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IST-Africa 2022 Conference Proceedings
Miriam Cunningham and Paul Cunningham (Eds)
IST-Africa Institute and IIMC, 2022
ISBN: 978-1-905824-68-7
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 1 of 11
Impact of Technophobia on the Digital Divide. A Preliminary
Case Study in the Eastern Cape Province of South Africa
Samuel T. FALOYE1, Sanjay RANJEETH2, Sonny M.A. AKO-NAI3
Discipline of Information Systems and Technology,
University of KwaZulu-Natal, Pietermaritzburg South Africa
Tel: +27 7812612601, +27 33 260 56412, +27 33 260 59963,
Email: temitayofaloye@gmail.com1, ranjeeths@ukzn.ac.za2, akonaia@ukzn.ac.za3
Abstract: Over the years, technology has become an essential part of our lives, with
technological advancement presenting on-going opportunities. However, it creates
negative emotions, anxiety and fear among some people due to an established set of norms
and individual behavior patterns. This has been described as technophobia, which
constrains individuals’ ability to use technology and thus puts them at a disadvantage. The
continuous emergence of new technologies has given rise to increased technophobia,
which is now believed to affect a third of every population. This article examines the
characteristics that influence technophobia as well as how technophobia impacts the
digital divide. A quantitative methodology was employed, and 384 questionnaires were
distributed to participants in East London in the Eastern Cape Province of South Africa.
The findings suggest that age, employment and educational level play a role in
technophobia. However, young adults with no formal education and employment
demonstrated lower levels of technophobia than other age groups. The results also showed
that technophobia impacts the digital divide (access and skill). The article concludes with
suggestions to manage technophobia.
Keywords: Technophobia, Technology, Digital divide, gender, computer anxiety
1. Introduction
The continuous emergence of new technologies presents new opportunities and benefits.
However, due to an established set of norms and behavior patterns, some people consider the
introduction of a new technology as a threat, resulting in negative emotions, anxiety and fear
(Baysal, 2020). In contrast, the same technology brings comfort to and inspires enthusiasm
among adopters. This ambivalence is demonstrated by technophobia and technophilia. In its
simplest terms, technophobia is defined as avoidance of various forms of technology while
technophilia refers to strong attraction to and enthusiastic embrace of technology in its various
forms (Di Giacomo, Guerra, Perilli, & Ranieri, 2020). Thus, technophobia and technophilia are
the relationships formed between people and technologies. However, despite the proliferation of
new technologies, both technophobia and technophilia are under researched as scientists focus
on new discoveries rather than investigating human behavior, attitudes and emotions towards
new technologies (Di Giacomo, Ranieri, D’Amico, Guerra, & Passafiume, 2019; Martínez-
Córcoles, Teichmann, & Murdvee, 2017).
When a new technology emerges, individuals subconsciously decide whether to continue
with their pattern of behavior and reject new technologies or adopt them (Nimrod, 2018).
Technology’s perceived complexity creates fear and anxiety which leads to their avoidance
(Aksoy, Alan, Kabadayi, & Aksoy, 2020; Hechanova & Dioquino Jr, 2004; Nimrod, 2018). In
contrast, a technophile will accept new technologies in a positive manner and view its adoption
as a means to improve his/her quality of life and address the issues confronting society
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 2 of 11
(Brosnan, 2002). While technophiles have no fear of technology in any form, technophobes
avoid it due to their personal fear despite its benefits (Odai Y Khasawneh, 2018a). In a few
cases, such fear escalates from mere avoidance to physical symptoms such as sweating,
shivering and palpitations. When forced to interact with technologies, technophobes take more
time, commit more errors and perform worse than others (Anthony, Clarke, & Anderson, 2000;
Nestik et al., 2018). They are placed at a disadvantage as they are not able to capitalize on the
positive benefits technology can bring to their daily lives (Anthony et al., 2000).
Technophobia has received more attention in recent years due to the continuous infusion of
technologies into everyday live. The notion that it is only associated with adults has been
disproved (Nikos Bozionelos, 2001; Di Giacomo et al., 2020; Nestik et al., 2018; Salamzadeh,
Mirakhori, Mobaraki, & Targhi, 2013). Current research shows that technophobia is experienced
by around 50% of all populations across different age categories (Osiceanu, 2015). Due to its
impact on individuals’ quality of life, it is regarded by many as a serious clinical condition
which is comparable in severity to other phobias (Nimrod, 2018). Individuals with high levels of
technophobia are less likely to adopt technologies due to their mental resistance to all forms of
new technologies. For their part, technophiles, for whom the proliferation of new technologies
has become a norm, continuously adopt and capitalize on emerging technologies (Di Giacomo et
al., 2020; Di Giacomo et al., 2019; Dinello, 2005). Technophobia’s impact on technology
adoption calls for an investigation into its relationship with the digital divide.
The digital divide has been described as the gap between individuals with access and skills
to use information and technology and those without such (Samuel T Faloye, Ajayi, Raghavjee,
& Faniran, 2020). The literature points to the existence of a deep digital divide in South Africa
(Cox, Cheng, & Forbes, 2018; Nyahodza & Higgs, 2017; Robb, 2020). In an attempt to bridge
it, the South African government and other organizations have focused on measures such as
provision of technological infrastructure, basic technological resources and skills acquisition,
and recent research has thus focused on such interventions. However, the digital divide persists,
highlighting the need to consider human factors as a tool to bridge it (Cox et al., 2018;
Nyahodza & Higgs, 2017; Peroni & Bartolo, 2018; Pierce, 2019; Robb, 2020). Technophobia is
one such factor. It is against this background that the research project on which this article is
based posed the following questions: What individual background characteristics influence
technophobia? How does technophobia impact the digital divide, in this case, access and skills?
The following sections present the study’s objectives, the methodology employed, and the
results of the data analysis. This is followed by a discussion of the results, and a conclusion and
recommendations.
2. Research Objectives
To understand individual background characteristics that influence technophobia.
To understand how technophobia impacts the digital divide.
3. Methodology
Studies on technophobia have typically used a quantitative approach (e.g. Di Giacomo et al.
(2020); Odai Y Khasawneh (2018a); (Nimrod, 2018). Our study also employed a quantitative
methodology, with primary data collected by means of a semi-structured questionnaire. The
study, which was part of a larger one on technophobia in South Africa, focused on the Eastern
Cape. A purposive sampling approach was adopted with the target population being members of
the public who are potential users of technological platforms to enhance their functionality and
productivity in society. The sample size was guided by Krejcie and Morgan (1970) who
suggested that when a population in excess of 100,000 is the focus of a study, an
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 3 of 11
adequate/minimum sample size would be 384. A sample of 384 respondents was identified from
the population of 267,007.
Data was gathered by means of a semi-structured questionnaire. The first page described the
aim of the research and set out instructions, and the researcher’s contact details. Participants
were requested to read the instructions and complete the questionnaire which typically required
10-15 minutes of their time. The questionnaire was pilot tested to assess the quality and validity
of the questions. It was observed that some questions could tax the participants’ memory. This
was addressed by constructing clear and understandable questions. Language barriers were also
experienced by some participants in the pretesting stage. The questionnaire was thus translated
into Xhosa which is the dominant language in the province. The reliability and consistency of
the research instruments were determined using a reliability test in the Statistical Package for the
Social Sciences (SPSS). A Cronbach value of 0. 862 was obtained, indicating that the questions
and responses in the questionnaire were consistent and reliable. In addition, the researcher
structured the questionnaire in accordance with the study’s conceptual model (Fig. 1). The
constructs used to design the research instrument were adapted from previous studies on
technophobia (Odai Y Khasawneh, 2018b; Martínez-Córcoles et al., 2017; Nimrod, 2018; Wei,
Teo, Chan, & Tan, 2011). The confirmatory factor analysis technique was also used to confirm
the internal validity of the questionnaire items. A Pearson’s correlation coefficient was
computed to determine the validity of the relationship between the study’s independent and
dependent variables and a significant correlation co-efficient was achieved (p<0.05).
3.1 Framework
Previous research lacks scale to measure technophobia in a general sense; this study therefore
employed the technophobia model developed by Odai Y Khasawneh (2018b).The framework
was developed in line with the etymology of technophobia as well as several definitions by
different authors. It conceives of technophobia as fear, discomfort and anxiety towards various
forms of technology. However, the framework was aligned with Wei et al’s (2011) digital divide
framework. The technophobia model used 16 items to measure the five dimensions of
technophobia. As shown in Table 1, these were techno paranoia, techno fear (4), techno anxiety
(4), cybernetic revolt (2) and communication device avoidance (2). The internal consistency of
these scales was assessed through Cronbach’s alpha using SPSS. Table 1 presents the Cronbach
Alpha values for the sub-dimensions of technophobia.
Base on a review of the literature (Khasawneh, 2015; Odai Y Khasawneh, 2018b), the five
contructs of the technophobia model appeared to be the determinants of technology acceptance.
Therefore, we propose that they play a vital role in access to technology and skills. In addition,
socio-economic and socio-demographic factors have been considered as moderating factors of
technophobia (Anthony et al., 2000; Crabbe, 2016; Gilbert, LeeKelley, & Barton, 2003;
Powell, 2013). Therefore, age, gender, employment status, and educational level were
considered as moderating factors. The dependent variable (digital divide), and independent
variables are discussed below.
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 4 of 11
Figure 1: Technophobia - Digital divide model S.T. Faloye, © 2022
Independent variable:
Technophobia
Due to continous technological advancement, technology has become an essential
component of daily life. However, technophobia remains a major factor that prevents
individuals from benefiting from the digital age as it hinders adoption of technologies. The
literature has shown that technologies trigger a heightened level of negative attitudes, anxiety
and fear in some people. Such individuals are less likely to adopt technology as their mental
resistance manifests in avoidance of all its forms (Di Giacomo et al., 2019; Hogan, 2009; Odai
Y Khasawneh, 2018b; Nimrod, 2018). Thus, technophobia will impact digital acess as well as
skills. The technophobia sub-dimensions and their application to the study are described as
follows:
Techno paranoia describes unjustified fear and mistrust of all forms of technology which
results in its avoidance even though such fear might not supported by evidence ((Odai Y.
Khasawneh, 2018). In our study, this construct was used to investigate individuals’ level (if any)
of unjustified fear and mistrust towards technology.
Techno fear refers to the fear people experience during their interaction with technology,
which may be perceived as a threat to their established set of norms (Odai Y. Khasawneh,
2018). Such fear is associated with all forms of technology. This construct was used to
determine whether individuals have unpleasant feelings towards technology.
Techno anxiety describes feeling of nervousness and unease people might have about
potential interaction with any form of technology. This construct was used to investigate the
individual level of comfort with potential usage of technology.
Cybernetic revolt is the fear linked to technologies such as artificial intelligence, computer
networks or any technology that spies on people. The construct was used to understand
individuals’ perceptions of these technologies.
Communication device avoidance is avoidance of communication devices such as
smartphones and personal computers, which stems from people’s anxiety or fear regarding the
potential consequences of such technologies. For instance, people are becoming aware of the
side effects of cell phones (O. Kennedy, 2018; Sinkovics, Stöttinger, Schlegelmilch, & Ram,
2002). This construct was used to determine whether individuals avoid such technologies based
on fear or anxiety.
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 5 of 11
Table 1 : Technophobaia Sub-Scale Reliability
Sub- scale dimension Cronbach Alpha Items
Technophobia .973 16
Technoprara-noia .916 4
Techno-fear .915 4
Techno anxiety .905 4
Cybernetic Revolt .870 2
Communication Device
Avoidance
.943 2
The results show that all the technophobia sub-dimension scales conform with the Cronbach
alpha relibility test (a value of .7 or higher).
Dependent variable
Digital divide: Wei et al. (2011) model uses three constructs to explain the factors pertaining
to the digital divide, namely, the digital access divide, which focuses on inequality in access to
IT, mainly hardware and software; the digital capability divide that relates to inequality in the
skills required to use technologies; and the digital outcome divide that focuses on inequality in
individuals’ productivity. Two (the digital access divide and digital capability divide) of the
three construts were used in this study since individual productivity relates to both access and
skills.
Table 2 : Reliability (Digital Access Divide and Digital Capability Divide)
Constructs Cronbach Alpha No of Items
Digital acess divide .972 3
Digital capability divide (A) .851 5
Digital capability divide (B) .793 6
Digital capability divide (C) .824 5
Previous studies on the digital divide have shown positive correlation between the digital
capability divide and access (to technologies), as well as computer self efficacy (CSE) and
access to technologies (Samuel T Faloye, Ajayi, & Raghavjee, 2020; Hasan, 2003; Karsten,
Mitra, & Schmidt, 2012; Vekiri & Chronaki, 2008; Wei et al., 2011). For instance, the
availability of IT resources has the potential to significantly influence students’ computer self
efficacy (Vekiri & Chronaki, 2008). Furthermore, individual skills levels are likely to increase
with time, as frequent use of technology (through access) brings about strong affinity and
confidence towards technology, which in turn leads to increased competency (Samuel T Faloye,
Ajayi, & Raghavjee, 2020). However, technophobic individuals often avoid tecnologies and are
thus less likely to have access. Therefore, technophobia is likely to impact both the digital
access divide and the digital capability divide.
The digital access divide was used to determine if the participants had access to technology
and the type of technologies they could access, while the digital capability divide was used to
investigate the participants’ level of competency in the use of technologies.
4. Findings
A chi-square test was used to ascertain whether there were significant relationships between the
different variables. This test generates a value “P” called Pearson value, which indicates the
statistical significance between the variables under consideration. If the “P” value is less than
0.05 (i.e., P < 0.05), this implies that there is a significant relationship between the variables.
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 6 of 11
However, if the “P” value is greater than 0.05 (i.e., P>0.05), there is no significant relationship
between the variables.
As shown in Figure 2, the participants were categorized as young (18-35), middle-aged (36-
55) and older adults (56 and over). The moderating factors investigated were gender, age,
employment and educational level. Participants were asked questions based on each construct of
the framework relating to their reactions to technologies. The findings below cover the five
constructs of technophobia, namely, techno paranoia, techno fear, techno anxiety, cybernetic
revolt and technology avoidance.
Figure 2: Age Distribution of Respondents
For techno paranoia, the participants were asked questions around unjustifiable fear of
technologies. The analysis showed that many of the older adults (88%, N= 108) demonstrated
high levels of technophobia; however, a few with formal education and formal employment
showed no signs of technophobia. Similarly, a high percentage (89%, N=120) within the young
adults group showed no signs of technophobia, while a few with no formal education or
employment demonstrated mid-to-high level technophobia. Participants (79%, N=97) within the
middle-aged group showed no signs of technophobia. Further analysis showed that participants
with mid to high levels of technophobia had no formal education and a low level of education.
The questions relating to techno fear revolved around unpleasant feelings of fear in the
presence of technology. The data analysis showed that, 90% (N=122) of the participants in the
young adults group showed no signs of technophobia. Within the middle-aged group, 69%
(N=87) of the participants showed no sign of technophobia, while a higher percentage (77%,
N=95) of older adults demonstrated high levels of technophobia. However, the majority of these
participants had no formal education or employment. Furthermore, participants with no formal
education and low levels of education in both the young and middle-aged groups were
technophobic.
For techno anxiety, of the 135 participants in the young adults group, a few (39%, N= 53)
demonstrated mid- to high-level technophobia; these participants did not complete high school
and lacked formal education. Similarly, 25% (N= 32) of the participants in the middle-aged
group with a low level of education and no formal employment demonstrated mild to high-level
technophobia, while a high percentage 90% (N = 111) of the participants in the older adult
group demonstrated mid- to high-level technophobia. However, these participants had no formal
employment and low educational levels. Similarly, participants in other age groups with no
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 7 of 11
formal employment and a low level of education demonstrated mid- to high-level technophobia,
although the mid-level was observed for the young adults group.
The data analysis for cybernetic revolt showed that a low percentage (20%, N= 27) of the
participants in the young adults group demonstrated low technophobia levels; a low percentage
(25%, N=32) in the middle-aged group exhibited mid-to high-level technophobia; and the
majority of the participants (79%, N = 97) in the older adult group exhibited a high level of
technophobia. All these participants had no former employment or education. However,
regardless of their education and employment status, few participants in the young adults age
group demonstrated mid-level technophobia.
With regard to technology avoidance, a high percentage (73%, N=90) of the participants in
the older adult group exhibited a high level of technophobia, while a low percentage (27%, N =
34) in the middle-aged group demonstrated low- to mid-level technophobia. Around 15%
(N=20) of the participants in the young adults group demonstrated low- to mid-level
technophobia. These were participants with no formal employment and low qualifications.
Chi-square analysis and cross tabulations were conducted on the variables to determine the
association between the background characteristics and technophobia level. Table 3 depicts the
result of the chi-square test. The technophobia constructs contain five variables, represented by
V1, V2, V3, V4, and V5, with:
V1 = Techno paranoia
V2 = Techno fear
V3 = Techno anxiety
V4 = Cybernetic revolt
V5 = Technology avoidance
Except for gender, the Chi-Square tests depicted significant differences between
technophobia and the moderating factors (employment level, qualification and age). Inferences
drawn from these findings are thus based on the variables.
Table 3: Chi-Square Result
V1 V2 V3 V4 V5
Chi-Square
Value
103.707 110.272 134.139 98.421 141.299
Age
Asymptotic Sig.
Value (P-Value) 0.00 0.00 0.00 0.00 0.00
Chi-square
Value
121.251 124.242 231.521 425.124 632.125
Gender Asymptotic Sig.
Value (P-Value) 0.321 0.121 0.234 0.642 0.821
Chi-Square
Value
144.240 4342.516 288.304 211.435 209.728
Educational
level Asymptotic Sig.
Value (P-Value)
*0.00 *0.00 *0.00 *0.00 *0.00
Chi-Square
Value
131.062 102.045 99.748 138.525 136.288
Employment
status Asymptotic Sig.
Value (P-Value)
*0.00 *0.00 *0.00 *0.00 *0.00
*P<0.05= Significant Relationship
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 8 of 11
5. Discussion, Conclusion and Recommendations
The above results depict no significant differences in the level of technophobia across gender
(Brosnan, 2002; Di Giacomo et al., 2020; Nimrod, 2018). However, the level of technophobia
varies with respect to the participants’ age, qualifications and educational level. This finding
supports previous studies (Gilbert et al., 2003; Ha, Page, & Thorsteinsson, 2011; Hogan, 2009)
which showed no positive correlation between age and technophobia level. However, our results
contrast with studies that concluded that older adults are prone to high levels of technophobia
(Anthony et al., 2000; Hogan, 2006; Rosen & Weil, 1995). In this study, educational level and
employment level were positively correlated with the level of technophobia among all age
groups although the young adults group showed a low level of technophobia regardless of age,
gender, or educational and employment status. The low technophobia level demonstrated by
young adults can be attributed to their early exposure to all forms of technologies (Anderson,
2011; Brosnan, 2002). They are believed to be more technologically savvy than their older
counterparts because they were raised amidst technologies. Due to this early exposure, they are
likely to have developed an affinity with technologies which often results in high competency
levels and in turn reduces technology anxiety, an antecedent of technophobia (Samuel T. Faloye
& Ajayi, 2021; Samuel T Faloye, Ajayi, & Raghavjee, 2020).
A high level of technophobia was found among participants with no former employment and
low educational levels across all age groups. However, low- to mid-level technophobia was
observed among participants in the young adults group. Further observation revealed high levels
of technophobia among older male and female participants than the young and middle-aged
adult groups. These results suggest that age influences the level of technophobia, with older
adults being more anxious than young and middle-aged ones (Celaya, 1996; Di Giacomo et al.,
2019; Dinello, 2005; dos Santos & Santana, 2018). Previous studies (Anthony et al., 2000;
Dinello, 2005; Dyck, Gee, & Smither, 1998; Loyd & Gressard, 1984; Martínez-Córcoles et al.,
2017) produced mixed results with some showing age differences and others not. In support of
this study’s findings, Brosnan (2002); Di Giacomo et al. (2020) suggest that the age difference
could be due to the fact that older adults are likely to have only encountered technology later in
life; they thus have less affinity with it and are often challenged by technology. This leads to
negative attitudes and anxiety, which are regarded as the antecedents of technophobia (Odai Y
Khasawneh, 2018a; Lee et al., 2019; Sigit, 2021). Significant differences were also observed
between the participants’ employment status, educational qualifications and level of
technophobia. People in formal employment are more likely to have access to technologies than
those in informal jobs (Anderson, 2011). Moreover, they are usually exposed to various forms of
technologies in the course of their career, boosting their confidence and competence, which
often results in less anxiety than those with no exposure.
The majority of the participants with high levels of technophobia lacked access to
technologies such as computers and the Internet, particularly those in the older and middle-aged
groups with no formal education and formal employment. This finding supports those of
Anthony et al. (2000) and Di Giacomo et al. (2020) which showed that technophobic individuals
are less likely to adopt technologies. In this study, further observation showed that, some
participants with access were incompetent in the use of computers, laptops and web surfing,
although they exhibited mid-level technophobia. Low competency may be attributed to
technology anxiety and the negative attitude exhibited by these participants (Dinello, 2005; dos
Santos & Santana, 2018). Anxiety and negative attitudes have been considered as the
antecedents of technophobia (Brosnan, 2002). Individuals who are anxious and have negative
attitudes are likely to find it difficult to use technologies (Ahmad & Daud, 2011). This is
because such individuals often avoid technology-related tasks due to fear, and this affects their
usage skills (Nimrod, 2021; Osiceanu, 2015). On the other hand, participants in the middle-aged
Copyright © 2022 The authors www.IST-Africa.org/Conference2022 Page 9 of 11
and young adult groups demonstrated high competency levels; these were participants with
formal employment and high educational qualifications who showed no signs of technophobia.
In conclusion, the study’s results suggest that age, education levels and employment status
influence technophobia and that technophobia impacts access (the digital access divide) and an
individual’s competency level (the digital capability divide). However, the negative attitudes
and anxiety (antecedents of technophobia) demonstrated by the participants indicate that, in
encouraging technology adoption and enabling people to realize its benefits, strategies to
manage anxiety and negative attitudes would be beneficial. It is thus recommended that
technological skills development programmes be developed for anxious and cognitive
technophobes, and uncomfortable users. This recommendation is based on the notion that a lack
of experience (or exposure) often results in high levels of anxiety and negative attitudes
(Brosnan, 2002; Celaya, 1996; Dinello, 2005; Epstein, 1985; C. E. Kennedy, 1988). In addition,
we recommend psychological interventions such as coaching and counselling to address
technophobia whose value has been demonstrated by several studies (Nicholas Bozionelos,
1996; dos Santos & Santana, 2018; Rosen & Weil, 1995).
Limitation and Suggestions for Further Research
The study only considered a sample from one of South Africa’s nine provinces. It is thus
suggested that future studies should be broader in scope and cover a more diverse population to
determine whether the findings are consistent. Questionnaires could be supplemented with
interviews to gain clear insight on certain questions. Future research could also consider
including other personal characteristics such as religious orientation, personal beliefs, and race.
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