Analysing the Factors Influencing Digital Technology Adoption in

Manufacturing Sectors: Leadership Effectiveness as a Mediator

AJAYANDARAN ARUMUGAM

Faculty of Business and Accountancy, Lincoln University College Malaysia,

MALAYSIA

HAMED KHAZAEI

Faculty of Malaysia Japan Institute of Technology,

University Technology Malaysia,

MALAYSIA

AMIYA BHAUMIK

Lincoln University College,

MALAYSIA

THAVAMARAN KANESAN

University of Northumbria,

Newcastle, UK

Abstract: The convergence of emerging technologies and commitments of manufacturing enterprises has

formed a trend in reshaping the global manufacturing landscape. Hence, electrical and electronic (E&E)

manufacturers should not be expected to ‘face it alone’ in the current manufacturing environment. Specifically,

manufacturing companies will profit from intelligent operational relationships with suppliers and government

programmes that promote and support the adoption and usage of advanced development tactics and technology.

Thus, the study aims to emphasize the influence of digital technology adoption on the E&E manufacturing

industry through the lens of leadership effectiveness. Additionally, the current study focused on identifying and

altering the dynamics of new technologies in the E&E manufacturing sector where nations are vigorously

competing for advanced manufacturing leadership.

Keywords: Perceived Cost Effectiveness, Digital Technology Adoption, Leadership Effectiveness, Digital

Literacy.

Received: June 19, 2022. Revised: September 19, 2022. Accepted: October 9, 2022. Published: November 11, 2022.

1 Introduction

The convergence of developing technologies and

manufacturing could completely transform the

global manufacturing scene. Considering the

constantly changing global development

environment, manufacturing organizations must

regularly evaluate their current technologies and

practices to remain profitable and competitive, [1].

The study aims to highlight the impact of digital

technology adoption in the electrical and electronic

(E&E) manufacturing industry through the

leadership effectiveness perspective. The

introductory chapter explains the current research

by establishing a brief history, problem statement,

aims, and definitions. Moreover, the current study

emphasized recognizing and influencing the

dynamics of emerging technologies in the E&E

manufacturing sector when nations are vying for

advanced manufacturing leadership.

Apart from the rapid development impacting

manufacturing and the intelligent revolution, digital

transformation is a step towards the integration of

digital technology, processes, and human

capabilities at multiple levels and functions within

an organization, industry, or ecosystem through

cultural, organizational, and operational change.

The digital transformation utilizes technology to

create value for multiple stakeholders (employers,

employees, and consumers) to expand and acquire

the capacity to respond immediately to changing

situations, [2]. Digital transformation concerns

technology and disruption and the employees’

interests, efficiency, and ability to adapt to the

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necessary intelligent use of technology and

information.

Although digital transformation is most prominent

in the industry, the aspect also affects other

organizations, including governments and public

and private sector entities that employ emerging

technologies to address social issues, such as

pollution and ageing populations. Additionally, the

digital technology revolution in several countries is

attempting to address all spheres of life through a

state-wide effort, [3]. Substantial studies have

examined the relationship between E&E

manufacturing firms and digital technology

adoption, but no definitive conclusions were drawn

due to a 'black box' effect. Macedo, [4] presented

conflicting findings on the elements influencing

employee acceptance of digital technologies.

Meanwhile, academics are encouraged to examine

the influencing factors that may increase digital

technology adoption to investigate the ‘black box’

of background interactions, [5]. Hence, ensuring

that digital technology stimulates and promotes

sound business processes is critical. Effective

executives are essential to guide E&E

manufacturing firms towards digital technology

adoption. Much scholarly discussion has been

based on leaders' readiness to integrate digital

transformation into their firms while encouraging

employees to accept the change, which is typically

considered a threat to the status quo, [6].

Additionally, past findings have fragmented and

dispersed leadership and digitalization

contributions, thus creating new obstacles for

leaders in organizational operational and

management perspectives on digital technology

adoption, [7]. Thus, the study aims to examine the

relationship between E&E manufacturing and

digital technology adoption through the leadership

effectiveness perspective and better understand the

micro-mechanism factors that most impact digital

technology adoption, [8].

2 Literature Review and Hypotheses

Development

The numerous functions that companies need to

play are underlined by the several theories formed

to justify organizational actions in varying

situations. Findlay and Thompson, [9] applied the

ideas to explore how organizations reacted to

digital technology adoption and observed that many

studies tend to focus “exclusively on a single

disciplinary domain, whether it be organizational

behavior, human resource management (HRM),

leadership, strategic management, finance and so

forth”. The study established two mainstream

management theories, [the Technology Acceptance

Model (TAM) and Diffusion of Innovation (DOI)]

to examine how organizations will respond to

introducing new technologies in E&E

manufacturing firms. The study of DOI and TAM

indicates that each theory has a different viewpoint

on how different sets of variables will impact firms'

digital technology adoption decisions in the

manufacturing sector, [10].

The DOI research is understudied, specifically

the role of usage and adoption of technically related

technology. Technology clusters define the

thresholds for technology acceptance, [11].

Innovations that concurrently spread are usually

interdependent and should be considered likewise

in adoption studies, [11]. The initial technology

adoption reduces confusion and improves the

opportunity to leverage a digital medium, [11].

New information technologies research examines a

specific technology where users do not apply

related developments in functionality, [12].

Practically, new technology or content solution,

specifically one deemed a desirable substitute, will

replace its comparable functionality. Technology

clusters impact assumed technical characteristics

and technology usage motives, [13]. Meyer 14]

stated that DOI research does not usually include

innovation clusters as they are more complex to

grasp than innovation despite its predictive validity.

Thus, a deeper view of a person's actions in

technological adoption could be achieved by

observing innovators who adopt various

innovations and assessing influential leaders’

influences.

The sub-section conceptualizes the study

variables based on past studies to ensure the current

study produces a specific, agreed-upon meaning for

the study concept. The study developed 11

hypotheses based on the in-depth literature review

to achieve the objectives as follows:

3 The Relationship between Perceived

Usefulness and Digital Technology

Adoption

Davis, [15] described perceived usefulness as “the

subjective possibility of a foreseeable user

increasing employees’ job performance in an

organizational context through the use of a

particular application system”. The notion defines

usability as the main predictor of usefulness and

purpose, [16] and an individual’s belief that the use

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of specific digital technology could enhance work

productivity, [17]. The importance of perceived

utility in technology has long been recognized,

[18], where technology could transform the way a

person works, which is a beneficial development.

Many studies demonstrate that perceived utility

impacts intentions to adopt emerging technology,

[10], [20]. Navimipour and Soltani, [21],

mentioned the perceived benefit of new technology

as a critical factor determining adaptation. Hence,

the more advantageous an organization perceives a

new technology to be, the more likely the new

technology will be implemented. The current study

examined the direct relationship between perceived

usefulness and digital technology adoption, hence

presenting the following:

H1: There is a significant relationship between

perceived usefulness and digital technology

adoption.

4 The Relationship between Perceived

Ease of Use and Digital Technology

Adoption

The term ‘perceived ease of use’ refers to the

“degree to which an individual is free of physical

and mental effort when utilizing a specific

technology”, [20]. Studies revealed that perceived

ease of use ensures that an individual agrees that

utilizing a complex instrument will be accessible to

that individual, [20], [21]. Wingo et al., [22]

identified a similar link between perceived ease of

use and employees’ capacity to understand

developing technology immediately. Additionally,

the elements that increase digital activities include

perceived ease of use, which combines simplicity

with simple internet connectivity, the availability of

secure and high-quality electronic equipment, and

the requirement for organizational resources.

Extensive studies over the past decade highlighted

the direct and indirect crucial impact of perceived

ease of use on the intention to use, [23]. Thus, the

study investigated the direct relationship between

perceived ease of use and acceptance of emerging

technology through the following hypothesis:

H2: There is a significant relationship between

perceived ease of use and digital technology

adoption.

5 The Relationship between Perceived

Cost Effectiveness and Digital

Technology Adoption

Dale and Plunkett, [24] defined cost-effectiveness

as the extent to which a person believes that using a

given item would be more expensive. Critical cost-

saving measures include the involvement in large-

scale digital technology initiatives or information

technology infrastructure establishment in the

hardware and software sectors. During the early

stages of new technology adoption, the perceived

value of developing breakthroughs is gradually

considered while business investment strategies are

being developed, [25]. Businesses often struggle to

appropriately incorporate developing technology

due to insufficient funds and local competence,

[26]. Corporations can eliminate wasteful spending

and reallocate funds to more valuable business

activities by understanding hidden infrastructure

costs.

Businesses should perform a cost-benefit

analysis to determine cost-effectiveness by

weighing the expenses of introducing new

technologies, procedures, or policies against the

realized benefits, [27]. Generally, a cost-benefit

analysis considers the monetary costs and future

gains linked with new technology adoption.

Nevertheless, cost-benefit analyses could create

potential risks connected with new technology

implementation that threaten workers or form non-

monetary benefits. Thus, the study examined the

direct relationship between perceived cost-

effectiveness and technology adoption, therefore

forming the hypothesis as follows:

H3: There is a significant relationship between

perceived cost-effectiveness and digital technology

adoption.

6 The Relationship between Perceived

Effective Communication and Digital

Technology Adoption

Persuading people to adopt a new technology

perspective from the top down is critical for any

organization and the most complex element. The

bureaucratic procedures are ingrained in the culture

and employees might be notoriously resistant to

change, which is a situation recognized by business

owners [28]. Additionally, although the digital

technology revolution has prioritized customer

service, staff engagement must remain a priority,

[29]. Another critical aspect is to communicate

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organizational values and how new technology

adoption benefits employees. Workers’ value is

obvious and fosters an environment conducive to

the seamless adoption of new technologies given

that employees benefit from a transparent

company, [30]. The type of ideal value alignment

increases competitiveness, stimulates innovation,

and introduces new technology, most notably in

areas undergoing an organizational upheaval.

Employees who believe they can support the

company mission statement would develop an

interest in their work and a desire to perform more.

Furthermore, considering that customers prefer

conducting business with firms that support causes

they care about, staff seek mutual ideals and

efficient connections, [31]. Resultantly, employees

may feel more empowered to form decisions about

digital technology. Thus, the study proposed a

direct relationship between perceived effective

communication and the performance as follows:

H4: There is a significant relationship between

communication effectiveness and digital

technology adoption.

7 The Relationship between Perceived

Digital Literacy and Digital

Technology Adoption

Employees with digital literacy could efficiently

identify, comprehend, and utilize digital technology

and facilities for digital tools identification,

navigation, control, incorporation, appraisal,

comprehension, and synthesis and to learn new

skills, establish media expression, and

communicate with others in the real world to

facilitate positive social action, [29]. Employees’

digital learning is critical as a source of expertise

for digital culture involvement. Moreover,

technologically skilled individuals should retain

sophisticated digital capabilities, the ability to

define computer interfaces, and computer network

skills.

The action enables people to examine data,

form logical inferences, and identify value-added

solutions. People can gain more familiarity with

emerging technological difficulties and raise their

ability to think critically about social issues. The

abilities required to succeed in a future global

economy would change dramatically. The changes

in business and educational culture and

governments worldwide have integrated digital

education into school curricula, [30]. The current

research analyzed the direct relationship between

employees’ digital literacy and their digital

technology use, thus forming the following

hypothesis:

H5: There is a significant relationship between

perceived digital literacy and digital technology

adoption.

8 Relationship between Leadership

Effectiveness and Digital Technology

Adoption

Researchers have recently investigated the

theoretical implications of leadership effectiveness,

which affects workers as a result of increasing

technologies, [31]. Although scholars have

explored technology use for leadership purposes,

such as connection, a comprehensive paradigm for

technology use in administration has not been

formed for over a decade, [32]. Neufeld et al., [33]

discovered that employees’ belief in successful

interaction indicates a chasm in their expectations

of leaders’ efficacy. Leaders and employees must

collaborate to ensure the success of developing

technologies within an organization, [34]. Past

studies have disclosed that administration

effectiveness varies significantly when leaders and

staff do not collaborate, [35]. Additionally, the

leader aids technical exchanges with employees

due to insufficient face-to-face connection, [36].

Venkatesh et al., [37], noted that changing

specific communications methods would have little

impact on the fundamental social structures linked

with leadership. Thus, the leadership effectiveness

approach to leadership provides the solution to the

challenge as it encourages leaders to use

technology to guide and inspire people to utilize

emerging tools to strengthen the business. Hence,

the study examined the direct relationship between

leadership effectiveness and the use of digital

technology through the following hypothesis:

H6: There is a significant relationship between

leadership effectiveness and digital technology

adoption.

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9 Mediating the Relationship between

Perceived Usefulness, Leadership

Effectiveness, and Digital Technology

Adoption

Consumer usage behaviors can be influenced by

perceived ease of use and perceived utility.

Perceived usefulness affects a person's confidence

and leadership, [38]. When evaluating the usage of

current technology, the assumed utility of an

information system was the most crucial factor.

New technology adoption was slightly higher than

the predicted correlation between the simplicity of

use and digital technology adoption, [20], [39],

[40]. Nonetheless, leaders have substantially

impacted how employees perceive new technology

as advantageous and how they utilize the

technology. Thus, attitude represents potential

employees’ readiness to understand and can thus be

positively connected with values and leadership

effectiveness, [41]. For the supposed value,

scholars have discovered that the leader's activities

indirectly impacted the acceptability of emerging

technologies, [42]. Incentives and value

interpretation are the primary motivating factors in

the adoption of developing technology in the

sector, [42].

The more straightforward the technology

operation, the more meaningful the employees’

sense of usefulness and personal autonomy, [43] in

terms of their ability to execute the sequences of

activities needed for the technology to function.

Nonetheless, leadership quality is a relatively

recent development and lacks investigation despite

the growing awareness of leadership efficacy

through the utilization of a digital technology

paradigm. Corporate leaders may be at a

competitive disadvantage if they lack training on

how to harness developing technology to influence

people, [44]. A lack of theoretical paradigm for

emerging technology leadership performance might

affect how digital technologies are implemented in

a company, [45]. Thus, leadership efficiency could

mediate the relationship between perceived ease of

use and digital technology adoption.

Van Laar, Van Deursen, Van Dijk and De

Haan, [46] listed several concerns surrounding the

application of emerging technologies including

cost-effectiveness versus leadership. Developing

technology may be a significant expense for

numerous firms and the leadership effectiveness in

digital technologies is critical in determining

organizational success, [47]. The corporation must

decide whether the capital budget is worth the

investment in the emerging digital technologies

considering that the extra value or the cost would

be better spent elsewhere, [48]. Moreover,

optimism about emerging advancements is the

main factor that motivates a manager to adopt

technology, [49]. Various studies described that

leadership performance and actions are integrated

into a single system, [50]. Nevertheless, the attitude

is influenced by various antecedent characteristics,

including technical skill, cognitive capacity, and

confidence in the necessity of technology.

Additionally, leadership in digital technology is

constantly developing in response to historical

variables, such as gaining a sound comprehension

of how digital networks operate or discovering new

digital technology applications, [51]. Therefore, the

more effective new technology is integrated into an

organization, the more likely the new technology

will be used in a perceived cost-effective manner.

The new technology adoption occurs at the

same rate as grapevines in an organization. As new

technology is introduced, all organizational

members must understand and participate in the

vision, priorities, and road map for the

transformation, [52]. Leaders must first consider

the overall view and the variables influencing the

growing technology adoption and transition in the

organization, then consider the methods and

expertise necessary to push others in the same

direction, [53].

The technology installation affects the

leadership comprehension and technology

acceptance, thus altering the form and classification

of adoption employed in education. New

technologies cause developments in digital literacy

and digital knowledge and transformed the format

of information distribution, [54]. Digital literacy

provides leaders and employees with the ability to

employ innovations for information analysis,

assessment, development, and collaboration that

entails the acquisition of non-cognitive and

technical abilities, [55]. Therefore, the emphasis

has shifted to the leaders who design a strategy for

introducing and promoting emerging technology

and digital technologies in the sectors, hence

altering fundamental business processes.

Consequently, the following hypotheses are

presented to test and strengthen the mediating

impact of leadership effectiveness on perceived

digital literacy and digital technology adoption:

H7: Leadership effectiveness mediates the

relationship between perceived usefulness and

digital technology adoption.

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h8: Leadership effectiveness mediates the

relationship between perceived ease of use and

digital technology adoption.

H9: Leadership effectiveness mediates the

relationship between perceived cost-effectiveness

and digital technology adoption.

H10: Leadership effectiveness mediates the

relationship between perceived communication

effectiveness and digital technology adoption.

H11: Leadership effectiveness mediates the

relationship between perceived digital literacy and

digital technology adoption.

10 Conceptual Framework

The framework below (see Figure 1) is employed

to convey the tested hypotheses.

Fig. 1: The Study Conceptual Framework

11 Data Collection

Kumar, [56] defines ‘respondent selection’ as the

set of population items where the sample will be

selected. Locating complete lists or documents

comprising all elements in the sample population is

challenging. The population sample unit was

identified as E&E manufacturing company

directors, managers, executives, supervisors, and

business leaders. The list of respondents was

gathered from the directories of the Malaysian

Manufacturers Federation (FMM) 2018 and the

Small Medium Enterprise (SME) Directory

Malaysia 2017. Information in both folders is

categorized by economic operation, company type,

product or service type sold, and geographic

region. Table 1 demonstrates the study population

including E&E companies in Malaysia selected for

data collection.

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Table 1. Respondent Selection

No

Selection of respondents

No. of

Company

1

FMM Directory

403

2

SME Directory

397

3

Overlapping between FMM

and SME Directories

(32)

Total Population Size

768

Note. FMM = Federation of Malaysian

Manufacturers; SME = Small Medium Enterprise

The analysis involved 768 E&E manufacturing

businesses where a sample size over 400 was

proposed for a population of 768. A convenient

sampling procedure was applied to randomly select

23 industrial practitioners from varying subsectors

in the automotive industry for the pre-test. In the

pilot study and primary data collection, probability

sampling and a systematic sampling approach were

applied to select respondents from FMM 2018 and

SME Directory Malaysia 2018 directories. Based

on the produced list, any odd number of E&E

manufacturing companies were chosen for the

survey. The following E&E manufacturing

companies were established after selecting the E&E

manufacturing SME if none of the selected E&E

manufacturing companies was contactable.

12 Instrument Development

Table 2 presents the operationalization of all

variables in the analysis. Wagner, Mendez,

Felderer, Graziotin, & Kalinowski, [57], described

operationalization as “developing specific research

procedures (operations) leading to empirical

observations representing those concepts in the real

world.” Wright, [58], stated that

“Operationalization is the process of precisely

delineating how to measure a construct; that is, the

variables to be specified in such a way as to be

potentially observable or manipulated.” Therefore,

all structures in the questionnaires were

operationally described to test the study

hypotheses.

Wright, [58] emphasized that the best response

scale can be clearly understood, ascertained, easily

interpreted, and has a response with minimal bias.

Therefore, a five-point Likert scale was used for all

study variables measured by strongly disagree,

disagree, neutral, agree, and strongly agree. The

Likert scale allows an item to be measured with a

scale ranging from negative to positive, [59].

Table 2. Questionnaire Development

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The study used an online survey (Google Form)

and email questionnaire to communicate with

respondents about technological innovation. The

justification for embracing online surveys and

email is immediate access to extensive

communities within a short timeframe, [59].

Furthermore, a web survey encourages information

collection from geographically scattered E&E

production firms.

An online survey provides an extensive range

of stylistic formats to present a questionnaire and

the option to filter the reasoning flow ensures

respondents’ uncertainty. Moreover, an online

survey minimizes missing answers by limiting

respondents to the next segment before finishing

the current section. Finally, using an online survey

produces fast data extraction and interpretation

research given that respondents’ responses are

registered in a database.

13 Findings

The data screening, reliability, and validity findings

are discussed below. The section also discusses the

partial least squares structural equation modelling

(PLS-SEM) results, which contain the

measurement and structural models including

results of descriptive analysis, the measurement

model assessment, the structural model assessment

and bootstrapping, and path analysis.

Demographic Background of Respondents

The study sample comprises 142 females and 269

males with most respondents between 31 to 40

years old. Moreover, most respondents were from

Kuala Lumpur, Labuan, and Putrajaya, Malaysia

(36.65 per cent). The respondents also graduated

from different disciplinary areas with a majority of

Degree holders (27.18 per cent) followed by

Master’s graduates (88 respondents). Most

respondents had working experience between two

to three years (27.67 per cent) (See Table 3).

Table 3. Respondents’ Demographic Background

Gender

Frequ

ency

Perce

ntage

Female

142

34.5

Male

269

65.3

Total

412

100.0

Age (years)

Frequ

ency

Perce

ntage

41 - 50

8

1.9

20 – 30

81

19.7

31 - 40

136

33.0

41 - 50

44

10.7

51 - 60

1

.2

States

Frequ

ency

Perce

ntage

Johor Baharu

44

10.68

Melaka

34

8.25

Penang

55

13.35

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Perak

24

5.83

Selangor

32

7.77

Wilayah Persekutuan (Kuala

Lumpur, Labuan, Putrajaya)

151

36.65

Education Qualification

Frequ

ency

Perce

ntage

Degree

112

27.18

Diploma

56

13.59

Bachelor

43

10.44

Masters

88

21.36

PhD

34

8.25

Secondary

33

8.01

Other

46

11.17

Marital Status

Frequ

ency

Perce

ntage

Married

198

48.06

Single

214

51.94

Working Experience

Frequ

ency

Perce

ntage

Under 2 years

56

13.59

2 - 3 years

114

27.67

4 - 6 years

94

22.82

7 - 8 years

74

17.96

9 - 10 years

33

8.01

> 10 years

41

9.95

Total

412

100.0

13.1 Missing Data Evaluation

Hair et al., [60] noted that “the researcher's first

concern should be to uncover the patterns and

linkages underlying the missing data to keep the

original distribution of values as closely as feasible

when any cure is implemented.” Typically, missing

data happens when a respondent fails to respond to

one or more questions, [60]. The two forms of

missing data are ignorable missing data where no

specific remedy is required and non-ignorable

missing data that occurs when respondents do not

complete the questionnaire in its entirety. Using the

Smart Partial Least Squares (SmartPLS) test, no

missing data were discovered in the data.

13.2 Evaluation of Outliers

The initial step in analyzing the latent variable

distribution is to identify outliers. Outliers are

observations that exhibit a specific mix of traits that

distinguish them from other observations, [60].

Outliers are defined as extremely high or extremely

low scores, which can produce data that are not

normally distributed and outcomes that are skewed

by unexpected or unrealistic data. When outliers

are identified, a decision to retain or delete the

cases must be made, [60]. The current study

applied the Box and Whisker (BoxPlot) approach

to identify outliers and discovered no outliers in the

data.

13.3 Normality Test

Kurtosis and Skewness statistics have been utilized

to determine the normality of data distribution. The

study used the statistical package for social

sciences (SPSS) version 24.0 for Windows to

conduct the tests. First, the most frequently utilized

critical value for the Kurtosis and Skewness tests is

2.58., [60]. The Kurtosis test confirms the data

normal distribution, while the Skewness test was

used to explain the distribution balance, [60].

Kurtosis and Skewness tests disclosed that all

variables in the study were normally distributed

(see Table 4).

Table 4. Descriptive Statistics

N

o.

Missi

ng

Mea

n

Medi

an

Standard

Deviation

Kurto

sis

Skewn

ess

PU1

8

0

4.05

4

1.153

0.828

-0.955

PU2

9

0

4.05

4

0.709

2.041

-0.898

PU3

10

0

4.03

4

0.741

1.628

-0.884

PU4

11

0

4.11

7

4

0.742

1.819

-0.908

PU5

12

0

4.29

9

4

0.68

1.446

-0.875

PU6

13

0

4.2

4

0.676

1.237

-0.693

PE1

14

0

3.89

8

4

1.027

0.689

-0.821

PE2

15

0

3.85

2

4

0.984

0.548

-0.791

PE3

16

0

3.93

9

4

0.983

1.136

-1.002

PE4

17

0

4.05

4

0.992

1.42

-0.917

PE5

18

0

4.01

5

4

1.008

1.022

-0.815

PE6

19

0

4.15

8

4

0.962

1.664

-0.896

PDL

1

20

0

3.96

1

4

0.993

0.839

-1.044

PDL

2

21

0

3.86

6

4

1.001

0.579

-0.928

PDL

3

22

0

3.95

1

4

1.071

0.755

-1.097

PDL

4

23

0

3.64

2

4

1.155

-0.323

-0.716

PDL

5

24

0

3.68

1

4

1.115

-0.262

-0.667

PDL

6

25

0

3.44

3

4

1.189

-0.526

-0.621

PCE

1

26

0

4.06

3

4

0.712

1.998

-0.823

PCE

2

27

0

3.86

9

4

1.147

0.501

-1.09

PCE

3

28

0

3.92

5

4

1.049

1.013

-1.18

PCE

4

29

0

3.88

6

4

1.03

0.702

-1.018

PCE

5

30

0

3.85

6

4

1.134

0.291

-1.012

PCE

6

31

0

4.07

5

4

0.719

2.869

-1.058

EO

C1

32

0

3.94

6

4

1.002

0.686

-0.999

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EO

C2

33

0

4.14

8

5

1.074

-0.057

-1.091

EO

C3

34

0

3.45

7

4

1.224

-0.545

-0.6

EO

C4

35

0

3.76

6

4

1.109

-0.847

-0.592

EO

C5

36

0

4.09

2

4

1.038

0.76

-1.209

EO

C6

37

0

4.01

4

0.979

1.59

-1.253

LE1

38

0

4.11

4

0.711

1.725

-0.822

LE2

39

0

4.16

3

4

0.669

1.455

-0.691

LE3

40

0

4.2

4

0.676

1.237

-0.693

LE4

41

0

3.49

6

4

1.113

-0.467

-0.368

LE5

42

0

4.09

5

4

0.734

1.025

-0.929

LE6

43

0

4.15

3

4

0.682

0.919

-0.665

TA1

44

0

4.12

2

4

0.704

1.739

-0.805

TA2

45

0

4.16

8

4

0.661

1.427

-0.655

TA3

46

0

4.21

2

4

0.667

1.215

-0.667

TA4

47

0

3.45

3

1.135

-0.48

-0.377

TA5

48

0

4.10

9

4

0.712

1.691

-0.813

TA6

49

0

4.16

8

4

0.664

0.853

-0.602

13.4 Internal Consistency Reliability

Internal consistency reliability testing assesses the

degree of consistency between numerous

measurements (indicators) of a construct, [61]. The

study used Cronbach's Alpha and composite

reliability to measure internal consistency.

Cronbach's Alpha is a broadly applied criterion for

evaluating internal consistency as it provides a

measure of dependability based on the observed

intercorrelation of indicator variables, [61].

Cronbach's Alpha indicates the instruments internal

consistency, which may be less than or over 0.7.

The results in Table 5 imply that the values for

Cronbach's Alpha are all greater than 0.70, which

ensures the instruments internal consistency, [60].

Table 5. Construct Reliability and Validity

Cronbach'

s Alpha

rho

_A

Composite

Reliability

Average Variance

Extracted (AVE)

Cost Effectiveness

0.721

0.7

04

0.778

0.506

Digital Technology

Adoption

0.800

0.8

06

0.862

0.555

Effectiveness of

Communication

0.711

0.7

69

0.813

0.526

Leadership

Effectiveness

0.811

0.8

17

0.869

0.569

P Digital Literacy

0.763

0.7

73

0.833

0.556

P Ease of Use

0.866

0.8

89

0.898

0.596

Perceived Usefulness

0.793

0.7

45

0.732

0.528

Composite reliability analysis considers the

indicator variable varying outer loading and does

not assume that every indicator produces the same

loading, [60]. A composite dependability value of

0.70 to 0.90 is considered satisfactory. A number

over 0.90 is undesirable as it indicates that all

indicator variables are redundant and hence the

constructed measure is unlikely to be genuine, [64].

13.5 Convergent Validity

Convergent validity is the extent to which a

measure correlates with alternative measures of the

same construct, [60]. Convergent validity can be

established by assessing an average variance

extracted (AVE). The AVE describes how a latent

structure determines the conflict of its measures,

[60]. Table 4.1 illustrates that all the AVEs are 0.5,

hence suggesting that the minimum requirement to

meet the convergent validity was achieved.

Therefore, at least 50% of each indicator variance

is explained by latent variables, [60].

13.6 Discriminant Validity

Discriminant credibility is how a concept varies

from other constructs, [60]. Therefore, forming

distinguishing validity means that an idea is

different and not represented in the proposed model

by other constructs, [60]. Table 6 indicates the

AVE square root to implicit correlation where all

constructs square root AVEs were higher than their

highest correlation with other constructs, [60].

Table 6. Discriminant Validity (Fornell-Larcker

Criterion)

Cost

Effect

ivene

ss

Digital

Technolo

gy

Adoption

Effectiven

ess of

Communic

ation

Leaders

hip

Effectiv

eness

Digit

al

Liter

acy

Eas

e of

Use

Percei

ved

Useful

ness

Cost

Effectiven

ess

0.621

Digital

Technolog

y

Adoption

0.478

0.745

Effectiven

ess of

Communi

cation

0.308

0.210

0.725

Leadershi

p

Effectiven

ess

0.472

0.972

0.206

0.755

P. Digital

Literacy

0.264

0.302

-0.029

0.297

0.67

5

P. Ease of

Use

0.199

0.130

0.342

0.143

-

0.00

3

0.7

72

Perceived

Usefulness

0.465

0.753

0.183

0.774

0.37

9

0.1

57

0.599

Cross-loading exhibited no lack of distinguishing

consistency if two concepts are ideally connected.

Meanwhile, the heterotrait-monotrait (HTMT) is an

aggregate of all indicator relationships around

indicators measuring the same construct relative to

average correlations of indicators measuring the

same structure, [60]. Thus, a disregarded

comparison between two constructs close to one

suggests that the concepts indicate a lack of

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distinguishing accuracy. Therefore, the HTMT

score should be less than 0.90, [60]. Table 7

demonstrates that all the HTMT scores were under

0.90.

Table 7. The HTMT values

Cost

Effe

ctive

ness

Digital

Technol

ogy

Adoptio

n

Effective

ness of

Commun

ication

Leader

ship

Effecti

veness

Digi

tal

Lite

racy

Ea

se

of

Us

e

Perce

ived

Usefu

lness

Cost

Effective

ness

Digital

Technolo

gy

Adoption

0.61

3

Effective

ness of

Commun

ication

0.70

8

0.262

Leadersh

ip

Effective

ness

0.62

7

1.207

0.253

P.

Digital

Literacy

0.33

3

0.370

0.098

0.362

P. Ease

of Use

0.62

2

0.149

0.431

0.161

0.06

8

Perceive

d

Usefulne

ss

0.63

5

0.935

0.302

0.954

0.54

4

0.1

79

13.7 The Structural Model Evaluation

The structural model assessment result determines

the model capability to predict target constructs,

[64]. The structural model assessment is

categorized into five components. First, identify

collinearity in the structural model. Second,

evaluate the structural model linkages importance

and relevance. Third, establish the coefficient of

determination level. The fourth step determines the

degree of impact size and the fifth step evaluates

the predictive significance.

13.8 Collinearity Assessment

A related measure of collinearity is the variance

inflation factor (VIF). A VIF of five and above

indicates a potential collinearity issue, [64].

Therefore, this study considers removing the

indicators with VIF of five and above, [64].

Nevertheless, the remaining indicators still capture

the construct content theoretically. Otherwise,

combine colinear indicators into one (new)

composite indicator. Multicollinearity can occur

due to a high correlation between two or more

additional independent variables, [61].

Consequently, a metric is required to quantify the

extent to which each independent variable is

affected by the collection of other independent

variables, [64]. The VIF and tolerance value are

two methods for determining multicollinearity. The

VIF was used to test for multicollinearity amongst

variables (VIF). Each construct VIF value should

be less than five to prevent multicollinearity, but

the VIF value is still acceptable if less than 10,

[61]. Tables 8 and 9 display that all the VIF values

are less than 5, which is acceptable.

Table 8. Inner VIF Values

Digital Technology

Adoption

Leadership

Effectiveness

Cost Effectiveness

1.583

1.538

Digital Technology

Adoption

Communication

Effectiveness

1.299

Leadership

Effectiveness

4.078

P. Digital Literacy

1.776

1.725

P. Ease of Use

1.305

1.303

Perceived

Usefulness

4.898

1.754

Table 9. Outer VIF Values

Factor

VIF

EOC1

1.208

EOC2

1.228

EOC3

1.890

EOC4

1.790

EOC5

1.773

EOC6

1.742

LE1

1.652

LE2

1.675

LE3

1.579

LE4

1.043

LE5

1.658

LE6

1.481

PCE1

1.328

PCE2

2.088

PCE3

2.114

PCE4

2.033

PCE5

1.878

PCE6

1.320

PDL1

1.568

PDL2

4.099

PDL3

1.693

PDL4

4.483

PDL5

1.381

PDL6

4.499

PE1

2.666

PE2

2.933

PE3

2.208

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PE4

3.711

PE5

3.959

PE6

2.039

PU1

1.448

PU2

1.396

PU3

1.282

PU4

1.279

PU5

1.221

PU6

1.488

TA1

1.629

TA2

1.657

TA3

1.547

TA4

1.038

TA5

1.595

TA6

1.429

Outer Loadings

Indicator dependability denotes the proportion of

indicator variance explained by the latent variable,

[61]. Manifest variables with an outer loading of

0.7 or over are considered highly satisfactory, the

values with an outer loading of 0.5 are deemed

acceptable, while outer loadings of 0.4 are regarded

as acceptable, [61]. Henseler et al., [62], suggested

that manifest variables with low loadings should be

considered for deletion. If omitting the signs

improves the composite dependability, they should

be deleted. The outer loadings for manifest

variables in the conceptual model are illustrated in

Table 8 with each loading exceeding. Most

loadings are deemed highly satisfactory and signify

that they fulfilled requirements for individual item

reliability. Nonetheless, the loading of less than 0.5

was deleted, [62]. Figure 2 depicts the structural

model after the low loadings were omitted.

Table 10. Outer Loadings

Communication Effectiveness

EOC1

0.232

EOC2

0.233

EOC3

0.671

EOC4

0.69

EOC5

0.788

EOC6

0.823

Leadership Effectiveness

LE1

0.754

LE2

0.769

LE3

0.768

LE4

0.242

LE5

0.766

LE6

0.706

Cost Effectiveness

PCE1

0.81

PCE2

0.602

PCE3

0.38

PCE4

0.308

PCE5

0.351

PCE6

0.756

Digital Literacy

PDL1

0.722

PDL2

0.855

PDL3

0.755

PDL4

0.885

PDL5

0.651

PDL6

0.909

Ease of Use

PE1

0.797

PE2

0.798

PE3

0.818

PE4

0.871

PE5

0.829

PE6

0.727

Perceived Usefulness

PU1

0.77

PU2

0.611

PU3

0.556

PU4

0.565

PU5

0.544

PU6

0.789

Digital Technology Adoption

TA1

0.753

TA2

0.766

TA3

0.76

TA4

0.208

TA5

0.755

TA6

0.683

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Fig. 2: The structural model after the low loadings

were omitted

13.9 Assessment of the Model Fit

The proposed structural model was assessed after

evaluating the measurement model validity and

reliability. The model was applied to derive

parameters that best predict endogenous

components from the sample data. The PLS lacks a

conventional goodness-of-fit measure, hence the

model quality was determined by its ability to

predict endogenous constructs. The coefficient of

determination (R2), cross-validated redundancy

(Q2), path coefficients, and effect magnitude (f 2)

all contribute to the judgment. The model

explanatory capability was tested using

bootstrapping, a technique that computes the

significance of Partial Least Coefficients (PLS)

coefficients using resampling approaches.

Digital technology adoption and leadership

effectiveness adjusted R-squared scores

(confidence interval bias-corrected) enable an

understanding of the amount of variance explained

by them and independent factors (see Table 11).

The resulting model enhanced predictability and R2

values (0.946 and 0.616). The PLS researchers

emphasised that including control variables

considerably diminishes the effect, regardless of its

importance, [61].

Table 11. R-Square Values

R

Square

R Square

Adjusted

Digital Technology

Adoption

0.946

0.945

Leadership

Effectiveness

0.616

0.611

The determination coefficient (R2) is a

function of statistical precision while the R2 is a

mixture, influence, and a discrete factor maximum

variability. The R2 ranges from 0 to 1 with higher

predictive accuracy levels. The R2 values should be

high enough for the model to achieve a minimum

explanatory power level. Additionally, Hair et al.,

[61], suggested that the R2 of endogenous latent

variables should be over 0.26 for a decent model.

The coefficient of determination (R2 value)

suggests the predictive accuracy of a structural

model and is derived as the squared correlation

between the actual and projected values of an

endogenous component. The R2 value implies the

variance proportion in endogenous constructs

explained by all exogenous constructs related to it.

The R2 value was between 0 and 1 with a value

closer to 1 indicating greater prediction accuracy

(see Table 11).

Table 12. Fit Statistics

Saturated Model

Estimated Model

SRMR

0.087

d_ULS

7.677

d_G

5.606

Chi-Square

7464.971

NFI

0.923

The root mean square residual (RMSR) is a

standardised root mean square residual (SRMR)

that is calculated by translating the sample and

projected covariance matrices into correlation

matrices. Additionally, Smart PLS calculates the

SRMR criterion bootstrap-based inference

statistics. The exact model fit was employed to

interpret the SRMR bootstrap confidence interval

results. The SRMR is defined as the difference

between the observed and implied correlation

matrix in the model, hence enabling the average

magnitude of actual and expected correlation

differences to be utilised as an absolute measure of

the (model) fit requirement. A number under 0.10

is deemed a satisfactory fit. The Structural

Equation Modelling or SEM analysis revealed that

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SRMR = 0.087, which is acceptable, [64]. SRMR

is a metric of model goodness of fit for PLS-SEM

that may be applied to prevent model

misspecification, [62]. The normed fit index as one

of the first fit measures in the SEM literature. The

index calculates the proposed model Chi-squared

value and compares it to a useful benchmark.

Considering that the proposed model Chi-squared

value does not provide adequate information to

determine model fit, the NFI uses the null model

Chi-squared value as a yardstick. Nevertheless, the

literature does not explain why the PLS-SEM Chi-

squared value is different from the Covariance

Based Structural Equation Modelling (CB-SEM)

value, [61].

The Normed Fit Index (NFI) is defined as 1

minus the suggested model Chi-squared value

divided by the null model Chi-squared value.

Consequently, the NFI produces values between 0

and 1 where a value that is closest to 1 indicates a

better match. The current study NFI score is 0.964,

which is more than 0.9 and suggests a satisfactory

match. he NFI computation of PLS route models in

detail. Nonetheless, the explanations are

complicated to comprehend for the practical user.

The NFI is a measure of incremental fit, thus a

significant disadvantage is that model complexity is

not penalised. The more parameters in the model,

the greater (and therefore more accurate) the NFI

result (see Table 12), [61].

The Level of Effect Size (f2) Assessment

The effect size can be evaluated via Cohen’s f2

(Cohen, 1998). The f2 evaluates the relative impact

of a predictor construct (independent variable) on

an endogenous construct (dependent variable).

Specifically, the f2 estimates how strongly one

exogenous contrast contributes to explaining a

specific endogenous construct. Cohen 1988)

mentioned that f2 values of 0.35, 0.15 and 0.02 are

considered large, medium, and small effect sizes

(see Table 13), [61].

Table 13. The f-Square Statistics

Digital

Technology

Adoption

Leadership

Effectiveness

Cost

Effectiveness

0.007

0.033

Digital

Technology

Adoption

Communication

Effectiveness

0.002

0.003

Leadership

Effectiveness

6.619

P. Digital

Literacy

0.003

0.000

P. Ease of Use

0.004

0.000

Perceived

Usefulness

0.000

0.916

13.10 Hypotheses Testing

After confirming the structural model validity, the

following stage evaluates the proposed structural

model path where each path corresponds to one of

the hypotheses. The study evaluated the

assumptions by applying a bootstrapping method

on subsample randomly generated (with

replacements) from the original dataset, [60]. The

sign, magnitude, and statistical significance of the

path coefficient between the latent variable (LV)

and its dependent variables were employed to

evaluate each hypothesis. The greater the route

coefficient, the more effective LVs are at affecting

the dependent variable. The proposed relationship

will be statistically significant at p < 0.05, [60].

13.10.1 Path Analysis

Bootstrapping ensures validity robustness and

outcome stability, [60]. After bootstrapping, the

structural model signified the strength of the

relationship between the independent factors and

the dependent variable through the route coefficient

estimates (see Table 14).

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Table 14. Path Coefficients

Origina

l

Sample

Sampl

e

Mean

Standard

Deviation

(STDEV)

T-

Statisti

cs

P-

Value

s

Cost

Effectiveness ->

Digital

Technology

Adoption

0.023

0.022

1.041

0.299

Cost

Effectiveness ->

Leadership

Effectiveness

0.334

0.137

0.045

2.991

0.003

Communication

Effectiveness ->

Digital

Technology

Adoption

0.211

0.012

0.016

0.726

0.042

Communication

Effectiveness ->

Leadership

Effectiveness

0.037

0.033

1.116

0.265

Leadership

Effectiveness ->

Digital

Technology

Adoption

0.962

0.963

0.024

40.148

0.000

P. Digital

Literacy ->

Digital

Technology

Adoption

0.203

0.013

0.010

1.374

0.010

P. Digital

Literacy ->

Leadership

Effectiveness

-0.006

-0.002

0.034

0.177

0.860

P. Ease of Use -

> Digital

Technology

Adoption

-0.015

-0.014

0.015

1.044

0.297

P. Ease of Use -

> Leadership

Effectiveness

-0.007

0.000

0.029

0.251

0.802

Perceived

Usefulness ->

Digital

Technology

Adoption

-0.007

-0.006

0.014

0.472

0.637

Perceived

Usefulness ->

Leadership

Effectiveness

0.70

8

0.70

7

0.037

19.37

8

0.0

00

Table 15 depicts a significant relationship

between Cost Effectiveness and Leadership

Effectiveness (Beta = 0.334, P-value < 0.05).

Moreover, Communication Effectiveness positively

influenced Digital Technology Adoption (Beta =

0.211, P-value < 0.05) while Perceived Digital

Literacy significantly influenced Digital

Technology Adoption (Beta = 0.203, P-value <

0.05). The results also indicated a direct strong

effect of Perceived Usefulness on Leadership

Effectiveness with a coefficient of 0.708 (P value =

0.000), [61]. Interestingly, the results presented a

strong relationship between Leadership

Effectiveness and Digital Technology Adoption

Beta = 0.962, P-value < 0.05).

Table 15. Indirect Effects

Origi

nal

Sam

ple

Sa

mpl

e

Me

an

Standar

d

Deviati

on

(STDE

V)

T

Stati

stics

P

Val

ues

Cost

Effectiveness ->

Digital

Technology

Adoption

0.12

2

0.0

25

0.015

1.51

3

0.0

13

Communication

Effectiveness->

Digital

Technology

Adoption

0.20

8

0.0

08

0.008

0.89

4

0.0

2

P. Digital

Literacy ->

Digital

Technology

Adoption

-

0.00

1

0.0

03

0.008

0.17

9

0.8

58

P. Ease of Use -

> Digital

Technology

Adoption

-

0.00

8

-

0.0

03

0.008

1.00

0

0.3

18

Perceived

Usefulness ->

Digital

Technology

Adoption

0.84

3

0.8

36

0.179

4.72

3

0.0

00

13.10.2 Mediation Analysis

Mediation occurs when a third variable is distorted

from exogenous variables by dependent ones. The

mediator regulates the relationship between

exogenous and endogenous variables, [64]. Tables

14 and 15 illustrate insignificant direct effects of

Cost-Effectiveness, Effectiveness of

Communication, Perceived Digital Literacy, and

Leadership Effectiveness on Digital Technology

Adoption. The indirect effects of Cost-

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Effectiveness, Effectiveness of Communication,

and Perceived Usefulness of Digital Technology

Adoption are also significant. Therefore, the results

indicated a full mediation of Leadership

Effectiveness on the relationship between Cost

Effectiveness and Digital Technology Adoption,

which signifies that the indirect influence of the

mediator, [61]. Full mediation implies that the

effect of Cost-Effectiveness on Digital Technology

Adoption is entirely transferred via another variable

Leadership Effectiveness. Additionally, the

condition Digital Technology Adoption entirely

absorbs the beneficial or detrimental impact of

Cost-Effectiveness, only under a specific condition

imposed by Leadership Effectiveness. The findings

also indicate a partial mediation of Leadership

Effectiveness on the relationship between

Effectiveness of Communication and Digital

Technology Adoption due to significant direct and

indirect influences on the DV. The direct and

indirect effects of Communication Effectiveness

point in the same (positive) direction, which

implies a complementary partial mediation, [61].

Thus, a component of Communication

Effectiveness effects on Digital Technology

Adoption was mediated by Leadership

Effectiveness, while Effectiveness of

Communication still adequately explained a portion

of DV independently of Mediation.

Similar to Cost Effectiveness, the results

suggested a full mediation of Leadership

Effectiveness on the relationship between

Perceived Usefulness and Digital Technology

Adoption. Table 16 demonstrates the Hypotheses

Testing results summary.

Table 16. Hypotheses Testing Summary

Hypothesis

Result

H1:

There is a significant

relationship between

perceived usefulness and

digital technology adoption.

Unsupported

H2:

There is a significant

relationship between

perceived ease of use and

digital technology adoption.

Unsupported

H3:

There is a significant

relationship between

perceived cost-effectiveness

and digital technology

adoption.

Unsupported

H4:

There is a significant

relationship between

communication

effectiveness and digital

Supported

technology adoption.

H5:

There is a significant

relationship between

perceived digital literacy

and digital technology

adoption.

Supported

H6:

There is a significant

relationship between

leadership effectiveness and

digital technology adoption.

Supported

H7:

Leadership effectiveness

mediates the relationship

between Perceived

Usefulness and digital

technology adoption.

Supported

H8:

Leadership effectiveness

mediates the relationship

between Perceived Ease of

Use and digital technology

adoption.

Unsupported

H9:

Leadership effectiveness

mediates the relationship

between perceived cost-

effectiveness and digital

technology adoption.

Supported

H10:

Leadership effectiveness

mediates the relationship

between perceived

communication

effectiveness and digital

technology adoption.

Supported

H11:

Leadership effectiveness

mediates the relationship

between perceived digital

literacy and digital

technology adoption.

Unsupported

14 Discussion

Malaysia developed a standard definition of SMEs

endorsed by the National SME Development

Council (NSDC) and adopted by all Ministries and

agencies, financial institutions, and regulators

involved in SMEs development programmes. The

creation and acceptance of a common definition of

SMEs are essential for aiding better identification

of SMEs across sectors and for the formulation and

execution of more effective SMEs policies and

development initiatives. Additionally, the definition

could provide a more precise assessment of the

contribution and SMEs advancement to the

Malaysian economy. The SME is a term that varies

by country, while SME characteristics include the

number of employees, invested capital, total assets,

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annual revenue, production capacity, and average

income. The respondents stated that the path

analysis results indicate no significant relationship

between perceived ease of use and technology

adoption. (P = 0.297).

Perceived usefulness assessed intrinsic

technological characteristics and was predicted to

impact the intended utilization only for activities

that are inherent to the technology- when the

technology performs the fundamental task activity.

The study presented that perceived utility is a

dynamic construct with varying degrees and

consequences based on the type of technology

used. Interestingly, the findings demonstrated that

perceived usefulness indirectly affected technology

adoption via the mediating role of leadership

effectiveness. The findings could contribute to

understanding a vital TAM research area, namely

when and why perceived usefulness affects the

intention to adopt technology in the manufacturing

business.

The respondents also mentioned that the path

analysis results suggested no significant

relationship between perceived ease of use and

technology adoption. (P = 0.860). Perceived ease of

use denotes the extent to which an individual is free

of physical and mental effort when operating a

specific technology, [20]. A simple-to-use

programme has a greater probability of being

approved by workers. Employee opinions of ease

of use signify how much effort is needed to use

automated technologies or how simple they are to

use, [20]. Ease of use is crucial to embracing and

utilize modern technologies. Davis, [20] proposed

TAM to examine the relationship between relative

advantage (perceived utility) and complexity in the

adoption process (perceived ease of use).

Additionally, perceived ease of use is a function of

users’ perceptions of the technological complexity,

which influences technology adoption and

highlights compatibility as an essential element

determining technology adoption. Innovation in

research has been defined in various ways,

including its uniqueness or exclusivity,

compatibility with adopters’ norms and skills, the

clearly visible benefits provided by innovation, the

visibility of innovation in society, uncomplex

technology use, and ease of trial before adoption.

Hence, individuals’ attitudes about technology use

may vary based on their demographic background.

Resultantly, several innovation characteristics may

affect the pace of adoption in numerous businesses

or cultures.

Cost-effectiveness is defined as the degree to

which an individual perceives that utilizing a

particular device will incur additional costs. Cost-

cutting solutions require the adoption of full-scale

digital technology or the establishment of an

information technology infrastructure in the

hardware and software domains. During the early

stages of new technology adoption, the perceived

value of developing breakthroughs is gradually

considered when business investment plans are

being developed, [24]. Nonetheless, SEM research

revealed no significant relationship between

perceived cost-effectiveness and digital technology

adoption in the Malaysian manufacturing business.

Therefore, the third hypothesis is rejected.

The findings indicated a significant relationship

between communication effectiveness and digital

technology adoption. Consequently, the fourth

hypothesis is confirmed. Other scholars concur

with this conclusion, [63]. Dale and Plunkett, [24]

described cost-effectiveness as an individual’s

belief that using a particular device would be

costly. Cost-cutting measures demand the adoption

of full-scale digital technology or the establishment

of an information technology infrastructure in the

hardware and software sectors.

Communication is a vital aspect of all

managerial tasks. Additionally, the capacity to

communicate well with people is a necessary skill.

Communication enables employees to obtain a

better understanding of one another, develop an

affinity for one another, influence one another,

form trust, and learn more about themselves and

how others view them. Effective communication is

a crucial duty for every organization. Rapid and

effective communication among the company

numerous sectors promotes organizational

flexibility, competency, and responsiveness to

change.

The results indicate that when an organization

suffers from an internal communication

breakdown, the way information is conveyed from

sender to receiver is normally disrupted.

Resultantly, when an organization undergoes

technological change, everyone will experience

difficulty to accomplish their goals efficiently.

Additionally, leaders’ main function should be to

disseminate knowledge to their subordinates. If

employees are not informed of changes, they risk

delivering false information to their coworkers. The

technology adoption process involves

communication hurdles that lead to

misunderstanding and uncertainty. Hence, the aim

should be to minimize the occurrence of the

barriers at each stage of the process through the

application of clear, brief, accurate, and well-

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planned communications to produce a successful

communicator and convey your message without

misunderstanding or confusion, [63]. Professional

communicators could form relationships with

coworkers and business associates, thus facilitating

the completion of changes promptly and

successfully. Another necessary requirement is

experienced personnel capable of resolving

conflicts and encouraging teamwork while

achieving the objective, [47]. Apart from errors and

missed deadlines, a lack of good communication is

at the root of a slew of other serious workplace

concerns, including low employee morale and poor

job performance.

The findings imply a considerable relationship

between perceived digital literacy and digital

technology adoption. The conclusion is consistent

with the findings of other studies, such as

suggested that developing an individual's digital

literacy enables them to be technology-driven or

project-based, [69]. Digital literacy workers are

individuals who are proficient at identifying,

comprehending, and utilizing digital technology

and facilities for the identification, navigation,

control, incorporation, appraisal, comprehension,

and synthesis of digital tools, and the acquisition of

new skills, media expression, and communication

with others in the real world to facilitate positive

social action, [55].

The DOI ‘technology adoption lifespan’

provides a glimpse of how humans view new

technologies. The theory describes that early

adopters are the first to discover novel applications

for new technologies. Prior to innovations reaching

the mainstream, they are tried, tested, accepted,

rejected, or changed by a so-called ‘digital elite.’

Therefore, asymmetric epistemologies might form

asymmetric literacies. Thus, the reason that

adoption behaviors are linked with technology-

mediated practices is distinct and may demand the

development of new identity-related literacies.

Activities mediated by technology differ from

other activities due to the community component.

Traditional literacy is based on a scarcity-based

educational philosophy and exclusionary beliefs,

which was initially conducted for positive reasons:

believing in meritocracy necessarily leads to the

exclusion of ‘everyone but the finest.’ When an

organizational goal is to incorporate new or

unknown technology, digital literacy scarcity is a

major concern. Thus, digital literacy enables

employees to understand logical inferences and

identify value-added solutions provided by

technology and facilitates its adoption. Many

businesses are gaining a holistic view of their

digital workers.

Stakeholders will make no concessions on their

course of action and there may be no reliable or

objective information about the company digital

staff available. Significant firms globally and the

government purchase the digital workforce.

Malaysia has regained interest while the

commercial enterprise has maintained its presence

in the region. Malaysians are currently accountable

for ensuring that the citizens play a crucial role in

developing and promoting digital technology skills

globally and investing in cutting-edge digital and

creative technologies from global ICT businesses.

The qualities required to prosper in a future global

economy will undergo major changes.

The findings indicated that leadership

effectiveness substantially impacted digital

technology adoption in Malaysian industrial

enterprises. Neufeld et al., [33] discovered that

employees’ belief in successful engagement

reflects a gap in their expectations of leaders’

efficacy. Resultantly, comprehending the

communication efficiency networks influences the

decision to incorporate new technologies into the

leadership model.

When a leader places a premium on in-role

behavior and efficiency, employees adopt a highly

disciplined approach to technology to maximize

their output quantity and quality. The leaders are

expected to operate the system in a specific way to

limit an individual's exposure to technological

complexity. If a CEO supports innovation and

openness, employees will develop a greater

tolerance for experimenting with new technologies

and procedures and catch up on features quicker.

Considering that past learning experiences foster

the development of latent inventiveness, the newly

presented technology could be simpler to utilize.

Although leadership styles are abstract and vast

in scope, organizational facilitators can be more

specific activities made by a leader or implemented

within an organization. Conditions and events that

promote technology adoption, such as training and

education and organizational technical assistance

could be considered parts of organizational

facilitators. Training, knowledge, and technical

support can affect an individual's ability to apply

technology effectively or find it simple to use.

Hands-on sessions and feedback could illustrate

technological capabilities and features, hence

influencing perceived usefulness.

In reality, leaders and employees must

collaborate to ensure the success of developing

technologies within an organization, [64]. Past

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research also demonstrated that when leaders and

staff do not collaborate, administration

effectiveness differs significantly, [35] where

evolving platforms and additional considerations

are required for leaders and people to effectively

embrace digital technology, [49].

The results presented that leadership

effectiveness mediated the relationship between

perceived usefulness and digital technology

adoption. Meanwhile, the TAM predicted that

factors indirectly affected behavior through their

influence on perceived usefulness, perceived ease

of use, or their relative weights, [20]. The findings

also revealed that leadership effectiveness mediated

the influence of perceived usefulness on digital

technology adoption. Therefore, leadership

effectiveness in Malaysian manufacturing sectors

plays a critical role in digital technology

acceptance behavior. In terms of perceived

usefulness, employees may consider it more helpful

to work with if a leader communicates the benefits

of technology by emphasizing that the system is the

only method to achieve defined goals. The

transactional leadership style is cost-effective,

which may disclose technology utility, given that

technologies are frequently introduced for cost-

cutting purposes. Another strategy employed by

transformational leadership encourages employees

to be more creative and inquisitive. Consequently,

people are more likely to grasp the technology

utility.

Leadership Effectiveness Mediating Effect

The results indicated no mediation effect of

leadership effectiveness on the relationship

between digital technology perceived ease of use to

digital technology adoption in the Malaysian

manufacturing sector employees. The results also

suggested that leadership effectiveness mediated

the relationship between perceived cost-

effectiveness and digital technology adoption.

Summarily, effective leadership performance for

emerging innovations is the main variable

encouraging the company to utilize technology.

Past studies have debated that leadership

performance and actions are a single system, [65].

Nonetheless, the notion is affected by numerous

antecedent variables, including technical expertise,

cognitive capacities, and confidence that the

technology is required for a specific reason.

Furthermore, digital technology leadership is

constantly evolving in response to historical

factors, such as acquiring more knowledge about

digital network operation or seeking new digital

technology applications, [65]. Resultantly, the more

effective leadership incorporates new technology,

the more likely it will be utilized in a perceived

cost-effective organization. Thus, the study

proposed to test the mediating influence of

leadership effectiveness between perceived cost-

effectiveness and digital technology acceptance.

The results presented that leadership

effectiveness mediated the relationship between

perceived communication effectiveness and digital

technology adoption. Meanwhile, leadership

effectiveness mediated the relationship between

perceived communication effectiveness and digital

technology adoption. Most research on the

influence of leadership traits on business

technology adoption originated in the literature on

strategic management and organizational behavior,

[65]. Meanwhile, many leadership attributes have

been analyzed in the context of organizational

innovation, only a few have been consistently

proven to be key contributors to enterprise adoption

decisions, specifically for perceived

communication effectiveness considering that

information and communication technologies could

revolutionize existing organizational procedures

and interactions with partners and stakeholders.

Based on the earlier discussions, the study

confirmed that management endorsement and

commitment demonstrated to organization

members that leadership approves, believes in, and

encourages new technology adoption. Past studies

revealed that senior management commitment and

support are a strong predictor of the success of new

technology initiatives, [66]. Although evidence

confirmed that emerging technology adoption, such

as Blockchain, is usually driven by a grassroots

movement, the study provided substantial evidence

that the technology adoption and implementation

process will stall or, in the worst-case scenario, fail

without top management support and commitment.

Another crucial leadership attribute that

influences organizational digital technology

adoption is organizational innovation level.

Innovativeness refers to a leader's willingness to

take risks and openness to change. Inventive

leaders tend to foster an environment that is

receptive to change and innovation and project and

communicate the mindset throughout the

organization. Thus, innovative leaders are crucial in

accelerating the adoption of developing new

technologies. Executives with a visionary mindset

create an environment conducive to ICT innovation

and assist firms in progressing with successful

implementations. Prior research suggested that

leaders with previous experience with technology

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adoptions and implementations are better prepared

to share knowledge about new technology and

related organizational change, [67]. The situation

indicates that knowledge sharing and effective

communication enable leaders to anticipate and

address potential dramatic challenges due to digital

technology implications. The path analysis results

demonstrated that leadership effectiveness did not

mediate the relationship between perceived digital

literacy and digital technology adoption, hence

rejecting the eleventh hypothesis.

15 Conclusion

The study investigated digital technology adoption

in the E&E manufacturing industry subsector. The

analysis unit is an E&E manufacturing organization

registered under FMM and SME Directory

Malaysia. The study investigated perceived utility,

ease of use, perceived cost-effectiveness, perceived

communication effectiveness, digital literacy, and

leadership efficacy in Malaysian E&E production.

The study developed and examined two

mainstream management theories, the TAM and

DOI to accomplish its objectives. The theories

indicate a different viewpoint on how different

variable sets will impact a company decision in the

manufacturing sector on digital technology

adoption.

The study employed the SEM method to

evaluate the adequacy of the conceptual model and

measurement assessment and the structural model.

The statistical analysis identified the key drivers of

Malaysian manufacturing SMEs technological

acceptance processes. The path analysis results

implied a significant relationship between

communication effectiveness, perceived digital

literacy, leadership effectiveness, and digital

technology adoption. Additionally, the study

confirmed that leadership effectiveness mediated

the relationships between perceived usefulness and

digital technology adoption, perceived cost-

effectiveness, perceived communication

effectiveness, and digital technology adoption.

16 Study Implications

The study contributes to the technology adoption

literature by developing a novel theoretical

framework for analyzing the factors influencing

industrial manufacturing technology adoption in

Malaysian manufacturing SMEs. The study also

facilitated industrial practices by providing insight

into the industrial manufacturing E&E technology

adoption experiences in Malaysia. Adopting current

technologies may assist Malaysian manufacturing

E&Es to maintain competitiveness and viability in

the business sector. Numerous factors could impact

technology acceptance. The study is a pioneering

examination of major components in the business

and is theoretically grounded in the process of

technical selection, the qualities of technology,

organizational culture, and external environment

framework.

Managers and owners of Malaysian

manufacturing E&Es should be aware of their

leadership role and the technical characteristics that

may impact the ease with which certain digital

technologies are adopted and implemented in their

enterprises. The E&Es must adopt the correct

approach to secure their survival during the

digitization process, which includes an efficient

communication system, effective digital literacy,

effective leadership style, and technical expertise

for the staff. The study could aid Malaysian

manufacturing E&Es in identifying organizational

communication systems, digital literacy, and

leadership styles by providing a validated and

quantified perspective of the entire organizational

culture to promote effective business

transformation. Additionally, the empirical findings

emphasized the critical function of top management

responsibilities in developing countries, such as

Malaysia regarding manufacturing E&E new

technologies adoption. Furthermore, examining

external environmental factors could provide

Malaysian manufacturing E&Es with a better

understanding of the complexities driving

technological decisions.

The conclusions emphasized the crucial nature

of digital and technology literacy as a digitalization

prerequisite. Therefore, the government should

consider providing relevant and adequate assistance

to Malaysian manufacturing SMEs. The study

could catalyze the Malaysian government agencies

to improve assistance to SMEs concerning

technology adoption.

The government must be able to facilitate

Malaysian manufacturing E&Es technology usage.

Understanding the factors that influence technology

adoption allows the government to offer or provide

incentives, training, or information in crucial areas

of Malaysian manufacturing E&Es and SMEs to

stimulate digital technology adoption.

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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.159

Ajayandaran Arumugam, Hamed Khazaei,

Amiya Bhaumik, Thavamaran Kanesan

E-ISSN: 2224-2899

1786

Volume 19, 2022

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

-Dr. AJayandaran A/L Arumugam carried out the

whole study, data collection, data analysis and

writeups.

-Dr. Hamed Khazaei carried out the SEM and the

statistical analysis and evaluated the whole process.

-Dr. Amiya Bhaumik critically evaluated the whole

process, supervised, organized and executed the

research gap, problem statement and the

implications.

-Dr. Thavamaran Kanesan, coordinated with

evaluation of the whole process, the organaziation

of the paper, and proofreading procedure.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

The author(s) reported there is no funding

associated with the work featured in this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.e

n_US

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.159

Ajayandaran Arumugam, Hamed Khazaei,

Amiya Bhaumik, Thavamaran Kanesan

E-ISSN: 2224-2899

1787

Volume 19, 2022