IEEE Paper Template in A4 (V1)

International Journal on Advances in ICT for Emerging Regions 2022 15 (1):

June 2022 International Journal on Advances in ICT for Emerging Region

User Acceptance of a Novelty Idea Bank System to

Reinforce ICT Innovations: Sri Lankan University-

Industry perspective
Chaminda Wijesinghe#1, Henrik Hansson2, Love Ekenberg3

Abstract— Information and communications technology (ICT)

represents an enormous opportunity to introduce significant and

lasting positive changes across the developing world. Several

attributes determine behavioural intention to adopt the

technology. Some elements will stimulate end users' intention to

use the technology, while others are detrimental. This study is

designed to measure the relationship between various factors on

individuals' perceptions of adopting an ICT-based instrument to

stimulate ICT innovations. A model was developed by combining

the technology acceptance model and the diffusion of innovation

theory. A survey questionnaire was distributed among students

and teachers in higher education and industry professionals

working with university collaborations. A sample of 202

responses was analysed using structural equation modelling. A

good insight into user acceptance and the adoption of a systematic

model to reinforce ICT innovations are provided in this study

with the derived results. Theoretical and practical implications

for the factors influencing the acceptance of the system are

discussed.

Keywords— Collaboration; Diffusion of Innovation; ICT-
Innovation; Structural Equation Model, Technology

Acceptance; Knowledge Management.

I. INTRODUCTION

his study is designed to measure individuals' perceptions

of adopting an instrument to stimulate ICT innovations

through university-industry collaborations. This

instrument is intended to be a tool between the university and

industry to disseminate knowledge and ideas for innovation.

Knowledge and innovation are considered crucial sources for

sustaining the competitive advantage of organisations [1].

Knowledge generates value by supporting an

organisation's capability to produce innovation [2][3][4] learn

and transfer best practices across boundaries [5][6]. Successful

companies consistently create new knowledge, disseminate it

widely throughout the organisation, and quickly embody it in

new technologies and products [7]. Knowledge management

implementations require a wide range of quite diverse tools

that come into play throughout the knowledge management

cycle. Gressgård et al. [8] claim that using ICT-based tools to

promote external knowledge flow into the organisation may

help realise their innovation potential. Many ICT-based tools

have gained popularity as instruments for disseminating

knowledge for innovation [9]. Organisational knowledge and

ICT refer to a distinct set of constructs, and several variables

need to be considered when assessing the relationship between

ICT and knowledge management. The technology primarily

facilitates communication, collaboration, and content
management for better knowledge capture, sharing, diffusion,

and application [10].

Diffusion is "the process by which an innovation is

communicated through certain channels over time among the

members of a social system" [11]. It is a particular type of

communication in which messages are concerned with new

ideas. The nature of diffusion demands at least some sort of

heterophily between the two parties. Ideally, it can be in

education, social status, and the like. The ICT tool is the

communication channel in the present study, enabling the

exchange of messages between two groups or units [11].

Universities and industries can benefit from adapting proper

channels and tools for their collaboration [12]. Students

engaging in research can be widely accepted by the industry

and impact society. A need has been created for a systematic

collaborative platform where industry professionals and

academics can engage and share expert knowledge. In contrast,

academic supervisors can empower and guide students in

theoretical aspects and future directions. Innovation consists

of an interactive process where diverse expertise are combined

through communication amongst and across organisational

borders [13].

Further, firms can absorb ideas from suppliers, users, and

knowledge institutions, as this innovation process demands

interaction with many disparate actors. Adequate support

mechanisms can further accelerate such interactions. It is

evident that there are substantial barriers regarding UIC and

the motivation to interact systematically, and a significant

obstacle is the lack of interconnections of this nature.

A. The Idea Bank System

The Global Idea Bank (GIB) [14] is a platform designed to

support these interconnection activities. More precisely, GIB

is a web platform where individuals can submit, exchange,

discuss, and fine-tune fresh ideas to produce new innovations.

Organisations may use the idea bank to collect users' feedback

and enhance their ideation process. When paired with a unique

innovation strategy and methodology, the idea bank creates a

holistic innovation solution that allows organisations to

collectively generate and elaborate ideas. To determine the

worth of an idea, the idea bank uses a voting mechanism. The

idea bank's underlying premise is that if a large group of

individuals collaborate on a project or develop an idea, that

project or idea would ultimately improve the performance of

those who worked on it [15]. Figure 1 illustrates the GIB

concept.

Chaminda Wijesinghe is with Stockholm University and NSBM Green
University (bandara@dsv.su.se). Henrik Hansson, Love Ekenberg are

with the Stockholm University (henrik.hansson@dsv.su.se,

lovek@dsv.su.se).
Manuscript received: 26-10-2021 Revised: 29-03-2022 Accepted: 17-06-

2022 Published: 30-06-2022.

DOI: 10.4038/icter.v15i1.7236

User Acceptance of a Novelty Idea Bank System to Reinforce ICT Innovations 2

June 2022 International Journal on Advances in ICT for Emerging Regions

Fig. 1 Global Idea Bank concept.

GIB mainly addresses collaboration barriers between

universities, governments, companies, and societies to acquire

and implement valuable ideas exerted through problems. The

authors propose the idea bank as an IT platform that is much

about ideals as it is about ideas. This fact fuels innovation since

the essence of innovation is about changing the world

according to a particular vision or ideal [7]. Because of the

originality of the notion toward proliferating ICT innovations,

the idea bank system itself may be regarded as an ICT

innovation.

The early phase of innovation is frequently referred to as

"fuzzy" [15] because it works best when a collaborative system

fosters confusion, disruption, and the fortuitous discovery of

ideas. An essential feature of GIB is to be a backbone to

develop an organisation's innovation culture supporting the

fuzzy front end of innovation. Every individual must create an

account and log into the system. Individuals may use the

platform to submit items of interest to other users, search for

information, comment on information, and find specialists in

the organisation when needed, resulting in everyone being well

informed. The platform functions as a knowledge management

system in this way. It allows users to submit ideas and keeps

track of these ideas by enabling others to remark on them,

moulding them further, grouping them with similar ideas, and

accepting some type of vote mechanism to determine their

merit. When ideas in the system receive the most votes,

comments, votes on comments, views, "follows," "alerts,"

bookmarks, and related ideas uploaded, they are automatically

promoted. All ideas can be clustered depending on how they

match the United Nations' Sustainable Development Goals. If

an idea does not match any of these seventeen goals, a new

group can be created, or it can be added to the "other" category.

Students who create an idea in the system and expect financial

assistance can specifically mention the requirement. Other

collaborating partners can then support the idea's

commercialisation.

GIB will thus be a potential solution for disseminating

knowledge across different social systems and, more

importantly, among students to obtain potential opportunities

for innovation. However, it is essential to evaluate how users

accept such systems, as there is no prior use, especially in

universities and industries.

B. Problem Description

Universities and industries can benefit from adapting

proper channels and tools for their collaboration [12]. Students

engaging in research can be widely accepted by the industry

and impact society. A need has been created for a systematic

collaborative platform where industry professionals and

academics can engage and share expert knowledge.

Further, firms can absorb ideas from suppliers, users, and

knowledge institutions, as this innovation process demands

interaction with many disparate actors. Adequate support

mechanisms can further accelerate such interactions.

Universities and industries are ideal for sharing knowledge,

best practices, and ideas for innovation. However, it is evident

that there are substantial barriers to university-industry

collaboration (UIC) and the motivation to interact

systematically, and a significant obstacle is the lack of

interconnections of this nature. The above-explained Idea

Bank is a platform intended to solve interconnectivity partly.

However, understanding the acceptance of the idea bank

system as a tool for stimulating innovation under the purview

of UIC is an essential factor. The system is intended to be used

by a mixed group of participants. Most users will be young

undergraduates, and their desires may differ from industry

professionals. There may be deviations from the system's

functional expectations between academics and industry users.

Can we use the idea bank system with its existing features as

the communication channel between the university and

industry to stimulate innovations? Do such systems require

more influential features to be included? The full benefit of the

university-industry collaborative platform cannot be achieved

unless students and industry partners can use the system.

Individuals' behavioural intention to use the system may

depend on several important factors. The platform will be a

potential solution for disseminating knowledge across

different social systems and, more importantly, among

students to obtain potential opportunities for innovation.

However, it is essential to evaluate how users accept such

systems, as there is no prior use, especially in universities and

industries. Therefore, this study was conducted to measure the

factors influential on user acceptance of an Idea Bank system

as a source of innovation.

C. Related Works

Over the years, evaluating user acceptance of technologies

has become a popular topic in numerous disciplines. These

disciplines include eLearning systems [16][17][18][19],

mLearning [20][21] in universities, knowledge management

systems [1][22][23], e-commerce and m-commerce

applications [24][25][26], social networking sites [27][28],

professional networking sites [29], and other educational

applications such as the adoption of Google in education

[17][30]. The e-collaboration system was a rare application

type, and Dasgupta et al. [31] conducted a study about twenty

years ago in a similar discipline. However, the aim of

collaboration and collaboration technologies is drastically

different from today's technological advancements and the

nature of collaborations. Hence, the scope of the study was

limited to measuring user acceptance of e-collaboration

technology, where technology was limited to emails and chat

messages. However, a collaboration system between

universities and industries is different from all the above

systems. Since most system users are young undergraduates,

3 Chaminda Wijesinghe
#1

, Henrik Hansson
2, Love Ekenberg3

International Journal on Advances in ICT for Emerging Regions June 2022

their intentions to use the system may be different from

industry users. There is no study aimed to identify the factors

influencing this nature of collaborative system between

universities and industries aiming for innovation.

D. Theoretical Background

Innovation and technology refer to a distinct set of

constructs, and several variables need to be considered when

assessing the relationship between technology acceptance and

innovation. When identifying a theoretical model,

understanding human behaviour and the determinants of

intention are of concern. Several competing theoretical models

demonstrate human behaviour for the user acceptance of

technologies. Previous studies such as the theory of reasoned

action (TRA) [32], theory of planned behaviour (TPB) [33],

technology acceptance model (TAM) [34], extended

technology acceptance models TAM2 [35], TAM3 [36], and

unified theory of acceptance and use of technology (UTAUT)

[37][38] demonstrate theories in the relevant domain.

The TRA is designed to predict people's daily life

volitional behaviour and understand their psychological

behaviours [32]. The theory is more oriented toward

individuals' attitudes toward behaviours and subjective norms.

Subjective norms refer to attitudes toward social pressure to

perform a behaviour. According to the TRA, the determinants

of behavioural intention (BI) to use the system are attitudes

toward behaviours and subjective norms. However, a person

may not perform an activity even if motivated by positive

attitudes because of a lack of control over the person's actions.

Therefore, TRA is extended to TPB [33], including perceived

behavioural control as an additional variable. The TPB model's

problem is that a person's attitude toward using the computer

system becomes irrelevant if the computer system is not

accessible to that person. TAM is derived from TRA [39] by

eliminating the uncertain and psychometric status of

subjective norms, including two essential factors, perceived

ease of use (PEU) and perceived usefulness (PU), to determine

BI. Then, the TAM model is extended to TAM2 by including

the factors for social influence required when evaluating

systems beyond the workplace. Venkatesh and Morris [37]

introduced UTAUT by comparing differences and similarities

in eight theories relating to technology acceptance and derived

14 constructs from these eight theories, including effort

expectancy, performance expectancy, social influence, and

facilitating conditions significant constructs [39].

TAM has been considered a parsimonious and powerful

theory by the information systems community [40]. Moreover,

the three constructs PEU, PU, and BI used in TAM are more

relevant in this study than those used in other models.

Constructs used in other models, such as social norms, image,

job relevance, output quality, usage behaviour, result

demonstrability, and behavioural controls, are considered less

relevant because of the nature of the current study. TAM has

received substantial empirical support during the past decades,

and it has probably become the most widely cited model in

technology acceptance studies [39]. Therefore, the TAM

introduced by Davis [34] is considered more relevant for

evaluating the user acceptance of the LIB.

Structural equation modelling (SEM) is a multivariate
statistical modelling technique used to analyse structural
relationships [41]. Furthermore, this has become a standard

method for confirming or disconfirming theoretical models in
quantitative studies [42]. This technique combines factor
analysis as well as multiple regression analysis. It is used for
analysing structural relationships between measured and latent
variables (constructs). SEM has several benefits over more
conventional data analysis methods, such as the linear
regression model. The capability to account for measurement
errors when estimating effects, examine the model's fit to the
data, and construct statistical models that more closely agree
with the theory, have all been advantageous.

This paper is organised as follows: The second section

follows the introduction with a conceptual model and

hypothesis' development. The research methodology is

presented in the third section. In the fourth section, data

analysis and findings are provided. The fifth portion presents

the discussion and conclusion, while the sixth section presents

recommendations for further research.

II. CONCEPTUAL MODEL AND HYPOTHESIS

The study's conceptual model is presented by modifying

the TAM with the diffusion of innovation theory (DIT) [11].

TAM requires external variables to support PU and PEU, and

integration efforts are needed to understand better technology

adoption [40]. The DIT presented by Rogers [11] helps

understand important innovation characteristics. Among the

five constructs presented in DIT, relative advantage (RA),

compatibility (CO), and trialability (TR) were considered

more relevant to the current study. Moore and Benbasat [43]

suggested reducing the instrument based on the research

objective and organisational context. In the present study, the

system under investigation was a novel system, and people

other than the survey participants did not use it. Therefore, the

complexity and observability of the other two constructs are

considered less important in this context. The conjunction

between TAM and DIT has been used in many previous

studies in various disciplines, including e-learning systems and

mobile apps (e.g., [16][25][44][45]).

PU is defined as "the degree to which a person believes

that using a particular system would enhance his or her job

performance" [34]. PEU refers to the degree to which a person

believes that using a particular system would be free of effort

[34]. BI to use is known as the learners' choice of whether to

continue using the new system. This term is seen as a factor

that determines the use of technology.

In this study, PU refers to how a person believes that using

the system would enhance ICT innovation. Davis [34] claims

that an application perceived as easier to use than another is

more likely to be accepted by users. Based on this, the authors

of the current study propose that.

H1: Users' PEU is positively related to the BI to use the system.

H2: Users' PU is positively related to the BI to use the system.

H3: PEU is positively associated with PU.

The DIT explains how innovations are adopted within a

population of potential adopters. Everett Rogers first

developed the theory in 1962 based on the observations of 508

diffusion studies [11]. The DIT theory consists of crucial

elements, innovation, communication channels, time, and

User Acceptance of a Novelty Idea Bank System to Reinforce ICT Innovations 4

June 2022 International Journal on Advances in ICT for Emerging Regions

social systems. The characteristics of innovation perceived by

individuals help explain the different adoption rates measured

in 1). Relative advantage (2) Compatibility, 3). Complexity, 4).

Trialability, and 5). Observability [11] as mentioned

previously.

RA is the degree to which an innovation is perceived as

better than the idea it supersedes [11]. What matters in RA is

whether an individual perceives innovation as advantageous.

The rate of adoption is higher when the RA of innovation is

higher.

Compatibility refers to the degree to which an innovation

is perceived as consistent with the existing values, needs, and

past experiences of potential adopters [11]. An idea

incompatible with the current norms and values of a social

system will not be adopted rapidly as a compatible innovation.

Trialability refers to the extent to which people think that

they need to experience innovation before deciding whether to

adopt it. Trailable innovation tends to have less uncertainty

perceived by individuals who consider adopting it, and those

individuals tend to learn through this experience. In the current

study, this concept refers to how students view their use of the

proposed system as having a significant impact on their

innovation performance.

Based on the identified characteristics of DIT, the authors

propose that,

H4: RA is positively related to the PU of the system.

H5: RA is positively related to the PEU of the system.

H6: Compatibility is positively related to the PU of the
application.

H7: Trialability is positively related to PEU

Fig. 2: Combined theoretical model (TAM & DIT)

TAM is combined with DIT to check the perceived usefulness
and perceived ease of use of the system described by Davis [34]
in TAM. Figure 2 explains how DIT is combined with the
TAM to derive a hybrid theoretical model with the derived
hypothesis.

This study contributes to the literature as a model developed

with the diffusion of innovation theory combined with the

technology acceptance model to evaluate users' perception of

a newly developed tool for escalating ICT innovations. The

study also helps practitioners focus on the criteria in a

university-industry collaborative tool to stimulate innovations.

III. METHODOLOGY

This study aims to understand some factors and how they

contribute to the system's usability. We wanted a broad

response from various stakeholders to achieve a reasonable

idea of these factors. We designed a questionnaire distributed

to 300 individuals, including 250 students in higher education,

30 academic staff members, and 20 industry representatives.

The survey was conducted in two phases, with a pilot study

using a convenient sample of higher education students,

followed by the main study. The scope of the study was limited

to measuring user acceptance of e-collaboration technology,

where technology was limited to emails and chat messages.

A. Measurement of Items

The questionnaire was developed from an extensive

literature review to test the constructs used in this research's

theoretical model. A total of 34 questions (see Appendix A)

were modified to suit the context under study, including five

demographic characteristics, 13 TAM factors, and 16 DIT

factors. Among the 29 items used to measure TAM and DIT,

21 items were selected from Moore and Benbasat [43] as

follows: for RA, out of nine items, eight were selected, and the

deselected item measured the control over individuals' work,

which is considered less significant in the study; further, all

three items for compatibility, all five items for trialability, and

all five items for PEU were extracted and modified. The

remaining eight items for TAM were selected as three items

for PU, and two items for BI were selected and modified from

Venkatesh and Bala [36]. Two items for PU were selected

from Davis [34] and modified. Based on the literature, one

item (Appendix A: BI2) was created for this study. All scales

used in this study were adapted from the existing literature.

The items in the constructs were measured using a five-

point Likert scale with answer choices ranging from "Strongly

Disagree (1)" to "Strongly Agree (5), "previously validated

scales operationalise the constructs.

A short video was created to understand the system's

features and distribute it with the questionnaire. A link to the

system was created, and access was granted to all respondents

via guest login credentials. This process was required because

the system is a novel system, and the respondents were novice

users

B. Pilot Study

A pilot study, a small-scale rehearsal of the larger research

design, was conducted to identify potential issues and check

the measurement items. A convenience sample of 50 students

studying in the second year in a higher education institute in

Sri Lanka was selected, and the questionnaire was distributed

using Google forms. All the students are computing

undergraduates and are therefore considered highly ICT

literate. In the Google Form, the study's title, the purpose of

the survey, the objective, and the intended audience to fill the

pilot survey were mentioned. A text area was included for each

construct, and respondents were asked to write feedback on

each construct measurement item. One questionnaire item was

H6 Compatibil
ity (CO)

Trialability

(TR)

Relative

Advantage

(RA)

Perceived

Usefulness

(PU)

Perceived

Ease of

Use
(PEU)

Behaviour

Intention

(BI)

5 Chaminda Wijesinghe
#1

, Henrik Hansson
2, Love Ekenberg3

International Journal on Advances in ICT for Emerging Regions June 2022

rephrased to improve clarity based on the respondents'

feedback.

C. Main Study

After the pilot study, university undergraduates in

computing and software industry professionals representing

university collaborations were selected to distribute the refined

questionnaire. The respondents were purposefully selected as

all software companies in Sri Lanka do not have UIC, and all

undergraduates do not know about ICT innovations or

collaborations at the early stage of their university education.

Generally, in Sri Lanka, students engage in various industry-

related activities while studying at universities. Therefore,

when choosing undergraduates, those in the second year and

onwards were chosen because they are mature enough to

understand the system's requirements related to university-

industry collaborations. In the survey, a forced response option

was used in the main section after the demographic data

section to avoid missing values. Thus, all respondents could

only move to the next question by answering the current

question. Finally, the questionnaire was distributed among

software industry professionals, academics, and the above-

described students, using a google form. All respondents were

active in Sri Lanka, a South Asian country.

Related literature suggests that a minimum sample size of

100 to 150 is required in SEM [42][47]. A minimum sample

size of 150 is required when the number of constructs is less

than seven, with modest (0.5) item communalities and no

under-identified constructs [47]. According to Barclay et al.

[48] and Gaskin & Happell [49], one guideline for sample size

is that the sample should have at least ten times more data

points than the number of questionnaire items in the most

complex construct in the model. It becomes ten times the

number of predictors, either from indicators from the most

complex formative construct or the most significant number of

antecedent constructs leading to an endogenous construct,

whichever is larger. Since Schumacker & Lomax [42] claim

that these multiple values should be ten times or 20 times, in

the current study, the most complex regression involves the

formative construct with eight items requiring a minimum

sample size of 20 times 8, which equals 160.

The system is intended to be used by university students,

especially after the first year, academic staff members, and

industry professionals. Therefore, the questionnaire was

distributed among 300 survey participants, including 250

undergraduates enrolled in the second, third, and fourth years

of computer and IT-related degree disciplines. Twenty

industry participants worked in ICT-related industries, were

familiar with university engagements, and had 30 academic

staff members. The respondents' involvement in university-

industry collaboration was questioned in the questionnaire and

required to be answered. A total of 210 responses were

received, with eight incomplete responses. These eight

respondents did not attempt the main questionnaire and were

therefore excluded, and the remaining 202 responses were

used for data analysis.

IV. DATA ANALYSIS AND RESULTS

A. Demographic Analysis

The participants were almost equal in terms of sex. Among

the 201 participants,47.3% were men, and 52.2% were women.

One participant opted not to indicate their gender. The

majority of participants were between the ages of 18 and 25

(86.6%), and 9.4% were 25 to 30. The next highest age

category was 35–40, which was 2.5%. Only 1% were from the

age group 30 to 35 years, and only 0.5% were reported from

the age group 40 to 45 years. The total number of responses in

the age group was 202. Among the three categories of

participants as students in higher education, academic staff

members, and industry respondents, the majority of

respondents (87.6%) were students in higher education. The

second and third categories were almost equal, 6.4% and 5.9%,

respectively. 46.8% of participants had direct university-

industry interactions, and 28.9% of participants had no

interactions. There were 24.4% reported as they may have had

university-industry interactions with uncertainty. Among the

undergraduate respondents, 60.6% studied in the third year,

and 17.1% studied in the fourth year. There were 7.3% in the

second year. The remaining 15% of the participants responded

to none of the above categories. Students are given an

internship in the second semester of the third year in the higher

education institute selected for the survey. Students can

continue their internship or become employees after the

internship. Therefore, some respondents can become

undergraduates, and at the same time, they can be employees.

B. Model Analysis

The model analysis was conducted with Partial Least
Squares Path Modelling (PLS-PM), which was used in two
stages: 1) assessing the validity and reliability of the
measurement model and 2) assessing the structural model. The
Statistical Package for Social Science (SPSS) AMOS was used
to analyse the questionnaire's data.

1) Assessment of the Measurement Model: The
measurement model describes the relationship between
constructs (latent variables) and their measures (observed
variables) [50]. One of the primary objectives of SEM is to
evaluate the construct validity of the proposed measurement
model [41]. The validity of the measurement model depends
on the model fit for the measurement model and construct
validity evidence [41]. Kline [51] and Hair [41] emphasise the
guidelines to measure the goodness of fit and suggest reporting
model fit statistics as the minimum of model chi-square (χ2)
and its degree of freedom (df), root mean square error of
approximation (RMSEA), and comparative fit index (CFI).
The initial model test values are reported as RMSEA= 0.066,
which indicates a good fit, GFI= 0.799 was below the cut-off
value for a good fit, CFI=0.937 reported as a good fit,
TLI=0.928 reported as a good fit, χ2 = 747.072, df= 362, χ2
/df= 2.064 reported as a good fit. Except for GFI, all the values
are within the cut-off values for an accepted model fit (see
Table I). The considered GFI value for a good model fit should
be greater than 0.9 to ensure the construct's validity. Then, the
model modification was conducted by specifying covariances
for the error terms. The highest Modification Indices (MI)
were paired (see Figure 4), as higher values of MI indicated
item redundancy [52].

TABLE I.
THE THREE CATEGORIES OF MODEL FIT AND THEIR LEVEL OF ACCEPTANCE

(AFTER THE MODEL MODIFICATION) INDICATE AN ACCEPTABLE MODEL FIT.

User Acceptance of a Novelty Idea Bank System to Reinforce ICT Innovations 6

June 2022 International Journal on Advances in ICT for Emerging Regions

Note: RMSEA-Root Mean Square of Error Approximation, GFI-Goodness of

Fit Index, CFI-Comparative Fit Index, TLI-Tucker-Lewis Index, ChSq - Chi-
Square, df- degree of freedom

Individual item reliability was evaluated by examining

loadings. A value of 0.7 or more is considered an indication of
acceptable reliability [52][53]. One item from the relative
advantage (RA4) reported a low factor loading of 0.62, as
shown in Figure 3. An item with low factor loading means that
a particular item is deemed useless to measure that construct
[52] and is therefore removed. Figure 3 and Figure 4 show the
parameter values before and after the model modification,
respectively.

After the model modification, the reported values were:
RMSEA= 0.066 (no change), GFI= 0.824 (improved),
CFI=0.937 (no change), TLI=0.928 (no change), χ2 = 619.6,
df= 331, χ2 /df= 1.872 (improved). GFI and the χ2 /df values
improved after the model modification. Although the GFI
value does not reach 0.9, a value above 0.8, should be
considered a reasonable fit and acceptable, c.f.,.[54],[55].
Every construct has reported AVE's value greater than 0.5 (see
Table II), confirming the measurement model's convergent
validity [53].

Discriminant validity was conducted to ensure that the

measurement model had no redundant constructs. For

adequate discriminant validity, the diagonal elements in Table

II should be greater than the corresponding off-diagonal

elements in the rows and columns [42]. Therefore, the

measurement model was verified with discriminant validity,

indicating that all diagonal values were greater than their

corresponding off-diagonal values.

2) Reliability of the Measurement Model: Reliability
measures were assessed to verify the latent construct reliability
and internal consistency of the measurement model. The
reliability of the measurement model was examined using
composite reliability (CR) and Cronbach's alpha (CA) [42]. If
the values of CR and CA are greater than or equal to 0.7, it is
considered that the composite reliability for a construct is
achieved [53], and if CA is greater than 0.8, it is a good level
[56]. The results show that estimates for CR and CA range
between 0.8161 and 0.9142 (see Table II), indicating a good
reliability level.

The structural model was used for assessment when the
measurement model was satisfied and confident with the
constructs' reliability and validity.

3) Assessment of the Structural Model: The structural
model specifies relationships between constructs [50]. We use

the standard of Gefen et al.[47] (p.45). Table III is developed

to test each hypothesis's support by investigating the

endogenous latent variables' coefficient of determination (R2).

The critical ratio (CR), or t value, has become a popular

statistic for evaluating the structural model.

Fig. 3 Initial model showing factor loadings of the measurement model before modifications.

Note: TR- Trialability, RA- Relative Advantage, CO- Compatibility, PU- Perceived Usefulness, PEU- Perceived Ease of Use, BI- Behavioural Intention

Name of

Category

Name of

Index

Level of

Acceptance

Level

achieved

Absolute fit RMSEA RMSEA<0.08 0.066

GFI GFI>0.90 0.824

Incremental fit CFI CFI>0.90 0.937

TLI TLI>0.90 0.928

Parsimonious fit ChSq/df Chisq/df <3.0 1.872

7 Chaminda Wijesinghe
#1

, Henrik Hansson
2, Love Ekenberg3

International Journal on Advances in ICT for Emerging Regions June 2022

Fig. 4 Measurement model after modifications.

Note: TR- Trialability, RA- Relative Advantage, CO- Compatibility, PU- Perceived Usefulness, PEU- Perceived Ease of Use, BI- Behavioural Intention

TABLE II.

CORRELATION MATRIX SHOWING INTERNAL CONSISTENCIES AND CORRELATION OF CONSTRUCTS INDICATING MEASUREMENT MODEL'S DISCRIMINANT

VALIDITY AND RELIABILITY

Note: CA- Cronbach's Alpha, CR- Composite Reliability, AVE- Average variance Extracted.

TABLE III.
HYPOTHESIS TESTING RESULTS OF THE STRUCTURAL MODEL SHOW FIVE SUPPORTIVE AND TWO UNSUPPORTIVE.

Note: IV- independent variable; DV- dependent variable; SE- standard error; CR- critical ratio; PU- perceived usefulness; PEU-

perceived ease of use; RA- relative advantage; CO- compatibility; TR- trialability

Hypothesis IV Relation DV Estimate SE CR P-value Result

H1 PEU BI 0.892 0.156 5.723 0.000 Supportive

H2 PU BI 0.096 0.136 0.702 0.483 Unsupportive

H3 RA PU 0.403 0.150 2.680 0.007 Supportive

H4 RA PEU 0.621 0.118 5.265 0.000 Supportive

H5 CO PU -0.095 0.135 -0.708 0.479 Unsupportive

H6 PEU PU 0.626 0.105 5.975 0.000 Supportive

H7 TR PEU 0.323 0.100 3.231 0.001 Supportive

No of

items

Correlation of constructs Internal consistencies

RA CO TR PU PEU BI CA CR AVE

RA 8 1 0.902 0.9142 0.579723

CO 3 0.81 1 0.859 0.8626 0.676857

TR 5 0.78 0.84 1 0.894 0.8946 0.62959

PU 5 0.77 0.69 0.70 1 0.903 0.9044 0.654916

PEU 5 0.76 0.71 0.71 0.80 1 0.877 0.8766 0.587306

BI 3 0.80 0.75 0.74 0.76 0.77 1 0.817 0.8161 0.597344

International Journal on Advances in ICT for Emerging Regions 2022 15 (1):

June 2022 International Journal on Advances in ICT for Emerging Region

Among all seven hypotheses, five were supportive, and
only two were unsupportive, based on the results depicted in
Table III. Three hypotheses were significant at p<0.001, and
two hypotheses were significant at p<0.01. Perceived ease of
use has a positive effect on the behavioural intention to use the
system. (Β=0.892, p<0.001), while perceived usefulness had a
negative impact on behavioural intention (β=0.096, p>0.05).
This result implies that the ease of using the system is more
decisive for the behavioural intention to use the system than its
usefulness. Relative advantage has a positive effect on
perceived usefulness (β=0.403, p<0.01) and perceived ease of
use (β=0.621, p<0.001). Compatibility had a negative effect on
perceived usefulness (β=-0.095, p>0.05). Perceived ease of
use was positively affected by perceived usefulness (β=0.626,
p<0.001), and trialability had a positive effect on perceived
ease of use (β=0.323, p<0.01).

V. DISCUSSION AND CONCLUSION

The TAM has been used in various educational studies to

evaluate the acceptance of different technologies for education.

Among these studies examining the behavioural intention to

use learning management systems (LMS) [18][19] and other

e-learning platforms [16][17][45][57], mobile learning

systems [20], and e-collaboration systems [31] are considered

to be more relevant. The examined information system is

different from the above systems in terms of its collaborative

features between educational institutes and industries for

stimulating ICT innovations. Therefore, the study's theoretical

and practical implications bring new insights to researchers,

information system designers, and administrators at UIC.

A. Theoretical Implications

The TAM proposed by Davis [34] claims that an

individual's adoption of information technology depends on

perceived usefulness and perceived ease of use. Our results

provide limited support for the original TAM. First, perceived

ease of use positively influences perceived usefulness and

behavioural intention to use the system. Second, perceived

usefulness has a negative effect on the behavioural intention to

use. While the first result supports the original TAM, the

second result contradicts the original TAM. In comparing our

results with previous studies of TAM, behavioural intention to

use e-learning systems (e.g. [16][17][45]) and learning

management systems [18][19] have been influenced by

perceived usefulness and perceived ease of use. However,

investigating the behavioural intention to use an e-

collaboration system in another study [31], the authors

concluded that perceived usefulness has a negative

relationship with the use of an e-collaboration system. This

result contradicts the results of the original TAM. However,

perceived ease of use has a strong positive effect on the

perceived usefulness of the system, supporting the results of

the original TAM. However, the e-Collaboration system has

not radically changed over the last few years (ibid).

Consequently, advanced system users may be efficient users

who are familiar with the navigational structure of the system.

This contradictory situation is supported in two other studies

[26][28], and perceived usefulness does not influence

behavioural intention due to environmental factors.

The possible reason for the contradictory relationship in

our study may be that participants already know the usefulness

of an IT-supported system for ICT innovations. That is not

considered an extrinsic motivational factor. The result may

have also been affected because all participants had a strong

background in IT. However, the current study's findings are

consistent with the original findings of the TAM [34].

Moreover, it is evident that external factors derived from

the diffusion of innovation theory comply with the existing

similar studies [16][25] in the literature regarding their

relationship with TAM. The relative advantage greatly

influences the perceived ease of use and perceived usefulness.

This result implies that when users regard the idea bank system

as better than the traditional collaboration system or

approaches, they may perceive the Idea Bank system to be

more useful [24][44]. The factor trialability also positively

supports the perceived ease of use. However, factor

compatibility negatively affects perceived usefulness.

However, many studies (e.g.[16][24]) have shown a positive

relationship between compatibility and perceived usefulness

B. Practical Implications

The idea bank system has many features that contribute

positively to ICT innovation. The relative advantage of using

the idea bank system compared to other systems significantly

influences perceived usefulness and perceived ease of use.

Perceived ease of use is an assessment of the cognitive effort

involved in using the system. In such situations, users' focus is

on the interaction with the system and not on objectives

external to the interaction. Since the system is mainly intended

to be used by young undergraduates in universities, they will

be more focused on the ease of using the system than its

usefulness. Previous studies have shown that factors affecting

user acceptance may vary between hedonic and utilitarian

systems [58]. Higher demand for perceived ease of use is

expected in hedonic systems than perceived usefulness.

Heijden [58] suggested that developers include hedonic

features to invoke other configurations to achieve user

acceptance.

Systems trialability before deciding to purchase is a

significant factor in the ease of using the system. This result

implies that when users had more opportunities to try the idea

bank system, they could view it as easier to use [45]. Since the

idea bank system is quite a new system, it would be good to

make it available for users on a trial basis and grow the system

with ideas before deciding to purchase. Additionally, cognitive

absorption may also be experienced with visually rich and

appealing technologies when designing an information system

[59].

Diversity in societies and organisational structures may

challenge the use of information systems. While some factors

such as IT proficiency and experience promote the system's

ease of use, technology acceptance and intention to use may

be moderated by its rules, policy, and IT guidelines [60].

Generalisability is commonly accepted as a quality criterion

in quantitative research [61]. Adopting a modelling framework

(SEM) that allows for a variety of statistical models in the

study increases the study's credibility. The reliability of the

survey is high since it was preceded by a thorough literature

review to adjust the scales used in the study. Respondents were

carefully chosen to adequately represent academic staff,

industry, and students involved in the university-industry

partnership, assuring the internal validity.

9 Chaminda Wijesinghe
#1

, Henrik Hansson
2, Love Ekenberg3

International Journal on Advances in ICT for Emerging Regions June 2022

C. Conclusion

A good insight into the user acceptance and adopting a

systematic model to rein-force ICT innovations are provided

in this study with its derived results. Combining the

technology acceptance model and diffusion of innovation

theory derives varied results based on the system under

investigation. The significance of the current study is the

identification of factors affecting users' perceptions of using a

collaborative tool for stimulating innovation. This study fills

the gap in the literature by providing valuable features in such

a collaborative system, primarily when young students mainly

use it in universities. Since the system is intended to be used

primarily by young undergraduates, developers of such

systems are encouraged to use hedonic features.

The study presents several essential findings for researchers in

a similar domain, information system designers, and

university-industry partnership managers. Our main

conclusion is that the usefulness and behavioural intention to

use the system will be determined by how far the system use

is free of effort. Second, the relative advantage is an excellent

determinant of perceived usefulness and the perceived ease of

use of the system. The free effort to use the system is an

influential factor for behavioural intention to use the idea bank

system. The authors recall that the relative advantage is the

degree to which a new product is superior to an existing

product. Therefore, new products should be incorporated with

ease of use. These results indicate that the system becomes

more valuable by increasing the ease of use, and hence, the

behavioural intention to use the system can be improved.

Finally, since there has been no prior study that has evaluated

user acceptance of an Idea Bank system or university-industry

collaborative system aiming at innovation, the results of the

study are unique

VI. LIMITATIONS AND SUGGESTIONS FOR FUTURE
WORKS

One limitation of the study is that the respondents have not

used the system for an extended period to become familiar

with all system functionalities. That can make respondents feel

that the system's ease of use is more important than its

usefulness. Enthusiastic researchers in the same domain can

examine hedonic features in educational systems to

consistently use such information systems.

APPENDIX A

QUESTIONNAIRE ITEMS-PART I DEMOGRAPHIC

CHARACTERISTICS

Gender

Age group

Status of the respondent (Undergraduate/

Graduate employee/ non-graduate employee)

Involvement in university-industry interactions

If undergraduate: year of study

QUESTIONNAIRE ITEMS-PART II SYSTEM

CHARACTERISTICS

PERCEIVED USEFULNESS

PU1. Using the system would improve my innovation

performance.

PU2. Using the system would help to accomplish innovation

tasks more quickly.

PU3. Using the system would improve the quality of

innovation.

PU4. Using the system would enhance the effectiveness of

innovation activity.

PU5. I feel that the system is useful to increase innovations.

PERCEIVED EASE OF USE

PEU1. Learning to operate the system would be easy for me.

PEU2. My interaction with the system is clear and

understandable.

PEU3. I believe it would be easy to get the system to do what

I want it to do.

PEU4. It is easy for me to remember how to perform tasks

using the system.

PEU5. Overall, I believe the system would be easy to use.

BEHAVIOURAL INTENTION TO USE.

BI1. Assuming I had access to the system, I intend to use it.

BI2. I intend to recommend others to use this system for future

work.

BI3. For future work, I would use the system.

RELATIVE ADVANTAGE

RA1. Using this system enables me to accomplish innovation

tasks more quickly than before.

RA2. Using the system improves the quality of innovation

activities.

RA3. Using the system makes it easier to do innovation

activities.

RA4. The disadvantages of my using the system far outweigh

the advantages.

RA5. Using the system improves innovation performance.

RA6. Overall, I find that using the system will be

advantageous for innovation activities.

RA7. Using the system enhances the effectiveness of

innovation activities.

RA8. Using the system increases my productivity.

COMPATIBILITY

CO1. Using the system would be compatible with all aspects

of innovations.

CO2. I think that using the system would fit well with the way

I like to collaborate with Industry/University.

CO3. Using the system would fit into my work style.

TRIALABILITY

TR1. I have a great deal of opportunities to try various

applications in the system.

TR2. I know where I can go to satisfactorily try out various

uses of the system.

TR3. The system will be available to me to test various

applications adequately.

TR4. Before deciding whether to use any system applications,

I will be able to properly try them out.

TR5. I will be permitted to use the system on a trial basis long

enough to see what it can do. I am able to experiment with the

system as necessary.

User Acceptance of a Novelty Idea Bank System to Reinforce ICT Innovations

June 2022 International Journal on Advances in ICT for Emerging Regions

APPENDIX B - ACRONYMS

AVE Average Variance Extracted

BI Behavioral Intention

CA Cronbach's alpha

CFI Comparative Fit Index

ChSq Chi-Square

CO Compatibility

CR Composite Reliability

df degree of freedom

DIT Diffusion of Innovation Theory

DP Dependent Variable

GFI Goodness of Fit Index

GIB Global Idea Bank

ICT Information and Communication Technology

IT Information Technology

IV Independent Variable

LIB Local Idea Bank

MI Modification Indices

NIB National Idea Bank

PEU Perceived Ease of Use

PLS-PM Partial Least Squares Path Modelling

PU Perceived Usefulness

RA Relative Advantage

RMSEA Root Mean Square Error Approximation

SDG Sustainable Development Goals

SEM Structural Equation Modelling

SE Standard Error

SPSS Statistical Package for Social Science

TAM Technology Acceptance Model

TLI Tucker–Lewis's index

TPB Theory of Planned Behaviour

TRA Theory of Reasoned Action

TR Trialability

UIC University-Industry Collaboration

UTAUT Unified Theory of Acceptance and Use of

Technology

REFERENCES

[1] A. Elmorshidy, "The impact of knowledge management systems

on innovation: An empirical investigation in Kuwait," VINE J. Inf.
Knowl. Manag. Syst., vol. 48, no. 3, pp. 388–403, 2018, doi:

10.1108/VJIKMS-12-2017-0089.

[2] G. Ahuja and R. Katila, "Technological acquisitions and the
innovation performance of acquiring firms: a longitudinal study,"

Strateg. Manag. J., vol. 22, no. 3, pp. 197–220, Mar. 2001, doi:

https://doi.org/10.1002/smj.157.
[3] J. Darroch, "Knowledge management, innovation and firm

performance," J. Knowl. Manag., vol. 9, no. 3, pp. 101–115, Jan.

2005, doi: 10.1108/13673270510602809.
[4] K. Z. Zhou and C. B. Li, "How knowledge affects radical

innovation: Knowledge base, market knowledge acquisition, and

internal knowledge sharing," Strateg. Manag. J., vol. 33, no. 9, pp.
1090–1102, Sep. 2012, doi: https://doi.org/10.1002/smj.1959.

[5] M. T. Hansen, "The Search-Transfer Problem: The Role of Weak

Ties in Sharing Knowledge across Organization Subunits," Adm.
Sci. Q., vol. 44, no. 1, pp. 82–111, Sep. 1999, doi:

10.2307/2667032.
[6] G. Patriotta, A. Castellano, and M. Wright, "Coordinating

knowledge transfer: Global managers as higher-level

intermediaries," J. World Bus., vol. 48, no. 4, pp. 515–526, 2013,
doi: 10.1016/j.jwb.2012.09.007.

[7] I. Nonaka, "The Knowledge-Creating Company," Harv. Bus. Rev.,

vol. Nov-Dec, no. 1, 1991.
[8] L. J. Gressgård, O. Amundsen, T. M. Aasen, and K. Hansen, "Use

of information and communication technology to support

employee-driven innovation in organisations: A knowledge

management perspective," J. Knowl. Manag., vol. 18, no. 4, pp.

633–650, 2014, doi: 10.1108/JKM-01-2014-0013.
[9] P. H. J. Hendriks, "Many rivers to cross: From ICT to knowledge

management systems," J. Inf. Technol., vol. 16, no. 2, pp. 57–72,

2001, doi: 10.1080/02683960110054799.
[10] K. Dalkir, "Knowledge Management Tools," in Knowledge

Management in Theory and Practice, 2011.

[11] E. M. Rogers, Diffusion of innovations, Third Edition. 1983.
[12] E. Bellini, G. Piroli, and L. Pennacchio, "Collaborative know-how

and trust in university–industry collaborations: empirical evidence

from ICT firms," J. Technol. Transf., pp. 1–25, 2018, doi:
10.1007/s10961-018-9655-7.

[13] L. Johnston, S. Robinson, and N. Lockett, "Article information :,"

Int. J. Entrep. Behav. Res., vol. 16, no. 6, pp. 540–560, 2010, doi:
10.1108/JHOM-09-2016-0165.

[14] "global idea bank," 2019. https://ideabank.global/launch/ (accessed

Mar. 17, 2021).
[15] C. Sandström and J. Björk, “Idea management systems for a

changing innovation landscape,” Int. J. Prod. Dev., vol. 11, no. 3–

4, pp. 310–324, 2010, doi: 10.1504/IJPD.2010.033964.
[16] W. M. Al-Rahmi et al., "Integrating Technology Acceptance

Model with Innovation Diffusion Theory: An Empirical

Investigation on Students' Intention to Use E-Learning Systems,"
IEEE Access, vol. 7, pp. 26797–26809, 2019, doi:

10.1109/ACCESS.2019.2899368.

[17] R. Cheung and D. Vogel, "Predicting user acceptance of
collaborative technologies: An extension of the technology

acceptance model for e-learning," Comput. Educ., vol. 63, pp.

160–175, 2013, doi: 10.1016/j.compedu.2012.12.003.
[18] S. Alharbi and S. Drew, "Using the Technology Acceptance Model

in Understanding Academics' Behavioural Intention to Use

Learning Management Systems," Int. J. Adv. Comput. Sci. Appl.,
vol. 5, no. 1, 2014, doi: 10.14569/ijacsa.2014.050120.

[19] N. Fathema, D. Shannon, and M. Ross, "Expanding The

Technology Acceptance Model (TAM) to Examine Faculty Use of
Learning Management Systems (LMSs) In Higher Education

Institutions.," MERLOT J. Online Learn. Teach., vol. 11, no. 2, pp.

210–232, 2015.
[20] N. Wei and Z. W. Li, "Telepresence and interactivity in mobile

learning system: Its relation with open innovation," J. Open Innov.

Technol. Mark. Complex., vol. 7, no. 1, pp. 1–18, 2021, doi:
10.3390/joitmc7010078.

[21] L. Briz-Ponce and F. J. García-Peñalvo, "An Empirical

Assessment of a Technology Acceptance Model for Apps in
Medical Education," J. Med. Syst., vol. 39, no. 11, Nov. 2015, doi:

10.1007/s10916-015-0352-x.

[22] M. Del Giudice and M. R. Della Peruta, "The impact of IT-based
knowledge management systems on internal venturing and

innovation: a structural equation modeling approach to corporate

performance," J. Knowl. Manag., vol. 20, no. 3, pp. 484–498,
2016, doi: 10.1108/JKM-07-2015-0257.

[23] J. Xu and M. Quaddus, "Development of a Research Model of
Adoption and Continued use Exploring the factors Influencing End

Users' Acceptance of Knowledge Management Systems :," J.

Organ. End User Comput., vol. 19, no. 4, 2007.
[24] J. H. Wu and S. C. Wang, "What drives mobile commerce? An

empirical evaluation of the revised technology acceptance model,"

Inf. Manag., vol. 42, no. 5, pp. 719–729, 2005, doi:
10.1016/j.im.2004.07.001.

[25] S. Min, K. K. F. So, and M. Jeong, "Consumer adoption of the

Uber mobile application: Insights from diffusion of innovation

theory and technology acceptance model," J. Travel Tour. Mark.,

vol. 36, no. 7, pp. 770–783, 2019, doi:

10.1080/10548408.2018.1507866.
[26] R. Septiani, P. W. Handayani, and F. Azzahro, "Factors that

Affecting Behavioral Intention in Online Transportation Service :

Case study of GO-JEK," in Procedia Computer Science, 2018, vol.
124, pp. 504–512, doi: 10.1016/j.procs.2017.12.183.

[27] E. Constantinides, C. Lorenzo-Romero, and M.-C. Alarcón-del-

Amo, "Social Networking Sites as Business Tool: A Study of User
Behaviour BT - Business Process Management: Theory and

Applications," pp. 221–240, 2013, [Online]. Available:

http://dx.doi.org/10.1007/978-3-642-28409-0_9.
[28] P. Tantiponganant and P. Laksitamas, "An analysis of the

technology acceptance model in understanding students' behavioral

intention to use university's social media," Proc. - 2014 IIAI 3rd
Int. Conf. Adv. Appl. Informatics, IIAI-AAI 2014, vol. 12, pp. 8–12,

2014, doi: 10.1109/IIAI-AAI.2014.14.

[29] W. A. Khan Afridi, H. Hashim, and M. Kuppusamy, "The critical

11 Chaminda Wijesinghe
#1

, Henrik Hansson
2, Love Ekenberg3

International Journal on Advances in ICT for Emerging Regions June 2022

success factors of professional networking sites'adoption by

university students," Test Eng. Manag., vol. 82, no. 1–2, pp. 653–
673, 2020.

[30] R. S. Al-Maroof, A. M. Alfaisal, and S. A. Salloum, "Google glass

adoption in the educational environment: A case study in the Gulf
area," Educ. Inf. Technol., vol. 26, no. 3, pp. 2477–2500, 2021,

doi: 10.1007/s10639-020-10367-1.

[31] S. Dasgupta, M. Granger, and N. Mcgarry, "User Acceptance of E-
Collaboration Technology : An Extension of the Technology ...,"

Gr. Decis. Negot., vol. 11, no. 2, p. 87, 2002.

[32] M. Fishbein and I. Ajzen, Belief, Attitude, Intention and Behavior:
An Introduction to Theory and Research. Addison-Wesley, 1975.

[33] I. Ajzen, "From Intentions to Actions-TPB.1985.pdf," in Action

Control: From Cognition to Behavior, 1985, pp. 11–39.
[34] F. D. Davis, "Perceived usefulness, perceived ease of use, and user

acceptance of information technology," MIS Q. Manag. Inf. Syst.,

vol. 13, no. 3, pp. 319–339, 1989, doi: 10.2307/249008.
[35] V. Venkatesh and F. D. Davis, "A Theoretical Extension of the

Technology Acceptance Model: Four Longitudinal Field Studies,"

Manage. Sci., vol. 46, no. 2, pp. 186–204, 2000, doi:
10.1287/mnsc.46.2.186.11926.

[36] V. Venkatesh and H. Bala, "Technology acceptance model 3 and a

research agenda on interventions," Decis. Sci., vol. 39, no. 2, pp.
273–315, 2008, doi: 10.1111/j.1540-5915.2008.00192.x.

[37] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "USER

ACCEPTANCE OF INFORMATION TECHNOLOGY:
TOWARD A UNIFIED VIEW," MIS Q., vol. 27, no. 3, pp. 425–

478, 2003, doi: 10.1006/mvre.1994.1019.

[38] V. Venkatesh, J. Y. L. Thong, and X. Xu, "Unified theory of
acceptance and use of technology: A synthesis and the road

ahead," J. Assoc. Inf. Syst., vol. 17, no. 5, pp. 328–376, 2016, doi:

10.17705/1jais.00428.
[39] H. Taherdoost, "A review of technology acceptance and adoption

models and theories," Procedia Manuf., vol. 22, pp. 960–967,

2018, doi: 10.1016/j.promfg.2018.03.137.
[40] Y. Lee, K. A. Kozar, and K. R. T. Larsen, "The Technology

Acceptance Model: Past, Present, and Future," Commun. Assoc.

Inf. Syst., vol. 12, no. December, 2003, doi: 10.17705/1cais.01250.
[41] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson,

Multivariate Data Analysis, 7th ed. 2014.

[42] R. E. Schumacker and R. G. Lomax, A Beginner's Guide to
Structural Equation Modeling, 3rd edn, vol. Third Edit. 2010.

[43] G. C. Moore and I. Benbasat, "Development of an instrument to

measure the perceptions of adopting an information technology
innovation," Inf. Syst. Res., vol. 2, no. 3, pp. 192–222, 1991, doi:

10.1287/isre.2.3.192.

[44] S. C. Chang and F. C. Tung, "An empirical investigation of
students' behavioural intentions to use the online learning course

websites," Br. J. Educ. Technol., vol. 39, no. 1, pp. 71–83, 2008,

doi: 10.1111/j.1467-8535.2007.00742.x.
[45] Y. H. Lee, Y. C. Hsieh, and C. N. Hsu, "Adding innovation

diffusion theory to the technology acceptance model: Supporting
employees' intentions to use e-learning systems," Educ. Technol.

Soc., vol. 14, no. 4, pp. 124–137, 2011.

[46] F. D. Davis, "IT Usefulness and Ease of Use," MIS Q., vol. 13, no.
3, pp. 319–340, 1989.

[47] D. Gefen, D. Straub, and M.-C. Boudreau, "Structural Equation

Modeling and Regression: Guidelines for Research Practice,"
Commun. Assoc. Inf. Syst., vol. 4, no. October, 2000, doi:

10.17705/1cais.00407.

[48] C. Barclay, D., Thompson, R., dan Higgins, "The Partial Least

Squares (PLS) Approach to Causal Modeling: Personal Computer

Adoption and Use an Illustration," Technol. Stud., vol. 2, no. 2, pp.

285–309, 1995.
[49] C. J. Gaskin and B. Happell, "On exploratory factor analysis: A

review of recent evidence, an assessment of current practice, and

recommendations for future use," Int. J. Nurs. Stud., vol. 51, no. 3,
pp. 511–521, 2013, doi: 10.1016/j.ijnurstu.2013.10.005.

[50] J. C. Anderson and D. W. Gerbing, "Some Methods for

Respecifying Measurement Models to Obtain Unidimensional
Construct Measurement," J. Mark. Res., vol. 19, no. 4, p. 453,

1982, doi: 10.2307/3151719.

[51] R. B. Kline, Principles and practices of structural equation
modelling Ed. 4. 2015.

[52] Z. Awang, "VALIDATING THE MEASUREMENT MODEL:

CFA The measurement model of a latent construct," in SEM Made
Simple, MPWS, 2015, pp. 54–74.

[53] C. Fornell and D. F. Larcker, "Evaluating Structural Equation

Models with Unobservable Variables and Measurement Error," J.
Mark. Res., vol. 18, no. 1, p. 39, 1981, doi: 10.2307/3151312.

[54] H. Baumgartner and C. Homburg, "Applications of structural

equation modeling in marketing and consumer research: A
review," Int. J. Res. Mark., vol. 13, pp. 139–161, 1996, doi:

10.1016/j.ecolecon.2015.08.017.

[55] W. J. Doll, W. Xia, and G. Torkzadeh, "A confirmatory factor
analysis of the end-user computing satisfaction instrument," MIS

Q. Manag. Inf. Syst., vol. 18, no. 4, pp. 453–460, 1994, doi:

10.2307/249524.
[56] G. Ursachi, I. A. Horodnic, and A. Zait, "How Reliable are

Measurement Scales? External Factors with Indirect Influence on

Reliability Estimators," Procedia Econ. Financ., vol. 20, no. 15,
pp. 679–686, 2015, doi: 10.1016/s2212-5671(15)00123-9.

[57] I. A. C. Jimenez, L. C. C. García, M. G. Violante, F. Marcolin, and

E. Vezzetti, "Commonly used external tam variables in e-learning,
agriculture and virtual reality applications," Futur. Internet, vol.

13, no. 1, pp. 1–21, 2021, doi: 10.3390/fi13010007.

[58] H. van der Heijden, “User Acceptance of Hedonic Systems,” MIS
Q., vol. 28, no. 4, pp. 695–704, 2004.

[59] R. Agarwal and J. Prasad, "A Conceptual and Operational

Definition of Information Technology," Inf. Syst. Res., vol. 9, no.
2, pp. 204–215, 1998.

[60] P. Ajibade, "Technology acceptance model limitations and

criticisms: Exploring the practical applications and use in
technology-related studies, mixed-method, and qualitative

researches," Libr. Philos. Pract., vol. 2019, 2019.

[61] F. N. Kerlinger Lee, Howard B., Foundations of behavioral
research. Fort Worth, TX: Harcourt College Publishers, 2000.