INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
Intrinsic and Extrinsic Construct as Prediction of Lecturer Anxiety  
and Intention to Attitude in Polytechnics Located in North-Central  
Nigeria.  
1 Dung Joseph Jong, 2 Paul Thomas Muge, 3 Bassi Jeremiah Yusuf  
1 Department of General Studies, Federal Polytechnic Nyak Shendam  
2 Department of Electrical Electronic Engineering Technology, Federal Polytechnic Nyak Shendam  
3 Department of Computer Engineering, Federal Polytechnic Nyak Shendam  
DOI : https://doi.org/10.51583/IJLTEMAS.2026.150100012  
Received: 07 January 2026; Accepted: 12 January 2026; Published: 23 January 2026  
ABSTRACT  
This study delved into how both intrinsic and extrinsic factors can predict lecturers' anxiety and their willingness  
to use instructional technologies in teaching electrical courses at polytechnics in North-Central Nigeria. With  
the increasing push for digital integration in higher education, it’s crucial to understand the psychological and  
systemic elements that affect educators' adoption of technology in technical fields. The primary goal of this study  
is to explore how both intrinsic and extrinsic factors influence lecturers' anxiety and their willingness to use  
instructional technologies while teaching electrical courses in polytechnics located in North-Central Nigeria.  
This study adopted population size of 250 lecturers. A mixed-methods approach was utilized, blending  
quantitative surveys with qualitative interviews, targeting lecturers from selected public and private polytechnics  
in the region. This study employed multiple linear regression analysis. The results show that motivational  
elements, intrinsic motivation, level of anxiety, and suggestion all significantly increase lecturers’ anxiety. This  
indicates that certain motivational pressures such as personal drive, expectations, or performance-related  
suggestions can unintentionally heighten anxiety levels. While institutional support had a positive but  
insignificant effect, it shows that existing support structures are not strong enough to reduce anxiety. Extrinsic  
motivation had a negative but insignificant effect, suggesting that external rewards alone do not meaningfully  
reduce anxiety. Overall, the findings highlight the need for stronger institutional interventions and healthier  
motivational environments to reduce lecturers’ anxiety. Establish counseling and wellness units dedicated to  
managing academic stress and mental health. Encourage self-paced professional development rather than  
excessive self-imposed pressure. Avoid using high-pressure performance metrics without providing adequate  
support. Train administrators and supervisors to give encouraging, clear, and supportive suggestions. Provide  
meaningful rewards such as promotion opportunities, research grants, or reduced administrative workload.  
Introduce mindfulness, relaxation, and mental health awareness programs within the institution.  
Key words: Intrinsic motivational elements, Extrinsic motivational aspects, Level of anxiety, Instructional  
technologies  
INTRODUCTION  
The way we incorporate technology into education has become a vital part of how we teach and learn today,  
especially in technical and vocational education and training (TVET) institutions like polytechnics. In Nigeria,  
particularly in the North-Central region, these polytechnics are tasked with producing skilled professionals in  
fields related to science and technology, such as electrical engineering. However, even with government  
initiatives and investments in tech infrastructure, many lecturers still aren't making the most of these instructional  
technologies.  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
One key reason for this disconnect is the mindset and motivation of lecturers when it comes to embracing  
technology. Research has shown that anxiety, resistance to change and a lack of motivation to use technology  
are significant hurdles for educators, particularly in developing regions. The choice to adopt or reject technology  
often hinges on a mix of internal factors like self-confidence, interest, and how relevant they find the technology  
as well as external factors, such as institutional support, access to ICT resources, training opportunities, and the  
influence of peers.  
Grasping how these internal and external factors interact is essential for crafting effective strategies to encourage  
lecturers to adopt technology. Additionally, examining how these factors predict lecturers' anxiety and their  
willingness to integrate technology into their electrical engineering courses can offer valuable insights for  
policymakers, administrators, and stakeholders involved in the polytechnic education system.  
This study aims to explore how both intrinsic and extrinsic motivational factors impact anxiety and intention  
among lecturers in polytechnics located in North-Central Nigeria. By concentrating on the teaching of electrical  
courses a discipline that significantly depends on simulations, virtual labs, and digital instruction this research  
shed light on the unique challenges and opportunities that come with digital transformation in technical  
education.  
Understanding intrinsic factors such as self-efficacy, digital competence, personal motivation, and attitudes  
toward technology help reveal internal barriers that may hinder lecturers’ adoption of modern teaching tools.  
Likewise, examining extrinsic factors such as institutional support, availability of ICT facilities, training  
opportunities, workload, and administrative encouragement provides insights into the external conditions that  
shape usage behavior.  
PROBLEM STATEMENT/JUSTIFICATION  
• Even though there’s been a surge in investment in ICT infrastructure at polytechnics in Nigeria, many lecturers  
in electrical engineering still struggle to effectively use technology in their teaching.  
• Many of these lecturers’ express feelings of anxiety and discomfort when it comes to using instructional  
technologies, which really gets in the way of smoothly integrating digital tools into technical education.  
• There’s a gap in understanding how both intrinsic factors (like interest, perceived competence, and self-  
efficacy) and extrinsic factors (such as institutional support, access to resources, training, and policy incentives)  
affect lecturers’ anxiety and their willingness to adopt new technologies.  
• Most research has been centred around barriers faced by students in e-learning or general adoption frameworks,  
with not enough focus on the unique challenges lecturers face when teaching practical, tech-heavy courses like  
electrical engineering in Nigerian polytechnics.  
• The absence of solid evidence on what drives lecturers’ anxiety and their intentions to adopt technology makes  
it tough to create targeted interventions and professional development programs.  
OBJECTIVE(s) OF THE STUDY  
The primary goal of this study is to explore how both intrinsic and extrinsic factors influence lecturers' anxiety  
and their willingness to use instructional technologies while teaching electrical courses in polytechnics located  
in North-Central Nigeria.  
Specific Objectives:  
1. To pinpoint the intrinsic motivational elements that drive lecturers to incorporate instructional technologies  
in their electrical course teachings.  
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2. To investigate the extrinsic motivational aspects (like institutional support, training opportunities, and  
resource availability) that affect lecturers' decisions to embrace instructional technologies.  
3. To evaluate the level of anxiety that lecturers experience regarding the use of instructional technologies.  
4. To analyse the connection between intrinsic motivation and lecturers' anxiety about using technology.  
5. To explore the relationship between extrinsic motivation and lecturers' anxiety concerning technology use.  
6. To assess how lecturers' anxiety acts as a mediator between motivational factors (both intrinsic and extrinsic)  
and their intention to adopt technology.  
7. To offer suggestions on enhancing lecturers' motivation and alleviating anxiety to promote better technology  
adoption in teaching practices  
LITERATURE REVIEW  
The use of educational technologies in higher education has become essential for enhancing teaching and  
learning outcomes. In fields like technical and vocational education, especially electrical engineering, tools such  
as simulations, computer-aided design, and virtual labs are incredibly effective for helping students grasp  
complex ideas (Yusuf & Balogun, 2021). However, in Nigerian polytechnics, the uptake of these technologies  
is often uneven, influenced by various institutional and personal factors (Adeoye et al., 2022).  
Intrinsic motivation plays a key role here, encompassing internal drivers like interest; enjoyment, perceived  
competence, and self-efficacy that affect how engaged someone is with a task (Deci & Ryan, 2000). For lecturers,  
this intrinsic motivation is a strong predictor of their willingness to embrace technology in their teaching. Those  
who feel capable and find personal value in technology are more inclined to use it effectively (Teo, 2011). Self-  
efficacy, in particular, is crucial in determining whether educators choose to engage with or shy away from  
technology (Bandura, 1997). In technical disciplines like electrical engineering, where hands-on experience is  
vital, this intrinsic motivation can be especially powerful.  
On the flip side, extrinsic motivators like training opportunities, institutional policies, peer support,  
administrative backing, and access to ICT tools also play a significant role in how technology is adopted  
(Venkatesh & Davis, 2000). Research in Nigerian institutions has shown that lecturers are more likely to embrace  
instructional technologies when they receive sufficient support and incentives from their institutions (Onasanya  
et al., 2020). Moreover, a supportive institutional culture and strong leadership commitment can create an  
environment that encourages on going digital transformation (Ajadi & Salawu, 2021).  
Feeling anxious about using technology often called technophobia can really get in the way of embracing and  
effectively using digital tools in education. This anxiety might come from a lack of confidence, fear of messing  
up, insufficient training, or even past negative experiences (Brosnan, 1998). Research shows that lecturers who  
experience high levels of anxiety related to technology are less likely to engage with digital tools, no matter how  
many resources are available (Agbo et al., 2020). Lately, there’s been a growing interest in how anxiety acts as  
a bridge between motivation and intention in educational research (Yerdelen-Damar et al., 2019).  
The desire to use technology in teaching is a major predictor of actual behavior, as highlighted in the Technology  
Acceptance Model (TAM) (Davis, 1989). This model points out that how useful and easy to use a technology  
seems plays a big role in shaping our intentions. In polytechnic education, where digital tools can really boost  
hands-on learning, the intentions of lecturers are crucial for successful implementation. But it’s important to note  
that intention isn’t just about perceived usefulness; it’s also shaped by motivation and anxiety levels (Teo, 2011;  
Mtebe & Raisamo, 2014).  
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Herman et al. (2016) developed the intrinsic-motivation (IM) course design method to make motivation theory  
accessible to faculty and to help faculty think more concretely about the costs that demotivate them and make  
their course designs untenable.  
Ourcoursedesignmethodcomplementsexistingcoursedesignmethodsbyprovidinganapproachtodesigning  
for  
motivational outcomes. In this paper, we describe the IM Course Design Method and then illustrate how this  
method was used to refine the design of a sophomore-level engineering course that enrolled over 200 students.  
The study then present  
evaluationevidencefromthiscoursetosuggestthatapplicationofthemethodcanincreasestudents’intrinsicmotivation  
in engineering courses.  
Al-Said (2023) examined the influence of teacher on student motivation: Opportunities to increase motivational  
factors during mobile learning. The study surveyed 200 students and 46 teachers of The University of Jordan  
and Jordan University of Science and Technology regarding the factors that influenced their motivation in terms  
of mobile learning. The results revealed that 178 out of 200 participants agreed that intrinsic motivation impacted  
their interest in mobile learning. Some 78% of the students approved of mobile learning, while the remaining  
22% believe it is necessary to return to the traditional face-to-face education format. The importance of feedback  
and communication with teachers and its impact on the process of mobile learning is considered. The role of  
built-in mechanisms in information systems and the positive role of gamification are equally important. Plug-  
ins compatible with the convenient Word Press system that is applications that help organize the educational  
process were examined in the scientific work. The specific recommendations for raising the motivation of  
students in the learning process, which can be used by relevant institutions worldwide presented.  
Dong et al. (2025) investigated barriers by developing a theoretical model that integrates elements from Social  
Ecological Systems Theory, considering both micro-level individual factors (such as neuroticism and personal  
innovativeness) and meso-level social factors (such as negative word-of-mouth). Data were collected from 500  
university teachers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and  
fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM results revealed that neuroticism and  
negative word-of-mouth (WOM) have a significant positive impact on technology anxiety, while personal  
innovativeness has no significant direct effect. Moreover, perceived invasiveness plays a key mediating role in  
the relationships between neuroticism and technology anxiety, as well as between negative WOM and  
technology anxiety, whereas perceived authenticity does not exhibit a significant mediating effect. The fsQCA  
findings further revealed that technology anxiety does not stem from a single causal pathway. Instead, four  
configurations that include the presence and absence of certain conditions can lead to this desirable outcome.  
Autio (2019) determined the elements motivating comprehensive school students to study technology. The  
research was carried out as a qualitative case study and the material was collected through individual theme  
interviews. Each test participant represented a different case of motivation towards technology education. In  
choosing individuals for the study the main criteria were gender, negative or positive motivation and competence  
in the field of technology. The study found that the artifact to be made in school and the student’s freedom of  
choice had significant effect on motivation in all test participants. Instead, curiosity and intellectual challenge  
seemed to be the main elements among technological talents. Although, we must be careful with final  
conclusions as the research group was relatively small, we can conclude that there were more signs of intrinsic  
motivation among students with high technological competence whereas extrinsic motivation was emphasized  
in the other students.  
Tsai and Chang (2013) investigated inner motivation and anxiety of English learning as it is experienced by  
English as Foreign Language (EFL) learners with respect to various majors, differences in genders and language  
proficiency. Specifically, it studies EFL students at a technical university in Taiwan. This study surveyed and  
analyzed 857 freshmen from a technical university in Taiwan. Based on the analyses of structural equation  
modeling, the results indicated that English learning anxiety impacted English learning motivation in different  
ways depending on genders and majors. On the other hand, English learning anxiety had little effect on English  
learning motivation for the different levels of language proficiency groups, especially for learners in the  
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intermediate group. Generally speaking, most of the learners were prone to instrumental rather than integrative  
motivation in terms of learning English, and their levels of English language class anxiety were higher than their  
levels of English use and test anxiety. The findings can help clarify the nature of both English learning anxiety  
and English learning motivation as psychological constructs to students.  
Teng (2024) based on a cross-lagged panel design, examined the directionality of the relationships between  
anxiety, self-efficacy, and motivation in the context of online English learning. A total of 420 university students  
in China completed self-efficacy belief, motivation, and anxiety measures twice, eight months apart. The  
findings suggest that self-efficacy belief mediates the relationship between motivation and anxiety in online  
English learning, whereas anxiety mediates the relationship between self-efficacy belief and students’  
motivation. The mediation models based on two times of data collection achieved a satisfactory fit. However,  
the second model demonstrated a better model fit, highlighting the importance of anxiety in the relationship  
between motivation and self-efficacy beliefs. Understanding the causes and effects of anxiety for students may  
lead to training and resource development that are important to maintaining students’ self-efficacy belief and  
motivation in online English learning.  
Khoudri (2024) delved into the origins of English as a Foreign Language (EFL) speaking anxiety in Moroccan  
high school students and offers potential remedies to reduce or prevent its occurrence, with a particular emphasis  
on encouraging a willingness to speak. The study involved 37 high school teachers who completed a  
questionnaire regarding their strategies to mitigate speaking anxiety and promote communication willingness  
among Moroccan EFL high school students. The data was collected and analyzed using SPSS. The findings  
reveal that anxiety stems from various factors, including linguistic issues (such as limited vocabulary, grammar  
challenges, and fluency), personal factors (such as learner personality and motivation), and teacher-related  
factors (including feedback quality and classroom activities). Moreover, the research suggests that teachers  
should prioritize strategies like providing positive feedback, offering praise, and incorporating collaborative  
work or task-based learning to reduce students’ EFL speaking anxiety. Additionally, teacher participants  
proposed additional strategies focused on a variety of activities and methods to foster a welcoming classroom  
atmosphere.  
While previous studies have looked into the obstacles to using technology in Nigerian higher education, there  
hasn’t been much empirical research on how intrinsic and extrinsic motivation, anxiety, and the intention to use  
technology are connected especially among lecturers teaching technical courses like electrical engineering. This  
study aims to fill that gap by examining how these factors interact and influence digital teaching practices in  
polytechnics in the North-Central region of Nigeria.  
METHODOLOGY  
This study used a quantitative, correlational survey research design. This approach is perfect for exploring the  
predictive relationship between intrinsic and extrinsic factors, anxiety, and the intention to use instructional  
technologies among lecturers. It enables a statistical analysis of how strong and in what direction the  
relationships between these variables are. The research took place in North-Central Nigeria, which is one of the  
six geopolitical zones in the country. This region includes states like Benue, Kogi, Kwara, Nasarawa, Niger,  
Plateau, and the Federal Capital Territory (FCT) in Abuja. These states are home to various federal, state, and  
private polytechnics that offer programs in electrical and electronic engineering, along with related technology  
fields. North-Central Nigeria was chosen for its diverse educational institutions, on-going ICT development  
projects, and a good mix of urban and semi-urban polytechnic environments.  
The study focused on lecturers who teach electrical and electronic engineering and related courses at selected  
polytechnics throughout North-Central Nigeria. These lecturers are key because they deliver technical content  
that increasingly relies on instructional technology for simulations, modeling, and practical demonstrations.  
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A multi-stage sampling technique was employed:  
• Stage 1: Purposefully selecting 6 polytechnics (2 federal, 2 state, and 2 private) from different states in the  
region.  
• Stage 2: Using stratified sampling based on the size of the departments to ensure proportional representation.  
• Stage 3: Implementing simple random sampling to choose respondents within each department.  
The sample size was calculated using Cochran’s formula for known populations, aiming for a confidence level  
of 95% and a margin of error of 5%. 250 lecturers took part in the study.  
Instrumentation  
The main tool used for gathering data is a structured questionnaire that has five key sections:  
1. Demographic Information (like age, gender, qualifications, years of experience, and type of institution).  
2. Intrinsic Motivation Scale (covering aspects such as self-efficacy, personal interest, and perceived  
competence).  
3. Extrinsic Motivation Scale (including factors like institutional support, peer influence, and access to ICT  
tools).  
4. Technology Anxiety Scale (addressing fears of failure, discomfort, and feelings of lack of control).  
5. Intention to Use Technology Scale (looking at willingness, future plans, and readiness to engage with  
technology).  
The questions adapted from established scales, including: The Technology Acceptance Model (TAM) (Davis,  
1989), The Computer Anxiety Rating Scale (CARS) (Heinssen et al., 1987) and The Self-Determination Theory  
Instruments (Deci & Ryan, 2000). Each item was rated on a 5-point Likert scale, ranging from Strongly Disagree  
(1) to Strongly Agree (5).  
• To ensure face and content validity, three experts in educational technology and psychology review the  
questionnaire. This study conducted a pilot study with 25 lecturers from a polytechnic outside the main sample  
area to fine-tune the instrument. Reliability was evaluated using Cronbach’s Alpha, with a threshold of α ≥ 0.70  
deemed acceptable for each construct.  
This study got ethical approval and the necessary permissions from the institution and distributed the  
questionnaires in person. Three research assistants were trained to help with the distribution and collection at  
selected institutions. This study guaranteed respondents' anonymity, confidentiality, and voluntary participation.  
The data collection process took four weeks.  
RESULT AND DISCUSSION  
Table 1: Demographic Information of Respondents  
Demographic Variable  
Category  
Frequenc  
y
Percentage (%)  
Male  
185  
74.0  
Gender  
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Female  
65  
26.0  
24.0  
44.0  
22.0  
10.0  
28.0  
58.0  
14.0  
16.0  
38.0  
28.0  
18.0  
56.0  
44.0  
100  
25 – 34  
60  
Age Range (Years)  
35 – 44  
110  
55  
45 – 54  
55 and above  
HND/Bachelor’s Degree  
Master’s Degree  
Ph.D.  
25  
70  
Highest Qualification  
145  
35  
Below 5 years  
5 – 10 years  
11 – 15 years  
Above 15 years  
Federal Polytechnic  
State Polytechnic  
40  
Years of Teaching Experience  
95  
70  
45  
140  
110  
250  
Type of Institution  
Total  
The demographic data show that most respondents are male (74%), reflecting the gender imbalance often found  
in technical and engineering disciplines in Nigerian polytechnics. This dominance suggests that men are more  
involved in teaching electrical courses, which could influence the study’s insights on anxiety and technology  
adoption patterns.  
The majority of lecturers (44%) are aged between 35 and 44 years, representing mid-career professionals who  
are experienced yet still adaptable to new teaching technologies. Their willingness to use instructional  
technologies may depend on adequate institutional support and perceived ease of use.  
Regarding academic qualifications, most lecturers (58%) hold a Master’s degree, implying that they possess a  
solid academic foundation, which could enhance their confidence and reduce anxiety toward technology  
integration. However, the smaller number of Ph.D. holders (14%) may indicate limited exposure to research-  
driven technological pedagogies.  
In terms of teaching experience, a considerable number (66%) have over five years of experience, suggesting  
familiarity with conventional teaching methods. Transitioning to digital or technology-based instruction may  
therefore trigger varying levels of anxiety depending on individual motivation (intrinsic factors) and institutional  
encouragement (extrinsic factors).  
Finally, the distribution between federal (56%) and state (44%) polytechnics highlights institutional diversity,  
which may influence the availability of resources, administrative support, and training opportunities. Federal  
institutions may have better access to technological infrastructure, which could reduce anxiety and improve  
willingness to adopt new instructional technologies.  
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The demographic profile suggests that while most lecturers are educated, experienced, and within a productive  
age range, their willingness to integrate instructional technologies is likely moderated by both intrinsic factors  
(such as motivation, self-efficacy, and attitude toward innovation) and extrinsic factors (like institutional  
support, training, and resource availability). This means that effective policy and professional development  
programs should target both personal and institutional determinants to reduce anxiety and enhance technology  
adoption in teaching electrical courses.  
Table 2: Descriptive Statistics  
Descriptive Statistics  
Indicators  
ME1  
ME2  
ME3  
ME4  
IS1  
N
Minimum  
1.00  
2.00  
1.00  
1.00  
1.00  
1.00  
1.00  
2.00  
1.00  
1.00  
1.00  
2.00  
1.00  
1.00  
1.00  
1.00  
1.00  
2.00  
1.00  
2.00  
2.00  
Maximum  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
Mean  
Std. Deviation  
.76152  
.69138  
.79152  
.85227  
.81646  
1.09837  
.86164  
.80397  
1.10549  
.82846  
1.11493  
.83082  
.84569  
1.21046  
.88885  
.85588  
.84349  
.84268  
.91317  
.83089  
.82749  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
4.2800  
4.3520  
4.0000  
3.8880  
4.0080  
3.5200  
3.8880  
4.1680  
2.5280  
3.9800  
3.8520  
4.3640  
4.1080  
2.2840  
4.3480  
4.0800  
3.8760  
4.1440  
4.1160  
4.1280  
4.1000  
IS2  
IS3  
IS4  
LA1  
LA2  
LA3  
LA4  
IM1  
IM2  
IM3  
IM4  
EM1  
EM2  
EM3  
EM4  
SG1  
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SG2  
250  
250  
250  
250  
250  
250  
250  
250  
2.00  
2.00  
1.00  
2.00  
1.00  
2.00  
1.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
5.00  
4.2120  
4.1600  
4.2800  
4.3520  
3.8520  
4.3640  
4.1080  
.77034  
.78028  
.76152  
.69138  
1.11493  
.83082  
.84569  
SG3  
SG4  
AX1  
AX2  
AX3  
AX4  
Valid N (listwise)  
Table 2 presents the descriptive statistics for 250 respondents across various indicators. The mean values range  
mostly between 3.5 and 4.4, indicating that respondents generally agreed positively with most statements.  
Indicators such as ME2 (M = 4.35), LA4 (M = 4.36), IM3 (M = 4.35), and AX3 (M = 4.36) show strong  
agreement among participants, reflecting favorable perceptions in those areas. However, items like LA1 (M =  
2.53) and IM2 (M = 2.28) have noticeably lower means, suggesting weaker agreement or dissatisfaction with  
those aspects. The standard deviations, which mostly fall between 0.7 and 1.1, indicate moderate variability in  
responses showing that participants’ views were generally consistent. Overall, the results suggest that most  
respondents had positive perceptions, with only a few areas showing relatively low ratings that might require  
improvement.  
Table 3: Out of Range Values  
Univariate Statistics  
Indicat  
ors  
N
Mean  
Std. Deviation  
Missing  
No. of Extremesa  
Count  
Percent  
Low  
High  
ME1  
ME2  
ME3  
ME4  
IS1  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
4.2800  
4.3520  
4.0000  
3.8880  
4.0080  
3.5200  
3.8880  
4.1680  
2.5280  
3.9800  
.76152  
.69138  
.79152  
.85227  
.81646  
1.09837  
.86164  
.80397  
1.10549  
.82846  
0
0
0
0
0
0
0
0
0
0
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
5
0
0
0
0
0
0
0
0
3
0
0
8
IS2  
15  
0
IS3  
IS4  
5
LA1  
LA2  
0
13  
10  
0
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LA3  
LA4  
IM1  
IM2  
IM3  
IM4  
EM1  
EM2  
EM3  
EM4  
SG1  
SG2  
SG3  
SG4  
AX1  
AX2  
AX3  
AX4  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
250  
3.8520  
4.3640  
4.1080  
2.2840  
4.3480  
4.0800  
3.8760  
4.1440  
4.1160  
4.1280  
4.1000  
4.2120  
4.1600  
4.2800  
4.3520  
3.8520  
4.3640  
4.1080  
1.11493  
.83082  
.84569  
1.21046  
.88885  
.85588  
.84349  
.84268  
.91317  
.83089  
.82749  
.77034  
.78028  
.76152  
.69138  
1.11493  
.83082  
.84569  
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
.0  
0
10  
13  
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12  
13  
1
13  
13  
12  
13  
9
9
5
3
0
10  
13  
a. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).  
Table 3 provides a summary of the univariate statistics for the same indicators. It shows that there were no  
missing responses for any item, confirming complete data collection. The mean and standard deviation values  
here align with those in Table 2, reaffirming the general consistency of responses. Additionally, the table reports  
a few low extreme values (outliers) for some items, such as LA1 (13 low extremes), IM1 (13), IM3 (12), and  
EM3 (13), but these are relatively minor and do not significantly affect the overall distribution. This implies that  
while a small number of respondents rated some items unusually low, the overall data pattern remains reliable  
and valid. In summary, Table 3 confirms that the dataset is complete, consistent, and free from major anomalies,  
reinforcing the reliability of the descriptive results presented in Table 2.  
Table 4: Model Summary  
Model Summaryb  
Model  
R
R Square  
Adjusted R Square  
Std. Error of the  
Estimate  
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1
.888a  
.788  
.783  
1.24133  
a. Predictors: (Constant), suggestion, intrinsic motivation, level of anxiety, institutional  
support, motivational element, extrinsic motivation  
b. Dependent Variable: Lecturers Anxiety  
The model summary in Table 4 shows that the model has an R value of 0.888, indicating a very strong positive  
relationship between the predictors and Lecturers’ Anxiety. The R Square value of 0.788 means that about  
78.8% of the variation in lecturers’ anxiety is explained by the independent variables motivational element,  
institutional support, level of anxiety, intrinsic motivation, extrinsic motivation, and suggestion. The  
Adjusted R Square (0.783) confirms that the model remains strong even after adjusting for the number of  
predictors. The Standard Error of the estimate (1.24133) shows a relatively small average difference between  
the observed and predicted values, indicating that the model fits the data well.  
Table 5: ANOVA  
ANOVAa  
Model  
1
Sum of Squares  
1392.316  
Df  
6
Mean Square  
232.053  
F
Sig.  
Regression  
Residual  
Total  
150.595  
.000b  
374.440  
243  
249  
1.541  
1766.756  
a. Dependent Variable: lecturers anxiety  
b. Predictors: (Constant), suggestion, intrinsic motivation, level of anxiety, institutional support,  
motivational element, extrinsic motivation  
The ANOVA in Table 5 shows the overall significance of the regression model predicting lecturers’ Anxiety.  
The regression sum of squares (1392.316) represents the variation explained by the predictors, while the  
residual sum of squares (374.440) represents unexplained variation. The F-value of 150.595 with 6 and 243  
degrees of freedom and a significance level (Sig.) of .000 indicates that the model is statistically significant.  
This means that the combination of predictors motivational element, institutional support, level of anxiety,  
intrinsic motivation, extrinsic motivation, and suggestion jointly have a significant effect on lecturers’  
anxiety.  
Ta Table 6: Coefficients  
Coefficients  
Model  
Unstandardized  
Coefficients  
Standardiz  
t
Sig.  
Collinearity  
Statistics  
ed  
Coefficient  
s
B
Std. Error  
Beta  
Toleran  
ce  
VIF  
1
(Constant)  
-2.827  
.706  
-4.005  
.000  
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Motivational  
Element  
.246  
.078  
.605  
.323  
-.010  
.041  
.047  
.051  
.047  
.042  
.043  
.061  
.230  
.065  
.506  
.292  
-.011  
.038  
5.228  
1.522  
.000  
.129  
.000  
.000  
.812  
.502  
.450  
.484  
.575  
.619  
.389  
.280  
2.224  
2.065  
1.739  
1.616  
2.571  
3.578  
Institutional  
Support  
Level of  
Anxiety  
12.98  
0
Intrinsic  
Motivation  
7.764  
-.238  
.672  
Extrinsic  
Motivation  
Suggestion  
a. Dependent Variable: Lecturers anxiety  
The coefficients Table 6 presents the results of a multiple regression analysis conducted to determine how  
several independent variables motivational element, institutional support, level of anxiety, intrinsic  
motivation, extrinsic motivation, and suggestion influence the dependent variable, lecturers’ anxiety.  
From the table, the constant (intercept) has a value of -2.827 with a t-value of -4.005 and a significance level  
of 0.000, indicating that when all predictors are held constant, the baseline level of lecturers’ anxiety is negative  
but statistically significant. This provides the starting point for the regression model.  
The variable motivational element has an unstandardized coefficient (B) of 0.246, a standard error of 0.047,  
and a standardized coefficient (Beta) of 0.230. The t-value (5.228) and p-value (0.000) show that it  
significantly contributes to lecturers’ anxiety. This study is consistent with the study conducted by Al-Said  
(2023). The results revealed that 178 out of 200 participants agreed that intrinsic motivation impacted their  
interest in mobile learning. Some 78% of the students approved of mobile learning, while the remaining 22%  
believe it are necessary to return to the traditional face-to-face education format. This implies that for every  
one-unit increase in motivational elements, lecturers’ anxiety increases by 0.246 units, assuming other factors  
remain constant. The VIF value (2.224) and tolerance (0.450) indicate no multicollinearity problem.  
Institutional support has a coefficient (B) of 0.078 with a t-value of 1.522 and p-value of 0.129, which is not  
statistically significant since it exceeds the 0.05 threshold. This suggests that institutional support does not have  
a meaningful influence on lecturers’ anxiety. Its VIF (2.065) and tolerance (0.484) also fall within acceptable  
limits, showing no multicollinearity issue. This study disagreed with the study conducted by Dong et al. (2025).  
The PLS-SEM results revealed that neuroticism and negative word-of-mouth (WOM) have a significant positive  
impact on technology anxiety, while personal innovativeness has no significant direct effect. Moreover,  
perceived invasiveness plays a key mediating role in the relationships between neuroticism and technology  
anxiety, as well as between negative WOM and technology anxiety, whereas perceived authenticity does not  
exhibit a significant mediating effect. The fsQCA findings further revealed that technology anxiety does not  
stem from a single causal pathway.  
The variable level of anxiety shows the highest impact with an unstandardized coefficient (B) of 0.605, a Beta  
of 0.506, a t-value of 12.980, and a p-value of 0.000. This means that a one-unit increase in anxiety levels leads  
to a 0.605-unit increase in lecturers’ anxiety, and the relationship is highly significant. This variable is the  
strongest predictor of lecturers’ anxiety in the model, and its VIF (1.739) confirms no multicollinearity. This  
study agreed with the study conducted by Tsai and Chang (2013). The results indicated that English learning  
anxiety impacted English learning motivation in different ways depending on genders and majors. On the other  
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hand, English learning anxiety had little effect on English learning motivation for the different levels of language  
proficiency groups, especially for learners in the intermediate group. Generally speaking, most of the learners  
were prone to instrumental rather than integrative motivation in terms of learning English, and their levels of  
English language class anxiety were higher than their levels of English use and test anxiety.  
Intrinsic motivation has a coefficient (B) of 0.323, Beta of 0.292, t-value of 7.764, and p-value of 0.000, all  
indicating a significant positive effect. This means lecturers with higher intrinsic motivation tend to experience  
higher anxiety levels, possibly due to internal pressure to perform well. Its VIF (1.616) and tolerance (0.619)  
show that this variable is statistically independent of others. This study is consistent with the study conducted  
by Khoudri (2024). The findings reveal that anxiety stems from various factors, including linguistic issues (such  
as limited vocabulary, grammar challenges, and fluency), personal factors (such as learner personality and  
motivation), and teacher-related factors (including feedback quality and classroom activities).  
Extrinsic motivation, however, has a coefficient (B) of -0.010, a Beta of -0.011, a t-value of -0.238, and a p-  
value of 0.812, which is not significant. This implies that external rewards or recognition do not significantly  
influence lecturers’ anxiety. This study is inconsistent with the study conducted by Khoudri (2024). The findings  
reveal that anxiety stems from various factors, including linguistic issues (such as limited vocabulary, grammar  
challenges, and fluency), personal factors (such as learner personality and motivation), and teacher-related  
factors (including feedback quality and classroom activities). The VIF (2.571) and tolerance (0.389) are within  
the acceptable range, meaning multicollinearity is not a concern.  
Lastly,  
has a coefficient (B) of  
of  
, and  
, which  
p-value of 0.502  
suggestion  
0.041, Beta  
0.038, t-value of 0.672  
are all statistically insignificant. This indicates that this factor does not meaningfully contribute to changes in  
lecturers’ anxiety. This study is not in tandem with the study conducted by Autio (2019). The study found that  
the artifact to be made in school and the student’s freedom of choice had significant effect on motivation in all  
test participants. Instead, curiosity and intellectual challenge seemed to be the main elements among  
technological talents. Although its VIF (3.578) is somewhat higher than others, it is still below the critical value  
of 10, implying moderate but acceptable multicollinearity.  
In summary, the regression model identifies level of anxiety, intrinsic motivation, and motivational element  
as the major significant predictors of lecturers’ anxiety, while institutional support, extrinsic motivation, and  
suggestion do not significantly predict anxiety levels. The absence of multicollinearity across all predictors (VIF  
< 10, Tolerance > 0.1) confirms that the results are statistically reliable. This implies that lecturers’ anxiety in  
polytechnics within North-Central Nigeria is primarily driven by internal psychological and motivational  
factors rather than institutional or external factors.  
Table 7: Direct effect of X on Y  
Direct effect of X on Y  
Effect  
.0016  
se  
t
p
LLCI  
ULCI  
.0804  
.0400  
.0411  
.9673  
-.0771  
Table 7 shows the direct effect of the independent variable (X) on the dependent variable (Y). The effect value  
is 0.0016, indicating a very weak positive relationship. The t-value (0.0411) and p-value (0.9673) show that this  
effect is not statistically significant since the p-value is much greater than 0.05. Additionally, the confidence  
interval (LLCI = -0.0771, ULCI = 0.0804) includes zero, confirming that the direct effect is not significant.  
This means that X does not have a meaningful direct influence on Y, suggesting that any relationship between  
them may occur through other variables, such as a mediator.  
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Table 8: Indirect effect(s) of X on Y  
Indirect effect(s) of X on Y  
Effect  
.1949  
BootSE  
.0412  
BootLLCI  
.1170  
BootULCI  
.2790  
Anxiety  
In Table 8, presents the indirect effect of the independent variable (X) on the dependent variable (Y) through  
anxiety as a mediating variable. The indirect effect value is 0.1949, indicating that Anxiety mediates about  
19.5% of the relationship between X and Y. The bootstrapped standard error (BootSE) is 0.0412, showing  
the precision of the estimate. The bootstrapped confidence interval (BootLLCI = 0.1170; BootULCI =  
0.2790) does not include zero, which means the indirect effect is statistically significant. In summary, Anxiety  
significantly mediates the relationship between extrinsic and extrinsic motivation. This study agreed with the  
study conducted by Teng (2024). The findings suggest that self-efficacy belief mediates the relationship between  
motivation and anxiety in online English learning, whereas anxiety mediates the relationship between self-  
efficacy belief and students’ motivation. The mediation models based on two times of data collection achieved  
a satisfactory fit. However, the second model demonstrated a better model fit, highlighting the importance of  
anxiety in the relationship between motivation and self-efficacy beliefs.  
Table 9: Model Summary  
Model Summary  
R
R-sq  
MSE  
F
df1  
df2  
p
.6964  
.4850  
2.9999  
116.3208  
2.0000 247.0000  
.0000  
The model summary in Table 9 shows that the model has an R value of 0.6964, indicating a strong positive  
relationship between the independent and dependent variables. The R-squared value of 0.4850 means that about  
48.5% of the variation in the dependent variable is explained by the predictors in the model. The mean square  
error (MSE) of 2.9999 indicates the average prediction error. The F-value of 116.3208 with degrees of freedom  
(df1 = 2, df2 = 247) and a p-value of .0000 shows that the overall model is statistically significant, meaning  
the predictors collectively have a significant impact on the dependent variable. This study agreed with the study  
conducted by Herman et al. (2016). The study present  
evaluationevidencefromthiscoursetosuggestthatapplicationofthemethodcanincreasestudents’intrinsicmotivation  
in engineering courses.  
CONCLUSION  
The results show that motivational elements, intrinsic motivation, level of anxiety, and suggestion all  
significantly increase lecturers’ anxiety. This indicates that certain motivational pressures such as personal drive,  
expectations, or performance-related suggestions can unintentionally heighten anxiety levels. While institutional  
support had a positive but insignificant effect, it shows that existing support structures are not strong enough to  
reduce anxiety. Extrinsic motivation had a negative but insignificant effect, suggesting that external rewards  
alone do not meaningfully reduce anxiety. Overall, the findings highlight the need for stronger institutional  
interventions and healthier motivational environments to reduce lecturers’ anxiety.  
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RECOMMENDATION  
The following recommendations were made:  
1. Establish counselling and wellness units dedicated to managing academic stress and mental health.  
2. Encourage self-paced professional development rather than excessive self-imposed pressure.  
3. Avoid using high-pressure performance metrics without providing adequate support.  
4. Train administrators and supervisors to give encouraging, clear, and supportive suggestions.  
5. Provide meaningful rewards such as promotion opportunities, research grants, or reduced administrative  
workload.  
6. Introduce mindfulness, relaxation, and mental health awareness programs within the institution.  
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