







































Humanitas: Indonesian Psychological Journal  

Vol. 21 (1), February 2025, 45-56 

ISSN:  2598-6368(online); 1693-7236(print)                                                                 

       humanitas@psy.uad.ac.id             http://journal1.uad.ac.id/index.php/Humanitas                              10.26555/humanitas.v22i1.1044 

Modification of the Indonesian Academic Cyberloafing Scale (IACS): 

A tool for assessing online deviance in educational contexts 

Muhammad Nurrifqi Fuadi, Rizal Galih Pradana, Mutmainnah Budiman, Farah Fauziyah, 

Karima Nada Medina, Syukron Ramadhan, Avin Fadilla Helmi, Wahyu Widhiarso 

Faculty of Psichology, Universitas Gadjah Mada, Yogyakarta, Indonesia  
Corresponding author: muhammadnurrifqifuadi@mail.ugm.ac.id 

 

This is an open access article under the CC BY-SA license. 

 

Introduction 

The phenomenon of cyberloafing is closely tied to technological advancements. The presence 
of digital devices such as laptops and smartphones enables individuals to easily access the 
internet and engage in cyberloafing (Metin-Orta & Demirtepe-Saygılı, 2023). Furthermore, 
the simultaneous rise of various social media platforms, including Facebook, Twitter, 
YouTube, Instagram, and TikTok, has accelerated the growth of the cyberloafing 
phenomenon (Keser et al., 2016; Yildiz & Yildiz, 2022). Social media platforms facilitate 
individuals in passing their leisure time or simply communicating with one another through 
text, audio, or even visual messages (Marangoz et al., 2012). 

Initially, cyberloafing behavior was employed as a coping mechanism to relieve stress. 
However, this purpose has gradually shifted, and cyberloafing has started to exert a negative 
influence on individuals. Various forms of cyberloafing manifest in activities such as using 
social media, online shopping, personal email usage, online gaming, news browsing, and 
even accessing inappropriate websites (Toker & Baturay, 2021). These behaviors have 
significant consequences for individuals in various contexts, especially in educational 

ARTICLE  INFO 

 

ABSTRACT  

 

Article history 

Received September 23, 2024 

Revised December 17, 2024 

Accepted January 11, 2025 

 The initial ease of internet use has led to new challenges, one of which 
is the phenomenon of cyberloafing. Cyberloafing refers to the activity 
of accessing the internet during learning processes. The aim of this 
study is to modify the cyberloafing scale within an educational setting 
in Indonesia using the dimensions of sharing, shopping, real-time 
updating, accessing online content, and gaming/gambling. The 
modifications include contextualizing the original and adding new 
relevant items. Data collection was conducted using purposive 
sampling, involving 235 university students from various higher 
education institutions in Indonesia. The method used to test the 
validity of the cyberloafing model was confirmatory factor analysis. 
The results showed that out of 65 items, 20 were found to be valid, 
with a satisfactory total Cronbach’s alpha of 0.73-0.93 and 
McDonald’s omega of 0.71-0.93 for measuring reliability for each 
dimension of cyberloafing. The practical implication of this 
measurement tool is that it can be used to assess the intensity of 
cyberloafing among higher education students in Indonesia.  

     

 
Keywords 
College Students 
Confirmatory Factor Analysis 
Cyberloafing 
Indonesian Version 
Psychometry 
 

 

 

mailto:humanitas@psy.uad.ac.id
http://journal1.uad.ac.id/index.php/Humanitas
https://doi.org/10.26555/humanitas.v22i1.1044
https://creativecommons.org/licenses/by-sa/4.0/


46   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

setting, ranging from decreased performance and productivity (Desnirita & Sari, 2022; Saleh 
et al., 2018) lower academic achievement (Wu et al., 2020), academic procrastination 
(Durak, 2020), reduced focus and attention (Li et al., 2022; Zhang et al., 2022), to diminished 
motivation (Mei et al., 2021) and a reduced sense of life meaning (Krishna & Agrawal, 
2023). These negative impacts arise from students being distracted during class by their 
gadgets, neglecting to engage with the lecture or learning activities (Widiastuti & 
Margaretha, 2016). 

Research on cyberloafing initially emerged within organizational settings. Lim (2002) 
suggested that cyberloafing behavior stems from three theories: social exchange theory, 
organizational justice, and neutralization. Social exchange theory refers to the motivation of 
individuals to engage in social exchanges that maximize rewards and minimize costs. 
Organizational justice pertains to perceptions of fairness and the quality of treatment 
received by individuals within their environment. Neutralization, on the other hand, refers 
to a theoretical perspective that rationalizes and justifies deviant behavior. Later, 
Anandarajan et al. (2004) explored the personal use of the web in workplace settings using 
the dimensions of opportunities versus threats and organizational versus interpersonal 
contexts. 

Further development was made by Blanchard & Henle (2008), who categorized 
cyberloafing into minor and serious types. Their study aimed to examine whether 
cyberloafing could be considered tolerable behavior or if it constituted serious misconduct. 
Minor cyberloafing involves engaging in deviant behavior with limited impact, such as 
checking sports scores while at work, whereas serious cyberloafing entails extreme impacts, 
such as online gambling or accessing adult websites during work. However, this research 
has not yet definitively categorized minor and serious behaviors, as the classification of 
cyberloafing depends heavily on the intent behind the behavior. 

The phenomenon of cyberloafing has also garnered attention in the field of education, 
not just within organizational settings. Kalayci (2010) conducted the first study on 
cyberloafing behavior in an educational context, proposing three new factors: personal 
works, socialization, and news-reading. Akbulut et al. (2016) then developed a cyberloafing 
scale targeting high school and university students, identifying five dimensions: sharing, 
shopping, real-time updating, gaming/gambling, and accessing online content. Akbulut et al. 
(2016) study critiqued the content validity of Kalayci’s (2010) modifications, pointing out 
that despite the adaptation for educational settings, the original scale remained too focused 
on workplace environments. Additionally, Kalayci’s (2010) modifications removed too 
many items, resulting in a loss of essential information from the original scale. 

Most recently, , Polat (2018) developed the Smart Phone Cyberloafing Scale in Classes 
(SPCSC), which consists of three dimensions: browsing-related cyberloafing, interactive 
cyberloafing, and entertainment cyberloafing, with an internal consistency coefficient of 
0.88 (Alanoğlu & Karabatak, 2021; Ozdamli & Ercag, 2021). Polat's scale adapts Blau et al. 
(2006) framework, focusing on cyberloafing via smartphones, based on the assumption that 
cyberloafing in the classroom primarily occurs through smartphone usage. However, this 
scale overlooks other digital devices commonly used today, both for learning and non-
learning purposes, such as tablets and laptops (Koay, 2018). 

This study focuses on Akbulut et al.’s (2016) theory, as it accurately reflects current 
internet usage and provides detailed explanations of its theoretical foundations. Akbulut et 
al. (2016) define cyberloafing in the educational context as internet activities performed for 
non-educational purposes during learning processes. In Indonesia, Pratama and Satwika 
(2022) adapted this measurement using Pearson's product-moment correlation. However, the 
adaptation is largely a literal translation, which may lead to cultural discrepancies. 
Meanwhile, the modifications in this study attempt to contextualize the original items from 
Akbulut et al. (2016) and add other items that are relevant to the current students’ 



Humanitas: Indonesian Psychological Journal 47 

 

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...)et.al  

cyberloafing behaviour. Therefore, this study aims to modify Akbulut et al.’s (2016) 
cyberloafing scale to better align with Indonesian academic settings and cultural nuances. 

Method 

This study employed a quantitative method to modify the cyberloafing scale developed by 
Akbulut et al. (2016) to fit the context of participants in Indonesia. Participants were selected 
based on predetermined criteria (purposive sampling). Eligible participants were asked to 
complete all items of the survey after providing informed consent. 

Participants 

This study involved 250 participants, consisting of university students from various 
institutions across Indonesia. However, after data cleaning, only 235 participants were 
included in the final analysis. This sample size aligns with the minimum number of 
participants recommended by Plichta and Kelvin (2013), which suggests at least 200 
participants for conducting confirmatory factor analysis (CFA). The inclusion criteria for 
participants were: (a) aged 18–35 years; (b) enrolled in a diploma (D3), applied bachelor’s 
(D4), bachelor’s (S1), master’s (S2), or doctoral (S3) program; (c) had been enrolled in their 
studies for at least one month. The exclusion criteria were: (a) incomplete questionnaire 
responses; (b) refusal to complete the informed consent form; (c) duplicate identities. As 
shown in Table 1, the majority of participants were female (73.62%), enrolled in 
undergraduate programs (76.17%), and attending public universities (83.83%). 

Table 1 

Demographic Data of Participants (N=235) 

Demographic N Percentage Cumulative 
Gender    

 Male 62 26.38 % 26.38 % 
 Female 173 73.62% 100 % 
Age    

 18-21 120 51.06 % 51.06 % 
 22-35 115 48.94% 100 % 
Educational Level    

 Post Graduate 56 23.83 % 23.83 % 
 Undergraduate 179 76.17 % 100 % 
Type of University    

 Public 197 83.83 % 83.83 % 
 Private 38 16.17 % 100 % 
Field of Study    

 Science 87 37.02 % 37.02 % 
 Social 148 62.98 % 100 % 

Region     

 Java, Bali, & Nusa Tenggara 133 56.60% 56.60 % 

 Kalimantan 18 7.66% 64.30 % 

 Sulawesi, Maluku, & Papua 53 22.55% 86.81 % 

 Sumatra 31 13.19% 100 % 

Construction and Establishment of the Blueprint 

The cyberloafing scale by Akbulut et al. (2016) originally consisted of 30 items with five 
dimensions. The researchers then modified the scale by adding items relevant to the context 
of Indonesian participants, resulting in a total of 65 items. Afterward, a readability test was 
conducted with five graduate students in psychology. The results of this test were used to 
revise the items to ensure linguistic clarity, align them with the context of the blueprint, and 



48   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

minimize social desirability bias. Table 2 presents the blueprint that served as the foundation 
for the modification process of the scale. 

Data Collection 

Data was collected through a Google Forms survey by distributing the modified cyberloafing 

scale. The primary reason for choosing a survey method was the need to test the 

measurement tool in a confirmatory manner, aligned with the real conditions of the 

participants, without requiring in-depth elaboration on the phenomenon (Sugiyono, 2018). 

The Google Forms platform was chosen for its practicality, allowing researchers to reach a 

larger number of participants within a relatively short time frame. To fill out the cyberloafing 

scale, participants were instructed to read the instructions at the beginning. The following 

are several statements regarding "internet access behavior carried out during learning in the 

last month." The scale used a five-point Likert scale (1: never; 2: rarely; 3: sometimes; 4: 

often; 5: always) to measure the intensity of cyberloafing behavior during learning activities 

over the past month. 

Table 2  

Cyberloafing Blueprint for Indonesian Version 

Dimension Definition Weight Target 

Sharing 

(SHAR) 

Behavior related to accessing the internet to 

share, check, and interact with others through 

social media during learning activities. 

27.69 % 
(18 items) 

20% 
(4 items) 

Shopping 

(SHOP) 

Behavior related to accessing the internet for 

online shopping activities, including selecting 

products, purchasing products, and tracking 

product deliveries during learning activities. 

23.08 % 
(15 items) 

20% 
(4 items) 

Real Time Updating 

(REAL) 

Behavior related to accessing the internet to 

obtain the latest information through social 

media during learning activities. 

15.38 % 
(10 items) 

20% 
(4 items) 

Game / Gambling 

(GAME) 

Behavior related to accessing the internet for 

gaming or online gambling activities during 

learning activities. 

12.31 % 
(8 items) 

20% 
(4 items) 

Accessing Online 

Content 

(ACCE) 

Behavior related to accessing the internet for 

listening to music, watching videos, reading 

articles, or other references outside the context 

of the ongoing learning activities. 

21.54 % 
(14 items) 

20% 
(4 items) 

Total 
100 % 

(65 items) 

100% 
(20 items) 

    

Data Analysis 

Before analyzing the data, the researchers conducted a normality test by examining the 

skewness (acceptable range = -2 < x < 2) and kurtosis (acceptable range = -7 < x < 7(Kim, 

2013). The researchers then performed confirmatory factor analysis (CFA) to test and 

confirm whether the constructs aligned with the theory. Factor loadings were analyzed to 

determine which items were valid in measuring each dimension. The criteria used were CFI 

(0.90), TLI (0.90), SRMR (0.08), and RMSEA (0.08), as recommended by Brown (2006). 

The estimation method for this interval (continuous) data was maximum likelihood (ML) 

(Umar & Nisa, 2020), using JASP version 0.18.1.0. To determine the model, researchers use 

a model with a smaller AIC, BIC information size as a model with a better fit (Kline, 2023; 

Wang & Wang, 2019). In addition, the researchers conducted multigroup confirmatory 

factor analysis (MG-CFA) to examine measurement invariance. 



Humanitas: Indonesian Psychological Journal 49 

 

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...)et.al  

Results 

The Normality Assumption 

After conducting a descriptive analysis on the 65 items of the cyberloafing scale, skewness 
and kurtosis values were obtained for normality testing. The results indicated that 61 items 
fell within the skewness range of +2 and the kurtosis range of +7, while 4 items related to 
gambling did not meet these criteria. Once the normality assumption was satisfied, the next 
step was to conduct confirmatory factor analysis. 

Confirmatory Factor Analysis 

In this section, the researchers performed confirmatory factor analysis in two steps. First, 
analysis process included the multidimensional (61 items) that met the normality 
assumption. Second, the researchers did residual correlation based on modification indices 
to drop the items that have many correlations with other items per dimensions (Umar & Nisa, 
2020). After that, items with highest factor loading for each dimension had been chosen to 
adjust with the blueprint. Moreover, researchers also consider the goodness of fit indices 
(GoF), resulting in 20 items.  

Table 3 

Goodness of Fit 

Model ꭕ2 df p CFI TLI RMSEA SRMR AIC BIC 

Multidimensional 

(61 items) 
4589.65 1759 < .001 0.70 0.69 0.08 0.09 37684.93 38141.59 

Multidimensional 

(20 items) 

286.24 160 < .001 0.96 0.95 0.06 0.05 11469.21 11642.19 

Second-order 

(20 items) 
306.89 165 < .001 0.96 0.95 0.06 0.06 11479.86 11635.54 

Based on the goodness of fit results in Table 3, the multidimensional model with 61 
items exhibited poor fit, with CFI, TLI, and RMSEA values of 0.70 < 0.90, 0.69 < 0.90, and 
0.08 > 0.08. The model showing satisfactory fit indices was the multidimensional (20 items) 
model, which indicated fit with CFI at 0.96, TLI at 0.95, and RMSEA at 0.06. Although the 
second order model also shows satisfactory fit indices with CFI= 0.96, TLI= 0.95, RMSEA= 
0.06, the multidimensional model has a better fit although the difference with the second 
order model is relatively small, and both can be considered quite good depending on the 
context. Furthermore, Table 4 shows that the correlation between dimensions is vary (some 
are too small). Therefore, researchers recommend using this scale in a multidimensional 
model. 

Additionally, Figure 1 shows positive factor loading values ranging from 0.51 to 0.97. 
The overall data indicated z-values greater than 1.96 and p-values less than 0.05, meaning 
all 20 items support construct validity, with all items being valid for measuring each 
dimension. Table 5 is presented the 20 items based on the goodness of fit indices (GoF) that 
exhibited the highest factor loadings according to the results of the confirmatory factor 
analysis. 

 
 
 
 
 
 



50   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

Figure 1 

Multidimensional Model 
 
 

 
 

Table 4 

Interdimensional Correlation 

Dimension ACCE GAME REAL SHOP SHAR 
SHAR 0.71 0.27 0.44 0.64 - 

SHOP 0.57 0.24 0.48 -  

REAL 0.59 0.41 -   

GAME 0.45 -    

ACCE -     

 
Reliability 

After identifying the valid items, the researchers reviewed the Cronbach's alpha values to 
assess the reliability or consistency of an instrument or test in measuring a specific concept 
or characteristic. Cronbach's alpha is used to measure the internal consistency of a set of 
questions or indicators designed to assess the same construct. Based on the analysis, the 
Cronbach's alpha values were as follows: 0.73 for sharing, 0.92 for shopping, 0.91 for real-
time updating, 0.93 for gaming, 0.81 for accessing online content.  

However, some research also suggests considering the use of McDonald’s omega to 
estimate reliability. This is because in some cases, Cronbach’s alpha is often very sensitive 
with sample size (Hayes & Coutts, 2020; Malkewitz et al., 2023). Therefore, the value of 
McDonald’s omega for each dimension presented as follows: 0.71 for sharing, 0.91 for 
shopping, 0.91 for real-time updating, 0.93 for gaming, 0.83 for accessing online content. 
According to Cronbach's alpha and McDonald’s omega criterion of 0.70, which indicates 
good internal consistency, all dimensions of cyberloafing demonstrated satisfactory 
consistency (Cortina, 1993). 

Measurement Invariance 

According to Putnick and Bornstein (2016), measurement invariance assesses whether a 
construct exhibits consistent psychometric properties across various groups or over different 
time periods. This is to determine whether the measurement tool yields consistent and valid 
results that are not influenced by group differences. In this study, the researcher conducts a 



Humanitas: Indonesian Psychological Journal 51 

 

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...)et.al  

measurement invariance test on postgraduate and undergraduate groups because these two 
groups have different academic experiences that may impact their cyberloafing behaviors. 

Table 5 

Item & Factor Loading 

Item Factor Loading 
Factor 1 – Sharing (Alpha: 0.73; Omega: 0.71) 
Saya memosting foto di media sosial (SHAR2) 

 

0.51 
Saya menandai teman pada foto yang saya bagikan di media sosial (SHAR4) 0.63 

Saya membuka media sosial (SHAR10) 0.66 
Saya mem-follow akun media sosial teman saya (SHAR17) 0.71 

 

Factor 2 – Shopping (Alpha: 0.92; Omega: 0.91) 
Saya mengunjungi situs belanja online (SHOP1) 

 

 

0.94 
Saya menelusuri beberapa produk pada situs belanja online (SHOP2) 0.92 

Saya menambahkan produk ke keranjang (SHOP3) 0.83 
Saya melakukan pembelanjaan online (SHOP8) 0.73 

 

Factor 3 – Real Time Updating (Alpha: 0.91; Omega: 0.91) 
Saya memastikan jadwal rilis tayangan favorit (REAL5) 

 

 

0.86 
Saya menunggu tayangan favorit di channel tujuan (REAL6) 0.92 

Saya me-reload halaman channel untuk menonton tayangan favorit (REAL7) 0.88 
Saya memastikan jadwal live streaming (REAL8) 0.71 

 

Factor 4 – Gaming (Alpha: 0.93; Omega: 0.93) 
Saya bermain game online (GAME1) 

 

 

0.93 
Saya membeli item terbaru pada game online yang saya mainkan (GAME2) 0.69 

Saya membuka aplikasi game online (GAME3) 0.97 
Saya mengupdate fitur terbaru pada game online yang saya mainkan (GAME4) 0.89 

 

Factor 5 - Accessing Online Content (Alpha: 0.81; Omega: 0.83) 
Saya mendengarkan lagu (ACCE2) 

 

 

0.77 
Saya menonton video (ACCE5) 0.84 
Saya membaca berita (ACCE6) 0.69 

Saya men-download aplikasi (ACCE11) 0.62 

Invariance testing or CFA based on specific demographic groups was conducted for 
postgraduate and undergraduate groups. Table 6 presents the results of invariance criteria 
testing, including configural, metric, scalar, and strict invariance, indicating equal variance. 
This allows for an accurate comparison in the analysis of cyberloafing behaviors between 
the two groups. The researchers used CFI (0.90), SRMR (0.08), and RMSEA (0.08) 
according to Brown (2006). 

 

Table 6 

Measurement Invariance 

Description ꭕ2 df p CFI TLI RMSEA SRMR 

Model Invariance Testing        

Multidimensional 

(20 items) 

Configural 527.37 320 < .001 0.94 0.92 0.07 0.06 

Metrics 548.42 335 < .001 0.93 0.93 0.07 0.07 

Scalar 577.95 350 < .001 0.93 0.92 0.07 0.07 

Strict 632.97 370 < .001 0.92 0.92 0.08 0.08 

 



52   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

First, configural invariance yielded CFI, TLI, and RMSEA values of 0.94, 0.92, and 
0.07, respectively. This indicates that the factor structure is similar between postgraduate 
and undergraduate groups. Second, metric invariance resulted in CFI, TLI, and RMSEA 
values of 0.93, 0.93, and 0.07, respectively, suggesting that the factor loadings are 
comparable between the two groups. Third, scalar invariance yielded CFI, TLI, and RMSEA 
values of 0.93, 0.92, and 0.07, indicating that scalar parameters, such as factor loadings and 
intercepts of the structural equation model, are constant across both groups. Lastly, strict 
invariance provided CFI, TLI, and RMSEA values of 0.92, 0.92, and 0.08, indicating that 
the parameters, including factor loadings, intercepts, and residuals, maintain a consistent 
model structure between the postgraduate and undergraduate groups. 

Discussion 

The measurement instrument for cyberloafing was initially developed and pioneered by Lim 
(2002) in a work setting. The loafing behavior that disrupted productivity in the workplace 
eventually spread to the educational field, leading Akbulut et al. (2016) to construct a 
cyberloafing scale within an educational setting. In the context of Indonesia, the 
development of cyberloafing is unique not only because Indonesia ranks among the largest 
internet users with 212.9 million internet users and 167 million social media users as of early 
2023, but also due to the shift in educational media trends (Kemp, 2023). This study focuses 
on developing a cyberloafing scale with strong validity and reliability based on the 
perspectives of Indonesian students. 

The theoretical framework of cyberloafing within an educational setting in Indonesia 
shows similarities to the model developed by Akbulut et al. (2016). The five-dimensional 
multidimensional model is also applicable in the Indonesian context with minor 
modifications. These modifications include (1) the elimination of items related to real-time 
updates via Twitter; and (2) the removal of items representing gambling as part of the 
gaming/gambling dimension. This suggests that Indonesian students no longer engage in 
cyberloafing via Twitter, and gambling behaviors are not well represented by these items. 

In the sharing dimension, Indonesian students' cyberloafing behavior is represented by 
items indicating content sharing, checking social media, and interacting on social media. 
This is highly relevant to the current situation, where many students cannot disengage from 
sharing activities on social media, even during learning sessions. This claim is supported by 
data from Kemp (2023), showing that the social media penetration rate for the age group of 
18-35 years is 26.3%, significantly higher than other age groups. Furthermore, the shift to 
online learning during the Covid-19 pandemic has transformed the culture and habits of 
learning activities from paper-based to digital-based formats. This shift has persisted even 
after the resumption of offline lectures in the post-pandemic period. 

Next, online shopping activities are observed through behaviors such as searching for 
and purchasing products. These items are relevant to the end-to-end process of online 
shopping today through various marketplace platforms. Considering the high level of 
consumption, especially among Indonesian students, these items are highly applicable to 
their daily lives (Databoks, 2018). Furthermore, the behavior of accessing the internet to 
obtain real-time information on social media is represented by actions such as waiting for 
favorite shows and live streaming. This differs from Akbulut et al.’s (2016) original items, 
which specifically referenced Twitter for real-time updates. This shift is due to the relatively 
low number of Twitter users in Indonesia, especially among students, with only 24 million 
users. In contrast, Instagram has 89.15 million users, and TikTok has 109.9 million users 
(Kemp, 2023). 

The gaming/gambling dimension is represented solely by items indicating online 
gaming behavior. This finding is noteworthy because all gambling-related items were 



Humanitas: Indonesian Psychological Journal 53 

 

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...)et.al  

eliminated. This can be attributed to cultural factors, as Indonesian society, particularly 
students, exhibits high levels of religiosity and attributes gambling as an activity that is 
prohibited and contradicts both religious and social norms (Choirina et al., 2021). This 
cultural context led to poor data distribution and low factor loading values, resulting in the 
removal of the gambling items. Lastly, accessing online content is represented by behaviors 
such as listening to music, watching videos, reading online articles, and accessing various 
applications. This is relevant to the daily habits of Indonesian students, who frequently use 
music streaming platforms like Spotify, video streaming platforms like YouTube, and other 
applications to access news. 

The researcher acknowledges that this study has several limitations related to the 
demographic characteristics of the participants, which are unevenly distributed, specifically 
(1) the number of female participants exceeds that of males; (2) postgraduate students are 
overrepresented compared to undergraduate and diploma students; and (3) the majority of 
participants are concentrated in the Java and Bali regions. These disparities may affect the 
research findings, as internet access habits can vary between genders. Similarly, 
postgraduate students, who are generally more mature, may exhibit different online 
behaviors compared to undergraduate and diploma students. Additionally, differences in 
culture and telecommunications infrastructure between Java and Bali and other regions may 
influence participants' internet usage patterns. 

Conclusion 

The process of modifying the cyberloafing measuring instrument in the Indonesian context 
was carried out by providing contextualization to the original items and adding new relevant 
items. Based on the goodness of fit indices (GoF), the multidimensional model with 61 items 
was not satisfactory in terms of psychometric properties. On the other hand, the 
multidimensional model with 20 items demonstrated a satisfactory fit, with all items being 
valid in measuring each dimension. Although the second-order model with 20 items also 
showed satisfactory fit, the multidimensional model had smaller AIC and BIC scores, so 
researchers recommend administering this scale in multidimensional form. Additionally, 
reliability testing using Cronbach's Alpha and McDonald’s Omega indicated that the 
cyberloafing dimensions have satisfactory internal consistency. The practical implication of 
this study is that the modified scale can be used to measure the intensity of cyberloafing 
among higher education students in Indonesia. 

 

Acknowledgment 

The authors express sincere gratitude to the instructors and teaching assistants for their 
invaluable time and insights during the consultation of ideas and research design. 
Additionally, the author extends heartfelt thanks to all participants who willingly took part 
in this study. 

 

Declarations 

Author contribution. MNF: Idea Conceptualization, Review Literature, Data Collection, 
Data Analysis, Method, Result and Discussion, Revision and Finishing. RGP: Idea 
Conceptualization, Review Literature, Data Collection, Method, Result and Discussion, 
Revision and Finishing. MB: Idea Conceptualization, Review Literature, Data Collection, 
Introduction, Revision and Finishing. FF: Idea Conceptualization, Review Literature, Data 
Collection, Introduction, Revision and Finishing. KNM: Idea Conceptualization, Review 
Literature, Data Collection, Introduction, Revision and Finishing. SR: Idea 



54   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

Conceptualization, Review Literature, Data Collection, Method, Revision and Finishing. 
AFH: Idea Conceptualization, Method, Revision, Finishing, and Supervision and Guidance 
throughout the research process. WW: Idea Conceptualization, Method, Revision, Finishing 
and Supervision and Guidance throughout the research process. 
Funding statement. The authors did not receive any funding support for the research, 
writing, or publication of this article. 
Conflict of interest. The authors declare no conflict of interest. 
Additional information. No additional information is available for this paper. 
 

References 
 

Akbulut, Y., Dursun, Ö. Ö., Dönmez, O., & Şahin, Y. L. (2016). In search of a measure to 

investigate cyberloafing in educational settings. Computers in Human Behavior, 55, 

616–625. https://doi.org/10.1016/j.chb.2015.11.002 

Alanoğlu, M., & Karabatak, S. (2021). Examining of the smartphone cyberloafing in the 

class: Relationship with the attitude towards learning and prevention of cyberloafing. 

International Journal of Technology in Education, 4(3), 351–372. 

https://doi.org/10.46328/ijte.84 

Anandarajan, M., Devine, P., & Simmers, C. A. (2004). A multidimensional scaling 

approach to personal web usage in the work place. In M. Anandarajan & C.A. Sim-

mers (Eds.), Personal web usage in the workplace: A guide to effective human resource 

management (pp. 61–79). 

Blanchard, A. L., & Henle, C. A. (2008). Correlates of different forms of cyberloafing: The 

role of norms and external locus of control. Computers in Human Behavior, 24(3), 

1067–1084. https://doi.org/10.1016/j.chb.2007.03.008 

Blau, G., Yang, Y., & Ward-Cook, K. (2006). Testing a measure of cyberloafing. Journal of 

Allied Health, 35(1), 9–17. https://pubmed.ncbi.nlm.nih.gov/16615292/ 

Brown, T. A. (2006). Confirmatory factor analysis for applied research. The Guilford Press. 

Choirina, V. N., Ayriza, Y., & Wibowo, Y. S. (2021). Religiosity and life satisfaction in 

Indonesia: Evidence from a community survey. Journal of Educational, Health and 

Community Psychology, 10(1), 38–47. https://doi.org/10.12928/jehcp.v10i1.19625 

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. 

Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-

9010.78.1.98 

Databoks. (2018). Bappenas notes shift in public spending. Databoks. 

https://databoks.katadata.co.id/datapublish/2018/07/24/bappenas-catat-pergeseran-

belanja-masyarakat 

Desnirita, D., & Sari, A, P. (2022). The impact of workload and cyberloafing behavior on 

employee performance at PT Dwidaya World Wide, DKI Jakarta branch area. Journal 

of Indonesian Accounting Academy Padang, 2(1), 1–13. 

https://doi.org/10.31933/jaaip.v2i1.540 

Durak, H. Y. (2020). Cyberloafing in learning environments where online social networking 

sites are used as learning tools: Antecedents and consequences. Journal of Educational 

Computing Research, 58(3), 539–569. https://doi.org/10.1177/0735633119867766 

Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than cronbach’s alpha for estimating 

reliability. But…. Communication Methods and Measures, 14(1), 1–24. 

https://doi.org/10.1080/19312458.2020.1718629 



Humanitas: Indonesian Psychological Journal 55 

 

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...)et.al  

Kalayci, E. (2010). The investigation of relationship between cyberloafing and self regulated 

learning strategies among undergraduate students [Unpublished master’s thesis]. 

Hacettepe University. 

Kemp, S. (2023). Digital 2023: Indonesia-DataReportal-Global Digital Insights. 

https://datareportal.com/reports/digital-2023-indonesia 

Keser, H., Kavuk, M., & Numanoglu, G. (2016). The relationship between cyber-loafing and 

internet addiction. Cypriot Journal of Educational Sciences, 11(1), 37–42. 

https://doi.org/10.18844/cjes.v11i1.431 

Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution 

(2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52. 

https://doi.org/10.5395/rde.2013.38.1.52 

Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). The 

Guilford Press. 

Koay, K.-Y. (2018). Assessing cyberloafing behaviour among university students: A 

validation of the cyberloafing scale. Pertanika Journal of Social Science and 

Humanities, 26(1), 409–424. http://www.pertanika.upm.edu.my/pjssh/browse/regular-

issue?article=JSSH-1974-2016 

Krishna, S. M., & Agrawal, S. (2023). Cyberloafing: Exploring the role of psychological 

wellbeing and social media learning. Behavioral Sciences, 13(8), 649. 

https://doi.org/10.3390/bs13080649 

Li, Q., Xia, B., Zhang, H., Wang, W., & Wang, X. (2022). College students’ cyberloafing 

and the sense of meaning of life: The mediating role of state anxiety and the moderating 

role of psychological flexibility. Frontiers in Public Health, 10. 

https://doi.org/10.3389/fpubh.2022.905699 

Lim, V. K. G. (2002). The IT way of loafing on the job: Cyberloafing, neutralizing and 

organizational justice. Journal of Organizational Behavior, 23(5), 675–694. 

https://doi.org/10.1002/job.161 

Malkewitz, C. P., Schwall, P., Meesters, C., & Hardt, J. (2023). Estimating reliability: A 

comparison of cronbach’s α, McDonald’s ωt and the greatest lower bound. Social 

Sciences & Humanities Open, 7(1), 100368. 

https://doi.org/10.1016/j.ssaho.2022.100368 

Marangoz, M., Yesildag, B., & Arikan Saltik, I. (2012). A Research on web and social 

network sites of e-commerce enterprises by content analysis method. Journal of 

Internet Applications and Management, 3(2), 53–78. 

https://doi.org/10.5505/iuyd.2012.87597 

Mei, T. K., Mahamood, A. F., Abdullah, S., Yakob, T. K. T., & Mokhdzar, Z. A. (2021). 

Cyberloafing behavior and its effects towards academic achievement among students 

in higher education institution cyberloafing behavior and its effects towards academic 

achievement among students in higher education institution. Journal of Human 

Development and Communication, 10, 115–133. 

https://johdec.unimap.edu.my/index.php/volume-10-2021 

Metin-Orta, I., & Demirtepe-Saygılı, D. (2023). Cyberloafing behaviors among university 

students: Their relationships with positive and negative affect. Current Psychology, 

42(13), 11101–11114. https://doi.org/10.1007/s12144-021-02374-3 

Ozdamli, F., & Ercag, E. (2021). Cyberloafing among university students. TEM Journal, 

10(1), 421–426. https://doi.org/10.18421/TEM101-53 



56   

               ISSN 2598-6368 (online) / ISSN 1693-7236 (print) 

  

Muhammad Nurrifqi Fuadi et.al (Modification of the Indonesian Academic Cyberloafing Scale (IACS):...) 

Plichta, S. B., & Kelvin, E. A. (2013). Munro’s statistical methods for health care research 

(6th ed.). Wolters Kluwer Health/Lippincott Williams &Wilkins. 

Polat, M. (2018). The smart phone cyberloafing scale in classes (SPCSC): A scale adaptation 

study for university students. Social Sciences Studies Journal, 4(21), 3114–3127. 

https://doi.org/10.26449/sssj.733 

Pratama, M. Y. A., & Satwika, Y. W. (2022). The relationship between self-regulation and 

cyberloafing behavior in psychology students of Surabaya state university. Character: 

Journal of Psychological Research, 9(1), 21–33. 

https://ejournal.unesa.ac.id/index.php/character/article/view/44551 

Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and 

reporting: The state of the art and future directions for psychological research. 

Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004 

Saleh, M., Daqqa, I., Rahim, M. B. A., & Sakallah, N. (2018). The effect of cyberloafing on 

employee productivity. International Journal of Advanced and Applied Sciences, 5(4), 

87–92. https://doi.org/10.21833/ijaas.2018.04.011 

Sugiyono. (2018). Quantitative Research Methods. Alfabeta. 

Toker, S., & Baturay, M. H. (2021). Factors affecting cyberloafing in computer laboratory 

teaching settings. International Journal of Educational Technology in Higher 

Education, 18(1), 20. https://doi.org/10.1186/s41239-021-00250-5 

Umar, J., & Nisa, Y. F. (2020). Construct validity test with CFA and reporting. Indonesian 

Journal of Psychology and Education Measurement, 9(2), 1–11. 

https://doi.org/10.15408/jp3i.v9i2.16964 

Wang, J., & Wang, X. (2019). Structural Equation Modeling. Wiley. 

https://doi.org/10.1002/9781119422730 

Widiastuti, R., & Margaretha, M. (2016). Personality factors and cyberloafing of college 

students in Indonesia. International Journal of Applied Business and Economic 

Research, 14(13), 9227–9238. https://www.serialsjournals.com/abstract/89800_30-

ratna.pdf 

Wu, J., Mei, W., Ugrin, J., Liu, L., & Wang, F. (2020). Curvilinear performance effects of 

social cyberloafing out of class: The mediating role as a recovery experience. 

Information Technology & People, 34(2), 581–598. https://doi.org/10.1108/ITP-03-

2019-0105 

Yildiz, H., & Yildiz, B. (2022). Testing the validity and reliability of a Turkish version of 

the social cyberloafing scale. Perspectives in Psychiatric Care, 58(4), 1291–1302. 

https://doi.org/10.1111/ppc.12930 

Zhang, Y., Tian, Y., Yao, L., Duan, C., Sun, X., & Niu, G. (2022). Teaching presence 

predicts cyberloafing during online learning: From the perspective of the community 

of inquiry framework and social learning theory. British Journal of Educational 

Psychology, 92(4), 1651–1666. https://doi.org/10.1111/bjep.12531 

  


