Behavior intention of animation usage among university students.

This research aims to test the antecedents that influence behavior intention of animation usage among marketing students in universities depending on the extended unified theory of acceptance and use of technology (UTAUT2) introduced by Venkatesh et al. (2012). Partial least square structural equation modeling approach was used to analyze information gathered from undergraduate marketing students in Jordanian universities. The results revealed that hedonic motivation, performance expectancy, students' innovativeness, learning value and effort expectancy were significant constructs influencing the behavior intention of animation usage. The research extended UTAUT2 in the field of animation usage by integrating the constructs of learning value and students' innovativeness to the model. The research provides practitioners and teachers in the marketing field with advantageous methods in their learning process.

Introduction

The revolution of technology allowed learners and innovators to collect massive supply of learning resources. Nevertheless, there is a difference between progressive technologies and the learners’ comprehending of how they can benefit from these technologies, such as animation (Stebner et al., 2017; McElhaney et al., 2015; Chandler, 2004). Recently, animation has been included in the learning environment that depends on technology in developed countries (e.g., Arguel and Jamet, 2009). Animation is defined as a changing graphic visual arrangement which stimulates the view of a constant modification (Schnotz and Lowe, 2008). In this research, animation is defined as an art and a craft that crosses and is part of many disciplinarians in the field(s) of computers and social science, model making, sculpture, performance, and dance to produce a motion picture illustrating the movement of the drawn objects (Suki and Suki, 2017; Luzon and Leton, 2015; Mayer and Moreno, 2002).

The effectiveness of animation may vary based on learning objective levels and the learning abilities of students. In a technological learning environment, animation is frequently employed to enhance and facilitate the students’ comprehension for difficult practices that change over time (Ainsworth and Van Labeke, 2004). Animation may serve various purposes and have many advantages. In marketing, animation use is entertaining and memorable (Hoyer and MacInnis, 2001). It provides the consumer with visual and emotional stimulation. It allows the brand to expand in an entertaining and professional method (Jin, 2011), and it gives an opportunity to the consumer to know about the product/service via virtual tours (Simon, 2006).

In addition, animation has a vital and creative role in the learning methods for marketing students. Students extensively watch online videos and online episodes (Lewis et al., 2013). During the year 2016, Techcrunch reported that more than 8 billion videos were seen daily using social media. This translates to an estimated 100 million hours of interactive animation that is imperative to use in the field of marketing (TechCrunch, 2016). This is important evidence for promoting marketing activities concerned with any kind of learning by using these new technologies. Currently, using animation in the education learning process is growing rapidly, and marketers must acquire methods for motivating student interest in this field (Dahl et al., 2018).

The students' perception and behavior intention to use animation is an initial step in implementing this new technology in the learning process. Extensive research has been directed in this area, such as the work of Suki and Suki (2017); Lowe and Boucheix (2016); Höffler et al. (2013); Wang et al. (2011); Moreno and Mayer (2007); Martins and Kellermanns (2004); and Tversky et al. (2002). However, there are limited or nonexistent studies investigating the behavior intention to use animation in less developed countries and specifically in Jordanian universities. Consequently, it is vital to accomplish this research to test factors that influence the students’ behavior intention of animation usage.

Theory

To determine consumer behavior, marketers use various behavior and technology models, such as the motivation-need theory by Maslow (1943); the theory of reasoned action (TRA) from Fishbein and Ajzen (1975); the diffusion of innovation theory (DOI) by Rogers (2003); the decomposed theory of planned behavior (DTPB) by Taylor and Todd (1995); the unified theory of acceptance and use of technology (UTAUT) by Venkatesh et al. (2003); and the unified theory of acceptance and use of technology (UTAUT2) model from Venkatesh et al. (2012). These models were originated from concepts in psychology and utilized to clarify technology usage.

Students' comprehension and usage of new technologies are one of the issues in marketing and includes the analysis of beliefs, attitude and behavior intention (Benbasat and Barki, 2007; Venkatesh et al., 2007). Numerous technology theories were used to explain the reasons behind students’ adoption of technology. For example, Davis (1989) examined what causes the acceptance or rejection of information technologies usage among students. The idea of (TAM) is to explain the influence of external factors on the construct of belief, attitude toward behavior intention, and system usage (Ibrahim and Jaafar, 2011).

Furthermore, Venkatesh et al. (2003) integrated 8 theories of technology usage to produce the unified theory of acceptance and use of technology. The UTAUT introduced by Venkatesh et al. (2003) has been used by marketing scholars in studying behavior intention and usage behavior. The UTAUT has been examined for its validity and can calculate 70% of the variance intention use; consequently, this makes it an important theory for examining intention and usage behavior (Lee et al., 2013; Venkatesh et al., 2012). The main constructs of the UTAUT: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) that influence the behavior intention (BI) of technology usage. The UTAUT illustrates gender, age, experience and voluntariness of use as moderators of the key relationships.

Recently, the unified theory of acceptance and use of technology (UTAUT) has been revised, resulting in the extended unified theory of acceptance and use of technology (UTAUT2) (Venkatesh et al., 2012). The UTAUT2 integrated 3 constructs, namely hedonic motivation (HM), price value (PV) and habit (H) to improve its predictive strength. Some demographic variables (age, gender, experience) were proposed to control the effects of constructs on behavior intention and technology usage (Venkatesh et al., 2012).

The literature indicates that the technology acceptance theories are significantly tested in the developed nations. The inadequate research effort on technology usage in the less developed countries shows conflicting results regarding the appropriateness and predictive power of these models in the developing countries (Dajani, 2016; Abu Shanab et al., 2010; Lin and Bhattacherjee, 2008). Researchers explain that the culture of a specific country and the type of technology investigated are the reasons behind this inconsistency (Abu Shanab et al., 2010; Steers et al., 2008). Consequently, this research validates and tests the aptness of the UTAUT2 in a different culture, specifically Jordan, using animation technology. Animation can create a competitive advantage among businesses worldwide to differentiate themselves from competitors; marketing students must motivate themselves to use new technologies such as animation to create innovative content in their marketing activities in the future (Oloko et al., 2014).

This research adapts UTAUT2 because of its comprehensiveness and power in explaining behavior intention compared to other technology theories. Additionally, a great number of the technology acceptance models, specifically UTAUT2, have not been investigated in less developed nations such as Arab countries (Alalwan et al., 2015; Kamoun and Almourad, 2014; Zhao et al., 2012). Moreover, Venkatesh et al. (2012) emphasized to examine UTAUT2 in various cultural settings to improve its applicability and substantiality. Venkatesh et al. (2012) stated that antecedents influencing the usage of new technologies differ according to the context, final user and technology. In comparison to UTAUT, UTAUT2 indicated an essential advancement in the distinction demonstrated in behavior intention (56%–74%) (Venkatesh et al., 2012). It is therefore coherent to adapt this model to examine the constructs that affect students' behavior intention to accept animation in the learning process at Jordanian universities.

Only a few or all of the UTAUT2 variables have been operationalized in different contexts. For example, Krishnaraju et al. (2013) investigated the effect of PE, EE, SI, FC, H and HM on the use of e-governance technology. In addition, Baptista and Oliveira (2015) considered the impact of PE, EE, H, and PV constructs on mobile banking and added cultural moderators to their conceptual model. Yuan et al. (2015) tested the effect of all the Venkatesh et al. (2012) constructs on the continuous usage of health applications without testing the effect of moderators. Moreover, the UTAUT2 model has been investigated in the learning context. For example, Lewis et al. (2013) explored the effect of PE, EE, SI and H constructs on the teacher's adoption of new technologies. Raman and Don (2013) analyzed the impact of PE, EE, SI, FC and HM constructs on learning management software.

Consequently, the literature indicated that the UTAUT2 theory has been examined in various settings. Additionally, researchers have operationalized some or all of the UTAUT2 constructs. The construct of the PV has been omitted from the UTAUT2 theory in the educational context and has not been replaced except for the Ain et al. (2015) study. The constructs of PV, HM and students' innovativeness (SINN) are inadequately discussed in the context of students’ behavior intention of animation usage. Hence, there is a call for a framework that reflects these constructs. Thus, the current research objectives are to:

Enhance the UTAUT2 model by adding the learning value (LV) construct and students' innovativeness (SINN) construct in the animation context.

Examine the impact of PE, EE, SI, FC, LV, SINN and HM constructs on the students' behavior intention of animation usage.

Examine whether the UTAUT2 model can be used in developing countries.

The research model and hypotheses

Fig. 1 illustrates the intended research model that explains the associations between the independent and dependent variable(s). Furthermore, the UTAUT2 model listed 3 moderating variables: age, gender and experience. This research excludes the controlling variable effect because other studies excluded them, such as the work of Morosan and DeFranco (2016); Alalwan et al. (2015); Oechslein et al. (2014); Raman and Don (2013). Moreover, this research eliminates the construct of habit because animation use is a recent knowledge that has not yet acquired general usage among students to form a habit (Oliveira et al., 2016). Moreover, several scholars removed or/and augmented constructs on UTAUT2 (e.g., Morosan and DeFranco, 2016; Baptista and Oliveira, 2015; Lian, 2015; Yang, 2013). This study integrated the learning value and students’ innovativeness constructs to the model. Therefore, this study made some adjustments to the Venkatesh et al. (2012) model.

Fig. 1

Proposed research model. Adapted from Venkatesh et al. (2012).

Behavior intention of animation usage

BI is referred to as the possibility of an individual to accomplish a task (Venkatesh et al., 2003). There is extensive usage of information technology to enhance the communication between students and teachers in universities (Adria and Rose, 2004). The construct of BI has an important effect on technology usage according to Venkatesh et al. (2003, 2012). In this research, the behavior intention is examined as a dependent variable (e.g., Lian, 2015) to measure students’ acceptance to use animation in their education system.

Performance expectancy

It measures the level to which utilizing a specific innovation may help consumers to attain their specific objectives (Venkatesh et al., 2012). The construct of PE has a strong impact on the behavior intention to use new technologies. Diversified characteristics of emerging technologies such as productivity, speed and clearance can promote the individuals' performance expectancy which can determine their technology use intentions and behavior (Suki and Suki, 2017; Baptista and Oliveira, 2015; Tosuntas et al., 2015; Lakhal et al., 2013). Thus, this research proposes the association between PE construct and BI of animation usage and applies it to the students’ learning process. Consequently, the following research hypothesis is suggested:

Performance expectancy has a positive impact on the students' behavior intention of animation usage.

Effort expectancy

It is known as the level of ease related to using the innovation (Venkatesh et al., 2012). In this research, EE construct shows the level of convenience and ease students feel when using new technology. In short, it measures the level of effort in the behavior intention to use animation. Yang (2013); Porter and Donthu (2006) expressed that EE construct has an influence on the students' use of new technologies. Therefore, this research predicts effort expectancy to be recognized as a significant indicator for the students’ behavior intention to use animation to facilitate their learning process. Hence, the subsequent hypothesis is proposed:

Effort expectancy has a positive impact on the students' behavior intention of animation usage.

Social influence

It is the level to which individuals believe that significant others think that they must use the system (Venkatesh et al., 2012). In universities, social influence refers to the support and motivation provided by instructors, friends and families to students. Oh and Yoon (2014) suggested that there is a strong association between the construct of SI and BI construct. Furthermore, we can infer that social influence plays a vital role in forecasting the intention of innovation usage in the learning process if faculty members and supervisors use the innovation. Hence, the next hypothesis is formulated:

Social influence has a positive impact on the students' behavior intention of animation usage.

Facilitating conditions

Referred to as the level of the accessibility to the means and possessions to accomplish a task (Venkatesh et al., 2012). Students have positive attitudes and perform their tasks if sufficient resources are available. Facilitating conditions are validated as an important interpreter of acceptance and usage of new innovation (Baptista and Oliveira, 2015; Venkatesh et al., 2003). The students’ interaction with new technologies is not available in the lack of facilitating conditions. Therefore, this research articulates the suggested hypothesis:

Facilitating conditions have a positive impact on the students' behavior intention of animation usage.

Hedonic motivation

It is the acquired entertainment caused by technology usage (Venkatesh et al., 2012). Several researchers proposed the positive association between HM construct and BI construct. This relationship is supported by the work of Thong et al. (2006); Escobar-Rodríguez and Carvajal-Trujillo (2014); Brown and Venkatesh (2005); Childers et al. (2001). Derived from the literature review, this study recognized the vital role of HM construct (e.g., enjoyment) in clarifying the motives behind students' usage of new technologies (Brown and Venkatesh, 2005; Childers et al., 2001). Thus, the research uses HM construct as an antecedent of the students’ behavior intention of new technologies usage. Therefore, this research proposes the subsequent hypothesis:

Hedonic motivation has a positive impact on the students' behavior intention of animation usage.

Learning value

Price value is the balance between the apparent advantages of using a system and the expense of that system (Venkatesh et al., 2012). This is a cost–benefit association that influences the individual's behavior intention of a new technology usage as indicated by Venkatesh et al. (2012). Consumers perceive products or services of good value if the benefits of these goods are higher than the cost. In the current research, the value is related to the benefits achieved from the intention to use animation. However, students do not pay any price to learn the system. They spend time and effort to achieve benefits from animation use, according to Ain et al. (2015). Therefore, agreeing with the definition by Venkatesh et al. (2012) of price value, learning value construct is a balance between the obvious advantage of animation, and time spent on it, according to Ain et al. (2015). The following hypothesis is suggested to examine the association between the learning value and behavior intention of animation usage:

Learning value has a positive impact on the students' behavior intention of animation usage.

Students’ innovativeness

Agarwal and Prasad (1998) stated that personal innovativeness has an important role in explaining the intention of new technology usage. Rogers (2003) described innovativeness as the level to which a person is fairly ahead of other people in using the innovation. He categorized adopters in different stages, beginning with innovators who are early adopters and ending with laggards who are the last to use an innovation. Goldsmith et al. (2015) described innovativeness as the eagerness to try new things.

Personal innovativeness reflects the level to which a person would likely direct the way in trying new technologies (Yi et al., 2006). Similarly, Colby and Parasuraman (2003) stated that individual innovativeness, including (the desire to try out the technology, learn more about it, influence others and having faith in the ability of technology to provide real benefit) contributes to the technology adoption, while discomfort, insecurity and lack of control over technology will inhibit the adoption.

Extensive research was conducted to explore the role of innovativeness to determine behavior intention in different contexts (Thakur and Srivastava, 2015; Escobar-Rodríguez and Carvajal-Trujillo, 2014; San Martín and Herrero, 2012; Crespo and Bosque, 2008). The students’ personal innovativeness has a great impact on the use of new technologies (Farooq et al., 2017; Lin et al., 2013; San Martín and Herrero, 2012; Herrero & Rodríguez Del Bosque, 2008; Citrin et al., 2000; Agarwal and Prasad, 1998). A student who is innovative is more likely to be ahead of his colleagues to experiment a new technology. Therefore, it is anticipated that high innovativeness among students will reinforce their behavior intention of animation usage. The explanation above led to the suggestion of the following hypothesis:

Students' innovativeness has a positive impact on the behavior intention of animation usage.

Methods

Sampling and questionnaire administration

The sample of the current research includes undergraduate marketing students in Jordanian universities. Self-administered questionnaires were distributed to gather data from five major universities over a period of two weeks in April 2018. The five universities are a good representation of public and private Jordanian universities. The selected universities introduced animation learning in their marketing and management programs and have the intention to integrate animation learning in their curriculum. Currently, animation is used more extensively in the marketing department than the management department but it is not an obligatory requirement in the program.

The suggested sample size for this study is 370 undergraduate marketing students which is suitable for the size of the population, according to Sekaran (2003). A pilot study was made before the dissemination of the questionnaires. The researchers selected 30 marketing students and asked them to complete the questionnaire. Students were introduced to the objectives of the study and the researchers made sure that the students understood the questionnaire and all the questions were clear for them. The students read the questionnaire in the Arabic language and stated that the questionnaire was clear and understandable.

Then, 370 self-administered questionnaires were distributed randomly among marketing students in classroom settings ranging from freshman to senior years. These students have used animation at least once in their marketing classes. The researchers sent 120 electronic questionnaires using Google form, and 250 questionnaires were distributed physically by the researchers. Of the 370 students involved, 320 provided complete questionnaires. As a result, the actual number of surveys used in the analysis was 320, with a gross response rate of 86.4%.

The questionnaire had closed questions: Section 1 requested sociodemographic information, and Section 2 contained (29) measurement items that tested the factors affecting the students’ behavior intention of animation usage as adapted from the literature review. A research survey was prepared in the English language and translated into Arabic then back-translated into English to ensure validity. A cover letter was added to each survey to ensure the confidentiality of the responses. The questionnaire contained 29 questions measured using a 5-point Likert scale varying from strongly disagree (1) to strongly agree (5). To increase the face validity, a list of academic arbitrators (eleven arbitrators) who specialized in the field of marketing, animation and innovation adoption evaluated the questionnaire before distributing it.

Measures

The dimensions used in the research were validated from prior research papers. Table 1 illustrates the applicable sources of the scale adjustment of each construct.

Table 1

Measurement adaptation.

Constructs	Sources
Performance Expectancy	Venkatesh et al. (2003),Venkatesh et al. (2012), Suki and Suki (2017)
Effort Expectancy	Venkatesh et al. (2003),Venkatesh et al. (2012), Suki and Suki (2017)
Social Influence	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
Facilitating conditions	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
Learning Value	Venkatesh et al. (2012), Paechter et al. (2010), Ain et al. (2015)
Hedonic Motivation	Venkatesh et al. (2012), Ain et al. (2015)
Students' Innovativeness	Yi et al. (2006), Agarwal and Prasad (1998), Mahat et al. (2012)
Behavior Intention	Venkatesh et al. (2003), Venkatesh et al. (2012)

Analysis

The respondents include (48.1%) males and (51.9%) females as illustrated in Table 2. These percentages show a fair distribution of the questionnaire between males and females. The age sample is categorized on various levels, and the majority of the study sample is between (18–20) years old, followed by (21–23) years old and then (24–26) years old. The minimum percentage was above (27) years old. This means that the highest percentage of the sample represents young people (18–20) years old.

Table 2

Demographic sample characteristics–gender, age.

	Frequency	Percent	Valid Percent	Cumulative Percent
Gender
Male	154	48.1	48.1	48.1
Female	166	51.9	51.9	100.0
Total	320	100.0	100.0
Age
18–20	166	51.9	51.9	51.9
21–23	111	34.7	34.7	86.6
24–26	31	9.7	9.7	96.3
27 and over	12	3.7	3.7	100.0
Total	320	100.0	100.0

The descriptive analyses of the data are illustrated in Table 3. The results show that means scored higher than 3.5 on all items (29 items) which is a relatively positive indication for the answers.

Table 3

Descriptive statistics of the research variables.

Items	Mean	Standard Deviation	Excess Kurtosis	Skewness
PE1	4.456	0.724	2.333	-1.444
PE2	4.404	0.742	2.105	-1.330
PE3	4.490	0.655	1.181	-1.161
PE4	4.370	0.679	1.751	-1.012
EE1	4.234	0.723	1.127	-0.832
EE2	3.826	0.897	-0.254	-0.383
EE3	4.099	0.778	-0.096	-0.533
EE4	3.927	0.897	-0.090	-0.574
SI1	3.858	0.846	-0.328	-0.393
SI2	3.806	0.869	-0.470	-0.333
SI3	3.957	0.863	-0.071	-0.583
SI4	3.897	0.935	-0.450	-0.497
FC1	3.643	1.115	-0.682	-0.464
FC2	3.826	0.817	-0.333	-0.351
FC3	3.594	0.859	-0.419	-0.197
FC4	3.578	0.946	-0.862	-0.025
LV1	4.226	0.775	-0.227	-0.678
LV2	3.856	0.759	-0.549	-0.114
LV3	3.983	0.730	-0.040	-0.379
HM1	3.875	0.714	0.779	-0.585
HM2	3.817	0.724	0.625	-0.568
HM3	3.551	0.915	-0.014	-0.446
HM4	3.854	0.727	0.903	-0.619
SINN1	4.344	0.685	0.794	-0.879
SINN2	4.121	0.712	0.471	-0.555
SINN3	4.237	0.777	0.462	-0.848
BI1	4.295	0.768	0.038	-0.833
BI2	4.071	0.851	0.698	-0.830
BI3	4.236	0.830	1.188	-1.054

PE: Performance Expectancy; LV: Learning Value; EE: Effort Expectancy; HM: Hedonic Motivation; SI: Social Influence; SINN: Students' Innovativeness; FC: Facilitating Conditions; BI: Behavior Intention.

A partial least square structural equation modeling approach using SmartPLS (Version 3.2.8) is employed to analyze the collected data through two stages. Step 1: Measurement model to assess the reliability and validity of the theoretical constructs. Step 2: Structural model to represent the (paths) associations between the constructs. PLS is used because it is an entrenched technique for calculating path coefficients in a structural model and has the ability to model latent constructs when data is not normally distributed and samples are small to medium size (Hair et al., 2013). PLS is considered as the most comprehensive analytical system (McDonald, 1996, p. 240) that is extensively employed in management information system research (Marcoulides and Saunders, 2006), marketing (Hair et al., 2012), and various disciplines. It can measure reflective models and forecast the behavior of the relationship between variables and investigate the fundamental theoretical concept (Hair et al., 2013).

Measurement model

The research examines the convergent validity, reliability, and discriminant validity to evaluate the measurement model. To fulfill convergent validity, three steps are needed (Hair et al., 1998; Fornell and Larcker, 1981). First, the entire item loadings should be higher than 0.65. Second, composite reliabilities (CR) must be above 0.8. Third, the average variance extracted (AVE) should be higher than 0.5. The results of this analysis confirmed that the entire item loadings were higher than the recommended value of 0.65. CR values are between 0.810 and 0.891. The AVE ranges between 0.520 and 0.780. Consequently, convergent validity is fulfilled in this study. To check the reliabilities of latent variables, CR and AVE should be above 0.8 and 0.5, respectively. The reliability for both is acceptable and presents clear confirmation of the reliability of the model's measurements as illustrated in Table 4. Discriminant validity illustrates that the square root of the AVE (Diagonal Values) of each variable is greater than its parallel correlation coefficients (Fornell and Larcker, 1981) as illustrated in Table 5. Thus, the measurement model showed that there was no great relationship between variables. This implies the model fitness for the proposed research.

Table 4

Convergent validity of the research constructs.

Items	Loadings	CR	AVE	Reference
Performance Expectancy
PE1: Using animation is a useful learning process.	0.782	0.891	0.671	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
PE2: Using animation helps me to better achieve my learning duties.	0.828
PE3: Using animation increases my learning process productivity.	0.829
PE4: Using animation enhances my possibilities of earning better grades.	0.837
Effort Expectancy
EE1: Using animation is clear and comprehensible.	0.704	0.823	0.539	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
EE2: It is simple to become proficient when using animation.	0.748
EE3: Using animation is easy.	0.708
EE4: Learning to work with animation is simple.	0.774
Social Influence
SI1: Individuals who affect my actions believe that I must use animation.	0.692	0.810	0.516	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
SI2: Individuals who are significant to me believe that I must use animation.	0.772
SI3: Teachers are supportive to use animation.	0.732
SI4: Overall, the university may support animation usage.	0.674
Facilitating Conditions
FC1: I get the important means to use animation.	0.683	0.821	0.535	Venkatesh et al. (2003), Venkatesh et al. (2012), Suki and Suki (2017)
FC2: I possess the important information to use animation.	0.794
FC3: Using animation is well-matched with other systems I use in the learning process.	0.767
FC4: Individual(s) is there to help me if I face any problem in using animation.	0.676
Learning Value
LV1: Animation provides me with the opportunity to control my own learning time.	0.731	0.873	0.697	Venkatesh et al. (2012), Paechter et al. (2010), Ain et al. (2015)
LV2: Animation provides me a chance to enhance my knowledge and to control my success.	0.864
LV3: Animation helps me to immediately and easily communicate my knowledge with others (e.g., short movies, animated images, forums, etc.)	0.901
Hedonic Motivation
HM1: Interaction with using animation is fun in my learning process.	0.796	0.866	0.619	Venkatesh et al. (2012), Ain et al. (2015)
HM2: I find using animation is thrilling in my learning process.	0.794
HM3: I find using animation is enjoyable in my learning process.	0.800
HM4: I find using animation is exciting in my learning process.	0.756
Students' Innovativeness
SINN1: If I knew about a new innovation, I would discover new ways to try it (e.g., animation).	0.818	0.841	0.638	Yi et al. (2006), Agarwal and Prasad (1998), Mahat et al. (2012)
SINN2: I would be the first among my friends to try out a new technology.	0.810
SINN3: Generally, I seek to experiment new technologies (e.g., animation)	0.768
Behavior intention
BI1: I expect to use animation in the next semester.	0.847	0.886	0.721	Venkatesh et al. (2012), Venkatesh et al. (2003)
BI2: I would advise my friends of animation usage in the following semester.	0.867
BI3: I would tell constructive things about animation usage in my learning process.	0.833

Table 5

Discriminant validity of the research constructs.

	BI	EE	FC	HM	LV	PE	SINN	SI
BI	0.849
EE	0.431	0.734
FC	0.242	0.350	0.732
HM	0.581	0.416	0.272	0.786
LV	0.504	0.378	0.259	0.563	0.835
PE	0.503	0.313	0.216	0.418	0.407	0.819
SINN	0.528	0.465	0.203	0.465	0.402	0.513	0.799
SI	0.423	0.535	0.357	0.504	0.383	0.303	0.485	0.718

Bold signifies the square root of the AVE.

Structural model

The research structural model and its associated hypotheses are tested depending on the calculated values of path coefficients (β) and significance T-level (T-Values), and the (R2) which reflects the strength of the predictive variables on the related construct. The coefficient of the R2 for the dependent variable (behavior intention of animation usage) indicates that the five independent variables (PE, EE, LV, HM and SINN) explain 48.4% of the intention to use variance. The value of SRMR = 0.068, RMStheta = 0.11, and the NFI = 0.918. Following a conservative approach, an SRMR (RMStheta) values of less than 0.08 (0.12) indicates good fit (Hair et al., 2017).

Bootstrap resampling methods were utilized to test the statistical significance of the propositions (Henseler et al., 2009). Table 6 illustrates the supported hypotheses of the adapted model. The results indicate a strong positive effect for five hypotheses H1, H2, H5, H6, H7, while H3, H4 is not supported. Consequently, H1 the path coefficient for performance expectancy is (β = 0.195) and has a value of (T-Value = 4.814) at a significant level of (p < 0.05). This outcome refers to the acceptance of the hypothesis. It could be concluded that there is a positive effect of PE on students' behavior intention of animation usage. H2 the path coefficient for effort expectancy is (β = 0.100) and has a value of (T-Value = 2.587) at a significant level of (p < 0.05). This consequence refers to the acceptance of the hypothesis. It could be concluded that there is a positive effect of EE on students’ behavior intention of animation usage.

Table 6

Results of the tested hypotheses.

	Path Coefficient (β	T-Values (Bootstrapping	P-Values (Bootstrapping
PE -> BI	0.195	4.814	0.000
EE -> BI	0.100	2.587	0.010
SI -> BI	0.025	0.603	0.547
FC -> BI	0.005	0.152	0.879
HM -> BI	0.277	6.058	0.000
LV -> BI	0.147	3.515	0.000
SINN -> BI	0.180	4.007	0.000

H5 the path coefficient for learning value is (β = 0.147) and has a (T-Value = 3.515) at a significant level of (p < 0.05). This outcome refers to the acceptance of the proposition. It could be concluded that there is a positive impact of learning value on students' behavior intention of animation usage. H6 the path coefficient for the HM is (β = 0.277) and has a value of (T-Value = 6.058) at a significant level of (p < 0.05). This outcome refers to the acceptance of the proposition. It could be concluded that there is a positive impact of HM on students' behavior intention of animation usage. H7 the path coefficient for the students' innovativeness is (β = 0.180) and has a (T-Value = 4.007) at a significant level of (p < 0.05). This outcome refers to the acceptance of the proposition. It could be concluded that there is a positive effect of students' innovativeness on students’ behavior intention of animation usage.

H3 the results for SI is β = 0.025, T-Value = 0.603 and p > 0.05. This result refers to the rejection of the proposition. Therefore, H3 is not supported, and SI has no positive impact on the students' behavior intention of animation usage. The results for facilitating conditions is β = 0.005, T-Value = 0.152 and p > 0.05. This result rejects the hypothesis. Therefore, H4 is not supported, and facilitating conditions (FC) have no positive impact on the students’ behavior intention of animation usage. The final findings of PLS analysis are illustrated in Fig. 2. In addition, the outcome of the tested hypotheses is illustrated in Table 6.

Fig. 2

Results of PLS analysis (path coefficients and T-Values).

Results & discussion

The research investigated the antecedents that have an effect on the students' behavior intention of animation usage by adapting the UTAUT2 model. The formulated hypotheses were tested by SmartPLS (Version 3.2.8). The proposed structural model is acceptable and the relevance constructs have a notable explanatory capacity in terms of the variance of the factors that influence the students’ behavior intention of animation usage.

Empirically, the outcome of the analyses indicated that the linkage between HM and students’ behavior intention of animation usage is the most significant. Additionally, students find that interaction with using animation is fun and thrilling in their learning process. Consequently, they felt good when using animation in the learning process. These results are consistent with Farooq et al. (2017); Escobar-Rodríguez and Carvajal-Trujillo (2014); Raman and Don (2013); Saadé and Kira (2006).

The construct of performance expectancy also has an important influence on the students’ behavior intention of animation usage. Students find using animation is a useful learning process and enables them to accomplish tasks better in the learning process. Consequently, this increases the productivity of the students and the chances of earning a good grade. These vital findings are aligned with the work of several researchers, such as Stebner et al. (2017); Chauhan and Jaiswal (2016); Lowe and Boucheix (2016); Luzon and Leton (2015); Tosuntas et al. (2015); Suki and Suki (2013).

Moreover, the students’ innovativeness has a significant influence on the behavior intention of animation usage. It is apparent that undergraduates who are aware of a new technology would seek methods to experiment with it (e.g., animation). A student who is innovative would be the first among his colleagues to experiment a new technology. Students would like to try out new technologies such as animation (San Martín and Herrero, 2012; Herrero & Rodríguez Del Bosque, 2008; Citrin et al., 2000; Legris et al., 2003; Agarwal and Prasad, 1998).

The hypothesized association between learning value and the students' behavior intention to use animation was significant. It shows that animation provides students with an opportunity to decide the pace of their personal learning. Animation provides them with a chance to improve their comprehension and to control their success and in less time. A related outcome was addressed by Ain et al. (2015), and Dodds et al. (1991) who reported that individuals’ eagerness to purchase a commodity depends on the perception of value.

Furthermore, the construct of EE has a significant impact on the students’ behavior intention of animation usage. Students who have great technological skills are more expected to use new innovations in their learning process (Baydas, 2015; Sadaf et al., 2012; Giallamas and Nikolopoulou, 2010; Sang et al., 2010). Students recognized that their experience with using animation is obvious, easy and comprehensible. This outcome clarifies the significant role of effort expectancy on the behavior intention (Raman and Don, 2013; Yang, 2013; Venkatesh et al., 2012; Dulle and Minishi-Majanja, 2011). The outcome reinforces the assumption that students are able to operate and use a new technology, despite of its complexity (Yi et al., 2006). Consequently, these results confirm that an advanced and convenient learning environment reduces the effort expectancy. Related outcome was also found by Heerink et al. (2009) and Cheng et al. (2008).

In addition, the outcome of this research analyses showed that SI did not have any influence on the behavior intention of animation usage. Moreover, students did not consider the community surrounding them to have an impact on their behavior. Perhaps the SI does not have an effect on the behavior intention because it is not used extensively in the department and it is also not incorporated in the curriculum. Thus, social influence is not supported. This outcome was coherent with the work of Suki and Suki (2017); Chauhan and Jaiswal (2016). Furthermore, facilitating conditions (FC) do not affect the students’ behavior intention of animation usage. Students conclude that they do not have the essential means to use new technologies, and the use of new technologies is not adaptable with other systems, such as the use of animation in their learning process, while Venkatesh et al. (2012) suggested that FC directly influence behavior intention. In contrast, the study finding does not support that claim. This finding aligns with previous research (El-Masri and Tarhini, 2017; Tarhini et al., 2017; Zuiderwijk et al., 2015; Joo et al., 2014).

Related work

Implications and further research

The present research enriches the scientific knowledge in the subject of education. The findings have meaningful implications that provide educators and practitioners with useful guidelines for successful e-learning methods at universities. Management and educators of Jordanian universities should recognize the animation usage as an effective educational method in students’ learning process. Different courses may be organized for marketing students to train them to use animation. Consequently, the same experience could be provided for various departments that may need to use animation in the future. This will provide graduates who are very advanced and innovative in their communication skills.

The results indicated that students should be offered an enjoyable exposure by the addition of animation in their learning process. The management of universities should guarantee the usefulness and the ease of using animation programs for their students. Including animation in the curriculum is a must to increase the creativity and communication skills among students. Teachers and managers should realize the positive learning value of animation and the positive students' innovativeness. This allows them to speed up the process of integrating animation learning in the students’ curriculum. Finally, management should provide the necessary resources (animation programs, technological equipment, and labs) to facilitate the use of animation in the classrooms.

The research has some restrictions that could be avoided in potential research. For example, the sample could be extended to other departments, such as management department. This will increase the sample size of the research and allow for testing the generalizability of the results. Furthermore, a replication of this research in other Arab countries will enhance generalizability. Additionally, further studies should explore cross-cultural differences among students in various parts of the world. Future research should examine the teachers’ acceptance to use animation. A comparative study between teaching with and without animation could be conducted. Finally, it would be useful to test the influence of vital moderators, such as age, gender and experience on the behavior intention of animation usage.

Conclusion

The current research tested the variables that influence the students' behavior intention of animation usage by adapting the UTAUT2 model introduced by Venkatesh et al. (2012). The data were analyzed and the hypotheses were tested by using SmartPLS (Version 3.2.8) and SPSS (Version 24) programs. The extended model had a good measurement model fit and can explain the behavior intention to use animation in universities. Additionally, the research structural model proved the match between the collected data and the research model and supported the hypothesized associations. The outcome of the research confirmed that hedonic motivation, performance expectancy, students' innovativeness, learning value and effort expectancy were significant, and explained (48.4%) of the students' behavior intention of animation usage. Hedonic motivation had the strongest and largest effects in explaining students’ behavior intention. This is followed by performance expectancy, students' innovativeness, learning value and effort expectancy, respectively.

The research offers enlightenment regarding the students' behavior intention of animation usage. The research extended the UTAUT2 model by adding the LV construct and the SINN construct. The findings adjust UTAUT2 in the field of animation acceptance. Studies that have used UTAUT2 have ignored the construct of price value in the field of education and did not replace it. Furthermore, students' innovativeness was scantly discussed in the context of animation usage. Students perceived the learning value of animation exceeds their time and effort learning it. Therefore, they had a positive behavior intention of animation usage. In addition, students' innovativeness led to a positive impact on the behavior intention of animation usage. Moreover, the analysis of the impact of PE, EE, SI, FC, LV, HM and SINN on students’ intention of animation usage validated the UTAUT2 model applicability in the animation learning context. Depending on the research outcome, it was concluded that the UTAUT2, which was initially suggested and examined in developed countries, can also justify the behavior intention of animation usage in Arab countries, such as Jordan.

Declarations

Author contribution statement

D. Dajani: Conceived and designed the experiments; Analyzed and interpreted the data Contributed reagents, materials, analysis tools or data; Wrote the paper.

A. Abu Hegleh: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.