The Influence of Hotel Price on Perceived Service Quality and Value in e-tourism

Empirical Models

To identify the influence of price on the quality and value in online traveler 

reviews, we developed the following regression model to test our proposed 

hypotheses. The definition of value suggests that it can be considered as a func-

tion of service quality and price, from the narrow perspective. In terms of qual-

ity, different prices may induce different quality standards. Accordingly, we 

propose the following:

 ValueRating = 

β

0

 + 

β

1

QualityRating + 

β

2

ln(Price) + 

µ

1

 otherFactors + 

ε

1

(1)

 QualityRating = 

α

0

 + 

α

1

ln(Price) + 

µ

2

otherFactors + 

ε

2

(2)

In the models, ValueRating and QualityRating represent a specific reviewer’s 

rating of the service quality and value of a given hotel in an online review. Price 

identifies the room rate paid by the customer (see Figure 1). Wooldridge (2009) 

states that it is better to use a natural logs model analysis so that dependent 

variables will satisfy the classical linear model assumption more closely. In our 

study, we use the loglinear form of Price and Ranking to narrow the range of 

variables with better explanatory power.

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30    JOURNAL OF HOSPITALITY & TOURISM RESEARCH

To highlight the general ideas and major variables in our study, we use oth-

erFactors as a comprehensive variable to represent all possible control variables 

and moderator variables in Equations (1) and (2). Then, to distinguish the spe-

cific data sets, we break down otherFactors into detailed variables in our 

empirical study and show all variables in Equations (3) and (4). The variable 

otherFactors includes all other factors that may influence reviewers’ ratings for 

a given hotel, including the star rating, usually from one- to five-star; the type 

of trip taken by customers, as posted in their review (see Figure 1); and the 

ranking of the establishment among all hotels in Beijing, which represents a 

popularity index, as a control variable (see Figure 1). Essentially, we use the 

following regression model for the analysis.

ValueRating = 

β

0

 + 

β

1

ln(Price) + 

µ

1

Comfort + 

µ

2

Location + 

µ

3

Cleanliness +  

µ

4

Service + 

β

2

StarRating + 

β

3

TripType + 

θ

1

StarRating 

× ln(Price) +  

                             

θ

2

TripType 

× ln(Price) + β

4

ln(Ranking) + 

ε

1

(3)

QualityRating = 

α

0

 + 

α

1

ln(Price) + 

α

2

StarRating + 

α

3

TripType +

 

θ

3

StarRating 

× ln(Price) + θ

4

TripType 

× ln(Price) + α

4

ln(Ranking) + 

ε

1

(4)

To test Hypothesis 1, we focused on the estimated coefficient 

β

1

 in Equation (3), 

which investigates the impact of price on customers’ value ratings after control-

ling for other variables observable from the review website. In line with previous 

work, comfort, location, cleanliness, and service are the components of quality 

on daodao.com.