Smartphone addiction among post-graduate management students: The Indian experience

Abstract

This study aimed to recognize the patterns of smartphone addiction among post-graduate management students in the North-East Region of India. The validated Smartphone Addiction Proneness Scale (SAPS) was administered to the respondents, and two different methods, namely SAPS method and median-based scoring method, were used to measure smartphone addiction. The measurement results of smartphone addiction show evidence that the student respondents are not vulnerable to smartphone addiction. Principal component analysis with promax rotation (Kaiser–Meyer–Olkin measure = 0.84; Bartlett’s test of sphericity = 0.000) demonstrated four crucial components that signify smartphone addiction which are habitual issues (issues relating to regularly or repeatedly doing or practicing something), withdrawal anxiety, tolerance, and usage outcomes. Gender was not seen to play a significant role in these components. The duration of use of a smartphone was seen to have a significant relationship with the component of habitual issues but not with the other components.

Keywords:

• addiction
• internet
• management students
• smartphone
• Smartphone Addiction Proneness Scale

1. Introduction

The internet, for diversity of activities, is very useful. It is used for the purpose of e-commerce, contacting and information sharing, entertainment, emotional support, etc. (Kraut et al., 1998; Morahan-Martin, 2009; Scherer, 1997). As of September 2021, there are 3.8 billion smartphone users worldwide, of which 346 million are in India (Statista, 2021). Even though the internet connectivity is poor in North-East Region (NER) of India, except in cities, the region is also not lacking behind the use of smartphones. The use of smartphones is also increasing among the student population. Besides occasional use of smartphones, it becomes a compulsory gadget during the coronavirus pandemic to attend online classes. It is projected that smartphone usage could significantly influence both educational and professional aspects of an individual’s life. Notably, smartphone ownership is predominantly high among the 18–29 years age group, which primarily constitutes the student population (Ameza & Baertb, 2020). Assuming the use of smartphones and uncontrollability are in the increasing trend, the present study targets the post-graduate management students in the universities of NER of India to assess the smartphone addiction.

2. Review of literature

The uncontrollable use of smartphones, termed as smartphone addiction, poses social, psychological, and health problems to those afflicted (Heron & Shapira, 2004; Young, 1999). It is opined that there is a high risk of smartphone addiction among the users (Chambers et al., 2003). After the introduction of smartphone, internet addiction has increased tremendously (Kim, 2013). Such addiction is a compulsive disorder linking to the overuse of mobile devices (Perry & Lee, 2008). This disorder can also be harmful to social life (Al-Barashdi, 2015). Smartphone addiction is becoming a norm among the users (Dvorac, 2015). As reported by smartphone users, they would not be able to survive without a smartphone (Wajcman et al., 2008).

Numerous features of smartphone addiction were advocated by various researchers. These include compulsion, functional impairment and tolerance (Lin et al., 2014), psychological symptoms (Bianchi & Phillips, 2005), and human–machine interaction (Griffiths, 1995). It is cited that smartphone use reduces stress, but it leads to addiction creating harmful consequences in financial, physical, psychological, and social aspects of life (Shaffer, 1996; Van Deursen et al., 2015; Young, 1999).

Smartphone use and addiction among students of higher studies have increased tremendously. The medical students in Andaman and Nicobar Islands are vulnerable to smartphone addiction (Sethuraman et al., 2018). There are evidences of students who cannot live without smartphones. The findings of Wajcman et al. (2008) and Klyoko and Hitoml (2005) may be mentioned in this regard. In another study, it is highlighted that the university students are induced to the harmful effects of mobile phone use (Szpakow et al., 2011). Smartphone addiction among students may reduce their academic performance (Ishii, 2010), mental health, and subjective well-being or happiness (Javid et al., 2011). It is also discoursed that gender, monthly income, hours of daily use of internet, and social status provide significant differences to the degree of smartphone addiction among the university students (Aljomaa et al., 2016).

Anxiety and smartphone use have a significant positive relationship (Hong et al., 2012). Depression, anxiety, and reduced sleep quality may be the effects of smartphone addiction (Demirci et al., 2015). The addicted users of smartphone face negative consequences like reduced quality of sleep, tendency to decrease the day-to-day physical activities, and psychological effects (Haripriya et al., 2019). Smartphone addiction can lead to an increase in ill health, psychological, physical, and social issues (Ding & Li, 2017).

Strong addiction to smartphone is harmful to classroom atmosphere (Soomro et al., 2019) and results in stress among students (Gligor & Mozos, 2019). Again, smartphone addiction strongly ties with depression, anxiety, and stress (Lei et al., 2020) and has adverse effect on sleep quality among the university students (Li et al., 2020). Further, it is also observed that smartphone addiction results in losing a sense of belongingness with family and looming of loneliness (Gökçearslan et al., 2021). Relying on smartphone too much has ill effects on sleep quality and daytime trouble (Lane et al., 2021). In the students’ life, smartphone addiction results in detrimental effects on students’ learning performance (Sunday et al., 2021), is linked with inadequate quality of sleep which results in poor academic performance (Rathakrishnan et al., 2021), and results in tendency to have lower grades due to physical and mental health issues (Alotaibi et al., 2022). Academic procrastination and quality of life are closely associated with students’ addiction in smartphone (Albursan et al., 2022) and correlate with students’ loneliness and aggression (Yilmaz et al., 2022). There is a negative impact of smartphone addiction on the well-being of a person (Moqbel et al., 2023). The excess use of smartphone leads to health-related complications (Chinwong et al., 2023); emotional, behavioural, cognitive, and interpersonal issues (Seo et al., 2023); and sleep deficiency (Hasan et al., 2023).

It has been highlighted that the deletion of app(s) that cause addiction to smartphone is one of the solutions to get rid out of such addiction, and, at the time, when people are aware of negative impacts of smartphone addiction, they emphasize more on the benefits of smartphone use (Al-Barashdi, 2015). The population of smartphone users is significantly increasing, and the communication technology targets the students who are in school and university level (Klyoko & Hitoml, 2005; Sethuraman et al., 2018; Wajcman et al., 2008).

On this backdrop, a study on smartphone addiction among post-graduate management students is taken into consideration.

3. Research questions and objectives

The study addressed the following questions:

1. Are the post-graduate management students addicted to smartphone?

2. Are there smartphone addiction differences between genders?

3. Are there smartphone addiction differences between duration of use?

To answer the above research questions, three objectives were formulated:

1. To assess smartphone addiction in a randomly selected group of post-graduate management students in eight universities in the NER of India,

2. To measure the smartphone addiction differences between genders, and

3. To measure the smartphone addiction differences between duration of use.

4. Materials and methods

4.1. Participants and procedure

Data were collected from 310 respondents during 17–27 August 2020 through an online Google Forms link. The link was distributed across the post-graduate management students of eight central universities in NER of India. Informed consent was given by all the respondents, and their participation to this research activity was entirely voluntary. A total of 306 responses were valid for further analyses. Structured questionnaires were used to collect data for this study. The questionnaire was divided into two major parts. The first part of the questionnaire deals with the demographic information of the respondents, including age, gender, university where the respondent is studying, and duration of smartphone usage. Table 1 shows the demographic characteristics of the respondents (n = 306).

Table 1. Demographic characteristics of the respondents

		Frequency	Percentage	Mean	Median
Gender	Male	177	58	1.42	1
	Female	129	42
Age (in years)	20	1	0.33	25	25
	21	5	1.63
	22	54	17.65
	23	81	26.47
	24	78	25.49
	25	39	12.75
	26	27	8.82
	27	13	4.25
	28	5	1.63
	29	1	0.33
	30	2	0.65
Smartphone usage duration	Less than 6 months	5	1.63
	6 Months to 1 year	6	1.96
	1 Year to 2 years	11	3.59
	2 Years to 3 years	12	3.92
	More than 3 years	272	88.90

Source: Researchers’ calculation.

The second part of the questionnaire includes the items of Smartphone Addiction Proneness Scale (SAPS) developed by Kim et al. (2014).

4.2. Smartphone Addiction Proneness Scale

There are various scales to measure smartphone addiction. The works of Kim et al. (2008), Kang and Son (2009), Korean Information Society Agency (2011), and Kwon et al. (2013) in the development or/and validation of the mobile phone/smartphone addiction scale cannot be undermined. In this paper, SAPS developed by Kim et al. (2014) was adopted as it mainly focuses on youths.

The SAPS (Torrecillas, 2007) consisted of four subdomains, namely disturbance of adaptive functions (five items), virtual life orientation (two items), withdrawal (four items), and tolerance (four items). The developers of this scale claimed that this scale seems to be a reliable and valid diagnostic scale for screening the youths who may be at risk of smartphone addiction. There are altogether 15 items in the scale, of which 3 are reverse-coded items (item 8, 10, and 13) (see Table 2). Each item was scored on a 4-point Likert scale as 1 = strongly disagree, 2 = disagree, 3 = agree, and 4 = strongly agree, respectively. The questionnaire for the study was prepared based on SAPS (Torrecillas, 2007) with slide modification on item 3. It was tested for inter-reliability of the scale by conducting a pilot study among 50 respondents. Cronbach’s alpha value of 0.77 suggested the inter-reliability satisfaction of items in the questionnaire.

Table 2. Overall responses in percentage

Items		SD	D	A	SA
1	My grades/marks dropped due to excessive smartphone use.	14	52	26	8
2	Using a smartphone is more enjoyable than spending time with family or friends.	31	55	12	2
3	I cannot imagine my life without a smartphone.	12	28	43	17
4	I tried to reduce the time I spend on a smartphone but I fail.	8	36	44	12
5	I have a hard time doing what I have planned due to using smartphone.	10	42	39	9
6	When I cannot use a smartphone, I feel like I have lost the entire world.	23	47	24	6
7	I get restless and nervous without a smartphone.	20	53	22	5
8	I can control my smartphone usage time. *	6	23	55	16
9	People frequently comment on my excessive smartphone use.	14	51	28	7
10	I am not anxious even when I am without a smartphone. *	6	33	49	12
11	Even when I think I should stop; I continue to use my smartphone too much.	8	32	48	12
12	Family or friends complain that I use my smartphone too much.	14	50	29	7
13	My smartphone does not distract me from studying. *	9	33	44	14
14	I panic when I cannot use my smartphone.	20	56	20	4
15	Spending a lot of time on a smartphone has become my habit.	11	31	43	15

*Reverse-coded items. Source: Researchers’ calculation. SD = strongly disagree, D = disagree, A = agree, SA = strongly agree.

The total score of the questionnaire ranges from 15 to 60. Regarding the scoring method of this questionnaire to measure smartphone addiction, the study used the following two different methods:

1. Clinical cutoff score of 42 of SAPS (Kwon et al., 2013) and

2. “Median”-based scoring method (Aljomaa et al., 2016; Fornell & Larcker, 1981): In this method, the participant whose score is equal or higher than the median score is considered as smartphone addict. Applying median formula for even set of numbers (1, 2, 3, and 4), it was measured at 2.5. Then, it was multiplied by the number of items (15) in the scale, yielding 37.5. Therefore, the participant whose score is equal or higher than 37.5 is considered as smartphone addict.

5. Data analysis

Descriptive analyses such as frequency, percentage, mean, and median were used to investigate the demographic variables of the study. Further, principal component analysis (PCA) with promax rotation was applied to identify the crucial components that signify smartphone addiction among the sampled respondents. Further, t-test was applied to identify smartphone addiction differences between genders, and analysis of variance (ANOVA) was conducted to draw smartphone addiction differences between duration of use.

6. Results

Out of 306 respondents, the male respondents constitute 58% and 42% by females, respectively. Most of the respondents have been using smartphone for more than 3 years (n = 272). The respondents had mix reaction to the statements of questionnaire (see Table 2). The responses indicated that the usage of mobile phone has not completely impacted their lives negatively. However, struggle to control the usage of mobile phone could be identified in their responses.

Out of the 15 items, the negative impact of mobile usage could be identified in four items (refer Table 3).

Table 3. Negative impact responses in percentage

Items	SD	D	A	SA
I cannot imagine my life without a smartphone.	12	28	43	17
I tried to reduce the time I spend on a smartphone but failed.	8	36	44	12
Even when I know I should stop; I continue to use my smartphone.	8	32	48	12
Spending a lot of time on a smartphone has become my habit.	11	31	43	15

Source: Researchers’ calculation. SD = strongly disagree, D = disagree, A = agree, SA = strongly agree.

Meanwhile, for remaining 11 items, we could see that mobile usage has not negatively impacted the lives of the respondents (refer Table 4).

Table 4. Positive impact responses in percentage

Items	SD	D	A	SA
My grades/marks dropped due to excessive smartphone use.	14	52	26	8
Using a smartphone is more enjoyable than spending time with family or friends.	31	55	12	2
I have a hard time doing what I have planned due to using smartphone.	10	42	39	9
When I cannot use a smartphone, I feel like I have lost the entire world.	23	47	24	6
I get anxious and nervous without a smartphone.	20	53	22	5
I can control my smartphone usage time.	6	23	55	16
People frequently comment on my excessive smartphone use.	14	51	28	7
I am not anxious without a smartphone.	6	33	49	12
Family or friends complain that I use my smartphone too much.	14	50	29	7
My smartphone does not distract me from studying.	9	33	44	14
I panic when I cannot use my smartphone.	20	56	20	4

Source: Researchers’ calculation. SD = strongly disagree, D = disagree, A = agree, SA = strongly agree.

The prevalence of smartphone addiction among post-graduate management students is shown in Table 5.

Table 5. Prevalence of smartphone addiction among post-graduate management students

Scoring method	Male	Female	Total
≥42 (Korean Information Society Agency, 2011)	22 (7.19)*	22 (7.19)*	44 (14.38)*
≥37.5 (Torrecillas, 2007; Suliman et al., 2016)	66 (21.56)*	51 (16.67)*	117 (38.23)*

*Numbers in the parentheses are the percentage to the total respondents (n = 306).

Source: Researchers’ calculation.

It is evident from Table 5 that, according to the measuring method through SAPS (Li et al., 2020), only 44 (14.38%) respondents out of the total 306 respondents are categorized under smartphone addiction. There were same number of addicted respondents among males and females which accounted to 7.19% each. However, the rate of addiction through median-based scoring method was measured at 117 (38.23%) of the total respondents which comprises 66 (21.56%) males and 51 (16.67%) females, respectively.

The Kaiser–Meyer–Olkin measure of sampling adequacy resulted in 0.840, suggesting that the sample size is adequate for conducting PCA. The significant value for Bartlett’s test of sphericity was 0.000, which indicates that the data are useful for conducting PCA. The PCA was capable of explaining 58.035% of the phenomenon related to smartphone addiction. PCA with promax rotation resulted in four components, which were renamed as habitual issues, withdrawal anxiety, tolerance, and usage outcome. However, two items (items 4 and 5) which had factor loading of less than 0.5 have been removed from further analysis as suggested by Fornell and Larcker (Pawlowska & Potembska, 2012). The factor scores of the remaining items have been recorded for further analysis.(refer Table 6).

Table 6. Component-wise factor loadings of smartphone addiction items

Items	Components
	Habitual issues	Withdrawal anxiety	Tolerance	Usage outcome
Even when I know I should stop, I continue to use my smartphone.	.870
Family or friends complain that I use my smartphone too much.	.753
People frequently comment on my excessive smartphone use.	.735
Spending a lot of time on a smartphone has become my habit.	.586
I cannot imagine my life without a smartphone.		.727
I get anxious and nervous without a smartphone.		.719
When I cannot use a smartphone, I feel like I have lost the entire world.		.698
I panic when I cannot use my smartphone.		.587
I can control my smartphone usage time.			.719
I am not anxious without a smartphone.			.703
My smartphone does not distract me from studying.			.611
Using a smartphone is more enjoyable than spending time with family or friends.				.816
My grades/marks dropped due to excessive smartphone use.				.572

Source: Researchers’ calculation.

T-test was applied to identify the role of gender in smartphone addiction (refer Table 7). However, the mean difference between male and female was found to be non-significant (p > 0.05). Therefore, the result suggests that gender does not play any role in smartphone addiction. Male and female might use mobile phone for different purposes, but smartphone addiction is at similar level in both genders. This finding is inclined to the finding of Potembska and Pawłowska (2010).

Table 7. Role of gender in smartphone addiction

Independent samples test
		Levene’s test for equality of variances		t-Test for equality of means
		F	Sig.	t	df	Sig. (2-tailed)	Mean difference	Std. error difference	95% confidence interval of the difference
									Lower	Upper
Smartphone addiction	Equal variances assumed	.111	.739	−.914	305	.361	−.615	.672	−1.937	.708
	Equal variances not assumed			−.920	283.971	.358	−.615	.668	−1.930	.701

Source: Researchers’ calculation.

ANOVA was conducted to identify if the differences in usage duration indicate any vital relationship with smartphone addiction (refer Table 8). However, the result indicated that there was no significant difference in means (p > 0.05) among the student respondents with different smartphone usage duration regarding the smartphone addiction. This finding is contradictory to the findings of Aljomaa et al. (2016). This indicates that smartphone addiction is not related to the duration of smartphone usage. People can get addicted to smartphone usage even though they are using smartphone for a short period of time.

Table 8. Role of usage duration on mobile addiction

ANOVA
	Sum of squares	df	Mean square	F	Sig.
Between groups	153.541	4	38.385	1.174	.322
Within groups	9839.901	301	32.691
Total	9993.441	305

Source: Researchers’ calculation.

7. Conclusion

This study intended to scrutinize smartphone addiction, addiction differences between genders, and addiction differences between duration of use of the student respondents. Four components, namely habitual issues, withdrawal anxiety, tolerance, and usage outcome, were drawn through PCA. The t-test has resulted that the role of gender in smartphone addiction is insignificant. Again, the usage duration of smartphone has a significant relationship with the habitual issues, but it is insignificant in the remaining three components, namely withdrawal anxiety, tolerance, and usage outcome, respectively. The result of the study indicates that, in most of the cases, students are able to limit the excessive smartphone usage in their lives. However, it can also be seen that excessive mobile usage has become habitual issue for most of the students, negatively impacting their academic performance and time management. Smartphone has become an important part of student’s life, and the absence of mobile phone is unimaginable for students.

The major limitation of this study is that the participants were from the eight central universities in NER of India, which limits the generalization of results to other youth groups and other parts of the same and different countries, respectively.

A stronger framework on smartphone addiction theories and concepts with bigger sample size is very much required for future research in this domain.

Author contributions

Khundrakpam Devananda Singh: Conceptualization, data collection, methodology, analysis, original draft writing, review and editing; Deep Jyoti Gurung: Methodology, analysis, original draft writing, review and editing; Ditalak Mpanme: Data collection, original draft writing, review and editing. All authors had approved the final version.

Acknowledgments

The authors acknowledge all the respondents who have responded to the Google Forms questionnaire.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Khundrakpam Devananda Singh

Khundrakpam Devananda Singh was born on 1st March, 1970 in Lilong Chajing Mamang Leikai, Imphal West District, Manipur, India. He has obtained M. Com, MBA, MTTM and Ph. D. Degrees from Indira Gandhi National Open University, New Delhi, India and Manipur University, Manipur, India, respectively. Currently, he is Professor, Department of Management, Nagaland University, Kohima Campus, Meriema, Nagaland, India. He has presented more than three dozen of research papers in international and national seminars and authored/coauthored/edited/coedited seven books. He has published fifty-three research papers in different reputed journals and edited volumes. Four Ph. D. Degrees were awarded under his supervision and also completed four research projects. His area of interest is on entrepreneurship and enterprise management.

Deep Jyoti Gurung

Deep Jyoti Gurung, was born on 26th August, 1987 in Tezpur, Assam, India. He is an MBA from NERIST, Arunachal Pradesh, India and obtained Ph.D. degree from Tezpur University, India. He is Assistant Professor at CHRIST Deemed to be University, Bengaluru, India. His research interest focuses on the impact of technology in human lives. Deep is interested in the field of consumer behaviour and tourism marketing. He has presented papers in various national and international conferences. His papers are published in various international journals. He has also authored many book chapters published by national and international publishers.

Ditalak Mpanme

Ditalak Mpanme was born on 23rd November, 1968 in Haflong, N.C. Hills, Assam, India and brought up from Dimapur, Nagaland, India. He obtained his PGDBA and LL. B from Bangalore University, MBA and Ph. D. from Assam University, Silchar, India and North-Eastern Hill University, Meghalaya, India, respectively. At present, he is working as Assistant Professor in the Department of Management, Nagaland University, Kohima Campus, Meriema, Nagaland, India. He has presented research papers in international and national seminars and coauthored some books. He has also published some research papers in the journals of repute.