Factors influencing higher education students to adopt podcast: An empirical study

Computers & Education 83 (2015) 32e43

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Factors influencing higher education students to adopt podcast: An empirical study Mohammad I. Merhi* Department of Decision Sciences, Judd Leighton School of Business & Economics, Indiana University South Bend, 1700 Mishawaka Avenue, South Bend, IN 46634, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 April 2012 Received in revised form 16 December 2014 Accepted 18 December 2014 Available online 3 January 2015

Podcast, which is one of the technologies that was developed for personal entertainment or for information usage, has become one of the fastest growing technologies in distance learning over the past several years. Using the Technology Acceptance Model and Diffusion of Innovation Theory as base models, this study investigates the technological, individual, and social aspects that influence the adoption of podcast use in education. Previous research on podcast use in education attempted to study its adoption and diffusion; however, these studies have been rather isolated small case studies than a holistic, integrative research. This study overcomes this limitation by examining the student podcast adoption with survey data collected from 352 students in a higher education institution using a comprehensive model. The hypotheses were confirmed using structural equation modeling analytical procedures and the findings supported the proposed model. Based on the findings, implications for theory and practices are discussed. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Podcast adoption Technology adoption Online learning Distance learning Mobile learning

1. Introduction Technology advancement has affected many aspects of peoples' lives and changed the dynamics of technology delivery options over the years, including use in education. Online and mobile learning (m-learning) tools have become widely applied forms of e-learning and/or hybrid learning by educational institutions. Literature on m-learning in the last decade overwhelmingly suggests that podcasting initiatives have been on the rise across many nations (Abdous, Facer, & Yen, 2012; Bennet, 2006; Copley, 2007; Lee, McLoughlin, & Chan, 2008; Vogt, Schaffner, Ribar, & Chavez, 2010). Podcasting is “the process of capturing an audio event, song, speech, or mix of sounds and then posting that digital sound object to a web site or blog in a data structure called an RSS 2.0 envelope (or feed). Using specialized news readers, users can subscribe to a web page containing RSS 2.0 tagged audio files on designated web pages and automatically download these files directly into an audio management program on their personal computer. When a user synchronizes their portable audio device with their personal computer, the podcasts are automatically transferred to that device to be listened to at the time and location most convenient for the user” (Meng, 2005, p. 1). Podcasts usage helps institutions to serve their current students and to target those students who do not have the ability to attend regular classes. A recent report by the Pew Internet and American Life Project suggested that mobile technologies may contribute to reducing the “digital divide” (Smith, 2010). This indicates that m-learning tools can provide under-served communities with the opportunity to access quality education. By adopting the use of podcast and changing it from an entertainment tool to a learning tool, educators are also able to personalize and humanize e-Leaning by including rich media components into online courses in order to engage students in active, meaningful learning environment (Lee, Tan, & Goh, 2004). This paper extends the existing research in podcast by proposing and empirically testing a comprehensive model that explains podcast adoption by students of higher education. Initiatives on podcast development have the potential to empower students and even involve them in the learning process. Podcasts are helpful tools for students to learn the material independently in a convenient way (Tavales & Skevoulis, 2006). Thus, podcasts can be seen as an essential tool in helping students acquire new skills and improve their academic achievement because they are active

* Tel.: þ1 574 520 4387; fax: þ1 574 520 4866. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.compedu.2014.12.014 0360-1315/© 2014 Elsevier Ltd. All rights reserved.

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participants in the fulfillment of the task and they become a conductor of their own knowledge (Cruz & Carvalho, 2007; Lazzari, 2009). It is true that podcasts can help both institutions and students (Heilesen, 2010); however its effectiveness is minimized if students do not adopt the technology. A key to achieve success in the use of podcasts is when students adopt it; otherwise there is no purpose for this new teaching method. In a study, Koole (2009) argue that m-learning tools encompass technological, individual, and social aspects. Thus, in order to achieve success in the student adoption of podcast initiatives, institutions should take these aspects into consideration. Educational institutions from around the world have been acquiring m-learning technologies to better serve their students. Demand for m-learning products and services has been rapidly increasing (Adkins, 2011). The growth of m-learning tools and specifically podcasts in education attracted many researchers to examine different aspects related to these technologies. For instance, some researchers were mainly interested in examining the benefits of podcasting and how this new method can influence the performance of students (Jarvis & Dickie, 2010; O'Bannon, Lubke, Beard, & Britt, 2011). Shim, Shropshire, Park, Harris, and Campbell (2007) examined student's preferences of media delivery richness of communication media using podcasts and webcasts. The main difference between podcasts and webcasts is that the latter requires users to be connected to the internet while playing or viewing the webcast files (Shim et al., 2007). Shim et al. (2007) found that personalization and usability are two main factors that affect media use. They also found podcasts to be a better communication tool rather than webcasts. Fernandez, Simo, and Sallan (2009) analyzed the use of podcasting to enhance distance students' personal study. They argued that podcasting is a powerful tool to complement traditional educational resources but not a complete substitute for them. Kemp, Mellor, Kotter, and Oosthoek (2012) suggested that student-produced podcasts enhance engagement, competence in e-technologies, creativity, science communication skills and a broader understanding of the instructional content. Despite the growing body of literature, there is still a lack of research on students' adoption of podcasts, consequently there is room for new studies that help both practitioners and researchers to understand the factors leading to student podcast adoption. The investigation of the adoption and diffusion of podcasts by students has been very scarce in the literature; limited to specific case studies (Kemp et al., 2012) or groups of small number of participants (Fernandez et al., 2009; Hill, Nelson, France, & Woodland, 2012). Almost all of the current studies are limited by small sample size preventing generalization of their findings to overall factors on podcast adoption. To overcome these limitations, a comprehensive model that explains the adoption of this phenomenon as well as data from a larger sample of students are needed to explore the potential of this technology as an educational tool. This paper bridges this gap in the literature by proposing a comprehensive model that explains podcast adoption and empirically testing it using data collected from 352 students. Drawing from the literature related to podcast, e-learning and mobile learning, and based on theoretical models such as the diffusion of innovation theory “DoI” (Rogers, 1983), and technology acceptance model “TAM” (Davis, Bagozzi, & Warshaw, 1989), this study examines the effect of technological, individual, and social factors on podcast adoption by students. Specifically, this study seeks to answer two main research questions: (1) what are the factors leading higher education students to adopt podcast? And (2) how do these factors influence podcast adoption? By answering these questions, this study makes the following contributions: - Expand the existing literature by identifying a list of important dimensions that influence podcast adoption by students. - Develop a conceptual comprehensive model of podcast adoption. - The research model is empirically tested using a large sample size in a higher education institution enabling validation of effects of the previously mentioned factors on podcast adoption in a relatively robust way. Studying the factors impacting student podcast adoption can help educational institutions which are planning to adopt this technology in their system, as well as it enriches the literature. IT developers and instructional designers may find the study useful since it highlights important factors that impact podcast adoption. By taking these factors into consideration, they can develop strategies that enhance the podcasts in order to make them more accepted by students. Additionally, researchers can make use of the model as a basis for research development and later try to build on top of it so as to enrich the body of knowledge in this area. In the next section is presented a brief literature and theoretical framework upon which the study relies, followed by the research model and a set of research hypotheses. Next is discussed the methodology used to test the proposed model and hypotheses followed by a discussion of the results and the analysis of the study. Finally, conclusions and implications are presented, indicating limitations for this study and proposed areas for future research. 2. Literature and theoretical framework The theoretical foundation for most technology adoption research is found in the diffusion of innovation (DoI) literature (Rogers, 1983) which explains the process of technology diffusion and the factors influencing technology adoption decisions. In his theory, Rogers explains how, why and at what rate new ideas and technologies spread through cultural systems. Diffusion is defined as the adoption of an innovation as it is transferred through communication channels within a social system (Rogers, 1995). The key elements of diffusion include innovation, communication channels, time and a social system. Innovation means “an idea, practice or object that is perceived as new by an individual or other unit of adoption” (Rogers, 1983, p. 11). Communication channels tell of the “means by which messages get from one individual to another” (Rogers, 1983, p. 18). The innovation-decision period is “the length of time required to pass through the innovationdecision process” (Rogers, 1983, p. 17) while the rate of innovation specifies “the relative speed with which an innovation is adopted by members of a social system” (Rogers, 1983, p. 22). Finally, Rogers defined a social system to be a “set of interrelated units that are engaged in joint problem solving to accomplish a common goal” (Rogers, 1983, p. 24). Each of these four elements plays an important role in the diffusion of an innovation. According to DoI theory, an innovation will be adopted slowly at first then increases its diffusion speed as more and more people adopt it. Thus, individuals can be classified into five adopter categories based on their innovativeness. These categories are: innovators, early adopters, early majority, late majority, and laggards. In addition to the characteristics of adopters, there are different characteristics of

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innovations that also help to explain the differences seen in adoption rates. Usually potential adopters judge an innovation based on their perceptions in regard to five characteristics of the innovation. These characteristics are: relative advantage; compatibility; complexity; trialability; and observability. In this study, the relative advantage could possibly be of good use for explaining the adoption of podcast because it measures the degree to which using the technology (podcast) is perceived as being better than using its precursor (Moore & Benbasat, 1991) or complement its precursor. Reported studies have viewed podcasts as innovations (Yang, 2010). Hence, the spread of podcasts in institutions could rightfully be considered as the diffusion of innovation, in a manner consistent with Roger's theory. In this current study, podcast diffusion is defined as the adoption of podcasts by students. In addition to the DoI theory, the Technology Acceptance Model (TAM), which was developed by Davis (1989), provides a wellestablished model for evaluating and predicting user acceptance of information technology (Davis, 1989; Venkatesh & Davis, 2000). The TAM has also been used to evaluate the use of Internet-based technology in higher education programs (Saade', Fassil, & Tan, 2007). A key principle of the framework is the assumption that user acceptance is likely greater if the user perceives the technology as useful and easy to use (Davis, 1989). The DoI theory and TAM have been modified in this study because of two reasons: a. Most of the work in TAM and the DoI theory was done on technologies that were introduced into organizations which do not allow the complete voluntary usage of technologies, as in the case of student use of podcasts. These theories have been applied in organizations were users most likely do not care about some factors such as mobility and image. These factors many have an impact on the students' intention to adopt podcasts contrary to the adoption of technology in an organizational work environment. b. The TAM and the DoI theory have been used in organizations where users may not have the ability to express themselves freely. Koole (2009) argue that m-learning tools encompass technological, individual, and social aspects. Thus, adding other factors such as enjoyment may be important for student adoption. This factor is defined by behavioral sciences and psychology as important determinant of users' intention to adopt and use new items or technology (Dickinger, Arami, & Meyer, 2008).

3. Research model and hypotheses In order to provide a broader comprehensive view and a better explanation of podcast adoption, this paper intends to present a model that extends the TAM and DoI models by adding other factors that affect the adoption of student use of podcasts. Fig. 1 illustrates the podcast adoption model. It asserts that the intention to use podcasts is a function of the individual's perceived ease of use, perceived self-efficacy, relative advantage, perceived usefulness, image, and perceived enjoyment. Mobility has a direct effect on ease of use, usefulness, image, and enjoyment. Self-efficacy influences ease of use and usefulness. Image affects enjoyment; and usefulness influences relative advantage. Intention to use is the extent to which the student would like to use the podcast. Below is a brief description of each of these factors along with the hypotheses. 3.1. Perceived ease of use (PEoU) PEoU is the degree to which a person believes that using a particular system would be free of effort (Davis, 1989). Previous studies using TAM found that when users believe that a technology is easy to use, they will tend to accept it and use it (Chatzoglou, Sarigiannidis, Vraimaki, & Diamantidis, 2009; Sanchez-Franco, 2010). In the context of this study, if students perceive that a podcast is easy to use, they will most likely adopt it and then use it. It is believed that since using a podcast does not need much experience and does not have complicated system, perceived ease of use can be an important factor toward the intention of students to adopt and use podcasts. Moreover, researchers such as Heijden (2004) argued that perceived ease of use has an indirect effect on intention to use through Perceived Usefulness. Therefore, it is hypothesized: H1.a. Perceived ease of using a podcast has a significant positive effect on perceived usefulness of a podcast. H1.b. Perceived ease of using a podcast has a significant positive effect on intention to use a podcast.

Fig. 1. Podcast adoption model.

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3.2. Perceived mobility Perceived mobility in this study is the extent to which students can access the podcast anytime and anywhere with no restrictions. The aim behind the invention of the podcast was to help people get the needed data and information easily at anytime and anyplace convenient to them (Bolliger, Supanakorn, & Boggs, 2010; Chan, Lee, & McLoughlin, 2006; Donnelly & Berge, 2006; Harris & Park, 2008; Shim, Shropshire, Park, Harris, & Campbell, 2006). Once downloaded, podcasts can be transferred and used with a variety of portable devices such as iPods, handheld computers, as well as many modern smart cell phones, tablet computers, and personal digital assistants (Boulos, Maramba, & Wheeler, 2006; Cebeci & Tekdal, 2006; Lee et al., 2008). Students are no longer obliged to stay in a specific place to study and prepare for their classes (Walls et al., 2010). It is proposed the greater students perceive podcasts as a mobile technology, the easier they will find it to access and use. In addition, mobility can give them the chance to show others that they are up-to-date with the technology as it relates to education. From these arguments, the following hypotheses are offered: H2.a. Perceived mobility has a positive influence on perceived ease of using a podcast. H2.b. Perceived mobility has a positive influence on perceived usefulness of a podcast. H2.c. Perceived mobility has a positive influence on image. H2.d. Perceived mobility has a positive influence on perceived enjoyment.

3.3. Perceived self-efficacy Perceived self-efficacy can be defined as the individuals' judgment of their ability to perform the actions required for success. In the use of podcasts, self-efficacy can be divided to two types: perceived efficacy toward the technology and academic self-efficacy. Students with higher academic self-efficacy would be expected to put more effort into tasks and be more persistent in their academic pursuits (Sander & Sanders, 2006). This in its turn affects the self-efficacy towards technology. In the context of podcasts, it is expected that the higher the level of technology self-efficacy, the more the students will find podcasts easy and useful for them. Thus, it is hypothesized: H3.a. Students' computer self-efficacy is positively related to his or her perceived ease of use regarding podcasts. H3.b. Students' computer self-efficacy is positively related to his or her perceived usefulness about podcasts. H3.c. Students' computer self-efficacy is positively related to his or her intention to use podcast.

3.4. Perceived usefulness Perceived usefulness is the degree to which a person believes that using a particular system would enhance his or her job performance (Davis, 1989). It is one of the factors that information systems research has reported as important for the users' intention to adopt new technology (Pikkarainen, Pikkarainen, Karajaluoto, & Pahnila, 2004; Taylor & Todd, 1995a, b; Yu, Ha, Choi, & Rho, 2005). This relationship was found direct as well as indirect through the perceived relative advantage of the technology (Wang, Meister, & Wang, 2011). Previous researchers also found that the majority of students in traditional courses rated podcasts as very useful (Copley, 2007), others claim that students are more receptive to learning material provided in the form of a podcast than a traditional lecture or textbook (Evans, 2008). Moreover, since podcasts have been used by students to address problems such as improving their academic achievement, reducing their anxieties, increasing their satisfaction by making up a missed class (Chan & Lee, 2005; Cruz & Carvalho, 2007; Tavales & Skevoulis, 2006), it is hypothesized: H4.a. Perceived usefulness of a podcast has a significant positive effect on perceived relative advantage. H4.b. Perceived usefulness of a podcast has a significant positive effect on intention to use podcast.

3.5. Relative advantage Relative advantage is the degree to which a new technology is perceived as better than the method or technique used before the introduction of the new technology (Moore & Benbasat, 1991). Convenience and satisfaction are considered important factors that play important role in leading people to adopt new technology and leave the old method (Wixom & Todd, 2005). Usually, what helps individuals to decide to adopt new technology is whether they perceive the innovation as advantageous or not (Rogers, 1983). The higher the perceived relative advantage of an innovation, the faster they will adopt it. Researchers reported that relative advantage is a crucial factor that leads users to adopt new technology (Moore & Benbasat, 1991; Rogers, 1983). From these arguments, the following hypothesis is offered: H5. Perceived relative advantage has a positive effect on intention to use a podcast.

3.6. Image Image in this study is the extent to which students would enhance their image or status in their social system and among their peers by using a podcast (Moore & Benbasat, 1991). If users feel by using a podcast they can improve their image among their peers, their intention to

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adopt this technology may increase. Moore and Benbasat (1991) found that image had a positive impact on Internet adoption and it explained 8.9% of the total variance of adoption. In the same vein, Rogers (2003) argued that the need to gain social status is one of the most essential motivations for almost any individual to adopt an innovation. Besides image being an influence on adoption, it can be argued that the higher the perceived image is, the higher the enjoyment is because students will be motivated to show their peers their new technology. Based on this discussion, it is hypothesized: H6.a. Image has a positive influence on Perceived Enjoyment. H6.b. Image has a significant positive effect on intention to use a podcast. 3.7. Perceived enjoyment Perceived enjoyment is the extent to which the usage of the technology is perceived to be enjoyable for the users apart from any consequences for using the technology. This factor was added by Heijden (2003) to the TAM and subsequently demonstrated that perceived enjoyment has a significant influence on the intention of users to adopt a website. Previous studies also found that perceived enjoyment is an important factor driving an individual's technology adoption (Bruner & Kumar, 2005; Davis, Bagozzi, & Warshaw, 1992; Pikkarainen et al., 2004). Users and especially adults, tend to try technology despite of its complexity. Podcasts facilitate “just-in-time” learning where learners can often take advantage of unexpected free time since they frequently have their devices with them (Evans, 2008). This again explains the importance of the mobility that supports intrinsic motivation and provides dual benefits: first enjoying the usage of the device as well as easily accessing the class materials. Accordingly, it is hypothesized: H7. Perceived enjoyment has a positive effect on intentions to use a podcast. 4. Methodology 4.1. Research method and data collection In order to assess the podcast adoption model, a survey methodology was utilized. The population for this study was undergraduate and graduate students from three different colleges (education, health sciences and human services, and social and behavioral sciences) within one higher education institution in the Southern part of the United States. Student access to course podcasts was optional and not mandatory, podcasts were used as a supplementary tool in order to help students review and catch up with missing information. The podcasts that students used in their classes were films of classroom lectures in their entirety. Each podcast covers one lecture. Students have access to previous podcasts. This means every week a student can listen and/or watch the lecture of the week plus any other podcast from before. Students who participated in the study were clear on what the questionnaire was referring to. There were not any other sorts of podcasts that students could have been responding to in their answers.

Table 1 Respondent characteristics. Measure Gender Male Female Age Less than 20 20e24 25e34 35 and above Education (highest level achieved) High School Two or more years of college College graduates Time you spend on podcast Less than 15 min 15e30 min 31e45 min 46e60 min More than one hour Often listen to podcast a week Rarely 1e3 times 4e6 times 7e10 times More than ten times a week Personal device supports podcast Yes No Internet Yes No

Frequency

Percentage

163 189

46.3 53.7

112 165 65 10

31.8 46.9 18.5 2.8

50 208 94

14.2 59.1 26.7

32 46 84 78 112

9.1 13.1 23.9 22.1 31.8

32 29 169 107 15

9.1 8.2 48.0 30.4 4.3

329 23

93.5 6.5

333 19

94.6 5.4

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Convenience sampling was used in this study and IRB approval was obtained before data collection. In total, 450 students were asked to complete the paper-based survey instrument. These were all the students who were taking classes that used podcasts. In other words, this is the total population of students who have experience in this particular semester in this institution. Of the 450 surveys, 98 were unusable because they were incomplete. In the end, a total of 352 valid surveys were used for data analysis. Thus, the effective response rate was 78 percent. Table 1 depicts the respondents' characteristics. Overall, 46.3% of the respondents are male and 53.7% are female. Most respondents were between 20 and 24 years of age (46.9%). Education levels were broken down into high school graduates (14.2%), two or more years of college (59.1%), and college graduates (26.7%). Most of the students (93.5%) had a personal device that allowed them to download and access the podcasted class session, and 94.6% have Internet access with their devices. Almost half (48%) of the respondents listen and watch course podcasts between four and six times a week. Regarding the times spent on podcasts, 13.1% of the respondents spend between fifteen minutes and half an hour, 23.9% of them spend between 31 and 45 min, and 22.1% spend between 46 min and an hour listening to podcasts. More descriptive statistics of respondents' characteristics are presented in Table 1.

4.2. Measures and pilot test Literature provides definition and arguments that help researchers develop measurement items to represent latent constructs. As long as the constructs are the same, previously validated measures should be used (Lee & Hubona, 2009; Straub, 1989; Straub, Boudreau, & Gefen, 2004). The measures used in this study are based on previously validated measures thus enhancing the validity and reliability of the measurement model (Boudreau, Gefen, & Straub, 2001). The majority of the scale items are adopted from the previous technology literature but adapted to the podcast context. The behavioral constructs intention to use, ease of use, usefulness were adopted from Davis (1989) with some modifications. Perceived enjoyment was adapted from Davis et al. (1992), while the mobility scale was adopted from Evans (2008). Image and relative advantage were adopted from Moore and Benbasat (1991); whereas, perceived computer self-efficacy was developed by Compeau and Higgins (1995). Perceived computer self-efficacy is defined as the individuals' judgment of their ability to use the technology for success. The eight latent variables were measured using 24 manifest variables. All items were five point Likert scale ranging from “Strongly disagree” to “Strongly agree.” Table 2 shows the primary sources of the measurement items for each of the construct. The questionnaire was pilot-tested to validate the psychometric properties of the instrument (Churchill, 1979; Straub, 1989). A group of 18 doctoral students participated in the pilot test. Minor changes were made to items that showed loadings issues (low or cross-loadings). The reliabilities and discriminant and convergent validities of the constructs were assessed using the data collected from the responses in the pilot test. The reliability of measurement items for each construct was assessed using Cronbach's alpha (Cronbach, 1951). Values of Cronbach's alpha were between 0.71 and 0.84 indicating that the measures are valid. Convergent and discriminant validity were assessed using factor analysis in order to ensure that measures of distinct constructs are valid (Hair, Black, Babin, & Anderson, 2010). Based on these tests, few measures were refined and the revised instrument was used for actual data collection.

Table 2 Construct operationalization. Construct Ease of use

Usefulness

Enjoyment

Image

Relative advantage

Mobility

Self-efficacy

Intention

Measures

-

Overall, podcasts are easy to use. I found it easy to play the podcast files. I found it easy to access the podcast files. I believe podcasts are helpful for my understanding of the lectures. Podcasts are helpful for preparing quizzes/tests. Using podcasts helps me learn the subject. I enjoyed listening to podcasts. It's fun to use podcasts. I enjoyed listening to podcasts. I often show podcasts to others. I often talk to others about podcasts. It is cool to use podcasts. Listening to the podcasts helps clarify my understanding of the subject. Reviewing through podcasts enables me to catch up with what I missed in the class. Podcasts help me focus more on the instructor's explanations because I can complete the notes afterward. I think it is important to be able to listen to the podcasts where and when I want I listen to podcasts while traveling I listen to the podcasts while doing something else I have the knowledge necessary to use podcasts. I have the resources necessary (e.g. computers, Internet access etc.) to use podcasts. If I need assistance in using podcasts, there are helps (e.g. tutorial, helpdesk) available. I intend to continue using podcasts in the future. Given that I have access to podcasts, I intend to use them. It is likely to continue using podcasts in the future.

Sources Davis (1989)

Davis (1989)

Davis et al. (1992)

Moore and Benbasat (1991)

Moore and Benbasat (1991)

Evans (2008)

Compeau and Higgins (1995)

Davis (1989)

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4.3. Analysis methods The data collected from the survey instrument were subjected to various statistical tests. The first analysis tests the data for outliers and normality. Outliers are those observations that are numerically distant from the rest of the observations. They arise from four different causes: errors of data entry, missing values, unintended sampling, and non-normal distribution (Hair et al., 2010). Outliers can change the outcome of analysis and are also violations of normality. To check for normality, Hair et al. (2010) proposed an absolute value of two for skewness and seven for kurtosis as maximum limits for satisfactory departures from normality. After assessing outliers and normality, compiled were descriptive statistics, such as the mean and standard deviation, for each construct. Next, a reliability test was executed in order to ensure that the variables in each construct are internally consistent. The reliability test was checked using the Cronbach's alpha. Normally the value of the Cronbach's should be higher than 0.70 to obtain a reliable model (Hair et al., 2010). Next, construct validity, convergent validity, and discriminant validity were checked. Construct validity is the extent to which a set of measured variables represent the theoretical latent construct they are designed to measure (Hair et al., 2010). A construct shows high validity when all items measuring that construct load on one factor. Construct validity is conducted by assessing convergent validity and discriminant validity. Convergent validity signifies that many variables were used to form the construct, while discriminant validity indicates that each construct correlates freely with its items. Convergent validity can be assessed using two measures: composite reliability and Average Variance Extracted (AVE). Composite reliabilities tests ensure that the variables in each construct are internally consistent. They are similar to Cronbach's alphas and thus their values should exceed 0.70 and the AVE estimates should exceed 0.50 (Hair et al., 2010). Discriminant validity can be assessed by comparing the correlation between pair constructs and the AVE of each construct. According to Anderson and Gerbing (1988), the squared correlation between a pair of latent variables (constructs) should be less than the AVE estimate of each variable. Therefore, each AVE value should be greater than the correlations in its row and column. In addition to convergent and discriminant validity, the model fit was checked as well. In the model fit, assessed is the chi-square, NFI, CFI, TLI, GFI, AGFI, and RMSEA. The chi-square should not be significant, therefore the p-value should be greater than 0.05 and its value should be small enough. Also, the other model fits indices, such as NFI, CFI, TLI, GFI, and AGFI, should be higher than 0.90 in order to have a good model fit and the RMSEA should be less than 0.08. After confirming the validity of the instrument, Structural Equation Modeling (SEM) was used to assess and investigate the hypothesized causal paths among the constructs by performing a simultaneous test. This helped to determine if the presented conceptual model had provided an acceptable fit to the empirical data gathered or not.

5. Data analysis and results Before applying statistical procedures, data abnormalities such as missing data and outliers were investigated. Violations of statistical assumptions were also checked. Missing data were checked before analysis and incomplete instruments were not included in the study as mentioned previously. Outliers' tests revealed that there was no need for a corrective treatment. Data also were checked for assumptions violations, such as normality and linearity, and results indicated that these assumptions were met in the data collected. Below are the statistical tests used to assess the model presented.

5.1. Descriptive analysis Table 3 presents descriptive statistics of the constructs used in this study. Results of this table indicate that students responded positively to intention to use a podcast for learning. Per the table, all the means exceeded three out of five on the scale except for image. The students participating in the study responded positively to ease of use. This same result is seen for usefulness and enjoyment. Students perceive podcasts as a tool that is useful for them educationally and at the same time as a source of enjoyment. Regarding image, the surveyed students seemed to be slightly less concerned with this factor. As for relative advantage, the respondents perceived the use of podcasts as a better method/technique than methods used prior to podcasts in class. Also, students in this study appreciate the mobility that podcasts offer them. They perceive this technology as a mobile technology that they can use at anytime/ anywhere. Finally, the surveyed students believed that computer self-efficacy is important when it comes to podcast adoption. Students believe that they should have certain technological ability in order to download and use podcasts.

Table 3 Descriptive statistics. Constructs

Mean

Standard deviation

Ease of use Usefulness Enjoyment Image Relative advantage Mobility Self-efficacy Behavioral Intention

3.78 3.96 3.92 2.89 4.09 3.95 4.03 4.14

1.30 1.31 1.19 1.22 1.32 1.19 1.17 1.10

Note: 5 point Likert scale was used: 1 ¼ Strongly disagree; 2 ¼ Disagree; 3 ¼ Neutral; 4 ¼ Agree; 5 ¼ Strongly agree.

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5.2. Measurement model assessment The acceptability of the measurement model was assessed by factor analysis, the reliability of the individual items, the internal consistency between items, the model's convergent and discriminant validity, and the fit indices. Factor analysis with varimax rotation was performed to ascertain that each construct presented in the model is distinct. The results confirmed the existence of eight factors with eigenvalues greater than 1.0 that accounted for 74.3% of the total variance. The criteria that was used in this study to interpret the factors was that each item should load 0.4 or greater in one factor and less than 0.4 in the other factors. Table 4 depicts the result of this test. It is clear in this table that each construct loaded on one and only one factor. Therefore, the results confirm that each of these constructs is unidimensional and distinct and that all items used to measure a particular construct loaded onto a single factor. After checking the factor analysis of the model, internal consistency was checked with composite reliabilities. All the constructs demonstrated acceptable values: the Cronbach's alpha coefficients of all the constructs were above 0.70. This indicates that the items used were reliable measures for their perspective constructs. Next, tests were conducted to check for convergent and discriminant validity with results presented in Table 5. These results demonstrate convergent validity as the AVE values of all constructs are equal or higher than the threshold of 0.5. Comparing the square root of the AVE (in bold in Table 5) to the correlations among the constructs, each construct was more closely related to its own construct than to the others which simply means that discriminant validity is demonstrated in this study. After evaluating the reliability and validity, the overall fit of the research model was tested. Table 6 shows the results. In addition to the chi-square fitness test, which is one of the best tests to measure the model fit, six other indices were used in this study. Results of all indices assure that the model fits the data. 5.3. Structural model assessment and hypotheses testing The SEM technique was used to assess the model and the proposed hypotheses among eight latent constructs. The analysis results are graphically presented in Fig. 2 and the results of hypotheses are shown in Table 7. Fig. 2 shows the path coefficients and the significance levels for each hypothesis as well as the variances for the six dependent constructs: ease of use, usefulness, image, relative advantage, enjoyment, and behavioral intention to use podcast. Mobility explains 19% of the variance of ease of use; and 12% of the variance of image. Image and mobility explain 54% of the variance of enjoyment. Mobility and self-efficacy explain 39% of the variance of usefulness which in its turn explains 81% of the variance of relative advantage. Enjoyment, image, usefulness, ease of use, self-efficacy, and relative advantage together explain 73% of the variance of intention to use. However, ease of use, self-efficacy, and image make almost no contribution to the variance in usefulness, ease of use, and behavioral intention to use podcast respectively. Thirteen of the fifteen hypotheses are supported (see Table 7). Inconsistent with Hypothesis 1.a, perceived ease of using podcast does not affect perceived usefulness; whereas, consistent with Hypothesis 1.b, perceived ease of using podcast has a significant positive effect (at 0.001 level) on intention to use podcast. Also, mobility in podcasting has a significant effect on ease of use (at 0.001 level); usefulness (at 0.001 level); image (at 0.05 level); and enjoyment (at 0.001 level), supporting Hypotheses 2.a, 2.b, 2.c, and 2.d. Inconsistent with Hypothesis 3.a, computer self-efficacy in podcasting does not affect perceived ease of use; while, self-efficacy has a significant effect (at 0.001 level) on usefulness and intention to use podcast, supporting Hypotheses 3.b and 3.c. Consistent with the prediction, usefulness in podcasting has a significant effect (at 0.001 level) on relative advantage and intention to use podcast, supporting Hypotheses 4.a and 4.b. Also, consistent with

Table 4 Loadings of items (factor analysis). Items Ease1 Ease2 Ease3 Use1 Use2 Use3 Enj1 Enj2 Enj3 Image1 Image2 Image3 RelAd1 RelAd2 RelAd3 Mobil1 Mobil2 Mobil3 SelfEff1 SelfEff2 SelfEff3 Int1 Int2 Int3

1

2

3

4

5

6

7

8

.798 .801 .770 .764 .744 .773 .815 .815 .810 .616 .709 .678 .871 .873 .875 .858 .847 .864 .799 .696 .840 .872 .869 .874

Note: Ease: Perceived Ease of Use; Use: Perceived Usefulness; Enj: Perceived Enjoyment; Image: Perceived Image; RelAd: Relative Advantage; Mobil: Mobility; SelfEff: SelfEfficacy; Int: Intention to Use Podcast.

40

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Table 5 Inter-consistent correlations: consistency and reliability tests. Construct

Chronbach a

AVE

Ease

Use

Enj

Image

RelAd

Mobil

SelEf

Int

Ease Use Enj Image RelAd Mobil SelEff Int

0.87 0.82 0.84 0.77 0.81 0.87 0.79 0.89

0.58 0.65 0.71 0.54 0.69 0.74 0.63 0.81

0.76 0.47 0.49 0.30 0.33 0.41 0.34 0.49

0.81 0.50 0.45 0.65 0.48 0.52 0.47

0.84 0.53 0.41 0.51 0.39 0.58

0.73 0.32 0.41 0.33 0.46

0.83 0.56 0.44 0.52

0.86 0.39 0.49

0.79 0.53

0.90

Note: AVE is Average Variance Extracted. Diagonal elements are the square roots of AVE. Off-diagonal elements are correlations among constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements.

Hypothesis 5, relative advantage has a significant effect (at 0.001 level) on intention to use podcast. Image does have significant effects (at 0.05 level) on enjoyment and behavioral intention to use podcast, consistent with Hypotheses 6.a and 6.b. Finally, consistent with Hypothesis 7, enjoyment has a significant effect (at 0.001 level) on intention to use podcast. 6. Discussion The results of this study show that student intention to use a podcast is influenced by perceived enjoyment, image, perceived usefulness, perceived ease of use, self-efficacy, and relative advantage (R2 ¼ 73%). The proposed model was generally supported by the empirical data. Thirteen of the fifteen hypotheses were supported. The coefficients in Fig. 2 show that relative advantage has a significant influence (b ¼ 0.57; Sig. at 0.001) on intention to use. This result indicates that students choose to use a podcast because they perceive it as a method/ technique that provides them more advantage or benefits. This finding affirms what was found in previous research. For instance, Evans (2008) found that students perceive podcasts as an efficient way to learn and as a more effective revision tools than their textbooks. Shim et al. (2007) found that students believe that the benefits of adopting podcasts outweigh the disadvantages and problems associated with their usage. Perceived usefulness was found to be the second important factor that influences students' intentions to use podcast (b ¼ 0.53; Sig. at 0.001). This result supports that podcasts are a useful technique that helps students access and learn the classes' material (Evans, 2008). In general these two factors, relative advantage and usefulness, inform instructional designers, decision makers, and instructors in higher education institutions that students do perceive this technique as a useful way to support their learning. These findings support using podcasts for students that might help institutions base decisions on evidence whether to use this instructional method or not. Perceived enjoyment was also found to be important to student podcast adoption and usage. This result indicates that students enjoy learning by using podcasts. This finding confirms Zacharis (2012) findings who found enjoyment to be a predictive factor of podcast adoption. Taking a closer look at the model, one can find that mobility in this study has an effect on four different constructs: ease of use, usefulness, image, and enjoyment. These relationships are all supported in this study. Comparable to this finding, Hill et al. (2012) found that podcasts are perceived as an effective tool in supporting learning, largely by offering a flexible and moveable learning experience. This again explains the importance of the mobility that helps students with intrinsic motivation and obtain dual benefits: first enjoying the usage of the device as well obtaining the knowledge for learning by accessing the class materials. Mobility also makes it easy for students to use podcasts. By using podcasts students can minimize the need to take notes and engage with the podcast material in addition to revisiting the podcast frequently to review the course materials. Results of this study indicate that ease of use positively affects intention to use podcast (b ¼ 0.35; Sig. at 0.001) supporting Hypothesis 1.b. This result is interesting and logical. Students choose to use podcasting because it is an easy method that can be used and does not require complicated steps to use the technology. Steps used to download the files are usually similar to any other audio/video files and once the podcast is downloaded, it is easy to use and can be easily transferred from any mobile device to another. This result confirms the findings of O'Bannon et al. (2011) in that students perceive podcasts as an easy technology to use. The data give support for Hypotheses 6.a and 6.b: That is, image positively affects enjoyment and intention to use a podcast. Rogers (2003) argued that the need to gain social status is one of the most essential motivations for almost any individual to adopt an innovation. To our knowledge, no research has investigated the influence of image on podcast adoption. On the other hand, Hypotheses 1.b and 3.a were not supported in this study. Inconsistent with previous TAM studies, ease of use did not affect usefulness in this study. This finding may signify that students perceive a podcast as a useful technique regardless of its ease of use. Table 6 Fit indices of the research model. Fit index

Recommended value

Results

c2/df

<3 >0.90 >0.80 >0.90 >0.90 >0.90 <0.08

1.56 0.94 0.91 0.92 0.94 0.95 0.04

Goodness of Fit Index (GFI) Adjusted Goodness of Fit Index (AGFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) TuckereLewis Index (TLI) Root Mean Square Error of Approximation (RMSEA) Note: Source of recommended value (Hair et al., 2010).

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Fig. 2. Model results.

Since it was found in previous TAM studies that ease of use does affect usefulness, it is advised that future researchers may examine this relationship in order to validate the results found in this current study. Also, inconsistent with the prediction, self-efficacy did not affect ease of use in this study. This is probably because students do not need high technical ability to access and use a podcast. 7. Limitations and future research As with all research, this study has some limitations that should be considered. First, the sample was collected from one university located in the Southern part of the United States. Thus, the opportunity to generalize the findings to other universities, countries, or cultures is limited. The model proposed in this study may need to be validated in different parts of the world and in other countries in future studies. Re-testing this model in other countries and taking the culture into consideration and introducing moderator effects such as gender, age, and educational level will enrich the body of knowledge and enhance understanding of the factors affecting the intention to adopt podcasts in the educational setting. Second, in this study undergraduate and graduate students participated and the data collected from both groups were combined. There may have been some variation in how each group used and perceived podcasts. This current study did not separate the data of these groups. Also in this study there was no measurement of the effect of podcasts on learning. Third, although the results indicate that ease of use, and self-efficacy have no significant impact usefulness and ease of use; they may have significant influence in another population given different groups and cultures have varied perceptions of technology. Different groups/cultures have different perceptions. This also suggests a possible avenue for future research. Finally, the relationships tested among the constructs in this model were simple in order to keep it parsimonious. There might be other relationships among these constructs that need to be investigated in future studies. The present study measured the intention to use the podcast, therefore a further evaluation of actual usage may extend our understanding of students' behaviors. 8. Conclusions and implications By using well-grounded theories (TAM and DoI), this study intended to provide both practitioners at universities and researchers a parsimonious framework that includes factors that are most likely to affect the behavioral intention of students to adopt and use podcasts. Podcasts embraces different factors such as technological, individual, and social to explain adoption of the technology (Koole, 2009). However, the impact of these different factors on podcast adoption was not previously investigated in a comprehensive model using a large sample size. This study fills this gap and the findings can inform different practitioners, namely IT developers, instructional designers,

Table 7 Results of hypotheses tests. Hypothesis H1.a H1.b H2.a H2.b H2.c H2.d H3.a H3.b H3.c H4.a H4.b H5 H6.a H6.b H7

Supported? Perceived ease of using a podcast has a significant positive effect on perceived usefulness of a podcast. Perceived ease of using a podcast has a significant positive effect on intention to use a podcast. Perceived mobility has a positive influence on perceived ease of using a podcast. Perceived mobility has a positive influence on perceived usefulness of a podcast. Perceived mobility has a positive influence on image. Perceived mobility has a positive influence on perceived enjoyment. Students' computer self-efficacy is positively related to his or her perceived ease of use regarding podcasts. Students' computer self-efficacy is positively related to his or her perceived usefulness about podcasts. Students' computer self-efficacy is positively related to his or her intention to use podcast. Perceived usefulness of a podcast has a significant positive effect on perceived relative advantage. Perceived usefulness of a podcast has a significant positive effect on intention to use podcast. Perceived relative advantage has a positive effect on intention to use a podcast. Image has a positive influence on Perceived Enjoyment. Image has a significant positive effect on intention to use a podcast. Perceived enjoyment has a positive effect on intentions to use a podcast.

No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes

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decision makers in universities, among others on decisions regarding integration of this technology as an educational strategy. Results showed that technological factors do play an important role on podcast adoption. Hence, in order to succeed and support students in adopting this technique, practitioners should present podcasts as a useful technology that can assist students in their academic success. Strategies should be employed to make it easy for students to increase the level of adoption. Moreover, personal and social factors were also found to be important and play significant roles in predicting students' behavioral intention to adopt podcasts. Due to its characteristics, podcasting provide students the ability to build their knowledge and learn individually in a unique way. Using this technique, students are able to access their materials at anytime from anywhere which seems to increase their motivation and enjoy learning while using this technology. 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