THE IMPACT OF SERVICE-BASED TECHNOLOGY ON CUSTOMER SATISFACTION AND PURCHASE INTENTION: EVIDENCE FROM DISNEY MAGICBAND USERS IN

WALT DISNEY WORLD®, ORLANDO, FL

By

DANNI WANG

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2017

© 2017 Danni Wang

To Mom and Dad

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ACKNOWLEDGMENTS

I would like to thank my parents for their financial and moral supports. Without

them I would not be able to study aboard and to concentrate on learning what I am

interested in. I am also grateful to my other family members and friends, for their

accompaniment in these two years.

I would like to thank my committee members, especially my advisor Dr. Svetlana

Stepchenkova, for their extraordinary support in this thesis process. Without their

passionate instruction, this thesis paper could not have been successfully conducted. At

the beginning of last semester, when I was not sure whether I am qualified to do a

research, it was my advisor who encouraged me to try on it and offered me suggestions

when I got confused. I sometimes asked myself how lucky I am to meet an advisor like

her, who was always there when I need help.

I would like to thank my fellow master and doctoral students for sharing their

experience and knowledge with me, and for their cooperation and friendship. I also want

to express my gratitude to faculty members in TRSM department, for their help and

support.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 INTRODUCTION .................................................................................................... 10

Problem Statement ................................................................................................. 10 The Evolution and Implementation of RFID ............................................................ 11 The Walt Disney MagicBand ................................................................................... 14 The Purpose of this Study ....................................................................................... 15 Theoretical Background .......................................................................................... 16

2 LITERATURE REVIEW .......................................................................................... 22

Delone and Mclean (D&M)’s Information System (IS) Success Model ................... 22 System Quality ........................................................................................................ 28 Information Quality .................................................................................................. 29 Customer Satisfaction ............................................................................................. 32 Purchase Intention .................................................................................................. 36

3 METHODOLOGY ................................................................................................... 41

Measurement Development .................................................................................... 41 Study Sample and Data Collection ......................................................................... 43

4 ANALYSIS AND RESULTS .................................................................................... 46

Sampling Profile ...................................................................................................... 46 Reliability and Factor Analysis ................................................................................ 48 Multiple Regression Analysis .................................................................................. 52 Further Analysis of Data ......................................................................................... 58

5 DISCUSSION ......................................................................................................... 63

Research Findings .................................................................................................. 63 Implication and Future Study .................................................................................. 65 Limitation ................................................................................................................ 67

6 CONCLUSION ........................................................................................................ 70

6

APPENDIX: QUESTIONNAIRE .................................................................................... 72

LIST OF REFERENCES ............................................................................................... 76

BIOGRAPHICAL SKETCH ............................................................................................ 88

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LIST OF TABLES

Table page 3-1 Measures of this study ........................................................................................ 42

4-1 Respondents’ Profile .......................................................................................... 47

4-2 Reliability of Derived Measures .......................................................................... 49

4-3 Factor Analysis of Functionality and Usability..................................................... 50

4-4 Factor Analysis of Satisfaction and Purchase Intention ...................................... 51

4-5 Pearson’s Correlation Matrix .............................................................................. 52

4-6 Statistic Value of Factors .................................................................................... 53

4-7 Results of Regression Analysis .......................................................................... 54

4-8 List of Hypothesizes and Results ........................................................................ 57

4-9 The Number of Responders Haven’t Used MagicBand's Functions Before ........ 58

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LIST OF FIGURES

Figure page 1-1 A conceptual model of MagicBand quality, user satisfaction and purchase

intention .............................................................................................................. 21

2-1 A conceptual model of service-based technology quality, user satisfaction and purchase intention. ...................................................................................... 40

4-1 Further analysis of data ...................................................................................... 60

4-2 Comparison of Disney App Users and Non-uses’ Satisfaction ........................... 61

4-3 Comparison of Disney App Users and Non-uses’ Intention to Purchase Using MagicBand.......................................................................................................... 62

9

Abstract of Thesis Presented to the Graduate School Of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

THE IMPACT OF SERVICE-BASED TECHNOLOGY ON CUSTOMER SATISFACTION AND PURCHASE INTENTION: EVIDENCE FROM DISNEY MAGICBAND USERS IN

WALT DISNEY WORLD®, ORLANDO, FL

By

Danni Wang

August 2017

Chair: Svetlana Stepchenkova Major: Recreation, Parks and Tourism

Understanding new technologies’ impact on customer satisfaction and behavior

intention has a number of benefits to service industry managers. Research has tested

the role of technology in various fields, but few studies have examined technology’s

effects on amusement park customers. This paper examines the effect of MagicBand’s

functionality and usability on customer satisfaction and customers’ intention of using this

technology to purchase merchandises. Data was collected by surveying Disney

MagicBand users. The research findings suggested that overall MagicBand’s

functionality and usability have a significant positive effect on customer satisfaction, but

only MagicBand’s functionality partially affects customers’ purchase intention while

customer satisfaction mediates this relationship. Implications and research limitation are

discussed.

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CHAPTER 1 INTRODUCTION

Problem Statement

The development and application of new technologies have always been the key

factors of the industry performance improvement. Walt Disney World® as one of most

successful companies in tourism industry owns 8 of the top 10 most popular theme

parks, while the Walt Disney World®’s Magic Kingdom attracted more than 50, 000

customers per day in 2015 (WWW. Temporary Tourist.com). To enhance customers

visiting experience and track customer activities, Disney introduced MyMagic+®

management system with new technologies including MagicBand, for which the core

technology is Radio-Frequency Identification. Radio-Frequency Identification (RFID) is a

technology that was widely used in movement tracking and supply chain management

(Harold & Samuel, 2007; Ron, 2005). The RFID system consists of tags and two-way

radio transmitter-receivers called Interrogators. Radio waves are generated and

transmitted by interrogators, then received and stored by tags. After processing, tags

send the response back through radio waves to the interrogators (Dwivedi et al., 2013;

Ampatzidis & Vougioukas, 2009). Once placing the RFID unique tag on an object or

person, this item or people can be identified and tracked automatically (Roberts, 2006;

Zhang & Li, 2012).

The study of RFID technology started at as early as 1948. Primary studies of

RFID were focused on test its impact on supply chain and retail management. Recently,

with the increasing awareness RFID’s functions, it has been applied in such industries

as aerospace, library management and tourism industry. Studies of RFID technology in

the tourism industry have been focused on how RFID affect service quality in hospitality

11

management (Öztayşi et al., 2009), how RFID can be applied to monitor customers in

casino management (Wyld, 2007) and how RFID can be applied to solve complex

supply chain management in cruise management (Veronneau & Roy, 2009; Dias,

2016). Compared with other industries, there are much fewer studies have been

conducted with the purpose to explore RFID’s role in theme parks industry. Existing

studies that investigate RFID in theme parks were focused on the understanding RFID’s

functions, such as exploring how RFID can be used to track customers’ location and

manage customers’ queuing time, but very little study has been done to test RFID’s

effects on amusement park customers (Yogesh et al., 2013).

Based on this situation, it is essential to develop a framework to test how RFID

technology affects theme park customers. Disney MagicBand contains the RFID

technology to perform all functions but it also wore by customers during their whole

visiting process, so in this study we developed a framework to test the effect of RFID’s

functionality and MagicBand’s usability on customer satisfaction and customers’

intention to purchase food and merchandises using MagicBand.

The Evolution and Implementation of RFID

RFID technology was studied as early as 1948 in Harry Stockman's landmark

paper (Roberts, 2006; Landt, 2005; Weis et al., 2004). In the 1960s, researchers and

inventors devoted to developing the theory of RFID technology and apply it to reality. In

1964, R. F. Harrington in his published paper studied the RFID related paper (Jerry &

Barbara, 2001; Landt, 2005), while in the same year RFID was applied in commercial

field. From the 1970s to 1980s, the application of RFID technology was gradually

expanded and then fully implemented. During that period, large companies such as

Raytheon®, RCA® and Fairchild® developed their own RFID module (Yang et al.,

12

2009). Long before the 1990s, scientists have started to test RFID's function of

collecting tolls, then the world's first highway electronic tolling system that used the

RFID technology has been installed in 1991 in the United States. Following the

electronic tolling system, more innovation that combined the electronic system with

transportation management system. With the technology's widespread and

development of mobile commerce, RFID technology has been applied into to more

industries and related closer to people's daily life in the 21st century. Besides industries

that have used RFID technology for many years, such like supply chain management

and the retail management industry, RFID technology has been applied wider fields in

recent years (Harun et al., 2008; Molnar & Wagner, 2004; Zeni et al., 2009).

RFID contains tags that have their own code that can individually be accounted

for and be scanned without human interaction (Wyld, 2008). RFID has been widely

applied in several fields. In the field of supply chain management, RFID was applied to

track shipping vehicles, improve the efficiency of tire management and control

construction process (Nambiar, 2009; Kim et al., 2008; Kovavisaruch et al., 2008).

Research of RFID in the supply chain management RFID were conducted around

examine the impact of RFID on supply chain management (Michael & McCathie, 2005;

Sarac et al., 2010) and exploring the method to test and improve the efficiency of RFID

in supply chain management (Sabbaghi & Vaidyanathan, 2008; Van Weele, 2009).

Studies of RFID implementation in inventory management were focused on introducing

designs and strategies of applying RFID in inventory management (Bae et al., 2016;

Holy et al., 2014) and on examining the effect of RFID on inventory management from

different perspectives (Cannella et al., 2005; Fan et al., 2015). From the organizational

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perspective, RFID was used in human tracking, property management, and contactless

payment systems in health-care industries (Chowdhury & D'Souza, 2009; Öztayşi et al.,

2009). Other researchers explored the cost, challenges, and privacy of implementing

RFID system (Christian & Elgar, 2008; Huang & Tang, 2008). RFID was also adopted to

prevent security issues since each of RFID tags has its own code and can only work

when the code matches the tag (Weinstein, 2005).

In recent years, a greater number of academic research has been conducted on

studying the application of RFID on tourism industry including hotels, casinos, cruise

ships and theme parks management. Most of these studies have been done in the

hospitality industry. Öztayşi et al. (2009) listed previous cases of RFID's applications in

the tourism industry and investigated the possibility of implementing RFID in the

hospitality industry by using a case study. Samidjen et al. (2013) stated that RFID

technology contributed to the effectiveness of queue management in hospitality

management. Zeni et al. (2009) measured the effects of the cultural event on tourism by

using RFID to tracking tourists in the destination using the city-card circuit. In casino

management, RFID was used as a method to prevent loss in casinos, using RFID

imprinted chips enabled casinos to virtually eliminate theft and gather customers' data

(Hozak, 2012). RFID was also claimed to be able to allow casino managers to prevent

cheating and get more information about customer behavior (Wyld, 2007). RFID

technology was also used in cruise ship management. By applying RFID tags on

merchandise and people and tracking them in the real time, RFID allowed cruise

managers to reduce number and cost of labor (Veronneau & Roy, 2009) and better

control inventory in cruise supply chain management (Kurt, 2012). Although now RFID

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has been implemented in some theme parks, water parks, and ski resorts to locate

visitors, especially children (Roh et al., 2009; Chen et al., 2006; Dugan et al., 2009),

assist queue Management (Mahoney &Ragsdale, 1996), and provide photo-souvenir

service (Durrant, 2011), relatively few research has comprehensively examined how

RFID technology affects consumers' travel experience from customer's' perspective

(Dwivedi et al., 2013).

The Walt Disney MagicBand

The Walt Disney World® is one of the most popular and successful amusement

parks in the world, averaged 56,142 visitors per day in 2015 totaling 20,492,000 for the

year visited only in the Walt Disney World®’s Magic Kingdom in Orlando, Florida

(www.temporarytourist.com/how-many-people-go-to-disney-world-every-day-2015-

edition/). In 2013, the Walt Disney Company® introduced MagicBand to the world at

Walt Disney World® in Orlando, Florida. MagicBands are colorful, waterproof

wristbands with which customers can enter places where contain the sensor called

“touch point”. MagicBand performs its functions relying on the RFID technology. With

the implementation of RFID, MagicBand can be used to enter Disney Resort hotel

rooms, theme and water parks, FastPass+, and can be used to link Disney PhotoPass

images with the customer account. For Disney resorts customers, MagicBand can also

be linked with debit or credit cards to purchase food and merchandise

(www.disneyworld.disney.go.com ).

Customers are able to select the basic MagicBand with pink, blue, red, green,

orange, gray and yellow color, while MagicBand can also be personalized to be more

colorful and stylish since more than thousands of MagicBand skins, covers and stickers

are offered online and in Disney stores. Disney world resort hotel guests or annual pass

http://www.disneyworld.disney.go.com/

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holders can obtain the MagicBand for free, while other customers are able to purchase

a MagicBand online or in the Walt Disney World®. The price of the MagicBand varied

from $12.95 to $19.95 depends on the decoration. (www.Disneystore.com ). The

MagicBand is linked to customers' My Disney Experience account, which holds all the

information relating to customers' experience in Walt Disney World®. To secure

customer information, the MagicBand was built with privacy controls from the outset and

is voluntary. MagicBand is one part of Disney’s vacation management system called

MyMagic+®, which was introduced by the Walt Disney Company® with the ambition of

collecting customers’ personal data, increasing customer loyalty and improving

customer visiting experience (Brooks, 2013, Jane 7).

The Purpose of this Study

Although RFID system has been widely applied in various fields, relatively few

studies have been focused on exploring the effect of RFID system on amusement parks

industry, the industry attracts about 375 million people only from North America (IAAPA)

and make huge economic benefits. The Walt Disney Company® as one of the largest

amusement parks in the world attract more than 10 million people around the world to

visit its theme parks every year. Walt Disney Company® introduced MagicBand to the

Walt Disney World® in Orlando, Florida in 2013.The MagicBand can be used by

customers to enter Walt Disney World® amusement parks, enter Disney Resorts room

and enter FASTPAST+. For those Disney Resorts guests, they can also link their debit

or credit cards with MagicBand to purchase food and merchandise inside Disney parks.

In this study, we aimed to examine how MagicBand affects tourist’s satisfaction and

purchase intention. Since MagicBand is a wearable technology, it will be measured by

RFID’s functionality and MagicBand’s usability.

http://www.disneystore.com/

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Theoretical Background

There are plenty of alternative theories have been used to examine the

information system implementation. Some of theories and models focus more on pre-

adoption variables, such like: Technology Acceptance Model, it was used to measure

factors that affect users’ acceptance of an information technology (Davis, Bagozzi &

Warshaw, 1992). Hossain & Prybutok (2008) in their study surveyed a group of students

who always use a RFID tag to pay their toll at university in Texas, and their study results

proved that several factors including convenience, culture, and security significantly

affected customer’s acceptance of RFID technology. Beside perceived system based

factors, customer's’ personality traits were proved in Pramatari & Theotokis (2009)’s

study to have a significant effect on RFID-enabled services. Similarly, there are plenty

of other studies that were conducted to explore factors that have a significant effect on

customers’ acceptance of RFID technology. Theory of Planned Behavior is another

commonly used theory in RFID studies that concentrated on explaining people’s RFID

using behavior. There are also some other theories that have been applied in RFID-

related studies that focus only on post-acceptance variables, such as the Expectation

Confirmation Model (ECM), and Acceptance and Use of Technology (UTAUT).

However, all of those theories are qualified in the adoption of information system

studies, but none of them will be appropriate for measuring aspects of both pre-adoption

and post-adoption behavior. Since our study was supposed to test the success of RFID

adoption and RFID’s effect on customers’ satisfaction and purchase intention, instead of

theories listed above, DeLone and McLean's Information System (IS) Success model

was selected as a theoretical basis for this study.

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DeLone and McLean's (2003) IS Success model is one of the most frequently

utilized theories in testing the success of information system (DeLone, 2003; Yogesh et

al., 2013; Wang, 2008). Six interrelated dimensions are included in the success model:

“system quality” that identifies the technology success, “information quality” that clarifies

semantic success, “IS user”, “user satisfaction”, “individual impact”, and “organization

impact” measure “effectiveness success” (Delone & McLean, 2003). The DeLone and

McLean's IS success model was used to be studied based on either the process model

or the causal model. The process model suggests that when users and managers

experience the features contained in the information system, they will get satisfied or

dissatisfied. Then the use of the system will affect users work and finally cause

organizational influence. The causal model is used to examine the existence of

interrelationship between dimensions mentioned above.

Testing the validity of DeLone and McLean's IS Success Model is main purposes

of empirical research (Delone & McLean, 2003). Iivari (2005) tested DeLone and

McLean's IS Success model by surveying 100 mandatory information system users and

found that there is significant relationship between "perceived system quality",

"perceived information quality" and "user satisfaction" between "User satisfaction" and

"individual impact" and between "Perceived system quality" and "system use". Petter &

McLean (2009) tested the DeLone and McLean's IS Success model at the individual

level by studying the results of 52 previous studies that tested the interrelationship of

dimensions within the IS Success model and found that majority of relationships

presented in the DeLone and McLean's IS Success model was valid. Empirical testing

the relationship among factors identified in DeLone and McLean's IS Success model is

18

another main purpose of research. Almutairi and Subramanian (2005)'s study about

"Kuwaiti Stock market" supported some of the association identified in the DeLone and

McLean's IS Success model that both information quality and system quality are

significant predictors of user satisfaction while system usage is related to individual

impact. Moreover, there are other studies tested the model by aggregating various

success dimensions then explore the association between them, including Bai et al.

(2008)'s study.

Oliver defined customer satisfaction as "Customer satisfaction is the consumer's

fulfillment response. It is a judgment that a product or service feature, or the product or

service itself, provided (or is providing) a pleasurable level of consumption-related

fulfillment" (Oliver, 1997). Customer satisfaction was extensively explored with different

variables in a number of various contexts. The relationship between service quality and

customer satisfaction is a highly discussed topic in marketing theories (Sureshchandar

et al., 2002; Taylor & Baker, 1994; Sivadas & Baker-Prewitt, 2000). Service quality has

been generally defined as an assessment of service (Ganguli & Roy, 2011), and it

occurs when customers' after- service experience exceeds their before- service

expectation (Parasuraman et al., 1985). Ganguli & Roy (2011) in their study suggested

that the concept of service quality was consist of service delivery process (Parasuraman

et al., 1985) and the service outcomes (Lehtinen& Lehtinen, 1991). Lehtinen & Lehtinen

(1991) introduced three dimensions in the service production process approach

including physical quality, interactive quality, and corporate quality, while they also

suggested process quality and output quality dimensions as another approach.

Parasuraman et al. (1988) introduced SERVQUAL as a measurement of service quality.

19

Five dimensions of the original SERVQUAL theory are “reliability, responsiveness,

tangibles, empathy, and assurance”. In the context of Information Technology (IT) and

Information System (IS), modified SERVQUAL theory was used as a measurement of

service quality. Kettinger and Lee's (1994) stated in their research that there is an

interrelationship between reliability, responsiveness, assurance, empathy, and user

information satisfaction (UIS). Moreover, Jiang et al. (2000) also supported that

SERVQUAL can be a useful measurement of information system service quality.

Service quality and customer satisfaction are commonly acknowledged as key

factors that influence consumers' purchase intentions in service industries (Taylor &

Baker, 1994). Ajzen (1985) said that behaviors are driven by behavioral intention and

attitudes are able to be used to predict behavioral intentions and behaviors. Existing

research explored the correlation between service quality, customer satisfaction and

behavioral intention in the traditional service industry, including tourism industry

(González, 2007; Alexandris et al., 2002; Kouthouris & Alexandris, 2005), hospitality

industry (Ladhari, 2009; Kim, 2006), and restaurant management (Ryu et al., 2012; Ryu

& Han, 2010). Taylor & Baker (1994) surveyed 426 individuals from four service

industries (health care, recreation, transportation, communication) to explore the

relationship between service quality, customer satisfaction and purchase intention.

Research results indicated that service quality, customer satisfaction, and customer

purchase intention are correlated, and customer satisfaction plays a mediating role

between service quality and customer purchase intention. With the development of

technology and the use of the website, more research of customer satisfaction, and

purchase intention has been done in the technology-based service environment. Kuo et

20

al. (2009) tested the interrelationship among service quality, perceived value, customer

satisfaction, and post-purchase intention in mobile value-added services by survey

graduate students in 15 major universities in Taiwan. Among all the dimensions, they

suggested that service quality positively affects customer satisfaction while indirectly

influence the post-purchase intention.

Bai et al. (2008) developed a conceptual model that based on DeLone and

McLean's IS Success model to examine the impact of website quality (System Quality

and Information Quality) on customer satisfaction and purchase intention.

Measurements used to measure website quality in his study are "Functionality",

"Usability". Results of Bai et al. (2008)'s study proved that customer satisfaction

mediating the relationship between website quality and customer purchase intention.

The study object in our research is MagicBand, which is different from web-site although

both of them are the implementation of an information system. We considered Disney’s

MagicBand as a wearable technology that performs its functions through RFID

technology and is also used as a bracelet, so it is not appropriate if we only measure

MagicBand’s quality from the technological perspective. Thus to critically test the value

of MagicBand, we measured it from both the RFID technology’s functionality and the

bracelet’s usability. Then we built a conceptual framework (Figure 1-1) to explore the

interrelationship between MagicBand’s quality, customer satisfaction and customers’

intention of purchasing merchandises inside Walt Disney World® using MagicBand.

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Figure 1-1. A conceptual model of MagicBand quality, user satisfaction and purchase intention

22

CHAPTER 2 LITERATURE REVIEW

Delone and Mclean (D&M)’s Information System (IS) Success Model

The Delone and McLean (D&M)'s Information System (IS) Success model was

developed by Delone and McLean in 1980 to measure the performance of information

system (DeLone & McLean, 1992). Six categories were introduced in the original model

by Delone and Mclean (1992) that including: “System Quality, Information Quality, Use,

User Satisfaction, Individual Impact, and Organizational Impact”. DeLone and McLean

suggested a model of interdependencies between these variables and commented that

this model needs "further development and validation before it could serve as a basis

for the selection of appropriate I/S (success) measures" (DeLone & McLean, 1992).

Because of the incompleteness of DeLone and McLean's IS Success model,

Seddon and Kiew (1994) then examined the interrelationship between four variables

(“System Quality, Information Quality, Use, User Satisfaction”) of DeLone and McLean's

model and with the considering that "usage" isn't the appropriate measurement of

system success, they replaced "Use" with "Usefulness" in their research (Seddon and

Kiew,1994). They collected data by surveying 169 users of a University's Departmental

Accounting System and after analyzing, the results of this empirical test suggested that

1) "User Satisfaction is the most general individual-user perceptual measure of

information system success", and 2) "Importance of task" should be controlled while

measuring "Usefulness" (Seddon and Kiew, 1994).

Besides, there are also some other empirical studies that tested the

interrelationship between variables identified in DeLone and McLean's IS Success

model. Igbaria & Tan (1997) collected data from survey 625 employees from a

23

Singapore company to explore the relationship between IT acceptance and its impact

on individual users. The structural model in this study was adapted from D&M's IS

Success model that contains three dimensions (User Satisfaction, System Usage, and

Individual Impact). Results indicated that there exists a relationship between variables

mentioned above that user satisfaction has a significant effect on individual impacts

while system usage can serve as a mediate variable and computer acceptance does

influence individual users' performance. Teo & Wong (1998) modified variables in

D&M's IS success model to "information quality, managerial satisfaction, improvements

in the work environment and organizational impact" and tested the impact of “IT

intensity” on these variables by collecting data from 2641 retail companies in Singapore.

Although the result of this study shows that “IT intensity” has no significant impact on IS

measures, the result supported DeLone and McLean's IS Success model by suggesting

that there is a positive relationship between IS success measures (Teo & Wong, 1998).

Moreover, some empirical articles tested multiple variables in DeLone and

McLean's IS Success model and explored the interrelationship among those variables.

Gelderman (1998) tested the relationship between “usage of information system”, “user

satisfaction” and “performance” by mailing questionnaires to 1024 Dutch managers,

information managers, and controllers. His study result showed that “User satisfaction”

has a significant impact on system performance and "User satisfaction" is more

appropriate than "Usage" as a variable to measure the success of information system

(Gelderman, 1998). Torkzadeh & & Doll (1999) tested four dimensions (“task

productivity, task innovation, customer satisfaction and management control”) of

information technology's impact on work in the pilot study, then they surveyed 409

24

participants to further explore the relationship between four constructs mentioned

above and other variables (“user involvement, user satisfaction, system usage”).The

author suggested at the end of this research that more research should be devoted to

studying the effects of information technology impacts instead of studying how to design

the information technology.

Among articles mentioned above that examined and validated DeLone and

McLean's IS Success model, authors of these studies are to some extent supported

D&M's model. However, Seddon (1997) argued in his study that DeLone and McLean's

model of IS success is confusing and ambiguous since in this model DeLone and

McLean tried to combine both variance and process model to measure IS success. He

said in his research that “the inclusion of both variance and process interpretations in

their model leads to so many potentially confusing meanings that the value of the model

is diminished”. Seddon (1997) argued that the meaning of "IS Use" in DeLone and

McLean's model is not as a success variable, it does not "cause" benefits and other

influences but performs as a variable that was represented for the “Benefits from use”,

so "Net Benefits" should replace "IS Use" to be a variable in measuring IS success.

Moreover, Seddon also introduced "Expectation", "Consequences", "Perceived

Usefulness" with "Net Benefits" to build a specified model and split D&M's model into

“partial behavioral model of IS Use and IS Success model” with the expectation to cover

the inefficiency of DeLone and McLean's model (Seddon, 1997). It is hypothesized in

Seddon's model that "Expectations about the net benefits of future IS use" drives "IS

use", more "IS Use" implies more "Consequences", "Consequences" may lead to "IS

success" through “Observation, Personal Experience and Reports from others”

25

(Seddon,1997). Inside the IS success model, the complex relationship between "system

quality", "Information quality", "User satisfaction', "Perceived usefulness" and "Net

benefits" are explored. Then Seddon hypothesized that "higher net benefits" will lead to

"higher expectation about future benefits", and the existence of "Feedback" is needed to

measure a more complex relationship between them (Seddon, 1997).

Pitt et al. (1995) claimed that it is dangerous that still using measures that focus

only on the product of IS system rather than service it offers. “Service Quality” was

suggested to be another indication of IS success with SERVQUAL as an instrument.

SERVQUAL is an instrument introduced by Parasuraman et al. (1988) to measure

service quality in all service industries. Five dimensions of the original SERVQUAL

theory are “reliability, responsiveness, tangibles, empathy and assurance”

(Parasuraman et al., 1988). After tested the suitability of SERVQUAL in the content of

“validity, reliability, convergent validity, nomological validity, and discriminant validity” by

survey internal computer users from organizations in three countries (Parasuraman et

al., 1988). The author indicated that “Service Quality” can be a proper indication of IS

success and SERVQUAL. Li (1997) supported Pitt et al. (1995)’s argument by proving

seven factors including “Service Quality” to be significant indicators in IS success

measurement.

Delone & McLean (2003) then argued in their article that they disagree with the

point raised by Seddon (1997) that “Use is appropriate for inclusion in a process model

but not in a causal model” (Delone & McLean, 2003). Seddon claimed that “Use must

precede impacts and benefits, but it does not cause them.” (Seddon, 1997). Delone &

McLean (2003) suggested that “Use” of a system is a voluntary activity and can be

26

discontinued based on manager's’ judgment. The nature of the system also affects the

performance of the system. Thus “Use” should be kept to use as an indication of IS

success. Based on empirical studies that have been done before, Delone & McLean

(2003) added in their updated IS success model “Service Quality” (Li, 1997), “Net

Benefits” (Seddon, 1997) and suggested that “intention to use” as an attitude can be

appropriate measure in some context. In this updated model, “Use” and “User

Satisfaction” can be described in process relationship and causal relationship (Delone &

McLean, 2003). And the results of “Use” and “User Satisfaction” may lead to “Net

Benefits” (Delone & McLean, 2003; Balaban, Mu, & Divjak, 2013; Xuan, 2007). Factors

affecting the achievement of success in e-tailing in China’s retail industry: a case study

of the Shanghai Brilliance Group (Doctoral dissertation, College of Management,

Southern Cross University, Australia). Because of the impact Information technology

has on commerce industry, the DeLone and McLean's IS Success Model was extended

and explored in several e-commerce studies. Delone & McLean (2003) suggested that it

is appropriate to use updated DeLone and McLean's IS Success model to measure e-

commerce system success. They described the indicators of the updated IS Success

Model that suited in e-commerce system as following: 1) “System Quality”, which

measures the “desired characteristics of an e-commerce system” can be valued by

“Usability, availability, reliability, adaptability and response time”. 2)"Information quality"

measures e-commerce system content, can be measured by “Completeness, Ease of

understanding, Personalization, Relevance and Security.”3) The metrics of “Service

Quality” are “Assurance, Empathy and Responsiveness”. 4)"Usage" “measures

everything from a visit to a Web site”, it can be measured by “Nature of use, Navigation

27

patterns, Number of site visits, and Number of transactions executed”. 5) “Customer

Satisfaction” represents customer’s opinion of e-commerce system while it is measured

by “Repeat purchases, Repeat visits, and User surveys”.6) “Net Benefits” was claimed

as the most important success measure by Delone & McLean and has many measures

including “Cost savings, Expanded markets, Incremental additional sales, Reduced

search costs, Time savings” (Delone & McLean, 2003; Xuan, 2007)

After DeLone & McLean (2003) suggested that further studies can apply their

updated IS Success Model to investigate e-commerce success, more studies have

been focused validating and examining the updated IS Success Model in the area e-

commerce. Some research focused on testing the effect e-commerce system has on

customer satisfaction. For example, Bharati & Chaudhury (2004) built a web-based

decision support systems (DSS) that based on D&M IS Success model to test the effect

of “Information Quality”, “System Quality” and “Information presentation” have on

“decision-making satisfaction”. The result shows that both “System Quality” and

“Information Quality” have positive effects on user satisfaction while “Information

Presentation” which reflects how information is displayed has no significant relationship

with “Decision- Making Satisfaction”. Some researchers assessed the content of IS

Success Model in the context of web-based system, such as Wang (2008), who tested

D&M IS Success Model in the context of e-commerce by developing a research model

that consists of “Information Quality, System Quality, Service Quality, Perceived Value,

User Satisfaction and Intention to Reuse”. In this study, Wang (2008) used “Intention to

Reuse” and “Perceived Value” to present “Net Benefits”. After analyzing the dataset, the

author of this study suggested that “Information Quality, System Quality and Service

28

Quality” have effect on “Perceived Value and User Satisfaction”, while “Perceived Value

and User Satisfaction” has a significant correlation with “Intention to Reuse”. There are

also some other researchers (Molla & Licker, 2001; Fang et al., 2011) who focused on

validating and modify the IS Success Model itself in e-commerce studies.

System Quality

Information Quality and System Quality are claimed to be the two essential

quality constructs in measuring system success (DeLone & McLean, 2003). System

Quality signifies the quality of information system itself (Delone & Mclean, 1992; Negash

et al., 2003; Lee & Chung, 2009). Perceptual measures that have been frequently used

in previous studies including “ease-of-use” (Rai et al., 2002; Doll & Torkzadeh, 1988;

DeLone & McLean, 2003), reliability (Gable & Chan, 2008; Fan & Fang, 2006), flexibility

(Bailey & Pearson, 1983; Dwivedi, Wade & Schneberger, 2011), accessibility (McKinney

et al., 2002; Fan & Fang, 2006) and integration (DeLone & McLean, 2003, Gable &

Chan, 2008). Besides, there are also some other measures such as “importance,

sophistication, functionality, system accuracy” that have been applied in empirical

studies. Among all those variable, “Perceived Ease of Use” is also a commonly used

factor in measuring system quality. Delone and McLean (2003) also claimed that

information technology together with the internet significantly affect the business

operation, and the six dimensions of updated D&M IS Success Model is appropriate in

measuring e-commerce success. They then suggested that “even though new business

models are emerging, the fundamental role of IT has not changed, and thus the

methodology for measuring the success of information systems (IS) should not change”

(Delone and McLean, 2004). Then after reviewing system quality metrics for e-

commerce in IS studies, Delone & McLean (2004) stated that “usefulness, usability,

29

responsiveness, reliability, and flexibility” are key measures of system quality, while

based on different environment some other measures are also used in e-commerce

studies. Generally, Delone & McLean (2004) describe system quality as “desired

characteristics of an e-commerce system” that can be valued by “Usability, availability,

reliability, adaptability and response time”.

Information Quality

Quality has long been considered as a core concept in the business field that

reflects how well a product or service meets customers’ requirement while information

quality (IQ) reflects the performance of the system (Juran et al., 1974; Kahn et al., 2002;

Negash et al., 2003). Information System (IS) contains at least four components (data,

interface, work/task design, and Soft-/Hardware system) Dedeke (2002). A lot of studies

that have been focused on studying assessment instrument for measuring Information

Quality (IQ) of different components of IS.

General, Information Quality can be defined from either intrinsic or a contextual

view (Nelson et al., 2005). The intrinsic IQ reflects the accuracy of information

presented by IS (Goodhue, 1995), while the contextual IQ reflects which degree

information quality is “relevant, timely, complete, appropriate in terms of amount” and

useful in completing a specific task (Lee, 2003; Nelson et al., 2005). Wang & Strong

(1996) expanded the view of IQ by using three approaches (intuitive, theoretical and

empirical) in capturing important categories of data quality from data consumers’

perspective. After analyzing data collected from system users, Wang & Strong (1996)

developed a framework that contains four categories: “1) Intrinsic Data Quality

(accuracy, objectivity, believability, and reputation), 2) Contextual Data Quality (value-

added, relevancy, timeliness, completeness, and appropriate amount of data), 3)

30

Representational Data Quality (interpretability, ease of understanding, representational

consistency, and concise representation), 4) Accessibility Data Quality (accessibility and

access security)”. Building on intrinsic, contextual, representational and accessibility IQ,

researchers continued to explore more dimensions and measures that can be used in

the further understanding of Information Quality. Delone and McLean (1992)

represented 23 factors to measure information quality by reviewing previous studies.

Dedeke (2002) also expanded Wang & Strong (1996)’s framework and identified five

quality categories in their study. These categories are “representation, contextual,

accessibility, transactional, and ergonomic quality”. Instead of Intrinsic Quality,

“ergonomic Quality” and “transactional quality” are included to measure to which degree

the system is qualified to meet customers’ needs and the “natural skills, expectations

and work preferences of workers” (Dedeke, 2002). Lee et al. (2002) suggested a core

set of IQ dimensions, including “accuracy, completeness and currency, and format”

based on the dimensions (intrinsic, contextual, representational) grouped in Wang &

Strong (1996)’s study. Van & Hendriks (1996) defined several indicators of software

quality by extending ISO model. Quality characteristics defined in the study, including:

“functionality, reliability, usability, efficiency, maintainability and portability” and 32

quality sub-characteristics.

Then this extended ISO model was examined by Leung (2001). After analyzing

the data collected by surveying participants, Leung (2001) identified three quality

characteristics of software quality, including “reliability, functionality, efficiency” and five

key sub-characteristics including “availability, accuracy, security, suitability and time

behavior”. Focusing on the field of World Wide Web, Klein (2002) surveyed more than

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300 students to identify factors that affect the information quality of World Wide Web.

Key dimensions that were tested in this study including “accuracy, completeness,

relevance, timeliness, and amount of information”. Similarly, Knight & Burn (2005)

proposed an approach “IQIP: Identify, Quantify, Implement and Perfect” to assess

information on the World Wide Web by reviewing past IQ studies. Then in the e-

commerce context, Delone & McLean (2003) concluded several measures of Internet

information quality, such as “completeness, ease of understanding, personalization,

relevance, and security”.

Information Quality was proved to be a key predictor of customer satisfaction

(Wang & Liao, 2008; Urbach &Müller, 2012; Dwivedi et al., 2013). Cheung & Lee (2008)

focused on the web-based information system and defined satisfaction into “web

information satisfaction” that reflects “user satisfaction pertaining to information quality

of the Web-based information system” and “web system satisfaction” which emphasis

on user satisfaction of system itself. After analyzing data from 515 university students,

they claimed that “understandability, reliability, and usefulness” are key measures of

information quality which affect user web system satisfaction.

Chung & Kwon (2009) explored the relationship between information quality,

system quality and customer satisfaction within mobile bank industry. Since mobile

commerce offers different user experience compared with the business transaction on

the personal computer, Chung & Kwon (2009) added “information presentation” as a

new dimension that emphasis on customer interface aspect. The result of this study

showed no interrelationship between information presentation and customer

satisfaction, while proved that there is a significant relationship between information

32

quality, system quality, and customer satisfaction. Chen (2010) in the empirical study

about taxi-filling user satisfaction demonstrated an important effect of information and

system quality on taxpayer satisfaction.

Customer Satisfaction

Customer satisfaction was introduced by Oliver as “Customer satisfaction is the

consumer’s fulfillment response (Oliver, 1997). It is a judgment that a product or service

feature, or the product or service itself, provided (or is providing) a pleasurable level of

consumption-related fulfillment” (Oliver, 1997). Customer satisfaction has been broadly

studied in various fields, such as management, organizational and marketing (Molla &

Licker, 2001). In the context of management, Rust & Zahorik (1993) explored the

relationship between customer satisfaction with “individual loyalty, aggregate retention

rate, market share, and profits” to offered financial managers a mathematical framework

that measures which element of customer satisfaction may lead to maximizing

profitability. Mithas et al. (2005) examined how managing customer relationship affects

customer satisfaction while using customer knowledge as a mediating factor. Data was

collected from IT managers in more than 300 US companies and the result showed that

customer relationship management application affects customer satisfaction. In the field

of marketing, studies were focused on exploring the interrelationship between customer

satisfaction and customer retention (Rust & Zahorik, 1993; Anderson, 1994; Gustafsson

et al., 2005), market share (Anderson et al., 1994; Rust & Zahorik, 1993) and

profitability (Anderson et al., 1994; Hallowell, 1996; Anderson et al., 1997). From the

organizational perspective, Koys (2001) based on organizational behavior theories

hypothesized that there is a relationship between “employee satisfaction, organizational

citizenship behavior and the turnover on organizational effectiveness”. Results showed

33

that employee satisfaction is correlated with customer satisfaction and HR outcomes

significantly affect customer satisfaction and profitability. Similarly, Rogg et al. (2001)’s

empirical study also supported that human resources practice mediating the relationship

between organizational climate and customer satisfaction.

Common used antecedents of customer satisfaction in previous studies including

“expectations, perceived quality, and disconfirmation” (Yi, 1990; Oliver, 1980, Anderson

& Sullivan, 1993). Service quality is also approved to be the antecedent of customer

satisfaction (Ruyter et al., 1997; Caruana, 2001). Service quality has been defined as

an assessment of service (Ganguli & Roy, 2011), and it occurs when customers’ after-

service experience exceeds their before- service expectation (Parasuraman et al.,

1985). The 22-item SERVQUAL instrument introduced by Parasuraman et al. (1988) is

widely used a tool in measuring service quality studies. Five dimensions of the original

SERVQUAL theory are “reliability, responsiveness, tangibles, empathy, and assurance”.

Generally, customer satisfaction was proved to mediate the quality of service and

products have on customer loyalty, customer behavior intention and even customer

behavior (Boulding et al., 1993; Oliver. 1999; Gustafsson et al., 2005). Caruana (2002)

examined the effects of service quality and customer satisfaction has on customer

loyalty by survey 1000 retail banking customers. They hypothesized that customer

satisfaction mediating the relationship between service quality and customer loyalty,

then this hypothesis was supported by research results. More recently, similar results

have been reported in Santouridis & Trivellas (2010)’s empirical study in mobile

telephone industry in Greece, Siddiqi (2011)’s study in the retail banking sector in

34

Bangladesh and Sheng & Liu (2010)’s empirical study about online customer loyalty in

China.

A number of studies have been done in exploring the interrelationship between

customer satisfaction and customer behavior intention. Among all those studies, the

theoretical framework proposed by Parasuraman et al. (1985) was seen as the

foundation that explores the relationship between service quality, customer satisfaction

and behavior intention (Woodside et al., 1989). Woodside et al. (1989) developed

models to further explore the model raised by Parasuraman et al. (1985) in the field of

hospital care. Findings in this research showed that there is relationship among service

encounter, service quality, customer satisfaction, and customer purchase. Similar

results that supported Parasuraman et al. (1985)‘s study can be seen in many other

studies (Choi et al., 2004; Yi, Y., & La, 2004; Chen & Chen, 2010), while some research

argued that this relationship should be tested and validated. For example, Hellier et al.

(2003) designed a complex conceptual model to test the relationship among customer

satisfaction, brand preference, and repurchase intention. Several other factors have

also been tested in this research, such as customer loyalty, perceived value, perceived

quality, and perceived quantity. After analyzing data collected from car insurance

customers, the author suggested that the result didn’t support the hypothesis that

customer satisfaction directly and positively affects customer repurchase intention.

Repurchase behavior as a form of loyalty is one of the customer behavior that

has been examined by a lot of researchers to explore its relationship with customer

satisfaction. Lao et al. (2004) designed a repurchase frequency model and a

satisfaction model to test the interrelationship among “customer satisfaction, repurchase

35

frequency, waiting time and other service quality factors” in fast food outlets. Results

showed that service related factors have a significant influence on customer satisfaction

and repurchase frequency, but the relationship between customer satisfaction and

repurchase frequency only exists under certain condition and still needed to be further

explored. Mittal & Kamakura (2001) surveyed more than hundred thousand automotive

customers to test the conceptual model of the relationship between “rated satisfaction,

true/latent satisfaction, repurchase behavior, and consumer characteristics”. After the

results analysis, the author stated that the correlation between satisfaction and

repurchase behavior is not significant and different with the link between satisfaction

and behavior intention (Mittal & Kamakura, 2001).

In the e-commerce field, measures of customer satisfaction varied with study

environment. Technology Acceptance Model and IS Success Model have been

commonly used to tested (Lin, 2008; Lee et al., 2009) e-satisfaction and behavior

intention. In the Delone and Maclean’s IS Success model (Figure 2), system quality and

information quality are hypothesized to affect use and user satisfaction, while use also

influences the degree of User Satisfaction (Delone and Maclean, 1992; Molla & Licker,

2001). Meuter et al. (2000) examined the relationship between technology-based

service (self-service technology) and customer satisfaction through the internet- based

survey while using a critical incident technique as a qualitative method. A range of self-

service technologies has been examined in the study by categorizing them into three

technology interfaces including “telephone-based technologies, direct online

connections and interactive free-standing kiosks” and several purposes such as

“Customer service, transactions, and self-help”. Then problem-solving ability,

36

customers’ perceived benefits and capability of technology are proved to be three key

factors that may lead to customer satisfaction. More recently, Fang et al. (2011) added

“justice” and “trust” into the updated Delone and Maclean’s IS Success Model to study

customer repurchase intention during the online shopping process. The finding of this

study supported their framework that information quality and system quality have effects

on customer satisfaction while customer satisfaction mediating the relationship between

quality perceptions, trust and purchase intention. Some other researchers designed

study framework based on other models but still accept items from Delone and

Maclean’s IS Success Model to test user e-satisfaction and customer purchase

intention. Hsu et al. (2012) built their framework based on stimulus-organism-response

(S-O-R) paradigm. Website quality that was proposed in Delone and Maclean’s IS

Success model was modified in this study to be the stimulus, along with organism

(perceived playfulness, perceived flow) were used to test their impact on customers’

satisfaction and purchase intention.

Purchase Intention

Service quality and customer satisfaction are commonly acknowledged as key

factors that affect consumers' purchase intention in service industries (Taylor & Baker,

1994). Compared with service quality, customer satisfaction was claimed as a more

influential variable in the formation of customer purchase intention (Cronin & Taylor,

1994). Previous studies have explored the relationship between service quality,

customer satisfaction and customer behavioral intention in the context of the traditional

service industry, including tourism industry Based on the study field, various factors are

explored in their relationship between customer satisfaction and behavioral intention.

Liu & Yang (2009) explored factors that affect customer satisfaction and purchase

37

intention in the context of Chinese restaurants in the United States by using importance-

performance analysis approach. After data analyzing, the author suggested that food

quality, service reliability, and environmental cleanliness are essential factors that

influence customer satisfaction and purchase intention.

In the tourism and recreation field, Baker & Crompton (2000) claims that behavior

intention is affected more by perceived performance quality than by satisfaction. Items

used to measure behavioral intentions in their study are classified into “loyalty” and

“willingness- to -pay more”. Results of their study showed that customer satisfaction

cannot be used as a factor to fully mediate perceived performance quality and

behavioral intention. By contrast, customer satisfaction was proved to be a mediating

factor in the relationship between a firm’s perceived quality and purchase intention in

Bou-Llusar et al. (2001)’s study. Bou-Llusar et al. (2001) developed a scale to measure

firm perceived quality (perceived service quality and perceived product quality), then

firm perceived quality’s direct effects on purchase intention and customer satisfaction’s

mediating role are investigated. Results of this study show that customer satisfaction

mediated the relationship between firm perceived quality and customer purchase

intention.

Web-site quality is proved to have a direct impact on purchase intention, with

customer satisfaction mediating this relationship by Bai et al. (2008). In Bai et al.

(2008)’s study, web-site quality was measured by usability and functionality since

functionality measures the richness of website content and usability measures whether

the web-site is easy to use. Bai et al. (2008) in his study also suggested that system

quality, service quality, and information quality are three dimensions of website quality

38

that may have a significant influence on customer satisfaction and thus affect customer

purchase intention. Ahn et al. (2007) categorized web-site quality into information

quality, system quality, and service quality to explore its relationship with customer

behavior intention. Similarly, Hsu et al. (2012) investigated factors that mediate the

relationship between web-site quality, customer satisfaction, and customer purchase

intention. By building the stimulus-organism-response framework, perceived playfulness

and perceived flow are hypothesized as factors that play the mediating role. Both of

these studies proved that web-site quality (system quality, information quality, and

service quality) has a significant effect on customer satisfaction and customer purchase

intention. A web-site is a form of the e-commerce system, since the quality of web-site

is proved to be related to customer satisfaction and purchase, it is possible to assume

there is a relationship among quality of other information systems, customer

satisfaction, and customer purchase intention. Also, system quality, information quality

and service quality that were originally raised by Delone and McLean (2003) to measure

the information system success should be also appropriate to measure the quality of

RFID system.

Taking into account the points raised in the literature review, a conceptual model

was developed based on the framework proposed by DeLone & McLean (2003) and the

conceptual model offered by Bai et al. (2008). In our study, we considered that

MagicBand contains the RFID technology, which is an information system, while

MagicBand itself is also a wearable technology. To measure how the quality of the

MagicBand affect customers’ satisfaction and purchase intention, it is necessary to

investigate both system quality (functionality of the system) and usability of MagicBand.

39

Functionality measures whether the information system provides functions that

needed to. In this study, we consider the functionality of MagicBand as the quality of the

RFID system. To measure the functionality of MagicBand is to measure whether the

RFID system inside MagicBand performs its functions such as entering the Disney

resort rooms, entering the park entrance and paying for purchases inside the Disney

World, etc. Accessibility, response time and reliability are three frequently used

measures of the system quality of information system. Accessibility measures whether a

system can be accessed easily, reliability refers to the “dependability of a system over

time”, response time measures whether a system can have immediate response to the

information (Nelson, Todd & Wixom, 2005). Since we want to test whether the RFID

system inside MagicBand performs its function well, these three variables will be

adopted to measure the functionality of MagicBand.

The definition of usability was derived from the term of “user-friendly” and now

usability is defined as ease of use or “the ability to use a product for its intended

purpose” (Bevan, 1995). Since usability is affected by the types of tasks to be

completed (Goodwin, 1987), different approaches are used to measure the usability.

Rauschnabel, Brem & Ro. (2015) in their research of smart glasses defined wearable

technology as a form of fashion accessory, while other researchers of wearable

technology related article also suggested that visibility as a fashion related factor has a

significant effect on customers reaction to wearable technologies (Chuah et al., 2016).

MagicBand is a wearable technology that was designed as a bracelet, so in our study,

we adopt visibility as one measure of MagicBand’s usability. Questions of visibility were

designed based on Fisher & Price. (1992) definition of visibility as “people's believes of

40

the extent to which smartwatches are noticed by other people”. We also considered that

factors as whether the MagicBand is easy to wear and convenient to be carried on play

important role in customer satisfaction and even purchase intention using MagicBand,

so variables including wearability, visibility and mobility are selected in our model to

measure the usability of the MagicBand and examine the relationship between usability,

customer satisfaction and purchase intention using MagicBand.

The conceptual model and three hypotheses can be formulated as following:

H1: There is a positive relationship between MagicBand’s functionality and customer satisfaction

H2: There is a positive relationship between MagicBand’s usability and customer satisfaction

H3: There is a positive relationship between customer satisfaction and purchase intention (customer intention of purchasing food and merchandises using MagicBand)

H4: There is a positive relationship between MagicBand’s usability and purchase intention (customer intention of purchasing food and merchandises using MagicBand), and this relationship is mediated by customer satisfaction

H5: There is a positive relationship between MagicBand’s functionality and purchase intention (customer intention of purchasing food and merchandises using MagicBand), and this relationship is mediated by customer satisfaction

Figure 2-1. A conceptual model of service-based technology quality, user satisfaction and purchase intention.

41

CHAPTER 3 METHODOLOGY

Measurement Development

Most of the measurement scales were adapted from the prior studies and were

used to operationalize research constructs in this study. As shown in Table 3-1,

functionality was measured by accessibility, reliability and response time. All these three

factors were measured by four items, adopt from Nelson, Todd & Wixom (2005) and

Dahlman & Coelho (2002). Usability was measured by wearability, visibility, and

mobility. And these factors were measured by four items that adopt from Park &

Jayaraman (2003)’s study that measured the quality of another wearable technology.

Customer satisfaction was measured by three items that adopt from Bai et al. (2008)’s

study. Bai et al. (2008) adopt several multi-item scales to measure customer satisfaction

in the web-site context of, this scale was modified in this study to measure RFID user’s

satisfaction. Purchase intention was measured by three item scale to reflect the

behavior intention of Walt Disney’s MagicBand users in their future visiting plan. Each

item was measured on a five-point Likert-type scale ranged from ‘‘1’’ being ‘‘Strongly

Disagree’’ to ‘‘5’’ being ‘‘Strongly Agree’’. Since there exists the possibility that some

customers didn’t experience all functions that MagicBand offers, participants were given

a choice of “I haven’t used this function.” Demographic data, including gender, age,

education, nationality, and annual household income before taxes were also collected in

the study. The questionnaire is presented in Appendix A together with the invitation

letter.

42

Table 3-1. Measures of this study

Construct Features Formulation Literature Source

Accessibility To enter the park, all I need is MagicBand.

Nelson, Todd & Wixom (2005)

To enter the Disney Hotel Rooms, all I need is MagicBand.

To purchase food and merchandise, all I need is MagicBand.

To enter FASTPASS+®, all I need is MagicBand.

Response Time

MagicBand has fast response when I use it to enter the park. Dahlman &

Coelho (2002) Functionality

MagicBand has fast response when I use it to enter Disney Hotel Rooms.

MagicBand has fast response when I use it to purchase food and merchandise.

MagicBand has fast response when I use it to enter FASTPASS+®.

Nelson, Todd & Wixom (2005)

Reliability MagicBand never malfunctions when I use it to enter the park.

MagicBand never malfunctions when I use it to enter Disney Hotel Rooms.

MagicBand never malfunctions when I use it to purchase food and merchandise.

MagicBand works never mulfunctions when I use it to enter FASTPASS+®.

Visibility

Other people would notice it if I wear MagicBand.

Park & Jayaraman

(2003)

MagicBand is very visible to other people.

MagicBand is recognized by people who see me.

Wearability MagicBand is easy to wear. Park & Jayaraman

(2003) Usability

MagicBand is easy to take off.

Wearing MagicBand is comfortable.

43

Table 3-1. Continued

Construct Features

Formulation Literature Source

Mobility

I feel I can wear MagicBand to go to any attractions in the Walt Disney World®.

Park & Jayaraman

(2003)

I feel I can wear MagicBand to take any ride in the Walt Disney World®.

I feel I can wear MagicBand all the way from my hotel to the Walt Disney World®.

Customer Satisfaction

I am satisfied with my decision to use MagicBand.

Bai et al. (2008) Using MagicBand is a good experience.

Wearing MagicBand makes me feel more satisfied with my overall experience in the Walt Disney World®.

Purchase Intention I will consider using MagicBand to purchase items when I visit Walt Disney World® in the future.

Cronin et al. (2000)

It is likely that I will use MagicBand to purchase items when I visit Walt Disney World® in the future.

I will use MagicBand rather than other purchase method for buying products when I visit Walt Disney World® in the future.

Study Sample and Data Collection

The target population would be users of the Walt Disney’s MagicBand at all

Disney resorts, and the sampling population for this study are MagicBand users at Walt

Disney World® in Orlando, Florida. Since the MagicBand was only introduced to the

theme and water parks in Orlando, Florida, the study sample includes customers who

are visitors to Walt Disney World® in Orlando, Florida and users of the Walt Disney’s

44

MagicBand. A survey instrument was designed to ask respondents who are past or

current users of Walt Disney’s MagicBand if and how the quality of Walt Disney’s Magic

Bands affect their satisfaction and purchase intention. The predominant measuring

device used in this study was survey. Data would be collected in one of the biggest

malls in Orlando, Florida. Participants would be asked whether they have used Walt

Disney’s MagicBand when visiting Walt Disney’s theme and water parks. Then

questionnaires would be given to those who answered yes.

The survey population came from Disney MagicBand users, including both

current and previous users. Considering that the Walt Disney Company® has strict

limitation for survey conduction on Disney-owned land, survey for this study was

conducted in one of the biggest malls in Orlando- Orlando Vineland Premium Outlets.

Volunteers were recruited from the University of Florida by posting research volunteers

recruiting information on University of Florida Facebook. These volunteers were trained

with basic research skills and acknowledged the research purpose of this study. Then

these volunteers were divided into two groups with four people each group, and these

two groups of volunteers were sent to Orlando to collect data in two separate weekends

in January 2017.Questionnaires were randomly given to Disney MagicBand users inside

the mall by asking whether they are or were Disney MagicBand users. It is not that

difficult to identify Disney MagicBand users since plenty of customers who were

shopping in the mail wear the MagicBand on their wrist. And there is a Disney store in

the mail, customers who were shopping there were supposed to be fans of Disney.

Thus when volunteers saw those customers who were shopping at the Disney store or

wear the MagicBand when they were shopping, they went to introduce themselves,

45

explain the research purpose and ask whether we can do a simple survey with

customers. Once got customer's’ permission, they would be given a questionnaire, in

which the introduction of this study was presented on the first page with the content be

revealed on the other two pages. Customers were double-checked by asking whether

they are or were MagicBand users in case that they got their MagicBand through other

ways. Volunteers also talking to people who are shopping in stores or eating in the food

court to find MagicBand users. Before participants started to answer the questionnaire,

they were asked to check whether they matched conditions to do the survey. Based on

IRB requirements, people who are older than 18 years old would be allowed to answer

the survey. Then seventy-eight responses were collected in the first round, and sixty-

two responses were collected in the second round, the total number of collected

questionnaires was 140. Some attributes contained missing values, however, the

number of missing entries was small compared with the sample size.

46

CHAPTER 4 ANALYSIS AND RESULTS

Sampling Profile

SPSS-24 was used for data analysis. A sample profile is analyzed by descriptive

statistics and summarized in Table 4-1. Of these 140 respondents, the number of

females (59%) respondents is more than the number of male respondents (40%).

Respondents that age 18-44 accounted for 64.7% of the sample. 32% of responders

are people who are 45 years old to 74 years old. Only two participants are 65 years old

or older. Compared to participants who have bachelors or higher than bachelor’s degree

(40%), relatively more participants have a lower educational level (60%).Among those

responders who have lower education level, 14% are high school graduated and 24%

have completed some college. According to Census, in 2015, 88% of adults are at least

high school graduated while 59% are some college graduated (U.S. Census Bureau),

different from the educational attainment of this research participants. The median

household income of participants is between $42,000 and $67, 999, which is similar to

the 2015 U.S median household income ($55,775) (U.S. Census Bureau). There are

17% of respondents who got household income more than $110,000, while the

percentage of U.S residents who got annual household income more than $110,000 in

2015 is 20% (www.money.cnn.com ). These numbers showed us that the annual

household income of our study sample is fit in with the normal range of annual

household income nationwide. There were roughly equal numbers of domestic and

international respondents. 41% of responders are from other countries, 7% less than

the number of American responders. Among all responders, there is just one from

China.

http://www.money.cnn.com/

47

Table 4-1. Respondents’ Profile

Variable Value Frequency Percentage (%)

Gender Male 56 40

Female 83 59

Missing 1 1

Total 140 100

Age 18-24 38 27

25 - 34 29 21

35 - 44 23 16

45 - 54 26 19

55 - 64 20 14

65 or older 3 2

Missing 1 .7

Total 140 100

Education Level No high school 10 7

High school graduate 20 14

Some college, no degree 33 24

Associate degree 18 13

Bachelor's degree 38 27

Master's or Doctorate degree 18 13

Missing 3 2

Total 140 100

Country USA 67 48

Other Countries 58 41

Missing 15 11

Total 140 100

Household annual income Less than $22,000 14 10

$22,000-$41,999 34 24

$42,000-$67,999 27 19

$68,000-$109,999 30 21

$110,000 and above 24 17

Missing 11 8

Total 140 100

48

Reliability and Factor Analysis

The reliabilities of the derived measures were evaluated by calculating coefficient

alphas and the results were reported in Table 4-2. All results appear adequate based on

Nunnally’s (1978)’s standard, Accessibility (alpha =0.772), Response Time

(alpha=0.76), Reliability (alpha=0.887), Visibility (alpha=0.812), Wearability

(alpha=0.745), Mobility (alpha=0.911), Satisfaction (alpha=0.846) and Purchase

Intention (alpha=0.888). From the Cronbach's alpha if item deleted we can see that

lower Cronbach's alpha would be got if we remove any question except the third

question in measuring customer satisfaction “Wearing MagicBand makes me feel more

satisfied with my overall experience in the Walt Disney World®”. However, this question

was not removed since the value for “Corrected Item-Total Correlation” is not very low

(0.66).

Exploratory Factor Analysis (EFA) was used to verify the underlying structure of

the functionality and usability constructs (Norris & Lecavalier, 2010). In this study, EFA

was used to test whether variables we selected have a similar structure as specified in

the conceptual framework. Two attributes (Q3 and Q5) had either low communalities or

factor loadings and were removed from further analysis. Firstly, we used EFA to

analysis variables in Functionality and Usability part to see whether variables we

selected have an underlying structure as we expected. The final KMO measure of

sampling adequacy was 0.711, communalities were all larger than 0.54; all factor

loadings are greater than 0.5. The total variance explained was 74.33%. As shown in

Table 4-3, generally we got a structure as we expected, three factors we conducted to

measure usability (visibility, accessibility, and wearability). Although response time and

reliability are combined into one, there are still two factors maintained to measure

49

functionality (response time & reliability and mobility). From this factor analysis, we got

three factors that were named as Functionality 1 (Response time & Reliability),

Functionality 2 (Mobility), Usability 1(Visibility), Usability 2(Accessibility) and Usability 3

(Wearability).

Table 4-2. Reliability of Derived Measures

Variables Scale mean

if item deleted

Scale variance if

item deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deleted

Accessibility Coefficient Alpha = .772

13.61 4.544 0.642 0.522 0.684

A2 13.58 4.724 0.652 0.524 0.685

A3 13.99 3.663 0.58 0.362 0.743

A4 13.53 5.404 0.498 0.303 0.757 Response Time Coefficient Alpha = .76

RT1 13.96 3.206 0.512 0.311 0.739

RT2 13.83 3.508 0.597 0.39 0.685

RT3 13.96 3.279 0.604 0.448 0.678

RT4 13.83 3.776 0.546 0.374 0.714

Reliability Coefficient Alpha = .887

R1 12.9 9.365 0.73 0.542 0.863

R2 12.73 10.128 0.739 0.57 0.861

R3 12.79 9.226 0.829 0.694 0.825

R4 12.79 9.344 0.721 0.56 0.867

Visibility Coefficient Alpha = .812

V1 8.46 3.011 0.606 0.372 0.812

V2 8.24 3.216 0.718 0.532 0.692

V3 8.24 3.216 0.677 0.495 0.729

Wearability Coefficient Alpha = .745

W1 9.13 1.749 0.634 0.405 0.589

W2 9.1 2.048 0.507 0.263 0.732

W3 9.2 1.623 0.585 0.363 0.65

Mobility Coefficient Alpha = .911

M1 9.61 1.197 0.77 0.593 0.929

M2 9.59 1.337 0.856 0.773 0.847

M3 9.58 1.323 0.857 0.775 0.845

50

Table 4-2. Continued

Variables

Scale mean

if item deleted

Scale variance ifitem deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if

Item Deleted

Satisfaction Coefficient Alpha = .846 SA1 9.22 1.74 0.761 0.682 0.775

SA2 9.27 1.472 0.802 0.718 0.705

SA3 9.58 1.23 0.657 0.438 0.896 Purchase Intention Coefficient Alpha = .888 PI1 8.61 3.413 0.789 0.651 0.849

PI2 8.7 3.053 0.836 0.709 0.799 PI3 8.99 2.529 0.769 0.597 0.883

Table 4-3. Factor Analysis of Functionality and Usability

Factors

Reliability &

Response Time

Mobility Visibility Accessibility Wearability

Variance Explained 20.18 17.72 12.66 11.89 11.88 Eigenvalue 6.04 2.91 1.84 1.49 1.1 Cronbach’s alpha 0.88 0.91 0.81 0.71 0.75

Variables and

Communalities Q12 Reliability, Enter FSTPASS+ 0.85 0.91 Q11 Reliability, Purchase 0.81 0.88 Q10 Reliability, Enter the hotel room 0.71 0.83 Q7 Response Time, Purchase 0.68 0.66 Q6 Response Time, Enter hotel rooms 0.54 0.61 Q8 Response Time, FASTPASS+. 0.69 0.57 Q21 Mobility 3 0.88 0.86 Q19 Mobility 1 0.76 0.83 Q20 Mobility 2 0.90 0.83 Q14 Visibility 2 0.79 0.87 Q15 Visibility 3 0.78 0.85 Q13 Visibility 1 0.79 0.84 Q2 Accessibility, Enter hotel rooms 0.87 0.78 Q4 Accessibility, FASTPASS+ 0.62 0.75 Q1 Accessibility , Enter the park 0.69 0.71 Q17 Wearability 2 0.63 0.79 Q18 Wearability 3 0.71 0.69 Q16 Wearability 1 0.68 0.67

51

The KMO measure of satisfaction and purchase intention was 0.76;

communalities were ranged from 0.70 to 0.88; all factor loadings were greater than

0.50. The total variance explained was 81.99%. As shown in Table 4-4, there are two

factors and were named as satisfaction and purchase intention. Then after we got our

factors, we did summated scales analysis and then got seven summated scale scores

to represent those seven factors that including 1) Reliability & Response Time, 2)

Accessibility, 3) Visibility, 4) Wearability, 5) Mobility, 6) Customer Satisfaction and 7)

Purchase Intention. Correlation analysis was performed by using Pearson’s product-

moment correlations to test whether or not there is an association between all factors

we got in factor analysis. As seen in Table 4-5, customer satisfaction is associated with

all independent variables. There is also a strong relationship between customer

satisfaction and purchase intention. The correlation between visibility and other

variables are weak, but there is a correlation between visibility and customer

satisfaction.

Table 4-4. Factor Analysis of Satisfaction and Purchase Intention

Factors

Purchase Intention Customer Satisfaction

Variance Explained 42.40 39.60

Eigenvalue 3.37 1.55

Cronbach’s alpha 0.89 0.85 Variables and

Communalities Q26 Purchase Intention 2 0.87 0.92 Q25 Purchase Intention 1 0.82 0.89 Q27 Purchase Intention 3 0.80 0.87 Q22 Satisfaction 1 0.86 0.92

Q23 Satisfaction 2 0.88 0.92 Q24