Understanding College Students’ Acceptance of Cloud Classrooms in Flipped Instruction: Integrating UTAUT and Connected Classroom Climate

Abstract

Cloud classrooms provide many advantages in higher education. However, little is known about the social influence of peer relationships on students’ acceptance of cloud classrooms. This study utilized structural equation modeling to examine a model that integrates the Universal Theory of Acceptance and Use of Technology (UTAUT) and Connected Classroom Climate (CCC). Effort expectancy, social influence, and CCC were found to significantly impact cloud classroom acceptance by college students. Performance expectancy and facilitating conditions, however, did not affect acceptance. These findings contribute understanding that can support decision-making for the cloud classroom, with particular emphasis on increasing college students’ acceptance and use of such technology. Administrators, researchers, and practitioners can use this knowledge to guide their implementation, improvement, and assessment of cloud classrooms. In addition, beyond the cloud classroom, our identification of this relationship between CCC and students’ acceptance represents new knowledge to guide other contexts of online learning.

Keywords

cloud classroom UTAUT Connected Classroom Climate structural equation modeling university tertiary

The cloud classroom is defined as a ubiquitous virtual space that hosts computer-mediated communication and the related processes of a shared formal learning experience (MacLeod, Yang, Zhu, & Shi, 2017). The underlying premise relies on cloud computing infrastructure, which enables unlimited access, storage, and sharing of information by participants (Hew & Kadir, 2016). Through the use of cloud classrooms, instructors are provided new cost-effective capabilities for leading online courses, as well as supplementing traditional face-to-face course interaction through blended learning approaches (cf. Bishop & Verleger, 2013; Bonk & Graham, 2005). In turn, students are provided with capabilities to interact with course-related information, tasks, and communications at any time, in any place, on any internet-connected device. Due to such capabilities, cloud classrooms are increasingly prevalent in higher education, particularly in response to fiscal reductions, demands for flexibility, and an interest in developing more diverse student and instructor communities (e.g., Malikowski, Thompson, & Theis, 2007; Pardeshi, 2014). Developing countries, such as China, India, and South Africa in particular, are pushing cloud technology to obtain a competitive advantage through greater reliability and reduced upfront service cost (Sabi, Uzoka, Langmia, & Njeh, 2016). However, despite the clear advantages and positive prospects for cloud computing in education, user adoption is still slow or in its early phases in many developing countries (Gital & Zambuk, 2011; Massadeh & Mesleh, 2013; Okai, Uddin, Arshad, Alsaquor, & Shah, 2014; Sabi et al., 2016). Therefore, identifying the factors that engender cloud classroom acceptance offers a great opportunity for developing processes that can improve education worldwide.

Students’ acceptance of cloud classrooms is especially critical to the success of flipped instruction. “Flipped” (inverted or reversed) instruction is a blended learning approach containing two parts: “interactive group learning activities inside the classroom, and direct, computer-based individual instruction outside the classroom” (Bishop & Verleger, 2013, p. 5). Flipped instruction has become very popular because the approach substantially boosts active learning processes during face-to-face class time, which is well documented as increasing student achievement (e.g., Deslauriers, Schelew, & Wieman, 2011; Freeman et al., 2014; Hung, 2015).

The learning gains from flipped instruction fundamentally rely on students’ out-of-class preparation and commitment to achieving certain levels of prior knowledge before arriving to class (Wilson, 2013). Such additional demands are often documented in students’ course evaluations as negatively impacting satisfaction (e.g., Berrett, 2012; Wilson, 2013). However, although cloud classrooms are becoming a common host for out-of-class preparation, it is still not clear how the students perceive the relative benefits and disadvantages of cloud classroom environments.

Our review of extant cloud classroom research suggests that little is known about unique social factors, such as Connected Classroom Climate (CCC). CCC is defined as “student-to-student perceptions of a supportive and cooperative communication environment in the classroom” (Dwyer et al., 2004, p. 267). Understanding the CCC is very important, given that the cloud classroom is a computer-mediated environment, and such environments have often been recognized as negatively affecting student participation and imposing communication-related challenges (Rovai & Jordan, 2004; Walther, 1992). Furthermore, the nature of a cloud classroom requires students to operate without direct face-to-face interaction with their instructors, which presents a more central role for student-to-student relationships in the learning experience (MacLeod et al., 2017). This procedural difference further accentuates the importance of CCC in cloud classrooms. However, to our knowledge, no studies have examined the influence of CCC (Dwyer et al., 2004) on students’ acceptance of cloud classrooms. Consequently, without an understanding of the factors that influence students’ acceptance, the choices of key academic decision-makers cannot be fully informed.

This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh, Morris, Davis, & Davis, 2003) with CCC (Dwyer et al., 2004) to expand understanding of the factors influencing students’ acceptance of cloud classrooms in higher education.

Our results make three main contributions to the field. First, we contribute toward filling the research gap in understanding students’ perspectives by empirically investigating students’ acceptance. Second, our research provides empirical evidence identifying the relationship between the CCC and the acceptance of an online learning environment, the cloud classroom. Third, we provide practical understanding relating to the CCC that can be used for decision-making when implementing, assessing, and improving cloud classrooms in higher education.

Theoretical Framework

Much related research exists on a broader perspective of cloud computing in education (Gonzalez-Martinez, Bote-Lorenzo, Gomez-Sanchez, & Cano-Parra, 2015). This broader perspective often emphasizes challenges to acceptance based on legal issues of privacy and intellectual property, as well as risks related to data security (e.g., Mircea & Andreescu, 2011; Sabi et al., 2016; Sultan, 2010). In addition, studies have focused on acceptance of cloud computing by administrators (e.g., Pardeshi, 2014; Sabi et al., 2016). For example, Sabi et al. (2016) utilized the Innovation Diffusion Theory (IDT; Rogers, 1995) and the Technology Acceptance Model (TAM; Davis, 1989) to analyze acceptance by decision-makers from an organizational perspective, including contextual factors such as infrastructure, sociocultural beliefs, cost, risk, and data security.

However, this broad exploration of cloud computing includes a variety of nonclassroom services such as operating systems, administrative tools, productivity applications, and malware detectors (Sultan, 2010). While this combined analysis is valuable for administrative decision-making, it is not relevant to developing processes to support the acceptance of more specifically educational services, such as the cloud classroom. In addition, deeper understanding of the students’ perspective is necessary to ensure their acceptance of cloud classrooms in higher education.

Among previous cloud classroom research, Xu, Liu, Liu, & Zhang (2017) examined students’ attitudes toward the cloud classroom and identified significant influences from material content, perceived responsiveness, and perceived usefulness. In addition, Shiau and Chau (2016) explored a multiple-model comparison of six theories to analyze college students’ behavioral intentions toward studying in a cloud classroom. These studies capture some valuable insights into the students’ perspective of cloud classrooms. However, information regarding actual use has not yet been examined. Furthermore, as noted by Shiau and Chau, more research is still necessary to understand the unique attributes of cloud classroom applications. This study expands knowledge in this area by integrating UTAUT with the CCC in relation to cloud classrooms.

Unified Theory of Acceptance and Use of Technology

Many studies support the association between the UTAUT and the acceptance of technology-supported learning environments (Abdullah & Ward, 2016; Al-Gahtani, 2016; Decman, 2015; Tarhini, Hone, & Liu, 2013). Venkatesh et al. (2003) developed UTAUT as a result of synthesizing eight prominently established models; the Theory of Reasoned Action (TRA; Fishbein & Ajzen, 1975), the Model of PC Utilization (Triandis, 1977), the Theory of Planned Behavior (TPB; Ajzen, 1985), Social Cognitive Theory (Bandura, 1986), the TAM (Davis, 1989), Motivational Model Theory (Davis, Bagozzi, & Warshaw, 1992), Combined TAM and TPB (Taylor & Todd, 1995), and the IDT (Rogers, 1995). It was found that UTAUT provided greater accuracy than any of the individual theories and was capable of explaining as much as 70% of the variance (Venkatesh et al., 2003). However, despite many advantages and parallel applications of UTAUT in education, it has rarely been used to explore the cloud classroom.

UTAUT consists of four major factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy is the degree to which an individual believes that using a system will help them to attain gains in performance. Performance expectancy is related to “relative advantage” in IDT, as well as the “perceived usefulness” factor of TAM (Venkatesh et al., 2003, pp. 448–449). In this study, the factor describes college students’ perception of the value that they get from the cloud classroom for learning, classroom collaboration, and achieving their academic goals.

Effort expectancy is the degree of ease associated with the use of the system. Effort expectancy emerged as a combination of factors such as “complexity” and “perceived ease-of-use” from IDT and TAM, respectively (Venkatesh et al., 2003, p.451). In this study, the factor describes college students’ perception of difficulty when using cloud computing in formal educational contexts. In addition, Venkatesh et al.’s (2003) study showed that effort expectancy was positively correlated to individuals’ performance expectancy. That is to say, when students do not find the system difficult, they expect greater success from it.

Social influence is the degree to which an individual perceives that important others believe they should use the new system. Social influence is similar to the “subjective norm” and “image” factors of TRA and IDT, respectively (Venkatesh et al., 2003, p.452). In this study, the factor describes the forces that important human relationships exert on college students’ behavior when studying in the cloud classroom.

Facilitating conditions refers to the degree to which an individual believes that an effective organizational and technical infrastructure exists to support the use of the system. This factor is similar to the perceived “behavioral control” factor of TPB, and the “compatibility” factor of IDT (Venkatesh et al., 2003, p.454). In this study, “facilitating conditions” represents college students’ personal knowledge of the required technology and access to support services needed to successfully operate the cloud classroom.

However, when considering the social context of cloud classrooms, which often rely heavily on student-to-student relationships, UTAUT alone may not be adequate to measure students’ acceptance. To account for the unique social characteristics of cloud classrooms, this study proposes a modified UTAUT application which integrates CCC to better understand the dynamics from the students’ perspective.

Connected Classroom Climate

CCC refers to the level of supportive and cooperative communication among peers in classrooms (Dwyer et al., 2004). In contrast to other, more inclusive, definitions of “connectedness” (cf. Hagerty, Lynch-Sauer, Patusky, & Bouwsema, 1993; Lee & Robbins, 1995; Rovai, 2002), Dwyer et al.’s interpretation is educationally focused and exclusively concerned with peer relationships. This distinction frames this study by providing a complementary, more specific measure of social influence (Venkatesh et al., 2003) to capture information relating to cloud classroom acceptance. This is important, given that CCC has been described as critical for capturing the essence of cloud classrooms, where there is greater emphasis on student relationships in the absence of face-to-face instructor interaction (MacLeod et al., 2017).

There is considerable evidence that CCC is positively related to students’ participation (Fassinger, 1996, 1997; Sidelinger & Booth-Butterfield, 2010) and learning (Frisby & Martin, 2010; Johnson, 2009; Sidelinger & Booth-Butterfield, 2010) in face-to-face educational environments. In addition, some research has suggested that CCC is related to classroom assimilation (Johnson & LaBelle, 2015) and that an instructor’s presence may not be directly associated with it (Frisby & Martin, 2010; Sidelinger, Bolen, Frisby, & McMullen, 2011). This notion has been empirically supported in comparative studies exploring face-to-face and blended learning instructional delivery methods (e.g., Ritter, Polnick, Fink, & Oescher, 2010; Xu, Yang, & MacLeod, in press). However, little research has focused on cloud classrooms. This is particularly the case within the context of flipped instruction, where acceptance of technology is likely to be more vulnerable, because of some students’ perception of the instructional model as making additional out-of-class demands (e.g., Berrett, 2012; Wilson, 2013). In addition, little research has examined CCC in the context of a cloud classroom, despite research suggesting that CCC may suffer in fully online environments (Ritter et al., 2010).

Our previous research (MacLeod et al., 2017) explored the relationships between technological factors and CCC in cloud classrooms. This study found positive and significant relationships between CCC and four technological factors: advanced computer self-efficacy, internet and entertainment computer experience, program and software computer experience, and computer importance. These findings imply that there are processes, such as training in 21st-century digital literacies (Van Laar, Van Deursen, Van Dijk, & De Haan, 2017), which can be used to assist in cultivating CCC in cloud classrooms. This is important because, if CCC engenders acceptance, then it becomes critical for researchers and practitioners to understand and address the cultivation of CCC in cloud classrooms. However, the influence of CCC on students’ acceptance of the cloud classroom has not yet been explored.

Research Model and Hypotheses

Based on our review of related studies, UTAUT is a strong theoretical approach that has been widely utilized to support the measurement of technology acceptance in related educational contexts. Therefore, as shown in Figure 1, we propose that the four UTAUT variables (performance expectancy, effort expectancy, social influence, and facilitating conditions) will influence students’ acceptance of cloud classrooms. Our research hypotheses for these variables are as follows:

Hypothesis 1 (H1): The level of performance expectancy is positively related to the degree of cloud classroom acceptance.

Figure 1.

The modified UTAUT research model.

Hypothesis 2 (H2): The level of effort expectancy is positively related to the degree of cloud classroom acceptance.

Hypothesis 3 (H3): The level of social influence is positively related to the degree of cloud classroom acceptance.

Hypothesis 4 (H4): The level of facilitating conditions is positively related to the degree of cloud classroom acceptance.

Hypothesis 5 (H5): The level of effort expectancy is positively related to the degree of performance expectancy.

Furthermore, our review indicates that CCC is crucial for explaining the unique social characteristics of cloud classrooms. Related studies have also shown that social influence can affect performance expectancy in other e-learning environments (cf. Abdullah & Ward, 2016). Therefore, as shown in Figure 1, the following hypotheses are proposed to integrate CCC in the measurement of college students’ acceptance of the cloud classroom:

Hypothesis 6 (H6): The level of social influence is positively related to the degree of performance expectancy.

Hypothesis 7 (H7): The level of CCC is positively related to the degree of social influence.

Hypothesis 8 (H8): The level of CCC is positively related to the degree of performance expectancy.

Hypothesis 9 (H9): The level of CCC is positively related to the degree of cloud classroom acceptance.

Methodology

Participants and Setting

Participants were 289 college students from a research-oriented normal university in central China; the term normal university refers to a teacher-training institution of higher education. Participants were purposely selected based on their current enrollment in a cloud classroom course. Five individuals’ responses were omitted from data analysis because of incomplete responses. Therefore, the valid response rate for this study was approximately 98% (n = 284).

The women-to-men ratio was about 3:1, typical of the gender composition of the student population at this and many other normal universities. The participants were a homogeneous group of college-age individuals (18–24 years) with similar introductory levels of experience using the cloud classroom. All participants were in their second or third year of undergraduate study, and were first exposed to cloud classrooms in higher education. Fourth-year students were not included because they were engaged in thesis work and therefore, were not enrolled in courses at the time of this study. First-year students were not included because, at the selected institution, students are not typically enrolled in cloud classroom courses until their second year of study.

The participants were all enrolled at a university that was conducting a large-scale cloud classroom initiative. As part of this in-progress initiative, the university developed a cloud classroom, called H-Star (MacLeod et al., 2017), which is a similar learning management system to Blackboard or Moodle. All university instructors were provided training opportunities with H-Star and encouraged to integrate the cloud classroom into their personal instructional practices. H-Star offers the capabilities of providing digital resources, user-specific tools and applications, and connections to third-party content. Essentially, the H-Star platform provides a ubiquitous virtual classroom for communication, as well as content distribution and collection. In addition, instructors can quickly analyze student progress, providing real-time feedback to their students.

All participants were enrolled in a general requirement course entitled, “Introduction to Marxist Principles.” The course consisted of six class sections that were all taught by the same experienced instructor during the fall of 2016. Each class comprised approximately 45 students and the cloud classroom only interconnected the students within their own class.

The format of cloud classroom integration was based upon flipped instructional principles (Bishop & Verleger, 2013). That is to say, the cloud classroom hosted before-class and after-class activities that supplemented traditional face-to-face classroom instruction. Before-class activities were self-paced and nonmandatory. These activities primarily consisted of selected readings, short videos, and self-assessment tests. After-class activities were mandatory and consisted of discussion forums, as well as a variety of individual and small-group assignments. This study explored students’ cloud classroom experience after two months of this utilization format.

Instrumentation

The survey used in this study first collected demographic information. Then, the survey utilized a UTAUT Scale and the CCC Inventory (CCCI) to measure the dependent and independent variables. These scales are described as follows.

Venkatesh et al.’s (2003) UTAUT Scale was adopted, consisting of five dimensions, namely performance expectancy (4 items), effort expectancy (4 items), social influence (4 items), facilitating conditions (4 items), and cloud classroom acceptance (4 items). In total, the UTAUT Scale consisted of 20 items and internal consistency reliabilities were all greater than .70. All UTAUT items were measured on a 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree. One representative item is included here for each dimension:

I think the cloud classroom is useful for my study (performance expectancy).

It is easy for me to use the cloud classroom (effort expectancy).

People who are important to me think that I should use the cloud classroom (social influence).

I have the knowledge necessary to use the cloud classroom (facilitating conditions).

I often use learning resources in my cloud classroom (cloud classroom acceptance).

Dwyer et al.’s (2004) CCCI consists of a single dimension with four items and was adopted to measure CCC. An introductory stem was added to the scale stating: “Based on my experience in the cloud classroom…” Two representative items of this scale are “The students in my class are friendly with one another” and “The students in my class show interest in what one another is saying.” The overall reliability on the scale is .94 (Dwyer et al., 2004, p. 268). All CCCI items were measured on a 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree.

Data Collection and Analysis Procedure

Data were collected during November 2016. The survey instrument was prepared in four steps. First, to administer the survey in the participants’ native language, all items were parallel translated (Guillemin, Bombardier, & Beaton, 1993) from English to Chinese by three researchers, using committee reconciliation for preliminary assessment of the translated draft (Harkness & Schoua-Glusberg, 1998). Second, a bilingual Chinese professor, who teaches English, reverse-translated the survey from Chinese to English to assess quality assurance. Third, the revised survey translation received independent bilingual assessment (Harkness & Schoua-Glusberg, 1998) from an educational technology expert with over 20 years’ teaching experience in the United States and China. Based on the expert’s feedback, several items were rephrased to improve readability. Finally, both a pretest and a pilot test were conducted. Confirmatory factor analysis was used to purify the items, which resulted in a final survey of 22 items. One item was trimmed from each of the social influence and facilitating conditions dimensions.

Before the survey was administered, the university granted permission to collect the data. Responses were collected voluntarily and anonymously via paper format during a mid-class break, then entered into Microsoft Excel and imported into SPSS 21.0 and AMOS 20.0 for data analysis. Structural Equation Modeling analysis was conducted to analyze the relationships between the independent variables and cloud classroom acceptance.

Results

Overview of the Survey

Table 1 provides an overview of the participants’ mean scores and standard deviations on the cloud classroom acceptance survey. The results showed responses that range between 2.96 and 3.93. CCC (M = 2.96, SD = .91) was shown to have the lowest average response value examined. Effort expectancy (M = 3.73, SD = .73) and cloud classroom acceptance (M = 3.93, SD = .71) were shown to have the highest average response values of the survey.

Table 1.

Descriptive Statistics.

Scale	M	SD
Performance expectancy	3.28	.82
Effort expectancy	3.76	.73
Social influence	3.73	.68
Facilitating conditions	3.72	.66
Connected Classroom Climate	2.96	.91
Cloud classroom acceptance	3.93	.71

Note. n = 284.

Confirming the Measurement Model

Convergent validity was assessed by three indicators: factor loadings, composite reliability (CR) coefficients, and the average variance extracted (AVE). All factor loadings were above .70. Then, as shown in Table 2, CR coefficients and AVE values showed a range of .76 to .98 and .51 to .72, respectively. These results were all greater than the recommended thresholds of .60 (CR coefficient) and .50 (AVE), which validates the convergent validity of the constructs (cf. Fornell & Larcker, 1981). In addition, as shown in Table 2, the square root of each AVE was greater than the respective correlation coefficient, which validates the discriminant validity of the constructs (cf. Fornell & Larcker, 1981). Accordingly, the constructs of the survey have high validity.

Table 2.

Validity and Reliability Analysis.

	Reliability		Convergent validity	Discriminant validity
	CR	α	AVE	1	2	3	4	5	6
1	.89	.88	.66	.81
2	.88	.87	.65	.56	.80
3	.89	.87	.72	.42	.29	.85
4	.76	.76	.51	.08	.20	.13	.71
5	.89	.89	.68	.65	.30	.41	.13	.82
6	.85	.85	.59	.23	.16	.18	.02	.32	.77

Note. 1 = performance expectancy; 2 = effort expectancy; 3 = social influence; 4 = facilitating conditions; 5 = Connected Classroom Climate; 6 = cloud classroom acceptance. Boldface numbers represent square roots of the AVE. AVE = average variance extracted; CR = composite reliability.

The overall alpha value of the survey was .88. In addition, as shown in Table 2, each alpha value and CR coefficient was within the range of .76 to .89. These values were all greater than .7, confirming satisfactory reliability (cf. Chin, 1998). Accordingly, the constructs of the survey used in this study have acceptable reliability.

As shown in Table 3, the chi-square value was 366.803 and the p value was .000, which was less than .05, indicating that the proposed model may not match the collected data well. However, the chi-square value was greatly affected by the sample size. For a sample size of more than 200, the chi-square value is often used only as a reference index, and the other fit indices are needed to assess the overall model fit. With the exception of the chi-square value, the values of all fit indices were acceptable. Accordingly, the model exhibited satisfactory values (cf. Kline, 2005).

Table 3.

Goodness-of-Fit Analysis.

	Fit index	Actual value	Recommended value	Judgment
Absolute fit indices	χ²	366.803 (p = .00)	p > .05	No
	χ²/df	1.287	≤ 3.00	Yes
	GFI	0.926	≥0.90	Yes
	AGFI	0.904	≥0.90	Yes
	RMSEA	0.032	≤0.08	Yes
Incremental fit measurement	CFI	0.983	≥0.90	Yes
	TLI	0.980	≥0.90	Yes
	NFI	0.929	≥0.90	Yes

Note. X² = Chi-square; df = Degree of freedom; GFI = Goodness of fit index; AGFI = Adjusted goodness of fit index; RMSEA = Root mean square error of approximation; CFI = Comparative fit index; TLI = Tucker-Lewis index; NFI = Normed fit index.

Structural Equation Modeling Analysis

To verify the research hypotheses, Structural Equation Modeling analysis was conducted. Figure 2 shows the path coefficients marked by standardized regression weights (β value) and p values. The results showed that, with the exception of the influence of performance expectancy (H1) and facilitating conditions (H4) on cloud classroom acceptance, all paths were significant.

Figure 2.

Structural model of cloud classroom acceptance. Note. ***p < .001; **p < .01; *p < .05.

As shown in Table 4, the results of the analysis indicate that seven of our nine hypotheses were supported. Effort expectancy (β = .380, p < .01), social influence (β = .232, p < .05), and CCC (β = .516, p < .001) were all positively related to acceptance of the cloud classroom. In addition, effort expectancy (β = .442, p < .001), social influence (β = .222, p < .01), and CCC (β = .413, p < .001) had significantly positive effects on performance expectancy. CCC (β = 0.496, p < .001) also had a significantly positive effect on social influence.

Table 4.

Test of Hypotheses.

Hypotheses			Path coefficient	CR	Result of hypotheses
H1. Performance expectancy	→	Acceptance	−0.067	−0.386	Not supported
H2. Effort expectancy	→	Acceptance	0.380**	2.855	Supported
H3. Social influence	→	Acceptance	0.232*	2.173	Supported
H4. Facilitating conditions	→	Acceptance	0.074	0.958	Not supported
H5. Effort expectancy	→	Performance expectancy	0.442***	6.250	Supported
H6. Social influence	→	Performance expectancy	0.222**	2.882	Supported
H7. CCC	→	Social influence	0.496***	3.558	Supported
H8. CCC	→	Performance expectancy	0.413***	5.863	Supported
H9. CCC	→	Acceptance	0.516***	3.910	Supported

Note. ***p<.001; **p<.01; *p<.05. CCC = Connected Classroom Climate; CR = composite reliability.

Discussion and Conclusion

The cloud classroom presents many valuable opportunities for educational stakeholders, especially among students in higher education (Lin, Wen, Jou, & Wu, 2014; Pardeshi, 2014; Sabi et al., 2016) and within the application of flipped instructional models. However, there is evidence that students sometimes perceive out-of-class work required by flipped instruction as an additional burden (e.g., Berrett, 2012; Wilson, 2013). This suggests that the adoption of the cloud classroom for out-of-class work may be vulnerable within such educational contexts. Given that learning gains from flipped instruction (e.g., Deslauriers et al., 2011; Freeman et al., 2014; Hung, 2015) fundamentally rely on students’ out-of-class preparation and commitment to achieving certain levels of prior knowledge before arriving to class (Wilson, 2013), encouraging students’ acceptance of cloud classrooms is an issue of critical importance.

This study incorporated UTAUT (Venkatesh et al., 2003) and CCC (Dwyer et al., 2004) to examine cloud classrooms, which themselves incorporate a unique range of variables that provide empirical evidence to expand understanding of ways to encourage the use of beneficial computing resources from the students’ perspective. In this study, effort expectancy, social influence, and CCC were all observed as significant and positively related to college students’ acceptance of the cloud classroom. Performance expectancy and facilitating conditions, however, were not found to be significant. When interpreting the nonsignificant results, it is important to consider two demographically related facts noted by Venkatesh et al. (2003) during the initial proposal of UTAUT. First, they stated that “the strength of [performance expectancy] varies with gender and age such that it is more significant for men and younger workers” (p. 467). Second, the effects of facilitating conditions on usage were described as “only significant when examined in conjunction with the moderating effects of age and experience—i.e., they only matter for older workers in later stages of experience” (p. 467). This information may provide some justification for our findings, as the sample examined in this study was comprised of relatively young students (18–24 years), was skewed toward women (3:1), and included individuals introduced to cloud classrooms during their university experience. That is to say, all students had less than 2 years of exposure and, on average, had only experienced cloud classrooms in a minority of their courses.

When compared with the long-standing face-to-face delivery of education, the utilization of cloud classrooms, or online learning more generally, is still in the developing stages (Gital & Zambuk, 2011; Massadeh & Mesleh, 2013; Okai et al., 2014; Sabi et al., 2016). In this context, the results may also be influenced by the premature conditions of applying the system, while institutions are still in the developing stages of formalizing instructor training programs and student support services. At this stage of implementation, students’ perceptions should be interpreted in light of the context, where instructors are still learning about and experimenting with the technology. Therefore, it is possible that students’ performance expectancy is based upon suboptimal experiences with cloud-classroom applications.

In addition, with respect to facilitating conditions, it is possible that our findings may be influenced by students’ lack of knowledge or confidence for operating in such a new and transitional environment. In light of these results, future research should continue to monitor performance expectancy and facilitating conditions, as well as examine the variables within samples drawn from different stages of cloud classroom implementation.

Theoretically, this study provides a new acceptance model that integrates UTAUT and CCC (Dwyer et al., 2004) to expand understanding of students’ usage behavior in cloud classrooms and within the online learning portion of a flipped instructional model. These contributions also provide value by exploring a new extraneous social variable that is critically important for understanding acceptance of cloud classroom methodology. Future research on this topic, as well as more general research into similar online learning environments, should use this acceptance model as a reference in considering the important social factors of peer relationships in educational contexts.

Practically, our research highlights that effort expectancy, social influence, and CCC have significant effects on college students’ acceptance of the cloud classroom. In addition, we found that social influence, effort expectancy, and CCC significantly affect performance expectancy. Therefore, instructors and instructional designers should consider this knowledge when developing and facilitating cloud classroom experiences, particular in encouraging the out-of-class preparations required for effective flipped classroom instructional models. For example, the design of a learning platform should reflect the simplest, most minimal configuration possible. Cloud classrooms with simple functionality can decrease students’ perceived effort expectancy, which, based on this study, is known to be correlated with an increased level of acceptance by students.

Furthermore, instructional design and learning activities should strategically cultivate peer relationships. Social isolation and loneliness in online learning has long been recognized as a serious issue (Kraut et al., 1992; Walther & Park, 2002), and this study recognizes the potential of CCC as a positive student-to-student force to decrease effort expectancy and increase students’ acceptance.

The practical and theoretical implications of this study must be considered with some limitations. For instance, the study was based upon a homogeneous sample, with similar age, culture, and cloud classroom experience. Therefore, future research should explore other ages, cultures, and levels of experience to help expand understanding of the generalizability of the results. In addition, this study only utilized quantitative research analysis. Therefore, future research should explore qualitative research methodologies to triangulate data and continue to examine the dynamics of students’ acceptance of cloud classrooms.

To conclude, the utilization of cloud classrooms in education is an ongoing and expanding global phenomenon. Researchers and practitioners should consider the critical influence that CCC contributes toward students’ acceptance of the cloud classroom when implementing, assessing, and improving their cloud classroom learning environments, particularly within models of flipped instruction. In addition, the implications of UTAUT and CCC observed in this study provide direction for future research related to computer-mediated student-to-student influence, as well as heightening the acceptance of cloud classrooms and similar online learning environments in higher education.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work was supported by the Key Projects of Philosophy and Social Research, Ministry of Education of China (Grant #: 14JDZ044).

Author Biographies

Harrison H. Yang is a professor in the School of Education at the State University of New York at Oswego, USA. He also holds the deanship in the School of Educational Information Technology at Central China Normal University, China. His research interests include distance/flexible education, technology literacy and integration, and e-learning experience management.

Lin Feng is a graduate of the Collaborative and Innovative Center for Educational Technology at Central China Normal University and currently holds an information technology teaching position at the Huoxian Secondary School in Beijing, China. During the time this research was conducted, she was a graduate student at Central China Normal University under the advisement of Professor Harrison H. Yang. Her research interests include e-learning experience management and mobile learning.

Jason MacLeod is a doctorate candidate in the School of Educational Information Technology at Central China Normal University. He is currently under the advisement of Professor Harrison H. Yang and a participating member in his research team. His research interests include intercultural interaction, computer-mediated collaboration, and e-learning experience management.

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	Reliability		Convergent validity	Discriminant validity
	CR	α	AVE	1	2	3	4	5	6
1	.89	.88	.66	.81
2	.88	.87	.65	.56	.80
3	.89	.87	.72	.42	.29	.85
4	.76	.76	.51	.08	.20	.13	.71
5	.89	.89	.68	.65	.30	.41	.13	.82
6	.85	.85	.59	.23	.16	.18	.02	.32	.77

	Reliability		Convergent validity	Discriminant validity
	CR	α	AVE	1	2	3	4	5	6
1	.89	.88	.66	.81
2	.88	.87	.65	.56	.80
3	.89	.87	.72	.42	.29	.85
4	.76	.76	.51	.08	.20	.13	.71
5	.89	.89	.68	.65	.30	.41	.13	.82
6	.85	.85	.59	.23	.16	.18	.02	.32	.77

	Reliability		Convergent validity	Discriminant validity
	CR	α	AVE	1	2	3	4	5	6
1	.89	.88	.66	.81
2	.88	.87	.65	.56	.80
3	.89	.87	.72	.42	.29	.85
4	.76	.76	.51	.08	.20	.13	.71
5	.89	.89	.68	.65	.30	.41	.13	.82
6	.85	.85	.59	.23	.16	.18	.02	.32	.77