Index

Abstract

The objective of this research is to investigate the relationship between the brand image of premium hotels in China, the level of customer engagement they experience, and their intention to make a purchase at one of those hotels. The study model that was developed made use of something called the Theory of Planned Behavior (TPB). Using a method known as convenience sampling, samples were collected from people living in Beijing, China. Participants in the poll who intended to make reservations at upmarket hotels were given the survey questionnaires. A grand number of 459 specimens were collected for the purpose of analysis. The process of data analysis and validation of the research hypotheses was conducted using SmartPLS. This research provides evidence that there is a connection between customer engagement and brand image, as well as the purchase intention. According to the findings of the study, the relationship between a brand's image and a consumer's propensity to make a purchase is diminished when customer engagement and brand image factors are both present. As a result, the efficacy of customer interaction as a mediator was shown to have been demonstrated. The academician is presented with new information in the form of this result, and further discussion is given regarding its application to practitioners.

Keywords: Brand image, China, Customer engagement, Premiun hotel, Purchase intention, Theory of planned behaviour.

Received: 3 May 2023/ Revised: 19 June 2023/ Accepted: 10 October 2023/ Published: 22 November 2023

Contribution/ Originality

According to this study, consumer interaction mediates the relationship between brand image and purchase intention. Customer engagement is a little-studied but important factor that influences brand image and purchasing intention in China's high-end hotels. The association between brand image and purchase intention is made clearer by this new technique.

1. INTRODUCTION

As a result of the proliferation of new types of internet-based accommodation, the traditional hotel business is facing significant challenges. Due to the change, it is vital to investigate what influences tourist’s online hotel booking decisions. According to China's online hotel booking data for the first quarter of 2019, Online Travel Agents (OTA) transactions accounted for 58.3% and ranked first, while hotel official websites and apps accounted for 18.8% (BigData Research, 2019). Most of the hotels are now focusing on boosting their market share by partnering with OTAs, demonstrating the importance of such partnerships for hotels to increase their revenue and sales (Laise, Filho, & Júnior, 2019).However, OTAs are leading the online hotel sector, resulting in numerous problems. For instance, a 15-25% increase in commissions affects hotel profitability (Liu, 2019).

The hotel's membership structure decreases the number of loyal guests (Analysys, 2020). For hotels, OTA has developed into a two-edged sword, which may be advantageous but also increases competition (Xue, Jo, & Bonn, 2020). At present, this partnership is both cooperative and competitive. In order to control resources, OTAs have begun investing in and constructing hotels, which has created a severe challenge for the hotel industry. Online travel agencies have made more efforts to invest in hotels, construct hotel groupings, and form strategic partnerships with hotels (Li, 2021).If they proceed in this manner, they will pose a significant threat to many hotels due to their inexpensive internet marketing costs, improved integration and cooperation, and internal technology systems.

As Teng, Wu, and Chou (2020) pointedout, in order to resolve the aforementioned problems, hotels must create and use the official online channel to its maximum capacity and gain market share for the initiative.It is vital to examine online booking behavior, particularly the intention to purchase online, in order to increase the competitiveness of official booking.  Despite the vast amount of research produced in tourism-related fields, there are few findings that examine the mediation role of customer engagement.

2. LITERATURE REVIEW AND HYPOTHESES

As consumers require goods and services for reasons other than their physical characteristics and functions, the concept of brand image was proposed, and it has since become a prominent study goal in consumer purchase behaviour research (Dobni & Zinkhan, 1990). In addition, hotel marketers and managers are concerned about the reputation and image of the brand because it is one of the most important ways to generate additional value and advantage (Wang & Tsai, 2014).

Because of increased familiarity with the idea, Keller, Parameswaran, and Jacob (2011) provided a more comprehensive definition of brand image. They made the observation that the associations that customers have with a brand reflect the image of the brand. Industry managers have a responsibility to make a distinction between customers who view the image of a brand in terms of its performance, the attributes of its imagery, and the benefits it provides, and customers who view the image in terms of their overall judgement, attitudes, and emotions. Therefore, brand image is the total connection and reaction with a brand that is based on the perceptions of unique brand qualities and benefits that reveal a strong association between entry and higher-level customers (Keller et al., 2011; Melisa, 2018). This is because brand image is based on the perceptions of unique brand qualities and benefits that reveal a strong association between entry and higher-level customers.

Since the turn of the century, one of the most discussed topics in marketing research has been how to keep customers engaged. According to the findings of some studies, the psychological process that generates customer engagement and loyalty is called client engagement (Bowden, 2009). According to Van Doorn et al. (2010), customer engagement is the term for consumer behaviors that match particular patterns and types of targeted engagement activities. This definition can be found in the article "Customer Engagement." Customer engagement can be defined as the activity of a customer that is based on or centred on a company or brand after the point of purchase but before the customer leaves the store.

Consumer engagement was categorised as different types of customer action in the research conducted by Jaakkola and Alexander (2014) as well as by Pham and Avnet (2009). According to Gummerus, Liljander, Weman, and Pihlström (2012), customers do a number of things in addition to making purchases that strengthen their engagement with a company's products and brand. Rishika, Kumar, Janakiraman, and Bezawada (2013) made the discovery that engaged customers are more lucrative than non-engaged customers. This is due to the fact that engaged customers are less price-sensitive and have a greater preference for premium products.

Standard marketing variables such as price and quality do not describe the level of relationships that customers establish with the products they purchase. As a result, customer engagement is a stronger determinant of brand loyalty-related results beyond the purchase than standard marketing variables such as price and quality (Bowden, 2009; Hollebeek, 2011). Therefore, it is anticipated that customer engagement will support the fundamental principles of relationship marketing, which are repurchase, engagement, and loyalty from customers (Verhoef, Reinartz, & Krafft, 2010). Bowden (2009) conducted an in-depth investigation into the question of whether or not involvement contributes to a better understanding of customer behaviour or the outcomes associated with loyalty.

2.1. Aims

This study investigates how customer interaction mediates brand image and purchase intention. This study examines brand image, consumer interaction, and whether brand image positively impacts online purchase intention. Customer involvement may mediate brand image's effect on online purchase intentions.

2.2. Theory of Planned Behaviour

The support for the study was established from the review above.  Theory of Planned Behaviour (Ajzen & Driver, 1991) acted as the theoretical support  for this study. A conceptual framework based on the mediation model was propos

2.3. Hypotheses

The independent variable is brand image, the dependent variable is online purchase intent, and the mediator is consumer engagement. The following visual paradigm, depicted in Figure 1 , makes it simple to comprehend.  Thus, the hypothesis (H) of this study are:
H1: Brand image has a significant positive effect on online purchase intention.
H2: Customer engagement has a significant positive effect on online purchase intention.
H3: Brand image has a significant positive effect on customer engagement.

H4: Brand image will have a significant mediation effect on online purchase intentions through customer engagement.

Figure 1. Conceptual framework.

3. METHODOLOGY

An online survey that consisted of close-ended questions with a 7-point Likert scale was distributed to the respondents in China using convenience sampling. This was done in accordance with the data collection process and procedure that were outlined by Xiaoyan, Hoo, Teck, Subramaniam, and Cheng (2022). After that, the data were analysed using a programme called SmartPLS 3.

According to Kwon (2018), a sample size of at least 300 participants is adequate when conducting quantitative research. According to the findings of another researcher, Bukhari (2014), the appropriate size of the sample should fall somewhere between 150 and 400 individuals, depending on the number of variables that are included in the framework. In the event that the structure contains more than six different variables, it is necessary to take a more extensive sample, and the total number of participants could reach 500.

Because the framework of this study includes three variables, a sample size of 300 is required. For the purpose of this study, a total sample size of 459 was collected, which significantly exceeds the minimum requirement. After that, the data were examined further for their reliability before being used.

4. RESULTS

The findings of the descriptive and predictive data analysis are presented below.

4.1. Samples' Demographic Profile

The research questionnaire also includes several demographic questions to determine the respondents' profiles. In Table 1, the descriptive statistical analysis of the project managers who completed the questionnaire is summed up.

Table 1. Participant demographic profile.

Measure

N

Percent

Gender

Male

211

46%

Female

248

54%

Age

<18

11

2.4%

18-25

79

17.2%

26-35

166

36.2%

36-45

180

39.2%

46-55

23

5.0%

56-65

0

0%

Above 65

0

0%

Education

Under high school

8

1.7%

High school

38

8.3%

College

121

26.4%

Bachelor

224

48.8%

Master

49

10.7%

Doctor

19

4.1%

Occupation

Student

72

15.7%

Government /Government-affiliated institutions staff

135

29.4%

Private sector staff

109

23.7%

Businessman/ Businesswoman

64

13.9%

Freelancer

27

5.9%

Retired

32

7.0%

Unemployed

20

4.4%

Income

Under 3,000 RMB

48

10.5%

3,000-5,000

96

20.9%

5,000-7,000

156

34%

7,000-9,000

81

17.6%

Above 9,000 RMB

72

13.5%

Dependent on others

16

3.5%

Experience with the internet

Less than 1 year

0

0.0%

1-3 years

54

11.8%

3-6 years

245

53.4%

More than 6 years

160

34.9%

Daily internet usage

Less than 1 hour

14

3.1%

1-2 hours

68

14.8%

2-3 hours

199

43.4%

More than 3 hours

178

38.8%

Familiarity with the internet

Very Poor

17

3.7%

Poor

43

9.4%

Moderate

130

28.3%

Good

146

31.8%

Very good

123

26.8%

IT proficiency

Very poor

13

2.8%

Poor

28

6.1%

Moderate

91

19.8%

Good

146

31.8%

Very good

179

39.0%

Online hotel booking experience in last 3 years.

Never

55

12.0%

1-3 times

142

30.9%

4 -6 times

176

38.3%

7 times and above

86

18.7%

Preferred booking channel

Online travel agency

152

33.1%

Group buying

173

37.7%

Non-standard hotel

71

15.5%

Others

63

13.7%

Experience with hotel official channel

Yes

187

40.7%

No

272

59.3%

Preferred payment method

Credit card

89

19.4%

Debit card

106

23.1%

Third party payment platforms

264

57.5%

Other

0

0.0%

It can be seen that the number of female respondents (54%) exceeded the number of male respondents (46%). More than seventy-five percent (75.4%), specifically, of the respondents were between the ages of 26 and 45. The vast majority of those who participated in the survey held college degrees or higher. More than sixty percent of respondents, or 63.6% to be exact, indicated that they held a Bachelor's degree or higher.

The vast majority of the individuals who participated in the survey were employed either by the government or institutions affiliated with the government (29.4%) or by private sector companies (23.7%). Students made up the largest percentage of respondents in the sample (15.7%). 13.9% of them owned their own companies, while only 7% were retired.

About one-third of the people who participated in the survey had a personal monthly income of 5,000 to 7,000 Chinese Yuan Renminbi (RMB after taxes, followed by 3,000 to 5,000 RMB (20.9%), 700,000 to 9,000 RMB (17.6%), less than 3,000 RMB (10.5%), more than 9,000 RMB (13.5%), and being dependent on others (3.5%).
In addition, each of the 459 people who participated in the survey stated that they had more than a year's worth of experience using the Internet. More than eighty-two percent of them go online for longer than two hours each day. Roughly nine in ten respondents to the survey rated themselves as either moderately or very good at their ability to use computers and the internet.

Regarding prior experience with online hotel reservations, the vast majority of respondents (88%) had made at least one reservation for a hotel room through the use of the internet. When asked how they typically book hotel rooms, group buying came in second with 37.7% of respondents, followed by online travel agencies (33.1%), boutique hotels (15.5%), and other channels (13.7%). According to the data in Table 1, 40.7% of the respondents have never made a reservation for a hotel room through the official website of a hotel. When it came to the various methods of payment, the respondents preferred using third-party payment platforms (57.5%), followed by debit cards (23.1%) and credit cards (19.4%).

Table 2. Measurement model assessment.

Constructs

Cronbach alpha

Composite reliability

AVE

Brand image

0.92

0.933

0.559

Customer engagement

0.928

0.938

0.582

Online purchase intention

0.817

0.872

0.577

Table 2 shows that each variable's Cronbach's was greater than 0.8, indicating the scales' strong reliability (Garson, 2016; Leguina, 2015; Tabri & Elliott, 2012; Waseem, Farzand, Khurshid, & Adil, 2019). All variables have high AVE values, indicating that their indicators explained over 50% of their variance.

Table 3. Fornell-Larcker.

Variable

BI

CE

OPI

BI

0.748

 

 

CE

0.569

0.763

 

OPI

0.517

0.468

0.760

Note:

BI=Brand image, CE=Customer engagement, OPI=Online purchase intention.

 Waseem et al. (2019) employed the Fornell-Larcker test by Fornell and Larcker (1981) to establish discriminant validity across all variables where the square root of each variable's average variance extracted value (AVE) is larger than its maximum correlation value with other variables.Table 3 shows that the Fornell-Larcker criteria calculate correlation by arranging the square root values of each variable's AVE diagonally (bold) and calculating correlations between variables off-diagonally. The estimations above show good discriminant validity for each variable (Waseem et al., 2019).

Table 4. Hypothesis test summary.

Hypothesis

Relationship

T statistics

P values

Results

H1

BI -> OPI

7.327

0.000

Significant

H2

CE -> OPI

4.952

0.000

Significant

H3

BI -> CE

14.556

0.000

Significant

H4

BI -> CE -> OPI

4.442

0.000

Significant

Note:

BI=Brand image, CE=Customer engagement, OPI=Online purchase intention.

This study examines how customer participation influences brand image and online purchase intent. Table 4 reveals that brand image significantly affects online shopping plans. Customer interaction significantly increased online purchase intention statistically. Brand image also had a positive impact on customer engagement. Customer involvement statistically links brand image with online purchase intent.

Also, Table 4 shows the t-statistics, which are another way to decide whether or not to accept the hypothesis.  Accept when the t-statistics is greater than 1.96 and the confidence interval is at 95%.

Figure 2.Partial least square SEM model.

Figure 2 shows the full estimates of the structural equation model, along with the mediating variable of Customer Engagement (CE).

5. DISCUSSION

5.1. Direct Relationship between Brand Image and Customer Engagement with Online Purchase Intention

Investigate whether brand image positively impacts online purchase intention to meet the research objective. Hypothesis: As shown in Table 4, brand image positively affects online purchase intention at a 95% confidence level. Previous studies (Lien et al., 2015; Xiaoyan et al., 2022) examined the relationship between brand image and online hotel booking, taking into account trust, price, and value. This study confirms previous research on brand image and online purchases.

Subsequently, to answer the research objective of examining whether customer engagement has a significant positive effect on online purchase intention. Hypothesis 2: Customer engagement has a significant positive effect on online purchase intention was found to be significant at a 95% confidence level, as shown in Table 4.  Prentice, Wang, and Loureiro (2019) conducted research on online purchase intention by focusing on identity-driven customer engagement and found a significant impact of customer engagement in the underlying linkage. The results and findings of this study support the existing literature on the importance of the variable.

5.2. Direct Relationship between Brand Image and Customer Engagement

To answer the research objective of examining whether brand image had a significant positive effect on customer engagement.  Hypothesis 3: Brand image has a significant positive effect on customer engagement was found to be significant at a 95% confidence level, as shown in Table 4. The results and findings of this study are in harmony with the previous literature on the link or relationship between brand image and customer engagement (Islam & Rahman, 2016).There has been extensive study of and support from academic research for the relationship between brand image and customer engagement

5.3. Mediation Effect of Customer Engagement between Brand Image and Online Purchase Intention

To answer the research objective of investigating whether the brand image had a significant mediation effect on online purchase intentions through customer engagement Hypothesis 4: Brand image has a significant mediation effect on online purchase intentions through customer engagement, which was found to be significant at a 95% confidence level, as shown in Table 4.  Customer engagement mediates the relationship or link between brand images and repurchase intention. This finding also answered the call by Yen, Teng, and Tzeng (2020) that, in addition to investigating the direct influence of brand image on online purchase intention or the mediating role of the substantial mediation role of customer interaction, the objective of this research is also to analyse the indirect effect of brand image on online purchase intention.

6. CONCLUSION

This study looks at the relationships between high-end Chinese hotel brands, customer engagement, and online purchase intentions. The association between brand image and Chinese tourists' aspirations to purchase premium hotels online is mediated through customer engagement. In this study, four research hypotheses were tested using SmartPLS. Positive correlations were discovered for every direct effect in the study. The study found relationships between brand image, consumer involvement, and online purchase intention that were positive. Customer involvement also mediates brand image and online purchase intention

Given its many limitations, the current study's significant theoretical and managerial implications should be further investigated. The study was first carried out using a cross-sectional research design, meaning that all the data were gathered at once. However, it is debatable whether the results of a study conducted over a short period of time should be expanded when the impact of the relationship must be determined on behavioural decisions, i.e., customer intentions. The choice of sample, sample size, sampling, and analytical techniques used in this study justify the use of a cross-sectional research design, despite the fact that the study employs this method. However, the author advises that subsequent studies should use a longitudinal research design to analyse the suggested theoretical model.

The study's second objective is to ascertain how brand image affects consumer engagement and online purchase intent among foreign tourists staying in China's top hotels. Future research should evaluate the suggested theoretical model in various cultural contexts.

The third section of the study examines how customer engagement, a mediator of brand perception, affects tourists' intentions to make online purchases in China's high-end hotel sector. Future research should, however, examine some other mediating variables to examine the relationship between brand image and online purchase intention.

This study offers valuable insights to researchers and marketers that online purchase intention for premium hotels was directly influenced by brand image and customer engagements. Customer engagement also significantly mediates online purchase intention. In the presence of customer engagement and brand image at the same time, the link between online purchase intention and brand image is suppressed. Thus, customer engagement has been proven to be a successful mediator. Subsequent studies may want to prove this point outside of China.

Funding: This research is supported by INTI International University, Malaysia (Grant number: T&E2508).
Institutional Review Board Statement: The Ethical Committee of the INTI International University, Malaysia has granted approval for this study (Ref. No. INTI/UEC/2023/017).
Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics.
Data Availability Statement: The corresponding author can provide the supporting data of this study upon a reasonable request.

Competing Interests: The authors declare that they have no competing interests.

Authors’ Contributions: All authors contributed equally to the conception and design of the study. All authors have read and agreed to the published version of the manuscript.

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