Introduction
The Internet and its increasing use for educational purposes (Jaiyeoba and Iloanya, 2019; Stavroulia et al., 2019) have become a lifestyle issue in the developed and developing world. Its popularity today is credited to the increasing availability of digital mobile devices like phones and tablets that are convenient for surfing the World Wide Web and its ubiquitous character. Interestingly, across the global divide, the youth who are mostly in school acquiring knowledge and vocations depend on online technologies (Watjatrakul, 2020) to meet digital goals (e-aspirations), particularly for social, entertainment and academic benefit (Al-Qaysi et al., 2020; Spante, 2019; Al-Qaysi and Al-Emran, 2017). Emphasizing the significance of having an online presence of the university community (Mehta and Aguilera, 2020), researchers have shown how digital ethnography motivates individuals and corporate organizations to leverage daily, if not hourly, on social media to satisfy e-aspirations (Dzogbenuku et al., 2019; Shepherd and Lane, 2019). The leverage of social media for learning realized a crescendo with the advent of COVID-19, due to the compulsory or mandatory pedagogical changes needed to prevent the discontinuation of educational curriculum across the word.
Generally, social media is an online-based application driven by conceptual and high-tech foundations of Web 2.0, which promotes the development and sharing of user-generated content (UGC) frequently (Kaplan and Haenlein, 2009, 2010), thereby adding value to sites. Some researchers perceive Internet-based communication as platforms wielding opportunistic interactivity, discriminatory self-presence in real-time interaction, targeting asynchronous audiences driven by online user-generated marketing content (Lund, 2019; Carr and Hayes, 2015; Rothschild, 2011). Kaplain and Haenlein (2009) categorized social media into blogs and micro-blogs such as Twitter; social networking sites (SNSs) such as Instagram, MySpace and Facebook, including content platforms such as YouTube. Other forms are weblogs, podcasts, pictures, videos, social bookmarking and WhatsApp. From the user and gratification theory (Blumler and Katz, 1974), it is obvious that social media users, often goal-oriented, actively select social media that best fulfills their individual needs such as information, entertainment and socialization among others.
In recent times, researchers on social media (Pahlevan Sharif and Yeoh, 2018; Recalde and Gutiérrez-García, 2017; Yazdanparast et al., 2016), social media brand communities (Jones and Glynn, 2019), acceptance in higher education (Dumpit and Fernandez, 2017), social media for learning and students' satisfaction (Orús et al., 2016), determinants of students' intent and use of social media (Balakrishnan, 2017), among others seek to understand the social media phenomena among consumers such as students across countries. For example, with the advent of the corona virus (COVID-19) in the early part of 2020, the social life of people globally was disrupted, denying them from gathering for any activity including education. As a remedy to augment lost academic time, various social media were adopted compulsorily or voluntarily as pedagogical changes to promote remote teaching (Stenman and Pettersson, 2020). Consequently, while some of the new pedagogical technology users easily navigated their way into the new platforms, majority of the users faced significant problems that stalked the learning process. These further raise questions regarding the role of media in education policies (Liu et al., 2019). For users who were successful however, the success factors are worth investigating for further policy and dynamic adaptation of pedagogical technologies.
Social media (SM) is utilized by students to obtain information for research and academic purposes as well as for entertainment due to their social needs. These include student integration into the university system (Xiao and Wilkins, 2015). However, in the case of tertiary students in sub-Saharan Africa, very few studies have been done, particularly on social media entertainment and information (content) and their effect on the performance of students. Consequently, this research attempts to examine the mediating function of social media entertainment on social media information and how it affected students' performance in sub-Saharan Africa during the COVD-19. The research, therefore, makes some contributions to the existing literature in relation to students' dependence on social media for entertainment and information as well as their subsequent performance post COVD-19.
Theoretical framework
Technology-specific theoretical models recommended for studying the utilization of technology, such as social media, at the personal level, propose that perceptual beliefs, values and the expected benefits to be gained are the determinants of why an individual will use technology. These include the technology acceptance model (TAM) (Davis, 1989; Venkatesh and Bala, 2008) and the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003). The UTAUT model is engaged as the underpinning theory in this research.
The UTAUT (Venkatesh et al., 2003) propose that performance expectancy, effort expectancy, social impact and other enabling conditions will drive behavior intentions to adopt a technology. Venkatesh et al. (2012) extend the UTAUT to integrate three more concepts into the proposed UTAUT2: hedonic inspiration, price value and habit.
The major performance expectancy of social media discovered by Whiting and Williams (2013) are social communication, search for information, passing time, entertainment (hedonic value), destressing, communicative efficacy, convenience, expressing opinions, dissemination of information and surveillance/knowledge about others. This implies that content and entertainment are two significant factors in social media usage.
Social media performance
SM performance is the ability to achieve an objective based on using social media tools (Ahmad et al., 2019; Janssen and Van Yperen, 2004). Students' academic performance promotes group discussions, building a strong student-lecturer relationship, interaction with classmates and lecturers, and ultimately, improving academic performance (Al-Rahmi and Othman, 2013). In the context of social media usage, academic performance is driven by coordination, communication, entertainment, collaboration and socialization (Al-Tarawneh, 2014).
The significance of examining academic performance is based on the assertion that social media networks have now become addictive (Pahlevan-Sharif and Yeoh, 2018; Recalde and Gutiérrez-García, 2017) and can, therefore, either hinder or improve a user's (student) wellbeing and performance. Various studies (Al-Rahmi and Othman, 2013; Guo et al., 2018; Al-Tarawneh, 2014) have indicated an improvement in academic performance, which is attributable to social media. Dzogbenuku et al. (2019) further argue that students' social media usage is dependent on students' information communication technology (ICT) ability to surf the Internet and use various social media tools.
Technology performance (Ahmad et al., 2018; Paniagua and Sapena, 2014) explains social media adoption and its benefits. Also, several other authors have acknowledged the positive effect of social media adoption on business performance (Paniagua and Sapena, 2014; Rodriguez et al., 2012). Again, Rodriguez et al. (2016) have discovered that the utilization of social media positively influences customer-interactions, and hence, sales performance.
The relationship between social media use and the wellbeing of students is worth noting (Pahlevan-Sharif and Yeoh, 2018). Wellbeing defines the happiness and quality of life of a group of people over time, including the type of fulfilment derived from technologies (Falter and Hadwich, 2020; Deci and Ryan, 2008). Peoples' (students) wellbeing in the long-term affects their physical, satisfaction, social and mental (academic) performance (Marks and Shah, 2004). In the services sector of the wider society, customer wellbeing remains essential for service providers; hence, leading researchers like Ostrom et al. (2015) have coined the term “transformative service research” (TSR) to emphasize the significance of how some services have and are improving the wellbeing, leading to performance of their users globally.
Social media information (content)
SM information per Chan and Fang's (2007) study describes the types, sources and uses of contents accessed on digital media. Content usage could be for fun, work, fashion, travel, shopping, socialization, education and health, among others. With regard to content, Yazdanparast et al. (2016) also established that social media marketing activities (contents) affect attitudes towards brands and propose that marketers provide social media content and create experiences (entertainment) in line with customers' motives for social media usage. For such social media, information to be of maximum influence and benefit to its users; however, Aladwani (2017) suggests that four types of content quality must be present. These are:
Reflective quality (a belief), which describes a user's beliefs regarding significant ways that social media information meets his or her needs.
Stimulated quality (a feeling), which describes a user's feelings toward the degree of social media content's usefulness in task performance.
Practiced quality (an implied behavior), which describes exceptional social media contents that support users' needs.
Advocated quality (an obvious behavior), which describes open user support for social media content that caters to his or her needs.
These content types are major drivers of individual and group actions (Jones and Glynn, 2019) and performance (Guo et al., 2018). del Rocío Bonilla et al. (2020) acknowledged the significance of social media as sources of information and engagement among stakeholders in the university system. In that regard, academic researchers (Al-Rahmi and Othman, 2013; Mingle and Adams, 2015; Guo et al., 2018) have reported how social media have improved students' academic performance through collaborative study (Guo et al., 2018).
We, therefore, hypothesize the following:
Social media information (content) positively affects academic performance.
Social media content has a strong positive effect on academic performance.
Social media entertainment
SM usage provides escapism (Korgaonkar and Wolin, 1999). Contents for entertainment, pleasure and relief of anxiety are described as social media entertainment (Whiting and Williams, 2013). Over time, social media has gained notoriety for its entertaining content (Lee and Ma, 2012) and has been reported to elicit positive attitudes towards a product or a brand (Sheth and Kim, 2017). The frequency of social media interactions has also become a regular indispensable routine (Sheth and Kim, 2017), such that its perceived utilitarian and perceived hedonic values have been reported to affect higher education service outcomes (Cao et al., 2019).
Even though Brooks (2015) reports a negative correlation between social media usage and performance, other studies such as Sledgianowski and Kulviwat (2009) and Dzogbenuku et al. (2019) reported a strong effect of social media entertainment content on performance and fulfilment. As a perceived hedonic benefit of social media (Venkatesh et al., 2012), entertainment has been predicted in the UTAUT to lead to usage. This presupposes that social media entertainment, as suggested by Yazdanparast et al. (2016), can influence performance (Mingle and Adams, 2015; Guo et al., 2018). Hence, we also contend that if social media information can influence entertainment (Yazdanparast et al., 2016), while entertainment leads to performance (Guo et al., 2018), then social media entertainment could mediate information (content) and performance.
We, therefore, hypothesize that:
Social media entertainment positively affects academic performance.
Social media entertainment mediates social media information and academic performance.
Methodology
Context, sampling and collection of data
Survey data were gathered from students of two tertiary institutions in Ghana. With the aid of random sampling techniques, students were engaged between March and April 2020 to share their opinions on the subject. This target group was considered appropriate to investigate the subject based on the fact that students spend long hours on the Internet (Gikas and Grant, 2013; Fujita et al., 2018). The institutions selected were the largest public and private universities in Ghana respectively. They are both located in Accra with over 46,000 student population. In all, 373 responses out of 400 were obtained and found to be useable for analysis. This represents 93% of response rate. The partial least square–structural equation modeling (PLS-SEM) and SmartPLS Release: 3.2.7 (Ringle et al., 2015) were used to analyze data. Sample size, as well as a researcher's biases, does not affect the application of this software (Hair et al., 2011).
Measurement
The research instrument comprised two main aspects. The first section which touched on the major constructs of the study was framed on a five-point Likert scale starting from 5 (strongly agree) to 1 (strongly disagree). Each of these constructs was adopted and linked to study social media information (Chan and Fang, 2007); entertainment (Dzogbenuku and Kumi, 2018); and students' performance (Ahmad et al., 2019; Lee et al., 2002) as shown in Table 1. The second section of the instrument contains respondents' demographic information, including favorite social media platform, ICT competence and how much time they spend on social media, as indicated in Table 2.
Results of analysis
Following the high response rate (93%) attained in the study, non-response bias was not tested. Common method variance bias was not present in the data because a variance explained of 33.943% (lower than 50%) was attained using the exploratory factor analysis with the extraction of solely a single factor (Podsakoff et al., 2003). Also, normality analysis indicated 0.176 < α < 0.347; p < 0.01 for all items for Kolmogorov–Smirnov test and 0.713 < W < 0.912; p < 0.01 for all items for Shapiro–Wilk test, indicating that the data was highly different from a usual distribution (Hair et al., 2013, p. 71).
All three concepts of the study that is social media information, entertainment and performance were evaluated reflectively. It was exigent to delete some items as they highly cross-loaded into other concepts with the application of the PLS software. Following purification, the study assessed the model's quality criteria: Cronbach's alpha, composite reliability and average variance extracted values all satisfy the minimum values of 0.7, 0.7 and 0.5, respectively as suggested by Hair et al. (2016). The results are given in Table 3. Also, all remaining item loadings were statistically significant utilizing bootstrap t-values (5,000 sub-samples) (Tortosa et al., 2009). The outcomes show that convergent validity has been effectively attained.
The study utilized both the Fornell and Lacker (1981) and heterotrait-monotrait ratio (HTMT 0.85) (Henseler et al., 2015) for measuring discriminant validity in variance-based SEM. The inter-construct correlations among the three constructs were smaller than the square root of the regular variance extracted values for every factor (Fornell and Lacker, 1981) as presented in Table 4. Again, the HTMT ratio of relationships utilizing a specificity principle of 0.85 (HTMT 0.85) demonstrated that all relations were lower than 0.85 (Henseler et al., 2015) as depicted in Table 4. Therefore, the three-construct model indicates discriminant validity.
Examining the predictive accuracy (R2) of the structural model, it revealed that social media information explicated about 47% of the discrepancy in social media entertainment. In comparison, both social media information and entertainment explicated about 45% of the discrepancy in social media performance, both of which showed a considerable explanatory power (Cohen, 1988). Q2 – values of 0.308 and 0.249 were attained for social media entertainment and performance, both of which were more than 0 demonstrating predictive relevance (Chin, 2010). Lastly, the effect sizes (f2) calculated for the independent variables revealed that social media information had a significant effect size on SM entertainment but a small effect size on academic performance, whereas SM entertainment had a medium effect size on social media performance. The outcomes of predictive accuracy (R2), predictive relevance (Q2) test and effect sizes (f2) are given in Table 5.
Hypothesis testing
The outcomes of the structural model are shown in Figure 1. All paths are statistically significant; therefore, the first three hypotheses of the study are established in the current context. Particularly, a positive and noteworthy association exists between social media information, entertainment and academic performance. Table 6 summarizes the conclusions drawn from the hypotheses.
Mediation effect of social media entertainment
With regard to testing for mediation in PLS-SEM, Nitzl et al. (2016) suggest checking the significance of the indirect impact of the exogenous variable (SM information) on the endogenous variable (SM performance) through social media entertainment. If the indirect effect is significant, there is mediation; otherwise, there is not. From Table 7, social media entertainment partially mediates the connection between social media information and social media performance. The high proportion of mediation (74.9%) suggests that social media entertainment is almost indispensable when building social media content to achieve optimum performance. Consequently, the fourth (H4) hypothesis of this study is affirmed.
Discussion of findings
This paper sought to ascertain the effect of social media content on students' academic performance and wellbeing (operationalized as social media performance) and entertainment, as well as how social media entertainment mediates information (content) and students' performance in sub-Saharan Africa.
The data showed that social media content leads to social media information (content), which then leads to social media performance at a significance level of 0.05. These findings lend support to previous studies (Al-Rahmi and Othman, 2013; Guo et al., 2018; Al-Tarawneh, 2014) that reported that social media leads to academic performance. This also confirms the user gratification theory (Blumler and Katz, 1974) of using an artifact which enables the user to achieve his objectives. Theoretically, the perceived usefulness construct in the TAM (Venkatesh and Bala, 2008) and IMTA (Venkatesh et al., 2002), as well as performance expectancy construct in UTAUT model (Venkatesh et al., 2003) are confirmed in this study. The implication of these findings is that social media content must be relevant to each target group; for instance, in order to engender performance.
The data also revealed that information on social media leads to entertainment at a significance level of 0.01. This finding supports the report of Whiting and Williams (2013) that users choose content that is entertaining to them. The finding also lends credence to Yazdanparast et al.'s (2016) proposal for organizations to ensure that they provide content that is entertaining. Theoretically, this finding likewise confirmed the adjustment factors in TAM3 (Venkatesh and Bala, 2008), intrinsic motivation in IMTA (Venkatesh et al., 2002) and hedonic motivation in UTAUT model (Venkatesh et al., 2003). This implies that social media content must be entertaining to each target group. This will also call for some strategic segmentation. We further discovered that social media entertainment leads to academic performance at a significance level of 0.01. This finding supports some previous studies (Mingle and Adams, 2015; Guo et al., 2018) and confirms hedonic motivation in UTAUT (Venkatesh et al., 2003) as relevant in the utilization of technology.
Finally, it has been discovered that social media entertainment mediates between content and performance. The high proportion of mediation (74.9%) suggests that social media entertainment is almost indispensable when building social media content to achieve optimum performance. This is because the direct link between social media content and performance is not as strong as the indirect link, which is routed through social media entertainment.
Theoretical contribution
The use of social media entertainment as a mediator between social media content and academic performance of students is quite unique and interesting since there are few such studies in Ghana and Africa. This contributes to the growing literature on social media studies and youth in Africa. The study confirms performance expectancy and the hedonic motivation of the UTAUT model, in understanding students' learning and behavior process. The study also extends the UTAUT model by linking the antecedents of adoption to performance (post-adoption effect). This is an extension to the UTAUT model. Furthermore, the use of SEM to investigate the connection between social media information, entertainment and performance of students within the Ghanaian context is quite unique.
Managerial implications
The research reveals that students actively utilize social media and, therefore, education stakeholders should use it effectively to achieve educational objectives. The findings of this COVID-19 experience suggest that faculty can effectively modify their pedagogical activities to integrate some entertaining social media content into their teaching materials since these have an influence on students' performance. In the medium to long term, therefore, educational institutions should explore interactive technologies for teaching and learning purposes. This must have some entertainment content which can enhance a longer attention span of the students who interact with the media. Ultimately, this will improve their overall wellbeing as students and more importantly their academic performance. The dilemma that might arise in the attempt to include content with entertainment value is what content will be appropriate to compliment the educational content?
Policymakers (both government and institutions) should also begin to craft policy guidelines on social media usage as a pedagogical medium for the different levels in the educational sector. We anticipate that the COVID-19 experiences will shift examinations also to online platforms. This also calls for extensive logistical support. Some of such support includes content policy framework, hardware as well as software support for educational institutions. The creation of a “content bank” is also another practical step to enhance the drive towards online teaching and learning. Some of the anticipated challenges in this new pedagogical approach would be the cost of data usage, logistics and connections. To hold students accountable, who pays for student data usage as well as faculty data usage? How will the institution ensure that all students have equal access to smart phones and reliable connection in developing country contexts in the digitally disadvantaged circumstances?
Limitations and recommendations for future study
Data for the study were collected from university students in Ghana only. It will be good to include secondary school level students to understand the social media phenomenon better since they would eventually move to tertiary institutions. The use of cross-sectional data alone for this study does not give us the opportunity to observe the social media activities of respondents over a longer period. Future studies should, therefore, include longitudinal data. It will also be interesting to investigate in future the moderating role of gender and kinds of social media platforms on students' performance.
Conclusion
This study concludes that social media has a great impact on student performance. Therefore, all educational stakeholders like university authorities, parents and guardians, university teachers, administrators and ministry of education as well as policymakers would need to examine how best they can utilize social media to enhance the achievement of educational goals.
Figure 1
Structural path results presenting the link between SM information, entertainment and academic performance
[Figure omitted. See PDF]
Scales of variables in the model
| Concept | Items | Source |
|---|---|---|
| Social media (SM) information | 1.1 Dependence on SM for my travel information | Chan and Fang (2007) |
| 1.2 Provision of fashion information | ||
| 1.3 Provision of sports information | ||
| 1.4 Dependence on SM for weather outlook | ||
| 1.5 Dependence on SM for information on health | ||
| 1.6 Information on SM helps me to shop efficiently | ||
| Social media (SM) entertainment | 3.1 I normally listen to music on SM | Dzogbenuku and Kumi (2018) |
| 3.2 I have so much fun on SM | ||
| 3.3 I usually watch movies or sports on SM | ||
| 3.4 Leisure impacts Internet use | ||
| Social media (SM) performance | 5.1 SM enables me to achieve certain academic tasks faster | Ahmad et al. (2019), and Lee et al. (2002) |
| 5.2 SM enhances my productivity as a student | ||
| 5.3 SM helps me to gain more knowledge about my subject area | ||
| 5.4 Helps in promoting or sharing my ideas with classmates and friends | ||
| 5.5 It improves my image among my classmates and networks | ||
| 5.6 It offers fresh opportunities |
Demographic data of respondents
| Variable | Frequency | Percent |
|---|---|---|
| Sex | ||
| Male | 190 | 50.9 |
| Female | 183 | 49.1 |
| Age | ||
| 17–20 | 42 | 11.3 |
| 21–24 | 211 | 56.6 |
| 24–27 | 24 | 6.4 |
| 28–31 | 17 | 4.6 |
| 32–35 | 48 | 12.9 |
| 36–35 | 20 | 5.4 |
| 36–39 | 3 | 0.8 |
| 40 and above | 8 | 2.1 |
| Program | ||
| The sciences | 218 | 58.4 |
| Business school | 114 | 30.6 |
| Communication studies | 28 | 7.5 |
| Social science | 13 | 3.5 |
| Educational background | ||
| Undergraduate | 298 | 79.9 |
| Postgraduate | 75 | 20.1 |
| ICT knowledge/skill level | ||
| Fair | 152 | 40.8 |
| Above average | 41 | 11 |
| Good | 61 | 16.4 |
| Very good | 28 | 7.5 |
| Excellent | 91 | 24.4 |
| Social media platforms/networks used | ||
| 53 | 14.2 | |
| 40 | 10.7 | |
| YouTube | 28 | 7.5 |
| Blog | 16 | 4.3 |
| 236 | 63.3 | |
| Average time spent on social media per day | ||
| Seldom | 29 | 7.8 |
| Less than 60 min | 47 | 12.6 |
| 60–120 min | 30 | 8 |
| 120–180 min | 106 | 28.4 |
| More than 180 min | 161 | 43.2 |
| Average daily frequency of SM use | ||
| Rarely | 37 | 9.9 |
| 1–3 times each day | 61 | 16.4 |
| 4–6 times each day | 21 | 5.6 |
| 7–9 times each day | 222 | 59.5 |
| More than 10 times a day | 32 | 8.6 |
| Devices frequently used to access SM networks | ||
| Desktop computers | 20 | 5.4 |
| Laptops | 223 | 59.8 |
| Tablets | 27 | 7.2 |
| Mobile phones | 103 | 27.6 |
| Total | 373 | 100 |
Reliability and convergent validity
| Construct | Codes | Loading | Bootstrap t-values | α | CR | AVE |
|---|---|---|---|---|---|---|
| Social media information | INFO1 | 0.871 | 49.439 | 0.767 | 0.868 | 0.692 |
| INFO2 | 0.950 | 145.335 | ||||
| INFO3 | 0.644 | 13.971 | ||||
| Social media entertainment | ENT1 | 0.909 | 100.698 | 0.775 | 0.872 | 0.700 |
| ENT2 | 0.944 | 129.612 | ||||
| ENT3 | 0.620 | 13.076 | ||||
| Social media performance | SMP3 | 0.692 | 19.422 | 0.768 | 0.852 | 0.593 |
| SMP4 | 0.821 | 41.112 | ||||
| SMP5 | 0.842 | 57.566 | ||||
| SMP6 | 0.714 | 22.888 |
Note(s): All bootstrap t-values are significant at 0.01 degree of significance
Discriminant validity (square root of AVEs in italic-diagonal)
| Construct | Fornell-Larcker criterion | Heterotrait-monotrait ratio (HTMT) | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 1 | 2 | 3 | |
| 1. SM information | 0.832 | |||||
| 2. SM entertainment | 0.685 | 0.837 | 0.848 | |||
| 5. SM performance | 0.523 | 0.662 | 0.770 | 0.673 | 0.845 | |
Predictive accuracy (R2), predictive relevance (Q2) and effect sizes (f2)
| Constructs | R2 | Q2 | f2(SM entertainment) | f2(SM performance) |
|---|---|---|---|---|
| 1. Social media information | – | – | 0.883(Large) | 0.02(Small) |
| 2. Social media entertainment | 0.469 | 0.308 | 0.315(Medium) | |
| 3. Academic performance | 0.447 | 0.249 | – | – |
Structural path outcomes
| Hypothesis | Structural path | Path coefficient | t-value (bootstrap) | Hypothesis results |
|---|---|---|---|---|
| H1 | SM Information → SM performance | 0.131* | 2.428 | Supported |
| H2 | SM Information → SM entertainment | 0.685** | 22.036 | Supported |
| H3 | SM Entertainment → SM performance | 0.572** | 10.860 | Supported |
Note(s): **Significant at p < 0.01; *significant at p < 0.05
Mediation of social media entertainment
| Mediation path | Path coefficient “a” | Path coefficient “b” | Path coefficient “c” | Indirect effect (a*b) | SD (ai*bi) | t | Mediation type | Proportion of mediation |
|---|---|---|---|---|---|---|---|---|
| Info > Ent > SMP | 0.685** | 0.572** | 0.131* | 0.391** | 0.041 | 9.526 | Partial | 0.749 |
Note(s): **significant at p < 0.01; *significant at p < 0.05; Info (SM Information), Ent (SM Entertainment), SMP (Social Media Performance)
© Robert Kwame Dzogbenuku, Joshua Kofi Doe and George Kofi Amoako. This work is published under http://creativecommons.org/licences/by/4.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.