Prevalence of phubbing behavior and consequences on self-efficacy: a cross-sectional study among the Palestinian population

Abstract

Background

Phubbing is the behavior of ignoring face-to-face conversation while using a smartphone. This cross-sectional study is one of the first studies in Palestine to examine the prevalence of phubbing behavior and its association with self-efficacy among the community.

Methods

This cross-sectional study was conducted in Palestine between March and April 2025. A total of 357 participants aged 18–65 years were recruited using convenience sampling. Data were collected using a structured questionnaire comprising three sections: sociodemographic information, the Phubbing Scale (PS), and the General Self-Efficacy Scale (GSES).

Results

More than 70% of participants reported moderate to high levels of phubbing. The average total phubbing score was 27.32 (SD = 7.04), with subscale averages of 10.95 (SD = 3.67) for Communication Disturbance and 16.38 (SD = 4.35) for Phone Obsession; the average self-efficacy score was 28.40 (SD = 5.01). Stepwise multiple regression identified daily phone use, Facebook use, TikTok use, and age as positive predictors of overall phubbing, while self-efficacy was a negative predictor. Daily phone use was the only significant predictor of Phone Obsession. The models explained 23.5% of the variance in overall phubbing, 20.3% in Communication Disturbance, and 16.3% in Phone Obsession.

Conclusions

The study found a moderate prevalence of phubbing, which was positively associated with phone and social media use, and negatively associated with self-efficacy. This suggests that enhancing self-efficacy may help reduce phubbing behavior.

Introduction

The number of smartphone owners has recently increased, and they have become an integral part of daily life at home, work, and university, serving as a vital tool for communication, entertainment, and information. The widespread availability of mobile technology, coupled with the pervasive use of social media platforms, has become an increasing phenomenon, leading to interpersonal and social consequences across various societies [1, 2]. Phubbing is a combination of two words, “phone” and “snubbing,” referring to the act of ignoring someone in a social setting by focusing on one’s smartphone rather than paying attention during face-to-face conversations. Phubbing behavior is multifaceted, with two primary subsections that are most important: Phone Obsession and Communication Disturbance [3]. Several researchers worldwide, including those in China, the US, Spain, and Lebanon, have studied phubbing behavior to examine its possible impact and consequences on social, self-perception, and physiological health [4].

The concept of self-efficacy describes individuals’ beliefs about their ability to achieve their goals. Bandura described self-efficacy as one of the main concepts of his cognitive theory [5]. Self-efficacy influences personality and various actions related to the workplace, education, and social relationships. Previous studies had shown that individuals with higher scores on the self-efficacy scale showed more confidence and control in their personality, while those with lower scores showed that they struggled with stress and confidence [6, 7].

Several studies have been conducted to discover the prevalence of phubbing behavior. Previous cross-sectional studies showed that phubbing behavior was high among university students, with prevalence rates from 45% to over 70%, depending on the country of the study [8]. Additionally, Chotpitayasunondh et al. (2018) found in his study that mobile phone use and social media addiction were significantly associated with phubbing behaviors among adults [3]. While in Arab countries, this phenomenon was tested by different researchers, such as Zeidan et al. (2021) who examined smartphone usage and phubbing behavior among young Lebanese adults. The study showed high rates of smartphone use, which strongly correlated with phubbing behavior [4]. Previous review studies have indicated that excessive smartphone use is associated with increased phubbing behavior [9].However, broader evidence also links high social media use to negative behavioral and psychological outcomes [10].

Individuals who have phubbed or have been phubbed reported low satisfaction with their personal and professional relationships, which were related to mobile use [8]. Furthermore, phubbing raises the risk of stress, depressive disorders, and social isolation. However, these studies suggest that individuals with lower self-efficacy are more likely to exhibit phubbing behaviors, particularly when avoiding real-life interactions, especially in challenging situations. Zeidan et al. (2021) demonstrated that smartphone use was associated with lower self-esteem among Lebanese youth [4]. In some studies, males have been observed to exhibit higher levels of phubbing, suggesting that phubbing differs by gender and that students are more likely to engage in phubbing than non-students [2, 4]. In the study by Chotpiaysunondh et al. (2018) results showed that individuals who used their smartphones for more than 4 h per day reported higher rates of phubbing [3]. Also, the excessive use of social media and daily communication has highlighted the higher phubbing rate, whereby individuals with high Facebook app use reported more depressive symptoms and phubbing behavior [2].

With the increasing reliance on smartphones, the relationship between phubbing and self-efficacy warrants further exploration. Additionally, few studies assessed the prevalence of phubbing in the Arab community, especially among the Palestinian population. Recent research has evaluated the prevalence of phubbing across several countries, including the United States, Spain, India, and others. These studies suggest that this emerging phenomenon has a negative impact on both physiological and mental health.

However, few epidemiological studies examined significant differences related to gender, age, and educational level. In this study, we aim to evaluate the prevalence of phubbing behavior among the Palestinian community and explore its correlation with self-efficacy and screen time. Moreover, we will compare potential risk factors associated with student phubbing behavior with those of non-students and determine whether there are significant differences in phubbing behavior across sociodemographic groups (age and gender).

Methods

Study design, setting, and sample sze

This cross-sectional study was conducted in March and April 2025 in Palestine to determine the prevalence of Phubbing and its association with self-efficacy.

Participants were recruited using a mixed-mode convenience approach through two methods. First, electronically via social media platforms such as Facebook, WhatsApp, and Instagram. This was done by sharing the Google Forms link on people’s feeds and in geographically defined Facebook groups representing different Palestinian cities (e.g., “Residents of Nablus”) to broaden participation. Second, identical paper-based surveys were administered in person by approaching individuals in public areas to increase participation among those with limited internet access. The questionnaire content, item order, and response scales were identical in both formats to ensure methodological consistency.

Eligibility criteria were defined prior to data collection. The inclusion criteria were: (1) adults aged 18 to 65 years (2), current main residence in Palestine (3), ability to read and understand Arabic, and (4) provision of informed consent. The exclusion criteria included individuals outside the specified age range and those not residing in Palestine. The online questionnaire used mandatory-response fields; therefore, incomplete electronic submissions were not possible. All submitted surveys met the inclusion criteria and were included in the final analysis.

A total of 357 individuals aged 18 to 65 years, with a mean age of 29.14 ± 10.16, participated in our study and resided in Palestine, including cities, villages, and camps. The distribution of participants by residency among the Palestinian governorates was as follows: 73.3% were from the northern West Bank, while 26.7% were from the southern West Bank. Of the final sample (n = 357), 81.7% completed the survey electronically, while 18.3% completed the identical paper-based version.

Measurement tools

The survey was developed in Arabic and structured into three main domains. The first domain consisted of 18 socio-demographic and behavioral items, such as age, gender, education level, employment status, marital status, place of residence, total daily phone usage in hours, and the amount of time spent on each mobile social media platform daily. Various mobile social media platforms were included, such as Instagram, TikTok, Facebook, LinkedIn, WhatsApp, Telegram, YouTube, as well as movies, games, educational apps, and online shopping. The second domain included the 10-item Arabic version of the Phubbing Scale to assess participants’ phubbing behavior. The third domain consisted of the 10-item General Self-Efficacy Scale (GSES) to assess participants’ self-efficacy across various situations. The electronic survey was created using Google Forms, and an identical paper-based questionnaire was produced following the same structure, item order, and response format to ensure methodological consistency. All items in the electronic version were mandatory-response fields to prevent missing data.

Prior to data collection, a pretest of the questionnaire was conducted with approximately 15 participants to assess its clarity, comprehensibility, and completeness. Participants were asked to provide feedback on item wording, structure, and whether additional items were necessary. Minor wording adjustments were made to improve clarity, and no additional items were required.

Phubbing Scale (PS)

Phubbing was assessed using the Phubbing Scale (PS), originally developed by Karadağ et al. [11]. The scale consists of 10 items divided into two subdimensions: Communication Disturbance (CD) and Phone Obsession (PO), with 5 items each. Items are rated on a 5-point Likert scale ranging from 1 (Never) to 5 (Always), with higher scores indicating greater phubbing behavior [11]. In the present study, the total phubbing score was calculated by summing responses across all 10 items, yielding a possible score range of 10–50. Scores for the two subdimensions were calculated by summing the responses of their respective five items, resulting in a possible score range of 5 to 25 for each subdimension.

The Arabic Phubbing scale demonstrated good validity and reliability. In this study, the translation of the phubbing scale was conducted using a double-blind procedure. The Arabic version was reviewed by five local experts to assess its cultural relevance, clarity, and comprehensibility, thereby supporting its content validity. Construct validity was further examined using Exploratory Factor Analysis with Principal Axis Factoring. The data were suitable for factor analysis (KMO = 0.849; Bartlett’s test of sphericity χ² (45) = 1326.571, p < 0.001). Two factors with eigenvalues greater than 1 (4.218 and 1.346) were extracted, explaining 55.63% of the total variance. The rotated factor matrix indicated that the items loaded appropriately on their respective factors, consistent with the proposed bidimensional structure of the scale. Internal consistency reliability was also evaluated in the current sample using Cronbach’s alpha, which indicated good reliability for the overall scale (α = 0.844).

To describe severity levels of phubbing behavior, categories of low, moderate, and high, according to the developer of the scale, scores over 45 indicate high phubbing behaviors [11]. However, the percentile-based cutoffs (25th and 75th percentiles) classifications were derived from the sample distribution, which, using the standardized cutoff values, may limit direct comparability. For the total phubbing score, values ≤ 22 were classified as low, 23–31 as moderate, and ≥ 32 as high. For Communication Disturbance, scores ≤ 8 were categorized as low, 9–12 as moderate, and ≥ 13 as high. For Phone Obsession, scores ≤ 13 were classified as low, 14–18 as moderate, and ≥ 19 as high.

General self-efficacy scale (GSES)

Self-efficacy was assessed using the General Self-Efficacy Scale (GSES), originally developed by Schwarzer and Jerusalem [12]. The GSES is a 10-item instrument designed to measure individuals’ perceived ability to cope with challenges, achieve goals, and manage unexpected situations. The scale has demonstrated good psychometric properties across diverse populations, with reported Cronbach’s alpha values typically ranging from 0.76 to 0.90. The Arabic version of the General Self-Efficacy Scale (GSES) has been previously applied in Palestinian populations and has demonstrated satisfactory psychometric reliability [13–15]. In the current study, the instrument also exhibited good internal consistency reliability, with a Cronbach’s alpha coefficient of 0.866.

In this study, each item was rated on a 5-point Likert scale ranging from 1 (Never) to 5 (Always). Total scores were calculated by summing all 10 response items, yielding a possible score range of 10–50, with higher scores indicating greater perceived self-efficacy. Self-efficacy was operationalized as the total GSES score. To describe the distribution of self-efficacy levels, categories of low, moderate, and high were determined using percentile-based cutoffs (25th and 75th percentiles) derived from the sample distribution. Scores ≤ 25 were classified as low, scores between 26 and 31 as moderate, and scores ≥ 32 as high.

Ethical consideration

Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the Faculty of Medicine and Allied Medical Sciences at An-Najah National University (approval number: Med. Nov. 2024/13). The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants prior to their enrollment in the study. Participants were clearly informed about the purpose of the study and the confidentiality of their data and were assured that the information collected would be used solely for research and publication purposes.

Statistical analysis

Microsoft Excel 2016 and SPSS version 27 were used for all calculations and analysis. We set a statistical significance (P < 0.05) in this study. Various descriptive statistics were used to summarize Mean (µ), standard deviation (SD), and percentage (%) to show demographic data such as gender, age, education, and maternal status. Stepwise multiple regression analysis was used to assess the phubbing scores. Significant predictors included self-efficacy, total hours of phone use per day, age, and daily use of social media platforms such as Facebook and TikTok.

To ensure the robustness of the regression models, categorical sociodemographic and behavioral variables were numerically coded prior to analysis. Dichotomous variables, such as place of residence, were dummy-coded (e.g., Urban = 1, non-urban = 2). Behavioral variables representing social media use (e.g., Facebook, TikTok, YouTube) and total daily smartphone use were treated as ordinal data. Daily social media usage was coded on a 5-point scale: 1 (I don’t use it), 2 (Less than 1 h), 3 (1–2 h), 4 (2–3 h), and 5 (More than 3 h). Total daily phone use was coded into four categories: 1 (Less than 2 h), 2 (2–4 h), 3 (4–6 h), and 4 (More than 6 h). This systematic coding allowed for the inclusion of non-continuous variables in the stepwise multiple regression analysis.

Results

A total of 357 participants were included in the study. The average age of the sample was 29 ± 10.11 years, with ages ranging from 18 to 65 years and the median age was 26 years.

A range of variables was used to describe the sociodemographic characteristics of the sample, as presented in Table 1. The gender distribution showed that 29.69% of respondents were male and 70.31% were female, indicating a predominance of female participants. Regarding educational attainment, 68.35% had completed higher education, 22.41% held a bachelor’s degree or diploma, and 9.24% had secondary education or less. Almost half of the participants (47.06%) were employed, whereas 31.65% were students and 21.29% were unemployed. In terms of marital status, 59.38% were not married, and 40.62% were married. Most participants (63.31%) lived in urban areas (cities), while 36.69% lived in non-urban areas (villages and camps). Regarding daily phone use, the largest group (35.60%) reported using their phones for 4–6 h per day, followed by 30.00% who used them for 2–4 h, and 28.90% who used them for more than 6 h.

Table 1.

Sociodemographic characteristics of participants

Variable	Level			Count	%
Gender	Male			106	29.69%
	Female			251	70.31%
Education	Secondary or less			33	9.24%
	Baccalaureate or Diploma			80	22.41%
	Higher Education			244	68.35%
Employment Status	Student			113	31.65%
	Unemployed			76	21.29%
	Employed			168	47.06%
Marital Status	Not married			212	59.38%
	Married			145	40.62%
Residency	Urban (city)			226	63.31%
	Non-Urban (village and camp)			131	36.69%
Social Media Usage Daily
Total Hours Spent on Phone Daily	Less than 2 h			20	5.60%
	2–4 h			107	30.00%
	4–6 h			127	35.60%
	More than 6 h			103	28.90%
Facebook	I don’t use it			25	7.00%
	Less than 1 h			143	40.05%
	1–2 h			107	29.97%
	2–3 h			59	16.53%
	More than 3 h			23	6.44%
TikTok	I don’t use it			147	41.18%
	Less than 1 h			70	19.61%
	1–2 h			53	14.85%
	2–3 h			44	12.30%
	More than 3 h			43	12.05%
WhatsApp	I don’t use it			3	0.80%
	Less than 1 h			111	31.09%
	1–2 h			123	34.45%
	2–3 h			72	20.17%
	More than 3 h			48	13.45%
Instagram	I don’t use it			63	17.65%
	Less than 1 h			67	18.78%
	1–2 h			83	23.25%
	2–3 h			89	24.93%
	More than 3 h			55	15.41%
Telegram	I don’t use it			157	43.98%
	Less than 1 h			151	42.30%
	1–2 h			24	6.70%
	2–3 h			11	3.08%
	More than 3 h			14	3.90%
LinkedIn	I don’t use it			284	79.55%
	Less than 1 h			58	16.25%
	1–2 h			13	3.64%
	2–3 h			2	0.56%
	More than 3 h			0	0.00%
Games	I don’t use it			240	67.23%
	Less than 1 h			72	20.17%
	1–2 h			22	6.16%
	2–3 h			14	3.90%
	More than 3 h			9	2.50%
Educational Websites	I don’t use it			128	35.85%
	Less than 1 h			122	34.17%
	1–2 h			71	19.89%
	2–3 h			30	8.40%
	More than 3 h			6	1.68%
Online Shopping	I don’t use it			199	55.74%
	Less than 1 h			125	35.01%
	1–2 h			23	6.44%
	2–3 h			8	2.20%
	More than 3 h			2	0.56%
YouTube	I don’t use it			79	22.13%
	Less than 1 h			125	35.01%
	1–2 h			86	24.09%
	2–3 h			38	10.64%
	More than 3 h			29	8.12%
Age (Years)	Mean (SD) 29 (10.11)		Median 26

Patterns of social media use also varied across platforms. For Facebook, 40.05% used it for less than 1 h per day, and 29.97% for 1–2 h. A substantial proportion of the sample (41.18%) reported no TikTok use, while 19.61% used it for less than 1 h per day. WhatsApp use was almost universal, with only 0.80% of respondents reporting no use. The largest group (34.45%) reported using it for 1–2 h per day. Instagram use was more evenly distributed, with 24.93% of participants spending 2–3 h daily and 23.25% spending 1–2 h daily. Telegram, LinkedIn, online games, and online shopping were the least frequently used platforms, with 43.98%, 79.55%, 67.23%, and 55.74% of participants, respectively, reporting no use. Regarding educational websites, 35.85% did not access them, whereas 34.17% used them for less than 1 h per day. Finally, 35.01% used YouTube for less than 1 h per day, while 24.09% reported 1–2 h of daily viewing.

Table 2 presents the descriptive statistics and distribution of participants across varying levels of phubbing behavior, its subdimensions, and self-efficacy. The results indicated a moderate level of phubbing behavior among the study population, with a mean total score of 27.32 (SD = 7.03). Specifically, nearly half of the participants (46.78%) were classified as moderate phubbers, while 27.17% and 26.05% were classified as high and low phubbers, respectively.

Table 2.

Descriptive statistics, and prevalence levels of phubbing, and its subdimensions, and self -efficacy (n = 357)

	Mean	Std. Deviation			Median	Minimum Maximum	Low (%)	Moderate (%)	High (%)
Phubbing	27.32	7.03			27	12 50	93 (26.05)	167 (46.78)	97 (27.17)
Communication Disturbance	10.94	3.66			11	5 25	100 (28)	144 (40.37)	113 (31.65)
Phone Obsession	16.37	4.34		17		6 25	94 (26.33)	137 (38.38)	126 (35.29)
Self-Efficacy	28.40	5	29			11 40	95 (26.6)	171 (47.9)	91 (25.5)

Regarding the subdimensions as shown in Table 2, both Communication Disturbance (M = 10.94, SD = 3.66) and Phone Obsession (M = 16.37, SD = 4.34) showed moderate mean scores. For Communication Disturbance, the majority of participants experienced moderate (40.37%) to high (31.65%) levels of disturbance. Similarly, Phone Obsession levels were predominantly moderate (38.38%) and high (35.29%). Finally, the mean score for general self-efficacy was 28.40 (SD = 5.01); according to the distribution, 47.9% of the participants exhibited moderate self-efficacy, while approximately 26.6% and 25.5% reported low and high levels of perceived self-efficacy, respectively.

Table 3 presents the results of a stepwise multiple regression analysis conducted to determine which independent variables most strongly predict phubbing behavior and its subdimensions, communication disturbance and phone obsession, with a significance level set at p < 0.05.

Table 3.

Stepwise multiple regression analysis of predictors for phubbing behavior and its subdimensions

Dependent Variable / Predictors	B	SE	Beta	T	p	VIF	95% CI [Lower, Upper]	F	P value	R²
Phubbing (Total)								21.604	0.000	0.235
Constant	17.557	2.638	-	6.655	0.000	-	[12.368, 22.746]
Total Hours Daily	3.338	0.429	0.424	7.786	0.000	1.362	[2.495, 4.181]
Facebook Usage	0.661	0.336	0.096	1.968	0.050	1.092	[0.000, 1.321]
TikTok Usage	0.697	0.257	0.141	2.714	0.007	1.236	[0.192, 1.201]
Self-Efficacy (GSES)	− 0.153	0.066	− 0.109	-2.301	0.022	1.022	[-0.283, − 0.022]
Age (Years)	0.082	0.038	0.118	2.174	0.030	1.359	[0.008, 0.157]
Communication Disturbance								13.453	0.000	0.212
Constant	6.699	1.434	-	4.670	0.000	-	[3.878, 9.521]
Total Hours Daily	1.272	0.234	0.310	5.445	0.000	1.439	[0.812, 1.731]
Telegram Usage	0.505	0.183	0.134	2.756	0.006	1.052	[0.145, 0.866]
Self-Efficacy (GSES)	− 0.101	0.035	− 0.138	-2.861	0.004	1.027	[-0.170, − 0.031]
Residence	0.139	0.066	0.101	2.108	0.036	1.010	[0.009, 0.269]
YouTube Usage	0.341	0.159	0.110	2.135	0.033	1.172	[0.027, 0.654]
Age (Years)	0.052	0.020	0.143	2.610	0.009	1.337	[0.013, 0.091]
TikTok Usage	0.268	0.136	0.104	1.975	0.049	1.233	[0.001, 0.535]
Phone Obsession								69.181	0.000	0.163
Constant	10.731	0.711	-	15.095	0.000	-	[9.333, 12.129]
Total Hours Daily	1.963	0.236	0.404	8.318	0.000	1.000	[1.499, 2.427]

P < 0.05 is considered significant

For phubbing behavior, the final model included five predictors and accounted for (23.5%) of the total variance (R² = 0.235). The most influential predictor was daily phone use, which alone explained (19.3%) of the variance, followed sequentially by Facebook usage, TikTok usage, self-efficacy, and age. The results indicate that increased time on the phone and greater use of Facebook and TikTok are associated with higher phubbing scores, whereas higher self-efficacy is associated with lower phubbing behavior. For communication disturbance, the model explained (20.3%) of the variance (R² = 0.203). Key predictors included total hours of phone use, Telegram usage, self-efficacy, residence, YouTube usage, and age. On the other hand, for phone obsession, the model identified daily phone use as the only predictor, accounting for 16.3% of the variance (R² = 0.163).

Other demographic and socioeconomic variables that were collected in the study, such as gender, education level, employment status, marital status, and place of residency, as well as usage patterns of additional social media platforms, including WhatsApp, Instagram, LinkedIn, games, and online shopping, were not reported in the regression analysis results. These variables did not reach statistical significance (p > 0.05). There was also no evidence to suggest a meaningful relationship between these variables and phubbing behavior or its subdimensions, communication disturbance, and phone obsession.

The regression equation to predict phubbing behavior:

• Phubbing Score = 17.63 + 3.33(Total Phone Use) + 0.66(Facebook Use) + 0.70(TikTok Use) – 0.15(Self-Efficacy) + 0.08(Age)

Discussion

This study investigated the prevalence of phubbing behavior in Palestinians and explored its associations with self-efficacy and how demographic and socioeconomic variables played a role in predicting phubbing behavior. As a result, more than 70% of participants exhibited moderate to high levels of phubbing behavior, with approximately one-third (27.17%) falling into the high range. The prevalence of phubbing aligns with patterns observed in previous studies. Although very few studies have examined phubbing prevalence in an entire population, as this study did, most existing research focused on specific groups, such as youth, students, or particular professions like nursing. School and university students in Spain had a phubbing rate of about 50%, while medical students in Noida, India, showed a prevalence rate of 42.7% [14, 16]. A study focusing on youth ages 15–29 years was also conducted in India, where the prevalence rate was 52% among youth in general, whether at home or in school [17]. In Lebanon, Zeidan et al. also discovered a 60% prevalence rate among youth in general [18]. A study in Turkey, on the other hand, aimed to evaluate phubbing among nurses, finding a moderate level of phubbing [19]. Although these previous studies did not focus on the general population like this study, they still exhibited the same patterns of phubbing, ranging from moderate to high, among students, youth, and nurses. These previous studies do not provide explicit prevalence rates for each subfactor but rather imply that both subfactors are important in understanding phubbing behavior overall. While this study does not deny the importance of both subfactors, it was noted that the smartphone obsession subscale was slightly more prominent, with 35.29% of participants falling into the high range, compared to 31.65% for communication disturbance [14, 16]. Phubbing behavior in Palestinians is moderate to high for several reasons, including increased smartphone access, as 83% of the West Bank population owned a smartphone as of 2022, and due to high reliance on digital usage [20]. Digital usage has grown for two main reasons. During the COVID-19 pandemic, Screen time has increased as a result of public health measures enforced by governments to curb the spread of the virus, and education shifted to distance learning. As a result, classroom activities, meetings, studying, and assignments were all performed on the computer. Teachers and professors also made use of social media platforms like Facebook and WhatsApp to create student groups for easy accessibility. Secondly, as a land under occupation, education has been interrupted continuously for political reasons. Thus, shifting to online teaching may occur at any time for any reason [21, 22].

Self-Efficacy

According to Labert Bandura, self-efficacy refers to a person’s confidence in their ability to take action and achieve their goals. Having high self-efficacy is crucial in many aspects of our lives, such as academic achievement and higher controlled motivation, and is predicted to reduce anxiety [23]. In contrast, low self-efficacy is more important to talk about as it can severely undermine an individual in many aspects. Low self-efficacy leads to increased fear, high levels of anxiety and depression [24, 25]. This study showed that low self-efficacy was a predictor of increased phubbing behavior overall, as well as in its subsection, communication disturbance. In other words, self-efficacy has a negative correlation with phubbing behavior and communication disturbance, but no significance was found with smartphone obsession. This negative correlation of phubbing and self-efficacy was also discovered by other researchers, like Zhao, who found that higher refusal self-efficacy weakened the relationship between peer phubbing and smartphone addiction among Chinese college students [26]. Kilic also found a similar result in his study among adults in Turkey, where there was a negative relationship between phubbing and general self-efficacy and body image [27] In addition to a study conducted in the Southern United States, self-control was found to have a negative association with phubbing [28]. An idea very similar to self-efficacy is self-esteem. Self-esteem is an individual’s overall sense of self-worth, encompassing their appreciation and acceptance of themselves. Phubbing was negatively correlated with self-esteem [16]. Individuals with high self-efficacy often develop higher self-esteem because repeated successes reinforce their sense of competence and self-worth. Low self-efficacy can be due to the economic and political situation in Palestine. Palestinians face poverty, high unemployment rates, economic dependence, violation of justice and equality, cultural pressures, and future insecurity, all negatively affecting self-worth. These conditions collectively contribute to a level of trauma and uncertainty, which can undermine individuals’ belief in their capacity to influence their circumstances [29].

Sociodemographic variables and phubbing behavior

The results of this study, after completing the stepwise multiple regression analysis, identified that total hours of phone use, Facebook usage, TikTok usage, Self-efficacy, and age were significant predictors for phubbing behavior. These factors collectively account for 23.5% of the variance, with total hours of smartphone use being the strongest factor. Individuals who spend four or more hours on the phone, a group comprising 64.5% of this study’s sample, are at a higher risk of phubbing behavior. A study on nursing students in South Korea showed a relationship between phubbing and total hours of phone usage, where students who spent more than 10 h a day on their phones had higher levels of phubbing than those who spent less than 6 h daily on their phones [30]. Beyond total daily phone usage time, other studies have also discovered similar results. Pirincci found that Phubbing was a mediator that increased the impact of fear of missing out on smartphone addiction among university students in Turkey [31]. In other words, high levels of the Fear of Missing Out Scale (FoMO) are likely because of high levels of smartphone addiction due to phubbing behavior. Smartphone addiction is defined as a severe form of uncontrollable problematic smartphone use that interferes with daily life. Pirincci emphasized the idea that smartphone addiction was partially due to phubbing. Castrejon claimed to have a positive correlation between phubbing and problematic internet usage [16, 31]. Juhyung also explained in his study about friends phubbing that individuals who experience dependence on their smartphones may phub others more often because strong desires or attachments toward their smartphones may be developed. It is thus plausible to suggest that smartphone dependence would be positively related to phubbing [28]. Smartphone addiction and problematic internet usage are both factors that have a strong association with total daily phone usage time, and therefore, it was worth delving deeper into their literature. As explained before, smartphone usage and engagement have increased, especially after COVID-19, for social, economic, educational, and political reasons [21, 22, 29].

Social media

Social Media plays a significant role in our daily phone usage, phone obsession, and problematic internet usage. In this study, daily engagement for more than one hour on Facebook and TikTok was associated with increased phubbing rates. The study sample revealed that 39.2% of participants used TikTok and 52% used Facebook for more than one hour daily. In previous literature, Habitual Facebook was discovered to have a positive effect on phubbing. This means that individuals who habitually use Facebook are more likely to engage in phubbing behavior, even if it irritates those around them. In terms of TikTok, another study showed that the viewing of carefully curated short TikTok videos, 30–60 s in length, undermines self-control, which is associated with increased phubbing behavior [28].

Social media addiction, in general, is proven to hold the most predictive power for phubbing among undergraduate students in STEM fields. Similar results were found by Chi, who identified that the Bergen Social Media Addiction Scale (BSMAS) and the Fear of Missing Out Scale (FoMO) were significantly positively correlated with phubbing, while the Ten-Item Personality Inventory (TIPI) displayed a significantly negative correlation with phubbing. Additionally, there was a significant impact of fear of missing out on social media addiction and phubbing behavior among young adults in India. Blanchino had the same results that social media addiction and high Facebook usage were related to lower self-esteem [2, 32, 33]. TikTok and other social media platforms are algorithmized to deliver a continuous stream of personalized content that can lead to compulsive usage patterns, which can explain their relationship with phubbing [34]. As mentioned previously, social media is not only used for socializing and catching up with friends and family, but it is now used in education to facilitate and distribute assignments and educational resources [21, 35]. Social media has also economically provided new business opportunities and increased engagement, generating better sales [36]. Furthermore, with political instability and frequent city closures, social media platforms are used for updates on transportation routes, road closures, and security situations. Additionally, social media serves as a primary source of news [37].

Finally, age, a less pronounced but still statistically significant predictor of phubbing, has controversial results. In this study, older participants in the sample were more likely to engage in phubbing behavior compared to their younger counterparts. It should be noted that although the sample ranged from 18 to 65 years old, the overall sample skewed young (mean age = 29.14; 64.9% aged 18–24). This suggests that the positive correlation with age indicates that phubbing is more prevalent among young adults than among adolescents or the elderly. Young adults may be at greater risk of phubbing due to increased autonomy, academic pressures, and frequent smartphone use for both professional and personal interactions and for daily tasks. Contrary to some studies that showed that phubbing is primarily associated with adolescents [18]. The current findings of this study suggest that young adults, particularly those aged 18–35, may be at greater risk. This is corroborated by Ulas [38], who found that phubbing levels increased with age among pre-service teachers in Turkey. Many previous studies focus on specific age groups, such as college students aged 18 to 23, and therefore, age was not a statistically significant variable [16]. A key point is that self-efficacy showed a positive correlation with age, driven by accumulated experience, maturity, and cognitive development. This study also demonstrates a positive correlation between phubbing and age. This suggests that as we age, our self-efficacy and phubbing behavior increase. However, this is not the case, as the results of this study showed a negative correlation between phubbing behavior and self-efficacy. However, there is a slight contradiction that can be explained by the study’s focus on young adults as the greatest risk group, given the sample’s young skew. Additionally, phubbing behavior may increase with age as a result of increased engagement with technology over time, such as greater smartphone dependency for communication, study, or work, rather than due to psychological maturity [39].

No significant gender differences in phubbing behavior were found in this study. This is similar to Ulas’ research on pre-service and in-service teachers [38]. Bitar and Chotpitayasunondh, on the other hand, found that females were positively correlated with higher phubbing [3, 18]. However, a study of undergraduate students found that phubbing scores and their subscale scores were significantly higher among male students than among female students [32]. Another study revealed gender differences in phubbing behavior, where females had a positive correlation to phubbing concerning habitual Facebook use, suggesting that women who frequently use the platform are more likely to engage in phubbing behavior. In contrast, males had shown a negative correlation with phubbing behavior [40]. It is noted that results regarding gender are not uniform and can vary across cultures and contexts. These findings align with Al-Saggaf [9], who found that not all digital behaviors equally predict phubbing. This may be due to the social shifts toward gender parity in digital access, rights, and responsibilities. Palestinian males and females today share similar educational roles, societal expectations, and digital literacy [41].

Conclusion

In this study, we aim to discover the phubbing phenomenon and found a moderately high prevalence of phubbing behavior. The main significant variable was a positive relationship with daily smartphone use, followed by social media use on TikTok and Facebook, as well as participants’ age. It was also revealed that there was a negative relationship between self-efficacy and phubbing behavior. The findings also highlighted the importance of enhancing self-efficacy as a preventive measure against phubbing behavior.

Strengths

To the best of our knowledge, this is the first study conducted in Palestine to systematically examine the prevalence of phubbing and its association with self-efficacy. A major contribution of this study is the development, validation, and implementation of the Phubbing Scale (PS) in Arabic. This newly developed tool offers a reliable instrument for future research in Arabic- speaking countries. The study used stepwise multiple regression, which enabled us to examine the complex interplay among sociodemographic factors, screen time, and psychological variables.

Limitation

Cross-sectional studies cannot determine the causal relationship between phubbing behavior and self-efficacy. The studies used self-reported scales, which may increase the risk of recall or reporting bias, especially regarding hours of daily phone use and phubbing behavior, which individuals may under- or over-report. Moreover, specific demographic and behavioral factors, such as mental health status, were not assessed as confounders. Finally, convenience sampling bias may limit generalizability to the Palestinian population.

Recommendations

Further longitudinal studies are recommended to examine the causal relationship between self-efficacy and phubbing behavior. Future studies should aim for greater geographical, gender, and age balance to enhance generalizability, as this study’s sample is predominantly from Northern Palestine, female, and young adult. Future studies are also recommended to include age groups for a more detailed comparison. It is advised to include other psychological constructs to have a well-rounded scope on mental health, as many are interrelated.

Interventions should be delivered in schools, universities, and workplaces to improve self-efficacy, such as goal-setting workshops or counseling services, which could indirectly reduce phubbing behavior. For younger populations, particularly adolescents, parental supervision should encourage balanced digital habits and foster open communication at home. Awareness campaigns should be launched to educate the public about the psychological and social risks of phubbing and to provide guidance on techniques to limit smartphone use.

Authors’ contributions

RS: Writing original manuscript preparation, methodology, data collection, and reviewing the final draft. RB: Data collection, data curation and analysis, investigation, validation results interpretation, and writing the final draft. AH: Conceptualization, supervising, reviewing & editing the final draft.

Funding

None.

Data availability

All data from the current work is obtainable from the corresponding author. [a.hanani@najah.edu].

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the Faculty of Medicine and Allied Medical Sciences at An-Najah National University (approval number: Med. Nov. 2024/13). The study was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants prior to their enrollment in the study. Participants were clearly informed about the purpose of the study and the confidentiality of their data and were assured that the information collected would be used solely for research and publication purposes.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.