Perceived Influence og online restaurant guides on visiting intentions of customers

PERCEIVED INFLUENCE OF ONLINE RESTAURANT GUIDES

We all eat food; some eat to live on, while some enjoy the experience of eating. This has

been around for ages, and every generation around the globe had their favorite cuisine and food

joints. But this generation has taken it to a different level.

Education and exposure to lot of cultures have helped our people enjoy and appreciate the

different style of cuisines (Solis, 2003). This in turn created a demand and now we can see a lot

of restaurants, each serving different types of food and beverage, from various locations and

cultures known to us. Also, we see lot of people from this generation have accepted these diverse

cultures around us, and thus we have an increase in food lovers around the world.

With the increase in food lovers, and the advances we see in technology and internet, it is

obvious that it have led to the creation of websites and apps that will aid the food lovers in their

subject of interest. As a result, we can see lot of websites being dedicated for cuisine and

beverages. Every style of cuisine has its own set of followers and pages on social media and they

are all constantly trying to make their interests famous amongst people.

Simultaneously, there is a boom in the restaurant industry too. Lot of new players have

come into the market, showcasing their own version of cuisine. Each food culture has many

restaurants around the place, and everyone is trying to differentiate their business with ambience,

space, service and other varieties of food.

Recently, this increase has made the restaurant business very dense and cluttered. As a

result, a new problem rose: the customers fail to know about a new player in the market, and

people have no track of the restaurants and how they are differentiated from one another. This

problem opened up new problems. Such as; where to get your favorite cuisine in a new city?

What would be the price range? What do others think about it?

This gave rise to online customer directories and guides. These websites give information

about various restaurants regionally. This may include menu cards, contact numbers, reservation

options, online ordering, etc. Some even have photos of the restaurant and location guides.

Reviews are also very popular, along with ratings. Popular websites such as Zomato, Burrp,

Timecity, Indiadelicacy, etc have already taken over the major portion of the Indian market.

2 PERCEIVED INFUENCE OF ONLINE RESTAURANT GUIDES

1.1 Review of Literature

Previous researches and articles have shown a significant level of correlation between

reviews and the direct effect on the behavioral patterns of the consulting individuals. A person

regards another individual’s opinions mainly when they face an ambiguity in the matter of

concern. In the case of hotels and restaurants the same situation arises frequently. With a large

variety of restaurants and cuisines popping up at every interval in Bangalore every now and then

and the lifestyle changes taking place, where the people are willing to step out from their comfort

zone and venture out and experience new things, the opinions and peer reviews have gained

greater value and significance. So much so, that it can either make or break an image.

Online product reviews provided by consumers who previously purchased products have

become a major information source for consumers and marketers regarding product quality (Hu

et al, 2009). From this, it can be understood that peer review online or information given online

about certain services and products are regarded important by those who seek to try out certain

products or services. Same is the case with restaurants.

Online consumer review websites improve the information available about product

quality. The impact of this information is larger for products of relatively unknown quality. As

this information flow improves, other forms of reputation such as chain affiliation should

continue to become less influential. On the consumer side, simplifying heuristics and signals of

reviewer quality seem to increase the impact of quality information (Luca, 2011).

It can be seen that online reviews have had a direct impact on the pricing power and

RevPAR in any hotels in countries in Northern Americas and in Europe. “What was remarkable

about the study is that positive online reputation doesn’t merely provide higher pricing power for

online sales, It is correlated to higher group booking rates and corporate negotiated rates in

addition to reservations made over the phone” (Anderson, 2012 by).

Though a lot of diners put a great amount of trust in the word of mouth especially from

those of known sources, they would also go an extra mile to ensure a good dining experience by

looking out for more information on the restaurants. This is done to ensure that they have a good

dining experience by taking into consideration different accounts of peer review. “Outside of

personal word of mouth recommendations, the channels that consumer’s relied on most were

user-generated review sites, such as YELP; the restaurant’s own website, and blogs” (Carin

Oliver, 2012).


1.2 Research Gap

Though there are many articles written on the influence of peer reviews on behavior of an

individual and factors that influence a person’s opinion to visit a restaurant, not many research

have been done on this specific topic as of yet. As of now there have been many articles that

have been published online and in newspapers and magazines that have formed a good part of

this research undertaking. Though the articles give a specific point of view to the topic under

concern, it does not give a hard and fast proof and cannot be viewed as completely reliable as

they are just the viewpoint of the article’s author or just a handful of people that were

interviewed for the article. Moreover they have a tendency to be biased to a certain cause.


1. Title: Service quality and customer satisfaction: Antecedents of customer’s re-patronage

intentions

Source: Yap Sheau Fen; Kew Mei Lian ,Kdu College, Sunway Academic Journal 4

Objective: To examine the relationship between service quality, customer satisfaction and

Customer’s re-patronage intentions in the context of the restaurant industry

Methodology:

The study was conducted using self-administered questionnaires with the consent from

the restaurant owner beforehand. Pilot testing was conducted using a convenience sample of 35

respondents. The survey was conducted in fusion restaurant from June 2006 to October 2006.

Data analysis:

Multiple linear regression analysis was conducted to analyze the influence of service

quality and satisfaction on re-patronage intention through SPSS 13.0. Three hypotheses were

tested to determine the relationship between the three factors.

H1: Service quality is positively related to re-patronage intentions.

H2: Customer satisfaction is positively related to re-patronage intentions.

H3: Customer satisfaction will be a stronger predictor of customer’s re-patronage intention than

service quality.

Findings:

This study focuses on the quality and satisfaction concepts and how they influence

customer behavioral intentions like purchase and loyalty intention, willingness to spread positive

word of mouth.

The study assumes three hypotheses based on previous literature review to measure

The direct effect of customer satisfaction on re-patronage intention

The direct effect of customer satisfaction on re-patronage intentions

The relative importance of service quality and customer satisfaction for the prediction of

customer re-patronage intentions.

The study shows that there is a direct effect of service quality and satisfaction on re-

patronage of customers. Also this study is also able to demonstrate that satisfaction is a stronger

predictor of re-patronage intentions.


2. Title: The influence of the quality of the physical environment, food, and service on restaurant

image, customer perceived value, customer satisfaction, and behavioral intentions.

Source: Authors: Ryu, Kisang; Lee, Hye-Rin; Kim, Woon Gon

Publication title: International Journal of Contemporary Hospitality Management

Volume: 24 Issue: 2 Pages: 200-223

Publication year: 2012 Publication date: 2012

Publisher: Emerald Group Publishing, Limited

Objective:

The purpose of this study is to propose an integrated model that examines the impact of

three elements of food, service quality dimensions(physical environment, food and service)on

restaurant image, customer perceived value, customer satisfaction and behavioral intentions.

Methodology:

First a focus group interview was conducted by eight graduate students who patronized

authentic Chinese restaurants for six months. One of authors functioned as a moderator.

Participants freely discussed their criteria in evaluating the quality of the physical

environment, food quality, service quality, and the restaurant image. Responses from the focus

group helped to construct and refine the questionnaire. Data was collected from customers at an

authentic upscale Chinese restaurant located in a Southeastern state in the USA via a self-

administered questionnaire. 300 questionnaires were used for the final data analysis.

Data analysis:

The testing of the hypotheses in this study adopted a structural equation modeling (SEM)

designed to simultaneously examine the structural relationships among the proposed constructs.

The SEM analysis was based on the maximum likelihood method as an estimation method for

model evaluation and procedures.

Cronbach's alpha was used to assess the reliability of multi-item scales for each construct.

Findings:

The study tries to examine the integrated relationship between quality dimensions

(physical environment, food, and service), restaurant image, and customer perceived

value, customer satisfaction, and behavioral intentions in a Chinese restaurant context.

It shows that the all three elements of restaurant service quality dimensions were significant

determinants of the restaurant image.


However, the food quality was also found to be a significant predictor of customer

perceived value, the quality of the physical environment and the service was not found to be a

significant antecedent of customer perceived value.

Also, the findings from the study indicate that the restaurant image is a significant

determinant of customer perceived value. While customer perceived value was the significant

determinant of customer satisfaction, the restaurant image was not a significant predictor of

customer satisfaction. In addition, the current study reinforces the positive impact of customer

satisfaction on loyalty behaviors.

3. Title: Reviews, Reputation, and Revenue: The Case of Yelp.com

Source: Michael Luca, Harvard Business School, September 2011

Methodology:

Data Collection: Two datasets were combined for this paper: restaurant reviews from Yelp.com

and revenue data from the Washington State Department of Revenue to gather revenues for all

restaurants in Seattle from 2003 through 2009.

To investigate the impact of Yelp, the author showed that changes in a restaurant’s rating are

correlated with changes in revenue, controlling for restaurant and quarter fixed effects.

Further, to support the claim that Yelp has a causal impact on revenue, the author exploits the

institutional features of Yelp to isolate variation in a restaurant’s rating that is exogenous

with respect to unobserved determinants of revenue.

In addition to specific reviews, Yelp presents the average rating for each restaurant, rounded

to the nearest half-star. The author implements a regression discontinuity design around the

rounding thresholds, taking advantage of this feature. He looked for discontinuous jumps in

revenue that follow discontinuous changes in rating.

Next he examined the impact of Yelp on revenues for chain restaurants.

Finally, he investigated whether the observed response to Yelp is consistent with Bayesian

learning.

Findings:

A one-star increase in Yelp rating leads to a 5-9 percent increase in revenue,

This effect is driven by independent restaurants; ratings do not affect restaurants with chain

affiliation chain restaurants have declined in market share as Yelp penetration has increased.


Consumers do not use all available information and are more responsive to quality changes

that are more visible

Consumers respond more strongly when a rating contains more information.

4. Title: An Experimental Study of How Restaurant‐Owners’ Responses to Negative Reviews

affect Readers’ Intention to Visit.

Source: Evans, D.C., Oviatt, J., Slaymaker, J., Topado, C., Doherty, P., Ball, A., Sáenz, D., &

Wiley, E. (2012b). An experimental study of how restaurant‐owners’ responses to negative

reviews affect readers’ intention to visit. The Four Peaks Review, 2, 1‐12.

Research Question: When responding to negative reviews, especially publicly, what should

businesses say? Or should they say anything at all?

Research Objectives:

To measure how badly a first negative review affects readers’ intention to visit

To test whether any response from the restaurant is better than no response at all

To test whether a combative response (taken from Yelp) is as harmful as is generally

assumed

To test whether two different constructive responses (one taken from Yelp and one based on

PR theory) would return perceptions of the restaurant back to the level they were before the

negative review

Methodology:

An online experiment was built in which participants were randomly assigned to view one of

5 rich‐media scenarios. A random sample size of 259 readers was chosen to carefully controlled

mockups of a Yelp business profile and gathered their reactions.

Scenario 1: showed only the Restaurant Profile.

Scenario 2: added one Negative Customer Review to the Restaurant Profile above, but

showed no Business Response.

Scenario 3: added one Combative Business Response (taken from Yelp) to the above

Negative Review and Restaurant Profile.

Scenario 4: added instead a Constructive Business Response (taken from Yelp) to the above

Negative Review and Restaurant Profile.


Scenario 5: added instead a Constructive Business Response (tailored to PR best‐practices) to

the above Negative Review and Restaurant Profile.

Findings:

The research was consistent with past research, a negative review significantly decreased

readers’ intention to visit a restaurant.

Of all the scenarios tested, readers were least likely to visit the restaurant when no response

was made to a negative review; even a combative response improved on silence.

The two more positive, constructive responses from the restaurant owners were able to

eliminate, but not reverse, the PR‐hit caused by a negative review.

The response that was tailored to PR best practices outperformed the polite but non‐expert

responses typically seen on Yelp.

5. Title: Beyond recommendations: local review websites and their impact.

Source: BARRY BROWN, Mobile Life, University of Stockholm

Introduction:

In this paper interviews with users, reviewers, and establishments is done to explore how

local review websites can change interactions around local places. Review websites such as Yelp

and Tripadvisor allow customers to ‘pre-visit’ establishments and areas of a city before an actual

visit. The collection of a large numbers of user generated reviews has also created a new genre of

writing - with reviewers gaining considerable pleasure from passing on word-of-mouth and

influencing others’ choices. Reviews also offer a new channel of communication between

establishments, customers and competitors.

Methodology:

First step that was carried out was scanning of literature on recommendations and in

particular the extensive work on producing sets of recommendations automatically from

preference data.

Secondly, a discussion on history of reviews of physical places and the development of

‘taste’ alongside a literature on what constitutes good taste was carried out.

The last step was research which looked specifically at online reviews and covered the

history of review collected on websites.


Participants:

There were 28 participants interviewed in total:

14 review site users, with differences in terms of what sites they used, and what sites they

regularly reviewed

14 establishments selecting from the most reviewed types on Tripadvisor and Yelp.

Findings:

Review websites have their effects on businesses and readers, and they also encourage and

manage reviewers.

Reviews change the search for new establishments, businesses react to being reviewed.

A community of reviewers had developed around Yelp in particular, and this community

contributes to the massive proliferation of reviews on the Yelp website.

Through rearranging the information that the researcher gained about new places and

establishments, review websites did not revolutionize his city spaces, but they do rearrange

the effects of locality and proximity.

6. Title: How online reviews are crucial to a restaurant's takings

Source: Jamie Doward, observer.guardian.co.uk/, 2nd September 2012

Methodology:

The author has taken a work done by two economists from the University of California,

Berkeley, Professor Michael Anderson and Jeremy Magruder, published in a monthly edition of

the Economic Journal.

It represents the first attempt to gauge the relationship between online star ratings and

customers’ purchasing decisions, focusing on the positive effects of online ratings of over 300

restaurants in San Francisco, which helped in the formation of a star system on Yelp.com.

The improvements of an increase of half a star in a 1 – 5 scale without changing the

service, food menu or quality of food.

Number rating on 1 – 5 in decimals can become an average while giving star rating,

which can make a significant difference in quality for the online consumers. The study gathered

reviews and daily reservation availability for 328 restaurants in San Francisco, and found the

increase in star from 3 – 3.5 and later 3.5 to 4 has increased the chances of selling out from 13%

to 34% and to 53% respectively.


Findings:

The online star ratings have a tangible influence in the visiting numbers of restaurants.

A half star increase can increase the chance of an increase by up to 19%.

Strong reviews have a positive influence in business than poor reviews, keeping the quality

of food and services, etc constant.

Social media and forums do not generate financial returns, but influences the consumers

judgement of the quality of goods and services.

7. Title: Restaurant Research

Source: Restaurant Research Jason Guenther, Barry Zimmerman

Objective: To find the assortment of reasons that persuades a person to dine at a particular

restaurant and also how big of a part the atmosphere plays.

Methodology: Two different restaurants were selected which were different from each other and

a primary research was conducted through a Questionnaire.

Findings:

Lighting can play a big role in making a restaurant seem comfortable and pleasing. The

lighting can make the patron feel comfortable or uneasy and thus their next visit will depend

on this.

Privacy is an important factor so people who want privacy would always go to the same kind

restaurants and vice versa

Comfort was another important factor. For a few, comfort can be as general as being

comfortable with your surroundings and more defining as how comfortable is your chair.

Vegetation seemed to be an important issue because it provides privacy, colour and natural

elements to an interior.

Architecture also is an important issue as some people enjoy seeing the old timber

architecture and others prefer the newer, fresher environment.

Colour can play an important role in eating behaviours and how a patron feels in the space.

The Colour of the interior can set the mood for the patron and make them either comfortable

or uncomfortable.


Though the main factors for selecting a restaurant can be the food and the price but the other

factors like the above also plays an important role.

8. Title : Analysing the Effects of Social Media on the Hospitality Industry

Source: Analysing the effects of social media on the hospitality industry Gaurav Seth University

of Nevada 4-1- 2012

Publication detail: Published as UNLV Theses/Dissertations/Professional Papers/Capstones

Objective: The intent of this study is not to quantify how many hospitality businesses actively

use social media, rather it is to explore the areas and functions these businesses use social media

for, and understand how consumers perceive these new vehicles of communication. The study

will bring out a consumer’s perspective of the advantages of social media over traditional

marketing methods

Methodology: This research paper was compiled by comparing a variety of research from

academic journal articles and other sources of research-intensive literature.

Findings:

Majority of hotel bookings were made over the internet (45% in 2010) which exceeded the

share of travel agents.

Interactive marketing will comprise 21% of all marketing spending by 2014, and social

media will represent 3% to 6% of the interactive marketing spend.

Companies that promote satisfied customers to share their experiences on web-based,

company approved social networking platforms are more likely to draw new customers.

Also companies that address negative reviews retain dissatisfied customers; promote a

positive impact on the word-of-mouth and also improve their bottom line performance.

9. Title: Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online

Review Database

Source: Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an

Online Review Database Michael Anderson, Jeremy Magruder, The Economic Journal, 5 oct

2011

Objective:

To estimate the effect of yelp ratings on restaurant reservation availability.


A second test for whether the Yelp effect is due to solving information problem groups

restaurants according to whether there are external sources of quality information.

Methodology:

In order to estimate the effect of yelp ratings on restaurant reservation availability two

independent sources were merged . The first data set consisted of the universe of Yelp

reviews for restaurants in San Francisco, California as of February 2011.The second data set

consisted of reservation availability data taken from a large online restaurant reservation

website from July 2010 through October 2010.

A second test for whether the Yelp effect is due to solving information problem groups

restaurants according to whether there are external sources of quality information. The

information for these was taken from San Francisco Chronicles annual Top 100 Restaurants

listing.

Findings:

Yelp aggregates consumer information on restaurant quality into convenient half-star ratings.

The higher ratings cause restaurant to sell out prime-time table 19 percentage points more

frequently. These effects are largest for restaurants where information is most scarce.

Restaurants that are not externally accredited sell out 27 percentage points more frequently

when they receive an extra half-star.

There is no evidence that these effects are due to manipulation of ratings, changes in

restaurant quality, or direct marketing effects of Yelp, and present additional supporting

evidence that customer flows change.

Yelp represents a highly efficient mechanism for social learning, and thus it is perhaps

unsurprising that its effects are so large when social learning effects have been documented

in many other less efficient contexts

10. Title: Electronic Meal Experience: A Content Analysis of Online Restaurant Comments.

Source: Cornell Hospitality Quarterly November 2010 vol. 51 no. 4 483-491

Methodology:

The author randomly selected 300 full-service restaurants from the 791 restaurants on the

London-eating site. Selection criteria were that the restaurant either had an online presence so

that the style of service could determine, or it had online articles that could serve the same


purpose. He applied the Davis et al. (2008, 42) classification of full-service restaurants to include

their fine-dining and popular-catering categories. The original set of data of 2,292 was compiled

and included comments during March 2007. A content analysis was made to identify key factors

in the consumers’ reflective commentaries and thus suggest the key values in consumers’

restaurant preference structure model.

The content analysis (Krippendorff 1980) included a total of 2,471 comments (the

original 2,292 plus 179 during the recession). The original data of 2,292 comments from twenty

months (July 2005 to February 2007) were analyzed separately from the 179 comments retrieved

for the same three hundred restaurants for the months of December 2008 and January 2009.Then

frequencies of key variables were converted to percentages to make meaningful comparisons.

Findings:

Anonymity of this site does not unleash a negative tsunami and instead allows satisfied

customers to give a positive restaurant review is a message of great hope to restaurateurs who

might be concerned about such sites.

Price ranks fourth in the list of most frequently mentioned factors, with food and service at

the top

11. Title: Groundbreaking Survey Reveals How Diners Choose Restaurants

Source: Oliver, C. (2012, August 7). Angel Smith. Retrieved June 10, 2013, from Marketing

Article Library: http://angelsmith.net/inbound-marketing/groundbreaking-survey-reveals-how-

diners-choose-restaurants/

Summary: In the study conducted by Angel Smith on how diners are influenced on which

places to visit and eat their food or how restaurant choices are made, a sample size of 500 was

taken. In the result it was seen that 48.9% or the respondents said that they depend on the

opinions of their near and dear ones but out of these it was seen that 8 out of 10 would also

conduct extra research mostly online basically depending on consumer generated review sites,

blogs and restaurant websites.

Respondents were asked to rank the most important places they use for additional

restaurant research after receiving a recommendation from a trusted friend. User generated

review sites (27.7 percent) and the restaurant’s website (27.0 percent) were in a near statistical

tie as the most important places consumers turn. Other friends (25.2 percent) and food blogs


(16.4 percent) were ranked as the second most important places for additional restaurant

research. Rounding out the top rankings, Google search was cited by 16.5 percent as the 3rd

most important place for those who do additional research.

12. Title: Cornell Hospitality Research: Online Reputation Directly Affects Pricing Power,

Occupancy & RevPAR

Source: Review Pro. (2012, November 16), Retrieved June 11, 2013.

Summary:

This article explored the relation between online reputation of restaurants and the

subsequent effects it had on different factors mainly the pricing power, occupancy and

RevPAR(Revenue per Available Room). The study was able to find not only a direct correlation

between positive online reviews and pricing power of the concerned restaurants but also saw that

it amounted to higher group booking rates and corporate negotiated rates in addition to

reservations made over the phone.

The findings were made on the basis of over 31,000 monthly observations over 2 ½ years

on midscale, upscale, and luxury hotels in 11 major metropolitan cities and by observing the

consumers in the North America and Europe: London, Milan, Rome, Madrid, Berlin, Prague,

Chicago, Los Angeles, New York, San Francisco, and Miami.


3.1 Problem Statement

Earlier, that is before the era of internet and social media, consumers had preference in

selecting restaurants which was usually influenced by word of mouth, popularity or frequency of

visits. Lack of awareness of new restaurants was very much prevalent. Consumer loyalty existed

to a very large extent which made the industry stagnant.

Now, in this age, technology boom has removed all the barriers and opened the gates to

tremendous information available on the finger tips. Information about restaurants is freely

available online which notifies the consumers about the restaurant locations, ratings & rankings,

peer review, menu & price, photos, events, booking status, etc. This has changed the outlook of

the industry and its functioning. The new generation seems to have accepted the changing trend.

3.2 Research Questions

1. To what extent online guides influence the visiting decisions of the customers?

2. How components of online restaurant guides are ranked on the basis of customers'

preference?

3.3 Objectives

1. To identify the major factors that influence visiting intention of the customer.

2. To test the significance of the influence of online restaurant guides on visiting decision of the

customer.

3. To determine the influence of each component of online restaurant guides.

3.4 Scope of the Study

The study will mainly focus on the online restaurant guides and its influence on

consumers. The study will try to estimate and analyze the consumer behavior in Bangalore

region. The study includes restaurants, lounges, etc. but doesn’t include lodges and start hotels.


3.5 Research Methodology

3.5.1 Type of Research

The study will adapt quantitative research method by administrating questionnaires to the

target group. We intend to first identify the factors that influence consumer visiting intention,

then concentrating mainly on determining the extent of influence of online restaurant guides. In

the later stage we will be evaluating, to what extent each component of the online restaurant

guide will impact its influence.

The research design is descriptive indicating the relationship between the online

restaurant guides and visiting intention controlling other factors.

3.5.2 Hypothesis

H1 Customer's visiting intention is influenced by the online restaurant guides

H2 Photos, ratings, peer comments, prices, menu card in the online restaurant guides influence

customers’ preference.

3.5.3 Sample Design

i. Target population: The target group will consist of people in the age group of 15 years to 45

years who regularly check for the online reviews of restaurants.

ii. Sampling Elements: Individuals who regularly check for the online reviews of restaurants

iii. Sampling Methodology: The sampling methodology used is Convenience Sampling.

Sampling will be done in shopping malls, Cafes and Gaming Centers where we can find large

target group.

iv. Sample Size: The sample size for the study is 283 respondents.

v. Data Collection Method: Data will be collected through Questionnaires for respondents. The

survey will be conducted in places where there is large crowd. Interviews will be also

conducted with the restaurant owners to get an insight on the issues.

3.6 Limitations of the Study

Previous researches and articles have shown a significant level of correlation between

reviews and the direct effect on the behavioral patterns of the consulting individuals. A person

regards another individual’s opinions mainly when they face an ambiguity in the matter of


concern. In the case of hotels and restaurants the same situation arises frequently. With a large

variety of restaurants and cuisines popping up at every interval in Bangalore every now and then

and the lifestyle changes taking place, where the people are willing to step out from their comfort

zone and venture out and experience new things, the opinions and peer reviews have gained

greater value and significance. So much so, that it can either make or break an image.

Looking at it from a larger view, an individual’s opinion rest upon a variety of factors most

of which are not quantitative in nature making it hard to be measured empirically.

The sample being taken is strictly from Bangalore with a population of over 5 million, a

sample size of 200, is not enough to collectively form a strict conclusion on whether the

information given on online restaurant guides play a strong role on controlling the visiting

intentions of restaurant goers.

Factors that influence an individual’s opinion vary from person to person. What one

individual scales as a very important factor on his decision to visit a restaurant may not be

seen as that significant by the next one. There are many factors that influence a person’s

decisions to visit a restaurant and it is hard to take each and every variable into consideration

as a part of the research.


4.1 Profile of Respondents

Chart 4.1.1: Age of the Respondent

Chart 4.1.2: Occupation of the Respondents


Chart 4.1.3: Monthly Income of the Respondents

Table 4.1.1 Monthly Income

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Less than 10000 122 43.1 43.1 43.1

10000-20000 52 18.4 18.4 61.5

20000-30000 26 9.2 9.2 70.7

30000-40000 22 7.8 7.8 78.4

More than

40000

61 21.6 21.6 100.0

Total 283 100.0 100.0


Chart 4.1.4: Frequency of Visiting a Restaurant

Table 4.1.2: Frequency of Visiting a Restaurant

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Less than 500 28 9.9 9.9 9.9

500-1000 51 18.0 18.0 27.9

1000-2000 61 21.6 21.6 49.5

2000-3000 67 23.7 23.7 73.1

More than

3000

76 26.9 26.9 100.0

Total 283 100.0 100.0


Chart 4.1.5: Monthly Spending on Restaurants

Table 4.1.3 Monthly Spending on Restaurants

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Never 58 20.5 20.5 20.5

1-3 times 99 35.0 35.0 55.5

4-6 times 48 17.0 17.0 72.4

7-9 times 29 10.2 10.2 82.7

More than 10

times

49 17.3 17.3 100.0

Total 283 100.0 100.0

Interpretation:

The Age group of the respondents varies from 16 to 45 years with the maximum numbers

of respondents are from the age group of 23 years. The maximum number of the respondents as

seen from the data is students. Since most of the respondents are students the maximum number

of respondents in the sample are from the income group of less than 10000 followed by the

income group of more than Rs.40000.It can be observed that most of the respondents visit the

restaurants only 1-3 times in a month.. Largest number of the respondents is seen to be spending


more than Rs.3000 on restaurants followed by the second largest number spending Rs.2000-3000

per month.

4.2 Research Questions

Research Question1: To what extent online guides influence the visiting decisions of the

customers?

HypothesisH1: Customer's visiting intention is influenced by the online restaurant guides

Null Hypothesis: Online restaurant guides have significant importance on the customer’s visiting

intention

Alternative Hypothesis: Online restaurant guides do not have significant importance on the

customer’s visiting intention

Table 4.2.1 reports descriptive statistics (mean and standard deviation) for each factor. It can be

seen that, amongst the eight factors the mean score (3.87) of online restaurant guide is the least

while food quality has the highest mean score of 6.35.

Table 4.2.1: Descriptive Statistics

Mean

Std.

Deviation N

Level of importance of

ambience

5.20 1.626 282


location

5.11 1.587 282


price

5.54 1.562 282


Food quality

6.35 1.223 282


service quality

5.69 1.554 282


word of mouth

4.91 1.657 282


popularity

4.76 1.652 282


ORG

3.87 1.990 282


Interpretation:

General Linear Model procedure in SPSS, version 19.0 is used to perform factorial

ANOVA analysis. GLM gives the flexibility to accommodate and assess the effect of more than

one categorical variable on the dependent variable.

Table 4.2.2 shows the pairwise comparisons of all the factors. Food quality has the

highest importance having significant positive mean difference with the other factors, followed

by Service quality and price. Service quality and price have almost equal level of importance,

however service quality has a positive mean difference of .145but a significance greater than

0.05( p=0.187). Ambience is the fourth important factor. Popularity and word of mouth have

low level of importance.

The mean difference of Online restaurant guides is negative with respect to ambience

(mean difference= -1.333; p=0.000).Also, Price is more important than online restaurant guides

as the mean difference between online restaurant guides and price is negative (Mean

difference=-1.677;p=0.000) .There is a significant negative mean difference between online

restaurant guides and food quality(mean difference=-2.489;p=0.000) indicating that food quality

has higher level of importance than online restaurant guides. Similarly, we can see that the mean

difference of online restaurant guides (ORG) is negative with respect to all the other factors and

also the significance is less than 0.05. Therefore it can be concluded at 0.05 level of significance

the null hypothesis is accepted. Hence, there is no significant importance of online restaurant

guides while considering visiting a restaurant.

Table 4.2.2: Pairwise Comparisons

(I) factor1 (J) factor1

Mean

Difference (I-J) Std. Error P value

95% Confidence Interval for

Difference

Lower Bound Upper Bound

Ambience Location .089 .090 .325 -.088 .266

Price -.344* .108 .002 -.556 -.132

Food quality -1.156* .088 .000 -1.330 -.982

Service

quality

-.489* .082 .000 -.650 -.328

Word of

mouth

.291* .102 .005 .090 .492


Popularity .436* .100 .000 .239 .633

ORG 1.333* .121 .000 1.095 1.572

Location Ambience -.089 .090 .325 -.266 .088

Price -.433* .102 .000 -.633 -.232

Food quality -1.245* .102 .000 -1.445 -1.044

Service

quality

-.578* .097 .000 -.769 -.387

Word of

mouth

.202 .108 .061 -.010 .414

Popularity .348* .098 .000 .155 .540

ORG 1.245* .124 .000 1.000 1.489

Price Ambience .344* .108 .002 .132 .556

Location .433* .102 .000 .232 .633

Food quality -.812* .097 .000 -1.002 -.622

Service

quality

-.145 .110 .187 -.362 .071

Word of

Mouth

.635* .113 .000 .411 .858

Popularity .780* .106 .000 .571 .989

ORG 1.677* .131 .000 1.419 1.936

Food

Quality

Ambience 1.156* .088 .000 .982 1.330

Location 1.245* .102 .000 1.044 1.445

Price .812* .097 .000 .622 1.002

Service

quality

.667* .085 .000 .500 .833

Word of

mouth

1.447* .097 .000 1.255 1.638

Popularity 1.592* .103 .000 1.390 1.794

ORG 2.489* .128 .000 2.238 2.741

Service

Quality

Ambience .489* .082 .000 .328 .650

Location .578* .097 .000 .387 .769

Price .145 .110 .187 -.071 .362

Food quality -.667* .085 .000 -.833 -.500

Word of

mouth

.780* .097 .000 .589 .971

Popularity .926* .102 .000 .725 1.126

ORG 1.823* .120 .000 1.587 2.058

Word of Ambience -.291* .102 .005 -.492 -.090


mouth Location -.202 .108 .061 -.414 .010

Price -.635* .113 .000 -.858 -.411

Food quality -1.447* .097 .000 -1.638 -1.255

Service

quality

-.780* .097 .000 -.971 -.589

Popularity .145 .086 .091 -.023 .314

ORG 1.043* .122 .000 .803 1.282

Popularity Ambience -.436* .100 .000 -.633 -.239

Location -.348* .098 .000 -.540 -.155

Price -.780* .106 .000 -.989 -.571

Food quality -1.592* .103 .000 -1.794 -1.390

Service

quality

-.926* .102 .000 -1.126 -.725

Word of

mouth

-.145 .086 .091 -.314 .023

ORG .897* .121 .000 .659 1.136

ORG Ambience -1.333* .121 .000 -1.572 -1.095

Location -1.245* .124 .000 -1.489 -1.000

Price -1.677* .131 .000 -1.936 -1.419

Food quality -2.489* .128 .000 -2.741 -2.238

Service

quality

-1.823* .120 .000 -2.058 -1.587

Word of

mouth

-1.043* .122 .000 -1.282 -.803

Popularity -.897* .121 .000 -1.136 -.659

Research Question 2: How components of online restaurant guides are ranked on the basis

of customers' preference?

HypothesisH1: Influence of Photos, ratings, peer comments, prices, menu card in the online

restaurant guides

Null Hypothesis: Influence of photos, ratings, peer comments, prices and menu card in the online

restaurant guides on customer’s preference is same.

Alternative Hypothesis: Influence of photos, ratings, peer comments, prices and menu card in the

online restaurant guides on customer’s preference is not same.


Table 4.2.3 reports descriptive statistics (mean and standard deviation) for each Component of

online restaurant guides. Peer comments have higher level of importance for the customers

(mean=5.21) closely followed by menu card (mean=5.12). Photos is considered least important

while considering online restaurant guides (mean=4.52).

Table 4.2.3: Descriptive Statistics

Mean

Std.

Deviation N


Photos component of

ORG

4.52 1.722 203


ratings component of

ORG

4.89 1.389 203


comments component

of ORG

5.21 1.498 203


price component of

ORG

4.99 1.678 203


menu card component

of ORG

5.12 1.585 203

Interpretation:

Peer comments are the most important component when compared to the other

components. The mean difference of peer comments with respect to other factors is positive

followed by menu card. Whereas Price is more important than the photos and Restaurant ratings.

Therefore it can be concluded that at 0.05 level of significance, the null hypothesis is rejected.


Table 4.2.4: Pairwise Comparisons

(I) factor1 (J) factor1

Mean

Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for

Differencea

Lower Bound Upper Bound

Photos Rating -.369* .120 .002 -.605 -.133

Peer

comments

-.685* .132 .000 -.944 -.425

Price -.463* .145 .002 -.748 -.178

Menucard -.596* .143 .000 -.878 -.314

Rating Photos .369* .120 .002 .133 .605

Peer

comments

-.315* .104 .003 -.521 -.110

Price -.094 .130 .472 -.350 .163

Menucard -.227 .126 .073 -.475 .022

Peer

comments

Photos .685* .132 .000 .425 .944

Rating .315* .104 .003 .110 .521

Price .222 .135 .102 -.044 .487

Menucard .089 .124 .475 -.155 .333

Price Photos .463* .145 .002 .178 .748

Rating .094 .130 .472 -.163 .350

Peer

comments

-.222 .135 .102 -.487 .044

Menucard -.133 .116 .253 -.362 .096

Menucard Photos .596* .143 .000 .314 .878

Rating .227 .126 .073 -.022 .475

Peer

comments

-.089 .124 .475 -.333 .155

Menucard .133 .116 .253 -.096 .362

Interpretation:

Table 4.2.5 shows the cross tabulation between the frequency of visit and the visiting online

restaurant guides. It is seen that as the frequency of visit increases the percentage of

respondents visiting an online restaurant guides for selecting a restaurant increases. 62.1%

of the respondents visit online restaurant guides for selecting a restaurant as compared to

37.9%.


Table 4.2.5: Cross Tabulation

Do you visit online

restaurant guide for

selecting a restaurant

Total Yes No

In a month, how

often do you visit a

restaurant

Never Count 24 34 58

% within In a month,

how often do you visit a

restaurant

41.4% 58.6% 100.0

%

1-3 times Count 58 40 98



restaurant

59.2% 40.8% 100.0

%




restaurant

68.8% 31.3% 100.0

%




restaurant

86.2% 13.8% 100.0

%

More

than 10

times

Count 35 14 49



restaurant

71.4% 28.6% 100.0

%

Total Count 175 107 282



restaurant

62.1% 37.9% 100.0

%


Chart 4.2: Visiting Online Restaurant Guides


5.1 Discussion of Findings

1. To identify the major factors that influence visiting intention of the customer.

From the analysis it can be concluded that the major factor that is influencing the

respondents is food quality. Service quality has also emerged to be an important factor being

equally important as the price. Ambience and location do not have much significance. However

word of mouth and popularity of restaurants do not have much influence on the visiting intention

of the respondents.

2. To test the significance of the influence of online restaurant guides on visiting decision of

the customer

Online restaurant guides have the least importance while considering visiting a restaurant.

Even though online restaurant guides are visited often, they do not necessarily influence the

visiting intention of the customer. From the analysis it is seen that the online restaurant guides

are considered least important of all the factors and contribute very little in influencing the

visiting intention of the customers. Therefore, it

Another important finding is that with the increase in the frequency of restaurant visits,

the percentage of respondents checking online restaurant guides increases.

3. To determine the influence of each component of online restaurant guides

Components of online restaurant guides i.e. Photos, Rating, Peer comments, Price and

Menu card have different level of influence on the customer preference. Peer comments have the

most influence on the customers’ preference. Menu Card displayed in the online restaurant

guides are the next influential factor.

5.2 Implications of Findings

Our study shows that Food quality and Service Quality are the major factors considered

by customers for visiting a restaurant which is in line with the findings of the researches already

conducted in this area. When it comes to online restaurant guides in the Indian context we can

observe that the online restaurant guides do not have as much significance as compared to the

research conducted in the same area internationally. Thus the study establishes that online


restaurant guides do not majorly alter the decision making of an Indian customer, and to the

extent it does, it is observed that this alteration is influenced by peer comments followed by the

menu card and price rather than ratings and the photos.

Conclusion


With the advances in technology and internet, we can see lot of websites being dedicated

for cuisine and beverages. Simultaneously, there is a boom in the restaurant industry too. Lot of

new players has come into the market and everyone is trying to differentiate their business with

ambience, space, service and other varieties of food. With the increasing popularity of the online

restaurant guides, it is generally thought that they are having higher influence on the customers’

visiting intention of the restaurants. However the study shows that the major factors influencing

the customers’ visiting intentions are food quality, service quality and price. Online restaurant

guides are not considered important in their decision making process.

Even though people visit online restaurant guides, it is may be just to have a check on the

restaurants rather than make their decisions on visiting a restaurant based on it. Also Peer

comments about the restaurant dining experience have more influence on the customers than the

other components of the online restaurant guides. Menu card is also considered as an important

component while checking an online restaurant guides. Photos are considered the least important

component.

Online restaurant guides are effective way for gathering information about the

restaurants. However they are merely looked at as a supporting tool. They are primarily

influenced by the food, service quality, and price. Online restaurant guides have lot of scope in

the future provided they communicate their purpose effectively.

References


Electronic Meal Experience: A Content Analysis of Online Restaurant Comments. (2010,

November). Cornell Hospitality Quarterly, 51(4), pp. 483-491.

Review Pro. (2012, November 16). Retrieved June 11, 2013, from

http://www.reviewpro.com/cornell-reputation-revenue-research-14442

Oliver, C. (2012, August 7). Angel Smith. Retrieved June 10, 2013, from Marketing Article

Library: http://angelsmith.net/inbound-marketing/groundbreaking-survey-reveals-how-

diners-choose-restaurants/

Solis, K. T. (2003). How Can I Learn about Different Cultures? Retrieved 2013, from

www.wisegeek.com.

QUESTIONNAIRE


This survey is being conducted in partial fulfillment of Market Research project as part of MBA program at Christ University

Institute of Management. In appreciation for your honest participation in this study by completing this survey and submitting it to

questionnaire administrator, we promise to keep this information confidential and the use of this information will be purely for

academic purpose.

Name: Age (in years) :

Occupation (Tick whichever appropriate):

Professional / Business / Employed by an organization / Students / Homemaker /

Others ________________

Please tick the appropriate answer for the questions below ()

Q1. What is your monthly income (INR)?

Less than 10000 30000 – 40000

10000 – 20000 More than 40000

20000 – 30000

Q2. In a month how often do you visit a restaurant?

Less than 3 times 10-12 times

3-6 times More than 12 times

7-9 times

Q3. How much do you spend monthly on restaurants (INR)?

Less than 500 2000 - 3000

500 – 1000 More than 3000

1000 – 2000

Q4. Rate the level of Importance (1 to 7) of factors you consider for visiting a restaurant.

(7 being - Extremely Important and 1 being – Not at all Important)


Please encircle the appropriate option

FACTORS

Ambience 7 6 5 4 3 2 1

Location 7 6 5 4 3 2 1

Price 7 6 5 4 3 2 1

Food Quality 7 6 5 4 3 2 1

Service Quality 7 6 5 4 3 2 1

Word of Mouth 7 6 5 4 3 2 1

Popularity 7 6 5 4 3 2 1

Online Restaurant Guide 7 6 5 4 3 2 1

Q5. Do you visit Online Restaurant Guide for selecting a restaurant?

Yes No

If yes, then continue.

Q6. Out of 10 restaurants that you visit, how many are selected on the basis of online restaurant

guides?

___ / 10

Q7. Which Online Restaurant Guide(s) do you refer seeking information on restaurants?

Zomato

Burp

Justeat

Others _____________________

Please tick the appropriate bubble for the questions below ()


Q8. Photos of restaurants on Online Restaurant Guides encourage me to visit a restaurant.

Q9. Restaurants ratings on Online Restaurant Guides can alter my impression about a restaurant.

Q10. Peer comments about restaurants on Online Restaurant Guides effect my decision making

to visit a restaurant.

Q11. Before selecting a restaurant, I check the prices on Online Restaurant Guides.

Q12. Depending on the menu card available on Online Restaurant Guides, I decide on the cuisine

and make a choice of a restaurant.

Thank you for your valuable participation

PROFILE OF PROJECT TEAM

Strongly

Agree Strongly

Disagree

Strongly

Agree

Strongly

Agree

Strongly

Agree

Strongly

Agree

Strongly

Disagree

Strongly

Disagree

Strongly

Disagree

Strongly

Disagree


Sl.

No. Name

Register

No. Role Background Personal Strengths & skills

1 Aswin Dan

Abraham 1221403

Point of

Contact

B.Com in

Computer

Application

Leadership quality, creative

thinking, Presentation skills

2 Chinmai

K.P. 1221444

Team

Member

B.E. in Civil

Engineering

Sound analytical and

statistical skills, Logical

Thinking, Presentation skills

3 Niharika

Rai 1221453

Team

Member

1 year of work

experience in

Infosys,

B.E. in

Telecommunication

Good analytical skills,

leadership quality,

Presentation skills

4 Vinisha

James 1221460

Team

Member

10 months work

experience in Dell,

B.Tech. in

Biotechnology

Creative thinking, Sound

analytical skills, Presentation

skills

5. Dhanashree

Sreesan 1221545

Team

Member

B.B.M in

Marketing

Presentation skills, Creative

mind, Communication skills

Perceived Influence og online restaurant guides on visiting intentions of customers

Documents

Transcript of Perceived Influence og online restaurant guides on visiting intentions of customers