The Impact of Online Food Delivery Services to the People of Solano, Nueva

Vizcaya

Submitt ed in Parti al Completi on of Research 1

Aldersgate College

Solano, Nueva Vizcaya

July 2021

Angela Rochelle Tuzon Ganado

 CHAPTER 1

The Problem and Its Background

Introducti on

According to the World Health Organizati on (WHO), the 2019 coronavirus

disease (COVID-19) erupted in China in December 2019 and expanded as a

global pandemic on 11 March 2020. Because COVID-19 has a high risk of death

and human-to-human transmission, self-quaranti ne, wearing a mask in public,

social distancing, and restricti on of people's movement have been strongly

recommended by WHO. Consequently, most of the United States required

residents to stay at home and forced foodservice operati ons to be closed or

restricted.

With the restricti on of dine-in service due to COVID-19, many restaurants

adapted and heavily relied on contactless and online food delivery systems to

survive. The number of foodservice and users using online food delivery

systems has surged during COVID-19. About 67 percent of residents preferred

using online delivery services to purchase food during the COVID-19 pandemic

in the United States. Online food delivery service refers to internet- based food

ordering delivery services that connect customers with partner foodservice

operati ons via their websites or mobile applicati ons. Online food delivery

services provide a wide range of restaurant lists, allowing customers to

compare menus, prices, and even reviews from other users by restaurant types.
Furthermore, the distributi on of mobile devices has provided customers with a

new platf orm—food delivery apps—that is available when they order food

online. Moreover, it is expected that more customers and restaurants uti lized

online food delivery services during and aft er the COVID-19 pandemic.

One of the most prolifi c applicati ons of this recent hybridizati on is in the

restaurant industry, with the emergence of online food delivery services.

Delivery transacti ons made up six percent of total Philippine restaurant sales in

2017 and are esti mated to reach 40 percent of all restaurant sales by 2020

(Morgan Stanley Research 2017).1 However, the extent to which these online

sales are incremental—causing overall restaurant sales to increase—or,

alternati vely, drawn away from brick-and-mortar sales, has not been

quanti fi ed. Online food delivery is a prime example of e-commerce disrupti ng a

traditi onal market. A fl ood of new food delivery fi rms has caused rapid growth

in the total number of transacti ons and revenue for the nascent industry.

Although online food delivery services provide extra channels for potenti al

revenue, they also create the risk of cannibalizati on in which brick and-mortar

sales suff er because consumers who purchase in-store have transiti oned to

mostly online purchasing behavior. The purpose of this study is to determine

the eff ects that the entry of these fi rms—and subsequent hybridizati on—has

had on restaurant sales by quanti fying the levels of substi tuti on between in-

person restaurant sales and online food delivery services.

 Online food delivery services have been around for quite some ti me.

Several chain restaurants created websites to order take-out, but these

services were limited to within the chain’s own restaurants.2 Individual

restaurants followed suit, creati ng their own websites for delivery. Even

grocery stores began off ering online delivery in the early 21st century (Pozzi

2012; Relihan 2017). However, generalized online food delivery services that

off er delivery from many diff erent restaurants have only become popular in the

past decade—and they have done so rapidly. By 2018, the online food delivery

service industry had an esti mated $82 billion in gross revenue and accounted

for 6 percent of the restaurant market in 2020 (Frost and Sullivan 2018; Morgan

Stanley Research 2020). These fi rms are backed by revenue growth more than

14 percent over the past four years and are on track to double their market

share by 2025 (Morgan Stanley Research 2020). The rapid expansion of these

fi rms has even infl uenced some restaurants to change their enti re layouts and

migrate to a “delivery only” model (Bond 2019). The restaurant market is

evolving. The fi rst online food delivery fi rm, GrabFood, was founded in 2004

with the goal of replacing all paper menus with a single website. Since then,

GrabFood has transiti oned to connecti ng delivery drivers from those

restaurants to deliver to customers. Food Panda, and other fi rms operate

slightly diff erently from GrabFood. These newer fi rms—which were founded in

2011 and 2013, respecti vely—provide menus from restaurants as well as

contracti ng out delivery drivers, much like Uber or Lyft .3 These fi rms adopted
very similar growth strategies in which they start in select citi es and expand to

others with their success.4 Consumers that use online food delivery services

also have a few empirically quanti fi ed characteristi cs. Delivery is ordered to the

consumer’s home 86 percent of the ti me, and 74 percent of sales occur on

weekends (Hirschberger et al. 2017). Further, in 2017, 43 percent of individuals

who ordered with online food delivery services say that it replaced an in-person

meal at a restaurant. This fi gure increased from 38 percent just the year

before, suggesti ng that there is incremental cannibalizati on with the

introducti on of online channels (Morgan Stanley 2017). Online food delivery

services oft en state that they are providing supplementary sales to restaurants.

In fact, a survey of several thousand restaurateurs found that off ering online

delivery has generated additi onal sales for 60 percent of restaurant operators

(Technomic Food Trends 2018). While online food delivery services claim, and

actually do, provide incremental sales, the profi tability of restaurants is

declining as online delivery increases (Dunn 2018; Thompson 2019). This is

mostly due to high fees that online food delivery services charge, not only as

service and delivery charges to the consumer, but also to the restaurant. Most

online food delivery services charge the restaurant between 20-30 percent of

each purchase. Online delivery oft en represents a large bulk of business for

restaurants, so it’s not an opti on to cut online sales channels. In the age of a

pandemic, the demand for online food delivery services sales is spiking. In fact,

in China, online food delivery service orders surged 20 percent during January
alone; fi rms such as Doordash have even started reducing or eliminati ng their

fees in response to the surge that is beginning in the United States (Keshner

2020). It is expected that consumers will conti nue to increase their usage of

online food delivery services so long as there are stay-at-home orders and sit-

down restaurants remain closed, although this likely will not completely

replace pre-pandemic restaurant spending. As COVID-19 conti nues to impact

the United States, the demand for non-contact food delivery services will likely

follow the example of China and expand greatly. Understanding consumer

behavior as it relates to online food delivery services is essenti al in this rapidly

changing environment.

Existi ng studies provide an understanding of customers' moti vati ons to

use online food delivery systems and factors aff ecti ng online food delivery

service usage. Additi onally, previous studies have examined the factors based

on the technology acceptance model that determine whether or not an

individual adapts to innovati on. Although TAM is a robust and powerful

theoreti cal framework of users' acceptance and usage of technology, testi ng

and extending TAM by integrati ng it with other factors (social infl uence, trust,

and enjoyment) may provide insight for food service industry management to

develop the strategies of online food delivery services. Moreover, few studies

have examined the factors infl uencing customers' decision making toward the

use of online food delivery services, especially under pandemic conditi ons. As

the COVID-19 pandemic has changed customers' dining and consumpti on

behaviors, it is necessary to consider the COVID-19 pandemic as a context

factor aff ecti ng customers' online food delivery service. Therefore, the purpose

of the study is to examine the factors aff ecti ng customers' online food delivery

services usage by applying TAM and other factors (e.g., enjoyment, trust, and

social infl uence) to provide a comprehensive model during the COVID-9

pandemic.

Research Locale

This research will be conducted in Solano, Nueva Vizcaya, Philippines.

The respondents will be interviewed in their houses or any comfortable place

that the respondent will choose to. The researchers chose the place of

implementati on because it will give the researchers the needed informati on for

people availing food through online delivery. The research will be conducted in

the fi rst semester of the academic year 2021-2022.

Framework of the Study

To bett er understand the big picture of this research, the proposed

conceptual model is shown in Figure 1. This study proposes that expected

service quality plays a mediati ng role in the relati onships between sources of

online reviews, promised waiti ng ti me and customer’s acceptable waiti ng ti me

in the online food delivery services setti ng. In this research, it is also crucial to
 examine to what extent the sati sfacti on with the service is aff ected by the

acceptable waiti ng ti me.

Figure 1. A proposed conceptual framework

Online Service Waiting Customer

Intention
Reviews Quality Time Satisfaction

Source (owners vs.

customers)
Expected Acceptable Satisfaction
Repurchase
Service Waiting time/ with the
Intention
Quality waiting tolerance Service
Promised Waiting
Time (short vs.
long) Objective Waiting
time (short vs.
long)

Statement of the Problem

General Problem

The impact of online food delivery services to the people of Solano,

Nueva Vizcaya.

Specifi c Problem

1. Which is more eff ecti ve? The customer-created positi ve online review or

the owner-created positi ve online review?

2. Which has a more positi ve eff ect on the expected service quality? The

short-promised waiti ng ti me or the long-promised waiti ng ti me?

 3. For the promised waiti ng ti me, which will have a stronger eff ect on the

expected service quality? The customer-created online review or the

owner-created online review?

4. Is the willingness of customers to wait greater if they perceive an

expected service quality?

5. Are the customers more sati sfi ed if they wait for a short period of ti me?

Assumpti on/Hypothesis

H1. The customer-created positi ve online review will have a more positi ve

eff ect on expected service quality than the owner-created positi ve online

review.

H2. The short-promised waiti ng ti me shown in review will have a more positi ve

eff ect on the expected service quality than the long-promised waiti ng ti me.

H3. The promised waiti ng ti me will have a stronger eff ect on the expected

service quality from the customer-created online review than from the owner-

created online review.

H4. The greater the expected service quality customers perceive, the longer

customers are willing to wait.

H5. The shorter the customers wait, the more sati sfi ed with the service

customers are.
Scope and Limitati ons

This research focuses on the impact of online food delivery services to

the people of Solano, Nueva Vizcaya. The data collecti on will be conducted to

randomly selected customers of 10 random food delivery service providers in

Solano, Nueva Vizcaya who will represent the populati on.

Each of the respondent was given the same questi onnaires to answer.

The result of this research is only be applicable to the respondents of this study

and is not to be used as a measure to those who do not belong to the

populati on of this research. The main source of data will be the questi onnaire,

which is prepared by the researchers.

Signifi cance of the Study

This research would be benefi cial to the following group of individuals:

FOOD DELIVERY SERVICE PROVIDERS. This study will help them bett er

understand the factors that aff ect the number of clients that they are able to

serve and help them devise ways to be able to cater to the needs and wants of

their clientele.

CUSTOMERS. This study would help them understand bett er online food

delivery services and also give their opinion on their choice of food delivery

service that they avail themselves and make them more knowledgeable on the
other opti ons that they have on the ever growing market of food delivery

services.

MARKETERS. The study would help them understand bett er the needs and

wants of the clients and gear toward programs that could help them be able to

capture a bigger and broader market base knowing the results of this study.

BUSINESS OWNERS. The study would make them bett er u nderstand the

demands of their clients and thus could focus some fi nancial resources in the

improvement of their businesses to cater to the needs and wants of their

consumers.

RESEARCHERS. The result of the study will provide informati on that serves as a

reference for the researchers to develop more programs regarding impact of

diff erent factors on online delivery services. The researchers will gain baseline

data for future researchers to formulate soluti ons to problems encountered by

online food delivery services.

FUTURE RESEARCHERS. This study will serve as reference material for future

researchers who are interested in the subject matt er of the study.

Defi niti on of Terms

The following terms are defi ned operati onally and conceptually for the

purpose of clarifi cati on and it use in the study.

Business. It is the process of making a profi t by purchasing and selling

goods, off ering products and providing services.

 Consumer. It is classifi ed as the individual who pays for the products and

services generated by a seller (i.e., a corporati on, an organizati on).

Delivery. It is the acti on of delivering something in exchange of money.

E-commerce. It refers to commercial transacti ons conducted

electronically on the internet.

Hybridizati on. It refers to the way people adapt to diff erent latest

trends.

Impact. It is having a major eff ect on someone or something.

Online. It is connected to, served by or available through a system and

especially a computer or telecommunicati ons system (such as the internet).

Services. It is the process of doing something for someone or something

that is primarily intangible.

 CHAPTER 2

Review of Related Literature and Studies

Literature Review

Recent studies have described a “retail apocalypse” in which e-commerce

has forced brick and-mortar retail establishments without online channels to

shut down across the nati on. However, physical stores are not quite fi nished.

The “bricks-and-clicks” hybrid model has become more and more popular—and

this trend has not been limited to just retail stores (Horta¸csu and Syverson

2015).5 This study seeks to quanti fy potenti al crowding-out eff ects and market

expansions that have occurred due to the entry of online food delivery services

and subsequent hybridizati on of restaurants. “Crowding-out” refers to sales

that usually occur in brick-and-mortar stores that are now happening via other

channels. Market expansions refer to new sales that are generated by creati ng

an online channel for purchases. Although opening new online channels could

potenti ally increase restaurant revenues and cause overall market expansion,

new channels also allow for cannibalizati on of offl ine sales, i.e. crowding-out.

Firms face a similar trade-off when introducing new products or opening a new

store (Shaked and Sutt on 1990; Holmes 2011; Mitsukuma 2012). Consumers

that would typically purchase meals in-person are now ordering take-out with

online food delivery services. A rich academic literature describes the eff ects of

opening new sales channels, especially relati ng to e-commerce. There is a

parti cular focus on the investi gati on of potenti al market expansions and

substi tuti on eff ects that could be introduced with online channels in traditi onal

markets. These studies have found signifi cant substi tuti on eff ects across

diff erent industries, such as groceries, newspapers, and consumer electronics

(Duch-Brown et al. 2017; Wang, Song, and Yang 2013; Pozzi 2013; Gentzkow

2007). Most studies in this literature describe the eff ects of Internet-based

substi tutes for traditi onal goods and services from the consumers’ perspecti ve.

Electronic goods and computers are found to have relati vely sensiti ve prices

between the online and offl ine purchasing channels (Goolsbee 2001; Prince,

2007). Online presence of adverti sements on Craigslist lowered those found in

newspapers and even reduced home and rental vacancy rates (Kroft and Pope

2014). However, in the context of restaurants, the impact of introducing online

food delivery services is not as well understood.

There is limited empirical evidence on the impact of adding an online

sales channel to a traditi onal industry from the fi rms’ perspecti ve. In the

newspaper industry, the introducti on of online arti cles caused signifi cant

substi tuti on eff ects that greatly reduced the readership of print media

(Gentzkow 2007). Grocery store sales are only moderately crowded-out with

the introducti on of an online channel and their overall revenues increase (Pozzi

2012; Relihan 2017). In fact, it is generally found that including an online sales

channel provides signifi cant increases in sales, inventory, and return on

investments, while costs decrease in a sample of more than one hundred

publicly traded companies (Xia and Zhang 2010).

The literature related specifi cally to online food delivery services is even

more limited. These types of fi rms have been studied only in very narrow

contexts. Survey-based descripti ve stati sti cs show what types of consumers use

online food delivery services (Yeo, Goh, and Rezaei 2017 2017). Traffi c and

routi ng of drivers is studied to determine the eff ects on customer sati sfacti on

(Pigatt o et al. 2017). Website quality—esti mated by the number of clicks—is

quanti fi ed, as is the correlati on between consumer rati ngs and brand loyalty

(Correa et al. 2019; Ilham 2018). Not only are these studies limited in scope,

but they have also been constrained to countries outside of the United States,

except for some non-academic survey methodologies. The eff ects of online food

delivery services are not quanti fi ed, especially in terms of crowding-out of

brick-and-mortar sales.

Crowding-out eff ects, although well understood in some industries, have

not been empirically studied in the context of restaurants. The case of online

food delivery services is especially interesti ng because a third party off ers the

delivery service, rather than the individual restaurant opening its own

specialized online channel. Further, the cannibalizati on of restaurant sales by

online food delivery services has recently become a large point of contenti on.6

This study fi lls a gap in the literature related to online food delivery services
and their impacts on restaurants, addressing growing concerns in the

restaurant industry, especially in light of COVID-19.

The progress in informati onal technology has introduced a new business

model into the food service industry. Along with the advent of internet

technology, some big fast-food chains, especially pizza franchises, have been

the pioneers to embrace online food ordering with their websites. Restaurants

have adopted online food ordering because it has met or exceeded expectati ons

in several ways for restaurant operati ons. Online food ordering has grown in

popularity among customers and restaurants because of its benefi ts.

Online food ordering through websites was introduced with several

diff erent concepts. Aside from the websites operated by restaurant chains, as

menti oned above, the predecessors of online food ordering services only

aggregated and listed restaurants' names with their basic informati on, such as

phone numbers or addresses, on their website platf orms. Those platf orms have

begun to provide more informati on, including menus or prices. Subsequently,

online food ordering websites have taken food orders from allied restaurants.

In this stage, the food ordering platf orms have grabbed the food orders solely.

Restaurants took care of the delivery by themselves if delivery was available.

The latest approach in the food ordering systems has been for the platf orm to

take care of the delivery. Conclusively, when restaurants uti lize online food

ordering, they may operate their websites or receive the orders through

multi ple-restaurant platf orms. In additi on, the food delivery may be carried out
directly by the restaurants to the customer (e.g., Domino's), or the platf orm

picks up the meals at the restaurant and delivers them to customers (e.g., Uber

Eats). Some platf orms (e.g., GrubHub) provide both services. The online food

delivery services began with online food ordering; the online food delivery

service is separately a signifi cant business model. Recently, online food

delivery was defi ned as the process that food ordered online is prepared and

delivered to the customers by connecti ng customers with partner foodservice

operati ons via their websites or mobile applicati ons.

The demand for online food delivery services has dramati cally increased

over the last few years and is expected to grow. The global online food delivery

platf orm market already amounts to US $31 billion. As COVID-19 has changed,

customers prefer a contactless and online-to-delivery system to face-to-face

and dine-in service. The online food delivery market conti nues to att ract new

customers. Therefore, factors moti vati ng customers to use online food delivery

services under the COVID-19 pandemic are needed to understand customers'

decision-making process and therefore help the foodservice business survive in

this era.

Online FD impacts the relati onship between consumers and their food by

changing the way consumers obtain, prepare and consume food. In turn, these

changes impact the human to human relati onships, which have led to

considerable debate on whether online FD enhances or reduces the quality of

family ti me and community interacti ons.

 Traditi onally, family members communicated with each other and

enjoyed the comfort of each other's company while undertaking the mundane

aspects of food-related family life—such as shopping for groceries, and

preparing and cooking food in their home. Indeed, in some instances, it has

been reported that married Korean women are less likely to use online FD

because they believe they have a moral obligati on to prepare meals for their

families. In contrast, other studies report that online FD is seen by some

Chinese and UK consumers as being a way to quickly and easily provide meals

which consequently enables them to spend ti me with their family. For example,

a qualitati ve study in Guangzhou (the largest city in South China) of people

aged between 18 and 35, who order takeaway meals at least once per week

found that they used online FD as it enabled them to enjoy the comfort of their

home and sti ll partake in the foods and lifestyles they enjoyed, without the

stress associated with the buying and cooking of food. 

There is no doubt that online FD can save ti me otherwise spent on

grocery shopping, cooking or cleaning up aft erwards. According to the research

carried out by the Research Centre for Network Economy and Knowledge

Management of the University of Chinese Academy of Sciences, at least 48

minutes is saved by each online FD order. The qualitati ve study from

Guangzhou found that at least two hours a day could be “saved" by choosing to

use online FD and that these consumers liked to order on online during their

commute, so that they could relax and enjoy the food on their arrival home .
 A news reporter who interviewed white-collar workers in Shanghai,

China, reported that many workers feel that they are expected to work at a fast

pace, and they believe that they have no ti me to go out for lunch. Online FD,

especially if ordered in conjuncti on with colleagues, saves them ti me and

promotes bett er communicati on as they are able to share their mealti mes

together, discussing which restaurants and meals to order online and chatti ng

with each other while eati ng. In Italy, the Just Eat Observatory witnessed a

137% increase in orders, for delivered lunches in 15 Italian citi es in 2017, which

they att ributed to employees increasingly ordering and eati ng meals that are

delivered directly to their offi ces.

There are diff ering views on how online FD impacts social relati onships

between friends. In a study of 365 students at Ningxia University, China, it was

found that 34.2% of the students choose to order online because they had no

one to go out for a meal with; the author's assumpti on was that university

students were unwilling to socialize. In the qualitati ve research from

Guangzhou, it was also reported that some early-career people, who despite

sharing a fl at with other people, prefer to order food and eat it alone in their

room. This practi ce has been put down to the fact that many young people in

China lead independent and individualized lives and are unwilling to socialize.

In general, it has been reported that people tend to share food only with close

family members, such as young couples who live together, colleagues who work

together, or students who live together in dormitories. Therefore, online FD

provides people who wish to eat alone the opportunity to do so without

compromising on taste, quality or value, while also providing groups that wish

to eat together the chance to share food and split the delivery fee.

In additi on, online FD provides access to a wide range of meal opti ons for

those who wish to eat late either owing to work or lifestyle choices. For

example, Eleme reported that in 2018, between 21:00 and 24:00, more than

170,000 lamb skewers, 100,000 beef skewers and 70,000 chicken burgers were

consumed in Shanghai. Most late-night orders came from the CBD and hospitals

(presumably due to people working overti me or pati ents who were hungry

outside of the hospital's regular meal-ti mes).

By increasing food availability and choice and decreasing the barriers to

consumpti on of price and eff ort, online FD poses an inevitable challenge to the

public health system by promoti ng a sedentary lifestyle. It does this by

enlarging the range of the food environment. Traditi onally, a neighborhood

food environment encompassed approximately 1.6 km, equivalent to a 20-min

walk from the home, workplace, or school. With online FD, the range of food

service can extend out to 10 km and potenti ally much further.

Moreover, oft en the community food environment is fi lled with unhealthy

opti ons. For example, a survey in Xi Hu district, Hangzhou, China found that the

availability of “unhealthy" food outlets was four ti mes greater than that of

“healthy" outlets, and while 41.86% of the total food outlets provided food-
delivery services; fast-food restaurants comprised 65.53% of these providers,

thereby increasing the likelihood of exposure to unhealthy food choices

available in fast-food setti ngs. In additi on, by making the obtainment of food

eff ortless, requiring only a few touches on a keyboard to have food delivered to

the doorstep, online FD could potenti ally be promoti ng a sedentary lifestyle

which is harmful to health. Researchers have expressed their concerns that

food delivery apps could have negati ve health impacts for Americans. Further, a

study of 1220 university students in Beijing, China, found that a high frequency

of online delivery food consumpti on was associated with a non-medical major,

a preference for high fat and high sugar foods, physical inacti vity and not

surprisingly a high BMI, with 11.6% of the students surveyed being overweight

or obese.

CHAPTER 3

Research Methodology
Research Design

The study uti lized a correlati onal method type of research design as the

main tool for gathering data to determine the factors aff ecti ng the impact of

online food delivery service to residents of Solano, Nueva Vizcaya.

The study was correlati onal in nature because the goal was to know the

signifi cant relati onship and signifi cant diff erence among the menti oned

variables.

A correlati onal study is a form of research technique in which the

researcher tries to fi gure out what kinds of connecti ons exist between naturally

occurring variables (Artem, 2018).

The primary goal of the study is to empirically demonstrate the eff ect or

impact of online delivery service to the people of Solano, Nueva Vizcaya and

that long or short promised waiti ng ti me menti oned in the review, together

with diff erent sources of reviews, can infl uence the expected service quality

and how this percepti on infl uences the acceptable waiti ng ti me. Subsequently,

the study assesses to what extent the sati sfacti on with the service is aff ected

by the acceptable waiti ng ti me and objecti ve waiti ng ti me, and whether the

repurchase intenti on is infl uenced consequently. The study uti lized a 2 (sources

of reviews: owner vs. customer) x 2 (promised waiti ng ti me: long vs. short) x 2

(objecti ve waiti ng ti me: long vs. short) between-subject experimental design.

The combinati ons of the variables within the eight scenarios are displayed.
Before the fi nal research was conducted, a pre-test was used to identi fy

promised waiti ng ti mes that are considered either long or short for most

parti cipants regarding online food delivery service.

Ethical Considerati ons

The present study is subject to several ethical concerns. In conducti ng

our study, all parti cipants received a writt en agreement regarding their

parti cipati on in the research, through a signed Consent. The aim of the consent

form was to reassure parti cipants that their parti cipati on in the research is

voluntary and that they were free to withdraw from it at any point and for any

reason. And all parti cipants were fully informed about the objecti ves of the

study, and they were ensured that their responses would be treated as

confi denti al and used only for educati onal purposes and for the purposes of the

study. In additi on, both physically and psychologically, parti cipants were not

harmed or mistreated during the study.

Sampling and Collecti on

 The researchers used strati fi ed sampling method to develop the sample

in this study. Strati fi ed sampling method, which belongs to the probability

sampling techniques, divides the enti re populati on into a smaller group. It is a

sampling method where the researchers divide subject into a stratum based on

the given characteristi cs they share. This technique was used to guarantee a

fair and equal representati on of the variables of the study and in which it will

give greater precision and smaller sample.

Instrumentati on

To determine the lengths of promised waiti ng ti mes that are considered

either long or short in this study, a pilot study was conducted. Parti cipants in

the pilot study were asked to describe the average waiti ng ti me for a pizza

delivery service to the city center, Enschede. Most subjects expressed 30 mins

as average waiti ng ti me in this case. They rarely objected when the wait was 5

minutes. As Hui and Tse (1996) suggest, waiti ng 5 mins more is considered as

short delay. We therefore decided to use 5 mins more or less than 30 mins as a

long (35 mins) and short (25mins) promised waiti ng ti me in this study. In

additi on, according to the study from Osuna (1985), customers consider waiti ng

15 mins longer as a long delay, thus we decided to select 15 mins more or less

than promised waiti ng ti me as a long or short objecti ve waiti ng ti me.

 CHAPTER 4

Data Interpretati on and Analysis

Ideally, the sample chosen in the study should be limited to a

representati ve fracti on of the populati on. A research shows that the citi zens

around 18-31 years in Solano, Nueva Vizcaya spends more ti me online and are

most likely to engage in online shopping compared to older individuals (CBS,

2016a, 2016b). Considering the feasibility to conduct the study as a student of

Aldersgate College, the representati ve fracti on was narrowed to students (18-

31 years) of Aldersgate College through strati fi ed sampling.

Table 1. Complete demographic informati on of the survey respondents.

Demographic Characteristi cs Frequency Percentage

Gender Male 35 41.18 %

 Female 50 58.82 %

Age 18-21 years old 23 27.06 %

22-24 years old 35 41.18 %

25-28 years old 22 25.88 %

29-31 years old 5 5.88 %

Total 100 %

Eventually, parti cipants were 35 men and 50 women fi lling out the

survey. However, 6 nonfi nished surveys were eliminated, thus the number of

valid questi onnaires from 85 respondents were used for analysis. Of the 85

respondents included in this study, 41.18% were males (n = 35) and 58.82%

were females (n = 50). Most of the respondents’ age (41.18%) ranged from 22

to 24 years old, then 27.06% of respondents were between 18 and 21 years old,

only 5.88% were varied between 29 and 31 years old.

Expected Sati sfacti on Repurchase

Service with the Intenti on
Quality Service
One One One
component component component
Expected Service Quality
The online food delivery service .735
supplier provides the service reliable
and consistently.
The online food delivery service .654
supplier provides the service in a
ti mely manner.
The online food delivery service .737
supplier has approachable and easy to
contact employees.
 The online food delivery service .827
supplier has courteous, polite and
respectf ul employees.
The online food delivery service .837
supplier has employees listen to me
and we understood each other.
The online food delivery service .699
supplier has employees who are neat
and clean.
Sati sfacti on with the Service
Overall, how sati sfi ed are you with this .882
online food delivery service?
Based on your experience, how likely .950
would you be to recommend this online
food delivery service to a friend?
Based on your experience, how likely .925
would you be to recommend this
restaurant to a friend?
Repurchase Intenti on
You intend to conti nuously purchase .878
this online food delivery service from
the same restaurant.
You will pay close att enti on to this .818
online food delivery service off ered
from the same restaurant.
RECODE: You intend to purchase other .468
alternati ve online food delivery
services from other restaurants.
Table 2. Rotated component Matrix

Table 3: Overview of the constructs, number of items, mean, standard deviati on

and Cronbach’s alpha.

Construct N of items Mean Standard Cronbach’s

Deviati on alpha
Expected 6 4.95 0.88 0.84
Service
Quality
Sati sfacti on 3 6.76 1.71 0.91
with the
service
Repurchase 3 4.17 0.97 0.58
intenti on

Results

Main Findings

A multi variate analysis of variance (MANOVA) was conducted to

investi gate the diff erences between sources of reviews (owner vs. customer)

and promised waiti ng ti me (long vs. short) for the expected service quality and

the acceptable waiti ng ti me. Considering that expected service quality depends

on more factors than only ti me related aspects, in terms of ti me, the item

about service responsiveness ‘the online food delivery service supplier provides

the service in a ti mely manner’ is selected for investi gati on. F-value of main

and interacti on eff ects can be seen in Table 4.

Table 4. 2*2 MANOVA Result Study 1

Multi variate Tests

Eff ect F Sig.
b
Sources of reviews .672 .570
Promised waiti ng ti me 7.162 b .000*
Sources of reviews * 1.599 b .191
Promised waiti ng ti me
Tests of Between-Subjects Eff ects
Source Dependent F Sig.
Scale
Promised waiti ng ti me Service 9.489 .002*
responsiveness
item
Expected .294 .588
service quality
Acceptable 7.324 .007*
 waiti ng ti me

In Table 4, the multi variate tests show that a main eff ect of sources of

reviews could not be found [F1, 206 = .672, p = .570]. Also, an interacti on

eff ect between sources of reviews and promised waiti ng ti me could not found

[F1, 206 = 1.599, p = .191]. Therefore, further tests are not performed.

Regarding promised waiti ng ti me, as can be seen from the multi variate tests,

there is a ‘Sig.’ value of .000, which means p < .001 [F1, 206 = 7.162]. We thus

conti nue with further tests. According to the results from Tests of Between -

Subjects Eff ects, the expected service quality does not signifi cantly diff er

between long and short promised waiti ng ti me [F1, 206 = .294, P = .588]. An

explanati on for the absence of this eff ect might be that expected service

quality is simply not decided by ti me only. By using the service responsiveness

item ‘The online food delivery service supplier provides the service in a ti mely

manner’, a stati sti cally signifi cant diff erence between the groups (long

promised waiti ng ti me vs. short-promised waiti ng ti me) could be found [F1, 206

= 9.489, p = .002].

Next, based on the proposed conceptual framework in Figure 1, objecti ve

waiti ng ti me is manipulated aft er measuring expected service quality and

acceptable waiti ng ti me, and mainly infl uences service sati sfacti on and

repurchase intenti on. Therefore, a multi variate analysis of variance (MANOVA)

was conducted to investi gate the diff erences among sources of reviews (owner
vs. customer), promised waiti ng ti me (long vs. short) and objecti ve waiti ng ti me

(long vs. short) for sati sfacti on with the service and repurchase intenti on. F-

value of main and interacti on eff ects can be seen in Table 5.

Table 5. 2*2*2 MANOVA Results study 2

Multi variate Tests

Eff ect F Sig.
Source of reviews .111 b .895
Promised waiti ng 2.551 b .081
ti me
Objecti ve waiti ng 22.889 b .000*
ti me
Sources of reviews * .015 b .985
Promised waiti ng
ti me
Sources of reviews * 3.553 b .030*
Objecti ve waiti ng
ti me
Promised waiti ng .928 b .397
ti me * Objecti ve
waiti ng ti me
Sources of reviews * .934 b .395
Promised waiti ng
ti me * Objecti ve
waiti ng ti me
Tests of Between-Subjects Eff ects
Source Dependent F Sig.
Scale
Objecti ve waiti ng Sati sfacti on 46.005 .000*
ti me with the service
Repurchase 16.776 .000*
intenti on
Sources of reviews * Sati sfacti on 4.792 .030*
Objecti ve waiti ng with the service
ti me
Repurchase 6.434 .012*
intenti on
 In Table 5, the multi variate tests show that a main eff ect of objecti ve

waiti ng ti me could be found [F1, 206 = 22.889, p < .001]. Also, an interacti on

eff ect between sources of reviews and objecti ve waiti ng ti me could be found

[F1, 206 = 3.553, p = .030]. Further, based on the results from Tests of Between

- Subjects Eff ects, a main eff ect of objecti ve waiti ng ti me could be identi fi ed on

the scale of sati sfacti on with the service [F1, 206 = 46.005, p < .001] and

repurchase intenti on [F1, 206 = 16.776, p < .001]. As can be seen in Table 7,

105 parti cipants, who are exposed to the short objecti ve waiti ng ti me

conditi on, appear to have 7.47 sati sfacti on with the service (SD = 1.49) and

4.97 repurchase intenti on (SD = .98) on average, whereas the average

sati sfacti on with the service and repurchase intenti on are 6.04 (SD = 1.61) and

4.33 (SD = 1.33) respecti vely for the rest of parti cipants who are exposed to the

long objecti ve waiti ng ti me conditi on. These results are the clear evidence that

the short objecti ve waiti ng ti me drives higher service sati sfacti on and stronger

repurchase intenti on than the long objecti ve waiti ng ti me.

Regarding the interacti on eff ect between sources of reviews and

objecti ve waiti ng ti me, there is a stati sti cally signifi cant interacti on eff ect on

sati sfacti on with the service [F1, 206 = 4.792, p = .030] and repurchase

intenti on [F1, 206 = 6.434, p = .012]. In Figure 3 and Figure 4, we would easily

fi nd the interacti on eff ect between sources of reviews and objecti ve waiti ng

ti me for sati sfacti on with the service and repurchase intenti on. Furthermore,

the descripti ve stati sti cs, in Table 7, show that 1). 50 parti cipants who are
exposed to the customer review and the long objecti ve waiti ng ti me, their

average sati sfacti on with service and repurchase intenti on are 5.85 (SD = 1.68)

and 4.13 (SD = 1.40) respecti vely; 2). 51 parti cipants who are exposed to the

customer review and the short objecti ve waiti ng ti me, their average

sati sfacti on with service and repurchase intenti on are 7.75 (SD = 1.43) and 5.19

(SD = .81) respecti vely; 3). 53 parti cipants who are exposed to the owner

review and the long objecti ve waiti ng ti me, their average sati sfacti on with

service and repurchase intenti on are 6.21 (SD = 1.54) and 4.51 (SD = 1.24)

respecti vely; 4). 54 parti cipants who are exposed to the owner review and the

short objecti ve waiti ng ti me, their average sati sfacti on with service and

repurchase intenti on are 7.21 (SD = 1.52) and 4.77 (SD = 1.08) respecti vely.

These results indicate that when the parti cipants, who are exposed to the

customer review and the short objecti ve waiti ng ti me, appear to have the

highest service sati sfacti on and strongest repurchase intenti on; whereas the

parti cipants, who are exposed to the customer review and the long objecti ve

waiti ng ti me, appear to have the lowest service sati sfacti on and weakest

repurchase intenti on. Moreover, the parti cipants, who are exposed to the

owner review and the short objecti ve waiti ng ti me, appear to have the higher

service sati sfacti on and stronger repurchase intenti on than the parti cipants

who are exposed to the owner review and the long objecti ve waiti ng ti me.

Test of the Model

 Hypothesis H1 concerns the main eff ect of sources of reviews. As can be

seen in Table 6, the multi variate tests show that the main eff ect of sources of

reviews could not be found [F1, 206 = .672, p = .570]. Thus, Hypothesis H1 is

not supported. Promised waiti ng ti me has no signifi cant eff ect on the expected

service quality [F1, 206 = .294, p =. 588]. Thus, Hypothesis H2 is not supported.

Nevertheless, it should be noted that a stati sti cally signifi cant eff ect of

promised waiti ng ti me could be found on the service responsiveness item [F1,

206 = 9.489, p = .002]. In Figure 2, it shows that the parti cipants, who are

exposed to the short, promised waiti ng ti me conditi on, have a higher service

responsiveness on average than the rest of parti cipants who are exposed to the

long-promised waiti ng ti me conditi on. These fi ndings indicate that the short,

promised waiti ng ti me has a stronger eff ect on service responsiveness than the

long-promised waiti ng ti me. In Hypothesis H3, an interacti on eff ect between

sources of reviews and promised waiti ng ti me on the expected service quality

was proposed. As can be seen in Table 6, no signifi cant interacti on eff ect could

be found [F1, 206 = 1.599, p = .191]. Thus, Hypothesis H3 is not supported. In

Hypothesis H4, it is assumed that customers who expect more service quality

are willing to wait longer. As correlati on between expected service quality and

acceptable waiti ng ti me is not signifi cant [r = .004, p = .952], no support is

obtained for this hypothesis, see Table 9. In Table 9, it can be seen that

objecti ve waiti ng ti me has a negati ve correlati on with sati sfacti on with the

service [r = -.440, p < .001] and repurchase intenti on [r = -.294, p < .001]. To
test the mediati ng role of sati sfacti on with the service, mediati on analysis was

conducted based on Baron and Kenny (1986). The results show that the role of

sati sfacti on with the service is a mediator. Furthermore, there is a proposed

linear relati onship, thus linear analysis is used to esti mate a linear relati onship

between objecti ve waiti ng ti me and sati sfacti on with the service. As can be

seen in Table 10, it seems that the objecti ve waiti ng ti me is a predictor of

sati sfacti on with the service [β = -.440, t = -7.032, F = 49.450, p < .001]. A chi-

square diff erence test on the equality of the parameters confi rms this [X2 =

113.012, p < .001]. These fi ndings support Hypothesis H5.

For the test of Hypothesis H6, it is proposed that a positi ve relati onship

between the positi ve disconfi rmati on of acceptable waiti ng ti me (longer than

objecti ve waiti ng ti me) and sati sfacti on. The variable ‘the positi ve

disconfi rmati on of acceptable waiti ng ti me’ was calculated by subtracti ng the

objecti ve waiti ng ti me from the acceptable waiti ng ti me. In Table 9, the

positi ve disconfi rmati on of acceptable waiti ng ti me has a positi ve correlati on

with sati sfacti on with the service [r = .254, p = .003]. Furthermore, an eff ect of

the positi ve disconfi rmati on of acceptable waiti ng ti me on sati sfacti on with the

service would be found [β = .254, t = 3.047, F = 9.284, p =. 003]. Testi ng the

equality of the parameters confi rms that a relati onship is between the positi ve

disconfi rmati on of acceptable waiti ng ti me and sati sfacti on with the service (X2

= 262.657, p =. 026), see Table 10. These results support Hypothesis H6.
 In Hypothesis H7, an eff ect of sati sfacti on with the service on repurchase

intenti on was proposed. As the correlati on between the two variables is

signifi cant [r = .653, p < .001], see Table 9. By using liner analysis, it can be

seen a linear relati onship between the two variables [β = .653, t = 12.390, F =

153.500, p < .001]. A chi-square diff erence test on the equality of the

parameters confi rms this [X2 = 414.529, p < .001], see Table 10. Thus,

Hypothesis H7 is supported.

CHAPTER 5

Discussion of Results

The research aimed to investi gate eff ects of online reviews and waiti ng

ti me on customers repurchase intenti on of the online food delivery service.

This study heeds the value of expected service quality, even though the

expected service quality is not proven to be a determinant of acceptable

waiti ng ti me, as it was suggested by Maister (1985). Additi onally, the study

reveals the relati onship between acceptable waiti ng ti me and objecti ve waiti ng

ti me, and how this relati onship would aff ect sati sfacti on with the service.

Through understanding the way customers repurchase intenti on would be

infl uenced because sati sfacti on with the service appears to have a strong eff ect

on repurchase intenti on. Specifi c discussion is stated below.

 This study shows that customers’ expected service quality does not

signifi cantly diff er between long and short promised waiti ng ti me because the

expected service quality depends on more factors than only ti me related

aspects. By using the item about service responsiveness, there is a signifi cant

diff erence between the long and short promised waiti ng ti me. The short-

promised waiti ng ti me has a stronger eff ect on service responsiveness than the

long-promised waiti ng ti me. Additi onally, diff erent promised waiti ng ti mes

aff ect customers’ acceptable waiti ng ti mes diff erently. This study indicates that

the long-promised waiti ng ti me results in higher waiti ng tolerance/ longer

acceptable waiti ng ti me than the short-promised waiti ng ti me. This would imply

that customers are willing to wait longer, when they are informed that the

service takes much longer. Besides, a main eff ect of objecti ve waiti ng ti me

could be found for sati sfacti on with the service and repurchase intenti on. The

short objecti ve waiti ng ti me drives higher service sati sfacti on and stronger

repurchase intenti on than long objecti ve waiti ng ti me. Also, there is a

stati sti cally signifi cant diff erence between objecti ve waiti ng ti me and sources

of reviews for sati sfacti on with the service and repurchase intenti on, the

results show that the parti cipants, who are exposed to the customer review and

the short objecti ve waiti ng ti me, appear to have the highest service sati sfacti on

and strongest repurchase intenti on; whereas the parti cipants, who are exposed

to the customer review and the long objecti ve waiti ng ti me, appear to have the

lowest service sati sfacti on and weakest repurchase intenti on. Further, the
parti cipants, who are exposed to the owner review and the short objecti ve

waiti ng ti me, appear to have the higher service sati sfacti on and stronger

repurchase intenti on than the parti cipants who are exposed to the owner

review and the long objecti ve waiti ng ti me. Next, hypotheses are discussed.

Firstly, based fi rst on the literature review, sources of online reviews

(customer vs. owner) were expected to aff ect customers’ expected service

quality diff erently, nevertheless, the signifi cantly diff erent eff ects between

these two reviews on expected service quality have not been found. But this is

sti ll in line with theories. According to Smith (1993), some customers believe

that an owner-created review highlights the selling points of a product or

service, which is useful to make purchase decisions. Other customers, on the

contrary, concern the credibility of the owner-created review, as it was

suggested by Zhang, Ye, Law, & Li (2010), an owner-created review is an

adverti ser supported media. On the other hand, the customer-created reviews

are mainly based on personal experience, which are quite subjecti ve because of

individual diff erences in taste preferences. With respect to promised waiti ng

ti me, the results show that it is not so much the number of minutes that a

customer has been promised to wait which aff ects the expected service quality

as it is the subjecti ve transformati on of minutes into a long/short judgement.

An explanati on could be that people might not build a link between long and

short promised ti me without a comparison. Therefore, it is not diffi cult to

understand that there is no interacti on eff ect between sources of reviews and

promised waiti ng ti me on the expected service quality.

Secondly, we proposed that customers who expected a higher service

quality are willing to wait longer, as suggested by Maister (1985). However,

again, no stati sti cally signifi cant eff ect of expected service quality on the

acceptable waiti ng ti me was identi fi ed in this study. Probably, customers do

not consider the pizza delivery service as a high-value service to them,

therefore the eff ect is missed out on the acceptable waiti ng ti me. It might also

be, that the unknown restaurant does not create the percepti on of exclusivity

like in other instances where customers accept even extreme long waiti ng ti mes

for the service. It is assumed, that up to a certain threshold of ti me, customers

are sti ll willing to wait longer for the service because of the brand exclusivity

(Chavelier & Mezzavalo, 2008).

Thirdly, objecti ve waiti ng ti me is one of independent variables. The

results show that objecti ve waiti ng ti me has a negati ve impact on customers’

evaluati on of services, as several studies (Katz, Larson, & Larson, 1991; Taylor,

1994; Tom & Lucey, 1995) suggest. Furthermore, in this study, objecti ve waiti ng

ti me is used as a criti cal point of reference: if, for example, acceptable waiti ng

ti me is greater than objecti ve waiti ng ti me, customers are more sati sfi ed with

the service. The fi ndings of this study demonstrate that the discrepancy

between acceptable waiti ng ti me and objecti ve waiti ng ti me is certainly one of

the main eff ects on the sati sfacti on with the service. This outcome is in line
with theories. According to Tse and Wilton (1988), customer sati sfacti on is

defi ned as the consumer’s response to the evaluati on of the perceived

discrepancy between expectati ons and the actual performance of the service. In

terms of ti me, acceptable waiti ng ti me is what customers expect the maximum

number of minutes they would like to wait, whereas objecti ve waiti ng ti me is

what customers actually wait for the service, by evaluati on of the discrepancy

between the two variables, the level of sati sfacti on with the service is

infl uenced.

Finally, as the only dependent variable in this study, repurchase intenti on

is infl uenced by sati sfacti on with the service. The fi ndings show that a strong

eff ect of sati sfacti on with the service on customers repurchase intenti on. This

is consistent with the expectancy disconfi rmati on model, which indicates that

customers repurchase decision is dependent on their sati sfacti on (Chen et al.,

2012).

Theoreti cal and practi cal implicati ons

Regarding theoreti cal implicati ons, this study off ers some important

fi ndings on the eff ecti veness of online reviews on the expected service quality,

although the results do not fully support our hypothesized relati ons. For

sources of reviews (customer vs. owner), it might be that customers hold two

diff erent opinions on the customer-created review and the owner-created

review, as menti oned above. It is therefore suggested to further investi gate

this concept in future studies. Also, concerning the promised waiti ng ti me,

there might be a signifi cant diff erence between subjects for expected service

quality, if parti cipants have the cue on long and short promised waiti ng ti me.

There is more in-depth research needed to investi gate it. On the other hand,

the study reveals that the short-promised waiti ng ti me has a stronger eff ect on

service responsiveness than the long-promised waiti ng ti me. And the long-

promised waiti ng ti me results in higher waiti ng tolerance/ longer acceptable

waiti ng ti me than the short-promised waiti ng ti me.

Additi onally, we introduced the variable ‘the positi ve disconfi rmati on of

acceptable waiti ng ti me’ which represents the diff erence (in minutes) between

the acceptable waiti ng ti me and the objecti ve waiti ng ti me. And we found that

the more ‘the positi ve disconfi rmati on of acceptable waiti ng ti me’ is, the more

sati sfi ed with the service customers are. Moreover, the study reveals that the

short objecti ve waiti ng ti me drives higher service sati sfacti on and stronger

repurchase intenti on than long objecti ve waiti ng ti me. These fi ndings are

possibly useful for some future studies on the objecti ve waiti ng ti me and the

acceptable waiti ng ti me.

Of course, the relati onship between expected service quality and

acceptable waiti ng ti me is sti ll a point for discussion, as it has not been proven

in this research. Presumably, people do not treat a pizza delivery service as a

valuable service, meanwhile, the restaurant chosen for this study does not

communicate its brand exclusivity. This would imply that, if a brand becomes
more desirable, the longer a customer would like to wait for the service. Taking

that into account, it might be wiser to include brand exclusivity in future

studies.

Regarding practi cal implicati ons, this study provides some insights into

the promised waiti ng ti me and sources of reviews as valuable tool for online

food delivery services, however, the results are not shown as expected.

Undoubtedly, this study remains very basic and needs to be built on. On the

other hand, this study reveals that customers are willing to wait longer, when

they are informed that the service takes much longer. Moreover, customers

repurchase intenti on is not solely evaluated based on objecti ve waiti ng ti me as

is the case in operati ons management. Customers’ acceptable waiti ng ti me,

diff erent sources of reviews, even promised waiti ng ti me could infl uence

customers repurchase intenti on. Service marketers, especially who are working

in the digital communicati on industry, could use some fi ndings of this study.

Limitati ons and Future Research

This study has several limitati ons. First, probably some respondents fi ll in

most socially desirable answer. However, it is not easy to tackle due to

anonymous survey. Future work would do some follow up interviews to validate

the vague constructs such as long and short promised waiti ng ti me aft er

collecti ng survey. Second, this research only considered the students from the

University of Twente, who mostly are in a similar situati on of life. Furthermore,

the online survey was distributed with the use of the convenient sampling.

Although there were diversiti es in study level, faculti es, and gender, most of

the parti cipants were female, master students, from behavioral, management

and social sciences (BMS). There is no guarantee about the representati veness

of samples. It is not possible to determine the actual patt ern of distributi on of

the populati on. Future studies would improve by collecti ng parti cipants

distributed equally, recruiti ng random samples outside of the University of

Twente. One benefi cial opti on is to have a bigger sample size. Also, a scenario-

based method with a screenshot of the website of thuisbezorgd.nl was applied.

It is appropriate to fi rst give insights into the eff ecti veness of sources of

reviews and promised waiti ng ti me manipulati on techniques on expected

service quality. Nevertheless, the generalizati on of this method is concerned

because it is not a real case. This would imply that, while parti cipants face a

real online food delivery service, it is not clear whether they would react

similarly in the experimental setti ng as in real life situati on. It would be

interesti ng to investi gate it for further studies.

Another topic for further research is required to enhance our model and

fi ndings. In this study, the main eff ect of sources of reviews was not found. It

should be noted, however, that people have diff erent opinions on sources of

reviews. It is suggested to test the main eff ect in a diff erent setti ng and for

other service categories. As promised waiti ng ti me did not appear to be an

infl uenti al factor in customer’s expected service quality, perhaps, promised

waiti ng ti me to perform diff erently in other environments where customers just

want to order something to eat in a lazy day (our scenario is to order a pizza

aft er an exhausti ng studying day). Customers might be so strongly internally

focused (hungry and exhausted) that they ignore promised waiti ng ti me in our

study. It would be interesti ng for further research to check the eff ect of

promised waiti ng ti me in diff erent setti ngs.

To conclude, it is one of the fi rst studies that measured that the

eff ecti veness of sources of reviews and promised waiti ng ti me on customers

repurchase intenti on in the context of online food delivery services. Although

some hypothesized relati ons have not been fully supported by the results of

this study, it provides valuable starti ng points for further research. And insights

gained in adequate management of ti me and online reviews usage have become

increasingly important for companies’ survival.