Understanding Students’ Mobile Technology Usage Behavior During COVID-19 Through Use & Gratification and Theory of Planned Behavior

Abstract

The main purpose of the study was to validate the Use and Gratification (U&G) theory and examine the effect of U&G (entertainment, information seeking, and socializing) on the theory of planned behavior (Perceived behavioral control and attitude toward behavior) among students regarding mobile technology usage in COVID-19. Quantitative research design was employed for conducting the study. Data was collected using the survey method. The population consisted of students currently enrolled in a public sector university in Lahore. A questionnaire in online form was used for collecting data from students. SPSS software was used for descriptive analysis and SmartPLS was used for path analysis and hypotheses testing. The study identified that there was an insignificant impact of getting entertainment on students’ attitudes and their perceived control toward mobile technology use during the COVID-19 pandemic. Findings showed that students used mobile technology for information seeking and socializing as there was a significant impact of information seeking and socializing on their attitude toward the use of mobile technology. Current study provides insight into the usage behavior of students regarding mobile technology during the COVID-19 pandemic. It also provides, how mobile technology motivators (getting entertainment, information seeking, and socializing) work for mobile technology usage behavior in crises, especially COVID-19 pandemic. Findings are helpful for government authorities in decision-making regarding the provision of information through mobile technology during emergencies.

Keywords

use and gratification for mobile technology mobile technology motivators COVID-19 and mobile technology attitude toward mobile technology perceived behavioral control for mobile technology

Introduction

Technological advancement during the few decades has made possible computing operations in mobile phones along with other facilities such as phone calls, cameras, and 3G/4G connectivity. Its portability and advanced features invoke individuals especially the young generation to use mobile technology. The term “mobile” originates from the Latin word “mobiles” which means “to move” or “able to move freely.” A mobile device is a handheld and portable device that can be used for communication, text messaging, and using different kinds of applications (Sharon, 2008). Usually, smartphones, tablets, and laptops are considered mobile devices (Fietzer & Chin, 2017).

Coronavirus (COVID-19) started in China and spread worldwide. On 30 January 2020, it was declared COVID-19 a public health emergency officially by World Health Organization (WHO) and got worldwide attention (Guo et al., 2020). On 26 February 2020 government authorities confirmed two cases of Coronavirus in Pakistan (Ali, 2020). Due to the lockdown situation, COVID-19 impacted all spheres of life especially students’ life. However, mobile technology played an important role in the lockdown situation. A recent study reported that smartphone usage has increased among students at the university level during COVID-19 (Saadeh et al., 2021).

Mobile devices have made people’s life easier in different ways and impacted positively their lives as well (Efe & Dalmış, 2019). The literature revealed plenty of studies have been conducted on mobile phones during COVID-19 with different contexts such as smartphone technology and telemedicine (Iyengar et al., 2020); changes in mobile phone use due to COVID-19 (Chae, 2020); mobile learning (Biswas et al., 2020); technology adoption during COVID-19 (Al-Maroof et al., 2023) and information through WhatsApp (Islam et al., 2020). However, only a few studies have applied gratification theory’ (U&G) in the COVID-19 context. Balakrishnan et al. (2021) conducted a study regarding fake news-sharing motives during the COVID-19 pandemic by using U&G and other theories. Islam et al. (2020) investigated COVID-19 information sharing through WhatsApp. The different theories/models including U&G for conducting the study. To the best of our knowledge, no comprehensive study has been conducted regarding students’ behavior toward technology usage in COVID-19 especially in developing countries like Pakistan. Moreover, previous literature also explored entertainment (Ha et al., 2015; Lin et al., 2014; Sanz-Blas et al., 2013), information seeking (Lin et al., 2014), and socialization (Lin et al., 2014; Sanz-Blas et al., 2013) as motivators for the different context of mobile phone usage. There have also been studies conducted regarding COVID-19 in different contexts at the national level. These studies included customers mistreatment during COVID-19 at coffee cafes (Ahmed et al., 2021); innovation ambidexterity model regarding COVID-19 (Islam & Munir, 2023); and voice behavior of health professionals during COVID-19 (Nawaz et al., 2022). However, to the best of the researcher’s knowledge, the relationship of motivators (entertainment, information-seeking behavior, and socialization) with attitude toward mobile and perceived behavioral control during COVID-19 in Pakistan was not examined in the extant literature.

Hence, considering the literature gap and the importance of mobile technology usage in COVID-19, it is imperative to examine students’ behavior toward the use of mobile technology with the theoretical lens of U&G and TPB. The main objective of the study was the validation of U&G theory and investigation of the impact of its major components (entertainment, information seeking, and socializing) on TPB (Perceived behavioral control and attitude toward behavior) within the context of mobile technology use during COVID-19.

The current study will fill the literature gap by identifying, how motivators (getting entertainment, information seeking, and socialization) work for and are related to the use of mobile technology during crises, especially COVID-19. With the combination of U&G and TPB, the current study enriches existing literature in behavioral research regarding the behavior and mobile technology usage the n the COVID-19 situation. The current research would be helpful for academicians and mobile app developers.

Objectives of the Study

The current study formulated the following objectives

To know the impact of entertainment, information seeking, and socializing on students’ attitudes toward the use of mobile technology furthering COVID-19 pandemic.

To examine the impact of entertainment, information seeking, and socializing on students’ perceived behavioral control regarding mobile technology usage in COVID-19.

Theoretical Underpinning and Literature Review

Use and Gratification Theory

Previous literature revealed that use and gratification provide an understanding of the particular media regarding the motivation to use it. A previous study explained Use and Gratification Theory (UGT) as the main goal of UGT is to enhance comprehension of the social and individual gratifications associated with different media choices by investigating the underlying factors that influence such preferences. Additionally, it aims to elucidate the motives of users when engaging with a particular medium (Hossain, 2019). Initially, researchers used U&G for traditional media regarding the motivation for using this media but with the advancement in technology now a day it is being used for the internet instead of traditional media (Islam et al., 2020). Use and gratification theory has provided an innovative method employed during the early phases of every emerging communication medium, such as newspapers, radio, television, and presently, the internet (Ruggiero, 2000).

However, now a day U&G is being used for mobile technology, especially mobile phones. Plenty of literature is available about the usage motivation of mobile phones in different contexts. Use and gratification theory has been used in different areas for measuring the use of different media in different settings. These are included mobile health applications (Putri et al., 2019); mobile applications for tourism innovation (Palos-Sanchez et al., 2021); mobile phone technology (Wasiaya et al., 2021); news media (Saeed & Ullah, 2021).

Previous studies used different components for UGT such as relaxation, perceived enjoyment, ease of use, information seeking, information quality, social influence, and social benefits (Putri et al., 2019). Luo and Remus (2014) used information seeking, entertainment, time pass, convenience, and interpersonal utility as components of use and gratification in the context of web-based information services. Previous studies also reported different gratifications used by individuals from mobile phone contexts. Chua et al. (2012) conducted a study regarding mobile phone content retrieval and contribution by applying use and gratification. They used entertainment, ease of access, socializing, strengthening the social relationship, retrieval of information sources, and information retrieval regarding content credibility as gratification factors. Hoştut (2010) used innovation, relaxation, status and fashion, and sociability and reassurance as factors of UGT in the context of mobile phone use. Alhassan et al. (2020) used different gratifications such as cognitive, hedonic, integrative, ease of use, convenience, and usefulness gratification to measure its effect on using mobile phone attitude for payment services. Khoa (2020) used convenience, entertainment, socializing, and experience sharing as use and gratification motivators in the context of perceived enjoyment regarding online courses. Wang (2021) used U&G (emotion management, information seeking, value-expression, and social interaction) for measuring attitudes toward the use of mainstream and alternative media in China during the COVID-19 pandemic. Pang (2016) identified four gratifications regarding the use of mobile apps namely WeChat. These gratifications were time pass, sociability, fashion, and affection.

Relationship of U&G With TPB and Related Variables (Hypotheses Development)

Attitude toward behavior (ATB) and perceived behavioral control (PBC) are the components of the TPB provided by Azjen (1985, 1991). PBC can be defined as the ease or difficulty associated with effectively executing a behavior, which can be impacted by previous encounters, observation, anticipated assistance, and possible hindrance (Thompson et al., 2012, p. 776). Khoa (2020) described that the use and gratification theory is a theoretical framework that can explore the connection between user-centered motivation and user attitude while utilizing media platforms. TPB has been used in different contexts such as covid-19 information sharing with a combination of TPB and use and gratifications theory (Islam et al., 2020); and the intention of older adults to live in nursing homes with the combination of TPB and psychological needs theory (Lei et al., 2022).

Lin et al. (2014) noted a significant relationship between entertainment with attitude toward mobile phone apps. Chen et al. (2018) investigated the impact of getting entertainment on attitude toward information sharing regarding the social crisis. Hameed and Qayyum (2018) identified a significant and positive relationship between entertainment with attitude toward mobile learning. In the same way, they also explored a positive significant impact of entertainment on behavioral intention toward mobile learning. Based on the evidence from previous literature we assumed the following hypotheses.

H1: Getting entertainment has a positive and significant impact on students’ perceived behavioral control toward the use of mobile technology during covid-19 epidemic.

H2: Getting entertainment has a positive and significant impact on students’ attitudes toward mobile technology-using behavior during covid-19 epidemic.

The relationship of information seeking with attitude toward behavior and perceived behavioral control was also investigated in different studies. Chen et al. (2018) found an insignificant effect of information seeking on the attitude toward the behavior of users of WeChat. Lin et al. (2014) noted a non-significant relationship between information seeking and attitude toward apps. Islam et al. (2020) also reported a non-significant impact of information-seeking in attitude toward behavior. Although studies reported a non-significant relationship between information seeking and attitude toward behavior with specific contexts studies proposed hypotheses were in a positive direction. Thus, we also formulated the following hypotheses.

H3: Information seeking has a significant positive impact on students’ perceived behavioral control toward the use of mobile technology during covid-19 epidemic.

H4: Information seeking has a significant positive impact on students’ attitudes toward mobile technology-using behavior during covid-19 epidemic.

Islam et al. (2020) noted a positive impact of socialization on perceived behavioral control and attitude toward behavior within the context of COVID-19 information-sharing behavior on WhatsApp. Sanz-Blas et al. (2013) reported that sociability found influenced positive attitudes toward mobile social networks. Lin et al. (2014) also noted a significant relationship between socializing and attitude toward mobile phone apps. Hence, we proposed that socialization has a positive significant impact on PBC and ATB regarding mobile technology-using behavior.

H5: Socialization has a significant positive impact on students’ perceived behavioral control toward the use of mobile technology during covid-19 epidemic.

H6: Socialization has a significant positive impact on students’ attitudes toward mobile technology-using behavior during covid-19 epidemic.

Research Framework

Based on previous literature it is identified that gratification such as getting entertainment, information seeking, and socialization may influence students’ attitude toward using mobile technology. These motivators may also have influenced students’ perceived behavioral control regarding the use of mobile technology during the COVID-19 pandemic. Hence, we proposed six hypotheses based on the proposed research framework (Figure 1). It was based on use and gratification and TPB discussed in the previous section.

Figure 1.

Proposed research framework.

A path analysis diagram, a directed graph, describes the pattern of relationships among variables. Variables are linked by straight arrows that indicate the directions of the causal relationships between them (Figure 2).

Figure 2.

Path analysis diagram.

Methodology

This research used a quantitative research design to achieve the objective. A survey research method was employed for collecting data from respondents. The population of the study was students currently enrolled in a public sector university in Lahore as this university is one of the oldest and largest universities in the country and it has a diverse student population belonging to all over Pakistan. There are 13 faculties consisting of 83 academic departments, research centers, and institutes. Data was collected by using a convenient sampling technique. The reason for choosing the convenient sampling technique was that the university was closed due to COVID-19 and there was no possibility to collect data by visiting classrooms. Data were collected in an online format through Google Forms from one faculty that consisted of five departments and institutes. The link to the questionnaire was sent to class teachers through WhatsApp and requested to circulate it among students. The estimated population was around 6,000 students. The sample size was calculated by using an online calculator with a 95% confidence level and a 50% margin of error. Thuthe s, estimated sample size was around 362 students questionnaire was distributed among 450 students using a convenient sampling technique, and 365 usable questionnaires were received. This response rate remained at 81%.

A questionnaire was used as an instrument for collecting data from respondents as it is an appropriate tool for collecting data from a large population. The questionnaire was developed with the help of a previous study (Al-Emran et al., 2016) and other relevant literature. There were three parts of the questionnaire. The first part of the instrument was demographic information; the second part was related to the use and gratification of mobile technology (getting entertainment, information seeking, and socialization); whereas the third part was related to TPB (Attitude toward mobile technology, and perceived behavioral control). Five-point Likert scale was used to measure the statements. After developing the questionnaire with the help of existing literature. Expert opinion was got from four experts in the field. They suggested minor changes and these changes were incorporated. After this, pilot testing was conducted, and Cronbach’s alpha was checked. It was found that all the constructs have an acceptable value of Cronbach’s alpha value.

SPSS and SmartPLS were used to analyze data. Descriptive analysis was performed through SPSS and SmartPLS was used for hypotheses testing. Before path analysis and hypotheses testing, the quality of the measurement instrument was assessed through SmartPLS software. Reliability, construct validity, concurrent validity, and cross-loadings of constructs were measured for quality assessment of the measurement scale. There are five variables in the study. Three variables were treated as independent variables and two were treated as dependent variables.

Independent Variables (Use and Gratification)

Getting entertainment

Information seeking

Socialization

Dependent Variables

Attitude toward behavior

Perceived behavioral control

Data Analysis and Findings

This section provides a detailed analysis. The current section provides demographic information of respondents, quality assessment of measurement instruments (reliability, constructs validity, and discriminant validity), and path analysis for proposed hypotheses testing.

Demographic Information

The demographic detail of students is presented in Table 1. Gender-based distribution of respondents showed that the majority of respondents were female 246 (67%) students and 119 (33%) were male students. Data showed the age of respondents in four groups. Analysis indicated that more than half (206; 56%) of the students fall in the age bracket of 21-30. Sixty-nine respondents (19%) belonged to the 18 to 20 years age group and 66 (18%) were from the 24 to 26 age group. Only 24 (07%) students were from the 26 years or more age group. Table 1 also shows the program in which students are currently enrolled. The majority of respondents were enrolled in MA 210 (57%) and BS 120 (33%). Less number of respondents were from research programs such as M. Phil 24 (07%) and Ph.D. programs 11 (03%).

Table 1.

Demographic Information of Respondents (N = 365).

Category	Frequency	Percentage
Gender
Male	119	33
Female	246	67
Age
18–20	069	19
21–23	206	56
24–26	066	18
>26	024	07
Program
BS	120	33
MA	210	57
M.Phil	024	07
PhD	011	03

Reflective Measures

The reflective measure was assessed through reliability and construct validity. The detail of the reliability and validity analysis is as follows:

Reliability

For assessing the reliability of measures, Cronbach’s alpha, rho_A, and composite reliability (CR) were measured. Table 2 showed that Cronbach’s Alpha of all the constructs remained .76 to .91 and the composite reliability of the constructs was .84 to .94. Previous studies also reported the value of Cronbach’s Alpha more than .7 (Ali & Warraich, 2022b; Zeng et al., 2021) and CR was also reported more than 0.7 (Zeng et al., 2021). The values of rho_A for all constructs were ranging from 0.76 to 0.91. All the values in Table 1 are in an acceptable range and it indicates that the measurement instrument is reliable.

Table 2.

Reliability and Validity of Constructs.

Constructs	Cronbach’s alpha	rho_A	Composite reliability	AVE
Attitude toward behavior	0.915	0.916	0.94	0.796
Socializing	0.888	0.901	0.919	0.696
Information seeking	0.828	0.850	0.898	0.746
Perceived behavioral control	0.760	0.762	0.862	0.676
Getting entertainment	0.769	0.798	0.844	0.578

Construct Validity

Construct validity was calculated through convergent and discriminant validity. Factors loadings of items and average variance extracted (AVE) of constructs were checked for assessing convergent validity. As Hair et al. (2014) described that convergent validity should be checked by examining AVE and outer loadings. The AVE of all the constructs ranged between 0.57 and 0.079 (Table 2) which is acceptable. Hair et al. (2019) explained that the construct’s acceptable AVE is ≥0.50. Previous studies also check the ked AVE of constructs and reported its value of more than 0.5 (Ali & Warraich, 2022a; Zeng et al., 2021). The factor loadings of all the items of the constructs were more than 0.70 except (Ent2 = 0.602 and Soc1 = 0.662) (Table 4). These two items were retained because they were not disturbing the reliability and AVE of its constructs and Table 2 showed the hat reliability and AVE of getting entertainment and socializing is perfect. However, four items (Ent4, Soc2, PBC1, and PBC2) were excluded because these items were disturbing the reliability and validity of its constructs. For examining the convergent validity, cross-loadings, and Fornell-Larcker criterion were checked. Cross loadings also showed (Appendix A) that all the items loaded with an acceptable value under its relevant construct and values of items under irrelevant constructs remained low which is an indication of establishing discriminant validity. Hair et al. (2017) described that discriminant validity pertains to the extent to which a construct is empirically distinguishable from other constructs. According to Goswami and Dsilva (2019), the method of assessing discriminant validity by Fornell and Larcker (1981) suggests that if the diagonal elements in the corresponding rows and columns exhibit higher values than the off-diagonal elements, it confirms the discriminant validity of the construct. Additionally, the square root of the Average Variance Extracted (AVE) for a specific construct represents a value of the diagonal elements. Hence, discriminant validity was also checked through the Fornell-Larcker criterion and presented in Table 3. Results in Table 3 showed that discriminant validity was established because the diagonal elements in the relevant rows and columns exceeded the off-diagonal values.

Table 3.

Discriminant Validity (Fornell-Larcker Criterion).

Constructs	Attitude toward behavior	Getting entertainment	Information seeking	Perceived behavioral control	Socializing
Attitude toward behavior	0.892
Getting entertainment	0.449	0.761
Information seeking	0.81	0.505	0.864
Perceived behavioral control	0.226	0.217	0.268	0.822
Socializing	0.753	0.548	0.879	0.223	0.834

Formative Measures

For assessing formative measures, outer loading, outer weights, lights, and VIF was checked.

The p-value showed that the outer weights of all the items were significant. Outer loadings of each retained item were greater than .60 and the p-value of all the items was significant (Table 4). It indicates that the outer loadings and outer weights of all the items are in an acceptable range. Hair et al. (2017) described that if an indicator’s weight lacks significance but its corresponding item loading is relatively high (i.e., ≥.50) or statistically significant, it is advisable to retain the indicator.

Table 4.

Formative Measures.

Constructs	Item loadings	p Values	Outer weights	p Values
Theory of planned behavior
Attitude toward behavior
ATB1	0.901	.000	0.292	.000
ATB2	0.907	.000	0.280	.000
ATB3	0.878	.000	0.283	.000
ATB4	0.883	.000	0.266	.000
Perceived behavioral control
PBC3	0.825	.000	0.430	.000
PBC4	0.850	.000	0.403	.000
PBC5	0.791	.000	0.383	.000
Use and gratification theory
Getting entertainment
Ent1	0.834	.000	0.426	.000
Ent2	0.602	.000	0.110	.009
Ent3	0.743	.000	0.357	.000
Ent5	0.838	.000	0.374	.000
Information seeking
IS1	0.776	.000	0.331	.000
IS2	0.922	.000	0.438	.000
IS3	0.888	.000	0.382	.000
Socialization
Soc1	0.662	.000	0.187	.000
Soc3	0.861	.000	0.243	.000
Soc4	0.910	.000	0.267	.000
Soc5	0.848	.000	0.246	.000
Soc6	0.868	.000	0.247	.000

Hypotheses Testing Results

The result of the hypotheses is presented in Table 5. It indicates that getting entertainment did not have a relationship with students’ attitude toward mobile technology using behavior (β = .030, t = 0.715, p > .05) and their PBC toward mobile technology (β = .126, t = 1.769, p > .05). Thus, hypotheses H1 and H2 were rejected. Results depict that students’ information seeking has a positive significant impact on their PBC (β = .306, t = 2.505, p < .05) and attitude toward mobile technology behavior (β = .650, t = 10.08, p < .01). So, hypotheses H3 and H4 were accepted. H5 was rejected because students’ socialization behavior on mobile technology did not impact significantly their perceived behavioral control toward mobile technology (β = −.115, t = 0.896, p > .05). The results regarding the impact of socializing on their attitude toward the use of mobile technology have been positive and significant (β = .164, t = 2.882, p > .05). Thus, hypothesis H6 was accepted (Table 5).

Table 5.

Hypotheses Results.

Hypotheses	β	T statistics	p Values	Result
H1: Getting entertainment -> Perceived behavioral control	.126	1.769	.078	Rejected
H2: Getting entertainment -> Attitude toward behavior	.030	0.715	.475	Rejected
H3: Information seeking -> Perceived behavioral control	.306	2.505	.013	Accepted
H4: Information seeking -> Attitude toward behavior	.650	10.08	.000	Accepted
H5: Socializing -> Perceived behavioral control	−.115	0.896	.370	Rejected
H6: Socializing -> Attitude toward behavior	.164	2.282	.022	Accepted

Discussion

The main purpose of the current study was to develop and validate a model by using U&G and major components of TPB (ATB, and PBC) for understanding students’ behavior toward mobile technology usage in developing countries in COVID-19. Researchers considered three motivators (getting entertainment, information seeking, and socialization) with the context of use and gratification theory and two components (attitude toward behavior and perceived behavioral control) from the TPB. Researchers examined six hypotheses from the responses of 365 students of BS, MA, and M. Phil and Ph.D. levels. We investigated the influence of these motivators on students’ perceived behavioral control (difficulty or ease) and attitude toward behavior. Results of the current study revealed that getting entertainment did not have any influence on students’ attitudes toward mobile technology usage during COVID-19 (Figure 3). These findings are contrary to the findings of previous studies. A previous study reported a positive effect of hedonic gratification on attitude toward mobile social network sites (Ha et al., 2015). Another study reported a positive relationship between entertainment with mobile apps (Lin et al., 2014). Sanz-Blas et al. (2013) also noted a positive significant influence of relaxation/entertainment on attitudes toward mobile social networks. Another study reported a negative impact of entertainment on attitudes toward COVID-19 information-sharing behavior on WhatsApp (Islam et al., 2020). Context, culture, and type of information create variations in the results of different studies regarding the impact of entertainment gratification on attitudes toward mobile behavior in different contexts. However, our study confirmed that entertainment gratification did not create any change (positive or negative) in the attitude of students toward mobile technology usage in developing countries.

Figure 3.

Results of proposed model (U&G, and constructs from TPB).

We further identified that students’ motivation for getting entertainment also has not influenced their perceived behavioral control (Figure 3). Previous studies considered different situational factors for mobile phone use for different purposes (Liang & Yeh, 2008). A recent study also discussed COVID-19 as a situational factor for WhatsApp information sharing as taking into account the situational factor of COVID-19, it was observed that individuals do not share COVID-19 information on WhatsApp to seek entertainment (Islam et al., 2020, p. 6). We also consider that due to the COVID-19 situation, respondents did not use mobile technology for entertainment.

In line with the findings of past studies, the current study finds out a positive impact of socialization on students’ attitudes toward mobile technology usage. As previous studies reported a positive influence of sociability on attitudes toward mobile social networks (Sanz-Blas et al., 2013) and a positive influence on socialization on attitudes toward COVID-19 information sharing through WhatsApp (Islam et al., 2020). However, a study also noted that socialization hurts attitudes toward mobile apps (Lin et al., 2014). Another study found a non-significant effect of socialization on attitudes toward mobile social network sites (Ha et al., 2015). We further identified that there was no significant effect of socialization on students’ perceived behavioral control toward the use of mobile technology.

Unlike the majority of studies in previous literature, our study found that students’ information-seeking motivation significantly influences the attitude of students toward mobile technology usage as well as their perceived behavioral control. The study revealed that students use mobile phone technology for seeking information during COVID-19. Contradiction appeared with the findings of past studies which reported an insignificant impact of information seeking on attitudes toward mobile phone apps (Lin et al., 2014) and attitudes toward WhatsApp information-sharing behavior related to COVID-19 (Islam et al., 2020). The justification for this contradiction is that respondents needed information regarding COVID-19 during this pandemic and consequently they used mobile technology for this purpose. Hence, information-seeking is significantly related to students’ attitudes toward the use of mobile technology and their perceived behavioral control during the COVID-19 pandemic. A previous study reported high demand for information during COVID-19 as data indicated a significant surge in the demand for information, as evidenced by the growing number of searches for coronavirus-related content across news sources (Mangono et al., 2021). However, a study also noted a positive significant impact of sociability/information on attitudes toward mobile social networking sites (Sanz-Blas et al., 2013) and these findings are aligned with the findings of our study.

Conclusion

By using U&G and major components of TPB, the current study examined the impact of U&G motivators on students’ attitudes and perceived behavioral control toward mobile technology usage. A serious behavior of students’ was identified regarding mobile technology usage, as there was an insignificant impact of getting entertainment on their attitude toward mobile technology usage, and the same results were found for their perceived behavioral control regarding mobile technology usage. It shows that students did not use mobile technology for entertainment purposes during the COVID-19 pandemic. Our study further concluded that there was a significant positive impact of information seeking on students’ attitude toward mobile technology usage as well as their perceived behavioral control. It clarifies that they used mobile technology for information-seeking purposes during the COVID-19 pandemic. A significant impact of socialization on students’ attitudes toward mobile phone usage showed a socializing behavior of respondents during the COVID-19 pandemic which indicates students’ used mobile technology for socializing during the pandemic.

Theoretical and Practical Implications

The current study fills the literature gap and enriches existing literature in behavioral research regarding the usage behavior of mobile technology in the COVID-19 situation. The current study provides, how mobile technology motivators (getting entertainment, information seeking, and socializing) work for mobile technology usage behavior in crises, especially COVID-19. Past research has been investigated to know the relationship of U&G with attitude toward mobile phone usage behavior in a generic context but the current study measured the same with specific context that is, the COVID-19 pandemic. Thus, the current study extends the existing literature.

Government authorities and other stakeholders should provide relevant information, which can be accessed through mobile technology especially smartphones because the findings of our study revealed that respondents used mobile technology for information seeking during the pandemic. Hence, the current study is helpful for authorities in providing insight into the usage behavior of people regarding the use of mobile technology. Social media app developers should take steps to make social media platforms more interactive as socialization has a significant impact on attitudes toward behavior. Training and workshops should be conducted regarding the use of social media to enhance socializing to deal with crises.

Limitations and Future Research

The current study used a sampling frame of university students (only from one university) due to the lockdown situation in Pakistan during data collection. Future research may be conducted by including a large and diverse population. We included commonly used motivators and two components of TPB. Other motivators may be used to know the impact of U&G on respondents’ attitudes and perceived behavioral control toward mobile technology during the pandemic. The current study identified that respondents used mobile technology for information seeking during the COVID-19 pandemic. Future research should be conducted to know which type of information students searched during pandemics. Although in a previous study (Zeng et al., 2023) digital divide was discussed however, the current study did not include the digital divide regarding the attitude of students toward the use of mobile technology. Future research may be conducted regarding the digital divide among students regarding mobile technology use. The type of information, different cultures, and contexts may have variations regarding the results of the impact of entertainment on attitudes toward the use of mobile technology. Research can be conducted by considering the type of information, culture, and context as mediators in the proposed framework in the current study.

Footnotes

Appendix

Appendix A.

Cross-loadings of Measurement Items.

Items	Attitude towards behavior	Getting entertainment	Information seeking	Perceived behavioral control	Socializing
ATB1	0.901	0.439	0.752	0.222	0.693
ATB2	0.907	0.379	0.726	0.224	0.661
ATB3	0.878	0.418	0.722	0.217	0.695
ATB4	0.883	0.363	0.688	0.138	0.633
Ent1	0.419	0.834	0.443	0.193	0.492
Ent2	0.101	0.602	0.147	0.065	0.170
Ent3	0.357	0.743	0.422	0.151	0.445
Ent5	0.354	0.838	0.402	0.198	0.431
IS1	0.600	0.461	0.776	0.198	0.587
IS2	0.793	0.426	0.922	0.263	0.808
IS3	0.690	0.435	0.888	0.228	0.867
PBC3	0.172	0.212	0.211	0.825	0.160
PBC4	0.228	0.146	0.240	0.850	0.204
PBC5	0.157	0.175	0.211	0.791	0.187
Soc1	0.495	0.599	0.511	0.132	0.662
Soc3	0.633	0.525	0.736	0.203	0.861
Soc4	0.704	0.433	0.806	0.193	0.910
Soc5	0.641	0.370	0.806	0.201	0.848
Soc6	0.648	0.410	0.772	0.192	0.868

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nosheen Fatima Warraich

Irfan Ali

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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