Understanding the academic learning of university students using smartphone: Evidence from Pakistan
Abstract
As the number of smartphone users have increased worldwide, research on the usage of the smartphone has received scholarly attention. However, different results were found in previous studies in different contexts. The main research question is whether adopting smartphones by students improve learning or generates interference. The purpose of this study is to examine the usage of smartphone among university students in Pakistan. Using a survey approach, data were collected from different universities/colleges of Karachi, Sindh, Pakistan. total, 585 entries was analyzed using SPSS 21.0 and AMOS 21.0 software. Results of analysis, validated most of the hypothesis showing the intention of Pakistani students towards smartphone usage for academic purpose. In addition, findings confirm that students' self-efficacy related to a smartphone works as a moderating role of intention to use a smartphone for learning and actual usage of the smartphone. The current study has several implications in terms of smartphone usage and student academic performance. Limitations and implications of the study are also discussed in the last section.
1 INTRODUCTION
A smartphone is a computer-like device that can be used to perform several computational functions. It can be used by individuals to perform their daily life activities (Anshari & Alas, 2015). According to scholars, several college students use a smartphone to access information through the internet (Gerlich, Browning, & Westermann, 2010). Like other developed countries, usage of smartphone among youth especially students is rapidly growing in Pakistan. Scholars recently reported that most college-going students of age 18 to 29 are addictive users of smartphone (Crompton & Burke, 2018; Klimova, 2018). Students also using a smartphone for academic learning purpose for several reasons (Vanden Abeele, 2016). For example, the smartphone has no limitation in terms of age, place, duration, and time; students can easily link with each other for their own objectives and interests. Recent evidence shows that most of the students are users of an advanced smartphone, which provides several functions such as multimedia players, camera, browser, and navigation system (Nishida, Tamura, & Sakakibara, 2019). These features brought by smartphone lead to an increasing scholarly interest in the usage of the smartphone by the students. However, this stream of research is at the initial stage, and research studies related to smartphone usage by the student is insufficient and conversely scare (Anshari, Almunawar, Shahrill, Wicaksono, & Huda, 2017; Nishida et al., 2019). For example, Nishida et al. (2019) investigated the significant relationship between smartphone usage and psychological depression among college students. Anshari et al. (2017) reported the positive relationship between smartphone usage and student classroom learning. Woodcock, Middleton, and Nortcliffe (2012) discussed the smartphone usage and students' academic learning and found that most of the students do not install educational applications on their phones. Tamura, Nishida, Tsuji, and Sakakibara (2017) also reported that excessive use of a smartphone for social networking creates depression among students. These theoretical studies suggest mixed findings related to smartphone and students usage. On the one hand, scholars agreed that smartphone usage increases the students' academic learning; on the other hand, scholars also pointed out the dark side of smartphone usage among student. To address this research gap, current study may provide a comprehensive report related to smartphone usage and students' academic learning by investigating its motivational factors.
Smartphone device, with its pervasive benefits and powerful functionality, would certainly change the student's intentions. Past scholars suggest that relative advantage is the key factor that attracts the individuals to accept new technology (H.-J. Kim, Lee, & Rha, 2017; S. Lee, 2013). Smartphone usage also provides several advantages for students including reuse of educational material and flexibility at the optimum time and space (Vogel, Kennedy, Kuan, Kwok, & Lai, 2007). In addition, previous scholars also suggested that ease of use of technology is the fundamental element, which motivates the individual towards technology (Chau, 2001). According to scholars, perceived ease of use (PEOU) is defined as the degree to which the individual understand that the use of the particular system is effortless (Davis, Bagozzi, & Warshaw, 1989). Despite the numerous well-documented benefits of smartphone usage for academic learning (Anshari et al., 2017), the researcher suggested that an individual uses specific technology for enjoyment and work purpose. Hedonic use is defined as the use of a smartphone, to exchange information, videos, games, and posts having fun and enjoyment for the intent of relaxing and time passing (Brandtzæg & Heim, 2009; Kanwal, Pitafi, Akhtar, & Irfan, 2019). Given the attractiveness of smartphone and their importance in students' academic usage, it is necessary to assess and identify the primary factors that promote smartphone usage.
Past information system (IS) research studies has attempted to identify several features affecting technology usage (Davis et al., 1989; Hatlevik, Throndsen, Loi, & Gudmundsdottir, 2018; Igbaria & Iivari, 1995). An individual's capability to adopt new technology positively has been recognized as a key element influencing his/her motivation to accept new technology. Scholars reported that self-efficacy plays an important role in affecting behavior (Bandura, 1986). In addition, self-efficacy is an important predictor that can be utilized to understand students' intention and attitudes with respect to their competences to accomplish specific tasks (Hatlevik et al., 2018). It is a well-known fact that when a user considers any technology more difficult and assume that they will never be capable to use these technologies will prefer to avoid and are less possible to use them. In the context of smartphone technology, self-efficacy is defined as the ability of an individual to use and understand the applications of the smartphone. Literature also suggests that self-efficacy is a motivational factor, which motivates the individual towards specific technology (Gist, 1989). Therefore, it is important to investigate the smartphone self-efficacy attributes as a moderator and provide evidence associated with the student intends to use a smartphone for academic learning purpose.
The objective of the current study is to investigate the usage of the smartphone by university/college student for academic purpose. This study is conducted in Pakistan. Because there is a dearth of research documented on the use of smartphones in Pakistan, especially in the education environment, this study may contribute in depicting the recent adoption of smartphones by students for academic purposes. Based on the literature of past studies, the research model was proposed, with different indicators, which shows the motivation factors of smartphone usage among students. The current study has several theoretical contribution and practical implications. First, according to the best knowledge of the author, this study is the first study in Pakistan that investigated the usage of the smartphone by students for academic purposes. Past studies focused on social networking addiction (Kanwal, Pitafi, et al., 2019) or social networking poor academic performance. Second, the current study provides advance knowledge related to smartphone, applications, and its usage as an academic purpose. Third, university officials can use the results of this study and design future policies related to smartphone usage on the campus.
This study is organized as follows: Section 2 focuses on a detailed literature review of past studies and hypothesis development. Section 3 indicates the research methods including data collection procedures and research instruments. Section 4 shows the results and several data analysis techniques. Discussion, implications, and limitations are summarized in Section 5. Finally, Section 6 shows the overall conclusion of the study.
2 LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
2.1 Relative advantage and intention to use a smartphone for learning
Learning with a smartphone provides flexibility and improves individual learning with the interdependence of time and space (Vogel et al., 2007). Smartphone learning also provides educational material and a user-friendly environment (H.-J. Kim et al., 2017). Smartphone is providing a lot of opportunities to probe the benefits to groom learning skills of students. Smartphone consists of several features such as ubiquitous, portable, and collaborative. Past literature suggests that students can avail anytime and anywhere teaching material using smartphone applications (Lee, Cho, Kim, & Noh, 2015). Learning with a smartphone allows students to experience more personalized and also overcoming the limitations of classroom learning. Scholars consider that a relative advantage is a key factor that facilitates the adoption of new technology (Lee, 2013). Based on the observations of past literature, the current study assumes that advantages of smartphone for learning are related to higher intention to use a smartphone for academic learning. Therefore, based on previous literature supports, this study outlines the following hypothesis:
H1.Relative advantage is positively related to Intention to use a smartphone for learning.
2.2 PEOU and intention to use a smartphone for learning
PEOU is defined as the degree to which the individual understands that the use of a particular system is of free of energy (Davis, 1989). Specifically, PEOU indicates a motivational factor of the technology as it assists in the adoption of new technology with relative ease. When students understand that a smartphone is easy to use and flexible, they view the smartphone as useful; they tend to utilize it more for academic purposes. On the other hand, when a student understands that using a smartphone for learning requires a huge amount of time and effort, their intention to use smartphone decreases. Additionally, Davis (1989) succinctly argued that technology usage based on output quality and ease of use. Several prior scholars used PEOU variable in the different context of technology acceptance (Elkaseh, Wong, & Fung, 2016; Hamid, Razak, Bakar, & Abdullah, 2016; Jan, de Jager, Ameziane, & Sultan, 2019). For example, Jan et al. (2019) examined the relationship between PEOU and users attitude towards adverting with a smartphone. In this context, advanced smartphones offer students ease of use due to the accessibility of important utilities just a few steps away. Such useful attributes allow students to use smartphones for academic learning purpose. Therefore, based on prior literature support, this study proposed the following hypothesis:
H2.PEOU is positively related to the intention to use a smartphone for learning.
2.3 Hedonic motivation and intention to use a smartphone for learning
Hedonic motivation use of a smartphone is defined as the use of a smartphone for enjoyment such as gaming, socialization, and enjoyment (Brandtzæg & Heim, 2009). Hedonic motivation has been found strong positive predictor of using IS and to helps in understanding the adoption of new technologies (Kim & Hall, 2019; Van der Heijden, 2004). Venkatesh, Thong, and Xu (2012) reported that hedonic motivation is directly related to behavioral intention. The hedonic setting of technology is necessary because the mere intrinsic enjoyment of using technology is a significant element of individual assessments and behaviors. Hedonic motivation has used previously different studies including the adoption of virtual games (Hamari, 2015), the intention of using social networking (Kanwal, Pitafi, et al., 2019), and virtual hotels (Mäntymäki & Riemer, 2014). Furthermore, hedonic motivation brings pleasure to the individual to use technology (Khatimah, Susanto, & Abdullah, 2019). Literature suggests that hedonic motivations have an important effect on the intention to use new technology. Therefore, based on prior literature support, this study proposed the following hypothesis:
H3.Hedonic motivation is positively related to intention to use a smartphone for learning.
2.4 Intention to use a smartphone for learning and actual usage
Actual usage refers to “how often” and the volume of the particular system used by the individuals (Davis, 1989; Venkatesh & Davis, 2000). Davis (1989) argued that individual intention influences the actual system usage. Several past studies have reported the positive relationship between intention and actual usage of the system in the different context (Ismail, Moya, Bwiino, & Ismael, 2017; Malima, Chachage, & Kamuzora, 2015; Moya, Nakalema, & Nansamba, 2018). For example, Moya et al. (2018) conducted a research study on system usage by student and staff and found that there is a positive relationship between behavioral intention and actual usage of the system by both the respondents. In addition, the invention is a driving factor for an individual that influence the use of a particular system (Jan et al., 2019). It is also a fact in the case of smartphone usage. A student with a positive intention towards smartphone tends to positively use a smartphone for academic learning. Therefore, based on prior literature support, this study proposed the following hypothesis:
H4.Intention to use a smartphone for learning is positively related to actual usage.
2.5 Moderating role of self-efficacy
The term self-efficacy is related to behavior (Davis, 1989; Igbaria & Iivari, 1995). Self-efficacy is an individual's conviction that he or she has the ability to perform a specific task successfully (Hsiao, Moser, Schoenebeck, & Dillahunt, 2018). Recently, the role of self-efficacy has increased significant attention in research on students' motivations and learning consequences. Smartphone self-efficacy is a user belief about her or his capability to competently utilize smartphone in their decision to use them. Past research related to virtual banking has discovered that in addition to factors such as gender, age, and education level, technology self-efficacy has a positive impact on intention to use a particular technology (Y.-S. Wang, Wang, Lin, & Tang, 2003). Past literature review summarized that computer self-efficacy plays an important role in students' learning in IS-based learning environments (Moos & Azevedo, 2009); results indicated that computer self-efficacy is associated with both students' learning processes and outcomes. Therefore, self-efficacy is likely to affect the user's beliefs and attitude. Specifically, it will influence system usage indirectly and directly. Based on prior literature support, this study proposed the following hypothesis:
H5.Smartphone self-efficacy has positively moderated the relationship between intention to use a smartphone for learning and actual usage.
3 RESEARCH METHODOLOGY
3.1 Data collection
To understand the objective of the current study, survey technique was used. Data were collected from the Karachi, Sindh, Pakistan. The author used survey approach for this research because the survey method offers an efficient way of data collection (Zikmund, Babin, Carr, & Griffin, 2013) as compared with case studies or interviews. The survey of the current research was conducted in the Karachi, Sindh, Pakistan, for several reasons. First, Karachi city has several high-class education institutes as compared with other cities of Pakistan, especially in Sindh Province. Second, the use of technology such as a smartphone is also higher. Third, in the Karachi city, students or people living belong to all over Pakistan. The focused respondents of the current study are university and college students. As compared with other developed countries, use of a smartphone is also popular in Pakistan. Students using a smartphone for several activities such as social networking, academic purpose, and gaming. All the respondents are students of different universities and colleges who are enrolled in different programs. For example, some students are undergraduate, others are master's, and some are PhD. The wording of some items was modified according to the purpose of the research study. In addition, the author invited some professor and PhD students for critical review and suggestions about survey items. The questionnaire was redesigned and modified according to audience suggestions and reviews.
To achieve the target samples, the author visited the several universities of Karachi city and discussed the study purpose with students and faculty members. Faculty members and some volunteer's students helped authors in the process of data collection. The survey was conducted from March 2018 to November 2018. After designing the questionnaire, the author conducted a pilot study on 55 respondents. Findings of the pilot study show good convergent validity and reliability such as composite reliability, and Cronbach's alpha was found higher than the suggested value of.70. In the final dataset, these 55 respondents were eliminated.
Within a period of 9 months, the author obtained a total of 700 filled questionnaire. Some questionnaire were not filled properly or incomplete; therefore, the author discarded that questionnaire. After assessing, the author settled 585 respondents, for final analysis. The author keeps on the approach of Armstrong and Overton (1977) and assessed the potential nonresponse bias by evaluating the initial 25% replies and the final 25%, of all items of all the constructs. The outcome confirmed that t statistics for the difference in all constructs means were not significant. These findings ensured that nonresponse bias was not an issue in the existing study. Table 1 shows the sample information.
| N | Percentage | |
|---|---|---|
| Gender | ||
| Male | 368 | 62.9 |
| Female | 217 | 37.1 |
| Age | ||
| 21–30 years old | 304 | 52.0 |
| 31–40 years old | 243 | 41.5 |
| 41–50 years old | 11 | 3.1 |
| Education level | ||
| College or below | 98 | 16.8 |
| Graduate | 398 | 68.0 |
| Master degree or above | 89 | 15.2 |
| Usage experience | ||
| 1 to 2 years | 73 | 12.5 |
| 3 to 5 years | 255 | 43.6 |
| 6 to 8 years | 257 | 43.9 |
3.2 Research instrument
To achieve the objective of the current study, several instruments and scales were used. All the research instruments were adopted by previous studies, which are designed well known in their specific context. The instrument was analyzed using a 5-point Likert scale, as past researcher proposed that 5-point and 7-point scales have no dissimilarities in the results (Cheng & Chen, 2015). In addition, 5-point scale was used by prior several studies (Kanwal, Chong, & Pitafi, 2019; Pitafi, Kanwal, Ali, Khan, & Ameen, 2018; Pitafi, Liu, & Cai, 2018). Research model of the study consists a total of six variables, namely, relative advantages of a smartphone, hedonic motivation, PEOU, intention to use the smartphone for learning, and actual usage of smartphone for learning, and Self-efficacy, which were already tested in different studies in different contexts. The scale of relative advantages was adopted by Moore and Benbasat (1991). The instrument of PEOU was adopted by Davis et al. (1989). The scale of hedonic motivation was adopted by Maier, Laumer, Eckhardt, and Weitzel (2015). The instrument of intention of smartphone for learning was adopted by Joo, Lim, and Lim (2014). The scale of actual usage was measured by Joo et al. (2014). The scale of self-efficacy was measure by Wei, Teo, Chan, and Tan (2011). However, to measure the actual usage of smartphone among students, gender, age, education level, and user experience was used as a control variable.
4 DATA ANALYSIS AND RESULTS
4.1 Assessment of bias
As the data were collected by a single source at the same time. Scholars suggested that possibility of bias issue in the data if author gathered independent variable and dependent variable data at same time (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Podsakoff, MacKenzie, & Podsakoff, 2012). Following this suggestion, the authors follow two different approaches to analyze the possibility of the common method bias. First, a single factor or Herman one factor were used on all the items. According to results, six factors were generated with Eigenvalues >1, with accounted %. The first factor is <50%. Second, common latent factor approach was used (Podsakoff et al., 2012). The authors first computed the standard regression weights; in the next step, common latent factor was included in the research model and again computed the regression weights. Finally, by comparing the regression weights of both the analysis, the author found that no dominate factor was found. Therefore, together with both the evidence shows the nonexistence of the bias issue in the dataset.
4.2 Validity and reliability
To check the entire arrangement of all the constructs, different analyses were used including convergent validity, content validity, and discriminant validity. The author computed the convergent validity by observing the values of factor loadings, Cronbach's alpha, composite reliability, and average variance extracted (AVE). Results in Table 2 indicated that loading of all the items are >0.60, Cronbach's alpha > 0.70, composite reliability > 0.70, and AVE > 0.50 as recommended (Bagozzi, Yi, & Phillips, 1991; Fornell & Larcker, 1981; Nunnally & Bernstein, 1978). Findings ensured that research model has good composite reliability and validity.
| Construct | Items | Loading | CA | CR | AVE |
|---|---|---|---|---|---|
| Relative advantage | 3 | 0.884 | 0.84 | 0.89 | 0.73 |
| 0.789 | |||||
| 0.892 | |||||
| Perceived ease of use | 4 | 0.837 | 0.85 | 0.88 | 0.76 |
| 0.907 | |||||
| 0.828 | |||||
| Hedonic motivation | 3 | 0.808 | 0.80 | 0.84 | 0.64 |
| 0.762 | |||||
| 0.841 | |||||
| Intention to use smartphone for learning | 3 | 0.648 | 0.78 | 0.83 | 0.62 |
| 0.886 | |||||
| 0.821 | |||||
| Self-efficacy | 6 | 0.800 | 0.88 | 0.91 | 0.64 |
| 0.832 | |||||
| 0.831 | |||||
| 0.908 | |||||
| 0.787 | |||||
| Actual usage | 3 | 0.696 | 0.72 | 0.80 | 0.57 |
| 0.794 | |||||
| 0.784 |
- Abbreviations: AVE, average variance extracted; CA, Cronbach's alpha; CR, composite reliability.
In addition, results of Table 3 shows that all AVE square root > intercorrelation matrix of all the construct, which confirmed the decrement validity of the research model (Hair, Ringle, & Sarstedt, 2011). Therefore, the results of Tables 2 and 3 showed that the research model has good convergent validity and reliability.
| Variable | M | SD | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|---|
| 1 Relative advantage | 5.13 | 1.53 | 0.85 | |||||
| 2. Perceived ease of use | 5.20 | 1.46 | 0.41 | 0.87 | ||||
| 3. Hedonic motivation | 5.72 | 1.19 | 0.46 | 0.35 | 0.80 | |||
| 4. Intention to use smartphone for learning | 5.09 | 1.40 | 0.44 | 0.58 | 0.35 | 0.78 | ||
| 5. Self-efficacy | 4.11 | 0.83 | 0.30 | 0.22 | 0.38 | 0.21 | 0.80 | |
| 6. Actual usage | 2.63 | 1.12 | 0.41 | 0.30 | 0.56 | 0.30 | 0.38 | 0.75 |
4.3 Measurement model
To examine the relationship among constructs, model fit was computed. Scholars suggested three different types of model fit threshold including incremental fit, absolute fit, and parsimonious fit, (Hair, 2006; Hair et al., 2011; Hair, Black, Babin, Anderson, & Tatham, 1998). Findings of Table 4 indicated that proposed model values are within range (χ2 = 737.42, df = 209, RMSEA = 0.06, CFI = 0.94, SRMR = 0.05, NFI = 0.92, IFI = 0.94, and PNFI = 0.87).
| Absolute fit measures | Incremental fit measures | Parsimonious fit measures | ||||||
|---|---|---|---|---|---|---|---|---|
| Model | χ2/df | SRMR | RMSEA | NFI | PNFI | CFI | IFI | TLI |
| MM | 3.52 | 0.05 | 0.06 | 0.92 | 0.87 | 0.94 | 0.94 | 0.93 |
| SEM | 1.86 | 0.05 | 0.07 | 0.91 | 0.89 | 0.95 | 0.95 | 0.95 |
4.4 Structural equation modeling
To understand the objective of the current study, Structural Equation Modeling (SEM) with maximum likelihood was performed as recommended by Hair et al. (1998), Hair et al. (2011), and Hair (2006). Table 4 shows the model fit of the structural model. (χ2 = 855.71, df = 112, RMSEA = 0.06, CFI = 0.94, SRMR = 0.05, NFI = 0.92, IFI = 0.94, and PNFI = 0.87) are within the range.
4.5 Hypothesis testing
The hypothesis was tested using the AMOS 21.0 software. Table 5 shows the results of all the proposed hypotheses. Results validated H1 (β = .21, p < .001), which was proposed that the relative advantage of the smartphone has a positive effect on the intention to use a smartphone for learning. In H2, we suggested that PEOU smartphone is positively associated with intention to use a smartphone for learning, also validated by current dataset with β = .42, p < .001. Hedonic motivation also shows a positive relationship with the intention to use a smartphone for learning with β = .32, p < .001, which is according to our assumption. Similarly, the intention to use a smartphone for learning has a positive relation with actual usage with β = .44, p < .001; H4 is also supported.
| Relationship | Standard coefficient | CR | p value | Result |
|---|---|---|---|---|
| ISPE ← RA | 0.21 | 6.88 | *** | Supported |
| ISPE ← PEOU | 0.42 | 10.43 | *** | Supported |
| ISPE ← HM | 0.32 | 7.80 | *** | Supported |
| AU ← ISPE | 0.44 | 8.59 | *** | Supported |
| Self-efficacy | 0.30 | 9.82 | *** | Supported |
| ISPE* Self-efficacy | 0.10 | 2.825 | .005 | Supported |
- Abbreviations: AU, actual usage; HM, hedonic motivation; ISPEC, Intention to use smartphone for learning; PEOU, perceived ease of use; RA, relative advantage.
- * p<0.5
- ** p<0.01
- *** p<0.001
4.6 Moderation test
Findings of Table 5 showed the moderating effect of smartphone self-efficacy with the relationship between intention to use a smartphone for learning and actual usage. The results reveal that smartphone self-efficacy (β = .30, p < .001) has a positive moderating effect. The interaction term of intention to use a smartphone and smartphone self-efficacy (β = .100, p < .05) is significant, which specifies that smartphone self-efficacy has a moderating effect. This authenticates H5, illuminates that the higher the smartphone self-efficacy, the higher the use of smartphone among students.
5 DISCUSSION, IMPLICATION, AND LIMITATION
5.1 Discussion
The objective of this study is to investigate the Pakistani students learning behavior using a smartphone. Data were collected using a survey technique from Karachi, Sindh, Pakistan. Findings of the current study support most of the proposed hypotheses showing the students' learning behavior through smartphone applications. H1 was proposed that the relative advantage of a smartphone is positively related to the intention to use the smartphone for learning. Results of data analysis validated H1, which is according to our assumption. These results are consistent with the previous studies (H.-J. Kim et al., 2017; Panda & Jain, 2018). H.-J. Kim et al. (2017) argued that the benefits of mobile phone motivated the university students towards learning. Findings also confirmed H2, PEOU of the smartphone is positively related to the intention to use a smartphone for learning. Several past studies have investigated the PEOU variable in a different context (Abdullah, Ward, & Ahmed, 2016; Amin, Rezaei, & Abolghasemi, 2014; J. Lee, Ryu, & Lee, 2019; Li, Sun, Yang, & Guo, 2019; Pookulangara & Koesler, 2011). For example, Pookulangara and Koesler (2011) found that PEOU is positively related to the intention to use social networking. Similarly, Jiyoon Lee et al. (2019) proposed that PEOU has a significant effect on intention to use mobile banking. Results show that hedonic motivation has a positive effect on intention to use a smartphone. H3 is validated by the current dataset. Several past studies validated that enjoyment feature of the technology is positively related with the intention to use (Kanwal, Chong, Pitafi, Akhtar, & Irfan, 2018; Kanwal, Pitafi, et al., 2019). In H4, we proposed that intention to use smartphone for learning has positive effect on actual usage. H4 was also supported by existing dataset, which is also related with past studies (Ernst, Pfeiffer, & Rothlauf, 2013; Moya et al., 2018; Xu, Ryan, Prybutok, & Wen, 2012).
Furthermore, findings show the significant moderating effect of smartphone self-efficacy with the relationship of intention to use a smartphone for learning and actual usage. H5 is also supported by a current dataset. Prior studies also used computer self-efficacy and social media self-efficacy variables in different contexts (Chen, 2017; Wan, Compeau, & Haggerty, 2012; Y. Wang & Haggerty, 2011). For example, Wan et al. (2012) investigated the employee's e-learning process with online job training and found that individual adopts several self-regulated learning techniques resulting in numerous e-learning outcomes. Chen (2017) also found that computer self-efficacy is also positively related to the students' learning performance. We reveal that improving students' technology self-efficacy is an effective approach to encourage them to involve in the academic learning process. Specifically, the author suggested that it will be more beneficial for students if smartphone self-efficacy should be increased during teaching and to design training and education programs for students.
5.2 Implication
The current study has several important implications in theoretical literature as well as practical implications. First, according to the author's literature survey, the current study is the first study in Pakistan that shows the smartphone usage among university/college students. Past scholars conducted the same related studies in a developed country (Han & Yi, 2019; Leung & Liang, 2019). For example, Leung and Liang (2019) examined the relationship between smartphone usage and student academic performance and found a positive relationship. Second, the results of this study also help the students and educational institutes the usage of smartphone for learning purpose. To improve further usage of smartphone for learning, universities/colleges can craft awareness among students by conducting workshops to explain the benefits of smartphone usage for learning. Even a small positive increase in the use of smartphone may help the students to increase their academic performance (Anshari et al., 2017).
Third, the academician should redesign the syllabus and make it flexible with the usage of a smartphone as a learning channel. Smartphone applications should be used in such a way that it can incorporate teaching resources with several interactions, information sharing, and interesting exercise to enable important e-learning process. For example, creating online tasks into a syllabus can be a better way to engage students in learning. It ultimately inspires students to find and assess online material for a specific topic being discussed. If teaching materials are available online, students can always access without interdependence of time and space.
Finally, smartphone and application designers can use the results of this study to act as a guide in the development of smartphone functions and promotions that are attractive for students such as flexible interfaces and usefulness. According to findings, smartphone applications add new functions to attract more students to use a smartphone, which would enhance the sustainable viability of smartphone application providers. This research gives insights into the status of usage of a smartphone. The findings of this study are important for parents and academicians, as these are the main stakeholders of students.
5.3 Limitation
Regardless of the attempts that we have dedicated to the current study, this research study has some limitations, which may be helpful for future scholars. First, the sample size of this study is small as compared with a number of students in Pakistan. Although recent studies also has recommended a small sample size (Sideridis, Simos, Papanicolaou, & Fletcher, 2014). Future scholars are advised to investigate the same conceptual model with massive sample size and compare the results. Another sampling issue is that the current study based on Pakistani university/college students, like Pakistan, is a developing country in terms of economy and technology. Scholars are advised to examine the smartphone use in the developed country and compared the results. Additionally, the current study did not investigate the types of smartphones and usage patterns of students, as past scholars also suggested that it is necessary to examine how a particular media type provides a solution to real-life problems in a more detailed way (Knobloch-Westerwick, Hastall, & Rossmann, 2009). Future scholars should examine whether different types of smartphones and their functions exhibit dissimilar motivations for their use; this separation of usage patterns may be more beneficial for identification the process behind the use of smartphones.
Second, smartphone application providers should increase the ease of use; relative advantages of smartphone especially provide a flexible and user-friendly interface. For instance, the main page of a smartphone application should be user-friendly enough to improve user a good experience, such as fast response and utility. Furthermore, several functions of smartphone applications are idle, which consume memory of mobile devices; mobile applications should be improved by cutting down redundant functions to increase the work speed and user good experience to continue to use the smartphone applications (Li et al., 2019).
Third, in the current study, we investigated the positive effect of smartphone usage and student learning. However, the smartphone provides several entertainment functions, which attracts young generations especially students, decreases academic performance, and produces several psychological harmful effects. Therefore, future scholars are suggested to investigate the dark side of the smartphone as well. As Pakistan is a developing country in terms of technology and there are no any smartphone application usage policies such as social networking sites usage policies as compared to a developed country like China. The Chinese government has very strict rules and policies related to social networking sites usage, and most of the social sites are blocked in China. For example, well-known Chinese social media application WeChat has several rules and policies for users.
Finally, it is also recommended for university authorities to provide free WIFI on campuses that student can take more benefit from this emerging technology. On the other hand, it also suggested that university authorities should take appropriate steps to control the excessive use of smartphone such as chatting and social networking. For example, they can implement a timer system and set a limit of time that cannot be exceeded and warning may be issued to users related to the amount of time spent on the system, which helps to increase the awareness of time spent on smartphone usage (Turel & Bechara, 2016). Scholars can also arrange some conferences, workshops, and training to highlight the benefits of a smartphone for academic learnings.
6 CONCLUSION
In the current research, the author invested the actual usage of the smartphone by Pakistani university/college students. The current data support all the hypotheses, showing the students usage of smartphone for learning purpose. As a developing country, Pakistani students should use a smartphone for academic purpose. Relative advantage and PEOU show the significant predictor of student's intention to use a smartphone for learning purpose. The moderating effect of self-efficacy also shows a positive relationship. Technological facilities such as free WIFI on university campuses can also increase the usage of smartphone for learning.
ACKNOWLEDGEMENT
The second and corresponding author (Doctor Abdul Hameed Pitafi) is grateful to Anuhai government and Hefei University of Technology of China for financial support during post doctorate research at the Hefei University of Technology of China. There is no any research funding or grant from any organization is associated with this manuscript.
CONFLICT OF INTEREST
No potential conflict of interest was reported by the authors.
Biographies
Shardha Nand is working as a Assistant Professor in Computer Science Department of Sir Syed University of Engineering and Technology Karachi. He has done MS (Computer Science) from Shaheed Zulfiqar Ali Bhutto institute of Information Technology (SZABIST). He is keeping more than two decades of teaching and research experience. His areas of interest are Information Systems and Data Mining.
Abdul Hameed Pitafi is currently doing post doctorate in Hefei University of Technology, China. He got PhD degree specialization in Information System from the University of Science and Technology of China. He completed his Bachelor of Engineering (Computer System) from Quaid-Awam University of Science and Technology, Nawabshah. He received his Master of Science degree in Networks and Telecommunication from Mohammed Ali Jinnah University, Karachi. He has more than 15 years of teaching experience. His current focus of research work was in the role of information technology and people and how the adoption of various technologies is in the workplace. He is also reviewer of different international journals. He has published over 20 publications in SSCI and peer-reviewed journals.
Shamsa Kanwal is presently final years of master's program towards the degree of Master of Public Administration (specialization in Public Management) in University of Science and Technology, Hefei, China. She has received a Bachelor of Science (Math), degree from Sindh University, Jamshoro, Pakistan. Her research interests include social media, social networking, and policy issues in developing countries. She has published over eight publications as a first author and more than 15 papers in collaboration in SSCI SSCI and other international journals.
Dr. Adnan Pitafi is working as an Assistant Professor of Entrepeneurship, at the Mehran University Instiute of Science, Technology and Development (MUISTD). He has a doctoral degree from the University of Science and Technology of China (USTC) in Management Sciences, and his research focus is Innovation and Entrepreneurship, Technopreneurship, Social Media and Technology Adoption Models.
Dr. Muhammad Imran Rasheed received his Ph.D degree in Business Adminsitration from University of Science and Technology of China. Currently, he is serving the Islamia University of Bahawalpur Pakistan as Assistant Professor. His current area of research includes leadership, technology, and employee psychology.
