The Effects of Attitudinal,Normative,and Control Beliefs on M-Learning Adoption Among the Students of Higher Education in Pakistan

Abstract

This study intends to analyze the influence of behavioral and psychosocial factors of higher education students of Karachi on acceptance of m-learning as a mode of getting education. The Theory of Planned Behavior and Technology Acceptance Model have provided the basic frameworks to formulate the hypotheses for this study. The analyses of the study reveal that attitudinal beliefs and subjective norms have significant and positive impact on m-learning. Additionally, control influences on Perceived Behavioral Control also make significant contributions in the adoption of m-learning methodologies. Collectively, all of these factors converge to form greater intent of the students to assimilate this new learning environment. It was also found that the characteristics and features of the mobile gadgets such as greater familiarity of the students with them, handiness and ease of use, influences them to utilize these devices in completing their academic tasks, hence, influencing their intention toward the adoption of m-learning. Also seamless learning experience, students get through m-learning, reduces their mental resistance toward it and encourages them to adopt it. Information pertaining to the defined criteria was gathered through a specimen of 300 questionnaire responses and was then analyzed using the reliability analysis, confirmatory factor analysis, and partial least square-structural equation modeling to assess the influence of these factors on m-learning adoption.

Keywords

attitudinal beliefs normative beliefs behavioral beliefs theory of planned behavior technology acceptance model mobile learning m-learning

The rapid development and innovation in the field of technology have influenced almost every service and business sector in the world today. The rapid innovation in the field of information and communication technology (ICT) and the introduction of computing devices, such as notebook computers, wireless phones, and handheld gadgets, have revolutionized the field of higher education as well (Green, 2000). One such advancement in ICT is mobile technology which has broadened the opportunities for gaining knowledge through both formal and informal learning methods, as the use of this technology allows instant and convenient access to the digital resources (Cheon, Lee, Crooks, & Song, 2012). Developments in this technology have enabled the transition from conventional learning bounded by time and space to the learning entrenched in our daily lives. Mobile technology facilitates educators, students, and the other employees of universities to access information, handle data, and fulfill their requirements by utilizing mobile services whenever and wherever they want (Althunibat, 2015).

Mobile learning or m-learning also plays a vital role in the area of formal and informal education. M-learning, a continuation of e-learning, refers to the use of mobile devices for the purpose of initiating learning environment within the universities regardless of time or place (Althunibat, 2015). According to Thornton and Houser (2002), “mobile learning (m-learning) is education that is enhanced with handheld mobile devices such as personal digital assistants (PDAs), mobile phones, and eBook readers” (p. 229). Alternately, m-learning refers to mobile devices featuring e-learning capabilities (Ktoridou & Eteokleous, 2005; Şad & Goktas, 2014), such as smart phones, personal digital assistants, and tablets. These handheld gadgets enable learners to seamlessly continue their studies irrespective of their locations or time zones; for example, they can connect with their facilitator or access course content online as and when required (Nihalani & Mayrath, 2010; Cavus & Ibrahim, 2009; Richardson & Lenarcic, 2008; Kukulska-Hulme & Shield, 2008). According to Ozdamli (2012), m-learning is a dynamic learning tool augmenting the process of getting education, also providing two-way learning opportunities (Peck, Deans, & Stockhausen, 2010), and “just enough, just in time, just for me” kind of the adjustable learning (Abu-Al-Aish & Love, 2013; Peters, 2007). M-learning creates an atmosphere in which students can participate and interact actively, and it also assists in promoting informal learning (Karimi, 2016). To make this learning process more efficient and successful, mobile learning can be customized and implemented according to the requirements of learners (Sun, Joy, & Grifﬁths, 2007). Thus, m–learning has enough capability to facilitate the attainment of educational goals (Şad & Goktas, 2014). However, in order to benefit from education through such gadgets, adoption of m-learning must be given priority by the students. The use of mobile phones for the purpose of getting education is not only supported by the easy availability of these devices but also by the extent to which students are ready to opt for mobile learning (Corbeil & Valdes-Corbeil, 2007; Keller, 2011).

Asia is considered to be one of the two largest markets of portable devices which include smartphones, notes, tablets, palm tops, and so forth. According to Waqar (2014), mobile connections in Asia have reached over three billion (GSMA-Kearney, 2011). Specifically, in Pakistan, a statistical report published by the Pakistan telecommunication authority stated that by the end of September 2013, around 129.6 million people were using handheld gadgets (Pakistan Telecom and IT News). The number of users increased to 149.2 million in 2015. Although, in Pakistan, the ratio of individuals using such devices is extensively high, the adoption of m-learning in higher education is still at its initial stage. M-learning has not yet been welcomed enthusiastically by higher education institutions in Pakistan, and proper knowledge about the benefits of incorporating m-learning is yet to be gained.

Studies are being conducted to investigate the usage of portable gadgets in higher education institutions (Karimi, 2016). Most of the studies explore technological features (Sarrab, Elbasir, & Alnaeli, 2016) or motivational aspects which impact teachers’ adoption of mobile learning (Sad & Goktas, 2014); however, very little research has been carried out exploring students’ motivational factors in the adoption of m-learning (Karimi, 2016), especially in the context of Pakistani higher education institutions. It is important to discover the students’ perspective of using portable electronic gadgets for the purpose of learning. Hence, the purpose of this study is to determine those factors which influence the m-learning adoption in higher education institutions in Pakistan.

This article comprises five sections. The previous section has provided the introduction and the next section is composed of theoretical background and empirical studies. Methodology is explained in Data Analysis section, followed by Path Analysis and Discussion sections. The final section concludes the paper by detailing conclusions and recommendations.

Literature Review

Theoretical Framework

This study uses the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as the basis of primary research. Technology acceptance model (TAM), suggested by Davis (1989), is a prime, established theory which covers the aspect of technology (Awa, Eze, Urieto, & Inyang, 2011; Benbasat & Barki, 2007). It is a widely utilized model in the realm of information system acceptance, in which actual behavior is analyzed by perceived usefulness (PU) and perceived ease of use (PEOU) through attitude and intention. To study m-learning adoption, we can comprehend that TAM is inappropriate, since the technology utilized is different from that of conventional IT and the normal business setting. Prior to the adoption of m-learning, users are supposed to have some knowledge regarding its benefits, security issues, and risks associated with it. Hence in this regard, the TPB given by Ajzen (1991) is used. This theory describes common behaviors of a person and suggests that the intention of an individual drives his or her behavior, whereas intention itself is determined by three factors; attitude of an individual, subjective norms, and perceived behavioral control. The integration of TAM with TPB provides a comprehensive theoretical view of user technology acceptance in the domain of m-learning.

Mobile Learning in Higher Education

M-learning refers to a particular kind of educational framework which utilizes mobile technology (Naismith, Lonsdale, Vavoula, & Sharples, 2004; Yuen & Yuen, 2008), whereas e-learning refers to an educational setting in which learning occurs by utilizing different kinds of computer technologies (Clark & Mayer, 2008; Horton, 2006).

Various perceptions of m-learning have been discussed in different studies. According to Mcconatha, Praul, and Lycnh (2008), mobile learning is the learning which is implemented by using small computing mobile devices, but Alzaza and Yaakub (2011) define m-learning as an extended form or next stage of e-learning which utilizes mobile technology. Al-Emran and Shaalan (2014) regarded mobile learning as a process in which information is shared between students and their teachers during their interaction with each other. It allows students and teachers to accomplish their routine tasks without spending much time through the usage of handy mobile devices (Al-Emran, Elsherif, & Shaalan, 2016).

The students of higher education institutions of today are already familiar and comfortable with using mobile gadgets and are therefore ideal test subjects for student-oriented m-learning programs (Cheon et al., 2012). Implementation of m-learning has been done in universities as teachers send formative assessment and responses to students through mobile gadgets (Crawford, 2007). Moreover, managerial tasks, such as dealing with attendance and progress reports of students, can also be done through mobile phones and devices (Cheon et al., 2012). According to Keller (2011), universities like Stanford and University of Washington have incorporated m-learning. However, according to some researchers (Corbeil & Valdes-Corbeil, 2007; Traxler, 2009, 2012), integration of m-learning in universities is still facing a lot of difficulties due to societal, cultural, and managerial aspects. Hence, in order to successfully integrate m-learning in the learning system, the perceptions of students and teachers about m-learning should be understood.

A few studies have been conducted to investigate the adoption of m-learning by students (Liu, Li, & Carlsson, 2010; Lowenthal, 2010; Wang, Wu, & Wang, 2009). Research carried out by Liu et al. (2010) on the students of a Chinese college by employing TAM (Davis, 1989) found that PU and personal innovation are the factors which impact the m-learning adoption. Wang et al. (2009), by applying Unified Theory of Acceptance and Use of Technology (Venkatesh & Davis, 2000), carried out a research on the students of a Taiwan College and discovered that performance expectations, effort expectancy, social inﬂuence, perceived playfulness, and self-management of learning influence the adoption of m-learning.

Constructs of TPB

The TPB is basically an extended version of the theory of reasoned action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1977). It explains that the behavior of an individual is directly driven by the intention and the intention is itself a function of attitude, subjective norm, and behavioral control (Ajzen, 1991). To elaborate further, attitude of an individual toward the behavior is basically a good or a bad feeling of an individual regarding the performance of a certain behavior. Subjective norm can be described as perceived societal pressure initiating from an individual’s opinion, while perceived behavior control is one’s opinion about the ease or difficulty of executing a certain behavior (Cheon et al., 2012).

While integrating TPB, three aspects must be taken care of. To begin with, behavioral control must be theoretically distinguished from attitude. According to Ajzen (2002), behavioral control does not represent the possibility that the performance of a specific behavior will create an expected result; however, it allows an individual to manage the performance of a behavior on an independent level. Hence, learners’ opinion about the ease or difficulty of m-learning can be termed as perceived behavioral control. Then, the intention is used as a dependent variable in place of actual behavior. According to Ajzen (1991), “intentions are assumed to capture the motivational factors that inﬂuence a behavior” (p. 181). Hence, greater intention toward the performance of a certain behavior will lead to better performance of actual behavior (Venkatesh & Davis, 2000; Venkatesh, Morris, & Ackerman, 2000). Finally, attitudinal, normative, and control beliefs are added in the perspective of m-learning.

Attitudinal Beliefs Toward Attitude

Attitude is regarded as the level to which one has a positive or negative opinion about the performance of a particular behavior. Former studies (Ajzen, 1991; Taylor & Todd, 1995) have suggested that attitude strongly predicts intention. Moreover, attitude is driven by attitudinal beliefs. To gage attitude, two precursors, PEOU and PU, which are the factors of TAM, are incorporated in this study.

Theory of acceptance model claims that causal relationships exist among PEOU, PU, attitude, and intention toward the use of a system (Davis, 1989; Teo, 2009). Many studies have been carried out in which PU and PEOU determine the intention of an individual toward the adoption of an information system in a learning system (Guriting & Ndubisi, 2006; Luarn & Lin, 2005; Wang, Wang, Lin, & Tang, 2003).

PU refers to the opinions about the level to which an individual’s productivity increases by using a particular system or technology (Lu, Yu, Liu, & Yao, 2003; Rauniar, Rawski, Yang, & Johnson, 2014). PU and PEOU both influence the attitude of a person toward the intention to use a technology (Rauniar et al., 2014).

In an individual’s point of view, the extent to which the use of a certain system or technology can be effortless is known as PEOU (Davis, 1989; Venkatesh & Morris, 2000). It is basically the perception of an individual related to the assessment of the efforts involved while using technology (Davis, 1989). Through above studies, it can be hypothesized that:

H₁: University students’ attitude towards m-learning significantly inﬂuences their intention to adopt m-learning.

H₂: College (???University) students’ perceived ease of use of m-learning positively inﬂuences their attitude towards m-learning.

H₃: University students’ perceived usefulness of m-learning significantly inﬂuences their attitude towards m-learning.

Normative Beliefs Toward Subjective Norm

Subjective norm is concerned with an individual’s opinion of the societal settings encircling the behavior. As explained by Venkatesh and Davis (2000), perceptions of those people who are important to an individual are vital in determining his or her intention, and this is how subjective norm and behavioral intentions are related to each other.

According to Ajzen (1991), subjective norm is influenced by the available normative opinions which explain the expectations of other individuals as a significant factor in behavioral intention. According to the studies conducted previously, instructors and peer students are prominent referent sets in the setting of higher education (Liu, 2008; Taylor & Todd, 1995); therefore, in this study, normative beliefs of instructors and students are considered to be the precursors of subjective norm.

According to Parasuraman (2000), technology readiness refers to the tendency to adopt and utilize new technologies in order to achieve both personal and professional goals. Here, technology readiness refers to the readiness of both teachers and students for the incorporation of portable technology in their learning system. Earlier researches have shown that readiness of students and teachers regarding the acceptance and use of technology plays a significant role toward the acceptance and adoption of m-learning (Abas, Li Peng, & Manso, 2009; Andaleeb, Idrus, Ismail, & Mokaram, 2010; Petrova & Sutedjo, 2004). Hence, it can be hypothesized that

H₄: University students’ subjective norm towards m-learning significantly inﬂuences their intention to adopt m-learning.

H₅: Perceived instructor readiness for m-learning significantly inﬂuences subjective norm for m-learning.

H₆: Perceived peer student readiness for m-learning significantly inﬂuences subjective norm for m-learning.

Control Beliefs Toward Perceived Behavioral Control

Behavioral control deals with the opinion of an individual regarding the management of a specific behavior, and this opinion about the control is linked with the intention toward the performance of a behavior. The researchers have concluded that if an individual is sufficiently sure of his aptitude to overcome any expected hurdles in embracing a behavior, then an increase in behavioral control occurs (Ajzen, 1987; Hartwick & Barki, 1994; Lee & Kozar, 2005).

Behavioral control is itself based on two parts, self-efficacy and controllability, where self-efficacy can be defined as the self-confidence of a person regarding the enactment of a certain behavior and which regulates behavior (Ajzen, 1991).

Self-efficacy covers the notions of personal capability and enthusiasm factor of an individual in accomplishing a task (Bandura, 1986, 1997). People who think of becoming proficient and gaining expertise in a certain task are inclined to have a stronger intention toward the performance of that particular task or skill. According to the study carried out by Hill, Smith, & Mann (1987), self-efficacy significantly influences the acceptance of a technology and also influences the decision of using it.

Moreover, another precursor for behavioral control used in the current study is learning autonomy. Learning autonomy refers to the level to which learners have the responsibility and the power to regulate their learning processes by using mobile phones or gadgets (Cheon et al., 2012). Liaw, Huang, and Chen (2007) claim that autonomy plays a significant role in the adoption of a system. Hence, learning autonomy can be considered as a vital precursor of behavioral control. Therefore, it can be hypothesized that:

H₇: University students’ perceived behavioral control towards m-learning significantly inﬂuences their intention to adopt m-learning.

H₈: University students’ perceived self-efﬁcacy towards m-learning significantly inﬂuences their behavioral control with m-learning.

H₉: University students’ perceived learning autonomy towards m-learning significantly inﬂuences their behavioral control with m-learning.

Instrument

The instrument used in this study is an adopted questionnaire which was pilot-tested and validated by academic professionals. It is constructed on a 5-point Likert scale and contains 30 items for 10 variables which were adopted from Cheon et al. (2012); hence, the criterion of having at least 25 items in a questionnaire, proposed by Hair, Black, Babin, Anderson, and Tatham (2006), is met. Using the convenience sampling technique, the questionnaire was administered to 300 students of higher education institutions who use mobile phones. Along with the data, the descriptive information, such as gender, age, and education, was also collected from the respondents who participated in the study voluntarily. To fulfill the ethical criteria in research, the confidentiality of the information shared by them was strictly ensured.

Demographics

The sample shows the responses gathered from the students of various higher education institutions which were 358 in total. However, the removal of outliers and missing values shrunk the data to 300 useable responses. Furthermore, the demographics of the participants, used in this study, are represented by Table 1, which shows that out of 300 responses, 178 are male students, whereas 122 are female students, constituting 59% and 41% of the sample, respectively. Moreover, the majority of the students are aged between 18 and 25 years, making up 44% of the total responses, while 35% are aged between 26 and 30 years, 15% are aged between 31 and 36 years, and only 6% are aged 36 years or more. Also the data of educational level shows that the majority of the m-learning students are undergraduates making up 43% of the total responses, followed by graduates who are 36%, whereas only 21% of the students are postgraduates.

Table 1.

Profile of Respondents.

Demographic items	Frequency	Percentile
Gender
Male	178	59%
Female	122	41%
Age
18–25	132	44%
26–30	104	35%
31–35	46	15%
36 or more	18	6%
Education level
Inter/A levels	128	43%
Graduate	108	36%
Postgraduate	64	21%

Data Analysis

This study utilizes the technique of smart PLS 3.2.3 (Ringle, Wende, & Becker, 2014) along with the resampling method of 5000 subsamples (Hair, Ringle, & Sarstedt, 2011). Partial least square structural equation modeling (PLS-SEM) is the technique which is perfect for estimating a complex model; therefore, it is applied to evaluate the measurement and structural framework (Hair et al., 2011; Henseler et al., 2014) and exactly assess the model (Hair et al., 2011; Hair, Sarstedt, Ringle, & Mena, 2012).

In cases where there is little a priori knowledge on structural model relationships or the measurement of the constructs, or when the emphasis is more on exploration than confirmation, PLS-SEM is an attractive alternative to covariance-based structural equation modeling (CB-SEM; Hair, Hult, Ringle, & Sarstedt, 2016, p. 18). The primary purpose of the PLS approach is to predict the indicators by means of the components expansion (Joreskog and Wold, 1982, p. 266). According to Hair et al. (2011, p. 144), if the goal is to predict key target constructs or identify key “driver” constructs, PLS-SEM should be selected, but if the goal is theory testing, theory confirmation, or comparison of alternative theories, CB-SEM should be selected. In addition, if the research is exploratory or an extension of an existing structural theory, PLS-SEM is the correct choice. PLS-SEM can be used in such situations, since it is not constrained by identification and other technical issues. PLS-SEM also exhibits a higher level of statistical power than CB-SEM (Hair et al., 2011; Reinartz, Haenlein, & Henseler, 2009). Consequently, PLS-SEM is better at identifying population relationships and more suitable for exploratory research purposes—a feature which is further supported by the less restrictive requirements of PLS-SEM in terms of model setups, model complexity, and data characteristics (Hair et al., 2016, p.79).

Wold (1975, 1980) and Joreskog and Wold (1979) proposed PLS, a method which explains the association between latent or hidden variables. According to Aibinu and Al-Lawati (2010), any dormant, unobserved, or hidden determinant which causes the association between the factors being evaluated is called a latent variable (LV). PLS is capable of working with the LVs as well as deducing the measurement error for improving these LVs (Chin, 1998). The perception-based items, integrated in this research, are formed on Likert scale with their distribution and normality being unrevealed. Now, the effectiveness of the model is evaluated by carrying out reliability analysis of each single item, convergent validity (Cook & Campbell, 1979), and discriminant validity (Campbell & Fiske, 1959).

PLS-SEM is capable of estimating very complex models. For example, if theoretical or conceptual assumptions support large models and sufficient data are available (i.e., meeting minimum sample size requirements), PLS-SEM can handle models of any size including those with dozens of constructs and hundreds of indicator variables. As noted by Wold (1975), PLS-SEM is virtually without competition when path models with LVs are complex in their structural relationships (Hair, Sarstedt, Hopkins, & Kuppelwieser, 2014). Model complexity is generally not an issue for PLS-SEM. As long as appropriate data meet minimum sample size requirements, the complexity of the structural model is virtually unrestricted (Hair et al., 2014).

The evaluation of the reliability of each item is carried out by the assessment of standardized loadings or simple correlation. Raza and Hanif (2013) and Tabachnick and Fidell (2007) have set a benchmark of 0.55 which means that for the items to be regarded as reliable, their Cronbach’s alpha should be equal or greater than .55. Thus, through Table 2, it can be deduced that all the items are reliable and meet the set criterion.

Table 2.

Measurement Model Results.

Constructs	Items	Loadings	Cronbach's α	Composite reliability	Average Variance extracted
Attitude	ATT1	0.808	0.845	0.896	0.684
	ATT2	0.858
	ATT3	0.830
	ATT4	0.816
Behavioral control	BC1	0.821	0.782	0.860	0.607
	BC2	0.777
	BC3	0.774
	BC4	0.740
Intention to adopt	INT1	0.848	0.784	0.874	0.699
	INT2	0.803
	INT3	0.858
Instructor's readiness	IR1	0.820	0.823	0.876	0.587
	IR2	0.782
	IR3	0.742
	IR4	0.754
	IR5	0.733
Learning autonomy	LA1	0.744	0.833	0.889	0.668
	LA2	0.829
	LA3	0.843
	LA4	0.851
Perceived Self-efficacy	PE1	0.855	0.859	0.905	0.705
	PE2	0.842
	PE3	0.811
	PE4	0.853
Perceived ease of use	PEOU1	0.790	0.809	0.867	0.570
	PEOU2	0.740
	PEOU3	0.729
	PEOU4	0.755
	PEOU5	0.765
Perceived usefulness	PU1	0.768	0.791	0.856	0.546
	PU2	0.718
	PU3	0.761
	PU4	0.718
	PU5	0.735
Subjective norm	SN1	0.750	0.819	0.874	0.583
	SN2	0.786
	SN3	0.830
	SN4	0.717
	SN5	0.731
Student Readiness	SR1	0.751	0.797	0.861	0.554
	SR2	0.744
	SR3	0.735
	SR4	0.709
	SR5	0.779

The following two criteria are considered by PLS-SEM for the determination of convergent validity, which were given by Fornell and Larcker (1981):

Cronbach’s alpha and composite reliability,

The average variance extracted (AVE)

A common measure to establish convergent validity on the construct level is AVE. This criterion is defined as the grand mean value of the squared loadings of the indicators associated with the construct (i.e., the sum of the squared loadings divided by the number of indicators). Therefore, the AVE is equivalent to communality of the construct. An AVE value of 0.5 or higher indicates that, on average, the construct explains more than half of the variance of its indicators. Conversely, AVE of less than 0.5 indicates that on average, more error remains in the items than the variance explained by the constructs (Hair et al., 2014, p. 103).

All the variables meet the first criteria of being reliable as they all have their values of Cronbach’s alpha above .55, as shown in Table 2. The composite reliability of all the variables is also greater than 0.7, benchmark value set by Nunnally (1978) and Raza, Jawaid, and Hassan (2015), which suggests that this requirement is also fulfilled by the variables. Finally, the third criterion of AVE to be greater than 0.5 (Fornell & Larcker, 1981) is also met by the variables. Hence, convergent validity of the variables is determined.

For the determination of discriminant validity, three considerations are taken into account.

Square root of AVE,

Cross-loadings,

Heterotrait-monotrait ratio of correlations.

The first requirement for discriminant validity is that in the correlation matrix, the square root of AVE (diagonal value) in each construct should exceed the correlation of latent constructs, which is fulfilled in the current study as shown by Table 3. The second requirement of discriminant validity, suggested by Gefen and Straub (2000), is that the loading of each item should be higher than the loading of its corresponding variable and the difference between cross-loadings should also be higher than 0.1. Thus, through Table 4, it can be inferred that second criterion has also been met. The third criterion of discriminant validity is that the values of heterotrait-monotrait ratio of correlation should be less than 0.85 (Raza, Qazi, & Umer, 2017), and Table 5 shows that the third criterion is also met by variables, thus proving that that discriminant validity is also determined.

Table 3.

Summary Statistics.

Correlation Matrix
	ATT	BC	INT	IR	LA	PE	PEOU	PU	SN	SR
ATT	0.828
BC	0.583	0.778
INT	0.541	0.688	0.837
IR	0.586	0.563	0.510	0.767
LA	0.562	0.682	0.658	0.610	0.818
PE	0.659	0.670	0.654	0.573	0.709	0.840
PEOU	0.535	0.476	0.363	0.471	0.424	0.435	0.756
PU	0.648	0.628	0.574	0.588	0.592	0.644	0.658	0.740
SN	0.593	0.594	0.593	0.580	0.643	0.709	0.457	0.540	0.764
SR	0.579	0.658	0.518	0.664	0.656	0.620	0.514	0.611	0.687	0.744

Notes: ATT = Attitude, BC = Behavioral control, PEOU = Perceived ease of use, PU = Perceived usefulness, INT = Intention to adopt, PE = Perceived Self-efficacy, IR = Instructor's readiness, LA = Learning autonomy, SN = Subjective norm, SR = Student's Readiness. The diagonal elements (bold) represent the square root of AVE (average variance extracted).

Table 4.

Loadings and Cross Loadings.

	ATT	BC	INT	IR	LA	PE	PEOU	PU	SN	SR
ATT1	0.808	0.421	0.455	0.510	0.433	0.503	0.493	0.549	0.494	0.472
ATT2	0.858	0.536	0.511	0.488	0.527	0.609	0.449	0.544	0.480	0.542
ATT3	0.830	0.491	0.429	0.508	0.468	0.535	0.466	0.488	0.514	0.483
ATT4	0.816	0.483	0.388	0.432	0.429	0.532	0.359	0.564	0.478	0.413
BC1	0.522	0.821	0.616	0.459	0.599	0.606	0.391	0.521	0.526	0.579
BC2	0.496	0.777	0.458	0.500	0.558	0.544	0.315	0.421	0.451	0.486
BC3	0.436	0.774	0.536	0.397	0.500	0.453	0.365	0.486	0.423	0.472
BC4	0.348	0.740	0.522	0.395	0.454	0.471	0.411	0.527	0.439	0.505
INT1	0.471	0.621	0.848	0.481	0.608	0.636	0.280	0.535	0.559	0.501
INT2	0.448	0.513	0.803	0.391	0.497	0.462	0.342	0.416	0.461	0.362
INT3	0.438	0.586	0.858	0.402	0.538	0.529	0.295	0.481	0.462	0.427
IR1	0.553	0.500	0.457	0.820	0.567	0.511	0.436	0.566	0.505	0.589
IR2	0.435	0.373	0.380	0.782	0.398	0.386	0.395	0.388	0.432	0.488
IR3	0.421	0.366	0.326	0.742	0.348	0.358	0.364	0.391	0.414	0.456
IR4	0.424	0.412	0.353	0.754	0.424	0.431	0.270	0.397	0.367	0.488
IR5	0.398	0.486	0.419	0.733	0.563	0.492	0.320	0.480	0.481	0.509
LA1	0.373	0.514	0.477	0.527	0.744	0.621	0.391	0.470	0.572	0.551
LA2	0.446	0.502	0.545	0.539	0.829	0.575	0.300	0.508	0.505	0.545
LA3	0.486	0.602	0.554	0.466	0.843	0.537	0.318	0.464	0.505	0.517
LA4	0.521	0.599	0.571	0.476	0.851	0.593	0.378	0.498	0.526	0.540
PE1	0.521	0.539	0.549	0.473	0.600	0.855	0.334	0.541	0.608	0.524
PE2	0.559	0.545	0.557	0.469	0.581	0.842	0.382	0.575	0.577	0.529
PE3	0.571	0.550	0.524	0.497	0.611	0.811	0.351	0.487	0.625	0.498
PE4	0.561	0.613	0.566	0.487	0.591	0.853	0.392	0.561	0.575	0.531
PEOU1	0.434	0.368	0.325	0.405	0.378	0.375	0.790	0.566	0.369	0.440
PEOU2	0.345	0.376	0.264	0.322	0.314	0.302	0.740	0.463	0.317	0.360
PEOU3	0.358	0.359	0.275	0.310	0.244	0.259	0.729	0.457	0.320	0.384
PEOU4	0.414	0.328	0.225	0.351	0.279	0.336	0.755	0.487	0.355	0.373
PEOU5	0.453	0.371	0.281	0.379	0.371	0.357	0.765	0.503	0.361	0.382
PU1	0.551	0.468	0.515	0.518	0.474	0.562	0.482	0.768	0.443	0.489
PU2	0.405	0.417	0.370	0.387	0.405	0.432	0.530	0.718	0.362	0.451
PU3	0.447	0.461	0.382	0.465	0.401	0.436	0.589	0.761	0.412	0.450
PU4	0.502	0.505	0.407	0.400	0.489	0.483	0.408	0.718	0.422	0.469
PU5	0.469	0.466	0.431	0.393	0.410	0.453	0.446	0.735	0.347	0.397
SN1	0.504	0.484	0.493	0.394	0.438	0.555	0.351	0.420	0.750	0.503
SN2	0.461	0.436	0.431	0.448	0.531	0.545	0.381	0.423	0.786	0.558
SN3	0.465	0.521	0.512	0.465	0.512	0.591	0.339	0.433	0.830	0.554
SN4	0.387	0.391	0.314	0.460	0.449	0.457	0.345	0.317	0.717	0.508
SN5	0.442	0.427	0.496	0.452	0.520	0.549	0.333	0.456	0.731	0.502
SR1	0.429	0.466	0.367	0.491	0.514	0.447	0.323	0.438	0.528	0.751
SR2	0.404	0.463	0.392	0.538	0.451	0.421	0.454	0.480	0.477	0.744
SR3	0.460	0.558	0.398	0.490	0.556	0.529	0.375	0.458	0.489	0.735
SR4	0.423	0.534	0.405	0.452	0.444	0.448	0.371	0.464	0.502	0.709
SR5	0.438	0.438	0.370	0.501	0.478	0.463	0.395	0.438	0.554	0.779

Table 5.

Heterotrait-Monotrait Ratio (HTMT) Results.

	ATT	BC	INT	IR	LA	PE	PEOU	PU	SN
ATT
BC	0.710
INT	0.659	0.869
IR	0.695	0.693	0.624
LA	0.663	0.835	0.807	0.729
PE	0.770	0.808	0.787	0.674	0.840
PEOU	0.636	0.598	0.456	0.564	0.510	0.513
PU	0.781	0.793	0.715	0.711	0.725	0.771	0.820
SN	0.710	0.734	0.729	0.699	0.779	0.841	0.558	0.661
SR	0.702	0.832	0.650	0.814	0.808	0.748	0.638	0.767	0.847

The explanatory strength of the model is assessed by calculating the amount of discrepancy in the dependent variables, anticipated through the model. According to Breiman and Friedman (1985), adjusted R² is very crucial for the evaluation of a structural model. This model has adjusted R², as shown in Figure 2, which is predicted to be 45.2% by its antecedents, PEOU and PU. Adjusted R² of subjective norm shows that 50.4% of its changes occur due to instructors’ readiness and students’ readiness, whereas Adjusted R² of behavioral control suggests that 54.3% of its changes are caused by perceived self-efficacy and learning autonomy. Finally, the adjusted R² of intention shows that 53.7% of the changes in the intention to adopt m-learning is due to attitude, subjective norm, and behavioral control.

Figure 1.

Conceptual model.

Figure 2.

Results of path analysis.

Path Analysis

Path analysis is demonstrated by Table 6 and Figure 2 together with the hypotheses. Wixom and Watson (2001) explained that hypotheses between dependent and independent variables are described by the sign, size, and significance of the coefficients. β or coefficient values demonstrate the intensity with which independent variables influence dependent variables, while p values determine the significance of the hypotheses which should be less than .1 for the hypotheses to be regarded as significant in the case of this study. Thus, from Table 6 and Figure 2, it can be comprehended that all the hypotheses are accepted at .1 level of significance and all the paths are positive.

Table 6.

Standardized regression weights for the research model.

Hypothesis	Regression Path	Effect type	SRW	Remarks
H1	ATT -> INT	Direct Effect	0.319*	Supported
H2	BC -> INT	Direct Effect	0.479***	Supported
H3	IR -> SN	Direct Effect	0.223***	Supported
H4	LA -> BC	Direct Effect	0.423***	Supported
H5	PE -> BC	Direct Effect	0.372***	Supported
H6	PEOU -> ATT	Direct Effect	0.200**	Supported
H7	PU -> ATT	Direct Effect	0.523***	Supported
H8	SN -> INT	Direct Effect	0.236***	Supported
H9	SR -> SN	Direct Effect	0.542***	Supported

Notes: SRW = Standardized regression weight; ATT = Attitude, BC = Behavioral control, PEOU = Perceived ease of use, PU = Perceived usefulness, INT = Intention to adopt, PE = Perceived Self-efficacy, IR = Instructor's readiness, LA = Learning autonomy, SN = Subjective norm, SR = Student's Readiness.

***

p < 0.01, **p < 0.05, *p < 0.10.

Discussion

The objective of this research is to determine the variables which influence m-learning adoption and explore the associations among those variables. Findings suggest that all three constructs of the TPB, attitude, subjective norm, and behavioral control, affect the intention of University students to adopt m-learning, which means that m-learning adoption should be observed from various viewpoints.

The above-mentioned findings imply that all the hypotheses of this study are supported. H₁ (β = 0. 319, p < .1) shows the path linking attitude with the intention to adopt m-learning, suggesting that the attitude undoubtedly enhances the intention of an individual toward the adoption of technology (Doll & Torkzadeh, 1988; Fishbein & Ajzen, 1977), and here it is m-learning. Here, the level of significance is chosen at 10% as done by various researchers (Baptista & Oliveira, 2015; Chuang, Weng, & Huang, 2015; Kun, 2015). Moreover, the attitudinal beliefs, PEOU (β = 0.200, p < .05), and PU (β = 0.523, p < .01) were represented by hypotheses H₄ and H₅, respectively. These outcomes suggest that both PEOU and PU significantly influence the attitude of those University students who perceive the use of m-learning to be easy and useful, and they are more inclined toward the incorporation of such gadgets into their learning process. Also, PU has a greater impact on the students’ attitude toward m-learning which implies that the beneficial use of mobile phones plays a vital role in encouraging the students of higher education to use m-learning (Liu et al., 2010). This result is congruent with the previous studies (Aldunate & Nussbaum, 2013; Cheon et al., 2012; Hanafizadeh, Behboudi, & Khedmatgozar, 2014; Luarn & Lin 2005; Thakur, 2014; Venkatesh, Morris, Davis, & Davis, 2003; Wessels & Drennan, 2010). Hence, it can be concluded that attitude along with its antecedents improves the intention of an individual toward m-learning adoption. Constructive learning practices should reduce university students’ psychological resistance toward m-learning.

H₂ (β = 0. 236, p < .05), representing the connection of subjective norm with intention to adopt m-learning, suggests that subjective norms significantly influence m-learning adoption; however, the impact of subjective norm is a bit lesser than the impact of attitude and behavioral control on the intention to adopt m-learning. Shiue (2007) displayed similar results in which subjective norm had a weaker impact on the intention to use technology. Moreover, the normative beliefs, instructors’ readiness (β = 0. 223, p < .01), and students’ readiness (β = 0. 542, p < .01) were represented by the hypotheses H₆ and H₇. These outcomes suggest that both normative beliefs significantly influence subjective norm, and both the students and teachers are willing to integrate m-learning in their learning process; however, students’ readiness has a greater impact than instructors’ readiness. These findings suggest that universities should understand the significance of the role played by faculty members while introducing m-learning. Also, intra-faculty dealings may have supported the users’ perception and acceptance of m-learning. Furthermore, students of higher educational institutions are ready to integrate m-learning, as for them, these experiences will train them for future jobs in the pervasive society.

H₃ (β₃ = 0. 479, p < .05), representing the path connecting behavioral control with the intention to adopt m-learning, suggests that behavioral control significantly influences intention to adopt m-learning. Moreover, control beliefs, perceived self-efficacy (β = 0. 372, p < .01), and learning autonomy (β = 0. 423, p < .01) represented by H₈ and H₉ enhanced behavioral control significantly. This shows that if students are self-confident and are given opportunities to integrate m-learning in their studies, the chances of m-learning adoption would increase. This result is similar to the study carried out by Cheon et al. (2012).

Conclusion and Recommendations

This study determines the factors influencing m-learning adoption among the Business University students of Pakistan. All the variables of TPB significantly and positively impact the intention to adopt m-learning. The outcomes suggest that the familiarity of students with the features and advantages of mobile gadgets, such as their handiness and ease of use, influences them to utilize these devices in completing their academic tasks, hence, positively influencing their intention toward the adoption of m-learning. Furthermore, the rewarding learning experience helps reduce students’ mental resistance toward the adoption of m-learning.

This study provides students and educators with a new tool for enhancing their learning and education system by revealing the factors involved in the adoption of m-learning. Findings also suggest that the readiness of teachers toward the use of m-learning greatly persuades students toward the adoption of m-learning, which implies that the management of higher education institutions should acknowledge the significance of the role of teachers in the course of integrating m-learning. Thus, the outcomes suggest that support and facilities should be provided to the students and faculty members to help them incorporate m-learning properly. Also the management of higher education institutions should provide opportunities and training to students to familiarize them with different features and functions of mobile gadgets and their effective use in learning inside and outside the classroom. Furthermore, an m-learning forum should be created in the university which would assist teachers to post information related to the course work online in order to create an m-learning environment for students. Care should be taken in incorporating m-learning strategies into the classroom learning, as these strategies should be easy to adopt and comprehend for students. Moreover, service providers should focus on providing user-friendly interfaces for using m-learning in education, as high effort requirement for using m-learning may discourage the users. The more users are aware of the usefulness and ease of use of this medium, the more positive their intent will be to use it. As students are deeply influenced by the views of their peers and colleagues, an encouraging environment provided by the management of educational institutions will be extremely helpful in the adoption of this new technology. Policy-makers also need to utilize the factors identified in this study in order to help and support students’ engagement and accelerate their m-learning usage.

Like any other study, this study also has a few limitations. First, the data were collected from university students who belonged to a similar age group and shared similar lifestyles, hence the results of this research cannot be generalized. Therefore, it is recommended to collect data from students belonging to different age groups and with different lifestyles in future research. Also the target audience used in this study has not yet used m-learning fully in their education. Therefore, it is recommended that future research should be conducted from the viewpoint of those students who have experienced m-learning and who have also integrated m-learning in their education process.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Syed Ali Raza is associated with IQRA University as director Academics. His areas of interest include financial economics, energy economics, tourism economics, corporate finance and behavioral sciences. He has published numerous paper in International Referred Journals including Tourism Management, Energy Economics, Energy Policy, Economic Modelling, Social Indicators Research, Current Issues in Tourism, Physica A, Quality and Quantity, International Migration, Total Quality management and Business Excellence, Journal of Business Economics and Management, Studies in Higher Education and others.

Amna Umer is associated with IQRA University as a research associate. She completed her MBA degree in Human Resource Management from IQRA University. Currently, she is pursuing her PhD from IQRA University. Her areas of interest are human resource management, training and development, strategic performance management, social sciences and behavioral sciences.

Wasim Qazi is associated with IQRA University as a professor and vice chancellor. He completed his PhD in Educational Management from Hamdard University, Pakistan and Post doctorate from Education Eastern Kentucky University, USA. He has published numerous papers in international refereed ISI indexed journals. Qazi is the Editor-in-Chief of Journal of Management Sciences (JMS), a biannual online Double Blind Peer Reviewed Research Journal in the area of management sciences. Qazi is also heading the Editorial Team of Journal of Education and Social Sciences (JESS) a biannual online Double Blind Peer Reviewed Research Journal in the areas of education and social sciences.

Murk Makhdoom is associated with IQRA University as a Business administration student. She has completed her Masters in Business Administration in the field of finance. Her areas of interest are financial economics, international economics, social sciences and behavioral sciences.

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