societal standards and how those acts would effect other people. According to Rohani et al. (2017), customers' preferences for hotel amenities and services are influenced by the reason for their trip. In their study, Nuseir (2020) explained that while gender has no correlation to brand loyalty or image development, demographic characteristics like wealth and age have a substantial impact on both.
Objectives
Viewing the past researches on image building the objective of current study is to explore the role of consumer demographics on image building of star category hotels. Also, it is to explore whether any relationship exist between purpose of stay and image building parameters in star category hotels of Delhi and NCR.
Hypothesis
H01: Demographics does not create a difference on parameters image building of star category hotels.
H1: Demographics create a difference on parameters image building of star category hotels.
H02: There is no correlation between parameters of image building and purpose of stay.
H2: There is correlation between parameters of image building and purpose of stay.
Methodology
Survey form was created to collect responses from 550 guests staying star category hotels of Delhi and NCR. Incomplete and inconsistent data was removed and finally 500 data was found to be usable. The responses were collected using google form. Convenience sampling was used to collect the data. Since the data for image building was found to be non parametric, so non parametric tests were used to find whether demographic profile shows significant difference on image building parameters. Pearson’s correlation was conducted to explore the relationship between parameters of image building and purpose of stay.
Analysis and Interpretation
Table 1 gives an overview of the demographic profile of the respondents.
|
DEMOGRAPHIC VARIABLES |
VALUES |
PERCENTAGE |
|
Gender |
Female Male |
32.0 |
|
Age |
18-30 Years |
62.4 |
|
Marital Status |
Single |
61.0 |
|
Nationality |
Indian |
98.6 |
|
Qualification |
Undergraduates |
18.4 |
|
Employment Status |
Private sector |
45.2 |
|
Income |
Up to 5 Lakhs |
25.8 |
|
Star Category Hotel Visited |
Up to 2 |
24.8 |
|
Star Rating |
3 Star 5 Star |
12.6 |
|
Times Visited |
Once |
16.0 |
|
Info About Hotel |
Advertisement |
11.0 |
Table 1: Demographic Profile
To measure the internal consistency, Cronbach’s alpha was calculated which came (.984), which is in the acceptable range. Hence further exploratory analysis was performed. The results of KMO and Bartlett’s test of sphericity (Kaise-Meyer –Olkin Measure of Sampling Adequacy: .978; Barlett’s test of Sphericity-significance: .000). As a result, it was determined that the first requirements for employing factor analysis were satisfied.
The researcher further performed Principal Component Analysis, on the 25 items with respect to various parameters identified for image building. Item which had almost the same factor loadings like (IB_class_interior with factor loading .587, .585) and item with factor loadings were below 5 like IB_corporate_identity, were dropped as they indicated that they did not fit well with the factor. Further Kaiser’s criterion analysis was used, the scree plot graph indicated clear three factors solution. The 5 components’ together account for 83.2% variance. Items that had eigen value >1 were considered for respective components. Number of iterations was 25 in the present study. The component matrix uses the factors identified in Kaiser’s criterion. The criterion analysis reflects that the first 5 factors show loadings of 0.5 and above. Based on the findings of the Scree plot, the Component Matrix, and the Criterion analysis, it was revealed that 5 factors explain hotel facilities the best.
The 5 components are
Component 1: Factor1_IB_contact;
Item of this component is IB_personal_contact.
Component 2: Factor2_IB_service;
Items of this component areIB_emp_willing_to_help, IB_prompt_service, IB_emp_aware_of_SOP, IB_physical_env, IB_quality_service and IB_variety_FB_services
Component3: Factor3_IB_different_image;
Items of this component areIB_national_chain, IB_international_chain. IB_world_class_service, IB_long_history and IB_differentiated_image
Component4: Factor4_IB_convinience;
Items of this component are IB_accesibility, IB_location and IB_parking
Component5: Factor5_IB_hospitable;
Items of this component are IB_courtsey_politeness, IB_best_dressed_neat_employee, IB_well_trained_emp, IB_well_furnished_decorated_room, IB_comfortable_environment, IB_permits_relax, IB_good_qiality_food and IB_offer_upscale_service
The normality of data was checked by using K-S test. Since the p value <.05, it was found that the data is non-normal as displayed in Table 2.
Mann-Whitney U test and Kruskal-Wallis were used to find difference created by the demographics on the dependent variables (Image building). If was found that the demographics which created a difference on the factors of image building are Qualification with significant difference in factor1_IB_contact (highest mean rank is for professional course= 296.36), factor3_IB_different_image (highest mean rank is for professional course= 314.55), factor4_IB_convinience (highest mean rank is for professional course= =316.65), and factor5_IB_hospitable highest mean rank is for professional course= =315.94). Qualification does not create any difference in factor2_IB_service.
|
|
Factor1_IB_contact |
Factor2_IB_service |
Factor3_IB_different_image |
Factor4_IB_convinience |
Factor5_IB_hospitable |
|
Kolmogorov-Smirnov Z |
5.178 |
2.969 |
3.240 |
3.742 |
3.248 |
|
Asymp. Sig. (2-tailed) |
.000 |
.000 |
.000 |
.000 |
.000 |
|
Table2: One-Sample Kolmogorov-Smirnov Test |
|||||
Second demographic that creates a difference is Employment Status. Employment Status creates difference only in factor2_IB_service (highest mean rank in private sector=271.80).
Further Annual Income creates difference in factor2_IB_service (highest mean rank in above 15 Lakhs=299.10), factor3_IB_different_image (highest mean rank in above 15 Lakhs=293.51) and factor5_IB_hospitable (highest mean rank in above 15 Lakhs=290.29)
No of star category hotels visited also creates difference in all the image building parameters with highest mean rank in 7-10 times category.
No of times visited also creates difference in all the image building parameters with highest mean rank in five or more times in all parameters of image building except factor1_IB_contact where highest mean rank is for 4 times visit in star category hotels.
The demographics which do not create any difference on the image building parameters are Age, Gender, Marital Status, Nationality and mode of Information About Hotel.
Further Pearson’s correlation was run to check if any relationship exists between purpose of stay and image building parameters. It was found that there exists positive correlation between purpose of stay and image building parameters. For purpose of stay to be professional, correlations exist between professional trip and Factor4_IB_convinience and Factor5_IB_hospitable. (Results displayed in Table 3(a – i)- Table 3(d - ii)
|
Test Statisticsa,b |
|||||
|
|
factor1_IB_contact |
factor2_IB_service |
factor3_IB_different_image |
factor4_IB_convinience |
factor5_IB_hospitable |
|
Chi-Square |
12.100 |
9.091 |
9.956 |
14.615 |
14.428 |
|
Asymp. Sig. |
.017 |
.059 |
.041 |
.006 |
.006 |
|
Table3 (a - i) a. Kruskal Wallis Test |
|||||
|
b. Grouping Variable: qualification |
|||||
|
Ranks |
|||
|
|
qualification |
N |
Mean Rank |
|
factor1_IB_contact |
Undergraduates |
92 |
211.19 |
|
Graduates |
194 |
256.58 |
|
|
Postgraduates |
168 |
256.94 |
|
|
Doctorate |
13 |
238.31 |
|
|
ProfessionalCourse |
33 |
296.36 |
|
|
factor3_IB_different_image |
Undergraduates |
92 |
227.68 |
|
Graduates |
194 |
249.52 |
|
|
Postgraduates |
168 |
254.49 |
|
|
Doctorate |
13 |
212.50 |
|
|
ProfessionalCourse |
33 |
314.55 |
|
|
factor4_IB_convinience |
Undergraduates |
92 |
215.46 |
|
Graduates |
194 |
259.22 |
|
|
Postgraduates |
168 |
249.68 |
|
|
Doctorate |
13 |
211.08 |
|
|
ProfessionalCourse |
33 |
316.65 |
|
|
factor5_IB_hospitable |
Undergraduates |
92 |
217.76 |
|
Graduates |
194 |
263.63 |
|
|
Postgraduates |
168 |
242.99 |
|
|
Doctorate |
13 |
217.27 |
|
|
ProfessionalCourse |
33 |
315.94 |
|
Table3 (a-ii)
|
Test Statisticsa,b |
|||||
|
|
factor1_IB_contact |
factor2_IB_service |
factor3_IB_different_image |
factor4_IB_convinience |
factor5_IB_hospitable |
|
Chi-Square |
6.622 |
12.990 |
7.302 |
4.342 |
3.983 |
|
Asymp. Sig. |
.085 |
.005 |
.063 |
.227 |
.263 |
|
Table3 (b-i) a. Kruskal Wallis Test |
|||||
|
b. Grouping Variable: employment_status
|
|||||
|
|
|||
|
|
employment_status |
N |
Mean Rank |
|
factor2_IB_service |
Privatesector |
226 |
271.80 |
|
Govermentsector |
46 |
270.54 |
|
|
Selfemployed |
86 |
221.43 |
|
|
Student |
142 |
227.72 |
|
|
Total |
500 |
|
|
Table3 (b-ii) Ranks
|
Test Statisticsa,b |
|||||
|
|
factor1_IB_contact |
factor2_IB_service |
factor3_IB_different_image |
factor4_IB_convinience |
factor5_IB_hospitable |
|
Chi-Square |
8.410 |
10.496 |
13.287 |
8.264 |
11.952 |
|
Asymp. Sig. |
.078 |
.033 |
.010 |
.082 |
.018 |
|
Table3 (c -i) a. Kruskal Wallis Test |
|||||
|
b. Grouping Variable: annual_income |
|||||
|
|
|||
|
|
annual_income |
N |
Mean Rank |
|
factor2_IB_service |
Upto5Lakhs |
129 |
258.91 |
|
5–10Lakhs |
123 |
250.33 |
|
|
10–15Lakhs |
50 |
231.15 |
|
|
Above15Lakhs |
56 |
299.10 |
|
|
Not earning |
142 |
230.66 |
|
|
factor3_IB_different_image |
Upto5Lakhs |
129 |
273.02 |
|
5–10Lakhs |
123 |
241.84 |
|
|
10–15Lakhs |
50 |
224.12 |
|
|
Above15Lakhs |
56 |
293.51 |
|
|
Not earning |
142 |
229.87 |
|
|
factor5_IB_hospitable |
Upto5Lakhs |
129 |
251.00 |
|
5–10Lakhs |
123 |
269.17 |
|
|
10–15Lakhs |
50 |
218.05 |
|
|
Above15Lakhs |
56 |
290.29 |
|
|
Not earning |
142 |
229.61 |
|
Table3 (c – ii)Ranks
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
factor1_IB_contact |
factor2_IB_service |
factor3_IB_different_image |
factor4_IB_convinience |
factor5_IB_hospitable |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Chi-Square |
6.866 |
12.956 |
16.054 |
10.509 |
9.793 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Df |
2 |
2 |
2 |
2 |
2 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Asymp. Sig. |
.032 |
.002 |
.000 |
.005 |
.007 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Table3 (d - i) a. Kruskal Wallis Test |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
b. Grouping Variable: star_cat_hotel_visited
Table 3(d-ii) Ranks
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
For purpose of stay to be personal all parameters of image building are correlated as displayed in Table 4
|
|
Factor1_IB_contact |
Factor2_IB_service |
Factor3_IB_different_image |
Factor4_IB_convinience |
Factor5_IB_hospitable |
|
|
Purpose of Trip –Professional |
Pearson Correlation |
.060 |
.055 |
.081 |
.105* |
.117** |
|
Sig. (2-tailed) |
.184 |
.221 |
.070 |
.019 |
.009 |
|
|
Purpose of Trip –Personal |
Pearson Correlation |
.317** |
.337** |
.299** |
.319** |
.329** |
|
Sig. (2-tailed) |
.000 |
.000 |
.000 |
.000 |
.000 |
|
Table 4 Correlations
Thus, null hypothesis H01: Demographics does not create a difference on parameters image building of star category hotels is partially rejected, and partially accepting alternate hypothesis H1: Demographics create a difference on parameters image building of star category hotels.
Further nulls hypothesis H02: There is no correlation between parameters of image building and purpose of stay is rejected, accepting alternate hypothesis H2: There is correlation between parameters of image building and purpose of stay.
Conclusion
The analysis of the data reveals that of the demographic variables does create a difference on image building parameters for star category hotels. Demographic variable - Qualification of the surveyed population creates a difference on all four parameters of image building i.e., personnel contact, different image, convenience and hospitable environment but does not create any difference on second factor of image building which is service. The highest mean rank is displayed by professional course. Demographic variable – employment status shows a difference only in Factor2_IB_service with highest mean rank in private sector. The aforementioned findings are consistent with a study by Ceylan et al. (2021), in which the authors looked at how consumer education levels and other demographic factors affect the creation of tourist types and the perception of a location. Customers with higher education do tend to adopt a good opinion of the business image and service quality, according to Chen & Chen's (2014) study. Demographic variable – annual income shows difference in Factor2_IB_service, Factor3_IB_different image, Factor5_IB_hospitable with highest mean rank in above income group above 15 lakhs. According to a previous study by Mohamad et al. (2017), middle class families are more likely to choose luxury products than are upper class households. However, this study reveals that those with higher incomes are more preoccupied with different aspects of enhancing the reputation of star-rated hotels. Respondents who earn who are in high income groups, look for better location, comforts additional services and stay more loyal to brand (Zaman et al., 2016; Klopotanet al., 2016). The number of star category hotels visited also creates a difference in all the image building parameters with highest mean rank in 7 to 10 times category. The number of times a consumer visits a star category hotel also creates difference in all image building parameters with highest mean rank for four times visits or more. The demographic characteristics like age, gender, marital status, nationality and mode of information about the hotel, do not create any difference on image building parameters of star category hotels. This finding is in congruence with the study by Nuseir (2020) who explained in their research that demographic characteristic such as income and age significantly impact the image building and loyalty towards a brand, however gender has no association with image building and brand loyalty. Das (2014) noted that research using male and female samples demonstrated that self-congruity and retail brand personality are complementing rather than substitutable variables.
While analyzing the relationship between purpose of stay and Image building parameters through Pearson’s Correlation it was seen that, when the consumer stay in star category hotel for professional reason he is concerned about only convenience and hospitable environment. However, when the purpose of stay of consumer is personal all five parameters of image building (contact, service, different image, convenience, and hospitable environment) are correlated. This finding is in line with the study of Han et al. (2015) who mentions that purpose of visit can be another important factor in assessing brand trust.Lehto et al. (2015). also mentions personal visits of the travelers are self paid in comparison to professional/business visits which are paid by their companies, so during personal visits/ leisure visits customers looks for value for money which in turn affect their image building of the brand. Therefore, it can be said that consumer demographics play an important role while evaluating various factors that affect image building of star category hotels.
Limitations and Recommendations
The current study used the convenience sampling method to get its data. This type of sampling is thought to occasionally produce skewed findings. Further research can be done to discover the true influences of various demographics on image-building factors. Although they have not been taken into account, online image building parameters are suggested for further study. Data collection was challenging during the current pandemic, which could have influenced the results. The current study has a cross-sectional research design. However, longterm studies on the same population with different demographics may be able to pinpoint the precise behaviour with regard to image construction parameters.
Delhi and the NCR were included in the population for data collecting. As a result, conclusions cannot be generalized, although additional research may be done.
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