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January 2023 | Volume 2, Number 1 

 

 

 

The Impact of Organizational Leaders’ Perceptions of the 
Fourth Industrial Revolution Technologies 
 

Adewoye Peter Ademola, DBA | Columbia Southern University, Orange Beach, AL, United States 

Michelle Kelly, PhD | Columbia Southern University, Orange Beach, AL, United States 

Contact: adewoyepeterademola@gmail.com  

 

Abstract 
Artificial Intelligence (AI), Big Data (BD), Blockchain (BC), Cloud Computing (CC), Fifth Generation 
(5G) Wireless Network, and Internet of Things (IoT) are the six technologies that constitute the Fourth 
Industrial Revolution Technologies (FIRT) in the context of this quantitative study. The purpose of the 
quantitative study which was based on Technology Acceptance Model (TAM) and Cognitive Model 
(CM) was to understand how the perceptions of the America-based organizational leaders would affect 
the leaders’ acceptance of the Fourth Industrial Revolution Technologies (FIRT). The researcher created 
a questionnaire which was pre-tested by conducting a pilot study with 13 participants after which a total 
number of 135 respondents participated in the full study by responding to a 5-point Likert scale 
questionnaire through an online data collection platform. Cronbach’s Alpha, Confirmatory Factor 
Analysis (CFA), and Kendall’s Tau Correlation techniques were adopted using R and SPSS statistical 
packages to determine the impact of the leaders’ perceptions on their acceptance of the technologies. 
The findings of the study indicated that there was a statistically significant relationship between 
organizational leaders’ Perceived Ease of Use (PEOU) and the leaders’ Perceived Usefulness (PU) of 
the six technologies, between Perceived Usefulness (PU) and the Intention to Use (ITU) Cloud 
Computing technology, between Perceived Ease of Use (PEOU) and the Intention to Use (ITU) Cloud 
Computing technology, and between Perceived Ease of Use (PEOU) and the Intention to Use (ITU) 5G 
Network technology. There was however no statistically significant relationship between the leaders’ 
Perceived Usefulness (PU) or Perceived Ease of Use (PEOU) and the Intention to Use (ITU) the 
Artificial Intelligence, Big Data, Blockchain, and Internet of Things technologies. There was also no 
statistically significant relationship between the leaders’ Perceived Usefulness (PU) and the leaders’ 
Intention to Use (ITU) 5G Network technology. Recommendations for future research include 

mailto:adewoyepeterademola@gmail.com


 

 

 

57 Business Management Research & Applications: A Cross-Disciplinary Journal 

investigating the impact of participants’ age on the acceptance of the technologies, targeting different 
and more narrowed sample populations, considering additional and new technologies, and repeating the 
research at a later point in time.   

Keywords: Leadership, Fourth Industrial Revolution Technologies, Perceived Usefulness (PU), 
Perceived Ease of Use (PEOU), Intention to Use (ITU).  

  



 

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January 2023 | Volume 2, Number 1 

Background of the Study 
The World Economic Forum (2021) described the different stages of industrial revolution the world had 
undergone. During the first industrial revolution, industries applied water and steam power for 
manufacturing their products, the second industrial revolution ushered in the use of electric power for 
mass production while the third applied electronics and information technology to automate production. 
The fourth industrial revolution enhances the third industrial revolution with smart and autonomous 
systems fuelled by data and machine learning (Marr, 2018). The fourth industrial revolution is an 
enhanced version of the third with modern technologies such as the Artificial Intelligence, Big Data, 
Blockchain, Cloud Computing, Fifth Generation (5G) Wireless, and Internet of Things (World 
Economic Forum, 2021).    

Organizational leaders converge around new technological changes with many concerns such as 
cyberattacks, loss of leadership power, lack of new skills required, robots’ lack of capacity of moral 
reasoning and inability to make ethical decisions in complex scenarios, high cost of deployment of new 
technologies, and replacement of human employees by new technologies ( Xu, David, Jeanne, & Suk, 
2018). Successful migration to new technologies therefore depends on organizational stakeholders’ 
adoption of the new innovations (Kelley et al., 2018). Whether the organizational leaders would accept 
to deploy and use the Fourth Industrial Revolution Technologies (FIRT) within their organizations is of 
great concern considering the various challenges the new technologies come with. The study therefore 
measured the relationship between the organizational leaders’ perceptions of the Fourth Industrial 
Revolution Technologies (FIRT) and the leaders’ acceptance or intention to use the technologies in 
American companies.   

Literature Review 
Organizational Leadership in the Fourth Industrial Revolution Era 

Leadership is the capacity to direct followers to achieve goals that the followers would not achieve 
ordinarily (Emler, 2019). According to the Center for Creative Leadership, CCL (2021), great leaders 
consistently possess certain qualities that make them stand out. Such leadership qualities are integrity, 
ability to delegate responsibilities, effective communication skills, gratitude, influence, empathy, 
courage, and respect (Tracy, 2021 & Enfroy, 2021). The challenge for leadership is to deploy new 
technologies in ways that not only yield fresh efficiencies but also amplifies human creativity, ingenuity 
and judgment (World Economic Forum, 2018). Organizational leaders therefore will have to search for 
different ways to do more with less, find value where innovations overlap, and strategically invest in 
technologies (Baig et al., 2023). 

Artificial Intelligence (AI) Technology 

Artificial Intelligence (AI) is described as computer systems that perform tasks requiring human-like 
intelligence (Garbuio & Lin, 2019). Haenlein and Kaplan (2019) defined Artificial Intelligence (AI) as a 
system’s ability to interpret external data correctly, learn from such data, and use the learning to achieve 
specific goals and tasks through flexible adaptation. 



 

 

 

59 Business Management Research & Applications: A Cross-Disciplinary Journal 

Big Data Technology 

The word “Big” in the name “Big Data” suggests a large volume of the Big Data concept (Paraschiv & 
Danubianu, 2019). Grover, Chiang, Liang and Zhang (2018) explained that the ways businesses 
generate, share, communicate, access, analyze data and adapt to environmental changes have been 
transformed by the number of connected people, devices, and sensors.  

Blockchain Technology 

Blockchain technology is a multiple global ledgers that permit assets to be transferred from one party to 
another simultaneously without the need for a third party’s involvement thereby guaranteeing utmost 
security (Khalifa, 2019). Blockchain technology enables businesses to generate required reporting 
information directly from financial data (Mosteanu, 2019). Among all the uses of Blockchain 
technology, the Bitcoin, which is a digital currency, is outstanding (Da Silva Momo, Sordi Schiavi, 
Behr, & Lucena, 2019).  

Cloud Computing Technology 

According to Pise (2019), Cloud Computing is one of the information technology service delivery 
methods which permit users to store data on distant devices rather than storing data on hard drive or 
local devices. Cloud Computing comprises of three main services namely Infrastructure as a Service 
(IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS) (Pise, 2019). 

Fifth Generation (5G) Wireless Technology 

Every new generation technology is an improvement on the previous one. The first generation (1G) 
wireless technology focused on the simple mobile voice (Mahmod, 2017) and offered extremely low 
degree of bandwidth efficiency and security (Cisar, & Maravic, 2019). The second generation (2G) 
focused on both better bandwidth and coverage (Mahmod, 2017) and offered a much higher security and 
new features such as text messaging and communication with low data transmission rates (Cisar, & 
Maravic, 2019). The third generation (3G) was concerned with higher data level, multimedia backing, 
and spread spectrum (Mahmod, 2017) in order to achieve faster data transmission (Cisar, & Maravic, 
2019). The 4G offered access to multiple communication facilities including developed smartphone 
services, and mobility application (Mahmod, 2017; Cisar, & Maravic, 2019). The Fifth Generation (5G) 
Technology has many features such as data capabilities, unlimited call volumes, unrestricted data 
broadcast, live camera, MP3 recording, video chat, huge phone memory, and many others (Mahmod, 
2017).   

Internet of Things (IoT) Technology 

Sethi and Sarangi (2017) defined Internet of Things (IoT) as a new kind of world where almost all the 
devices and appliances are connected to a network by using these devices and appliances collaboratively 
to achieve complex tasks that require a high level of intelligence. Internet of Things (IoT) is not a single 
technology; it is rather a collection of various technologies that work together in tandem (Sethi, & 
Sarangi, 2017).  



 

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January 2023 | Volume 2, Number 1 

Table 1 

Definitions of Constructs and Indicators 

Table 1 helps to understand the acronyms used in the study. The parent constructs (variables); Perceived 
Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (ITU) were measured through 
the lens of the six technologies (items or indicators)-Artificial Intelligence (AI), Big Data (BD), 
Blockchain (BC), Cloud Computing (CC), Fifth Generation (5G) Network, and Internet of Things (IoT).  

Constructs (Varia-
bles) 

Indicators 
(Items) Definitions of Indicators (Items) 

Perceived Usefulness 
(PU) 

PU_AI Artificial Intelligence (AI) indicator for measuring or representing 
Perceived Usefulness (PU) construct 

PU_BD Big Data (BD) indicator for measuring or representing Perceived 
Usefulness (PU) construct 

PU_BC Blockchain (BC) indicator for measuring or representing Perceived 
Usefulness (PU) construct 

PU_CC Cloud Computing (CC) indicator for measuring or representing Per-
ceived Usefulness (PU) construct 

PU_FG Fifth Generation (5G) Network indicator for measuring or repre-
senting Perceived Usefulness (PU) construct 

PU_IoT Internet of Things (IoT) indicator for measuring or representing 
Perceived Usefulness (PU) construct 

Perceived Ease of 
Use (PEOU) 

PEOU_AI Artificial Intelligence (AI) indicator for measuring or representing 
Perceived Ease of Use (PEOU) construct 

PEOU_BD Big Data (BD) indicator for measuring or representing Perceived 
Ease of Use (PEOU) construct 

PEOU_BC Blockchain (BC) indicator for measuring or representing Perceived 
Ease of Use (PEOU) construct 

PEOU_CC Cloud Computing (CC) indicator for measuring or representing Per-
ceived Ease of Use (PEOU) construct 

PEOU_FG Fifth Generation (5G) Network indicator for measuring or repre-
senting Perceived Ease of Use (PEOU) construct 

PEOU_IoT Internet of Things (IoT) indicator for measuring or representing 
Perceived Ease of Use (PEOU) construct 



 

 

 

61 Business Management Research & Applications: A Cross-Disciplinary Journal 

Intention to Use 
(ITU) 

ITU_AI Artificial Intelligence (AI) indicator for measuring or representing 
Intention to Use (ITU) construct 

ITU_BD Big Data (BD) indicator for measuring or representing Intention to 
Use (ITU) construct 

ITU_BC Blockchain (BC) indicator for measuring or representing Intention 
to Use (ITU) construct 

ITU_CC Cloud Computing (CC) indicator for measuring or representing In-
tention to Use (ITU) construct 

ITU_FG Fifth Generation (5G) Network indicator for measuring or repre-
senting Intention to Use (ITU) construct 

ITU_IoT Internet of Things (IoT) indicator for measuring or representing In-
tention to Use (ITU) construct 

 

Note. The table helps to understand the acronym used for each item. The items are the six technologies 
while the constructs are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to 
Use (ITU).   

Methods 
Hypothesis Testing  

The study was guided by three research questions (RQ1, RQ2, and RQ3) with each having two 
hypotheses (Null- H0 and Alternate- HA) which measured the relationship between the organizational 
leaders’ Perceived Usefulness (PU) of the Fourth Industrial Revolution Technologies and the leaders’ 
Perceived Ease of Use (PEOU), the leaders’ Perceived Usefulness (PU) and Intention to Use (ITU) the 
technologies, and the leaders’ Perceived Ease of Use (PEOU) and Intention to Use (ITU) the 
technologies. Bevans (2022) explained that p-values are used in hypothesis testing to help decide 
whether to reject or accept a hypothesis. The most common p-value threshold is 0.05. Any hypothesis 
result with p-value that was less than 0.05 (p<0.05) was considered significant and accepted, however, 
any hypothesis result with p-value that was greater than 0.05 (p>0.05) was considered insignificant and 
rejected. Kendall Rank-Order Correlation Coefficient was selected as the test statistic for each 
hypothesis.  

Research Questions and Hypotheses 

RQ1: What is the relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Perceived Ease of Use (PEOU) of the 
technologies? 



 

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January 2023 | Volume 2, Number 1 

H10 - There is no relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Perceived Ease of Use (PEOU) of the 
technologies   

H1A - There is a relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Perceived Ease of Use (PEOU) of the 
technologies   

RQ2: What is the relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies? 

H20 - There is no relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies   

H2A - There is a relationship between the organizational leaders’ Perceived Usefulness (PU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies   

RQ3: What is the relationship between the organizational leaders’ Perceived Ease of Use (PEOU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies? 

H30 - There is no relationship between the organizational leaders’ Perceived Ease of Use (PEOU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies   

H3A - There is a relationship between the organizational leaders’ Perceived Ease of Use (PEOU) of the 
fourth industrial revolution technologies and the leaders’ Intention to Use (ITU) the technologies   

Models 
Fourth Industrial Revolution Technology Acceptance Model (FIRTAM) and the Cognitive Model (CM) 
were the two research models that guided the study.   

Fourth Industrial Revolution Technology Acceptance Model (FIRTAM) 

Figure 1 shows the Fourth Industrial Revolution Technology Acceptance Model (FIRTAM) adapted 
from the Technology Acceptance Model (TAM) of Davis and Venkatesh (1996) and Koul and Eydgahi 
(2018). The FIRTAM applied the TAM to the domain of Fourth Industrial Revolution Technologies in 
order to build more specificity by examining relationship between the organizational leaders’ 
perceptions and the leaders’ intention to use the Fourth Industrial Revolution Technologies.   

 

 

 

 



 

 

 

63 Business Management Research & Applications: A Cross-Disciplinary Journal 

Figure 1 

 

 

Note. This fourth industrial revolution technology acceptance model (FIRTAM) explains the impact of 
technology users’ perceptions on their acceptance of the fourth industrial revolution technologies, which 
was transformed by Ademola (2021) from Koul and Eydgahi (2018) and Davis and Venkatesh (1996). 

The Cognitive Model 

Cognitive theory is based on the idea that people’s thoughts and beliefs influence their behaviors and 
responses (DiGiuseppe, David & Venezia, 2016). The ease of using a new technology influences the 
users’ decision in adopting the technology while the difficulties in using a new technology influences 
the users’ decision in rejecting the technology (Koul & Eydgahi, 2018; Arvie & Tanaamah, 2019; 
Primasari, Sudjono, & Abriani, 2019; Bayraktaroglu, Kahya, Atay, & Ilhan, 2019).  

Instrumentation 
Pilot Study  

In order to gather the data required to assess the participants’ perceptions of the Fourth Industrial 
Revolution Technologies, a questionnaire was created based on questions adapted from previous 
researches by Sevim, Yuncu, and Erogluhall (2017); Koul and Eydgahi (2018); Diop, Zhao, and Duy 



 

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January 2023 | Volume 2, Number 1 

(2019) and Lee, Kriscenski, and Lim (2019). The questionnaire was a 5-item Likert scale survey 
instrument which was distributed through an online survey provider which allowed for downloading the 
results into an excel spreadsheet for further data analysis. The instrument measured three main 
constructs namely Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and the Intention to Use 
(ITU) Fourth Industrial Revolution Technologies. A pilot study with 13 participants was subsequently 
conducted before the distribution of the questionnaire to 135 participants for full study participation. 
Running a pilot study, according to Bhandari (2022), helps test the validity and reliability of a 
questionnaire, catch any errors or confusing points, find if any questions were particularly difficult to 
answer, unclear or inconsistent, and make necessary changes before performing a full study. The 13 
pilot study participants from organizations based in the United States helped ensure the interview 
questions were valid for meaningful data collection by reviewing the wordings of the questionnaire, 
pointing out any errors, and checking the clarity of the questions.  

The participants gave feedbacks in the comment section of the questionnaire. Eleven (11) pilot study 
participants indicated that all the questions were clear and well understood while two (2) gave useful 
recommendations. Responding to a security-related question in the questionnaire, the first of the two 
pilot study participants explained that the Fourth Industrial Revolution Technologies still had a long way 
to go before showing any proof of security and that the spread of 5G infrastructure in the rural areas was 
still lacking, consequently, most organizations would probably not invest in the technologies until they 
had more proven indicators that the technologies could add value to their companies’ operations. The 
second pilot study participant suggested that the questionnaire choices be randomized because of 
respondents’ tendency to argue positive bias if ‘Strongly Agree’ was at the top of the questionnaire 
choices, and negative bias if ‘Strongly Disagree’ was at the top of the choices.  

Validation and Reliability 
Validity, according to Field (2013), is defined as the determination of whether an instrument measures 
what it is designed to measure while reliability is defined as the determination of whether an instrument 
can be interpreted consistently across different situations. Cronbach’s Alpha and Confirmatory Factor 
Analysis (CFA) statistical techniques were used to validate and confirm the consistency of internal 
reliability of the questionnaire items relating to Perceived Usefulness , Perceived Ease of Use , and the 
Intention to Use (Sujatha & Sekkizhar, 2019).  

Cronbach’s Alpha Technique 

The researcher tested the reliability of the survey instrument by computing the Cronbach’s alpha 
coefficient using the Statistical Package for Social Science (SPSS) tool. Based on the Cronbach’s alpha 
coefficient, the questionnaire was tested to be reliable with the Cronbach's Alpha value of 0.805 (Table 
2). The Cronbach's Alpha’s value of 0.805 is acceptable as it is greater than the minimum value (0.70) 
recommended by Hair et al. (2014).  

 

Table 2 

Reliability Statistical Test Using SPSS 



 

 

 

65 Business Management Research & Applications: A Cross-Disciplinary Journal 

Reliability Statistics 

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items 

0.805 0.813 18 

 

Internal consistency reliability testing of the three constructs (Perceived Usefulness, Perceived Ease of 
Use, and Intention to Use) in the survey instrument was done and the Cronbach’s alpha values derived 
(Table 3) satisfied the minimum required value (0.70) for acceptable internal consistency reliability.  

Table 3 

Constructs’ Cronbach’s Alpha 

Dimension Cronbach's Alpha α (95% 
CI) 

Perceived Usefulness (PU) 0.832 

Perceived Ease of Use (PEOU) 0.799 

Intention to Use (ITU) 0.781 

 

Confirmatory Factor Analysis (CFA) Technique 

Confirmatory Factor Analysis (CFA) was conducted using R statistical technique to determine specific 
goodness-of-fit measures such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root 
Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual 
(SRMR). While the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) are acceptable as they 
are below the recommended cut-off value of 0.95 (Hu & Bentler, 1999), the Standardized Root Mean 
Square Residual (SRMR) exceeds the acceptable value (< 0.08) recommended by Hu & Bentler (1999). 
The Root Mean Square Error of Approximation (RMSEA) exceeds the acceptable value (0.07) as 
recommended by Steiger (2007). Hence, the CFA results (Table 4) indicate the survey instrument has 
internal inconsistencies that may impact the reliability of results.  

 

Table 4 

Confirmatory Factor Analysis Goodness of Fit Measures 



 

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January 2023 | Volume 2, Number 1 

Goodness of Fit Index Test Statistic 

Comparative Fit Index (CFI) 0.781 

Tucker-Lewis Index (TLI) 0.747 

Root Mean Square Error of Approximation (RMSEA) 0.123                                                           
90% CI (0.108; 0.138) 

Standardized Root Mean Square Residual (SRMR) 0.119 

 

Data Collection and Analysis 
The research study investigated the organizational leaders’ intention to use the Fourth Industrial 
Revolution Technologies based on the leaders’ perceptions of usefulness and ease of use of the 
technologies. Quantitative research method was used to examine the relationship between the 
perceptions of organizational leaders about the Fourth Industrial Revolution Technologies and the 
leaders’ acceptance of the technologies. A total of 135 participants responded to a 5-point-Likert-scale 
questionnaire. The questionnaire was delivered through SurveyMonkey platform and data were collected 
from the organizational leaders who worked with companies within the United States. SurveyMonkey 
maintained a reliable panel of respondents by processing all responses with machine learning to check 
for quality and compliance with the research methodology and the confidentiality of the respondents 
(SurveyMonkey, 2021).  

Population and Sample Selection 

Using a confidence interval of 95%, margin error of 5%, and effect size of 0.3, the required sample size 
was calculated using G*Power Software (Faul, Erdfelder, Buchner, & Lang, 2009) to be 134.  

Descriptive Data 

A total of 135 respondents working within America-based organizations completed the survey. Nine 
participants were disqualified for lack of leadership qualities. Responses were converted to numeric 
values for the purpose of statistical data analysis. The raw survey data collected were coded and the 
results from the negatively-worded questions were reverse coded using the Statistical Package for Social 
Science (SPSS) tool (Sonderen, Sanderman, & Coyne, 2013). The Median Time to Complete (MTC) the 
survey was 2 minutes, 17 seconds while the Margin of Error was +/- 8.607%. The margin of error is a 
range of values above and below the actual survey results. The smaller the margin of error the higher the 
confidence level in the results (SurveyMonkey, 2021). 



 

 

 

67 Business Management Research & Applications: A Cross-Disciplinary Journal 

Demographic Profile of Respondents 
Age 

Table 5 shows the age distribution of the respondents; 29.63% (40 respondents) of the sample is in 18-
29 age range, 17.04% (23 respondents) in 30-44 age range, 27.41% (37 respondents) in 45-60 age range, 
and 25.93% (35 respondents) in age range that is above 60 years. There was no participant below the age 
of 18. The highest age range of the respondents was 29.63% (40 respondents).    

Table 5 

Ages of Respondents 

Age 

Answer Choices Percentages Number of Respondents 

< 18 0.00% 0 

18-29 29.63% 40 

30-44 17.04% 23 

45-60 27.41% 37 

> 60 25.93% 35 

Total 135 

 

Gender 

The gender breakdown (Table 6) shows there were 70 female respondents constituting 52% and 65 
female respondents constituting 48% of the sample population. The total number of female participants 
was higher than the male by 5 respondents.  

 

 

 

Table 6 

Percentage Gender Breakdown 



 

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January 2023 | Volume 2, Number 1 

Gender Percentages Number of Respondents 

Male 48.15% 65 

Female 51.85% 70 

Total 135 

 

American Regions 

The survey participants were from 9 regions of the United States (Table 7). The highest percentage 
(21.05%) of the respondents was from Pacific Region while the lowest percentage (1.50%) was from 
(West North Central). Two participants skipped the question about their region.   

Table 7 

Regional Locations of the Respondents 

Region Percentage Number of Respondents 

East North Central 15.79% 21 

East South Central 4.51% 6 

Middle Atlantic 13.53% 18 

Mountain 12.78% 17 

New England 0.75% 1 

Pacific 21.05% 28 

South Atlantic 18.05% 24 

West North Central 1.50% 2 

West South Central 12.03% 16 

  Skipped 2 

Total 135 



 

 

 

69 Business Management Research & Applications: A Cross-Disciplinary Journal 

 

Discussion and Summary of Findings 
Based on the three research questions (RQ1, RQ2, and RQ3) in the study, Kendall Correlation test was 
conducted to determine the relationship between the leaders’ Perceived Usefulness (PU) and Perceived 
Ease of Use (PEOU) in relation to RQ1, between Perceived Usefulness (PU) and Intention To Use (ITU) 
in relation to RQ2, and between Perceived Ease of Use (PEOU) and Intention To Use (ITU) in relation 
to RQ3. The three correlation test scenarios were conducted through the lens of the six Fourth Industrial 
Revolution Technologies- Artificial Intelligence (AI), Big Data (BD), Blockchain (BC), Cloud 
Computing (CC), 5G Network (FG), and Internet of Things (IoT). As shown in Table 8, for each 
technology, Kendall Correlation coefficient value (τ ) was generated with its corresponding p-value. If 
the corresponding p-value was less than 0.05 (p < 0.05), the hypothesis result was considered significant 
and accepted, however, if the corresponding p-value was greater than 0.05, the hypothesis result was 
considered insignificant and rejected.  

Table 8 

Kendall’s Correlation Test Results and Decisions 

Research 
Question Correlation Hypothesis 

Type 
Z-

Value 
tau 
(Ʈ) 

P-
Value 

Hypothesis 
Result Decision 

RQ1            
(What is the 
relationship 
between the 

organizational 
leaders’ 

Perceived 
Usefulness 
(PU) of the 

fourth 
industrial 
revolution 

technologies 
and the 
leaders’ 

Perceived Ease 
of Use (PEOU) 

of the 
technologies?) 

PU_AI-->PEOU_AI 

Null (H10)         Rejected 

Alternate 
(H1A) 3.4716 0.2681353 0.0005174 Statistically 

Significant Accepted 

PU_BD-->PEOU_BD 

Null (H10)         Rejected 

Alternate 
(H1A) 3.965 0.3082377 7.34E-05 Statistically 

Significant Accepted 

PU_BC -->PEOU_BC 

Null (H10)         Rejected 

Alternate 
(H1A) 5.735 0.4470702 9.75E-09 Statistically 

Significant Accepted 

PU_CC -->PEOU_CC 

Null (H10)         Rejected 

Alternate 
(H1A) 4.5339 0.3514791 5.79E-06 Statistically 

Significant Accepted 

PU_FG -->PEOU_FG Null (H10)         Rejected 



 

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January 2023 | Volume 2, Number 1 

Alternate 
(H1A) 5.5154 0.4243429 3.48E-08 Statistically 

Significant Accepted 

PU_IoT -->PEOU_IoT 

Null (H10)         Rejected 

Alternate 
(H1A) 6.5581 0.5256835 5.45E-11 Statistically 

Significant Accepted 

RQ2             
(What is the 
relationship 
between the 

organizational 
leaders’ 

Perceived 
Usefulness 
(PU) of the 

fourth 
industrial 
revolution 

technologies 
and the 
leaders’ 

Intention to 
Use (ITU) the 
technologies?) 

PU_AI -->ITU_AI 

Null (H20)         Accepted 

Alternate 
(H2A) 0.67703 0.0510185 0.4984 Insignificant Rejected 

PU_BD -->ITU_BD 

Null (H20)         Accepted 

Alternate 
(H2A) 1.2744 0.0987181 0.2025 Insignificant Rejected 

PU_BC -->ITU_BC 

Null (H20)         Accepted 

Alternate 
(H2A) 0.74675 0.0587028 0.4552 Insignificant Rejected 

PU_CC -->ITU_CC 

Null (H20)         Rejected 

Alternate 
(H2A) 4.2688 0.3342134 1.97E-05 Statistically 

Significant Accepted 

PU_FG -->ITU_FG 

Null (H20)         Accepted 

Alternate 
(H2A) 1.0004 0.0757775 0.3171 Insignificant Rejected 

PU_IoT -->ITU_IoT 

Null (H20)         Accepted 

Alternate 
(H2A) 0.08433 0.0065714 0.9328 Insignificant Rejected 

 

  



 

 

 

71 Business Management Research & Applications: A Cross-Disciplinary Journal 

Research 
Question  Correlation Hypothesis 

Type 
Z-

Value tau (Ʈ) P-
Value 

Hypothesis 
Result Decision 

RQ3             

(What is the 
relationship 
between the 

organizational 
leaders’ 

Perceived Ease 
of Use (PEOU) 

of the fourth 
industrial 
revolution 

technologies 
and the leaders’ 

Intention to 
Use (ITU) the 
technologies?) 

PEOU_AI ->ITU_AI 

Null (H30)         Accepted 

Alternate 
(H3A) 0.59474 0.045596 0.552 Insignificant Rejected 

PEOU_BD ->ITU_BD 

Null (H30)         Accepted 

Alternate 
(H3A) 0.18825 0.0145997 0.8507 Insignificant Rejected 

PEOU_BC ->ITU_BC 

Null (H30)         Accepted 

Alternate 
(H3A) 0.25371 0.0198619 0.7997 Insignificant Rejected 

PEOU_CC ->ITU_CC 

Null (H30)         Rejected 

Alternate 
(H3A) 3.4767 0.270338 0.0005076 Statistically 

Significant Accepted 

PEOU_FG ->ITU_FG 

Null (H30)         Rejected 

Alternate 
(H3A) 3.9534 0.2988926 7.71E-05 Statistically 

Significant Accepted 

PEOU_IoT ->ITU_IoT 

Null (H30)         Accepted 

Alternate 
(H3A) 1.2202 0.0948238 0.2224 Insignificant Rejected 

 

Theoretical and Practical Implications 
 
The practical implication of the positive and statistically significant results mentioned is that businesses 
need to start engaging stakeholders by sensitizing them on the importance of the use of Cloud Computing 
and 5G Wireless Network technologies. Failure to do so would mean that organizational leaders would 
refuse to deploy new technologies that would benefit businesses. The non-statistically significant results 
make it difficult to know the true perceptions of the organizational leaders. It is very important to first of 
all establish that organizational leaders’ Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) 



 

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January 2023 | Volume 2, Number 1 

have impacts on the leaders’ Intention to Use (ITU) the technologies before proffering any real-world 
solutions. Consequently, it is recommended that researchers repeat the study at another time to see if there 
could be different results from the ones found in the current study as it is possible that the knowledge 
about the technologies could have increased and consequently influenced the organizational leaders’ 
acceptance of the technologies  

Research Limitations and Assumptions 
Limitations   

• Although, the research population was narrowed to only American employees who occupied leadership 
positions in their various organizations, the population selected is still considered to be too broad. It is 
strongly recommended that future researchers narrow the population to leaders from specific industries 
such as telecommunications, oil and gas, health, and banking.   

• Regardless of the acceptable value of the Cronbach’s Alpha achieved, the survey instrument was still 
found to be less than optimum when evaluated using Confirmatory Factor Analysis (CFA). The CFA 
results in Table 5 indicate the survey instrument has internal inconsistencies that could affect the accu-
racy of the results. 

Assumptions  

• Survey participants would respond to each question truthfully 

• The survey instrument would accurately measure the Perceived Usefulness (PU), Perceived Ease of Use 
(PEOU), and the Intention to Use (ITU) the technologies 

• Survey respondents would be organizational leaders who truly worked with companies within the 
United States of America  

Recommendations for Action 
Future research could include the replication of the study to include the impact of the age of the 
participants on the participants’ Intention to Use new technologies and in different geographical 
locations. The study could help decision makers prepare for any form of oppositions that might erupt 
from organizational leaders in the process of migrating to new technologies. Organizational leaders 
might use the study as a reference when developing guidelines for implementing the fourth industrial 
revolution technologies initiatives. It is strongly recommended that technology industry experts who 
already understand the great benefits of the fourth industrial revolution technologies create more 
awareness about the benefits and the importance of adopting new technologies. 

 

Conclusion 



 

 

 

73 Business Management Research & Applications: A Cross-Disciplinary Journal 

Although the emergence of the Fourth Industrial Revolution Technologies comes along with different 
fears and concerns, organizational leaders and other industry stakeholders should consider mitigating the 
challenges that come along with the new technologies instead of rejecting the technologies because the 
future belongs to only businesses that adopt new technologies. The integration of the Fourth Industrial 
Revolution Technologies (Artificial Intelligence, Big Data, Blockchain, Cloud Computing, Fifth 
Generation (5G) Networks, and the Internet of Things) into the existing business processes should be 
done in a way that keeps organizational disruption to a minimum. 

  



 

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	Abstract
	Background of the Study
	Literature Review
	Internet of Things (IoT) Technology
	Methods
	Hypothesis Testing
	Research Questions and Hypotheses
	The Cognitive Model
	Instrumentation
	Validation and Reliability
	Confirmatory Factor Analysis (CFA) Technique
	Data Collection and Analysis
	Population and Sample Selection
	Demographic Profile of Respondents
	Age
	Gender
	American Regions

	Discussion and Summary of Findings
	Based on the three research questions (RQ1, RQ2, and RQ3) in the study, Kendall Correlation test was conducted to determine the relationship between the leaders’ Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in relation to RQ1, between Pe...

	Theoretical and Practical Implications
	The practical implication of the positive and statistically significant results mentioned is that businesses need to start engaging stakeholders by sensitizing them on the importance of the use of Cloud Computing and 5G Wireless Network technologies. ...
	Research Limitations and Assumptions
	Limitations
	Recommendations for Action


