انگیزه دانشگاهی دانشجویان، تعامل رسانه ها و هراس از دست رفته
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|30115||2015||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Human Behavior, Volume 49, August 2015, Pages 111–119
The concerns about the consequences of mental problems related to use of social media among university students have recently raised consciousness about a relatively new phenomenon termed Fear of Missing Out (FoMO). Drawing on the self-determination theory and on the assumption that low levels of basic need satisfaction may relate to FoMO and social media engagement, the aim of the present research was to examine for the first time possible links between FoMO, social media engagement, and three motivational constructs: Intrinsic, extrinsic and amotivation for learning. Data were gathered from 296 undergraduate students by using the following scales: Social Media Engagement (SME), Fear of Missing Out (FoMOs) and Academic Motivation. The SME is a new scale, specifically designed for this study to measure the extent to which students used social media in the classroom. This scale includes three categories: Social engagement, news information engagement and commercial information engagement. Path analysis results indicated that the positive links between social media engagement and two motivational factors: Extrinsic and amotivation for learning are more likely to be mediated by FoMO. Interpretation of these results, their congruence within the context of the theoretical frameworks and practical implications are discussed.
Students attending colleges today, known as the ‘Millennials’ (Jonas-Dwyer & Pospisil, 2004), are heavy users of social media tools relative to the general population, and use them extensively for communication with peers, including other students in their courses (Ophus and Abbitt, 2009 and Subrahmanyam et al., 2008). These technologies might play a key role in keeping college students connected to family and friends to obtain social support (Gemmill & Peterson, 2006). However, extensive social media use could also negatively affect psychological outcomes, such as well-being (Alabi, 2013 and Alavi et al., 2011). These concerns about the consequences of mental problems related to use of social media among university students have recently raised consciousness about a relatively new phenomenon termed Fear of Missing Out, popularly referred to as FoMO. This phenomenon has been defined as a “pervasive apprehension that others might be having rewarding experiences from which one is absent, FoMO is characterized by the desire to stay continually connected with what others are doing” (Przybylski, Murayama, DeHaan, & Gladwell, 2013, p. 1841). Drawing on the self-determination theory (SDT; Deci and Ryan, 1985 and Deci and Ryan, 2008), Przybylski et al. (2013) suggest that FoMO could serve as a mediator linking deficits in psychological needs to social media engagement. Their study showed that FoMO plays an essential role in the explanation of social media engagement over and above several individual factors, such as levels of need satisfaction. Based on this motivation-based perspective, the current study aims to further explore FoMO and its set of connections to Millennials’ social media engagements in higher education settings. Motivation is considered to be a significant psychological construct in the learning process, and highly connected to academic achievement and persistence in college (Donche et al., 2014, Linnenbrink and Pintrich, 2002 and Ratelle et al., 2007), therefore seems as a useful perspective for framing an empirically based understanding of FoMO. The current study aims to assess this psychological construct’s connections to college students’ social media engagement during lessons, mediated by FoMO, hence enables to delve further into the newly defined phenomenon of FoMO by investigating its correlates with learning motivations. The current work represents a twofold effort. First, from a methodological point of view and with the dearth of empirically-based assessment instruments, a new scale, designed to measure features of social media activities in higher education settings, will be constructed and validated. Moreover, Przybylski et al.’s (2013) single-factor scale will be adapted to include different facets of FoMO, corresponding to the different social media utilities suggested by theory. Thus, in contrast to previous work, the current study could point to specific elements of FoMO and social media engagements, which may be connected to learning motivations. These efforts might allow for the examination of more components or dimensions based on theoretical considerations than have been assessed thus far. Second, with relation to college students’ learning processes, this study could illustrate the role of motivational constructs in explaining FoMO and social media engagement, when the latter is not harnessed for pedagogical purposes. This potentially new avenue of research might encourage a future discussion related to Millennials’ engagement in current higher education learning environments, and to the investigation of new instructional approaches incorporating social media usages into current pedagogical applications.
نتیجه گیری انگلیسی
4. Findings Structural equation modeling (SEM) was employed to empirically test the current research hypotheses and to further assess the construct validity of the SME and FoMO scales, using a confirmatory factor analysis. Data used for the SEM were analyzed with the maximum likelihood method. Three fit indices were computed in order to evaluate model fit (the parenthetical values by the fit indices indicate the suggested cut-offs for good quality of fit): χ2(df) (p > .05), CFI (>0.9), and RMSEA (<0.08) ( Bentler, 2006). 4.1. The first hypothesis (H1) A structural model (Fig. 2) was constructed to measure the connections between the motivational and SME constructs. The model included the SME latent factor with its three latent sub-factors: Social engagement (M1), news information engagement (M2), and commercial information engagement (M3); and the motivational latent factors of extrinsic motivation (EX), intrinsic motivation (IN), and amotivation (AM). Observed items were entered in accordance with the aforementioned measurement descriptions. The goodness-of-fit of the data to the model yielded sufficient fit results (χ2 = 478.10, df = 183, p = .000; CFI = .910; RMSEA = .071). Results indicated positive, low to moderate, significant coefficients between SME and the following constructs: Amotivation (β = .46, p < .001), and extrinsic motivation (β = .30, p < .001). An insignificant path coefficient was found between the intrinsic motivation and SME constructs. The model’s capacity to explain the variation in each dependent variable was measured by the squared multiple correlation (SMC) values, for each structural equation (path) in the model. This coefficient is a measure of how well a given variable can be predicted using a linear function of a set of other variables. According to the results, the motivational factors explained 30% of the SME factor variance. 4.2. The second hypothesis (H2) In order to test the second hypothesis, several background variables and the FoMO latent variable accompanied by three latent variables: Social FoMO (FO1), news information FoMO (FO2), and commercial information FoMO (FO3) were entered into Model 3. The path model (Fig. 3) was constructed as follows: Paths were specified between the following student characteristic variables and several latent factors: Age, gender (Male = 1, Female = 2), and Cultural group (CG: Jewish students = 1, Non-Jewish students = 2). The latter dummy variable was created due to insignificant differences found among the non-Jewish groups (Muslim, Christian, and Druze) on the dependent variables. The student characteristic variables were entered into the analysis based on the results of several linear regression analyses, in which the FoMO, SME, and the motivational factors were separately measured as dependent variables, and the following student characteristic variables were entered into the analyses as independent variables: The student’s cultural group, gender, age, year of study, FEA, MEA, EC, and GPA. Full-size image (101 K) Fig. 3. Model 3. The structural model, with standardized parameter estimates for the assessment of H2. Figure options Paths were specified between the three motivational variables and the FoMO variable; and, based on Model 2, between the following factors: Amotivation, extrinsic motivation and SME. An additional path was created between the FoMO and SME factors (χ2 = 1219.68, df = 609, p = .000; CFI = .912; RMSEA = .058). The results showed a positive high significant coefficient between the FoMO and SME factors (β = .68, p < .001), and positive (moderate) significant coefficients between the FoMO factor and the following variables: Amotivation (β = .36, p < .001), and extrinsic motivation (β = .21, p < .01). Insignificant coefficient results were indicated between the FoMO and the intrinsic motivation variables, and between the motivational and SME factors. Regarding the student characteristic factors, positive connections were found between the following factors: Age and intrinsic motivation (β = .23, p < .01); gender (females) and intrinsic motivation (β = .14, p < .05); cultural group (non-Jewish students) and the variables of FoMO (β = .49, p < .001) and amotivation (β = .21, p < .001). An insignificant connection result was found between the non-Jewish group and SME. A negative connection was indicated between gender (females) and amotivation (β = −.16, p < .01). An inverse correlation was indicated between the non-Jewish group and the age variable (β = −.38, p < .001). The amotivation factor explained 38% of the FoMO factor variance (with additional 10% of the variance explained by the cultural group variable and 1% explained by the extrinsic motivation variable) which in turn explained 67% of the SME construct variance. In order to gain further insights into how the FoMO and SME sub-factors are interrelated, an additional analysis was conducted to assess these connections. Table 3 displays the bivariate correlation analysis results between these sub-factors. Results indicated positive statistically significant correlations between all factor pairings. As can be learned from Table 3, the correlation coefficient between the social FoMO (FO1) and social engagement (M1) sub-constructs was relatively higher (r = .325, p < .01) than the results found between this FoMO sub-construct and other SME sub-factors. Similar results were indicated for the fear of missing news information (FO2) and news information engagement (M2) sub-factors (r = .364, p < .01); and between the fear of missing commercial information (FO3) and commercial information engagement (M3) sub-constructs (r = .524, p < .01). Table 3. Bivariate correlation matrix for the FoMO and SME sub-factors. FoMOs sub-factors SME sub-factors Social engagement News information engagement Commercial information engagement Social FoMO .325⁎⁎ .218⁎⁎ .277⁎⁎ News FoMO .140⁎ .364⁎⁎ .322⁎⁎ Commercial FoMO .231⁎⁎ .520⁎⁎ .524⁎⁎ ⁎ p < .05. ⁎⁎ p < .01.