An integrated perspective of TOE framework and innovation diffusion in broadband mobile applications adoption by enterprises

Chiu, Chui-Yu; Chen, Shi; Chen, Chun-Liang

Article

An integrated perspective of TOE framework and

innovation diffusion in broadband mobile applications

adoption by enterprises

International Journal of Management, Economics and Social Sciences (IJMESS)

Provided in Cooperation with:

International Journal of Management, Economics and Social Sciences (IJMESS)

Suggested Citation: Chiu, Chui-Yu; Chen, Shi; Chen, Chun-Liang (2017) : An integrated perspective of

TOE framework and innovation diffusion in broadband mobile applications adoption by enterprises,

International Journal of Management, Economics and Social Sciences (IJMESS), ISSN 2304-1366,

IJMESS International Publishers, Jersey City, NJ, Vol. 6, Iss. 1, pp. 14-39

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International Journal of Management, Economics and Social Sciences

2017, Vol.6(1), pp.14 – 39.

ISSN 2304 – 1366

http://www.ijmess.com

An Integrated Perspective of TOE Framework

and Innovation Diffusion in Broadband Mobile

Applications Adoption by Enterprises

Chui-Yu Chiu

1

*Shi Chen

2

Chun-Liang Chen

3

1

IE & M, National Taipei University of Technology, Taipei, Taiwan

2

Graduate Institute of Industrial and Business Management, National Taipei University of Technology, Taipei, Taiwan

3

Graduate School of Creative Industry Design, National Taiwan University of Arts, New Taipei City, Taiwan

This study aimed at exploring the critical factors for

enterprises to adopt broadband mobile applications. The results

are expected to guide enterprises to strengthen their

competitiveness. Further, since the broadband mobile

applications were integrated with many characteristics of

information communication technologies, this study combined the

Technology-Organization-Environment (TOE) framework and

Diffusion of Innovation Theory in an effort to establish a

comprehensive view and to increase the level of understanding.

The Structural Equation Modeling and AMOS were applied for

analysis; which discovered that the adoption of broadband mobile

applications by enterprises is significantly affected by

technological, organizational and environmental contexts. This

paper also identified eleven critical factors from

technological, organizational and environmental aspects, as well

as two vital control variables. Based on the research outcome,

this paper conducted an in-depth discussion and drew

conclusions. Finally, the research implications were provided.

Keywords: Broadband mobile application, technology-

organization-environment framework (TOE framework),

diffusion of innovation theory, enterprise’s

adoption, critical factors

JEL: L96, O32

Global mobile broadband communication technology

has been evolving into the fourth generation

of wireless mobile tele-communications technology

standards. With broadband mobile network, the

development of innovative mobile applications will

then be massively accelerated. Lubbe and Louw

(2010) explained from the marketing perspective

that the superior advantage of mobile

communication and mobile commerce is that the

suppliers can now access to the customers via

mobile devices anytime anywhere. The rapid

evolution of broadband mobile applications has led

to a more advanced mobile commerce, which

attracts new customers and brings significant

revenue for mobile application service providers

(Nikou and Mezei, 2013). The characteristics of

mobile technology such as connectivity, agility,

interactivity and location positioning can bring

Manuscript

received January 2, 2017; revised March 5, 2017; accepted March

17, 2017. © The Author(s); CC BY-NC; Licensee IJMESS

*Corresponding author: chenshi5668@gmail.com

15

Chiu et al.

various advantages to enterprises, enhance the

efficiency and effectiveness of enterprises, and

improve enterprises’ competitive advantage (Porter

and Millar, 1985)

Barnes (2002) pointed out that the challenge for

information based industries such as

telecommunication, hardware and software,

contents, entertainment and financial industry

(Symonds, 1999) is the transformation from a

traditional fixed network infrastructure based

business model to a whole new mobile network

business model. The smartphone, as well as the

mobile application platforms (iOS and Android) will

also bring significant changes to the value network

of manufacturing and service industries (Suh and

Kim, 2015).

Broadband mobile network service is widely

implemented by telecommunication service

providers from all over the world, and the technical

characteristics of growing bandwidth have become a

core subject in mobile applications development.

Many of the past studies focus on the adoption of

mobile commerce and mobile applications from the

use of traditional 2G/3G/3.5G mobile network (High

Speed Downlink Packet Access, HSDPA, and

below), yet the researches on broadband mobile

applications and enterprise-focused topics remain

limited. Yang (2012) revealed that the high data

transmission rate of the 4G broadband mobile

network will drive more innovative forms of mobile

applications, which may also influence user

preferences and behavior (Kwak and Yoo, 2012), as

such, the enterprises’ adopting of broadband mobile

application may be triggered and affected by

different factors. In this case, this study is targeted at

enterprises to analyze by integrating the TOE

framework (i.e., Technology, Organization, and

Environment framework) (Depietro, Wiarda and

Fleischer, 1990; Tornatzky and Fleischer, 1990) and

diffusion of innovations theory (Rogers, 1962) in

order to further explore various key factors affecting

the adoption of broadband mobile applications.

Results of this study attempt to serve as a

reference for the continuous innovation and

development of mobile applications for enterprises.

The rest of this paper is structured as follows.

Literature review and the hypotheses were proposed

in the next section. The section three presents the

method and process of analysis as well as the

results. In the fourth section a discussion based on

the findings was mentioned. Then the conclusion in

final section with implications, limitations and

directions for future study were provided.

16

Chiu et al.

Barnes (2002) pointed out that the challenge for

information based industries such as

telecommunication, hardware and software,

contents, entertainment and financial industry

(Symonds, 1999) is the transformation from a

traditional fixed network infrastructure based

business model to a whole new mobile network

business model. The smartphone, as well as the

mobile application platforms (iOS and Android) will

also bring significant changes to the value network

of manufacturing and service industries (Suh and

Kim, 2015).

Broadband mobile network service is widely

implemented by telecommunication service

providers from all over the world, and the technical

characteristics of growing bandwidth have become a

core subject in mobile applications development.

Many of the past studies focus on the adoption of

mobile commerce and mobile applications from the

use of traditional 2G/3G/3.5G mobile network (High

Speed Downlink Packet Access, HSDPA, and

below), yet the researches on broadband mobile

applications and enterprise-focused topics remain

limited. Yang (2012) revealed that the high data

transmission rate of the 4G broadband mobile

network will drive more innovative forms of mobile

applications, which may also influence user

preferences and behavior (Kwak and Yoo, 2012), as

such, the enterprises’ adopting of broadband mobile

application may be triggered and affected by

different factors. In this case, this study is targeted at

enterprises to analyze by integrating the TOE

framework (i.e., Technology, Organization, and

Environment framework) (Depietro, Wiarda and

Fleischer, 1990; Tornatzky and Fleischer, 1990) and

diffusion of innovations theory (Rogers, 1962) in

order to further explore various key factors affecting

the adoption of broadband mobile applications. The

results of this study attempt to serve as a reference

for the continuous innovation and development of

mobile applications for enterprises.

The rest of this paper is structured as follows.

Literature review and the hypotheses were proposed

in the next section. The section three presents the

method and process of analysis as well as the

results. In the fourth section a discussion based on

the findings was mentioned. Then the conclusion in

final section with implications, limitations and

directions for future study were provided.

LITERATURE REVIEW

Broadband Mobile Communication Technology

The technical standards guideline published by

International Telecommunication Union (ITU)

specifies that data transmission rate can reach up to

17

Chiu et al.

100 Mbps peak in high-speed mobility state and 1

Gbps in low mobility state, it can be called the

fourth generation of wireless mobile tele-

communications technology (IMT-Advanced). The

Global mobile Suppliers Association (GSA)

estimates that by Q2 2016 the global Long-Term

Evolution (LTE) telecommunication subscriptions will

reach 1.4 billion. 166 operators have deployed

networks of LTE-Advanced or 4.5G/LTE-Advanced

Pro in 76 countries including the United States,

Europe, Australia and Japan (GSA, 2016).

Moreover, the Next Generation Mobile Networks

Alliance (NGMN) announced that the 5th generation

mobile networks (5G) will become operational by

2020 (NGMN 5G White Paper, 2015), which can

carry much more data in faster speed than ever, and

support more innovative applications such as the

Internet of Things (IoT).

Kwak and Yoo (2012) considered that 4G

broadband mobile network can not only solve 3G

system deficiencies, but also offers other services

3G doesn’t offer such as high quality voice

transmission, high-definition audio and video

broadcasting, and identified five key services

attributes of 4G: high-speed data transmission,

communication service, channels, on-demand video

and other additional services. Jung and Kwon (2015)

further found that 3G service users are more

concerned about the call quality and customer

service, whereas LTE service users are more

interested in data transmission quality and price.

Mobile Applications

Shih and Shim (2002) affirmed that mobile

commerce built upon the use of mobile applications

by enterprises or in a business environment can

stimulate transactions among businesses and

improve productivity. As such, mobile application is

deemed as one of the key driving forces for the

development of mobile commerce. Further, different

purposes of mobile applications can be developed

diversely. Nysveen, Pedersen and Thorbjørnsen

(2005) found from the earlier literature that it leads to

different methods to distinguish features of mobile

applications in view of different importance of

characteristics, such as by interaction (Device vs

Person) and by the process (Goal-oriented vs

Experience-oriented). Nikou and Mezei (2013) have

sorted out five categories of mobile services from

the past literature and those are communication,

entertainment, information, web 2.0, and transaction.

Unhelkar and Murugesan (2010) stated that

many enterprises adopt mobile technologies to

improve operational efficiency, increase

responsiveness and competitiveness, and meet

18

Chiu et al.

customer needs, in consequence, mobile

applications can provide new business opportunities

for companies. While mobile applications

incorporate a variety of information communication

characteristics, there are also studies about

individuals or businesses adopting mobile

technologies, and which provide references for this

study in exploring various possible factors affecting

the adoption of the broadband mobile applications in

enterprises. Some aforementioned studies are cloud

computing (Almudarra and Qureshi, 2015), mobile

innovation (Song, 2014), mobile payment services

(Slade

et al

., 2015) etc. However, today’s mobile

applications are far more capable than merely

providing voice communication and data

transmission back in 2G or 3G era. In addition,

mobile broadband network nowadays possesses

much more powerful features than the mobile

networks of the past, and this network advancement

has led to a radical change in the mobile

applications to become more integrated and

innovated, and thus change the customer habits and

business models, and become more conducive to

business. Moreover, a significant cognitive gap of

business managers generally occurs between the

varieties of mobile applications in the latest

broadband environment and limited speed of

network and bandwidth in old days, as a

consequence, it is necessary to conduct further

research.

TOE Framework

Technology-Organization-Environment Framework

(TOE framework) (DePietro

et al

., 1990; Tornatzky

and Fleischer, 1990) is an application level

framework for research from the organization-level

perspective (Piaralal,

et al

., 2015). TOE framework

proposes three main facets to explore the factors

that affect the organization's acceptance of

innovation technology. The technological context

includes the characteristics and the usefulness of

the innovative technology; the organization context

contains the internal issues within the company such

as management, employee, products and services;

and the environmental context involves the issues

exist in the business related field, such as the

competitors and business partners.

Zhu, Kraemer and Xu (2006a) indicated that the

TOE framework has proven to be fairly effective

from the past research. A lot of studies about

innovation technologies have been done by adopting

the TOE research method, including information

systems (DePietro

et al

., 1990), e-commerce (Rowe,

Truex and Huynh, 2012), web service (Lippert and

Govindarajulu, 2015), e-CRM (Racherla and Hu,

19

Chiu et al.

2008), and cloud computing (Lian, Yen and Wang

2014).

From the above literature, it can be well informed

that the TOE framework is widely used on the

adoption of different innovative technologies and

proven to be validated (Ramdani and Kawalek,

2007). Given that the theme of this study is in regard

to the application of innovative technology and from

the enterprises perspective, the TOE framework is

adopted as the main research model in this

research.

Diffusion of Innovations Theory

The concept of diffusion of innovation has long been

proposed by scholars. It was widely used in

agriculture, medicine, communication, marketing

and other fields (Greenhalgh

et al

., 2005). Rogers

(1962) on the other hand, summarized the studies in

the fields of anthropology, education, industry,

medicine etc., and proposed the diffusion theory of

innovation. Rogers elaborated that innovation is “any

idea, practice, or object that is perceived as new by

an individual or another unit of adoption”. Diffusion

means “the process by which an innovation is

communicated through certain channels over time

among the members of a social system”. In most

studies, innovators are individuals, organizations,

clusters, social networks, and even countries

(Meyer, 2004).

Rogers (1995) also pointed out that innovation

will go through five stages of the adoption process:

Knowledge, Persuasion, Decision, Implementation,

and Confirmation. In the persuasion stage, the

potential adopter will be more involved as compared

to the knowledge stage and begin to actively seek

out relevant information. Individuals or decision-

making units will generate a positive or negative

attitude towards an innovation; an innovative

perception will also develop, so the perceived

characteristics of innovation are particularly

important in the persuasion stage. Hence, five

perceptual characteristics of innovation i.e. relative

advantage, compatibility, complexity, trialability, and

observability were identified by Rogers and help to

assess the adoption rate. (Rogers, 1995; Bensley

and Brookins-Fisher, 2003)

As with the TOE framework, diffusion of

innovations theory has also been widely used in

recent years in the field of information technology

application research, such as web site adoption

(Beatty, Shim and Jones, 2001), ERP systems

implementation (Bradford and Florin, 2003), mobile

banking (Al-Jabri and Sohail, 2012) and e-

commerce adoption by small and medium

20

Chiu et al.

enterprises (SMEs) (Hussin and Noor 2005;

Limthongchai and Speece, 2003; Kendall

et al

.,

2001)

Integrating DOI and TOE

There have been numbers of literature in the past

exploring the use of innovative technologies that

combine the TOE framework with the diffusion of

innovation theory to better explain the diffusion of

innovation from the organizational perspective (Hsu,

Kraemer and Dunkle, 2006), and better focus on the

impact of both internal and external factors of

innovation technology adoption and diffusion (Zhu

et

al

., 2006a; Tornatzky and Fleischer, 1990), the

relevant factors are also validated effectively. As

reported by Piaralal

et al

. (2015), innovation

diffusion theory combined with TOE framework

provides a useful theoretical framework for small

and medium-sized logistics enterprises to adopt

green technology and explore the use of innovation

technology by the overall consideration of internal

and external factors. Wang, Wang and Yang (2010)

combined the important factors of both theories to

explore the key factors influencing the adoption of

Radio Frequency Identification (RFID) in the

manufacturing sector. Ramdani, Kawalek and

Lorenzo (2009) combined the TOE framework and

diffusion of innovation theory to explore the factors

of adoption of enterprise system. In addition,

Alshamaila, Papagiannidis and Li (2013) combined

both theories to identify the influence factors in the

adopting of the cloud computing by SMEs. Oliveira,

Thomas and Espadanal (2014) also explored the

key factors of cloud computing adoption in service

industry and manufacturing by using the TOE

framework in combination with innovation diffusion

theory.

As Thong (1999) suggested that due to the rapid

evolution of information technology and its

characteristic, whether a single theoretical model

can be applied to all the subjects is still arguable.

Oliveira and Martins (2011) also indicated that it

would be important to combine more than one

theoretical model in future studies in order to have a

better understanding of the adoption of complicated

innovation technologies. Hence, this study integrates

the innovation diffusion theory with the TOE

framework and proposes an integrated research

model.

Innovation Technology Adoption Stage

Organizational innovation usually has different

stages of processes (Grover and Goslar, 1993). As

noted earlier, Rogers (1995) pointed out that

innovation experiences five stages: knowledge,

persuasion, decision, implementation and

21

Chiu et al.

confirmation. Grover and Goslar (1993) also divide

organizational innovation into three stages: initiation,

adoption and implementation. Furthermore, Zhu

et

al

. (2006a) identified three important stages of

initiation, adoption and routinization from the point of

view of the adoption of e-business. The “Initiation”

stage begins with the perception and assessment of

an innovation, to measure the performance

improvement and potential benefits of an

enterprise's value chain activities. In the “Adoption”

stage, decisions are made to formally allocate

resources needed to fully deploy the innovation.

Then, in the “Routinization” stage, the innovation

must be accepted by members of the enterprise;

which can be widely used and deployed

Considering that the characteristics of broadband

mobile applications which may involve the personal

mobile devices and user habits of all members of the

enterprise, as well as customers and suppliers;

therefore, it is very crucial in the adoption and

implementation stage rather than in the initiation

stage. As a result, this study divides the process of

adoption into three stages: Initiation, Adoption and

Implementation, in order to have a full spectrum of

facets and factors through the model structure

analysis. In addition, this study measured the three

enterprises’ adoption stages with nine gradual

questions in the questionnaire, e.g., “I wish the

company could adopt the broadband mobile

applications”, “My company has just adopted the

broadband mobile applications”, and “We would

recommend our clients or suppliers to adopt the

broadband mobile applications”.

Hypotheses and Research Model

Given the diversified characteristics of information

and communication technology that broadband

mobile applications possessed, which have been

widely discussed in varieties of literature; also the

determinant factors of technological, organizational

and environmental context were tested, this study

sourced from mentioned literature to explore the

possible factors that may affect the adoption of

broadband mobile applications accordingly. The

theoretical basis for each factor is provided as

follows.

-The Technology Context

Alshamaila

et al

. (2013) in the study on SMEs’

adoption of cloud computing combined the TOE

framework with diffusion of innovation theory, which

adopted the five perceptual characteristics from

Rogers’ innovation diffusion theory. Ramdani

et al

.

(2009) integrated the innovation diffusion theory as a

theoretical basis of technological factors for

exploring the impact on SMEs’ adoption of

22

Chiu et al.

Enterprise system. As such, this study integrated the

five perceptual characteristics of innovation in the

persuasion stage of innovation diffusion theory as

factors in the technological context, and also

referred the previous research of related innovation

technology topics. Zhu

et al

. (2006b) found that

compatibility is the most important factor influencing

the post-adoption in European enterprises’ adoption

of digital transformation. The research results of

Wang

et al

. (2010) showed that due to the ongoing

development of RFID, there is no common standard

developed yet, and there are still issues in system

integration with the company's existing internal

information system, so the complexity and

compatibility for the manufacturing industry adopting

RFID have respectively significant negative and

positive impacts. Low, Chen and Wu (2011)

identified that the relative advantages and

complexity on cloud computing are inversely related

to enterprises adoption. Sin Tan

et al

. (2009) found

that relative advantage, compatibility, complexity

and observability in the technology context are the

main factors influencing the SMEs’ adoption of ICT

in Malaysia. Alshamaila

et al

. (2013) in the study of

SMEs’ adoption of cloud computing found factors in

the technology context which have significant

impact, including relative advantage, compatibility,

and complexity, as well as the trialability which

contributes to reduce uncertainties in adoption. To

sum up, this study proposed the five factors of

innovative diffusion theory with the following

hypotheses:

-The Organization Context

Low

et al

. (2011) in the research of cloud computing

found that the support of high-level executives in the

organization is a significant factor influencing

enterprises' adoption. Lin (2014) in their research

about electronic supply chain management system

adoption stated that since the user is encouraged to

participate and to solve problems between trading

partners, and also the enterprises need to quickly

strengthen the systematic knowledge in order to

23

Chiu et al.

enhance the awareness and reduce the resistance,

the management support and absorptive capacity,

as well as the competitive pressures in the

environment context are significant factors in both

“Likelihood of e-SCM Adoption” and “Extent of e-

SCM adoption” stages. Hollenstein (2004) argued

that the knowledge capital generated through

learning and experience will bring the advantage in

applying new technology to enterprises, so

absorptive capacity is one of the most important

factors in adopting innovative technology. In

addition, Thong (1999) revealed that the information

intensity of company’s products and services has a

significant impact on the adoption of information

technology by small businesses. Al-Qirim (2008)

also indicated that the information intensity of

products and services is a significant factor

influencing the adoption of e-commerce websites by

small businesses. Moreover, Mirchandani and

Motwani (2001) found that one of the major

problems faced by enterprises was the lacking of

knowledge in information system by the employees,

so employees’ knowledge was also a significant

factor influencing the adoption of e-commerce by

small businesses. Considering the characteristics of

innovative technology, this study propose the

following hypotheses based on the four factors

discussed above:

-The Environment Context

There are results from the past studies shown that

competitive pressures are a significant factor

influencing the adoption of information technologies

by enterprises (e.g., Ghobakhloo, Arias-Aranda and

Benitez-Amado, 2011). Li (2008) found that both

competitive pressures and external support were

significant factors of the use of e-procurement in

manufacturing. Teo, Lin and Lai (2009) found that

trading partners will also significantly affect the

adoption of e-procurement. Similarly, Stockdale and

Standing (2006) found that key trading partner is an

important factor influencing the adoption of e-

commerce by SMEs. Besides, the government’s

attitude is also considered to be one of the important

factors influencing the adoption of innovative

24

Chiu et al.

technology. Dahnil

et al

. (2014) pointed out that

government attitudes, policies and initiatives are

important factors influencing the adoption of

innovative technology by SMEs. Lee

et al

. (2014)

considered government support as one of the

conditions to promote the adoption of cloud

computing among the enterprise. In this study, we

incorporated the above mentioned important factors

into the following hypotheses:

-Control Variables

Different types business of enterprises for the

application of an innovative technology will take a

different attitude; Hsu

et al

. (2006) found that for US

firms, the manufacturers compare the distribution

and financial industries tend to be more willing to

adopt e-business. However, Teo

et al

. (2009)

conducted a study in adopting e-procurement by 141

firms in Singapore and the industry type was used

as the control variable, the results showed no

significant difference. Moreover, this different

attitude may also occur in organizations of different

sizes, such as Ifinedo (2011) in the study of

internet/e-business technologies for Canadian SMEs

using company size and industry sector as control

variables. Siamagka

et al

. (2015) also used the size

as a control variable in the study of social media

adoption by B2B organizations. Thus, the industry

type (service/manufacturing) and company size are

incorporated as control variables with reference to

the above studies and were further examined in the

three stages of adoption.

Sample and Questionnaire

Given that the broadband mobile applications

possess the features of various applications, this

study referred to the previous literature and

developed the questionnaire. The factors definition

and references are summarized in Table 1 (see

Appendix-I).

The responses were tapped by the Likert 5-point

scale. First of all, two Ph.D teachers reviewed and

provided feedbacks on items or questionnaire, then

the pre-questionnaires were given to 40 managers

and employees (22 high-level and 18 mid-level)

selected from the project database of the Ministry of

25

Chiu et al.

Economic Affairs. The respondents were asked to

read the instruction before filling the questionnaire. A

total number of 40 valid pre-questionnaires were

collected, and the test results of reliability and

validity were all found up to the standard. The

semantic amendments have been made into four

questions according to the feedbacks and

suggestions. In the formal stage, the main

databases were chosen from the project system of

industrial promotion organizations in cooperation

with economic department of government, such as

the National Association of Small and Medium

Enterprises and Corporate Synergy Development

Center, by following random sampling. The

respondents hold mid-level or above positions in

their organizations. 411 questionnaires were

distributed in both hard copy and via email, which

obtained a valid response rate of 73.72 percent and

has reached to the acceptable level (Rigdon, 2005).

The statistical data are shown below in Table 2; and

the sample characteristics are shown in Table 3

-The Analysis of Measurement Model

In this study, the structural equation modeling (SEM)

was used to analyze the data. Based on Hair

et al

.

(1998), the two-stage analysis was proceeded. First,

all variables were tested in the measurement model,

the cronbach's

α

of technological, organizational and

environmental context and adoption were .89, .89,

.93 and .87 respectively, which showed that the

questionnaire had good reliability. Secondly, the

confirmatory factor analysis (CFA) was used to test

the convergent validity and discriminant validity of

each facet and to validate the appropriate model for

structural model analysis (Pijperset

et al

., 2001). The

model fit, factor loading and convergent validity of

each facet were individually examined. According to

Bagozzi and Yi (1988), two factors’ loading showed

offending estimate (i.e., complexity did not reach .50

and government supports showed over .95) which

then been removed. Then the combined reliability

after the test was above .70, the average variance

extracted (AVE) ranged from .55 to .80, which were

in accordance with the standard proposed by

Bagozzi and Yi (1988) - the combined reliability

should be above .60, and the AVE should be above

.50. Further, the validities were in accordance with

the requirement value of .50 as proposed by Fornell

and Larcker (1981). Thus, the results indicated that

Distributed Return Invalid Valid Valid Percentage

Via Email 135 101 8 93 68.89%

Hard Copy 276 231 21 210 76.09%

Total 411 332 29 303 73.72%

Table 2: Questionnaire Distribution and Returns

26

Chiu et al.

the model has reached an acceptable level. Detailed

are given in Table 4 (see Appendix-II). In summary,

the proposed research architecture is shown below

in Figure 1 (see Appendix-III).

-The Analysis of Structural Model

First, in the goodness of fit test, the overall model fit

is shown in Table 5. The χ

2

/

df

ratio was 1.88 which

fitted the parsimonious fit value (less than 3) in

reference to Hair

et al

. (1998). Then Browne and

Cudeck (1993) pointed out that the GFI should be

greater than .80; and according to the study of Wu

and Wang (2006), that the goodness of fit index can

be in accordance with the recommendation

of Hadjistavropoulos, Frombach and Asmundson

Subject

Valid

Samples

Percentage

Cumulative

Percentage

Number of

Employees

Under 4

5~19

20~49

50~99

100~200

Over 200

33

77

64

32

44

53

10.89

25.41

21.12

10.56

14.52

17.49

10.89

36.30

57.42

67.98

82.50

100

Industry Type

Services

Manufacturing

228

75

75.25

24.75

75.25

100

Company Age

Under 1 year

1~3 years

3~7 years

7~10 years

10~13 years

Over 13 years

13

24

59

45

28

134

4.29

7.92

19.47

14.85

9.24

44.22

4.29

12.21

31.68

46.53

55.78

100

Position Level

Mid-Level

High-level

CEO

Board member

other

136

104

26

1

36

44.89

34.32

8.58

0.33

11.88

44.89

79.21

87.79

88.12

100

Table 3

:

Sample Characteristics

Indicator of

Goodness-of-fit

Standard Value Examination Result

RMR <0.08 (Jöreskog and Sörbom 1993) 0.066

RMSEA <0.08 (Hu and Bentler 1999) 0.054

TLI(NNFI) >0.9 (Bentler and Bonett 1980) 0.918

CFI

>0.9 (Bentler and Bonett 1980)

0.924

NFI

>0.8 (Hadjistavropoulos et al. 1999, Hair et

al. 1998)

0.852

GFI >0.8 (Browne and Cudeck 1993) 0.825

AGFI

>0.8 (Hadjistavropoulos et al. 1999)

0.800

χ2/d.f. <5 (Bentler and Bonett, 1980) 1.882

Table 5: Overall Fits of Models

27

Chiu et al.

(1999) and Hair

et al

. (1998): AGFI> .80, NFI> .80.

In this study, GFI was .82, AGFI was .80 and the

NFI was .85 which reflected the good values.

Besides, the RMSEA was .05, which was in

accordance with the suggestion of Hu and Bentler

(1999) that the value of RMSEA should be less than

.08. It is concluded that the fit of the structural model

data in this study was in a good range.

The results of the verification of factors are

shown in Table 6 (see Appendix-IV). A total number

of 11 factors showed significant results.

-Control Variable Test

First of all, in the variable “company size” analysis,

the ANOVA showed different results in various

stages. In the “adoption” and “implementation”

stages it showed insignificant results, only in the

“Initiation” stage, the

f

-value showed 10.54 and the

p

value showed .001, which reached the significant

level. Yet the homogeneity assumption was rejected

after the homogeneity of variance test (Levene's

test: 8.78,

p

= .001), the results were further

compared by Brown and Forsythe (1974) and Welch

(1951) test and showed that value in the group of

company size with more than 200 employees is

significantly lower than in the groups which under

200 employees. It can be deduced that the size of

the company and the adoption of broadband mobile

application have an inverse correlation.

Next, in the analysis of “industry type”, the results

of independent sample

t

-test indicated that the

Figure 2:

Results of Structural Modeling Analysis

Technology

Organization

Environment

Initiation

Adoption Implementation

0.301***

0.316***

0.471***

0.815***

0.933***

Sum of Squares

df

Mean Square

f

Sig.

Between Groups 40.251 5 8.025 10.545 .000

Within Groups 226.736 297 .763

Total 266.987 302

Table 7: ANOVA of Business Size

28

Chiu et al.

manufacturing and service industries showed

different results at various stages. In the final

“implementation” stage, there was no significant

difference. Yet, in the “initiation” and “adoption"

stages the results were significant, and the value of

service industries group was higher than the

manufacturing group after the comparison. The

results of the three-stage analysis are shown in

Tables 8 and 9 below.

DISCUSSION

The Technology Context

In terms of technology context, the relative

advantage and compatibility showed significant

results in line with the research results of

Premkumar and Roberts (1999) for enterprises

adopting innovative information technologies and

online data access, and Sin Tan

et al

. (2009) on the

adoption of information technology, indicated that

the broadband mobile applications possess a similar

information communication technology

characteristic. Zhu

et al

. (2006b) in the study of

digital transformation for European companies also

revealed that the e-business value chain activities,

which rely on communication tools, are now shifted

from paper/telephone/fax to digital form, and the

priority of digital assets and information flow within

the enterprise has gone up, thus the compatibility of

communication has become a key driving force.

Levene' test t-test for Equality of Means

f Sig. t df

Sig.

(2-tailed)

Mean

Difference

Std. Error

Difference

Initiation .012 .914 -3.160 301 .002 -.38974 .12334

Adoption 1.928 .166 -2.084 301 .038 -.26746 .12832

Implementation 1.374 .242 -1.951 301 .052 -.21132 .10833

Table 8: Independent Sample t-test

Industry N Mean Std. Deviation Std. Error Mean

Initiation

1 75 2.71 .88978 .10274

2 228 3.1031 .93824 .06214

Adoption

1 75 2.6733 .85224 .09841

2 228 2.9408 .99769 .06607

Table 9: Group Statistics

29

Chiu et al.

Further, trialability has reached to a significant level,

as Ramdani

et al

. (2009) in the study of the adoption

of enterprise systems stated that getting a trial

version before the adoption is important to the

enterprise. The results of observability also showed

a significant result and matched the researches of

both Hussin and Noor (2005), and Limthongchai and

Speece (2003) in adoption of e-commerce. In

addition, this study also suggests that it’s easy to

observe the diversity and wide range of use of the

mobile broadband application around the people, the

observability of mobile broadband application has

become more important.

The Organization Context

The four factors within the organizational context all

showed significant results. First, the information

intensity showed significant result in line with the

study results of Al-Qirim (2008) on the adoption of e-

commerce communications and applications

technologies. Ghobakhloo

et al

. (2011) suggested

that companies will consider the technology relevant

to their products and services, enterprises with high

IT relevance are more likely to use e-commerce to

improve competitiveness. Next, the significant result

of top management support is consistent with

previous studies such as cloud computing (Oliveira

et al

., 2014; Borgman

et al

., 2013), e-procurement

(Teo

et al

., 2009), Enterprise System (Ramdani and

Kawalek, 2007), e-commerce (Stockdale and

Standing, 2006), EDI (Premkumar and Roberts,

1999). This validates that the characteristics of

broadband mobile technology are similar to the IT

tools for data exchange used inside or between the

enterprises. Oliveira

et al

. (2014) claimed that top

management in the enterprise can show their

support for adoption of cloud computing by

supporting money and resources. Employees’

knowledge showed a significant result, which is

compatible with the study results of Mirchandani and

Motwani (2001), and Scupola (2009); the former

research indicated that employee’s knowledge was

one of the most important factors in company’s

website adopting. Scupola (2009) also reported that

employees’ knowledge in both Australian and

Danish companies is important factor for e-

commerce adoption, especially in the case that

Australian CEOs who have paid more attention to

the e-commerce recommendations from employees.

Absorptive capability reached to a significant result,

which is consistent with the results of research by

Lin (2014) in e-SCM (supply chain management),

and Hollenstein (2004) in information communication

technologies. As Park, Suh and Yang (2007)

elaborated that the knowledge of ERP systems

30

Chiu et al.

includes multiple functional aspects (e.g.,

positivistic/anti-positivistic and constructive aspects),

hence, the status of absorptive capacity is easy to

perceive by employees and regarded as very

crucial. This study believes that multi-functional

features are also embodied in today's rapid

developed broadband mobile applications.

The Environment Context

Business partner in the environment context showed

a significant result. Li (2008) revealed in the

research that pressure from suppliers or trading

partners, like competitive pressures, drives

companies to adopt e-procurement to gain and

maintain competitive advantage. While competitive

pressure also revealed a significant result in this

context. Zhu

et al

. (2006a) found that in some low

ICT-intensity countries, the incentive to use e-

business is not necessarily due to perceived

competitive advantage, but to avoid lagging behind

the technological curve, therefore, the competitive

pressure is more apparent than in high ICT-intensity

country. Ifinedo (2011) also claimed in the research

of the Internet/e-business technologies in SMEs

from Canada that SMEs are more likely to adopt

innovative technologies in a high competitive region.

At last, external support also produced a significant

result. Ramdani and Kawalek (2007) in the research

on the adoption of enterprise system commented

that the external support can prove to be the most

challenging factor for vendors. Similarly, Attewell

(1992) also pointed out that external support can

help SMEs to cross knowledge barriers.

Controls

The test of first control variable “company size”

showed different results in the three stages of

adoption, and reached to the significant level only in

the initiation stage, wherein a reverse correlation

appeared. That is, the smaller the enterprise size,

the higher the willingness in adopting broadband

mobile applications. This study suggests that

although the large enterprises have a higher

financial capability and more skilled employees, the

traditional bureaucracy may be disadvantageous to

initiate an innovative technology. In comparison,

SMEs have higher flexibility and more willingness to

try the innovative technology with relatively lower

difficulty level. However, the company size was

insignificant in both adoption and implementation

stages, which explained that once the company has

started the innovative process, the company sizes

show no significant influences in the latter stage.

The test of the second control variable has

different results from the first control variable.

“Industry type” reached to the significant level in

31

Chiu et al.

both initiation and adoption stages and failed in the

implementation stage. In addition, the result of the

analysis revealed that the service sector had a

higher adoption rate than the manufacturing sector.

This study suggests that unlike the big IT system

such as SCM or ERP, broadband mobile application

is not so sophisticated, and does not need to require

an information department or IT human resources in

order to operate, or even no longer need a desktop

or laptops to run the system, but through the more

convenient individual mobile devices that allow each

member of the enterprise to use it at any time and

location. Hence, the service industry relies more on

people’s direct contact and communicate, easy

access information and content delivery, has a

higher possibility and intention of adopting

broadband mobile applications in compare with

manufacturing sector. Thereafter due to the

approaching cognition, making the differences

between these two sectors gradually eliminate in the

implementation stage.

PRACTICAL IMPLICATIONS

This study discussed three aspects of the TOE

framework, namely technological, organizational and

environmental contexts having positive impact on

the broadband mobile applications adoption. There

are eleven factors explored, and the impact of the

two control variables i.e. company size and industry

type are also identified. All these results bring in

several critical insights for both enterprises and

broadband mobile applications providers. For the

enterprise, the management team should make sure

that the new application must be compatible with the

existing system, then gets involved with the change,

helps employees to absorb the information and

builds up the knowledge assimilating structure within

the enterprise. Continued supports from external

actors along with the willingness and ability of the

business supplier and buyer must also be taken into

account in order to ensure successful and effective

use. The results also showed that in attempts to

enhance customer communication, as well as to

reduce cost and increase profits, SMEs with higher

flexibility are more willing to adopt broadband mobile

applications; on the contrary, management level

from large enterprises must have the mindset to

change the attitude of acceptance in innovative

mobile broadband applications, in order to help

enterprises’ sustainable development. Similarly,

manufacturing companies must consider the

advantages of broadband mobile applications, and

invest more resources to keep up with rapidly

changing of mobile trends and to ensure long-term

competitiveness. Further, from the perspective of the

32

Chiu et al.

application providers, aside from good functional

design, the trial version and platform optimization

should be included in the product development and

marketing stage, so as to be more attractive and

accepted by the enterprise and the market. In

addition, after-sales support and knowledge-sharing

with the customers will increase the willingness to

adopt the application, and ensure clients’ successful

operation afterwards.

Theoretical contributions

The continuous improvement of technical

characteristics leads to more powerful mobile

applications into the market, and remains a key

element for sustainable development of enterprises

and industries. Yet, there exists few studies focused

on the topic from enterprise perspective, this study

aims to propose a research framework and

commence the empirical research, in an effort to

establish a preliminary basis for related research

and discussion subsequently. Then, this paper

combined the TOE framework with diffusion of

innovation theory, hoping to have a broader view

and extensive insights in environmental and

organizational aspects apart from technology

diffusion perspective, and then to develop a

reference model for the follow-up research. Finally,

this study applied company size and industry type as

control variables; as a result, it may also be useful

for the follow-up study in exploring the possibility of

different variables.

LIMITATIONS AND FURTHER DIRECTIONS

Because of the limitation of research scale, it was

hard to incorporate all the variables presented in

past literature of innovation technology.

Nonetheless, this study has still tried to achieve

unbiased results through data collection,

comparisons, and selections from related papers as

many as possible. Also, to keep on further in-depth

researching into distinct enterprises via the field

interviews to acquire an intensive perspective in the

adoption of broadband mobile applications will be

instructive. Finally, this research adopted the point of

view of overall mobile applications in consideration

of the understanding of the participants and

enterprises, thus, for applications with specific

characteristics such as voice, data, video or other

interactive features, the researches can be

continued hereafter considering customer's point of

view and value in the future.

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36

Chiu et al.

Appendix-I

Context Factors Definition References

Technology

Relative Advantage

An innovation is perceived as being

better than the idea it supersedes

(Rogers, 1995)

Ghobakhloo et al., 2011; Premkumar

and Roberts, 1999; Kendall et al.,

2001

Compatibility

An innovation is perceived as

consistent with the existing values,

past experiences, and needs of

potential adopters (Rogers, 1995)。

Beatty et al., 2001; Lian et al., 2014

Complexity

An innovation is perceived as

relatively difficult to understand and

use. (Rogers, 1995)。

Premkumar and Roberts, 1999; Al-

Jabri and Sohail, 2012

Trialability

An innovation may be experimented

with on a limited basis. (Rogers,

1995)。

Kendall et al., 2001; Al-Jabri and

Sohail, 2012

Observability

The results of an innovation are

visible to others. (Rogers, 1995)

Kendall et al., 2001; Al-Jabri and

Sohail, 2012

Organization

Information Intensity

Information intensity of product or

service measured by the currency of

information, reliability of information

and timeliness of information. (Thong,

1999)

Thong, 1999

Top Management

Support

Through business management, time

and resources investment and effective

execution, to resolve related issues.

(Alshamaila et al., 2013)

Premkumar and Roberts, 1999; Lin,

2014; Lian et al., 2014

Employees’ knowledge

The level of innovative technology

knowledge of the employee within the

organization. (Thong, 1999)

Rowe et al., 2012

Absorptive Capability

The organization can gain and apply

relative knowledge both from external

and internal, in order to creating a

profitable opportunity (Lin, 2014)

Lin, 2014

Environment

Competitive pressure

The level of pressure from

competitors within the same industry.

(Alshamaila et al., 2013)

Premkumar and Roberts, 1999;

Ghobakhloo et al., 2011

Business Partner

Relationship with suppliers and

customers. (Lin, 2014)

Ghobakhloo et al., 2011; Lin, 2014

External Supports

Supports from technical service

providers, training partners or relative

associations. (Stockdale and Standing,

2006)

Premkumar and Roberts, 1999;

Ghobakhloo et al., 2011

Government Supports

Government policy, measures or

incentives. (Dahnil et al., 2014)

Lian et al., 2014; Rowe et al., 2012;

Li, 2008

Control

Variables

Industry Type Service sector/ Manufacturing sector Oliveira and Martins, 2008

Company Size

Total number of the Employee (Zhu et

al., 2006a)

Lin, 2014

Table 1: Factor Definition and References

37

Chiu et al.

Appendix-II

Variables Question Item Loading

Factor

Loading

CR Value AVE

Relative Advantage

RA1 0.873

0.624 0.859 0.671

RA2 0.806

RA3 0.775

Compatibility

CP1 0.923

0.774 0.924 0.803

CP2 0.940

CP3 0.821

Complexity

CX1 0.804

0.405 0.718 0.499

CX2 0.883

CX3 0.269

Trialability

TR1 0.787

0.738 0.838 0.634

TR2 0.749

TR3 0.850

Observability

OB1 0.845

0.727 0.778 0.642

OB2 0.919

OB3 0.663

Information Intensity

II1 0.770

0.622 0.802 0.575

II2 0.789

II3 0.714

Top Management

Support

TM1 0.859

0.868 0.887 0.724

TM2 0.863

TM3 0.830

Employees’ knowledge

EK1 0.685

0.847 0.796 0.569

EK2 0.862

EK3 0.703

Absorptive Capability

AC1 0.821

0.925 0.786 0.553

AC2 0.754

AC3 0.646

Business Partner

BP1 0.724

0.912 0.823 0.609

BP2 0.833

BP3 0.780

External Supports

ES1 0.802

0.863 0.877 0.704

ES2 0.869

ES3 0.844

Competitive Pressure

CP1 0.866

0.915 0.785 0.703

CP2 0.933

CP3 0.699

Government Supports

GS1 0.709

0.972 0.758 0.511

GS2 0.723

GS3 0.713

Table 4: Measurement of Potential Variables of Loadings, CR Value and AVE

38

Chiu et al.

Appendix-III

Implementation

DOI

Theory

TOE

Framework

Initiation

H1a- Relative Advantage

H1b- Compatibility

H1c- Complexity

H1d- Trialability

H1e- Observability

T- Technology

H2a- Information Intensity

H2b- Top Management Support

H2c- Employees’ knowledge

H2d- Absorptive Capability

O- Organization

E- Environment

H3a- Competitive pressure

H3b- Business Partner

H3c- External Supports

H3d- Government Support

Adoption

Controls:

Company Size

Industry Type

ADOPTION

Figure 1: Research Model

39

Chiu et al.

Appendix-IV

Estimate S.E. C.R. P

H1a Relative Advantage <--- Technology 0.799

0.098

8.145

***

H1b Compatibility <--- Technology 1.223

0.133

9.191

***

H1d Trialability <--- Technology 1.095

0.131

8.38

***

H1e Observability <--- Technology 1.059

0.127

8.346

***

H2a Information Intensity <--- Organization 0.794

0.099

8.052

***

H2b

Top Management

Support

<--- Organization 1.746

0.196

8.918

***

H2c Employees’ knowledge <--- Organization 1.591

0.198

8.049

***

H2d Absorptive Capability <--- Organization 2.439

0.442

5.515

***

H3a Business Partner <--- Environment 2.215

0.349

6.347

***

H3b External Supports <--- Environment 1.708

0.195

8.752

***

H3c Competitive pressure <--- Environment 2.269

0.313

7.241

***

Table 6: Results of Regression Weights