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Quality & Quantity https://doi.org/10.1007/s11135-022-01580-w

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Examination of non‑cognitive variables affecting academic achievement: a conceptual model proposal

Hatice Yildiz Durak1  · Zeynep Şimşir Gökalp2 · Tolga Seki3 · Mustafa Saritepeci1 · Bülent Dilmaç3

Accepted: 2 November 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022

Abstract Psychological factors have a significant role in better understanding mechanisms that affect students’ academic performance. The intense and long-term stress of the pandemic process has made it necessary to rethink the components which effect the academic achievement of pupils. The purpose of this study is to examine the variables that predict the academic achievement of university students during the pandemic process and to present a model on these variables. The study group has 241 students who continue their undergraduate education in Turkey. The data were collected with a self-description form and 6 scales. The partial Least Squares (PLS) Structural Equation Model was used to analyses the devel- oped research model. In consequence of the study, a relationship was obtained between academic procrastination (AP) and multi-screen addiction (MSA). Covid-19 burnout has a crucial effect on AP, multiscreen addiction, and psychological well-being variables. Moti- vation and self regulation-attention variables are explanatory of AP. This study contributes to expanding the nomological network regarding the effects of Covid-19 on the psychologi- cal well-being and behavior of individuals.

Keywords Academic achievement · Multi-screen addiction · Psychological well-being · Motivation · Covid-19 burnout · Academic procrastination · Self-regulation

* Hatice Yildiz Durak hatyil05@gmail.com

Zeynep Şimşir Gökalp zey.simsir.93@gmail.com

Tolga Seki tlg.seki@gmail.com

Mustafa Saritepeci mustafasaritepeci@gmail.com

Bülent Dilmaç bulentdilmac@gmail.com

1 Eregli Faculty of Education, Department of Educational Science, Necmettin Erbakan University, Konya, Turkey

2 Faculty of Education, Department of Educational Sciences, Selçuk University, Konya, Turkey 3 Department of Educational Sciences, Necmettin Erbakan University, Konya, Turkey

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1 Introduction

Academic achievement at the university is of great importance as it is beneficial for stu- dents to obtain a bachelor’s degree and find a job (Suhlmann etal. 2018). For this reason, educational psychologists have often conducted research on the components that affect stu- dents’ academic achievement (e.g., Bücker et al. 2018; Edman and Brazil 2007; Mega et al. 2014). Although the status of intellectual abilities in the academic achievement of pupils in all professional domains is well known, psychological factors and individual differences that affect achievement are less known (Duckworth et al. 2007; Tape et al. 2021). Recent studies have indicated that the university students’ academic performance is affected by non-cognitive factors such as motivation (Kusurkar et al. 2013; Ning et al. 2010), self-reg- ulation (Ning et al., 2010), self-efficacy (Honicke and Broadbent 2016), personality traits (McCredie and Kurtz 2020), AP, positive parenting, self-esteem (Batool 2020), resilience (Kotzé and Kleynhans 2013), grit, achievement orientation goals (Alhadabi and Karpinski 2020), and emotional intelligence (MacCann et al. 2020). Also, a positive correlation was obtained between university students’ life quality (Chattu et  al. 2020), happiness (Önder 2020), subjective well-being (Ayyash-Abdo and Sánchez-Ruiz 2012), psychological well- being, (Kotzé & Kleynhans 2013; Tape et al. 2021) academic performance and level of life satisfaction (Vela et al. 2017).

The pandemic caused some changes and difficulties in the lives of students all over the world (Son et al. 2020). Governments have taken precautions like full lockdown and stay- ing home to inhibit the expanding pandemic, and numerous higher education institutions have switched from face-to-face classes to online education (Biwer et  al. 2021; Chandra 2020; Senol et al. 2021). Rapid changes in the lives of university students in a very short time required them to adapt to new life conditions and online education platforms (Biwer et al. 2021). As a result of these changes, the factors affecting the academic achievement of students have changed (Xu et al. 2021). For example, compared to the pre-pandemic period, university students have more difficulties to be motivated and organizing their learning pro- cesses (Biwer et al. 2021; Santamaría-Vázquez et al. 2021). In this respect, self-regulation, and motivation has a significant role in online learning environments. Moreover, the lack of these abilities is among the important causes of AP (Rakes and Dunn 2010).

Due to the excessive increase in the time that students spend in front of the screen, (TV, Computer, Smartphone, Tablet, etc.) (Jahja et al. 2021; Şimşir-Gökalp et al. 2022) and their use of digital technologies (Montag and Elhai 2020) during the pandemic, it may be use- ful to consider screen addiction while examining the factors affecting their achievement. According to the current literature, there is a negative relationship between university stu- dents’ smartphone addiction (Samaha and Hawi 2016), internet addiction (Iyitoğlu and Çeliköz, 2017), and video game addiction (Wright 2011) and academic performance in the pre-pandemic period. Screen addiction not only negatively effects pupils’ academic perfor- mance but also generates risks in terms of mental and psychological well-being, negatively affecting well-being (Akulwar-Tajane et al. 2020). However, there is no research examining the effect of screen addiction on academic achievement and psychological well-being dur- ing the pandemic. Therefore, the purpose of this study is to reveal the predictive power of university students’ multiple screen addiction on academic achievement and psychological well-being levels.

One of the psychological factors that should be emphasized to better understand the academic performance of students and the mechanisms mentioned above that affect their academic performance is the Covid-19 burnout. This concept, called pandemic fatigue by

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the World Health Organization (2020), was described by Queen and Harding (2020) as physical, emotional, and mental exhaustion arising from the intense and prolonged stress of the epidemic. Researchers emphasized that burnout reduces motivation and causes feel- ings of helplessness, hopelessness, and resentment. Recent research indicates that Covid- 19 burnout is quite common (e.g., Haktanır et al. 2021; Morgul et al. 2021; Stavem et al. 2021). For example, Morgul et al. (2021), which was conducted with the participation of 4700 people in Turkey, showed that 64.1% of the participants felt psychological burnout.

1.1 Purpose of the research

This study aims to examine the variables that predict the university students’ academic achievement in the process of epidemic and to present a model on these variables. For this purpose, the hypotheses of the research can be listed as follows:

1. H1: There is a relationship between AP and MSA. 2. H2: There is a relationship between covid-19 burnout and AP. 3. H3: There is a relationship between covid-19 burnout and GPA. 4. H4: There is a relationship between covid-19 burnout and MSA. 5. H5: There is a relationship between covid-19 burnout and psychological well-being. 6. H6: There is a relationship between motivation and AP. 7. H7: There is a relationship between MSA and GPA. 8. H8: There is a relationship between MSA and psychological well-being. 9. H9: There is a relationship between self-regulation and AP. 10. H10: There is a relationship between GPA and psychological well-being.

2 Conceptual and theoretical framework

2.1 Theoretical background

The theoretical background supplies a framework to understand the relationships between variables. There are various theories explaining the psychological factors and academic achievement as part of this research. The theoretical framework is referred to as the Self- Determination Theory (SDT). SDT is a meta-theory that provides a comprehensive frame- work for explaining personality, development, well-being, and motivation (Deci and Ryan, 1985; Deci and Ryan 2012; Ryan 2009). SDT focuses on social-contextual conditions that facilitate self-motivation, quality of performance, and healthy psychological development. SDT emphasizes the importance of meeting three innate psychological needs (autonomy, relationship, and competence) for intrinsic motivation, personality development, social development, and personal well-being (Ryan and Deci 2000). Empirical studies within the framework of SDT explain screen addiction as concepts related to the need for relation- ships, self-regulation with the need for autonomy, and academic achievement as concepts related to the need for competence (Neubauer et al. 2018; Ryanet al. 2021; Sheldon et al. 2011). In addition, the concepts of AP and burnout are associated with low motivation, well-being, and high motivation (Grund and Fries 2018; Nyanamba et  al. 2021). It was reported that conditions that support an individual’s experience of autonomy, relationship with others, and competence encourage will, motivation, and participation in activities, including school performance (Amholt et al. 2020; Ryan and Deci 2000). In this regard,

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this study investigates the role of non-cognitive factors which correspond to basic psycho- logical needs in the academic achievements of college students.

2.2 Academic procrastination

Humans tend to procrastinate at a variety of times in life. While most of these have become an individual’s lifestyle, some may be specific to a certain moment or situation (Zacks and Hen 2018). AP is defined as voluntarily delaying a planned action regarding the study, although worse outcomes are expected due to delay (Steel and Klingsieck 2016). Studies show that procrastination is quite usual for university students (Durak 2020; Kim and Seo 2015). Various studies in the related literature show that AP is a common behavior among university students (Day et al. 2000; Jin et al. 2019; Kathleen and Basaria 2021; Onwue- gbuzie 2004; Rabin et  al. 2011; Yockey 2016). Procrastination, which usually manifests itself in academic assignments such as writing papers, practicing for exams, and fulfilling reading homework every week, can negatively affect not only the student himself but also other people who trust him, even the organization (Zacks and Hen 2018). It was reported in various studies that procrastination or AP increases the probability of displaying problem- atic behaviors such as problematic smartphone and social media use (e.g. Davis et al. 2002; Rozgonjuk et al. 2016).

2.3 Covid‑19 Burnout

Covid-19 burnout, in other words, pandemic fatigue, is the feeling of burnout that increases the indifference of individuals in their protective actions against the epidemic, triggers avoidance behaviors from other individuals, develops due to reduced mental and physical effort and causes feelings of extreme nervousness (Lilleholt et al. 2020; Wen et al. 2020). When examined physiologically, covid-19 burnout is defined as the transformation of enthusiasm, enthusiasm, and adrenaline feelings produced by individuals to cope with the epidemic at the beginning of the epidemic process, into feelings of exhaustion and stress (Murphy 2020). The measures taken to cope with the epidemic (Zou et al. 2020), the news presented in various environments about the pandemic process (Taylor 2019), the signifi- cant decrease in physical activities in this process (Zou et al. 2020), and the long duration of the epidemic process. (Murphy 2020) increases covid-19 burnout.

The levels of covid-19 burnout experienced by individuals during the epidemic process can be affected by various factors. MacIntyre et al. (2021) concluded in their research that being young, underperceiving the severity of Covid-19, the prevalence of the epidemic and the low number of cases positively affect covid-19 burnout. There is evidence that covid- 19 burnout negatively affects functionality (Schwartz and Pines 2020; Teng et  al. 2020) and causes physical and psychological disorders (Teng et al. 2020; Zhan et al. 2020). In studies dealing with the negative impacts of covid-19 burnout, it has been emphasized that social support (Teng et al. 2020), self-care skills (Haktanir et al. 2021a, b), and psycho- logical well-being (Dozois 2020) that individuals receive in the epidemic are influential in decreasing the severity of covid-19 burnout.

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2.4 Multi‑screen addiction

MSA is expressed as extreme consumption of media with more than one device with a screen (Sarıtepeci 2021). Today, media usage possibilities have developed consider- ably and there are many usage options (Cain and Mitroff 2011). These increased choices have caused a marked rise in the simultaneous use of multiple media formats, especially among young people (Yildiz Durak and Saritepeci 2019; Yildiz Durak 2018). This increase increases the dependence of individuals on these devices and the opportunities they pro- vide. As a matter of fact, according to the We Are Social (2022) report, it is seen that indi- viduals aged 16–64 watch an average of 3H20M TV per day, spend time on social media 2H27M, and spend time playing 1H12M console games.

Screen addiction attributes to a process of erratic media behaviours with multiple dis- play devices, ranging from compulsive media consumption to highly problematical and pathological behaviours (Lin et  al. 2020; Yildiz Durak 2020). Increased screen time has been defined as a significant risk for well-being (Sigman 2014). Screen addiction is likely to result in deterioration of physical and psychosocial well-being and inadequate academic or task performance because of overuse (Lin et al. 2020).

2.5 Academic procrastination and multi‑screen addiction

Steel (2007a) expresses procrastination behavior as “to voluntarily delay an intended course of action despite expecting to be worse off for the delay”. Based on this definition, Steel and Klingsieck (2016) described AP as “to voluntarily delay an intended course of study-related action despite expecting to be worse off for the delay”. As a result of procras- tination, which is characterized by a lack of self-control in the literature, individuals may turn to activities that they think are more enjoyable for them (Rebetez et al. 2016). When the task to be completed is perceived to be less enjoyable, the individual will be more likely to procrastinate and will turn to online activities that are experienced as fun (spend- ing time on social media, playing games, etc.) (Geng et  al. 2018). A significant portion of the activities that promise relatively more entertaining activities, especially for young people, involve spending more time with screen devices such as mobile devices and con- soles. Accordingly, it can be claimed that the intensification of AP behavior may be one of the antecedents of MSA. In the literature, many studies are examining the correlation between AP and screen events such as using social media disorder (Al Shaibani 2020), Internet (Hayat et al. 2020), and smartphone addiction (Li et al. 2020). However, there is not an empirical study examining the relationship between AP and MSA.

2.6 The relationship of Covid‑19 burnout with academic procrastination and GPA

To control the Covid-19 epidemic, educational institutions had to take a break from their activities, and many academic activities began to be implemented in a virtual environ- ment. Ultimately, the academic life of many students around the world has been affected and even a major disruption has occurred in the education system (Alqahtani and Rajkhan 2020). Uncertainty about how the pandemic will progress and when it will end adversely affected mental health and caused burnout (Haktanir et al. 2020). Studies have shown that the pandemic reduces student-to-student communication and participation in student-to- student studies, increases students’ level of psychological distress, and causes procrastina- tion (Peixoto et  al. 2021). In general, students who have positive emotions cannot show

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procrastination behavior, while those who experience negative emotions tend to AP. There was reported the same-way relationship between AP and academic burnout in various stud- ies (Balkıs 2013; Hall et al. 2019). In this respect, procrastination can also be understood as a form of emotional regulation to reduce the negative emotions students experience while working on academic tasks (Rahimi and Vallerand 2021). In addition, students’ stress and burnout levels were related to lower academic performance (Balkıs 2013; Shadid et  al. 2020). In this regard, students’ Covid-19 burnout levels will be effective on AP and GPA.

2.7 The relationship of Covid‑19 burnout with multi‑screen addiction and well‑being

Quarantine, uncertainties about the future of the virus, changes in daily routines, restric- tion of social life, etc. stress factors increase the burnout levels of individuals related to Covid-19 (Arslan et al. 2020). Burnout is the result of a long-term response to stress fac- tors and poses a risk to the well-being of individuals (Maslach and Leiter 2016; Yıldırım and Solmaz 2020). On the other hand, internet and screen activities offer many interesting and fun experiences that reduce stress. This may cause people to have more time on screen to get rid of stress (Şimşir Gökalp et al. 2022). As a result, it can increase the risk of devel- oping MSA, which refers to an addiction to devices with multiple screens. In this context, Covid-19 burnout is thought to be related to well-being and MSA.

2.8 Multi‑screen addiction and psychological well‑being

While early research on digital media tools treated these technologies as an escape from the constraints and frustrations of daily life, recent research has concentrated on the nega- tive impacts of various screen activities on psychological well-being (Reinecke and Oliver 2016). The service provided by these tools without the time and place restrictions and the technological unconsciousness that exists in the background has revealed the problem of how to establish a healthy balance in individuals (Vanden Abele 2021), and caused indi- viduals to develop an addiction to one or more screen activities as well (Cho et al. al. 2014; Lemmens and Bushman 2009; Kwon et. al. 2013; Sarıtepeci 2021). Studies on this subject have reported that excessive screen time increases the probability of individuals experi- encing socio-emotional problems in proportion to watching hours (Balhara et  al. 2018). Moreover, Psychological well-being is negatively associated with problematic technology use such as the Internet and social media addiction (Casale et al. 2015; Nikbin et al. 2021). According to the evidence in the literature, there is a negative relationship between MSA and psychological well-being.

2.9 Multi‑screen addiction and GPA

Today, university students use electronic media much more actively than their predeces- sors. However, recent studies show that it is associated with many negative academic out- comes (Jacobsen and Forste 2011). The problematic use of these technologies (cyberloaf- ing, etc.) both reduces the attention of students to the lesson in the classroom and prevents students from working regularly by spending more time on the Internet and social platforms (Arefin et al. 2018; Yildiz Durak 2019). It was highlighted that the relationship between the use of digital technologies and academic achievement resembles a humpback-shaped

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curve and that optimum use can be advantageous (Organization for Economic Cooperation and Development 2011). However, frequent use in class or at night can effect the attention, concentration, and learning of individuals and have a negative effect on academic achieve- ment (Gerosa et al. 2020; Przybylski and Weinstein 2017).

2.9.1 Academic procrastination and self‑regulation

Many studies on procrastination view procrastination as an occupation of low self-regula- tion (Klassen et al. 2008). Accordingly, researchers expressed procrastination as a “leaning to delay completing or starting a task/assignment due to lack of self-regulation” (Balkış and Duru 2016). Ferrari (2001) proposed that procrastination is considered a failure of self- regulation. Many researchers have highlighted the effect of self-regulation on AP in the lit- erature (Abdi Zarrin and Gracia 2020; Zhang et al. 2018). Many studies agree that the use of self-regulation strategies positively affects academic life (Balkış and Duru 2016).

3 Method

In this study, the effects of burnout experienced by university students in the process of the Covid-19 epidemic on AP, MSA, and psychological well-being and the predictive effects of these variables on GPA were discussed. The relationships between these variables were examined using prediction design, one of the correlational research models. A prediction design is a model aiming to examine the dependent variable or the predictors of the vari- ables. The relationships between the discussed variables of this study were shown in Fig. 1.

3.1 Participants

The study group of this research consists of 241 students, 37 male, and 204 female, contin- uing their undergraduate education in Turkey. Participants are from different faculties and programs, and due to the pandemic, all courses are held online. The age range of the partic- ipants is from 17 to 48, with an average of 21.17. GPA question of the previous semester, 40% of the participants answered as 3.50, 24% as 4.00, 31% as 2.50–3.00, and 4% as 2.50. Participants have been using digital technologies for an average of 8.57 years. In addition, the participants evaluated their proficiency in using digital technologies on a scale of 1–5 (1: very low, 5: very high), with an average of 3.69. 62% of the participants gave 4 or 5

Fig. 1 Research model

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answers, stating that they are competent in using digital technologies. However, 34% of the participants (who gave 3 answers) thought that they had a medium level of proficiency, while only 5% of them (who gave 1 and 2) did not consider themselves sufficient using digital technologies.

3.2 Data collection tools

The data were collected using a self-description form and 6 scales.

3.2.1 Self‑description form

There are six questions in the personal information form. These questions are created to collect information about gender, age, grade level, digital technology use experience, digi- tal technology use competency level, and GPA.

3.2.2 Academic procrastination

McCloskey (2011) suggested a 5-item short form in addition to the 25-item long form in the study of the academic procrastination scale. Yockey (2016) conducted a psychometric analysis of the short form. According to the presented findings, the short form has a single- factor structure, and item factor loads range from 0.73 to 0.86. The scale is a 5-point Likert scale (1- Strongly Agree …., 5- Strongly Disagree). We translated the items in the shorts form into Turkish. We examined each item’s contextual meaning and intelligibility with two field experts fluent in Turkish and English. One linguist retranslated the resulting draft form. We discussed the differences in the items as a result of translation and re-transla- tion. In the last stage, we evaluated the scale items with three undergraduate students. As a result of our examinations and evaluations, we created the final form of the scale form. In this study, we found factor loads between 0.78 and 0.87 on the five-item academic procras- tination scale. In the analyzes related to the reliability and convergent validity of the scale, we found the average variance extracted (AVE) value to be 0.713, the composite reliability value to be 0.925, and the Cronbach Alpha internal consistency coefficient to be 0.899. These results show that the scale has reliability and convergent validity.

3.2.3 Covid‑19 burnout

YıLDıRıM and Solmaz (2020) replaced the concept of “your work” with “Covid-19” in the articles of Malach-Pines (2005) “Burnout Measure-Short Version” and adapted the “Covid-19 BurnOut Scale”. The scale with a single-factor structure is in the form of a 5-point Likert scale (1- Never, …, 5- Always). A high score on the scale is accepted as an indicator of a high level of covid-19 burnout.

3.2.4 Motivation

Pintrich et  al.’s (1991) “Motivated Strategies for Learning Questionnaire” consists of 81 items. The instrument consists of “the motivation scale” and “the learning strategy scale”. The scale Büyüköztürk et al. (2004) adapted to Turkish. The motivation scale has six fac- tors, and -in this study- we used the dimensions of intrinsic goal regulation and extrinsic goal orientation (intrinsic goal orientation, extrinsic goal orientation) from these factors.

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There are four items in each dimension. The scale is a 7-point Likert scale (1- Absolutely wrong for me, …, 7- Absolutely true for me).

3.2.5 Multi‑screen addiction

The multi-screen addiction scale by Saritepeci (2021) consists of 15 items and three sub- scales: eight items in Compulsive Behavior, three in Loss of Control, and four items in Excessive Screen Time. The scale is a 5-point Likert scale. A high score on the scale indi- cates a high level of MSA. Monothetic and polythetic formats can be used as dependency criteria. According to the monothetic format, those who give three or more answers to all the items are considered addicted. In the polythetic format, those who give three or more answers to more than half of the items (at least eight items) are considered addicted.

3.2.6 Psychological well‑being

DIENER et al. (2009) developed the psychological well-being scale and adapted it to Turk- ish by Telef (2013). This scale consists of eight items and there is no reverse item in the scale. The scale, adapted as it was in the original, has a one-dimensional structure. The single-factor structure explains 42% of the total variance. It is a 7-point Likert scale.

3.2.7 Self regulation‑attention

Schwarzer et  al. (1999) developed the psychological well-being scale, and Diehl et  al. (2006) adapted it to English. Demiraslan-Çevik et  al. (2017) made the adaptation of the scale to Turkish. The scale, which has a single-factor structure, consists of seven items. The scale is in the form of a 5-point Likert scale.

3.3 Data analysis

In the review of the research model developed for the study, the Partial Least Squares (PLS) Structural Equation Model was used with the SmartPLS 3.0 software. (Ringle et al. 2015). The reason for applying the PLS method to evaluate the structural and measure- ment models used in the study is its capacity to perform simultaneous analyzes that result in more exact evaluations (Barclay et al. 1995). In this study, first of all, the measurement model was examined, and then the findings related to the structural model were presented. While measurement models represent the measurements of structures, structural models describe the relationships between structures (Hair et al. 2017).

4 Findings

4.1 Assessment of measurement model

Construct validity and reliability were used to test the measurement model. Variance infla- tion factor (VIF) values were examined to determine whether there was indicator collinear- ity or not. Factor loads were used to evaluate the indicator reliability. The average vari- ance extracted (AVE) was used to evaluate convergent validity which states the degree to which measures are positively correlated with corresponding alternative measures of the

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same construct. The fact that the factor loads are greater than 0.70 and the average vari- ance extracted value is above 0.50 indicates that the convergent reality is acceptable (Hair et al. 2017). In this context, in the first stage of this study, items with a factor load of less than 0.70 in the model were excluded from the measurement model. Thus, the factor load of all items in the measurement model was found to be greater than 0.70. AVE values are above 0.50. To evaluate construct reliability, the Cronbach’s alpha, Composite Reliability, and rho_A coefficients of all research variables were evaluated in the measurement model of this study. The values obtained in the study exceed the recommended value of 0.7 for Cronbach’s alpha and Composite Reliability coefficients (Nunnally and Bernstein, 1994). Accordingly, the measurement model meets the specified criteria. Relevant values were presented in Table 1.

DFornell-Larcker and heterotrait-monotrait ratios (HTMT) were used to assess discri- minant validity.

Fornell-Larcker shows that the square roots of AVEs are higher than their correlation with other structures. Table 2 shows that the square roots of the AVEs on the diagonals were found to be greater than the correlations between the constructs, thus ensuring discri- minant validity of all constructs.

For discriminant validity, HTMT values were also examined. HTMT values were found to be below the recommended limit of 0.90 (Hair et al. 2017). In Table 3, HTMT values were presented.

4.2 Assessment of structural model

The structural model was calculated based on a sampling of 300 (bootstrapping). Findings of the evaluation of the structural model were shown in Fig. 2 and Table 4.

As in Fig. 2 and Table 4, there is a relationship between AP and MSA was supported (H1 supported). There is a relationship between Covid-19 burnout and AP was supported (H2 supported). There is no significant relationship between Covid-19 burnout and GPA (H3 Not supported). There is a significant relationship between Covid-19 burnout and multiscreen addiction (H4) and psychological well-being (H5). A correlation was found between motivation and AP (H6). The path between multiscreen addiction and GPA and psychological well-being is not significant (H7-H8). The H9 hypothesis, which includes the relationship between self-regulation attention and AP, was also accepted. Additionally, the percentages of variance explained were calculated as 15.2% for AP, 0.8% for GPA, 25.9% for MSA, and 13.6% for psychological well-being.

5 Discussion

The Covid-19 crisis, which influenced all over the world, caused many differences in peo- ple’s lives such as economic, political, social, physical, and mental well-being, as well as changing the educational activities and learning environments of students (Biwer et  al.., 2021; Xu et al.., 2021). While students are trying to overcome the negative mental health problems due to the pandemic (Son et al. 2020), they are also trying to adapt to the chang- ing educational conditions (Chandra 2020). These changes have resulted in the changes in the conditions that effect the academic achievement of students (e.g., Biwer et  al. 2021; Zheng, et al. 2021). However, the studies examining the factors affecting the academic per- formance of university pupils during the pandemic process are very limited in the current

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literature. Therefore, this research aimed to present a model of the variables that may be associated with academic achievement during the pandemic process.

Although almost 2.5 years have passed since the Covid-19 pandemic arose in November 2019, the continuation of restrictions, uncertainty about how long the pandemic will last,

Table 1 Measurement assessment results

Constructs Items Loading Cronbach’s Alpha rho_A Composite Reliability

Average Variance Extracted (AVE)

AP AP1 0.850 0.899 0.901 0.925 0.713 AP2 0.846 AP3 0.775 AP4 0.873 AP5 0.872

Covid-19 Burnout CB1 0.700 0.916 0.932 0.932 0.636 CB2 0.700 CB3 0.874 CB4 0.899 CB5 0.870 CB6 0.859 CB7 0.701 CB8 0.754

Motivation M1 0.835 0.827 0.838 0.882 0.652 M2 0.809 M3 0.725 M4 0.854

Multiscreen Addiction MA1 0.795 0.932 0.933 0.942 0.620 MA2 0.788 MA3 0.792 MA4 0.714 MA5 0.815 MA6 0.820 MA7 0.740 MA8 0.752 MA9 0.837 MA10 0.812

Psychological Wellbeing PW1 0.831 0.920 0.955 0.933 0.668 PW2 0.813 PW3 0.808 PW4 0.751 PW5 0.895 PW6 0.814 PW7 0.802

Self regulation-Attention SR1 0.776 0.839 0.864 0.891 0.672 SR2 0.825 SR3 0.834 SR4 0.843

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and the constant change in people’s sense of control over their lives cause them to experi- ence a sense of mental and physical exhaustion (Haktanir et al. 2021; WHO 2020). This negative mood, which we can express as pandemic burnout, has begun to threaten people’s

Table 2 Fornell-Larcker Criterion

1 2 3 4 5 6 7

1-AP 0.844 2-Covid-19 Burnout 0.239 0.797 3-GPA -0.047 0.029 1.000 4-Motivation -0.191 0.071 -0.003 0.807 5-Multiscreen Addiction 0.372 0.426 0.082 0.180 0.787 6-Psychological well-being -0.213 -0.367 0.031 0.360 -0.125 0.817 7-Self Regulation-Attention -0.298 -0.166 0.035 0.179 -0.233 0.372 0.820

Table 3 Heterotrait-Monotrait Ratio (HTMT) results

1 2 3 4 5 6

1-AP 2-Covid-19 Burnout 0.253 3-GPA 0.087 0.052 4-Motivation 0.200 0.121 0.008 5-Multiscreen Addiction 0.397 0.460 0.097 0.240 6-Psychological wellbeing 0.221 0.344 0.056 0.415 0.130 7-Self Regulation-Attention 0.332 0.185 0.035 0.189 0.259 0.410

Fig. 2 Structural model

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mental health (e.g., Morgul et  al. 2021; Moroń et  al. 2021; Ye et  al. 2020). The model established in this study revealed that pandemic burnout predicted university students’ AP, MSA, and psychological well-being (H2, H4, H5). Similarly, the existing literature shows negative correlations between COVID-19 fear and well-being (Deniz 2021; Özmen et al. 2021), AP (Biricik and Sivrikaya 2020), smartphone addiction (Kayis et al. 2021), and the internet has positive relationships between addiction (Servidio et al. 2021). However, in the model established in the study, the path from pandemic burnout to academic achievement was found to be insignificant and the established hypothesis could not be confirmed (H3). When the literature was reviewed, it revealed inconsistent findings regarding burnout and academic performance in students. For example, a study by Hendriksen et al. (2021) found a significant correlation between covid-19 fatigue and reduced academic performance in undergraduate and Ph.D. students. On the other hand, the study of Saberi et  al. (2020) conducted with stagers and interns from Guilan University of Medical Sciences supports research findings. The authors reported that GPA is not statistically associated with corona disease anxiety and academic burnout levels of participants during the covid-19 pandemic pandemic period. Basri et al. (2022) demonstrated that academic burnout negatively predict predict perceived learning among college students. Research findings may differ depending on the sample used or the country’s educational policies during pandemic.

Compared to face-to-face learning environments, the online learning context requires students to have high levels of motivation and self-regulation skills (Biwer et  al. 2021; Sansone et al. 2011). In the model established in the study, the paths drawn from motiva- tion and self-regulation-attention to AP were found to be significant (H6, H9). Procrastina- tion is the most well-known consequence of self-regulation failure (Rebetez et  al. 2016; Steel 2007a). Procrastination behavior is seen as a problematic behavior both in daily life and academic life (Steel 2007a). AP is a very familiar issue that negatively affects pupils’ academic achievement and academic life satisfaction (Balkis 2013; Xu 2021). During the pandemic process, students’ transition to distance education and living in quarantine condi- tions pose a greater threat to academic postponement. In addition, the internet, particularly smartphones and online games, offers students new paths to procrastinate in the process of pandemics (Xu 2021). Accordingly, the model established in the research indicates that AP increases MSA (H1). These results are not surprising considering that the time spent by students on the screen increased in the pandemic (e.g., Jahja et al. 2021; Şimşir Gökalp et al. 2022). Moreover, this finding was supported from the studies in the literature. For

Table 4 Hypotheses test results

Hypothesis Path Beta t-value p-value Decision

H1 AP—> Multiscreen Addiction 0.286 4.342 0.000 Supported H2 Covid-19 Burnout—> AP 0.212 3.681 0.000 Supported H3 Covid-19 Burnout—> GPA 0.010 0.155 0.877 Not supported H4 Covid-19 Burnout—> Multiscreen Addiction 0.358 5.787 0.000 Supported H5 Covid-19 Burnout—> Psychological wellbeing -0.383 6.421 0.000 Supported H6 Motivation—> AP -0.164 2.955 0.003 Supported H7 Multiscreen Addiction—> GPA 0.083 1.249 0.212 Not supported H8 Multiscreen Addiction—> Psychological wellbeing 0.038 0.543 0.588 Not supported H9 Self Regulation-Attention—> AP -0.234 3.633 0.000 Supported H10 Psychological wellbeing- > GPA 0.045 0.616 0.538 Not supported

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example, Şimşir Gökalp et al.. (2022), it was revealed that there is a positive relationship between procrastination and MSA in high school students during the pandemic process. In the study of Geng et al. (2018), significant relationships were found between university students’ internet addiction levels and procrastination levels.

In the established model, there was no significant relationship between MSA and GPA (H7). This result differs from the results of the research conducted before the pandemic. Studies conducted with university students in the pre-pandemic period revealed behavio- ral addictions such as smartphone addiction (Samaha and Hawi 2016), internet addiction (Iyitoğlu and Çeliköz, 2017) and technology addiction (Sert et al. 2019) negatively affect academic performance. However, in the study of Avaznia et  al. (2021) with university students during the pandemic process, it was found that there was no significant relation- ship between internet addiction and academic achievement, similar to the findings of this research. This difference can be explained by the increase in the time students spend in front of the screen because of online class applications in the pandemic process. Students had to do all their lessons and most of their homework on computers, tablets, and smart- phones during the distance education period. However, although higher education institu- tions have switched to face-to-face education in the 2021–2022 education period in Turkey, some of the educational activities are carried out with online education applications. Con- sequently, it can be inferred that screen addiction does not contribute to the decrease in academic success because students are spending more time on screen for academic goals.

The increase in the time people spend in front of the screen during the pandemic period is a risk source for mental and physical problems such as hypertension, depression, sleep disorders, type 2 diabetes, obesity, and myopia (Sultana et  al. 2021). According to the research findings, no significant relationship was found between MSA and psychological well-being (H8). Research before and during the pandemic has revealed that excessive use of platforms such as the internet, smartphone, and social media negatively affects well- being (eg, Afroz 2016; Avaznia et al. 2021; Duradoni et al. 2021; Horwood and Anglim 2019). The difference between the research findings and the results in the literature may be due to the characteristics of the participants.

Well-being has recently been seen as one of the important dimensions of schooling for effective and sustainable learning. For this reason, it has become a hot topic in learn- ing research (OECD 2021). In the model established in this study, no significant relation- ship was found between psychological well-being and GPA (H10). Amholt et  al. (2020) reviewed the relationships between psychological well-being and academic achievement with the systematic review method, and the results of the research in the literature were inconsistent; revealing that positive, insignificant, or conflicting results were reported between variables. Bücker et  al. (2018) examined the relationships between academic achievement and subjective well-being, a low level of relationship was found between the variables and the researchers emphasized that it would not be possible to make a causal inference. Moreover, Oishi et al. (2007) noted that although positive moods are generally associated with positive cognitive performance, they may be associated with poor cogni- tive performance in some conditions. According to the researchers, there may not be a lin- ear relationship between well-being and various life outcomes. In conclusion, considering the heterogeneity of research results in the literature, it can be said that more studies are needed on this subject.

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6 Implications

In this study, examining the relationship between Covid-19 burnout, AP, MSA, and GPA with a comprehensive model has important contributions to the relevant literature. The direct effects of Covid-19 burnout on psychological well-being, MSA, and AP are significant. Accordingly, this study contributes to the expansion of the nomological net- work regarding the effects of Covid-19 on the psychological well-being and behavior of individuals.

Another important effect of the model is that the direct effect of AP on MSA is sig- nificant. In addition, the direct effect of Covid-19 burnout on MSA and AP is significant, causing the relationships between these three variables to become unclear. Qualitative or exploratory mixed studies, in which the relationships between these variables were dis- cussed in-depth, may have important contributions to the literature.

A negative relationship was found between the control dimension of self-regulation and motivation and AP. On the other hand, none of the paths between the dependent vari- able GPA and the other variables are significant. Data of the study were collected in the 2021–2022 fall term, and the weight of the 1.5-year distance education period is important to GPA scores. The fact that the measurement and evaluation activities in the emergency distance education or distance education period consist of exams, which are mostly pre- ferred in face-to-face education environments and where abuses are prevented more easily in face-to-face education, increases the possibility of GPA scores not reflecting the exist- ing situation. Accordingly, the organization of studies containing multi-group analyzes in which the relevance of the measurement-evaluation activities used in the distance educa- tion period is considered as a group variable of the relationships between the GPA and the variables discussed can provide important contributions to the literature.

Acknowledgements None.

Funding There was no funding source for this study.

Declarations

Conflict of interest We here declare no conflict of interest related to the present manuscript.

Human participants and/or animals The data used was collected, ensuring that all credentials were anony- mous.

Informed consent An informed consent form created to fill out the data collection tools was approved by the participants.

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  • Examination of non-cognitive variables affecting academic achievement: a conceptual model proposal
    • Abstract
    • 1 Introduction
      • 1.1 Purpose of the research
    • 2 Conceptual and theoretical framework
      • 2.1 Theoretical background
      • 2.2 Academic procrastination
      • 2.3 Covid-19 Burnout
      • 2.4 Multi-screen addiction
      • 2.5 Academic procrastination and multi-screen addiction
      • 2.6 The relationship of Covid-19 burnout with academic procrastination and GPA
      • 2.7 The relationship of Covid-19 burnout with multi-screen addiction and well-being
      • 2.8 Multi-screen addiction and psychological well-being
      • 2.9 Multi-screen addiction and GPA
        • 2.9.1 Academic procrastination and self-regulation
    • 3 Method
      • 3.1 Participants
      • 3.2 Data collection tools
        • 3.2.1 Self-description form
        • 3.2.2 Academic procrastination
        • 3.2.3 Covid-19 burnout
        • 3.2.4 Motivation
        • 3.2.5 Multi-screen addiction
        • 3.2.6 Psychological well-being
        • 3.2.7 Self regulation-attention
      • 3.3 Data analysis
    • 4 Findings
      • 4.1 Assessment of measurement model
      • 4.2 Assessment of structural model
    • 5 Discussion
    • 6 Implications
    • Acknowledgements
    • References

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99 NACTA Journal • Volume 67 • 20239999 NACTA Journal • Volume 67 • 202399

Fast Friends: A Quasi-Experimental Design Fast Friends: A Quasi-Experimental Design Among Two Racially Diverse Among Two Racially Diverse

Student PopulationsStudent Populations

Stacy K. Vincent, Tiffany C. Monroe, and Brett M. Wasden

Department of Community and Leadership Development, University of Kentucky

Stacy K. Vincent - ORCID: 0000-0003-0004-1059 There are no conflicts of interest and this research is not part of a funded project

Correspondence regarding this article should be addressed to: Dr. Stacy K. Vincent, 306 W. P. Garrigus Building, Lexington, KY 40383. Email: stacy.vincent@uky.edu

Abstract

This exploratory study sought to determine if the Fast Friend intervention (Aron, Melinat, Aron, Vallone, & Bator, 1997) improve racial cognizance of cross-group dyads. Data were collected from freshmen (n=34) enrolled in a college of agriculture. The treatment and control groups included cross-group, same-sex, dyads composed of African American students and Caucasian students. In this quasi- experimental, nonequivalent comparison group, descriptive statistics revealed both the treatment and control participants failed to establish a difference between pretest/posttest and between control/treatment participants in Implicit Theory of Intelligence Scale, Color Blind Racial Attitude Scale, and Communal Orientation Scale. However, results did indicate significance in the Collective Self-Esteem Scale among treatment group participants and control group participants overtime. At the end of the study, a significant difference existed as treatment participants were more adaptive

while the control were more maladaptive. Results indicate that engaged conversations among interracial groups over a short period of time, does not have large impacts on cognizance, but makes substantial differences among perception of whom they feel comfortable talking to. Further research should be conducted to establish interventions that measure racial cognizance through longitudinal studies, cross-institutional studies, and an increase quantity of participants.

Keywords: implicit, color blind, race, attitude, quasi- experimental

A population report by Colby and Ortman (2015) of the United States Census indicated changing demographics. The Non-Hispanic White population is currently the majority group by race; by 2060 the group will decrease to 44%, making the non-Hispanic White population a racial minority.

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FAST FRIENDS Changing demographics are observed in the overall United States population, and changes in student demographics in colleges and universities have recently been noted. For example, according to the University of Kentucky Institutional Research & Advanced Analytics (2016), in 2015 the college of agriculture had an enrollment of 14.3 percent Black or African American students, the highest of all racial minority populations. This percentage of Black or African American students in the University of Kentucky’s College of Agriculture, Food, & Environment is higher than what is reflected across that nation. Given that there is a growing population of racially and ethnically diverse students, possibilities of initial implicit bias and prejudice behavior are great, impacting the experiences, performance, and friendship formations of all students (Guzman-Lopez, 2015). Continuous changes in the United States demographics have magnified the importance of preparing students for engagement in society and the workforce It has also increased the importance of providing an education that values and fosters diversity (Locks et al., 2008).

Colleges and universities prepare students for future careers, life experiences, and social interactions that are enhanced by providing students with an education that expands mindsets (Locks et al., 2008). The changing demographic of the United States population has increased the need for education that fosters diversity of experiences and thought. Prior to college, students are likely to come from backgrounds of similar socio-economic, ethnic, and racial identities that have been conditioned due to social homophily, institutional racism, and residential segregation (Fisher, 2008). Incoming freshmen at universities may likely be exposed to cross-group populations for the first time after entering college. Furthermore, students are likely to form relationships with students with like attributes rather than those who are different due to the need for a sense of belonging (Walton & Cohen, 2011). This social homophilic behavior encourages implicit bias to occur among student groups and makes it harder to notice and disrupt it. Conditions of perception, attitudes, stereotypes, self-esteem, self-concept, racial colorblindness, and bias are linked to implicit social cognition (Greenwald & Krieger, 2006) and occur within student populations (Wyatt et al., 2019). Despite this knowledge, there is limited research on implicit social cognition among student populations and even less literature of racial implicit research within an agricultural college where implicitness is even more likely to occur due to enrollment of rural and nontraditional students.

While there is substantial evidence that support the concept that having more racially diverse students on college campuses increase educational outcomes for all students, the area of research in education is relatively new and can be strengthened with empirical data (Denson & Chang, 2009). Furthermore, there is little documentation of cross-group relationships or members of different groups that can be differentiated by social, cultural, racial, or ethnic composition characteristics (Page-Gould et al., 2010), being examined within a college of agriculture. This quasi- experiment seeks to determine if the Fast Friends Program originally designed by Aron et al. (1997) would reduce racial anxiety, implicitness, prejudice, and racial color-

blindness among college freshman students in the college of agriculture. The Fast Friends Program experiment implements three 45-minute meetings, where participants complete self-disclosing and relationship-building tasks that gradually escalate in intensity over time. Such programs are aimed to understand how cross-group relationships between racially diverse students can increase cultural competence (Page-Gould et al., 2008).

Friendship formations are impacted by status similarity (relationship where one is more interested in the other based upon one’s status), reciprocity (relationship of someone of the same status level or of less stature), and most influentially, homophily (Fischer, 2008). According to Borgatti and Cross (2003) homophily research suggests that people are more likely to have strong social ties with people they find like them based on socially important characteristics such as race, sex, education, and age. Thus, students have natural tendencies to develop relationships with those like them. Consequently, elements of skin tone, religious preference, and socio-economic status are the major dividing factors. Therefore, examining the cross- group relationship of students who come from dissimilar backgrounds can help address the need for prepared and professional college students in an increasingly diverse workforce and society.

Theoretical/Conceptual Framework

Implicit bias, also known in the literature as implicit memory, implicit psychology, implicit social cognition, or unconscious bias, is an automatic and involuntary attitude or stereotype formed through cognitive processes (Fischer, 2008). Explicit bias, unlike implicit bias, is an active and conscious affirmation of attitude. Implicit attitudes are formed through internally unidentified reminiscences of former experiences “that mediate attributions of qualities to members of social categories” (Greenwald & Banaji, 1995 p. 15). Exposure to environmental stimuli and memory impulsively activate and trigger an implicit attitude or implicit bias (Stepanikova et al., 2011). The implicit bias model has developed over decades of research from both social psychologists and cognitive scientists. Implicit biases are involuntary responses that arise without little or no awareness from individuals who have them (Gallegos De Castillo, 2018). Banaji and Greenwald (2013) accredited implicit bias as a human adaptation to avoid danger. For example, the association of snake with the concept of danger creates a bias that bolsters survival. Even though, all snakes are not dangerous. The implicit bias emerges when an individual avoids specific areas – without realizing – for a potential threat of a snake.

According to Hong et al. (2004), implicit biases are constructed frameworks that individuals use to interpret and evaluate their social world (p. 1036). As such, a network of subconscious beliefs creates actions that may lead to a manifestation of stereotyping and prejudice (Levy, 1999). Implicitness is believed to be worth studying as a deterrent to social injustice. It is important to note, implicit actions of prejudice are typically unconscious and involuntary, even by individuals who consider themselves culturally responsive

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FAST FRIENDS

Methods

Study Population

The study’s population included freshman enrolled in a major within the college of agriculture and self-identified as African American or White, non-Hispanic. Both the treatment group and the control group consisted of White, non-Hispanic and African American students. The treatment (n = 20) included African American students (n = 10) and White, non-Hispanic students (n = 10) and the control group (n = 14) consisted of African American students (n = 7) and White, non-Hispanic students (n = 7). Fowler (2014) stated sampling should not be based on the norms of other research studies but rather based on analysis plans. The overall population was limited within the college; thus, convenient sample size, based upon availability and consent, was used to meet the research objectives and plans for analysis (Purswell & Ray, 2014).

During a freshman opportunity fair where 86% of enrolled freshmen attended, a booth was stationed where students could register for the opportunity to participate in the study. From funds allocated through a college mini- grant proposal, all participants chosen received a $100 gift card at the conclusion of the study. A convenient sample of 66 students registered to participate in the study, of the nearly 600 incoming freshmen. Students self-identified the racial composition that best describes them. An additional email was sent to encourage participation as well as an announcement at a required freshman agriculture course. From the two additional recruitment strategies, an additional 13 names were acquired. Of the 79 volunteers, 36 attended an interest meeting and 34 remained active throughout the process. The nine students that did not complete the study,

(Levy & Dweck, 1999). Many theorists argued the implicit bias model serves as an explanatory force that perpetuate inequality of marginalized populations (Fischer, 2008; Locks et al., 2008; Page-Gould et al., 2010).

Purpose and Objectives

This exploratory quasi-experimental design study's purpose was to ascertain whether the Fast Friends program would help reduce racial anxiety, implicit bias, and racial colorblindness among incoming freshman at an agricultural college. The following objectives guided the study...

1. Describe the difference in the Implicitness Intelligence scale of the treatment and control group participants.

2. Describe the difference in the Almost Perfect scale of the treatment and control group participants.

3. Describe the difference in the Color-Blind Attitude scale of the treatment and control group participants.

4. Describe the difference in the Communal Orientation scale of the treatment and control group participants.

5. Describe the difference in the Collective Self- Esteem scale of the treatment and control group participants.

provided reasons of “too busy”, “no longer interested”, “did not know that it would consume so much time” or simply did not respond.

Research Design

This study was a quasi-experimental, nonequivalent comparison group design that sought to explore the effects that the Fast Friends program on a select group of students. In a nonequivalent quasi-experimental design, the experimental groups and the control group are not randomly selected and both groups are required to take a pretest and a post-test evaluation (Creswell, 2014). The control group did not participate in the Fast Friends program. At the beginning of the semester, both control and treatment participants received the pretest and a post-test seven weeks later. Between the pretest and posttest, the treatment group was exposed to the Fast Friends program, where paired same-sex cross groups of African American and White, non-Hispanic students, met and interacted three times throughout the semester. At the conclusion of the third and final meeting, the students took a posttest. The authors received approval of the university’s Institutional Review Board prior to participant recruitment.

Intervention

The treatment selected for this study was the Fast Friends program. This program was originally developed and validated by Aron et al. (1997) to examine interpersonal closeness of cross-cultural groups and to identify the characteristics of a relationship that could be manipulated variables (Aron et al., 1997). The variables for this specific intervention were selected to evaluate perceived intelligence, perfectionism, color blindness, care for others, and self-confidence in social settings. These variables are all indicators of implicitness allowing the researcher to measure potential change in implicit bias of students. Originally inspired by the work of Collins and Miller (1994) on self-disclosure, Fast Friends resulted from the researchers recognizing a need for a procedure that expanded to on- going interactions of partners. The need for expanded interaction sculpted the overarching procedural purpose of encouraging a feeling of closeness between individuals (Aron et al., 1992; Aron, Aron, Tudor, & Nelson, 1991), defined as including others in the self- an interconnectedness of self and other, (Aron et al., 1997, p. 377). After later modification by Page-Gould et al. (2008), this procedure became known as Fast Friends. This process has impacted the scientific community by allowing social physiological research to (a) measure individual difference variables before, during or after interactions, (b) control who is in the relationship, (c) directly manipulate relationship variables, and (d) created a setting that can be observed (Aron et al., 1997).

In this study, the treatment group was exposed to the Fast Friends program and were paired same-sex, cross- groups of African American and White, non-Hispanic students, met and interacted, a minimum of, three times over the course of three months. Each interaction lasted one-hour, uninterrupted with no one in the room but a video

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FAST FRIENDS recorder. At the control and treatment participants’ pretest meeting, all student participants completed the Implicitness Scale, Color-Blind Racial Attitude Scale, Communal Orientation Scale, and the Collective Self-Esteem Scale. Also, during the first meeting, the students were informed whether they were a part of the control or treatment group, and the first treatment group meeting was scheduled. At the first treatment group meeting, the participants were paired and met in a room to discuss a series of questions pre-designed by the Fast Friends program. At the second meeting, the dyads met in the designated room to discuss the second round of topic questions. During the third and final meeting, the paired participants met for a one-hour social interaction by playing a Hasbro’s ® Jenga® game. They were not given prompted questions. Following the interaction, the participants completed a posttest that was identical to the pretest along with the completion of the Almost Perfect Scale. During each student pair interaction, a log that included steps and times were recorded to assist in providing a similar experience for each student that participated at the time they were assigned. Time was the only factor recorded that was not exact within the log. The researchers followed the steps provided by Hagermoser and Luh (2020) to maintain treatment fidelity.

Instrumentation

The protocol for this experiment was originally developed by Aron et al. (1997) as a procedure for measuring experimental interpersonal closeness and was later modified by Page-Gould et al. (2008). The pretest and posttest for both the treatment group and the control group were identical and were based on the Fast Friends experiential protocol (Page-Gould et al, 2008). The following served as indicators of racial implicitness in the questionnaire: (a) implicit theory of intelligence, (b) Almost Perfect Scale, (c) Color Blind Racial Attitude Scale, (d) communal orientation, and (e) collective self-esteem. Data were collected via online survey (Survey Monkey®) and proctored by the research assistant in an on-campus computer lab.

Implicit theory of intelligence Implicit theory of intelligence evaluates whether an

individual believes their intelligence is fixed or if their intelligence and ability can change. Implicit theory of intelligence originated from Dweck and her colleagues (Dweck, 1999; Dweck & Leggett, 1988) and is centered upon the idea that a person’s intelligence is recognized as a malleable trait. Individuals that believe that intelligence is fixed are characteristic of an entity, and those that believe intelligence is malleable and can accumulate are characters of an incremental mindset (Blackwell et al., 2007); that is, they believe in the value of effort and have been associated with higher academic goals. If an individual believes that intelligence is fixed (entity), it is likely that with or without a social intervention, their implicit mindset will not change. Conversely, if an individual believes their intelligence can change (incremental), it is possible that their implicit mindset and their ability can be altered due to an intervention. Fujii

&Uebuchi (2010) evaluated the online Implicit Theory of Intelligence theory among postsecondary undergraduate students and determined the instrument to be highly reliable.

Almost perfect scale The almost perfect scale reveals characteristics

associated with perfectionism. Those that obtain perfectionist qualities are either maladaptive or adaptive, while those that are not perfectionist are identified as non- perfectionists. Maladaptive individuals strive for unattainable ideals and adaptive individuals express flexibility based upon their motivation to achieve. Non-perfectionists are not naturally goal oriented and are not motivated by achievement. According to these classifications, in social settings maladaptive and non-perfectionist individuals may not naturally adjust to the conditions associated with a social interaction, while adaptive individuals can alter their behaviors to match a setting. Therefore, if an individual’s implicitness does not change, it could be due to having a maladaptive or non-perfectionist identity, and if an individual’s implicitness does change, it could be due to obtaining an adaptive identity. The Almost Perfect Scale was originated by Slaney and Ashby (1996), resulting in three different patterns: high standards, order, and discrepancy that differentiate individuals as having adaptive or maladaptive perfectionism. The research team utilized Rice et al.’s (2014) revised questionnaire which was tested among 749 individuals and determined to be valid and reliable. The higher score reflects a more maladaptive perfectionist trait that includes setting unrealistic standards, overreacting when not reaching such standards, and needing always to be in control. A lower score reflects a more adaptive perfectionist trait that includes the completion of tasks in good time and have high standards for their work. Adaptive perfectionists consider their strengths and limitations and don’t overexert themselves unless it really matters. In this study, the researchers utilized the Almost Perfect Scale at the Post- assessment phase, rather the pre- and post- phase.

Color-blind racial attitude Sculpted by the defining characteristics by Schofield

(1986) and Frankenberg (1993) on color-blind racial attitudes, the Color-Blind Racial Attitude Scale was developed and validated by Neville et al. (2000). Aspects of color-blind racial attitude are similar to what is commonly referred to as racial color blindness. The scale is based on the following assumptions:

(a) racism exists on structural and ideological levels (Thompson & Neville, 1999); (b) racism creates a system of advantages for Whites, mainly White elite, and disadvantages for racial and ethnic minorities (Thompson & Neville, 1999); (c) denial of these realities is the core component of color-blind racial attitudes; (d) people across racial groups can maintain a color-blind perspective; and (e) color- blind racial attitudes are cognitive in nature; they are part of a cognitive schema used to interpret racial stimuli. (Neville et. al., 2000, p. 61)

With these grounded assumptions focus on racial

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FAST FRIENDS attitudes and awareness, the Racial Color-Blind Attitude Scale evaluate individuals on three components: racial privilege, intuitional discrimination, and blatant racial issues (Neville et al., 2000).

Communal orientation Communal orientation measures how much an individual

cares for another’s wellbeing and how much one may value another person’s needs or feelings. Communal orientation was developed on the premise that relationship rules affect the giving and receiving of benefits depending on the type of connection and was studied, validated, and proven dependable by Clark et al. (1987). Specifically, according to Clark et al. (1987) high levels of communal orientation are associated with people who are more helpful than individuals with low communal orientation. If the communal orientation of an individual increases, the intervention may have increased their care, empathy, and willingness to help others of different racial ethnic backgrounds. If communal orientation does not change, then the intervention had no impact on how much an individual cares for others.

Collective self-esteem Personal identity and social identity are distinct attributes

of Tajfel and Turner’s social identity theory, meaning that social and personal are each a part of the construction of one’s identity (Riia & Crocker, 1992). However, theories in social psychology on self-esteem were considered a self- concept and individualistic. Riia and Crocker (1992) argued that individualistic attributes of self-esteem only revealed part of one’s self-concept and social behavior. To develop an approach to evaluating self-esteem that assesses individual differences of collective, Riia and Crocker (1992) developed the Collective Self-Esteem Scale. Collective self- esteem evaluates self-perception from how an individual interacts in social groups or with others. After exposure to the intervention, if collective self-esteem increases, the confidence and comfortability of individuals interacting with those that are different has increased. If collective self- esteem decreases, then the confidence and comfortability of interacting with those that are different has decreased. In 2010, Rossouw reevaluated the Riia Crocker questionnaire and still found it to be reliable and the elements of the collective self-esteem through a factor analysis evaluation.

Data Analysis

The quantitative data were analyzed using Statistical Package for the Social Sciences® (SPSS) version 24. The research objectives of this study guided the analysis. To address the five research objectives, descriptive statistics were used to describe each scale. Mean scores, standard deviations, and t-scores were calculated using SPSS. Such analysis was appropriate for the sampling size and research objectives in an exploratory study (Creswell, 2014).

Findings

This study consisted of incoming freshman majoring within a College of Agriculture. The student participants self-identified as either White, non-Hispanic or African American. After attending an orientation meeting, the students were separated into treatment and control groups, while treatment groups were paired with one participant in each pair representing a different racial identity. Students within the treatment group followed the Fast Friend protocol, a product originated from the University of California- Berkeley (Page-Gould et al., 2008).

Apparent limitations occurred with the small study population of both the treatment and control groups. Incoming students declared in the college of agriculture at the university that identified as African American, or White, non-Hispanic students were eligible for this study. Recruitment of African American students was challenging due to a small population of size within the agriculture college. Students selected for the treatment group were required to meet three times throughout the semester.

Research objective 1 sought to describe the difference in implicitness theory of intelligence of freshman college students that participated in the Fast Friends intervention and those that did not. An analysis of descriptive statistics shows the mean of the control group pretest as 3.7, and the mean of the control group posttest 3.4. The treatment group’s pretest means 3.4 and a 3.3 for the posttest (see Table 1). By the end of the semester, both the control and treatment group provided a slight decrease in their racial implicitness.

Table 1. Student Participants Implicitness by Assigned Group (n=34)

Implicitness Control (n = 14)

Treatment (n = 20)

m(SD) m(SD)

Pretest 3.7(.16) 3.4(.49)

Posttest 3.4(2.5) 3.3(.55)

Difference of Mean Score -.3 -.1

t-score -1.99 -.49

Cohen’s d 0.14 0.19

Note. Instrument based on a 6-Point Likert scale (1 - Strongly Agree, 6 – Strongly Disagree).

Research objective 2 sought to describe the difference in Almost Perfect Scale by the two groups. The Almost Perfect Scale was only reviewed at the end of the Fast Friends study. The Almost Perfect scale was based on a 7 – point Likert scale. As seen in Table 2, the mean of the control group was 5.1 and the treatment group showed a mean of 4.5. The mean differences were significant among the control group which reflected a large difference and being more maladaptive perfectionist (Urdan, 2010).

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FAST FRIENDS Table 2. Student Participants Almost Perfect Scale by Assigned Group (n=34)

Control Treatment

m(SD) m(SD)

Almost Perfect Scale 5.1(.64) 4.5(.65)

t-score difference 3.42*

Cohen’s d 0.93

Note. * = p < .05, Instrument based on a 7-Point Likert scale (1 - Strongly Disagree, 7 – Strongly Agree).

Research objective 3 sought to describe the difference in color blind attitude of freshman college students that participated in the Fast Friends intervention and those that did not. The Color-Blind Attitude scale was based on a 6 – point Likert scale. Table 3 shows a control group pretest mean of 3.5 and posttest of 3.6. The treatment group showed a pretest mean of 3.6 with no change on the posttest. Even after three months of conversation with someone of the differing race, the treatment group had zero change on the Color-Blind Attitude scale.

Table 3. Student Participants Color Blind Attitude by Experimental Group (n=34)

Color Blind Attitude Control Treatment

m(SD) m(SD)

Pretest 3.5(.25) 3.6(.36)

Posttest 3.6(.64) 3.6(.37)

Difference of Mean Score .1 0

t-score .23 .65

Cohen’s d 0,21 0.0

Note. Instrument based on a 6-Point Likert scale (1 - Strongly Agree, 6 – Strongly Disagree).

Research objective 4 sought to describe the difference in communal orientation of freshman college students that participated in the Fast Friends intervention and those that did not. The Communal Orientation scale was based on a 7 – point Likert scale. The control group pretest mean was 3.5 and the posttest was 3.9. The treatment group showed a pretest mean of 3.6 and a 3.6 for the posttest. No difference in the treatment and the control group occurred on the Command Orientation scale (see Table 4).

Table 4. Student Participants Communal Orientation Scale by Experimental Group (n=34)

Communal Orientation Control Treatment

m(SD) m(SD)

Pretest 3.5(.51) 3.6(.38)

Posttest 3.9(.52) 3.6(.31)

Difference of Mean Score .4 0

t-score 1.18 .29

Cohen’s d 0.78 0.0

Note. Instrument based on a 7-Point Likert scale (1 – Extremely Uncharacteristic of me, 7 – Extremely Characteristic of me).

Table 5. Student Participants Collective Self Esteem by Experimental Group (n=34)

Collective Self Esteem Control Treatment

m(SD) m(SD)

Pretest 3.9(5.1) 4.1(3.4)

Posttest 4.2(.32) 3.8(.39)

Difference of Mean Score .3 -.3

t-score 1.16* 2.33*

Cohen’s d 0.08 0.12

Note. * = p < .05, Instrument based on a (1 – Extremely Uncharacteristic of me, 7 – Extremely Characteristic of me).

Research objective 5 sought to describe the difference in collective self-esteem. The Almost Perfect scale was based on a 7 – point Likert scale. Although significance was not necessary, it was determined that both the treatment group and the control group had a small (d < .20) significant change (p < .05) in collective self-esteem. Table 5 describes the control group significantly increased while the treatment group significantly increased over the course of three months.

Summary

This study explored the effects of the treatment of cross- group friendships (Fast Friends) on racial implicitness. Previous research supports that cross-group friendships reduce racial anxiety (Page-Gould et al., 2008), and close relationships develop through social interactions with outgroup strangers predicted by positive experiences (Page- Gould et al., 2010). This study did not have findings like previous research. Individuals that received the treatment and those who did not receive the treatment failed to have significant difference between pretest and posttest or between control and treatment participants in evaluations

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FAST FRIENDS based upon implicit theory of intelligence, almost perfect attitude, color blind racial attitude, and communal orientation. However, results did indicate a significant decrease in collective self-esteem among treatment group participants.

Implicit Theory of Intelligence

Human psychology and behaviors are factors of implicit theories of intelligence (Cabello & Fernandez-Berrocal, 2015). Individuals with incremental intelligence tend to be goal oriented and consider making effort as necessary that is positive for improvement of malleable traits (Blackwell et al., 2007; Cabello & Fernandez-Berrocal, 2015; Dweck and Leggett, 1989). Based upon the findings, throughout the Fast Friends project, both the control and treatment group declined in their implicit intelligences. The research team was looking for efforts of incremental implicit change. Unfortunately, the results showed a fixed intelligence over the course of three months.

Almost Perfect Attitude

The Almost Perfect scale classifies individuals between adaptive perfectionism and maladaptive perfectionism or non- perfectionists and measures attitudes of individuals towards themselves, their performance, and attitudes towards others (Slaney et al., 2001). At the end of the timeline, a significant difference existed between the two groups. The control group revealed scores reflecting more maladaptive perfectionist traits, primarily in both Standards and Order (Wang & Slaney, 2015). Participants from the treatment group revealed more adaptive perfectionisms overall, but primarily in the deficiency domain. According to Slaney and Ashby (1996), people with maladaptive perfectionisms tend to be highly self-conscious and develop negative attitudes when events do not go as they desire. The negative attitudes are linked to psychological disorders such as depression and anxiety (2015). The time to address maladaptive personalities is at the post-secondary level as students are exposed to multicultural audiences. The lifetime damage that comes from maladaptive perfectionist traits correlates with imposter fears (Herman et al., 2013), internalized racism (Dancy & Jean-Marie, 2014), and resistance in multicultural awareness (Wang et al., 2014).

The importance of identifying students’ perfectionist traits among student interaction should be of high need within colleges of agriculture. By knowing the adaptive and maladaptive personalities, faculty can begin to navigate in efforts that help students obtain more adaptive traits. Faculty and academic advisors are encouraged to design assignments that allow students to establish ‘achievement- oriented’ mindsets rather than ‘failure-oriented’ mindsets. Achievement oriented students understand their limitations and work toward what they determine as successful rather than comparing to others and determining a failure. Group work can assist with transitioning students into an adaptive perfectionism. In the realm of multiculturalism, having students placed in culturally diverse group dynamics with assignments specific to everyone assist the maladaptive student to realize the project’s achievement is limited to the group.

Racial Color Blindness Attitude

Color Blind Racial Attitude scale is based on an individual’s awareness and racial attitudes. Color blind racial attitude evaluates individuals on three components: racial privilege, intuitional discrimination, and blatant racial issues (Neville et al., 2000). Based upon the findings, it was determined that the control group’s racial color-blindness increased over the three-month span of the study. Following the intervention, no change in color blind racial attitude was determined among the treatment group indicating the intervention had no impact on color blind racial attitude in both populations.

Communal Orientation

Communal Orientation scale measures how much an individual cares for another’s wellbeing and how much one may value another person’s needs or feelings. According to Clark et al. (1987) high levels of communal orientation are associated with people who are more helpful than individuals with low communal orientation. Individuals with high levels of communal orientation also expect others to help in return; much like a mutually beneficial transaction. Based upon the findings of both the control and treatment groups, only the control group improved their communal orientation over the span of the Fast Friends project, however, it was nonsignificant. The treatment group showed no change in communal orientation from pre- to post- assessment.

Collective Self-Esteem

Collective Self-Esteem scale measures self-perception from how an individual interacts in social groups or with others (Luhtanen & Crocker, 1992). According to the results, the control group improved over the timeline devoted to the Fast Friends project, while the treatment group decreased. The improvement of the control group was not significant; however, the decrease of the treatment group was determined to be a significant change. In addition, based on their pre- and post-results, there was a substantial difference between the control or treatment groups.

According to Cabello and Fernandez-Berrocal (2015) theories of implicitness act as structures of knowledge (Chiu et al. 1997; Plaks et al., 2009) and the way that people interpret and process individually, or of other objects such as people, generally mirrors implications of implicit theories (Dweck, 2012). Therefore, “implicit theories profoundly affect human behavior, and understanding natural variation in those theories may help predict how people will respond to particular stimuli, psychotherapy, or behavioral training,” (Cabello & Fernandez-Berrocal, 2015, p. 6). Prior to this study it was predicted that the Fast Friends intervention would impact the outlook, behavior, and implicitness of students by the interaction of cross-groups and the formation of cross-group friendships. More specifically it was projected that the Fast Friends program would assist in lowering racial anxiety, implicitness, prejudice, and racial colorblindness among entering freshmen.

Collective self-esteem measures self-image and self-

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FAST FRIENDS perception from how an individual interacts with others or in social groups (Luhtanen & Crocker, 1992). Therefore, it indicates a person’s self-perception and confidence when interacting with others of another social group. In this study, the treatment group’s collective self-esteem significantly decreased after participating in the Fast Friends intervention. The decrease of collective self-esteem for the treatment group and lack of change in all other categories potentially indicates that personal interaction with an individual of a cross group three times is not strong enough to change an entire mindset or perception of person. It is, however, influential enough to generate a new schema due to interactions, thus altering their collective self-esteem. The treatment group consisting of paired students, one African American and one White, non-Hispanic, had a decrease in their collective self-esteem or in other words, a decrease in their confidence when interacting with a cross- group member. This implies the intimacy of the Fast Friends project lowered the realization that incoming freshman are not as confident as they thought toward interacting with a cross group member.

Although a considerable drop in the treatment group's overall self-esteem may seem like a bad thing, it actually shows that there is an increase in intergroup self-awareness. After three intimate interactions with cross-group partners, over seven weeks, treatment group members decreased their self-perception and self-confidence when engaging in social settings. These interactions made an impression and changed their perception of interacting with a person of another race. Growth in self-awareness and the decrease in confidence suggests a change in perceptions of people that are different which could implicitly alter future social behaviors of students.

Recommendations

Changing students’ self-perception through interaction with a cross-group member can serve as a catalytic or beginning platform for a progression of building cultural competency throughout the course of their education. If this study was to be repeated, it is recommended that the study population be recruited from one or more freshman courses and the study be conducted during class hours. This would increase the student population and increase the accountability of students. It is also recommended future studies should employ a larger sampling size that would justify inferential statistical analysis.

Based on the negative change in collective self- esteem of treatment group members it is recommended that the colleges and universities foster more opportunities for individuals to have cross-group interactions that are integrated into curricular and non-curricular activities for students. Many of the current opportunities for students on campus are formed utilizing recruitment methods that are based on ‘like’ interests and characteristics. Examples of cross-group engagement include increasing active involvement in cultural groups on campus such as campus cultural centers and the creation of immersion experiences through students’ organizations. Off-campus engagement is also recommended such as cultural immersion and

relationship building with community organizations and stakeholders that will increase exposure and interaction with diverse communities, neighborhoods, and families. When engaging in the community Participatory Action Research (Reason & Bradbury, 2008) approaches are recommended in order to enrich experiences for both university and community members.

As shown in this exploratory study, three interactions may not be enough to alter the behavior of students. It is recommended that future studies extend beyond the scope of minimal cross-group interaction opportunities for students during their freshman year and consistently provide and measure these interactions throughout the entire college experience. Since developing culturally competent and self- aware students that are on track to reach autonomy during or soon after their college experience is advantageous for universities, future studies may reveal methods to achieve the desired outcomes of institutions. This can be greater achieved through quasi-experimental designs that measure the effects of programs and activities that go far beyond bringing students from diverse backgrounds into the same space. Future research in this line of inquiry may measure interactions on an intimate level, development cross-group friendship, student’s implicitness, pseudo-independent experiences, and cultural autonomy.

The research team recognized in the recruitment process, that many students were not ready to participate in a conversation with someone of a different racial group. When beginning the study, we were encouraged by the enthusiasm of college faculty and college administrators with the hopes that positive changes could encourage college-wide programs. Unfortunately, the results were alarming and new approaches are being taken for students to engage in more conversations. Since this study, the movement of the Office of Diversity has occurred, so it is near the students’ common area. Additional funds for undergraduate and graduate scholarships are established to assist in the increase of enrollment. Finally, another round of Fast Friends is already in the planning stages with the hopes of utilizing the college’s living communities (dormitories) and college-wide courses.

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ijerph-19-11981 (1).pdf

Citation: Si, W.; Tian, J.; Yan, Q.;

Wang, W.; Zhang, M. Research on the

Influence of Non-Cognitive Ability

and Social Support Perception on

College Students’ Entrepreneurial

Intention. Int. J. Environ. Res. Public

Health 2022, 19, 11981. https://

doi.org/10.3390/ijerph191911981

Academic Editors: Jesús De La

Fuente, Evangelina

Karagiannopoulou and Silvia

Pignata

Received: 15 August 2022

Accepted: 19 September 2022

Published: 22 September 2022

Corrected: 19 June 2025

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International Journal of

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Article

Research on the Influence of Non-Cognitive Ability and Social Support Perception on College Students’ Entrepreneurial Intention Wentao Si 1,†, Jiayi Tian 1,†, Qi Yan 1, Wenshu Wang 1 and Maocong Zhang 1,2,*

1 School of Public Administration, Shandong Normal University, Jinan 250014, China; 201829010205@stu.sdnu.edu.cn (J.T.)

2 Research Center for Educational Policy and Management, Shandong Normal University, Jinan 250014, China * Correspondence: zhangmc@sdnu.edu.cn † These authors contributed equally to this work.

Abstract: The entrepreneurship of college students is an important issue related to the harmony and sustainable development of society as a whole. At present, the existing research in the industry pays less attention to the influence mechanism of non-cognitive ability and social support perception on college students’ entrepreneurial intention. Using 450 survey data, this paper examines the relationship between non-cognitive ability and college students’ entrepreneurial intention in terms of five dimensions: openness, conscientiousness, extraversion, agreeableness, and emotional stability. At the same time, it focuses on the role of social environmental factors, namely, social support perception in the relationship between the non-cognitive ability and entrepreneurial intention, and explores the influence path. The results show that openness, conscientiousness, extroversion, and emotional stability have significant positive effects on entrepreneurial intention; agreeableness has no significant effect on entrepreneurial intention; openness, conscientiousness, extraversion, agreeableness, and emotional stability have significant positive effects on social support perception. The mediating effect of social support perception is as follows—it is part of the intermediary effect between openness, conscientiousness, extraversion, and emotional stability on entrepreneurial intention; within the influence of agreeableness on entrepreneurial intention, it plays a complete intermediary role. This paper enriches the research results on the impact of non-cognitive ability on entrepreneurial intention, reveals the intermediary effect of social support perception on the impact of non-cognitive ability on college students’ entrepreneurial intention, and broadens the field of vision for the study of college students’ entrepreneurial intention. The research results can provide a decision-making reference for the promotion of the entrepreneurial intention of college students, alleviating the employment pressure of college graduates in China and promoting sustainable economic development.

Keywords: non-cognitive ability; social support perception; college students’ entrepreneurial intention; intermediary effect

1. Introduction

With the acceleration of social transformation in China, the employment pressure of college students is increasing. Entrepreneurship has gradually become an important career choice for college students and graduates. Encouraging college students’ innovation and entrepreneurship is of great significance in order to relieve the employment pressure of col- lege students and promote economic development [1]. Therefore, to guide and encourage college students to start their own businesses, the first problem to be solved is to help college students to form their entrepreneurial intention. The entrepreneurial intention of college students is essentially an inner activity that predicts the possibility of college students start- ing a business in the future [2]. Therefore, entrepreneurial intention is the best predictor of entrepreneurial behavior [3]. Of course, no human action can be initiated without intending

Int. J. Environ. Res. Public Health 2022, 19, 11981. https://doi.org/10.3390/ijerph191911981 https://www.mdpi.com/journal/ijerph

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to do it. However, simply intending to become an entrepreneur does not provide a certain type of entrepreneurial behavior [4]. In this matter, clarifying the concept of entrepreneurial behavior is of utmost importance. However, a study found that entrepreneurial behavior is driven by entrepreneurial intention, and without entrepreneurial intention, there would be no entrepreneurial behavior among college students [5]. Therefore, entrepreneurial intention has gradually become a focus of researchers.

The entrepreneurial intention of college students is increasingly affected by a wide range of social backgrounds and social structures. The two most important factors in the influencing mechanism of entrepreneurial intention are the personal factors of college students and external social environment factors. First of all, from the perspective of college students’ own psychological factors, human capital is an important factor affecting their entrepreneurial intention [6]. Human capital factors such as gender, age, skills, physical fitness, knowledge, and awareness levels have a profound impact on this process [7,8]. In addition, Heckman proposed, in the new human capital theory, that non-cognitive ability is a comment on personal ability and plays an important role in personal develop- ment [9]. Previous studies have shown that non-cognitive ability can significantly improve the adaptability of workers in unstable systems and ensure that individuals still maintain the same thinking, feeling, and behavior patterns in unexpected situations [10]. At the same time, non-cognitive ability also has an important impact on workers’ income market competitiveness, career choices, and the social behavior of workers [11]. Specifically, in the aspect of college students’ entrepreneurship, non-cognitive ability affects the process of college students’ entrepreneurship to a certain extent by affecting their behavior in the face of external social aspects [12,13]. Second, college students have the personality characteristics of entrepreneurs—that is, non-cognitive ability—but they also need support from society to aid their entrepreneurial success [14]. Entrepreneurship is an economic activity; it concerns human action initiated in an economic environment based on voluntary exchange [15]. The entrepreneurial intention of college students is inevitably affected by the external social environment. As highlighted in some economic literature, college student entrepreneurship requires an appropriate institutional framework [16]. In the study of institutional framework, most scholars divide the institutional environment into formal institutions and informal institutions [17]. As two inseparable parts of the system, both systems are a unity of opposites, which are interdependent and can be transformed into each other under certain conditions [18]. Formal institutions are always associated with state power or an organization, and refer to such behavioral norms, such as various written laws, regulations, policies, rules, contracts, etc. [19]. They are identified in some definite form and are monitored and enforced by the actor’s organization. Strong formal institutions can increase the efficiency of business transactions and reduce transaction costs, thereby enabling individuals to profit from business activities [20]. A well-developed formal system increases the possibility of potential entrepreneurs to obtain business value from entrepreneurial opportunities, thus increasing their entrepreneurial willingness [21]. In environments that support entrepreneurship, regulatory-related barriers to entrepreneur- ship are lower, and individuals with high entrepreneurial self-efficacy are more willing to start new businesses. Informal systems refer to the unwritten restrictions on human behavior, which is a concept opposite to formal systems such as law, and include social code of conduct, social norms, moral concepts, customs, and cultural values [22]. Different from formal institutions, informal institutions are spontaneous, non-coercive, extensive, and persistent. Informal institutions indicate a state’s expected behavior and sanctions for behavior that does not adhere to social norms and values [23]. In the context of a culture that supports entrepreneurship, individuals will feel the supportive attitude of the entire society towards entrepreneurship [24]. This will stimulate individuals’ confi- dence in successful entrepreneurship and increase their willingness to start a business [25]. However, if a country (region) has a negative prejudice against the public image of en- trepreneurs, the utility of entrepreneurial activities is likely to be underestimated, and people will be reluctant to participate in entrepreneurial activities [26]. Therefore, a coun-

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try’s (region’s) attitude towards entrepreneurship will affect entrepreneurial activities. A culture that supports entrepreneurship makes individuals feel secure in the environment and encourages them to participate more actively in entrepreneurship [27]. At this point, individuals will also appreciate the entrepreneurial career more and believe in their ability to overcome obstacles. Therefore, other things being equal, the more positive the informal system about entrepreneurship, the stronger the influence of entrepreneurial self-efficacy on entrepreneurial intention.

The importance of institutions to the entrepreneurial intention of college students is self-evident. In addition, social support perception is a force or factor that promotes human development in the social environment and is defined as the subjective feeling and evaluation of entrepreneurial individuals for their degree of support from the outside world [28]. Therefore, perception of social support is also one of the important factors affecting entrepreneurship. Existing studies have also shown that college students with a high perception of social support are more likely to engage in entrepreneurial activities. The essence of social support is the intimate relationships between people. These supports come from both tangible material support, action support, information support, and feedback support, as well as intangible spiritual support and emotional support. At the same time, social support is not only a type of one-way care or help, but also a form of social exchange and social interaction between people in most cases [29].

Based on the above research background, this paper takes the university town of Jinan City, Shandong Province as a case area, and systematically studies the relationship between college students’ non-cognitive ability and entrepreneurial intention from a multi- dimensional perspective, as well as the mediating role of social support perception in the relationship between the two, with a view to fundamentally understand the factors and mechanisms that affect college students’ entrepreneurial willingness and provide entrepreneurial guidance and services to college students in a targeted manner. At the same time, it provides decision-making reference for promoting social harmony, stability, economic health, and sustainable development.

2. Theoretical Framework 2.1. Concept Definition 2.1.1. The Definition of Non-Cognitive Ability

In psychology, non-cognitive ability is considered a personality trait, and refers to the psychological factors that are displayed by workers; they have an important impact on individual social, economic, life, and other behaviors. It is widely used in psychology, economics, education, and other fields [29,30]. Based on the Big Five Personality Scale, this study divided non-cognitive abilities into five dimensions: openness, conscientiousness, extraversion, agreeableness, and emotional stability [31,32]. Openness refers to the ten- dency to accept new things; the more open individuals are, the stronger their innovation ability and curiosity [31,32]. Conscientiousness refers to having a sense of responsibility and diligence; the more conscientious the individual is, the stronger the persistence of their goal-oriented behavior, and the higher their sense of achievement in their learning or career [31,32]. Extraversion means that the individual’s attention is not focused on the subjective inner world, but on the external world of people and things; the stronger the ex- troversion, the more positive their attitude towards facing challenges and the stronger their social ability [31,32]. Agreeableness refers to an individual’s tendency to deal with affairs in a cooperative and selfless manner; the stronger the agreeableness, the easier it is to res- onate, empathize, and gain more trust and support in interpersonal communication [31,32]. Emotional stability refers to the predictability and consistency of emotional feedback, and individuals with strong stability experience no drastic emotional changes [31,32].

2.1.2. The Definition of Entrepreneurial Intention

Entrepreneurial intention is the subjective attitude of possible entrepreneurs regarding whether to carry out entrepreneurial activities, and it is a good predictor of entrepreneurial

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behavior [33]. This study measures entrepreneurial intention from the three dimensions of entrepreneurial feasibility, entrepreneurial propensity, and entrepreneurial desirability [34]. Entrepreneurial feasibility includes personal control and a sense of responsibility. Personal control is a self-influencing process in which individuals achieve their expected goals through self-guidance and self-motivation, and this process mainly focuses on individ- ual behavioral norms [35]. Responsibility awareness is the psychological characteristic of individuals consciously and conscientiously fulfilling their responsibilities in the pro- cess of starting a business and transforming responsibility into actions. This is a type of conscious awareness and an indispensable personal ability in the entrepreneurial process. Entrepreneurial propensity refers to the probability that entrepreneurial behavior may occur—that is, an individual’s subjective willingness to engage in entrepreneurial activ- ities in the future [36]. The entrepreneurial behavior tendency is the key link between entrepreneurial intention and formal entrepreneurial behavior, which can provide a more reliable basis for predicting whether an individual can truly start a business in the fu- ture [37]. Entrepreneurial desirability includes innovation orientation and achievement orientation. The first trait is innovation orientation. Entrepreneurship is the process of realizing innovation. The premise for entrepreneurs to obtain profits is to use new business models, new technologies, new services, and new products to meet market demands, thereby creating unique value. The second trait is achievement orientation, which shows that individuals pay attention to the consequences, efficiency, and standards, pursue the improvement of products or services, and strive to optimize the use of resources in the organization. Achievement orientation is also an important part of entrepreneurship [38].

2.1.3. Definition of Social Support Perception

Social support perception is an individual’s sense of support and care from oth- ers [28,39]. For college students and college graduates, the perception of social support specifically involves the material and spiritual support received from five types of people: family, relatives, friends, classmates, and lovers [40]. This paper selects the Perceived Social Support Scale (PSSS) compiled by Zimet, which is used to measure the degree of individual perception of support from various social support sources, and the total score reflects the total degree of social support felt by the individual [41]. In other words, the perception of social support is measured from the perspective of three sources of social support, namely family, friends, and others, emphasizing the individual’s understanding and comprehension of various sources of social support. Family support refers to the material and spiritual support that parents and relatives can provide to college students, such as providing entrepreneurial capital support, assisting in entrepreneurial decisions, and spiritual incentives. Friends support is mainly the encouragement and help given by friends when entrepreneurs encounter difficulties, including emotional support, informa- tion support, instrumental support, etc. Support from others can be understood as the help that individuals receive from teachers, relatives, classmates, colleagues, and other important individuals through social connections to reduce psychological stress, relieve mental tension, and improve adaptability.

2.2. Theoretical Analysis 2.2.1. The Direct Influence of Various Dimensions of Non-Cognitive Ability on Entrepreneurial Intention

As a type of implicit human capital, non-cognitive ability plays a significant role in promoting individuals’ entrepreneurial intention and entrepreneurial decision-making [42]. Openness is a concentrated expression of individual wisdom and creativity. The en- trepreneurial ability of individuals is closely related to their innovation ability, and indi- viduals with a high sense of achievement, self-confidence, creativity, pressure resistance, independent preference, and other non-cognitive factors are more likely to accept new things. Therefore, such entrepreneurs with open endowment tend to possess more in- novative spirit and stronger entrepreneurial intention [43–45]. Conscientiousness is the

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embodiment of responsibility and perseverance. Due to the coexistence of high returns and high risks in entrepreneurial activities, only individuals with strong conscientiousness face risks such as capital loss and entrepreneurial failure, and higher entrepreneurial returns can help to stimulate their personal entrepreneurial intention [43–45]. Extroversion includes the characteristics of being active and lively, possessing strong social skills, and being helpful and hopeful regarding the future, and these characteristics are helpful to enhance the confidence and hope of entrepreneurial success and enhance personal entrepreneurial motivation and willingness [43–45]. Agreeableness is reflected in being able to gain the trust and support of others. For entrepreneurial activities, entrepreneurs need to be more decisive and determined to make decisions. However, the rapidly changing social and market environment has put forward increasingly high requirements for the entrepreneur’s decision-making and execution ability. In this process, the more the entrepreneur can gain the trust of others, the more able they are to make and execute decisions [43–45]. Emotional stability is mainly manifested in the absence of obvious emotional fluctuations. Entrepreneurs with stable emotions are not easily affected by negative emotions, nor are they prone to anxiety and depression, and their entrepreneurial intentions are easy to achieve [43–45]. In conclusion, hypothesis H1 is proposed: all dimensions of non-cognitive ability have a positive effect on entrepreneurial intention.

2.2.2. The Mediating Effect of Social Support Perception on Non-Cognitive Ability Dimensions and Entrepreneurial Intention

1. The direct influence of non-cognitive ability on social support perception

Each dimension of non-cognitive ability is closely related to social support percep- tion [46]. Openness tends to be conducive to innovation rather than being constrained by the current environmental resources; individuals will identify and make use of various opportunities, and it is easier to perceive social support in a timely manner [47,48]. In terms of conscientiousness, the emergence of conscientious behavior can bring more pleasure to others, and the pleasure of others can also lead individuals to actively obtain more social support [46]. In terms of extraversion, entrepreneurs with strong extraversion expand their social capital by building interpersonal networks and improving their social communica- tion ability. They usually have relatively strong interpersonal communication ability and resource transformation ability, and the interpersonal network circle is often more diver- sified, so it can provide increasingly higher-quality external support for entrepreneurial activities [46,48]. In the aspect of humanity, the more cooperative entrepreneurs are, the more honestly they can communicate with people, and their ability to obtain and maintain social support and control their emotions and individual feelings is usually outstand- ing [46,48]. In terms of emotional stability, entrepreneurs who can easily understand and accept others and whose emotions are not affected by the outside world are less likely to deny themselves, and their social support perception is also stronger [46,48]. Based on the above analysis, the following hypothesis is proposed: H2: non-cognitive abilities have a positive effect on the perception of social support.

2. The direct impact of social support perception on entrepreneurial intention

Entrepreneurship is a social activity, and choosing to start a business is a major decision in the career planning of college students. Therefore, college student entrepreneurs will seek advice and support from those around them [29,49]. When college students face a complex entrepreneurial environment, good social support is also conducive to better reducing entrepreneurial pressure, adapting to the entrepreneurial environment, and increasing their entrepreneurial rate [50]. For entrepreneurial feasibility, the perception of social support is helpful for college students to adjust psychologically, enhance their self-efficacy, strengthen their personal control, and improve their ability to deal with negative events [36,49]. For entrepreneurial tendency, the perception of social support, as an important factor affecting people’s physical and mental health, can enhance the ability of individuals to respond to stressful situations [36,49]. For entrepreneurial aspiration, positive opinions of others can allow entrepreneurs to have a higher evaluation of their ability to control and cope with

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multiple tasks [37,49]. Therefore, the support environment in which the individual lives and the level of support that the individual perceives will directly affect the individual’s corresponding changes in their own behavior and decision-making. Based on the above analysis, the following hypothesis is proposed: H3: the perception of social support has a positive effect on entrepreneurial intention.

3. Mediating effect between perception of social support and the influence of non- cognitive ability on entrepreneurial intention

The degree to which individuals perceive that they are being supported and cared for by others is not only directly related to the quantity or quality of their supportive interactions with others, but is also affected by various dimensions of the non-cognitive abilities of the recipients [46]. Social support perception can also guide individuals to reduce their psychological stress, reduce tension, and enhance their adaptability [28], so as to stimulate greater entrepreneurial enthusiasm and entrepreneurial intention among college students [46]. When an individual’s strong non-cognitive ability is coupled with entrepreneurial support from important groups (parents, teachers, friends, etc.), their entrepreneurial intention will be significantly improved [51]. In other words, while non-cognitive ability directly affects entrepreneurial intention, it also indirectly affects entrepreneurial intention through social support perception [52]. Therefore, the three variables have a logical relationship of “non-cognitive ability (openness, conscientiousness, extraversion, agreeableness, and emotional stability)→ social support perception→ en- trepreneurial intention”. Accordingly, the following research hypothesis is proposed: H4: the perception of social support plays a mediating role between non-cognitive ability and entrepreneurial intention.

To summarize, this paper attempts to incorporate non-cognitive ability, social support perception, and entrepreneurial intention into the same analysis framework (Figure 1). It is intended to identify the functional path of non-cognitive ability and social support percep- tion with regard to entrepreneurial intention by constructing the structural relationship model of “non-cognitive ability—social support perception—entrepreneurial intention” in relation to college students. The model consists of two parts: the first part is the mechanism of non-cognitive ability’s effect on entrepreneurial intention; the second part is the mediat- ing effect of social support perception between non-cognitive ability and entrepreneurial intention (Figure 1).

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Figure 1. Theoretical framework diagram.

3. Method 3.1. Participants

The empirical research data in this paper were obtained from questionnaires. The “Questionnaire for entrepreneurial intention of College Students in Jinan” was created, which includes a total of 77 items of investigation. As the provincial capital city, Jinan has a certain representativeness of the whole country, so the research conclusions obtained by taking Jinan as a case area have certain reference value for the exploration of the entrepre- neurship of college students in large- and medium-sized cities throughout the country.

This study adopted a combination of stratified sampling and random sampling for research. First, we took districts as the primary sampling unit in Jinan City; according to the regional economic development status, four districts—Lixia District, Zhangqiu Dis- trict, Licheng District, and Changqing District—were selected as the sample survey areas. Among them, Lixia District had the highest economic development level in Jinan City, Zhangqiu District and Licheng District were in the middle level, and Changqing District had a relatively weak economic development level. Second, one undergraduate and one college were randomly selected in each district. Finally, 40–70 college students were ran- domly selected from each sample college according to a certain proportion, and the survey was conducted in the form of one-on-one interviews. Data collection was divided into two stages: pre-investigation and formal investigation. From July to September 2021, the pre- investigation stage was carried out. One hundred questionnaires were collected. After re- liability and validity analysis, some items were improved. In order to ensure the effective- ness of the questionnaire, the research team orally modified the questionnaire questions. During October–December 2021, the formal research stage took place, wherein 600 ques- tionnaires were officially submitted, and 450 questionnaires were considered valid. The sample descriptive statistics are shown in Table 1.

Figure 1. Theoretical framework diagram.

3. Method 3.1. Participants

The empirical research data in this paper were obtained from questionnaires. The “Questionnaire for entrepreneurial intention of College Students in Jinan” was created,

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which includes a total of 77 items of investigation. As the provincial capital city, Jinan has a certain representativeness of the whole country, so the research conclusions ob- tained by taking Jinan as a case area have certain reference value for the exploration of the entrepreneurship of college students in large- and medium-sized cities throughout the country.

This study adopted a combination of stratified sampling and random sampling for research. First, we took districts as the primary sampling unit in Jinan City; according to the regional economic development status, four districts—Lixia District, Zhangqiu District, Licheng District, and Changqing District—were selected as the sample survey areas. Among them, Lixia District had the highest economic development level in Jinan City, Zhangqiu District and Licheng District were in the middle level, and Changqing District had a relatively weak economic development level. Second, one undergraduate and one college were randomly selected in each district. Finally, 40–70 college students were randomly selected from each sample college according to a certain proportion, and the survey was conducted in the form of one-on-one interviews. Data collection was divided into two stages: pre-investigation and formal investigation. From July to September 2021, the pre-investigation stage was carried out. One hundred questionnaires were collected. After reliability and validity analysis, some items were improved. In order to ensure the effectiveness of the questionnaire, the research team orally modified the questionnaire questions. During October–December 2021, the formal research stage took place, wherein 600 questionnaires were officially submitted, and 450 questionnaires were considered valid. The sample descriptive statistics are shown in Table 1.

Table 1. Descriptive statistics of sample.

Project Category Frequency Percentage

Gender Male 234 52

Female 216 48

Grade

Freshman 93 20.7 Sophomore 105 23.3 Junior year 99 22 Senior year 153 34

Type of School Undergraduate 354 78.7 Specialist 96 21.3

3.2. Instruments

Based on domestic and foreign mature scales, combined with the research objectives, the scales of non-cognitive ability, social support perception, and entrepreneurial intention were designed. Each variable used a 5-point Likert scale, with “1” to “5” representing “strongly disagree”, “disagree”, “generally”, “agree” and “strongly agree”, respectively.

3.2.1. Independent Variable: Non-Cognitive Ability

According to the “Big Five personality” model, with reference to the relevant research of Liu Chuanjiang [53], Roger [54], and Neneh [55], and in combination with the Revised NEO (Neuroticism-Extraversion-Openness) Personality Questionnaire, the non-cognitive ability questionnaire was designed, focusing on the five dimensions of openness, conscien- tiousness, extraversion, agreeableness, and emotional stability (Table 2).

3.2.2. Mediating Variable: Perception of Social Support

We selected the Perceived Social Support Scale (PSSS) compiled by Zimet. The Per- ceived Social Support Scale is divided into three dimensions and twelve items, including family support, friends’ support, and others’ support. It is used to measure the individual’s perception of the support received from family members, friends, and other people, and

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to reflect the total social support felt by the individual with the total score. It is shown in Table 3.

Table 2. Evaluation indicators of non-cognitive ability.

Dimension Subdimension Index Mean Standard Deviation

Openness

Curiosity

C1: Interested in many different things 3.69 1.118

C2: A thoughtful person 3.78 1.145 C3: Hope to experience a new way of life 3.75 1.052

Action force

C4: Likes to take on challenges 3.74 1.049 C5: Activities organized by participating units 3.71 1.082

C6: Have their own hobbies and be able to stick to them 3.73 1.055

Imagination

C7: Can find smart ways to do things 3.68 1.073

C8: Imaginative people 3.69 1.023 C9: Be creative and come up with new ideas 3.7 1.085

Conscientiousness

Sense of responsibility

C10: I can concentrate on completing the work 3.88 0.929

C11: Trustworthy 4.03 0.964 C12: People around me praise me for being responsible 3.98 0.999

Organized

C13: Be organized 4.05 1.087 C14: Habit of keeping things neat and orderly 3.98 0.947

C15: Have a plan 3.94 0.915

Effort level

C16: At work, I try my best to do everything 4.01 0.963

C17: Efficient, work from beginning to end 4.11 1.105

C18: Perseverance and perseverance to get things done 3.97 0.964

C19: Work hard to achieve your goals 4.03 0.971

C20: People who constantly demand improvement 3.97 0.966

Extraversion

Social contact

C20: I like to make friends 3.69 1.192 C21: Talkative 3.62 1.021 C22: I will not reject attending gatherings with many people 3.7 1.116

Decisive

C23: Dare to express one’s opinion 3.67 1.131 C24: Strong and confident character 3.61 1.029

C25: Affect others 3.69 1.105

Vitality

C26: When I’m around, I’m usually not cold 3.67 1.088

C27: Energetic 3.65 1.105 C28: Passionate 3.69 1.141

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Table 2. Cont.

Dimension Subdimension Index Mean Standard Deviation

Agreeableness

Altruism

C29: Willing to pay time cost for others 3.85 1.106

C30: Make people around you feel at ease 3.79 1.089

C31: Do my best to help others 3.71 1.01

Compliance

C32: Obey social order 3.75 1.146 C33: Willing to make friends with locals 3.73 1.038

C34: Always be polite to others 3.77 1.101

Trust

C35: If others have bad experiences, I will be very sympathetic

3.72 1.079

C36: Think of people in the best way 3.78 1.085

C37: Easy to get close to others 3.73 1.062

Emotional Stability

Anxiety

C38: Rarely feel anxious 3.86 1.076 C39: Calm and good at dealing with pressure 3.78 1.008

C40: Don’t worry too much 3.73 0.97

Depression

C41: Be satisfied with yourself 3.9 1.122 C42: Feel safe in life 3.79 1.077 C43: Rarely unhappy in life 3.83 1.022 C44: Stay positive despite setbacks 3.8 0.986

Vulnerability C45: Mood is not easy to swing 3.81 1.106 C46: Rarely gets angry with others 3.78 1.133 C47: Can control one’s emotions 3.8 1.038

Table 3. Evaluation index of local adaptability.

Dimension Index Mean Standard Deviation

Family Support

B1: My family can help me in a concrete way 3.69 1.118 B2: I am able to get emotional help and support from my family when needed 3.78 1.145

B3: I can talk to my family about my problems 3.75 1.052 B4: My family is willing to help me make decisions 3.74 1.049

Friends Support

B5: My friends can really help me 3.88 0.929 B6: I can count on my friends in times of trouble 4.03 0.964 B7: My friends can share happiness and sadness with me 3.98 0.999 B8: I can discuss my problems with my friends 4.05 1.087

Other Support

B9: Some people (teachers, relatives, classmates) will be by my side when I have a problem 3.69 1.192

B10: I can share happiness and sadness with some people (teachers, relatives, classmates) 3.62 1.021

B11: Some people (teachers, relatives, classmates) are a real source of comfort when I’m in trouble 3.7 1.116

B12: There are people in my life (teachers, relatives, classmates) who care about my feelings 3.67 1.131

3.2.3. Dependent Variable: College Students’ Entrepreneurial Intention

Referring to the entrepreneurial problems of college students discussed by Kim and Park, and the entrepreneurial intention scale developed by Fan Wei and Wang Chong- ming, in this paper, entrepreneurial intention was evaluated using 25 questions focusing

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on three dimensions, namely entrepreneurial feasibility, entrepreneurial propensity, and entrepreneurial desirability [56–58], as shown in Table 4.

Table 4. The evaluation index of entrepreneurial intention.

Dimension Index Mean Standard Deviation

Entrepreneurial Feasibility

A1: I started a business because it gave me the opportunity to make a difference 3.69 1.118

A2: I already have the interpersonal skills needed to start a business 3.78 1.145

A3: I feel energized when working in a creative, passionate and dynamic environment 3.75 1.052

A4: I have the self-confidence needed to start a business 3.74 1.049

A5: I already have the organizational and management skills needed to start a business 3.71 1.082

A6: I already have the ideas needed to start a business 3.73 1.055

Entrepreneurial Propensity

A7: I like to do challenging work 3.88 0.929 A8: I think my experience can meet the needs of future work 4.03 0.964

A9: I have now started to prepare to start a business 3.98 0.999

A10: The current entrepreneurial environment is suitable for me to start a business 4.05 1.087

A11: I am excited when there are unusual solutions to work problems 3.98 0.947

Entrepreneurial Desirability

A12: I have communicated my intention to start a business with my family or friends 3.69 1.192

A13: I already have the teamwork skills needed to start a business 3.62 1.021

A14: I’m already spending time learning about entrepreneurship 3.7 1.116

A15: I already have the perseverance needed to start a business 3.67 1.131

A16: I already have the financial conditions needed to start a business 3.61 1.029

A17: I already have the learning skills needed to start a business 3.69 1.105

3.3. Procedure

All study procedures were approved by the researchers’ institutional review board and the school’s administration. On the day of college student data collection, trained study staff (graduate researchers and undergraduate research assistants) conducted random interviews on campus. All 600 (100%) college students provided consent and completed the survey. Survey items were read aloud by researchers while college students responded on their own paper copy. Other members of the research team were available to answer questions and provide assistance as needed. Surveys were typically completed within 30 min. For college student participation, each student received a $1 donation for school supplies.

3.4. Data Analysis

This study applied Amos21.0 (IBM, New York, NY, USA) to test and analyze the internal mechanism of the impact of non-cognitive ability and social support perception on entrepreneurial intention. The structural equation model consists of three latent variables, namely non-cognitive ability, social support perception, and entrepreneurial intention, each of which was measured by multiple items in the scale. In order to further verify

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the mediating effect and influence mechanism of social support perception, the bootstrap method was used.

4. Empirical Test and Result Analysis 4.1. Analysis of Group Differences 4.1.1. Analysis of Variance

One-way analysis of variance (one-way ANOVA) was used to compare the means of multiple samples in a completely random design. Its statistical purpose is to infer whether the means of the population represented by each sample are equal. If the p-value is less than 0.05, it indicates that there is a significant difference.

1. Differences in entrepreneurial intention of different grades

Through one-way analysis of variance, we compared the entrepreneurial intention of different grades and obtained the above results. There were significant differences in entrepreneurial intention among different grades (p < 0.05). From the average value, it can be seen that the entrepreneurial intention of senior students is stronger (Table 5).

Table 5. Differences in entrepreneurial intention of different grades.

Variable Category N Mean Standard Deviation F Salience

Entrepreneurial Intention

Freshman 93 3.262 0.588

4.687 0.003 Sophomore 105 3.355 0.557 Junior year 99 3.356 0.578 Senior year 153 3.520 0.519

2. Differences in entrepreneurial intention with frequent participation in entrepreneurial forum lectures

Through one-way analysis of variance, the above results were obtained by comparing the willingness to participate in entrepreneurial forum lectures. There was a significant difference in the willingness to start a business depending on whether the respondents often participated in the entrepreneurial forum lectures (p < 0.05). From the average value, it can be seen that those who regularly participate and those who always participate have stronger entrepreneurial intention (Table 6).

Table 6. Differences in entrepreneurial intention with frequent participation in entrepreneurial forum lecture.

Variable Category N Mean Standard Deviation F Salience

Entrepreneurial Intention

Never participate 88 3.283 0.604

3.362 0.01 Participate occasionally 198 3.358 0.545

Uncertain 45 3.366 0.504 Participate often 63 3.493 0.602

Always participate 56 3.591 0.508

4.1.2. T-test

1. Analysis of differences in entrepreneurial intention in different school types

Using a t-test to compare the entrepreneurial intention in different school types, the above results were obtained. There were significant differences in entrepreneurial intention among different school types (p < 0.05). It can be seen from the average that the number of college students was larger than that of undergraduate students (Table 7).

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Table 7. Analysis of differences in entrepreneurial intention in different school types.

Category N Mean Standard Deviation t p

Entrepreneurial Intention Undergraduate 354 3.3544 0.56121 −2.741 0.006Specialist 96 3.5306 0.5491

2. Analysis of differences in entrepreneurial intention at the university level and above regarding participation in college student entrepreneurial competitions and win- ning awards

Through the t-test, the entrepreneurial intention in terms of participation in college entrepreneurship competitions and winning awards at the school level and above was compared, and the above results were obtained (p < 0.05). From the average value, it can be seen that those who have participated in an entrepreneurship competition are more willing to start a business than those who have not (Table 8).

Table 8. Analysis of differences in entrepreneurial intention at school level and above regarding participation in college student entrepreneurship competitions and winning awards.

Category N Mean Standard Deviation t p

Entrepreneurial Intention Yes 164 3.487 0.546 2.733 0.007NO 286 3.338 0.566

4.2. Reliability and Validity Test 4.2.1. Reliability Test

After reliability analysis of 48 items for the 5 dimensions of the independent variable of non-cognitive ability, 12 items of the intermediary variable of social support perception, and 17 items of the dependent variable of entrepreneurial intention, the Cronbach α coefficients were found to all be greater than 0.7, indicating that this part of the questionnaire had good reliability (Table 9).

Table 9. Reliability analysis of variables.

Variable/Dimension Number of Items Cronbach’s Alpha

Entrepreneurial Intention 17 0.88 Entrepreneurial Feasibility 6 0.904 Entrepreneurial Propensity 5 0.883 Entrepreneurial Desirability 6 0.914

Social Support 12 0.921 Family Support 4 0.882 Friends Support 4 0.9 Other Support 4 0.877

Non-Cognitive Abilities 48 0.956 Openness 9 0.944

Conscientiousness 11 0.943 Extraversion 9 0.915

Agreeableness 9 0.928 Emotional Stability 10 0.948

4.2.2. Validity Test

As shown in Table 10, the KMO (Kaiser-Meyer-Olkin) values of non-cognitive ability, perception of social support, and entrepreneurial intention were 0.961, 0.921, and 0.917, which are all greater than 0.70, indicating that the questionnaire was suitable for factor analysis. The Bartlett sphericity test results showed that the significant probability corre- sponding to the approximate chi-square value was 0.000 (p < 0.01), so the validity structure was good. The total variance explained rate was 68.717% and 68.16%, greater than 60%, so

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the validity of the scale was considered to be good. The loading of each measurement item was higher than 0.5, there was no double factor loading, and the measurement items under each dimension were aggregated according to the theoretical distribution, indicating that the questionnaire had good content validity (Table 10).

Table 10. KMO and Bartlett test.

Variable KMO

Bartlett’s Sphericity Test

Approximate Chi-Square df Sig.

Entrepreneurial Intention 0.917 4434.751 136 0.000 Social Support 0.921 3502.37 66 0.000

Non-Cognitive Abilities 0.961 15285.919 1128 0.000 Overall Questionnaire 0.952 24583.693 2926 0.000

As shown in Table 11, confirmatory factor analysis results showed that the standard- ized factor loading of each item was greater than 0.5, and the standard error value S.E. was also less than the standard of 0.5, which proved that the validity of the questionnaire was good. At the same time, the AVE (Average Variance Extracted) of each dimension was greater than 0.5, and the square root of the AVE was greater than the correlation coefficient between the variables, indicating that the scale had good convergent and discriminant validity among the variables (Table 11).

Table 11. Factor analysis results of the overall scale.

Measurement Item Ingredients

1 2 3 4 5 6 7 8 9 10 11

A1 0.778 A2 0.791 A3 0.8 A4 0.745 A5 0.786 A6 0.775 A7 0.703 A8 0.747 A9 0.745 A10 0.759 A11 0.751 A12 0.757 A13 0.78 A14 0.755 A15 0.784 A16 0.801 A17 0.802 B1 0.714 B2 0.75 B3 0.753 B4 0.773 B5 0.741 B6 0.686 B7 0.748 B8 0.748 B9 0.708

B10 0.734 B11 0.703 B12 0.724

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Table 11. Cont.

Measurement Item Ingredients

1 2 3 4 5 6 7 8 9 10 11

C1 0.795 C2 0.79 C3 0.773 C4 0.769 C5 0.764 C6 0.778 C7 0.817 C8 0.785 C9 0.81

C10 0.736 C11 0.723 C12 0.716 C13 0.719 C14 0.696 C15 0.729 C16 0.734 C17 0.755 C18 0.748 C19 0.727 C20 0.743 C21 0.772 C22 0.757 C23 0.706 C24 0.723 C25 0.682 C26 0.761 C27 0.731 C28 0.731 C29 0.792 C30 0.74 C31 0.714 C32 0.661 C33 0.707 C34 0.717 C35 0.709 C36 0.682 C37 0.727 C38 0.783 C39 0.776 C40 0.782 C41 0.757 C42 0.748 C43 0.758 C44 0.732 C45 0.748 C46 0.774 C47 0.779 C48 0.771

Eigenvalues 21.93 4.71 4.61 4.12 3.61 3.50 3.0 2.42 1.98 1.55 1.06 Variance Explained Rate 28.48% 6.11% 5.99% 5.35% 4.68% 4.54% 3.89% 3.15% 2.57% 2.01% 1.38% Total Explanation Rate 68.16%

4.3. Correlation Analysis

In order to verify the interaction between multiple variables, it is necessary to carry out correlation analysis. If the correlation coefficient is positive and passes the significance test, there is a significant positive correlation between the variables; if the correlation coeffi-

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cient is negative and passes the significance test, there is a significant negative correlation between the variables. This study conducted a pairwise correlation analysis on the dimen- sions of non-cognitive ability, social support perception, and entrepreneurial intention. The results are as follows (Table 12).

Table 12. Correlation analysis.

1 2 3 4 5 6 7

Openness 1 Conscientiousness 0.389 ** 1

Extraversion 0.226 ** 0.337 ** 1 Agreeableness 0.412 ** 0.527 ** 0.313 ** 1

Emotional Stability 0.341 ** 0.457 ** 0.287 ** 0.464 ** 1 Social Support Perception 0.373 ** 0.438 ** 0.349 ** 0.466 ** 0.444 ** 1 Entrepreneurial Intention 0.398 ** 0.497 ** 0.422 ** 0.441 ** 0.496 ** 0.563 ** 1

Note: ** Significantly correlated at the 0.01 level (two-sided).

The above table shows the results of the correlation analysis. The p-values corre- sponding to the correlation coefficients between each dimension of non-cognitive ability, perception of social support, and entrepreneurial intention were all less than 0.05, which is statistically significant, indicating that there are significant correlations between the studied dimensions, namely, the perception of social support and entrepreneurial intention. Subsequent impact relationship analysis was performed.

4.4. Structural Equation Model Fitting Index

The fitting index of the structural equation model showed that the value of X2/df was 1.230, which is less than 3. The RMSEA (Root Mean Square Error of Approximation) was 0.023, which is less than the standard level of 0.08, indicating a good fit. GFI (Goodness of Fit Index) = 0.884, AGFI (Adjusted Goodness of Fit Index) = 0.873, NFI (Normed Fit Index) = 0.904, IFI (Incremental Fit Index) = 0.980, CFI (Comparative Fit Index) = 0.980, TLI (Tucker-Lewis Index) = 0.979. All goodness-of-fit indicators met the general standard, indicating that the structural equation model established in this study was effective and consistent with the recovered data. The match was better.

4.5. Analysis of Data Results

Through the path analysis of the structural equation model, the path coefficient value and C.R. value of the structural equation model were obtained. The results are shown in Table 13.

Table 13. Path analysis results.

Way Standardized Path Coefficient S.E. C.R. p

Social Support Perception <— Openness 0.142 0.043 2.738 0.006 ** Social Support Perception <— Conscientiousness 0.148 0.052 2.478 0.013 * Social Support Perception <— Extraversion 0.17 0.041 3.362 *** Social Support Perception <— Agreeableness 0.214 0.055 3.493 *** Social Support Perception <— Emotional Stability 0.212 0.045 3.799 *** Entrepreneurial Intention <— Openness 0.176 0.028 2.941 0.003 ** Entrepreneurial Intention <— Conscientiousness 0.185 0.033 2.707 0.007 ** Entrepreneurial Intention <— Extraversion 0.28 0.028 4.548 *** Entrepreneurial Intention <— Agreeableness 0.018 0.034 0.266 0.79 * Entrepreneurial Intention <— Emotional Stability 0.216 0.03 3.318 ***

Entrepreneurial Intention <— Social Support Perception 0.446 0.048 5.24 ***

Note: * means p < 0.05, ** means p < 0.01, *** means p < 0.001.

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4.5.1. Path Analysis of the Impact of Various Dimensions of Non-Cognitive Ability on Entrepreneurial Intention

The standardized path coefficients of openness, conscientiousness, extraversion, and emotional stability to entrepreneurial intention were 0.176, 0.185, 0.28, and 0.216, respec- tively, which were significantly established at the level of 0.001, indicating that these factors have significant positive effects on entrepreneurial intention. Among them, extraversion has the greatest impact on entrepreneurial intention.

The standardized path coefficient of agreeableness on entrepreneurial intention was 0.018 (t value = 0.266, p = 0.79 > 0.05), indicating that agreeableness has no significant effect on entrepreneurial intention. Theoretically speaking, college students with a more agreeable personality can obtain more understanding and support, so that they can start a business more smoothly. However, from the current data, this finding may be related to the fact that the local environment causes college students to feel unfairly treated and dissatisfied, and they do not have a sense of belonging to, identity with, or dependence on the local environment, thus affecting their willingness to start a business there. College students with a high level of agreeableness have an optimistic and positive attitude towards human nature, and they believe that the mentality of human nature will promote their subjectively more supported positive emotional experience, but they may lack entrepreneurial motivation.

4.5.2. Analysis of the Impact Path of Each Dimension of Non-Cognitive Ability on Perception of Social Support

The standardized path coefficients of openness, conscientiousness, extroversion, agree- ableness, and emotional stability on social support perception were 0.142, 0.148, 0.17, 0.214, and 0.212, respectively, indicating that these factors have significant positive effects on so- cial support perception. Extraversion, agreeableness, and emotional stability are significant at the level of 0.001, openness is significant at the level of 0.01, and conscientiousness is significant at the level of 0.05.

4.5.3. Path Analysis of the Impact of Social Support Perception on Entrepreneurial Intention

The standardized path coefficient of social support perception on entrepreneurial intention is 0.446, indicating that social support perception has a significant positive effect on entrepreneurial intention, which is significantly established at the level of 0.001.

4.5.4. Analysis of the Mediating Effect of Social Support Perception

This study used the bootstrap method to test the mediating effect of social support per- ception among the five dimensions of non-cognitive ability and entrepreneurial intention. The analysis results of the intermediary effect are shown in Table 14.

Table 14. Test results of intermediary effect of bootstrap.

Parameter Estimate Lower Upper p

Openness-Social Support Perception-Entrepreneurial Intention 0.063 0.018 0.12 0.004

Conscientiousness-Social Support Perception-Entrepreneurial Intention 0.066 0.012 0.129 0.01

Extraversion-Social Support Perception-Entrepreneurial Intention 0.076 0.032 0.145 0.001

Agreeableness-Social Support Perception-Entrepreneurial Intention 0.095 0.037 0.176 0.001

Emotional Stability-Social Support Perception-Entrepreneurial Intention 0.094 0.047 0.171 0.000

1. The mediating effect of social support perception on the effect of openness on en- trepreneurial intention

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The effect value of openness on entrepreneurial intention through social support perception is 0.063, the 95% confidence interval is [0.018–0.12], and the p-value is less than the significant level of 0.05, indicating that there is an intermediary effect, so the hypothesis is tenable. As the independent variable is significant for the dependent variable, it is still significant after adding the intermediary variable, which confirms that social support perception plays a part in the mediating role of openness in entrepreneurial intention.

2. The mediating effect of social support perception on the effect of conscientiousness on entrepreneurial intention

The effect value of due diligence on entrepreneurial intention through social support perception is 0.066, the 95% confidence interval is [0.012–0.129], excluding 0, and the p-value is less than the significant level of 0.05, indicating that there is an intermediary effect, so the hypothesis is tenable. As the independent variable is significant for the dependent variable, it is still significant after adding the mediator variable, indicating that it is a partial mediator.

3. The mediating effect of social support perception between extraversion and en- trepreneurial intention

The effect value of extraversion influencing entrepreneurial intention through social support perception is 0.076, the 95% confidence interval is [0.032–0.145], excluding 0, and the p-value is less than the significant level of 0.05, indicating that there is an intermediary effect, so the hypothesis is tenable. As the independent variable is significant for the dependent variable, it is still significant after adding the intermediary variable, which indicates that it is part of the intermediary.

4. The mediating effect of social support perception between agreeableness and en- trepreneurial intention

The effect value of agreeableness affecting entrepreneurial intention through percep- tion of social support is 0.095, the 95% confidence interval is [0.037–0.176], excluding 0, and the p-value is less than the significant level of 0.05, indicating that there is a mediating effect, so the hypothesis is established. As the independent variable is not significant for the dependent variable, the mediation is significant after adding the mediator variable, indicating that it demonstrates a complete mediation.

5. The mediating effect of social support perception on the influence of emotional stabil- ity on entrepreneurial intention

The effect value of emotional stability affecting entrepreneurial intention through the perception of social support is 0.094, the 95% confidence interval is [0.047–0.171], excluding 0, and the p-value is less than the significant level of 0.05, indicating the existence of a mediating effect, so the hypothesis is established. As the independent variable is significant for the dependent variable, it is still significant after adding the mediator variable, indicating that it is a partial mediator.

5. Discussion

This paper focuses on the problem of college students’ entrepreneurial intention and constructs a theoretical analysis framework of “non-cognitive ability—social support perception—entrepreneurial intention”. Based on field research and using a structural equation model, we analyzed the impact of various dimensions of non-cognitive ability (openness, conscientiousness, extroversion, agreeableness, and emotional stability) on entrepreneurial intention. On this basis, the bootstrap method was used to further verify the mediating effect of social support perception on the influence of various dimensions of non-cognitive ability on entrepreneurial intention.

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5.1. Openness, Conscientiousness, Extroversion, and Emotional Stability Have Significant Positive Effects on Entrepreneurial Intention

The mechanisms of action may be as follows. More open entrepreneurs are willing to accept new things and tend to invest time and energy in identifying entrepreneurial oppor- tunities, analyzing entrepreneurial risks, finding entrepreneurial partners, and formulating entrepreneurial plans. However, those who are less open may have scattered energy, weak motivation, and a tendency to retreat from difficulties encountered in achieving the goal of entrepreneurship. Individuals with a high sense of responsibility have a strong sense of responsibility for entrepreneurial work, work hard, do things efficiently, do things in an orderly manner, and strive to achieve their entrepreneurial goals. The more conscientious college students are, the more willing they are to start a business, because they have the elements of entrepreneurship. Extroverted college students are willing to participate in interpersonal communication, have the courage to undertake more social activities, and can better integrate into local social life. This will also improve the individual’s ability to bear entrepreneurial risks and reduce loss aversion, thus contributing to the growth of individual entrepreneurial intention. College students with strong emotional stability have strong self-regulation ability and have the advantage of eliminating negative emotions regarding entrepreneurship in time, so as to ensure the full exploitation of their personal entrepreneurial ability and stabilize their entrepreneurial intention.

5.2. All Five Dimensions Have a Significant Positive Impact on Perception of Social Support

Their impact paths may be as follows. Extroverted college students tend to have stronger interpersonal skills, and their social support sources are wider and higher in qual- ity, so the perception of social support obtained is also more significant. College students with stronger agreeableness have a stronger sense of cooperation and responsibility, a stronger sense of support from others, and a stronger sense of social support. College students who are emotionally stable tend to have a calm personality, can calmly deal with problems that they encounter and seek help, and can detect the support and help provided by others in a timely manner. College students with strong openness are more creative and have the ability to recognize and utilize various opportunities, and they can sense the emergence of social support and make use of it in time. The sense of responsibility and the conscientious behavior of entrepreneurs can bring a more positive emotional experience to the relevant groups, so as to obtain positive social feedback and allow them to experience more social support.

5.3. Perception of Social Support Has a Positive Impact on Entrepreneurial Intention

First, the stronger the social support obtained, the more the college student en- trepreneurs can affirm themselves and strengthen their sense of responsibility under the support of important others, so as to adhere to their original entrepreneurial intention and maintain a relatively strong entrepreneurial intention. Second, when college students deal with crisis and stress events, social support perception enables them to better adapt to their environment, relieve pressure, and reduce the withdrawal psychology of entrepreneurs. Third, active social support can enhance the innovation level and achievement level of college student entrepreneurs. At the same time, as entrepreneurs, when college students are concerned about respecting other people’s feelings and needs, they will also more firmly respect their own feelings and needs, thereby affirming the individual’s entrepreneurial intention and promoting entrepreneurial activities.

5.4. Social Support Perception Acts as a Mediator in the Influence of Non-Cognitive Ability on Entrepreneurial Intention

The perception of social support in the influence of openness, conscientiousness, extro- version, and emotional stability on entrepreneurial intention is partially mediated; in the influence of agreeableness on entrepreneurial intention, it shows a complete mediating role. Its mechanisms of action may be as follows. Openness indirectly affects entrepreneurial

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intention by influencing social support perception to a certain extent. Specifically, college students with higher openness tend to have a stronger social support perception, and a relatively strong social support perception can stimulate college students’ entrepreneurial intention. College students with high conscientiousness can influence their entrepreneurial intention by influencing their social support perception. These students can better achieve the emotional experience of being understood, respected, and supported in society be- cause of their degree of self-control, completing the generation of behaviors that affect the entrepreneurial tendency, and they have a positive attitude towards entrepreneurial intention promotion. The college students with a high extraversion level are perceived to be psychologically strong due to their extraverted personality characteristics, enthusiasm, optimism, vitality, and positive emotions. It is also this psychological strength that means that they have more positive emotions that are supported and understood subjectively, so as to increase their interaction with people, thereby improving their entrepreneurial intention and promoting the generation of entrepreneurial behavior. College students with a high level of agreeableness maintain a positive and optimistic attitude towards people, have full trust in their interpersonal relationships, and believe that human nature is inherently good. Such people are more likely to obtain, trust, and use support. These supports are transformed into entrepreneurial behavior tendencies and the desire for entrepreneurial behavior improves the conversion rate of entrepreneurial intention to a certain extent. College students who are emotionally stable have a large number of positive experiences, and most of them have a positive attitude towards objective factors; emotionally stable personalities are not easily affected by the external environment, individuals can remain alert and rational, and they tend to have higher social status. Support perception, and relatively deeper social support perception, stimulate greater entrepreneurial intention.

Finally, due to the limitation of the research group, the results of this research must also have certain limitations, which are expected to be improved in future research. In terms of research samples, only universities in Jinan City, Shandong Province, were selected for research. How to further improve the research framework, increase the number of spatial samples, and the number of questionnaires remains to be further explored. Future research can expand the sample size or conduct comparative studies on different types of samples in different regions to increase the applicability of the research conclusions. In the follow-up consideration, the entrepreneurial willingness will be divided into multiple dimensions, and the internal mechanism that affects the entrepreneurial willingness will be discussed in depth.

6. Conclusions

This study verifies that both non-cognitive ability and the perception of social sup- port have an impact on entrepreneurial intention, and they are involved in a sequential development process. College students’ perceptions of social support are affected by individual non-cognitive abilities, which will continue to affect their entrepreneurial in- tention. Therefore, there are two paths by which college students’ non-cognitive ability can influence their entrepreneurial intention. First, the four dimensions of non-cognitive ability, namely openness, conscientiousness, extroversion, and emotional stability, can directly affect entrepreneurial intention. Second, the five dimensions of non-cognitive ability, namely openness, conscientiousness, extraversion, agreeableness, and emotional stability, affect entrepreneurial intention through the mediating role of perceived social support. According to the research conclusions, the following suggestions are put forward according to three levels: college students, colleges and universities, and the government.

6.1. College Students Should Cultivate the Characteristics of Innovation and Entrepreneurship and Improve Their Entrepreneurial Ability

From the results of this research, college students’ high levels of openness, consci- entiousness, extroversion, and emotional stability have a significant positive impact on entrepreneurial intention. The entrepreneurial process is complex and volatile, and there are

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many uncertainties. Therefore, entrepreneurs are required to have personal characteristics such as good literacy, sufficient entrepreneurial skills, and tempered entrepreneurial prac- tices. In other words, college students must have the qualities of perseverance, courage to make breakthroughs, and resilience towards hardships, as well as the strength to maintain self-confidence and resist setbacks. Therefore, college students must purposefully cultivate these qualities in their studies and life, carry out comprehensive and systematic exercises, and build a reliable foundation for entrepreneurship. To summarize, college students should fully exploit their own advantages, learn comprehensively with multiple resources, and participate in practical activities independently to cultivate their entrepreneurial char- acteristics and optimize their entrepreneurial skills.

6.2. Colleges and Universities Should Improve the Quality of Entrepreneurship Education and Promote Entrepreneurial Actions

Institutions of higher learning should attach great importance to entrepreneurship and innovation education, and they should focus on cultivating innovative and highly skilled individuals who keep pace with the times. We must comprehensively revise the training program for innovative talent; integrate innovation and entrepreneurship education into professional education, ideological and political education, and other education; and build a curriculum system that pays equal attention to “professional ability + project orientation + innovation and entrepreneurship module”. We should promote teaching research and ed- ucation reform by rewarding innovation and entrepreneurship, and we should encourage teachers to take the initiative to meet the needs of enterprises and carry out innovation and entrepreneurship activities, so as to create an innovation and entrepreneurship teaching team with “school–enterprise interoperability, combination of professional and part-time”. We may set up an off-campus entrepreneurship tutor library to include outstanding alumni and entrepreneurs. We should strengthen school enterprise cooperation and establish a sound entrepreneurial practice system. It is far from sufficient to rely solely on the faculties of colleges and universities. Schools should invite company experts to impart some experi- ence to students and address their doubts. They should also lead students to the company to observe and participate in internships, so that students can effectively improve their ability to solve practical problems in the established training base and enhance the practical effect of innovation and entrepreneurship. It is also necessary to integrate school resources and build a cultural environment for innovation and entrepreneurship. Universities and colleges can regularly invite entrepreneurs and outstanding alumni to give lectures and make reports by holding entrepreneurship competitions inside and outside the campus, exhibit innovation achievements, and hold special lectures on entrepreneurship, alumni salons, and other activities, so as to introduce excellent cases of successful entrepreneurship into campus culture and create a cultural circle of innovation and entrepreneurship. At the same time, a variety of entrepreneurship education activities should be held, such as the Maker Culture Festival, the entrepreneurship planning competition, and the speech contest around the theme of entrepreneurship, etc. Relying on these diversified entrepreneurial cultural activities, we may achieve integration with entrepreneurial education to improve the promotion of an entrepreneurial culture. In addition, it should be noted that the target of the entrepreneurial initiative is not all college students, and it should not blindly en- courage college students’ entrepreneurial willingness to avoid adverse effects. For college students with a certain entrepreneurial willingness, they can cultivate their entrepreneurial thinking, innovative ideas, and innovative entrepreneurial skills in an all-around way, thereby promoting progress in innovative and entrepreneurial work.

6.3. Create a Good Entrepreneurial Environment for College Students to Start a Business and Improve Their Willingness to Start a Business

Society should create a good entrepreneurial environment for college students, in- cluding institutional, financial, and cultural environments. First, it should improve en- trepreneurship regulations, bankruptcy regulations, and intellectual property protection regulations; escort entrepreneurs; strengthen national supervision; and promote fair com-

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petition. A strong legal system can improve the efficiency of business transactions and reduce transaction costs, thereby enabling individuals to profit from business activities and stimulating their entrepreneurial desire. Second, in the initial stage of entrepreneurship, due to the uncertainty and high risk of entrepreneurship, financial institutions are less likely to provide financial support to entrepreneurs. Therefore, improving the financial environment and creating more diversified financing channels for college students’ en- trepreneurs can improve their entrepreneurial willingness. Finally, human capital is the most active and positive factor for innovation-driven development. Therefore, national general education should be vigorously strengthened in order to provide better conditions for college students entrepreneurs to create and operate enterprises. In the context of a culture that supports entrepreneurship, individuals will feel the supportive attitude of the entire society towards entrepreneurship. The cultural environment that supports entrepreneurship makes individuals feel the sense of security brought by the environment, which will stimulate individuals’ confidence in successful entrepreneurship, improve their entrepreneurial willingness, and promote their more active participation in entrepreneur- ship. Therefore, a social and cultural environment that supports entrepreneurship should be created.

Author Contributions: Conceptualization, W.S. and W.W.; methodology, W.S. and J.T.; software, W.S.; validation, W.S.; formal analysis, W.S.; investigation, W.S.; resources, W.S.; data curation, W.S.; writing—original draft preparation, W.S.; writing—review and editing, W.S., Q.Y., W.W. and M.Z.; visualization, W.S.; supervision, W.S.; project administration, W.S.; funding acquisition, W.S. and M.Z. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by Key Program of Pedagogy of National Social Science Founda- tion of China (Grant No. AFA 190018).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The data underlying the results presented in the study are all available. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments: We greatly appreciate the editor and four anonymous reviewers for their very constructive and helpful comments, which led to significant improvement on the exposition of the paper.

Conflicts of Interest: The authors declare no conflict of interest.

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  • Introduction
  • Theoretical Framework
    • Concept Definition
      • The Definition of Non-Cognitive Ability
      • The Definition of Entrepreneurial Intention
      • Definition of Social Support Perception
    • Theoretical Analysis
      • The Direct Influence of Various Dimensions of Non-Cognitive Ability on Entrepreneurial Intention
      • The Mediating Effect of Social Support Perception on Non-Cognitive Ability Dimensions and Entrepreneurial Intention
  • Method
    • Participants
    • Instruments
      • Independent Variable: Non-Cognitive Ability
      • Mediating Variable: Perception of Social Support
      • Dependent Variable: College Students’ Entrepreneurial Intention
    • Procedure
    • Data Analysis
  • Empirical Test and Result Analysis
    • Analysis of Group Differences
      • Analysis of Variance
      • T-test
    • Reliability and Validity Test
      • Reliability Test
      • Validity Test
    • Correlation Analysis
    • Structural Equation Model Fitting Index
    • Analysis of Data Results
      • Path Analysis of the Impact of Various Dimensions of Non-Cognitive Ability on Entrepreneurial Intention
      • Analysis of the Impact Path of Each Dimension of Non-Cognitive Ability on Perception of Social Support
      • Path Analysis of the Impact of Social Support Perception on Entrepreneurial Intention
      • Analysis of the Mediating Effect of Social Support Perception
  • Discussion
    • Openness, Conscientiousness, Extroversion, and Emotional Stability Have Significant Positive Effects on Entrepreneurial Intention
    • All Five Dimensions Have a Significant Positive Impact on Perception of Social Support
    • Perception of Social Support Has a Positive Impact on Entrepreneurial Intention
    • Social Support Perception Acts as a Mediator in the Influence of Non-Cognitive Ability on Entrepreneurial Intention
  • Conclusions
    • College Students Should Cultivate the Characteristics of Innovation and Entrepreneurship and Improve Their Entrepreneurial Ability
    • Colleges and Universities Should Improve the Quality of Entrepreneurship Education and Promote Entrepreneurial Actions
    • Create a Good Entrepreneurial Environment for College Students to Start a Business and Improve Their Willingness to Start a Business
  • References