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Comparative Study of Students' Perception and Behavioral Intention in MOOC Context: Undergraduates in China and Spain

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Title: Comparative Study of Students' Perception and Behavioral Intention in MOOC Context: Undergraduates in China and Spain
Language: English
Authors: Kai Wang (ORCID 0000-0002-7765-8658), Josep Rialp Criado, Stefan Felix van Hemmen
Source: Asia-Pacific Education Researcher. 2024 33(5):1129-1137.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 9
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Student Attitudes, Student Behavior, Intention, MOOCs, Electronic Learning, Undergraduate Students, Cross Cultural Studies, Foreign Countries, Cultural Influences, Cultural Differences, Educational Technology, Usability
Geographic Terms: China, Spain
DOI: 10.1007/s40299-023-00781-7
ISSN: 0119-5646
2243-7908
Abstract: This paper addresses undergraduates' perception and behavioral intention (BI) toward Massive open online courses (MOOCs). It considers the moderating effect of culture to further explore the perceptions of students with different cultures to engage in MOOCs, providing MOOC suppliers with practical suggestions. 770 undergraduates in Spain and China are involved in this study. The proposed research model incorporating the technology acceptance model and theory of planned behavior is confirmed as a desirable theoretical model. Additionally, the moderating effect of culture has been confirmed between Spanish and Chinese students. The findings show Perceived usefulness and Perceived ease of use are effective in forming attitude (ATT). Besides, ATT and subjective norm, and perceived behavioral control are confirmed as vital factors impacting BI. Regarding the moderating effect of culture, significant differences have been detected to account for their behavior.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1437311
Database: ERIC
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  Value: <anid>AN0179295307;[gchw]01oct.24;2024Aug30.05:32;v2.2.500</anid> <title id="AN0179295307-1">Comparative Study of Students' Perception and Behavioral Intention in MOOC Context: Undergraduates in China and Spain </title> <p>This paper addresses undergraduates' perception and behavioral intention (BI) toward Massive open online courses (MOOCs). It considers the moderating effect of culture to further explore the perceptions of students with different cultures to engage in MOOCs, providing MOOC suppliers with practical suggestions. 770 undergraduates in Spain and China are involved in this study. The proposed research model incorporating the technology acceptance model and theory of planned behavior is confirmed as a desirable theoretical model. Additionally, the moderating effect of culture has been confirmed between Spanish and Chinese students. The findings show Perceived usefulness and Perceived ease of use are effective in forming attitude (ATT). Besides, ATT and subjective norm, and perceived behavioral control are confirmed as vital factors impacting BI. Regarding the moderating effect of culture, significant differences have been detected to account for their behavior.</p> <p>Keywords: MOOCs; Theory of planned behavior; Technology acceptance model; TAM–TPB; Hofstede cultural dimensions theory</p> <p>Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s40299-023-00781-7.</p> <hd id="AN0179295307-2">Introduction</hd> <p>MOOCs have satisfied the diverse learning goals of global learners beyond the constrain of time, physical, and geographical boundaries to acquire high-quality education services (Qi et al., [<reflink idref="bib31" id="ref1">31</reflink>]). Universities, where online education is becoming common as the new standard of educational services (Kang & Park, [<reflink idref="bib23" id="ref2">23</reflink>]), around the world are offering their classes available through MOOCs (Kim & Song, [<reflink idref="bib25" id="ref3">25</reflink>]). Besides, the practicability of MOOCs advances the perception of university students to further enroll themselves in an appropriate MOOC as a complimentary resource for their residential courses (Lung-Guang, [<reflink idref="bib26" id="ref4">26</reflink>]) or to fulfill the diverse objectives (Sun et al., [<reflink idref="bib36" id="ref5">36</reflink>]).</p> <p>Additionally, MOOCs' openness attracts global learners with different nationalities and cultural backgrounds to enroll in the MOOC movement. Hofstede's cultural dimensions theory argues that culture explains individual psychological activities in a particular environment and distinguishes people from others (Huang & Crotts, [<reflink idref="bib20" id="ref6">20</reflink>]). Therefore, the cultural influence should be considered when studying students' intention and behavior toward MOOCs. Whereas few comparative studies have been conducted to explain the moderating effect of culture on students' behavior toward MOOCs, and people with diverse cultures behave their own culture patterns (Huang et al., [<reflink idref="bib22" id="ref7">22</reflink>]; Wang et al., [<reflink idref="bib42" id="ref8">42</reflink>], [<reflink idref="bib43" id="ref9">43</reflink>]). For instance, Chinese people are more collectivistic cultural orientations, while European-Americans are considered more individualistic orientations (Huang et al., [<reflink idref="bib21" id="ref10">21</reflink>]). Sánchez-Franco et al. ([<reflink idref="bib34" id="ref11">34</reflink>]) mentioned that cultural values affect the technology acceptance process, given that Spain is a country from the Latin European cluster (Gupta et al., [<reflink idref="bib13" id="ref12">13</reflink>]) and coordinated by regional or central governments, the Spanish higher education system is relatively less marketized than other European countries (Wang et al., [<reflink idref="bib42" id="ref13">42</reflink>], [<reflink idref="bib43" id="ref14">43</reflink>]) and China is a country from Confucian Asian cluster with important cultural differences (Gupta et al., [<reflink idref="bib13" id="ref15">13</reflink>]). Besides, Sánchez-Prieto et al. ([<reflink idref="bib35" id="ref16">35</reflink>]) indicated that the students both from China and Spain keep a considerable disposition toward the use of learning technology with high scores both in their behavior intention and attitude.</p> <p>This study aims to collect data from universities in China and Spain to provide an opportunity to conduct cross-cultural comparisons with a view to assess the influence of culture on students behavior toward MOOCs. TAM (Davis, [<reflink idref="bib7" id="ref17">7</reflink>]) and TPB (Ajzen, [<reflink idref="bib1" id="ref18">1</reflink>]) are utilized as an incorporated theoretical framework to explain students' perception and intention toward MOOCs. TAM is widely accepted and employed to study human behavior addressing the technology acceptance and usage (Teo & Dai, [<reflink idref="bib39" id="ref19">39</reflink>]). PU and PEOU are two theoretical constructs connected to the construct of ATT in TAM. Besides, TPB is adopted for predicting the causal relationship of BI by numerous researchers associated with three conceptual determinants of ATT, SN, and PBC. Hence, in this research, PU, PEOU, ATT, SN, and PBC are considered independent variables and BI is viewed as the dependent variable. Furthermore, this study considers culture as a moderator to verify the potential differences of students from China and Spain to further detect the moderating effect of culture.</p> <hd id="AN0179295307-3">Literature Review</hd> <p></p> <hd id="AN0179295307-4">MOOCs</hd> <p>The main intention and fundamental mission of MOOCs are to make the course content available and delivery course content freely to global learners ranging from tens of thousands with different backgrounds. Students' engagement is an essential issue in MOOC learning context but previous studies focused on the engagement patterns (Sun et al., [<reflink idref="bib36" id="ref20">36</reflink>]). Addressing the extant research of students' engagement in MOOCs, Lung-Guang ([<reflink idref="bib26" id="ref21">26</reflink>]) investigated the behavior of students to participate in a MOOC and found that individuals who choose MOOCs in school life follow critical foresight that is closely related to the planned behavior. Sun et al. ([<reflink idref="bib36" id="ref22">36</reflink>]) found the three basic psychological needs of autonomy, competence, and relatedness are critical to form the intrinsic motivation, which can increase students' psychological engagement in MOOCs. Furthermore, Padilha et al. ([<reflink idref="bib30" id="ref23">30</reflink>]) assessed MOOCs as an educational resource to enhance self-management intervention skills and revealed the easiness and quality have been perceived. Riehemann et al. ([<reflink idref="bib33" id="ref24">33</reflink>]) investigated how course type and relevance of individual participation impact online learners' engagement. The findings showed that participants in a MOOC condition referred to themselves and their peers to a greater extent when their participation was introduced as being highly relevant.</p> <hd id="AN0179295307-5">Hofstede's Cultural Dimensions Theory</hd> <p>Along with the globalization and advanced information and communication technology, people with different culture backgrounds interact with each other easily so that the original cultures are contaminated and changed (Craig & Douglas, [<reflink idref="bib6" id="ref25">6</reflink>]). As Hofstede's framework identifies a set of universal dimensions of national culture on which all societies can be located, allowing for conducting intercultural comparisons, many empirical studies have examined the impact of culture on a range of issues related to international management, global strategy, and cross-country education (Beugelsdijk et al., [<reflink idref="bib3" id="ref26">3</reflink>]; Wang et al., [<reflink idref="bib42" id="ref27">42</reflink>], [<reflink idref="bib43" id="ref28">43</reflink>]).</p> <p>Hofstede ([<reflink idref="bib16" id="ref29">16</reflink>]) first collected data from 72 countries from 1967 to 1973 to constitute the cultural framework and initially identified four aspects of national culture: (<reflink idref="bib1" id="ref30">1</reflink>) Individualism/Collectivism, which expresses the extent to which society treats people as individuals taking care of themselves (high individualism) or mainly as a close-knit community member (low individualism); (<reflink idref="bib2" id="ref31">2</reflink>) Power distance, which reflects the degree to which people in a society expect and accept the uneven distribution of power; (<reflink idref="bib3" id="ref32">3</reflink>) Uncertainty avoidance, which measures the comfort level of social members in unstructured situations (a culture of high uncertainty avoidance is characterized by a strong need for predictability and control of the environment); (<reflink idref="bib4" id="ref33">4</reflink>) Masculinity/femininity, reflecting the society's emphasis on caring for others, unity and quality of life (femininity), as opposed to achievement and success (masculinity). Later, the fifth and sixth dimensions were added, namely, long-term orientation and indulgence and restraint. Their scores are based on the items and data of the World Value Survey (Hofstede & Minkov, [<reflink idref="bib19" id="ref34">19</reflink>]). Referring to the Hofstede cultural dimensions, Table 1 shows an extreme reverse position in which Chinese people are more power distance orientation, masculinity orientation, long-term orientation compared to Spanish people, and Spanish people are more individualism orientation, uncertainty avoidance orientation and indulgence orientation compared to Chinese people.</p> <p>Table 1 Values of China and Spain in Hofstede's cultural dimensions</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Country</p></th><th align="left"><p>Power distance</p></th><th align="left"><p>Individualism</p></th><th align="left"><p>Masculinity</p></th><th align="left"><p>Uncertainty avoidance</p></th><th align="left"><p>Long-term orientation</p></th><th align="left"><p>Indulgence</p></th></tr></thead><tbody><tr><td align="left"><p>Spain</p></td><td align="left"><p>57</p></td><td align="left"><p>51</p></td><td align="left"><p>42</p></td><td align="left"><p>86</p></td><td align="left"><p>48</p></td><td align="left"><p>44</p></td></tr><tr><td align="left"><p>China</p></td><td align="left"><p>80</p></td><td align="left"><p>20</p></td><td align="left"><p>66</p></td><td align="left"><p>30</p></td><td align="left"><p>87</p></td><td align="left"><p>24</p></td></tr></tbody></table> </ephtml> </p> <p>Own elaboration based on Hofstede Insights ([<reflink idref="bib18" id="ref35">18</reflink>]): https://<ulink href="http://www.hofstede-insights.com/product/compare-countries/">www.hofstede-insights.com/product/compare-countries/</ulink></p> <p>To detect a cultural effect on students' perception and BI, the most common way of measuring cultural distance is based on the difference in Hofstede's cultural dimensions scores between countries (Beugelsdijk et al., [<reflink idref="bib3" id="ref36">3</reflink>]). Therefore, this paper considers Hofstede's cultural dimension theory to further explore the potential difference between students from Spain and China.</p> <hd id="AN0179295307-6">Incorporated Theoretical Framework</hd> <p>TPB (Ajzen, [<reflink idref="bib1" id="ref37">1</reflink>]) was proposed to predict human behavior and has been considered as one of the most preeminent theories among social cognition theories associating with three determinants of ATT, SN, and PBC. Among the three critical determinants of TPB, ATT refers to an individual assesses the particular behavior positively or negatively (Moon, [<reflink idref="bib28" id="ref38">28</reflink>]); SN refers to the perceived social pressure that may have an impact on an individual's behavioral intention toward a specific activity (Wang et al., [<reflink idref="bib42" id="ref39">42</reflink>], [<reflink idref="bib43" id="ref40">43</reflink>]); PBC refers to an individual's perception of own capacity to perform and engage in a given activity (Lung-Guang, [<reflink idref="bib26" id="ref41">26</reflink>]). Besides, TAM proposed by Davis ([<reflink idref="bib7" id="ref42">7</reflink>]) has been widely applied in different academic contexts as a ground theory to predict an individual's willingness and intention to adopt a specific technology (Teo & Dai, [<reflink idref="bib39" id="ref43">39</reflink>]). PU and PEOU are two constructs related to ATT and PU refers to an individual's perception toward a particular technology that can improve the performance on his or her jobs. PEOU refers to a belief that an individual can manage a particular technology free of effort (Davis, [<reflink idref="bib7" id="ref44">7</reflink>]).</p> <p>More researchers have focused on integrating TAM and TPB to examine IT usage and e-service acceptance because of the two theories' complementation. The findings show that the incorporated model keeps better exploration capabilities than TAM and TPB individually (Choe et al., [<reflink idref="bib5" id="ref45">5</reflink>]; Wang et al., [<reflink idref="bib42" id="ref46">42</reflink>], [<reflink idref="bib43" id="ref47">43</reflink>]). Gómez-Ramirez et al. ([<reflink idref="bib11" id="ref48">11</reflink>]) explored the factors that influence students' adoption of mobile learning through the incorporated model of TAM and TPB. Addressing the MOOCs, Yang and Su ([<reflink idref="bib44" id="ref49">44</reflink>]) proposed the incorporated model of TAM and TPB to explain how learners respond to MOOCs with a new teaching method when practice-oriented courses are online. Wang et al. ([<reflink idref="bib41" id="ref50">41</reflink>]) considered the low completion rate of MOOCs in China and incorporated TAM and TPB as a theoretical model to explore the determinants behind the MOOC performance. Wang et al. ([<reflink idref="bib42" id="ref51">42</reflink>], [<reflink idref="bib43" id="ref52">43</reflink>]) mentioned that the incorporated model of TAM and TPB is effective in assessing learners' BI and engagement in MOOCs.</p> <hd id="AN0179295307-7">Hypothesized Relationship</hd> <p>Figure 1 summarizes the research model including seven constructs that explain the causal relationships among PU, PEOU, ATT, SN, PBC, and BI as well as the moderating effect of culture. Each of the hypotheses is detailed below.</p> <p>Graph: Fig. 1 The proposed research model</p> <p>PU is one of the crucial variables of TAM and is posited to predict ATT. Teo and Dai ([<reflink idref="bib39" id="ref53">39</reflink>]) stated PU has a significant effect on individual's ATT toward using personal protective equipment. Hence, in this research, the hypotheses is proposed as following:</p> <hd id="AN0179295307-8">H1</hd> <p>A learner's perceived usefulness has a positive impact on the attitude toward using MOOCs.</p> <p>PEOU is another crucial variable of TAM for predicting ATT. Yang and Su ([<reflink idref="bib44" id="ref54">44</reflink>]) found PEOU has a significant and positive effect on student's ATT toward computer use. Hence, in this research, the hypotheses is proposed as following:</p> <hd id="AN0179295307-9">H2</hd> <p>A learner's perceived ease of use has a positive impact on the attitude toward using MOOCs.</p> <p>ATT, SN, and PBC are three crucial variables of TPB and are posited to together predict BI. MOON ([<reflink idref="bib28" id="ref55">28</reflink>]) confirmed ATT has a significant impact on intention to visit green restaurants. Hence, in this research, the hypotheses is proposed as following:</p> <hd id="AN0179295307-10">H3</hd> <p>A learner's attitude toward using MOOCs has a positive impact on the behavioral intention.</p> <p>Wang et al. ([<reflink idref="bib42" id="ref56">42</reflink>], [<reflink idref="bib43" id="ref57">43</reflink>]) found SN keeps a positive impact on BI and Luang-Guang ([<reflink idref="bib26" id="ref58">26</reflink>]) verified the significant relationship between SN and BI to adopt MOOCs for students. Hence, in this research, the hypotheses is proposed as following:</p> <hd id="AN0179295307-11">H4</hd> <p>A learner's subjective norms has a positive impact on the behavioral intention.</p> <p>Choe et al. ([<reflink idref="bib5" id="ref59">5</reflink>]) in his paper revealed the significance of PBC in human behavior. Wang et al. ([<reflink idref="bib42" id="ref60">42</reflink>], [<reflink idref="bib43" id="ref61">43</reflink>]) verified that PBC impacts teachers to work with MOOCs. Hence, in this research, the hypotheses is proposed as following:</p> <hd id="AN0179295307-12">H5</hd> <p>A learner's perceived behavioral control has a positive impact on the behavioral intention.</p> <p>Researchers have applied Hofstede's cultural dimensions in a variety of empirical studies associated with national culture values (Tarhini et al., [<reflink idref="bib38" id="ref62">38</reflink>]). The authors hypothesize that national culture could affect human behavior as culture is defined as the collective programming of the mind which distinguishes a group of people from others (Hofstede, [<reflink idref="bib17" id="ref63">17</reflink>]). Tarhini et al. ([<reflink idref="bib38" id="ref64">38</reflink>]) studied the differences in intention to use educational Really Simple Syndication between Lebanese and British students based on the Hofstede's cultural dimensions and hypothesize culture moderates the student's behavior. As such, in this study, further to the five hypotheses proposed above, it follows that there may be potential differences between students in different culture context.</p> <hd id="AN0179295307-13">H6</hd> <p>Culture moderates the relationships among learner's perceived usefulness, perceived ease of use, attitude, subjective norms, perceived behavioral control and behavioral intention.</p> <hd id="AN0179295307-14">Methodology</hd> <p></p> <hd id="AN0179295307-15">Measurement Instruments</hd> <p>This study considers an approach of a questionnaire survey to collect the requested data, and all the items for measuring and explaining the constructs of PU, PEOU, ATT, SN, PBC, and BI are based on extant literature and suggestions of experts on how students perceive MOOCs (See Online Appendix A). Hence, nineteen items are proposed: four items for ATT, three items for PU, three items for PEOU, three items for SN, and three items for PBC and three items for BI accordingly.</p> <hd id="AN0179295307-16">Data Collection and Demographic Profile</hd> <p>The questionnaires were distributed in Spain and China, with the specific objective only for undergraduates who have experience in MOOCs. Regarding the Spanish students, the questionnaires were distributed in Universitat Autònoma de Barcelona, Universitat de Barcelona, and Universitat Politècnica de Catalunya BarcelonaTech in Barcelona following convenience sampling (Taherdoost, [<reflink idref="bib37" id="ref65">37</reflink>]). A total of 300 questionnaires were distributed, and 260 questionnaires were retrieved. After excluding the incomplete questionnaires, 245 out of 300 qualified questionnaires were obtained. Regarding the Chinese side, the questionnaires were distributed to the students who have experience in MOOCs through WJX, an online questionnaire platform, to the undergraduates in Fudan University, Zhejiang University, China University of Petroleum, and Capital Normal University. A plan of 1000 students is expected to participate in the online questionnaire survey. Finally, 525 responses were retrieved after excluding the incomplete questionnaires, and a totally of 525 out of 1000 qualified questionnaires were obtained, which presents a 52.5% rate of non-refusal and satisfy and overcome the range of 15–20% stated by Menon et al. ([<reflink idref="bib27" id="ref66">27</reflink>]).</p> <p>According to Table S1 (see Appendix B), a total of 770 students have answered the questionnaire. 245 are from Spain, and 525 are from China. Regarding gender, 322 are males, and 448 are females. Addressing the Spanish side, 89 are males, and 156 are females. Addressing the Chinese side, 233 are males, 292 are females. Table S1 also presents the average age is 20.54 ranging from 17 to 38. Table S2 (see Appendix B) shows that 76.23% of the students do not have MOOC certificates, and 23.77% of students have certificates at least one. Table S3 (see Appendix B) presents that students from Arts and Humanity are 323 and students from Health Science are 76, students from Science are 89, students from Social Science and Law are 205, and students from Technology science are 77. Table S4 (see Appendix B) presents that the first-year students are 248, the second-year students are 174, the third-year students are 192, and the fourth year students are 156.</p> <hd id="AN0179295307-17">Data Analysis</hd> <p>SmartPLS (version 3) is utilized to analyze the data as PLS-SEM is effective in evaluating exploratory theories and a normal distribution of data is not necessary and even can handled well with small sample sizes (Henseler et al., [<reflink idref="bib15" id="ref67">15</reflink>]). Therefore, confirmatory factor analysis (CFA) and PLS-SEM are utilized to analyze the convergent and discriminant validity of the measurement model. Bootstrapping approach is used to evaluate all the proposed hypotheses. Additionally, multi-group analysis (MGA) is utilized to detect the moderating effect of culture.</p> <hd id="AN0179295307-18">Results</hd> <p></p> <hd id="AN0179295307-19">The Measurement Model: Reliability and Validity Assessment</hd> <p>Addressing the common method bias, Harman's single factor test was run through SPSS (version 26), and the eigenvalue of the first factor in this study presents 46.619% of the variance (Table 2), and the value is smaller than 50%, which shows there is no common method bias in this study and the analysis is free of measurement error (Hair et al., [<reflink idref="bib14" id="ref68">14</reflink>]).</p> <p>Table 2 Common method bias test</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Total</p></th><th align="left"><p>% of variance</p></th><th align="left"><p>Cumulative %</p></th></tr></thead><tbody><tr><td align="left"><p>13.053</p></td><td char="." align="char"><p>46.619</p></td><td char="." align="char"><p>46.619</p></td></tr></tbody></table> </ephtml> </p> <p>The convergent validity of the measurement model is verified through three aspects of the factor loading that should be significant and higher than 0.5 as the lowest threshold, the values of CR that should be higher 0.6 (Fornell & Larcker, [<reflink idref="bib9" id="ref69">9</reflink>]) and the values of AVE should be higher than 0.5 (Chin, [<reflink idref="bib4" id="ref70">4</reflink>]). Additionally, Cronbach's alpha is considered an indicator for measuring the reliability of the internal consistency of a scale and the acceptable threshold should be higher than 0.6 (Nunnally & Bernstein, [<reflink idref="bib29" id="ref71">29</reflink>]). According to Table 3, most of the factor loading of items is higher than 0.8 except ATT1 (0.798) and PU3 (0.716), and the highest value is 0.902, which indicates the model is reliable when all items' factor loading is higher than 0.6, and the AVE is more significant than 0.6, and the highest value is 0.787 indicating the measurement model has an excellent convergent effect. All values of Cronbach's alpha are higher than 0.7, and CR of the constructs are higher than 0.917, which indicates the internal consistency among the constructs is desirable.</p> <p>Table 3 Factor loading, Cronbach's alpha, CR, AVE of constructs</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Construct</p></th><th align="left"><p>Item</p></th><th align="left"><p>Factor loading</p></th><th align="left"><p>Cronbach's alpha</p></th><th align="left"><p>CR</p></th><th align="left"><p>AVE</p></th></tr></thead><tbody><tr><td align="left"><p>ATT</p></td><td align="left"><p>ATT 1</p></td><td char="." align="char"><p>0.798</p></td><td char="." align="char"><p>0.852</p></td><td char="." align="char"><p>0.900</p></td><td char="." align="char"><p>0.693</p></td></tr><tr><td align="left" /><td align="left"><p>ATT 2</p></td><td char="." align="char"><p>0.860</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>ATT 3</p></td><td char="." align="char"><p>0.838</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>ATT 4</p></td><td char="." align="char"><p>0.834</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>BI</p></td><td align="left"><p>BI 1</p></td><td char="." align="char"><p>0.872</p></td><td char="." align="char"><p>0.865</p></td><td char="." align="char"><p>0.917</p></td><td char="." align="char"><p>0.787</p></td></tr><tr><td align="left" /><td align="left"><p>BI 2</p></td><td char="." align="char"><p>0.902</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>BI 3</p></td><td char="." align="char"><p>0.877</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PBC</p></td><td align="left"><p>PBC 1</p></td><td char="." align="char"><p>0.816</p></td><td char="." align="char"><p>0.757</p></td><td char="." align="char"><p>0.861</p></td><td char="." align="char"><p>0.673</p></td></tr><tr><td align="left" /><td align="left"><p>PBC 2</p></td><td char="." align="char"><p>0.807</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>PBC 3</p></td><td char="." align="char"><p>0.837</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PEOU</p></td><td align="left"><p>PEOU 1</p></td><td char="." align="char"><p>0.885</p></td><td char="." align="char"><p>0.846</p></td><td char="." align="char"><p>0.907</p></td><td char="." align="char"><p>0.764</p></td></tr><tr><td align="left" /><td align="left"><p>PEOU 2</p></td><td char="." align="char"><p>0.882</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>PEOU 3</p></td><td char="." align="char"><p>0.856</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PU</p></td><td align="left"><p>PU 1</p></td><td char="." align="char"><p>0.845</p></td><td char="." align="char"><p>0.735</p></td><td char="." align="char"><p>0.850</p></td><td char="." align="char"><p>0.656</p></td></tr><tr><td align="left" /><td align="left"><p>PU 2</p></td><td char="." align="char"><p>0.861</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>PU 3</p></td><td char="." align="char"><p>0.716</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>SN</p></td><td align="left"><p>SN 1</p></td><td char="." align="char"><p>0.858</p></td><td char="." align="char"><p>0.820</p></td><td char="." align="char"><p>0.892</p></td><td char="." align="char"><p>0.735</p></td></tr><tr><td align="left" /><td align="left"><p>SN 2</p></td><td char="." align="char"><p>0.871</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left" /><td align="left"><p>SN 3</p></td><td char="." align="char"><p>0.842</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p>Adopting the correlation coefficient between the square root of AVE and all possible constructs to compare, the value of the square root of AVE must be stronger than the value of all possible constructs to show that there is discriminant validity in the measurement. According to Table 4, all the values of the square of roots of AVE are more robust than the values of all the potential constructs, indicating an excellent discriminant validity of the measurement.</p> <p>Table 4 Simple correlation matrix and discriminant validity</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Construct</p></th><th align="left"><p>ATT</p></th><th align="left"><p>BI</p></th><th align="left"><p>PBC</p></th><th align="left"><p>PEOU</p></th><th align="left"><p>PU</p></th><th align="left"><p>SN</p></th></tr></thead><tbody><tr><td align="left"><p>ATT</p></td><td char="." align="char"><p>0.833</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>BI</p></td><td char="." align="char"><p>0.578</p></td><td char="." align="char"><p>0.887</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PBC</p></td><td char="." align="char"><p>0.643</p></td><td char="." align="char"><p>0.665</p></td><td char="." align="char"><p>0.820</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PEOU</p></td><td char="." align="char"><p>0.690</p></td><td char="." align="char"><p>0.605</p></td><td char="." align="char"><p>0.707</p></td><td char="." align="char"><p>0.874</p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>PU</p></td><td char="." align="char"><p>0.787</p></td><td char="." align="char"><p>0.553</p></td><td char="." align="char"><p>0.624</p></td><td char="." align="char"><p>0.706</p></td><td char="." align="char"><p>0.810</p></td><td char="." align="char" /></tr><tr><td align="left"><p>SN</p></td><td char="." align="char"><p>0.514</p></td><td char="." align="char"><p>0.476</p></td><td char="." align="char"><p>0.481</p></td><td char="." align="char"><p>0.472</p></td><td char="." align="char"><p>0.522</p></td><td char="." align="char"><p>0.857</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0179295307-20">The Structural Model</hd> <p>The predictive ability of the structural model can be assessed through <emph>R</emph><sups>2</sups> value of each endogenous construct that should be higher than 0.1 (Falk & Miller, [<reflink idref="bib8" id="ref72">8</reflink>]) and the Stone-Geisser test of predictive relevance (<emph>Q</emph><sups>2</sups>), whose values are divided into 0.02, 0.15, and 0.35 as small, medium, and large effects, respectively (Geisser, [<reflink idref="bib10" id="ref73">10</reflink>]). According to Fig. 2, the values of <emph>R</emph><sups>2</sups> (0.655 and 0.496) and <emph>Q</emph><sups>2</sups> (0.45 and 0.387) indicate that the structural model has a great predictive ability.</p> <p>Graph: Fig. 2 Structural model PLS results</p> <p>Table 5 shows the five research hypotheses are positively supported. The structural model indicates the coefficients of PU to ATT, PEOU to ATT, ATT to BI, SN to BI, and PBC to BI are 0.597, 0.269, 0.204, 0.149, and 0.463, respectively, which are all significant.</p> <p>Table 5 The <emph>t</emph>-value of research hypotheses and path coefficients</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>No</p></th><th align="left"><p>Research hypotheses</p></th><th align="left"><p><italic>t</italic>-value</p></th><th align="left"><p>Path coefficients</p></th><th align="left"><p><italic>p</italic>-value</p></th><th align="left"><p>Validated result</p></th></tr></thead><tbody><tr><td align="left"><p>H1</p></td><td align="left"><p>PU → ATT</p></td><td char="." align="char"><p>14.760</p></td><td char="." align="char"><p>0.597</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H2</p></td><td align="left"><p>PEOU → ATT</p></td><td char="." align="char"><p>6.780</p></td><td char="." align="char"><p>0.269</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H3</p></td><td align="left"><p>ATT → BI</p></td><td char="." align="char"><p>4.961</p></td><td char="." align="char"><p>0.204</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H4</p></td><td align="left"><p>SN → BI</p></td><td char="." align="char"><p>3.953</p></td><td char="." align="char"><p>0.149</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H5</p></td><td align="left"><p>PBC → BI</p></td><td char="." align="char"><p>11.819</p></td><td char="." align="char"><p>0.463</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr></tbody></table> </ephtml> </p> <p>**<emph>p</emph> < 0.05; ***<emph>p</emph> < 0.01</p> <hd id="AN0179295307-21">The Cultural Dimension (Multi-group Analysis)</hd> <p>In order to test the moderating effect of culture in the model, the 770 students participated in the survey divided into Spanish students (Group 1) and Chinese students (Group 2). Before proceeding to multi-group analysis, we measured the invariability of the measurement models through MICOM (Rialp-Criado & Rialp-Criado, [<reflink idref="bib13" id="ref74">13</reflink>]). Following the approach of Moon ([<reflink idref="bib28" id="ref75">28</reflink>]), the MICOM procedure comprises three steps: (<reflink idref="bib1" id="ref76">1</reflink>) configural invariance, (<reflink idref="bib2" id="ref77">2</reflink>) compositional invariance, (<reflink idref="bib3" id="ref78">3</reflink>) the equality of composite mean values and variances. Table 6 shows that the configurational and compositional are observed but the third one, so a partial measurement invariance can be established, which allows us to proceed with multi-group analysis.</p> <p>Table 6 Configural invariance and compositional invariance</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2"><p>Factor</p></th><th align="left" rowspan="2"><p>Configural invariance</p></th><th align="left" colspan="3"><p>Compositional invariance</p></th><th align="left" rowspan="2"><p>Measurement invariance</p></th></tr><tr><th align="left"><p>Original Correlation</p></th><th align="left"><p>Permutation p-values</p></th><th align="left"><p>Compositional invariance?</p></th></tr></thead><tbody><tr><td align="left"><p>ATT</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>1.000</p></td><td char="." align="char"><p>0.138</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr><tr><td align="left"><p>BI</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>1.000</p></td><td char="." align="char"><p>0.092</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr><tr><td align="left"><p>PBC</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>1.000</p></td><td char="." align="char"><p>0.912</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr><tr><td align="left"><p>PEOU</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>1.000</p></td><td char="." align="char"><p>0.274</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr><tr><td align="left"><p>PU</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>1.000</p></td><td char="." align="char"><p>0.566</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr><tr><td align="left"><p>SN</p></td><td align="left"><p>Yes</p></td><td char="." align="char"><p>0.999</p></td><td char="." align="char"><p>0.480</p></td><td align="left"><p>Yes</p></td><td align="left"><p>Partial</p></td></tr></tbody></table> </ephtml> </p> <p>Table 7 presents the results of the hypotheses further developed on the moderating effect of culture. The MGA results support H6 (the moderating effect of culture) because the coefficients testing H1, H3, and H5, being significant for both groups present significant differences among them; and the coefficient relate to H2 also but at 90% of confidence.</p> <p>Table 7 Causal hypotheses testing and multi-group comparison test results for culture</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2"><p>No</p></th><th align="left" rowspan="2"><p>Path</p></th><th align="left" colspan="3"><p>Path coefficients</p></th><th align="left" rowspan="2"><p><italic>p</italic>-value (culture)</p></th><th align="left" rowspan="2"><p>Validated result</p></th></tr><tr><th align="left"><p>Group1 (<italic>β</italic><sup>Spain</sup>)</p></th><th align="left"><p>Group2 (<italic>β</italic><sup>China</sup>)</p></th><th align="left"><p>Culture</p></th></tr></thead><tbody><tr><td align="left"><p>H1</p></td><td align="left"><p>PU → ATT</p></td><td char="." align="char"><p>0.362***</p></td><td char="." align="char"><p>0.670***</p></td><td align="left"><p>− 0.308</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H2</p></td><td align="left"><p>PEOU → ATT</p></td><td char="." align="char"><p>0.365***</p></td><td char="." align="char"><p>0.223***</p></td><td align="left"><p>0.143</p></td><td char="." align="char"><p>0.096*</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H3</p></td><td align="left"><p>ATT → BI</p></td><td char="." align="char"><p>0.322***</p></td><td char="." align="char"><p>0.110**</p></td><td align="left"><p>0.212</p></td><td char="." align="char"><p>0.014**</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H4</p></td><td align="left"><p>SN → BI</p></td><td char="." align="char"><p>0.148**</p></td><td char="." align="char"><p>0.151**</p></td><td align="left"><p>− 0.003</p></td><td char="." align="char"><p>0.971</p></td><td align="left"><p>Not supported</p></td></tr><tr><td align="left"><p>H5</p></td><td align="left"><p>PBC → BI</p></td><td char="." align="char"><p>0.223***</p></td><td char="." align="char"><p>0.594***</p></td><td align="left"><p>− 0.371</p></td><td char="." align="char"><p>0.000***</p></td><td align="left"><p>Supported</p></td></tr></tbody></table> </ephtml> </p> <p>*<emph>p</emph> < 0.1;**<emph>p</emph> < 0.05; ***<emph>p</emph> < 0.01</p> <p>Focusing on a multigroup analysis, some differences are identified in participants across the two culture. The differences observed between the two groups allow us to interpret culture's moderating effect on Spanish and Chinese students. Addressing the path coefficients of the groups of PU to ATT, the difference among the coefficient between China and Spain is 0.308 and it is significant (<emph>β</emph><sups>Spain</sups>–<emph>β</emph><sups>China</sups> = − 0.308, <emph>p</emph>-value = 0.000<bold>***</bold>) being higher than the path coefficient for China than the one for Spain, indicating PU more likely influences Chinese students' attitudes. Addressing the path coefficients of the groups of PEOU to ATT, the path coefficient of Spain is 0.143 (<emph>β</emph><sups>Spain</sups>–<emph>β</emph><sups>China</sups> = 0.143, <emph>p</emph>-value = 0.096*) higher than the path coefficient of China, presenting Spanish students' attitudes are slightly more susceptible to PEOU. Furthermore, concerning the groups of ATT to BI, the path coefficient of Spanish students is 0.212 (<emph>β</emph><sups>Spain</sups>–<emph>β</emph><sups>China</sups> = 0.212, <emph>p</emph>-value = 0.014**) higher than Chinese students, demonstrating the Spanish students' behavior intentions are more likely affected by ATT. For the groups of PBC to BI, the path coefficient of Chinese students are 0.371 (<emph>β</emph><sups>Spain</sups>–<emph>β</emph><sups>China</sups> = − 0.371, <emph>p</emph>-value = 0.000<bold>***</bold>) higher than the path coefficient of Spanish students, showing PBC has more direct influences on Chinese students. Regarding the groups of SN to BI, the path coefficients of the two groups are almost identical, indicating SN has almost the same influence on BI of Chinese students and Spanish students. In this regard, there is no difference between the two groups.</p> <hd id="AN0179295307-22">Discussion</hd> <p>This paper utilizes the incorporated model of TAM–TPB and investigated the antecedents of MOOC-related students' behavior intention in China and Spain. PU, PEOU, ATT, SN, and PBC were proposed as the predictors of BI. PU and PEOU are found statistically effective in influencing the attitude, which is in line with the studies of Unal and Uzun ([<reflink idref="bib40" id="ref79">40</reflink>]) and Alfadda and Mahdi ([<reflink idref="bib2" id="ref80">2</reflink>]), indicating the functionality of technology can impact students' intrinsic attitude and perception. Compared with ATT, SN, and PBC are also positively confirmed to influence BI toward MOOCs, which is consistent with previous studies explaining the acceptance toward MOOCs (Wang et al., [<reflink idref="bib42" id="ref81">42</reflink>], [<reflink idref="bib43" id="ref82">43</reflink>]). Besides, PBC is confirmed as a crucial factor significantly affecting the BI in keeping with the studies of Lung-Guang ([<reflink idref="bib26" id="ref83">26</reflink>]) and Wang et al. ([<reflink idref="bib42" id="ref84">42</reflink>], [<reflink idref="bib43" id="ref85">43</reflink>]). Particularly, PBC as a non-volitional part of behavior explains most of the proposition among the constructs in this study, demonstrating students are more likely to construct their BI based on their actual conditions.</p> <p>In this study, we examined potential moderating effects of culture of culture on proposed relationship. The research suggested the cultural dimensions moderate the relationships of PU to ATT, PEOU to ATT, ATT to BI, PBC to BI except SN to BI. The culture differences could be explained by Hofstede's cultural dimensions (Hofstede Insights, [<reflink idref="bib18" id="ref86">18</reflink>]) and according to Hofstede Insights ([<reflink idref="bib18" id="ref87">18</reflink>]), China has a Masculine culture, where people are driven by assertiveness, achievement, and heroism (Gonzalez-Rodriguez et al., [<reflink idref="bib12" id="ref88">12</reflink>]). These can be fulfilled by the empowerment function of MOOC involvement, as students can exert significant self-value fulfillment and confidence over peers through MOOCs based on their perceived behavior control. Besides, China is also reported with a strong Long term orientation culture, where people are affected by the Confucianism and educated as pragmaticism, thrift, and tenacity (Hofstede & Minkov, [<reflink idref="bib19" id="ref89">19</reflink>]). Therefore, Chinese students more focus on the utility of MOOCs and they will choose and accept MOOCs based on the perceived usefulness. In contrast, Spanish are reported with higher tendency for uncertainty avoidance, which indicates that Spanish culture feels more threatened by ambiguous or unknown situations and has created beliefs that try to avoid these more than Chinese culture (Gonzalez-Rodriguez et al., [<reflink idref="bib12" id="ref90">12</reflink>]). Thus, Spanish students are more likely to be affected by PEOU, which means once they perceived MOOCs as easy to perform precisely, they would form the intention to accept MOOCs. Additionally, individualism is also featured in the Spanish culture. This result may be explained by Spain's inclusion in the Latin-European cluster, which is characterized as possessing some collectivistic features within an individualistic system of values (Huang et al., [<reflink idref="bib21" id="ref91">21</reflink>]), where people are featured as low levels of interdependency and follow their own personal goals (Beugelsdijk et al., [<reflink idref="bib3" id="ref92">3</reflink>]). Therefore, this study is in line with the research and found Spanish students are more likely be driven by their own intrinsic attitude toward MOOCs. As Kilinc et al. ([<reflink idref="bib24" id="ref93">24</reflink>]) and Huang et al. ([<reflink idref="bib21" id="ref94">21</reflink>]) found, when Spanish people adopt technology related to education, they tend to not only consider educational benefits but also the risks related to the technology adoption. The results for Spanish sample suggest that the students who perceived themselves as avoiding doing tasks they were not used to or considered risky, focused more on PBC side compared to Chinese students.</p> <p>Additionally, the research showed that the relationship of SN to BI was positively supported in the Chinese and Spanish samples, but no potential difference moderated by culture was detected. The finding may be attributable to the valuing harmony among group members and pursuing conformity in behavior in both Chinese and Spanish cultural contexts and both Chinese and Spanish students perceive themselves as being more inclined to follow the suggestions of significant figures.</p> <hd id="AN0179295307-23">Contributions, Limitations, and Future Research</hd> <p>Regarding the limitations of this study, firstly, due to the limitation of data collection, this study only involves undergraduate students in Spain and China, which may impact the research results. Hence, the follow-up research can consider including students with different academic years to enrich the scale of the research groups. Second, despite different aspects that may impact the behavior of students from Spain and China toward MOOCs, culture is more predominant according to the values of Hofstede's cultural dimensions. However, this study only considers the Hofstede's values to justify the moderating effect of culture. Therefore, the future research could involve empirical culture data along with the Hofstede's cultural theory. Third, the approach for collecting data should be modified by future researchers as this study used two approaches of convenience sampling and online questionnaires to collect data which may cause a particular impact on the results.</p> <hd id="AN0179295307-24">Conclusions</hd> <p>Results provide evidence that students are motivated to enroll themselves in MOOCs by PU, PEOU, ATT, SN, and PBC. The students' decision-making process toward MOOCs has been well documented in the literature, focusing on the psychological factors. Besides, this research has confirmed the incorporated model of TAM and TPB, which is in accord with precious studies (Choe et al., [<reflink idref="bib5" id="ref95">5</reflink>]; Yang & Su, [<reflink idref="bib44" id="ref96">44</reflink>]; Wang et al., [<reflink idref="bib41" id="ref97">41</reflink>]). Moreover, this study further contributes to the MOOC-related students' behavior literature by providing empirical evidence from China and Spain. The moderating effect of culture has been confirmed between Spanish students and Chinese students, which also contributes to the cross-culture research filed.</p> <hd id="AN0179295307-25">Funding</hd> <p>No funding has been received.</p> <hd id="AN0179295307-26">Declarations</hd> <p></p> <hd id="AN0179295307-27">Conflict of interest</hd> <p>The authors have no conflict of interest.</p> <hd id="AN0179295307-28">Supplementary Information</hd> <p>Below is the link to the electronic supplementary material.</p> <p>Graph: Supplementary file1 (DOCX 25 kb)</p> <hd id="AN0179295307-29">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0179295307-30"> <title> References </title> <blist> <bibl id="bib1" idref="ref18" type="bt">1</bibl> <bibtext> Ajzen I. The theory of planned behavior. 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  Data: Comparative Study of Students' Perception and Behavioral Intention in MOOC Context: Undergraduates in China and Spain
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  Data: <searchLink fieldCode="AR" term="%22Kai+Wang%22">Kai Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-7765-8658">0000-0002-7765-8658</externalLink>)<br /><searchLink fieldCode="AR" term="%22Josep+Rialp+Criado%22">Josep Rialp Criado</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Felix+van+Hemmen%22">Stefan Felix van Hemmen</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Asia-Pacific+Education+Researcher%22"><i>Asia-Pacific Education Researcher</i></searchLink>. 2024 33(5):1129-1137.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: <searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22MOOCs%22">MOOCs</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Cross+Cultural+Studies%22">Cross Cultural Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+Influences%22">Cultural Influences</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+Differences%22">Cultural Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Usability%22">Usability</searchLink>
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  Data: This paper addresses undergraduates' perception and behavioral intention (BI) toward Massive open online courses (MOOCs). It considers the moderating effect of culture to further explore the perceptions of students with different cultures to engage in MOOCs, providing MOOC suppliers with practical suggestions. 770 undergraduates in Spain and China are involved in this study. The proposed research model incorporating the technology acceptance model and theory of planned behavior is confirmed as a desirable theoretical model. Additionally, the moderating effect of culture has been confirmed between Spanish and Chinese students. The findings show Perceived usefulness and Perceived ease of use are effective in forming attitude (ATT). Besides, ATT and subjective norm, and perceived behavioral control are confirmed as vital factors impacting BI. Regarding the moderating effect of culture, significant differences have been detected to account for their behavior.
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