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Integration of TAM in IoT (Internet of Things) adoption: The Mediating
Role of Service Quality
Nahida Sultana1
KEYWORDS: IoT, TAM, SEM, Service Quality, Bangladesh.
1 Department of Management Information Systems, Faculty of Business Studies, University of Dhaka, Dhaka- 1000, Bangladesh.
(CORRESPONDING AUTHOR)
ARTICLE HISTORY: Received: 19 Nov 2023; Revised: 23 May 2024; Accepted: 06 Nov 2024
© 2024, Department of Management Information Systems (MIS), University of Dhaka.
This work is made available to the public under the conditions of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0/), which allows for the unrestricted use, distribution, and reproduction of the work in any
format as long as the original work is properly cited.
Bangladesh Journal of MIS
2024, Vol.10, No.01
https://doi.org/10.61606/BJMIS.V10N1.A1
ISSN:2073-9737 (Print), 2410-7077 (Online)
ABSTRACT
The revolution of the Internet of Things (IoT) has significantly enhanced modern living and intelligent
activities. However, the eectiveness of IoT-driven innovative technology relies on users' behavioral
evaluations of this new technology. This study investigates users' acceptance of IoT using the widely
adopted Technology Acceptance Model (TAM). A structured questionnaire was distributed to elicit
responses, and a total of 260 responses were accumulated, resulting in an 86.67% response rate.
Structural Equation Modelling (SEM) was employed to assess TAM using SmartPLS 4 and SPSS 28.0.
The study's empirical findings indicate that Perceived Ease of Use (PEOU) significantly impacts users'
Perceived Usefulness (PU), IoT Service Quality (SQ), and users' Behavioral Intentions (BI). SQ is a
mediator in this context, revealing direct and indirect relationships among the TAM antecedents. This
study provides a basis for future research, giving useful insights for policymakers, designers, and
scholars to create strategies and policies for implementing and promoting IoT in dierent sectors in
Bangladesh.
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1. Introduction
Our daily activities are being influenced by the Internet revolution, combined with emerging technologies for
many objectives. This system can be viewed as an international network with many devices connecting to
conduct tasks. Furthermore, these nodes, which include specialized and embedded devices, connect
across multiple hardware and software stands (Kortuem et al., 2010). In this composite varied environment,
the Internet of Things (IoT) is typically used as an open standard (Al-Fuqah et al., 2015).
The impression of the Internet of Things is to incorporate all innovative tools into a network, which can be
dealt with from the web and, in turn, deliver real-time information with person-to-person interaction (Gómez
et al., 2013). The Internet of Things (IoT) embodies a move towards a technologically enabled setting linking
brilliant stu and operators. The technologies of IoT are dierent from earlier inventions as they are more
universal, intellectual, and autonomous (Kahlert, 2016). The quick evolution of IoT has been creating a
growing number of topics that produce excitement and anxiety worldwide. Kahlert (2016) presented that in
predicting customers' intentions, significant roles are played by Usefulness, compatibility, enjoyment,
social influence, and behavioral control. IoT is being used multiply. The adoption starts with home
automation and progresses to wearable stu. Ghazaleh and Zabadi (2020) focused on the application of
IoT in Bangladesh to bring considerable insight into the dierent horizons of Customer Relationship
Management (CRM). They further contributed to making using IoT to link customers and businesses more
productive. To address issues during COVID-19, Singh et al. (2020) revealed a computerized and
transparent approach. Many indications reveal that the IoT will change numerous segments, including
higher education institutes, especially universities (Ning and Hu, 2012; Aldowah, 2017). Learning
experiences are impacted by technology in several ways. Adopting the Internet of Things can boost learning
upshots by enabling enriched learning knowledge, enhanced operational competence, and accessing real-
time vision into students' participation (Aldowah, 2017).
Reception and implementation of novel technology are always influenced by the user's attitude and
behavior toward the system. Yau et al. (2016) focused on smart mobility, which is becoming more critical
in this period of rapid innovation when the technologies of the fourth industrial revolution are all embedded
around us. The adoption and maintenance of modern systesuch as RFID services in conjunction with
portable wallets, is an individual decision influenced mainly by social attitudes and actions (Gao & Bai,
2014).
1.1 Significance of the study:
Embracing digital technology requires an innovative mindset and digital skills, which include the
ability to understand the technology and a desire to apply it in everyday activities. Convenience, ease
of use, and usefulness are key factors that aect user adoption (Verkijika and De Wet, 2018).
Numerous studies have been conducted on IoT using technology adoption models. However, more
studies need to contribute to identifying the mediation of IoT service quality in the technology
adoption model. This research endeavors to close the research breach contributing to exploring the
mediating role of IoT service quality in its adoption using the Technology Acceptance Model (TAM).
Hence, this paper aims to identify users’ attitudes and willingness to implement the Internet of
Things (IoT) considering TAM model factors (IoT service quality, users' perceived ease of use, and
perceived Usefulness), which will inline, pave the way for policymakers in dierent sectors of
Bangladesh to design and develop strategies for the adoption of this emerging technology.
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2. Theoretical background and hypotheses development
The Internet of Things is a cutting-edge technology that links all intelligent items without involving any
human intervention (IoT), which is getting significant conclusive study ground in contemporary years in
the industrial and academic disciplines (Mohammed, 2020). Müller (2020) defines the Internet of Things
(IoT) as a computer system in which digital and physical machines communicate in a network without
the need for human intervention. In other words, it refers to the ability of the objects we use daily to
connect via the web and cloud computing (Morpus, 2017). Acceptance and the adoption of newer
technology constantly hinge on the perceptions and attitudes of the operator to the system. Industry 4.0,
cloud–based services, and the Internet of Things are pervasive in this era of technological innovation
(Yau et al., 2016). Sultana and Tamanna (2021) evidenced the benefits and challenges of IoT service use
in dierent sectors of Bangladesh during the pandemic period. They found that the adaptability of these
new applications is increasing for this new normal situation.
Banica et al. (2016) revealed that the eect of the Internet of Things is felt in several aspects of
education, including customized content, course presentation, activities of learning, and sharing of
knowledge and contents. The application of IoT can lead to substantial deviations in the educational
sector: restructuring of education, changes in instruction, transformation of learning methods, tentative
and real-world transformation, teaching resources changes, and changes in grounds (Tianbo, 2012).
With its substantial development, the potential adoption of IoT depends on three things: developing
educational middleware, current teaching facilities, and gradually assessing students (Zhiqiang &
Junming, 2011). Gómez et al. (2013) proved that adopting the IoT in schooling improves students'
learning and eases evocative education as it lets them relate explicit knowledge to the actual context.
Ane et al. (2020) showed that in Bangladesh, about 80-90% of instructors agree that IoT greatly
influences education; students' perceptions agree with teachers' views in three parameters, i.e., e-
learning, research, and hyper-connectivity.
Sultana and Tamanna (2022) evaluated the benefits and challenges of using IoT services in the corporate
and service areas and the education sector. The topmost benefits in corporate and service industries
include preserving physical remoteness and saving time in the education sector. Challenges in all fields
comprise growing public remoteness and falling individual interaction (Sultana &Tamanna, 2022). Singh
et al. (2017) mentioned that the IoT revolution is reformation the recent health care arrangement,
economics, and other incorporated technology and society prospects. Radanliev and Roure (2021)
didn’t stress the eects of manifold and relative risks associated with IoT, yet mentioned the update of
IoT design and ethics. To lead the framework development of the health establishments, medical
practitioners, and governments, IoT conveys higher menace. Newer technology must be aligned with the
supply chain with cyber risk consideration (Radanliev, Roure & Carvalho, 2021). Intelligent homes are
another trend in the trending industry. IoT has extended professional networks for improving traic and
waste management and features inundation and fire sensors to make the house safer. Besides, the
deployment of smart devices such as infrared cameras, fever-detecting devices, and software to
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analyze video feeds in large public oices have been speeded up during the COVID-19 situation (Pike et
al., 2014).
In 1986, Fred Davis developed Technology Acceptance Model (TAM) as a development of management
information systems (MIS). Since its creation, the model in Figure 1 has made significant advancements
in the field of research by examining the aspects that influence operators' receiving or denial of
information technology (Davis, 1985). Due to the TAM model's flexibility and reliability in various
scenarios, it has become the most often used model in the information system (Giovanis et al., 2012;
Mercurio et al., 2020). TAM has also been updated to work with the most recent technology, for example,
online, WWW, and e-commerce (Ha, 2009; Kaewwit & Kaewwit, 2010).
Figure 1: Technology Acceptance Model (Davis, 1989)
According to Castiblanco Jimenez et al. (2021), there is proof that external factors influence two
personal beliefs in dierent ways than Davis (1985) predicted in the TAM. Perceived Ease of Use (PEOU),
followed by perceived enjoyment, system, and information quality, and perceived Usefulness (PU) were
shown to be the main determinants of perceived Usefulness (PU) by Castiblanco Jimenez et al. in 2021.
Self-eicacy substantially predicted the PEOU, with perceived satisfaction and experience coming in
second and third. Another study by Farahat (2012) that looked at 153 undergraduate Egyptian university
students revealed that the PEOU, PU, ATU (Attitude Towards Using), and social influence of the students
substantially impacted their desire to participate in digital classrooms.
The TAM model was further expanded by Sanchez et al. (2013). In the context of virtual education, they
looked at six dimensions: technical services (TS), computer self-eicacy (CSE), perceived ease of use
(PEOU), perceived Usefulness (PU), attitude to Use (ATU), and system usage (SU). The findings showed
that TS had a significant impact on students' PEOU and PU, and that PU has a significant influence on
students' acceptance of technology. Finally, in the setting of the learning management systems (LMSs)
utilized by university academic sta in the USA, Fathema, Shannon, and Ross (2015) explained an
extended TAM. The findings supported the hypothesis that all of the TAM's essential characteristics,
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system quality, perceived self-eicacy, and enabling conditions, had a substantial impact on behavioral
intentions to utilize LMS in university education.
There are strong correlations between user-related variables such as service quality, perceived
Usefulness (PU), user contentment, and system usage performance when using electronic services.
According to Alsamydai, Yousif, and Al-Khasawneh (2012), the utility of a service is substantially
influenced by its quality, which in turn aects how consumers behave. Service quality models assessing
numerous modern services, such as the Service quality model, can be used to determine service quality
for IoT services (Parasuraman, Valarie, and Leonard, 1988).
Sultana and Tamanna (2022) evidenced that 55% of people have positive intention toward using IoT in
the academic segment, and 52% have positive behavioral intention toward using IoT in the business and
service industries; these intentions are derived from their perceived benefits from IoT services during the
pandemic situation. There are justifications for many studies about Perceived Usefulness (PU) as the
most influential item in the TAM model as it describes the insight of users into the significance of
information technology (Pfoser, Schauer & Costa, 2018). The extent to which a user perceives using
technology will be easy is perceived as ease of use (PEOU) (Davis, 1989). It is a strong determinant in
shaping users' attitudes through improving system usefulness that, in the end, aects the users'
behavioral intention (Davis, Bagozzi, and Warshaw, 1989).
The researcher has formulated subsequent hypotheses grounded on the previous works. The model with
established hypotheses is shown in Figure 2.
H1: PEOU posits a positive association with BI.
H2: PEOU posits a positive association with SQ.
H3: PEOU posits a positive association with PU.
H4: PU posits a positive association with BI.
H5: SQ posits a positive association with BI.
H6: SQ posits a positive association with PU.
H7: SQ mediates the link between PU and PEOU.
H8: SQ mediates the link between PEOU and BI.
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Figure 2: IoT TAM Model with moderating role
3. Methodology
3.1 Constructs for measurement:
The comprehensive measurement items and their relative sources are presented in Table 1 to support the
validity of all observed variables for the latent constructs in the study model that was created from earlier
data.
Table 1: Summary of variables with sources for measurement
Latent variables
Corresponding variables
Item sources
Perceived
Usefulness
PU1: IoT services are available at anytime
PU2: IoT reduces my manual job
PU3: IoT facilitates location-based service
PU4: Allows contactless work during Covid
PU5: Helps to maintain physical distance
Castiblanco Jimenez et al.
(2021), Sánchez et. al.
(2013), Farahat (2012).
Perceived ease of
use
PEOU1: IoT saves time
PEOU2: Use of IoT saves my cost.
PEOU3: IoT services are convenient
PEOU4: It is easy to use
PEOU5: IoT facilitates communication
Castiblanco Jimenez et.
al. (2021), Sánchez et. al.
(2013), Farahat (2012).
Service quality
SQ1: I find IoT flexible
SQ2: IoT ensures security in data protection
SQ3: IoT provides easy access to
information
Fathema, Shannon, and
Ross, 2015; Sánchez et
al., 2013.
Behavioral intention
BI1: I have a positive attitude toward using
IoT
BI2: I will continue using IoT in the future
Fathima, Shannon &Ross
(2015), Farahat (2012).
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3.2 Questionnaire design and data collection:
The data for this analysis was collected through a structured questionnaire survey. The questionnaire was
divided into two sections, Parts A and B. Part A included questions about the respondents' age, gender,
educational background, profession, areas where they use IoT, and the types of IoT technologies they use.
Fifteen questions in Part B related to the four variables in the study framework shown in Figure 1 were
included. The construct items were assessed using a 5-point Likert-scale ranging from (1) "Strongly
Disagree" to (5) "Strongly Agree."
The literature on the choice of sample size for various types of data analytics revealed a substantial
dierence in viewpoints (Hair et al., 1998). 200 is considered a decent sample size, and 300 is considered
adequate for data examination using structural equation modeling (SEM) (Kline, 2015). Hair et al. (1998)
endorsed using a sample size of 200 to evaluate a framework with SEM. Mandeville and Roscoe (1971)
assert that to do multivariate research, the sample must be at least ten times the quantity of items in the
constructs of the study. The total number of study constructs in our study is 18. For data analysis utilizing
SEM, a sample size of 300 has been chosen based on Roscoe (1971) and other earlier works.
This study was collected during the period of 3 months – May, June, and July 2022. The sampling method
used for data collection was convenient random sampling, where IoT users of dierent sectors were
selected conveniently and randomly. Three hundred questionnaires were distributed; 260 (response rate of
86.67%) completed questionnaires have been returned and found to be valid for further analysis. The
participation of the respondents was voluntary, and no compensation was given to the participants.
3.3 Analytical method:
This study uses the Partial Least Square (PLS) approach, a statistical analysis method based on structural
equation modeling (SEM), to examine and confirm the study framework and the relationships between the
predicted constructs. A well-known model to assess the rationality of a hypothesis using empirical data is
the structural equation model (SEM) (Gotz, Liehr-Gobbers, & Krat, 2010). One of the well-known PLS-SEM
data analysis software programs is SmartPLS (Hair, Hult, Ringle, & Sarstedt, 2013). To do the necessary
statistical analysis, such as building a research model and computing a measurement model, data is first
loaded into Microsoft Excel (.csv) and then imported into the SmartPLS program. Following that, data are
entered into the IBM SPSS 20.0 program to do a linear regression analysis and assess the hypotheses.
4. Results
4.1 Demographic traits of participants:
Table 2 lists the demographic details of the respondents. 99 participants were female and 161 (62%) were
male for the surveys examined. Male participants (31%) and female participants (19%) were between the
ages of 26 and 34 respectively. In addition, most of the respondents (49%) had mastered educational
qualifications. Furthermore, the field of IoT use shows that most participants were from the education
sector (40%) and workplaces (36%).
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Table 2: Demographic Details of Participants
Variable
Description
Frequency (n=260)
Percentage (%)
Gender
Male
Female
161
99
62%
38%
Age
Male
Below 25
Below 35
Below 45
Below 55
55 or above
55
81
13
7
5
21%
31%
5%
3%
2%
Female
Below 25
Below 35
Below 45
Below 55
55 or above
31
50
10
5
3
12%
19%
4%
2%
1%
Education
Bachelors
Masters
PhD
118
127
15
45%
49%
6%
Fields of IoT use
Education
Medical
Wearables
Workplaces
Merchandise
Bank
Smart Home
106
39
32
95
24
74
41
40%
15%
12%
36%
9%
28%
15%
4.2 Measurement model:
The internal consistency, convergent validity, and composite reliability are the metrices to closely
investigate the measurement model (Ketchen, 2013). Cronbach's alpha (α), which identifies the reliability
of the scale items inside the construct, is used to assess the internal reliability (DeVellis, 2021; Hinkin,
1995). Internal uniformity is calculated by Cronbach's alpha and composite reliability, with a level of 0.70
indicating satisfactory internal dependability (Joe, 1993). Using the Average Variance Extracted (AVE) and
item loadings with at least 0.50 of AVE for item validity, the convergent validity is evaluated. The estimated
loadings, AVE, Composite Reliability (CR), and Cronbach's alpha (α) for each component are displayed in
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Table 3. The values of Average Variance Extracted range from 0.537 to 0.862, and loadings range from 0.501
to 0.809 are above the standard suggested level. Therefore, the convergent validity of the study is
satisfactory.
Table 3: Values of Factor Loading, Cronbach’s Alpha, Composite Reliability, and Average Variance
Extracted.
Constructs
Items
Loadings
Average Variance
Extracted (AVE)
Composite
Reliability
(CR)
Cronbach’s
Alpha (α)
Behavioral
intention
BI1
0.539
0.862
0.933
0.882
BI2
0.525
Perceived
Usefulness
PU1
0.635
0.537
0.864
0.705
PU2
0.501
PU3
0.569
PU4
0.510
PU5
0.662
Perceived
ease of use
PEOU1
0.631
0.621
0.792
0.718
PEOU2
0.565
PEOU3
0.712
PEOU4
0.623
PEOU5
0.633
Service
Quality
SQ1
0.602
0.681
0.726
0.726
SQ2
0.541
SQ3
0.696
4.3 Structural Model Evaluation:
This study assessed the correlations between dependent and independent variables using the path
coeicient (β) and p-value (Figure 3).
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Figure 3: Path Diagram with P-Values and Path Co-eicient.
The PLS outcomes for the structural model are shown in Table 4. The findings display that there is a
significant association between the variables PEOU and BI (β= 0.303, P 0.008), PEOU and SQ (β =
0.654, P 0.000), PEOU and PU (β = 574, P 0.000), PU and BI (β = 0.183, P 0.054), SQ and PU (β = 0.213,
P 0.001), and SQ and BI (β = -0.142, P 0.140).
H1, H2, H3, and H6 were therefore supported at a significance level of 0.05 (p < 0.05).
Variance Inflation Factor (VIF) has been calculated for every relation to test the collinearity. The
presence of a VIF more significant than 3.3 is suggested as an indicator of pathological collinearity,
as well as an indicator that common method bias (CMB) may aect a model; hence, if all VIF values
in the inner model are equal to or less than 3.3, the model can be regarded free of common method
bias (Kock, 2015). The presented values in Table 4 show that all values are below 3.3, hence it
concludes that the model is free of common method bias. The structural model with a series of
regression equations determined the association between the components in the research model.
Collinearity must be examined to confirm that the regression findings were not influenced. The
Variance Inflation Factor (VIF) measures collinearity (Hair et al., 2019). Hair et al. (2019)
recommended a cut-o value 5 for VIF. In this study, VIF values (Table 4) of each construct are lower
than the cut-o value five, which indicates that collinearity matters between the factors were vague.
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Further, f2 specifies the eect size, and values more than 0.35, 0.15, and 0.02 signify upper, average,
and lower eect sizes, respectively (Cohen, 1988). Following Cohen, the study found that the
relationships between PEOU > SQ (H2: f2 > 0.35) and PEOU > PU (H3: f2 > 0.35) have a higher
significant eect size.
Table 4: Structural Model Evaluation for Direct Associations
H
Relation
T
-
values
P
-
values
BCL LL
BCL UL
f
2
VIF
Decision
H1
PEOU > BI
2.64
0.008
0.081
0.508
0.043
2.454
S
H2
PEOU > SQ
16.586
0.000
0.561
0.719
0.746
1.000
S
H3
PEOU > PU
10.517
0.000
0.449
0.668
0.405
1.746
S
H4
PU>BI
1.928
0.054
-
0.017
0.356
0.018
2.148
NS
H5
SQ>BI
1.477
0.14
-
0.323
0.045
0.013
1.844
NS
H6
SQ>PU
3.431
0.001
0.093
0.338
0.056
1.746
S
Abbreviations:
BCL LL – Confidence Interval bias-corrected at the lower limit; BCL UL – Confidence Interval bias-corrected at
the upper limit; VIF – Variance Inflation Factor; S – Supported; NS – Not Supported.
The mediating eect of service quality (SQ) was inspected in Table 5 to find whether it mediated the
relationship between PU, PEOU, and BI. SQ (H7: t = 3.3, p < 0.01) mediated the relationship between
PU and PEOU, while SQ (H8: t = 1.484, p > 0.01) didn’t mediate the relationship between PEOU and
BI.
Table 5: Structural Model Evaluation for Indirect Associations
H
Relation
T Statistics
P values
BCI LL
BCI UL
Decision
H7
PEOU -> SQ-> PU
3.3 0.001 0.061 0.228 S
H8
PEOU -> SQ-> BI 1.484 0.138 -0.212
0.031 NS
Abbreviations:
BCL LL – Confidence Interval bias-corrected at the lower limit; BCL UL – Confidence Interval bias-corrected at
the upper limit; VIF – Variance Inflation Factor; S – Supported; NS – Not Supported.
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5. Discussion
The TAM model is employed in the study to determine the determinants influencing the
implementation of the Internet of Things (IoT) in diverse sectors of Bangladesh. In the current
research, four variables, including Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and
Service Quality (extrinsic moderating factor), are identified to discern the behavioral intentions
toward using IoT services. Perceived Usefulness is evaluated based on the five statements labeled
PU1, PU2, PU3, PU4, and PU5. Similarly, Perceived Ease of Use is measured based on five
statements labeled from PEOU1 to PEOU5, Service Quality is evaluated based on four statements
labeled from SQ1 to SQ3, and Behavioral Intention is calculated based on two statements labeled
BI1 and BI2. The reliability of the four items (Cronbach's alpha and Composite Reliability) is above
the standard level of 0.70. In addition, the items' convergent validity (Average Variance Extracted and
factor loadings) is well above the reasonable range of 0.50. Thus, this research's constructs'
reliability and convergent validity are satisfactory.
The demographic features of participants of this study demonstrate that young people aged (26 – 34
years) are prone to use IoT services. The fields of IoT use ensign that most of the usage incurred in
the education sector (40%). This is not surprising. During the pandemic time of covid-19, the
educational sectors of Bangladesh have been impacted immensely. As a result, the students and
educators started using IoT services such as intelligent meeting apps, classroom activities through
digital platforms, etc. The following frequently used fields are workplace, banks, medicals, smart
home, wearable, and merchandise, respectively.
In the hypotheses analyses, Perceived Ease of Use (IoT saves time, Use of IoT saves cost, IoT services
are convenient, It is easy to use, and IoT facilitates communication) presents a positive relationship
with users’ Behavioral Intention (they have a positive attitude toward IoT, and they will continue using
it) and Service Quality (IoT is flexible, secured, and easier access to information). Similarly, PEOU
and SQ show a positive relationship with users’ Perceived Usefulness (IoT services are available
anytime, reduce manual jobs, facilitate location-based service, contactless work during covid-19,
and help maintain physical distance). Hence, H1, H2, H3, and H6 are accepted. These outcomes are
in convergence with the findings of Gao & Bai, 2014 Liew et al., 2017 Mital et al., 2017 Park et al.,
2017 Lu, Y., 2021 Liu et al., 2022) but in dierence with (Bao et al., 2014). Moreover, SQ mediates the
relationship between PU and PEOU – thus, H7 is accepted.
On the contrary, three alternative hypotheses are rejected. First, users’ Perceived Usefulness
doesn’t have any positive relationship with their Behavioral Intention. Consequently, H4 is not
stayed. This eect opposes the findings of Hsu and Lin (2018) and supports Prayoga and Abraham
(2016). Likewise, IoT Service Quality does not significantly influence users' behavioral intention to
use IoT – thus, H5 is not supported. Finally, the indirect moderating relationship between SQ, PEOU,
and BI was found to be insignificant – hence, H8 is not supported.
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6. Conclusion, and Implications of the Study
6.1 Conclusion
The study aimed to inspect the variables prompting the acceptance of the Internet of Things (IoT)
grounded on the widely used Technology Acceptance Model (TAM). The application of this emerging
technology is new in Bangladesh. So, to explore their acceptance and adoption, the TAM is used to
comprehend the factors that aect their intention. Since many sectors became greatly aected by
the Covid-19 outbreak, the use of IoT emerged as an incredible tool to expedite the activities of
diverse fields. This study investigated several factors driving users' behavioral intentions toward IoT.
Perceived Usefulness and Perceived ease of use are one of the important factors shaping users'
intention and acceptance (Abu-Khadra & Ziadat, 2012), and service quality is one of the influential
factors in identifying users' acceptance (Parasuraman et al., 1988). The empirical findings of this
research identify that Perceived Ease of Use (PEOU) significantly aects users' Perceived Usefulness
(PU), Service Quality (SQ), and Behavioral Intentions (BI), respectively. The mediating factor
measures the indirect relationships – IoT Service Quality (SQ), where SQ mediates the relationship
between PU and PEOU.
6.2 Implication
Theoretical implications:
There needs to be more pieces of literature about the Internet of Things (IoT) in the context of Bangladesh.
As technology is new, more and more studies should be conducted regarding this. The current study will
contribute to the academic aspect by disseminating knowledge about the adoption of IoT among the people
in Bangladesh. Moreover, the intervening eect of IoT service quality on its adoption will enrich the existing
literature. Hence, this will form the foundation for IoT research based on the mediator role in Bangladesh.
Practical implications:
The findings present a novel outcome that might benefit the managers and policymakers. As the
result shows the intervening role of IoT service quality in adopting IoT, the managers and
policymakers may improve the quality of IoT service so that not only the tech-savvy people can use
it, but also the general people can adopt IoT in their regular activities. Thus, these findings will deliver
fruitful results for policymakers, planners, and scholars to make strategies and policies for the
positive application and enhancement of the Internet of Things (IoT) in diverse sectors of
Bangladesh.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Citation
Sultana, N. (2024). Integration of TAM in IoT (Internet of Things) adoption: The Mediating Role of Service
Quality. Bangladesh Journal of MIS, 10(01),01-17. https://doi.org/10.61606/BJMIS.V10N1.A1
pg. 14
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