Academic Integrity Crisis: Exploring Undergraduates’ Learning Motivation and Personality Traits over Five Years

Abstract

Academic misconduct is ubiquitous, a fortiori during crisis periods. The present research examines undergraduates’ learning motivation, based on Self-Determination Theory and personality traits factors, according to the Big Five Factor Model, affecting academic misconduct across different time spans: Before, during, and after a life-changing event. Using online questionnaires, we measured the level of academic misconduct, learning motivation, and personality traits of 1090 social sciences students during five different time spans pre-COVID-19, during COVID-19 (before and after vaccination), and after COVID-19 (post and long post). The results showed significant differences in students’ self-reported academic misconduct levels among the different periods and similar misconduct levels in pre-COVID-19 and long post-COVID-19. Additionally, the findings exhibited that external motivation significantly increases academic misconduct and that two out of five personality traits (agreeableness and emotional stability) reduce their occurrences. We conclude that higher education preparedness for academic integrity during an emergency is still a desideratum and that ethical concerns should not be abandoned but rather be fully addressed during emergency periods. This could be addressed by instructors allocating tasks during emergency groups involving students with pro-social personalities (agreeableness and emotional stability) and intrinsic motivation to serve as social agents in deterring academic misconduct.

1. Introduction

Since their inception, computers have revolutionised education [1], reshaping educational practices through the advancement of information and communication technology as well as online education [2]. Traditional face-to-face teaching integrated digital or hybrid educational systems [3], a transition further accelerated by the unexpected pandemic, i.e., COVID-19 [4], forcing mandatory online instruction [5], sometimes termed emergency remote teaching (ERT) [6,7]. This phenomenon affected academic integrity across [8] all education disciplines [9], presenting new challenges to the educational experience [10], demanding adaptability skills [2] and coping strategies [11].

Higher education prepares students for their future [12] and develops graduates who are not only highly skilled and technically proficient but also embody honesty, ethical responsibility, and a strong commitment to serving their profession and society [13]. An added value to education is desired conduct [14,15], such as academic integrity [11]. Traditionally, evaluating academic integrity has been limited to assessing student conduct. However, it is becoming clear that a more contemporary approach going beyond academic integrity breaching is necessary [16].

Academic integrity for researchers covers research ethics, data presentation, and authorship. For students, it involves plagiarism, cheating, or bribing for grades [17]. Previous research on academic integrity among students has focused on four main areas: prevalence, severity, causes, and solutions [18]. Students’ academic integrity breaches, like academic misconduct or dishonest academic behaviour, incite posterior counterproductive or fraudulent behaviours [19], are ubiquitous [20] and widespread, and have, unfortunately, become normalised globally [21].

The probability of future worldwide closures due to natural incidents such as floods, tsunamis, wildfires, or human situational incidents—including war, terror attacks, or shootings—may lead educational institutes to new challenges [22]. Thus, it is a desideratum to unveil the impacts of emergencies on learning and teaching [2] and the post-crisis trend and its long-lasting impact [23]. Despite the myriad research on academic dishonesty [24], to the best of our knowledge, the literature has not inquired into undergraduates’ motivation and personality traits factors that have influenced academic dishonesty before, during, and in the aftermath of COVID-19. The present research aims to analyse the relationship among the above variables and provide new insights into the research literature and practice.

The probability of future worldwide closures due to natural incidents such as floods, tsunamis, wildfires, or human situational incidents- including war, terror attacks, or shootings—may lead educational institutes to new challenges [22]. Thus, it is a desideratum to unveil the impacts of emergencies on learning and teaching [2] and the post-crisis trend and its long-lasting impact [23]. Despite the myriad research on academic dishonesty [24], to the best of our knowledge, the literature did not inquire into the undergraduates’ motivation and personality traits factors influencing academic dishonesty before, during, and in the aftermath of COVID-19. The present research aims to analyse the relationship among the above variables and provide new insights into the research literature and practice.

2. Theoretical Background

Events like natural disasters or global health pandemics have affected how academic content is delivered in higher education [25] and studying habits [26]. Thus, the COVID-19 pandemic introduced unprecedented uncertainties into the global education system [27]. Moreover, it hastened the establishment of online learning delivery platforms [28]. In other words, the global COVID-19 pandemic has compelled higher education institutions to transition from in-person instruction to emergency remote teaching (ERT), sparking heightened concerns about academic integrity [29].

Academic integrity is a transdisciplinary field of research, nuanced and complex [30]. It entails behaviours characterised by trustworthiness, respectfulness, fairness, and responsibility [31]. Defined as “compliance with ethical and professional principles, standards, practices and consistent system of values, that serves as guidance for making decisions and taking actions in education, research and scholarship” [32], it means following ethical standards in education and research [18].

Breaching or violating those principles in higher education is regarded as academic misconduct [8], fraudulent behaviour [18], or academic dishonesty [33,34] and is detrimental to the learning process [35]. A vast majority of post-secondary students admitted engaging in some form of academic misconduct [36]. Research suggests that academic misconduct, such as plagiarism, is more common among younger males who are poorly motivated and procrastinators [37], although other findings show that female misconduct rates are now comparable [38].

The rise of online education has led to an increase in academic dishonesty [39]. The literature has examined multiple factors that elucidate the concept of academic dishonesty [40]. Students commit academic dishonesty for various reasons [37]. Some factors contributing to academic dishonesty include stress and pressure, peer misbehaviour, insufficient knowledge [39], lack of motivation, and procrastination [37], to mention some. Nonetheless, there is no consensus regarding its primary causes.

Certain scholars have demonstrated that various factors influence academic misconduct, such as motivational research [41,42] or personality [38,40]. Motivation to learn involves improving an individual’s learning behaviours, directing their learning goals, and maintaining their educational engagement [43]. Motivational psychology highlights that an individual’s drive to pursue a specific objective is influenced by both personal traits and situational contexts [44,45]. Nonetheless, others proposed through their accentuation hypothesis that personal attributes become more pronounced when external factors (like COVID-19) disrupt established social balances [45,46].

The five-factor model (FFM) of personality traits is the most influential and widely used personality theory [47]. According to trait activation theory, there are situations that can potentially trigger behaviours [48]. In other words, research studies have emphasised the role of contextual factors [49] like COVID-19. Changes in the academic and social lives of university students, such as those caused by the COVID-19 pandemic, can highlight the role of their characteristics in dealing with these circumstances. These personal traits play a pivotal role in students’ capacity to adapt to such life-altering circumstances [45].

COVID-19 has dramatically transformed higher education worldwide, affecting learning, teaching methods, assessment strategies, the experiences of students and academics, the learning environment, and policymaking in higher education [50]. Nonetheless, the literature on education in the aftermath of the COVID-19 pandemic is still evolving [51], including a plethora of subjects, including pedagogic practices in online learning environments [25]. The learning environment and teacher–student interactions also play crucial roles in shaping student motivation [52]. Traditional learning environments include face-to-face (F2F) and planned online learning (POE), with special emphasis on instructors’ readiness [53]. Student performance in POE is often lower than in F2F settings [54].

Despite digital technology’s potential for teaching and learning [27], like the widespread automatic paraphrasing tools [55] and artificial intelligence [55,56], there is still a need to unveil digital-integrity behaviour [57]. It is imperative to understand dishonest behaviour to foster the academic and professional growth of undergraduates [58] and enhance positive strategies for academic integrity [59] and policies [60]. Thus, considering potential future disruptions [22], whether from human situational incidents or natural disasters, coping skills become paramount [29].

Studying the motivations behind academic dishonesty behaviours could provide insights into understanding these actions better [61]. Thus, based on Self-Determination Theory (SDT) [62] and expanding on previous research [63], the present research aims to fill the literature gap on the aftermath of an unexpected event (COVID-19) in higher education settings and its influence on academic dishonesty. Self-Determination Theory is a widely studied perspective on human motivation, defining it as the underlying reason for behaviour [64], as people’s actions are influenced by their motives, needs, and incentives [65]. This theory posits that there are three essential needs for well-being: autonomy (control and choice), competence (skill and effectiveness), and relatedness (connection and belonging) [66]. Autonomy involves being in control of one’s actions and having the freedom to make choices. Competence refers to the feeling of being effective and skilled in one’s activities. Relatedness is about feeling connected and belonging to others and a community [65]. Our research question is as follows: How do individual characteristics (motivation and personality traits) influence academic dishonesty according to contextual factors (before, during, and in the aftermath of COVID-19)?

2.1. Individual Characteristics

2.1.1. Learning Motivation

Motivation triggers and sustains one’s actions toward a goal, making it among the most significant factors impacting students’ learning achievements [67] and playing a crucial role in students’ learning [68]. Developing strong study habits is essential for boosting learning [26]. The Self-Determination and Intrinsic Motivation Theories [62] are a broad theory of human motivation, encompassing various types of motivation guiding human behaviours [67]. It posits three forms of academic motivational regulations based on distinct underlying reasons or goals for an individual’s actions: intrinsic, extrinsic and amotivation. Intrinsic motivation stems from engaging in an activity because of its inherent enjoyment or interest rather than external factors. Conversely, extrinsic motivation arises from pursuing an activity for external, separable outcomes [40,69]. Amotivation indicates the degree to which a person lacks the motivation to engage in specific activities or exhibits behaviour lacking intentionality [67,69,70].

Education entails recognising the factors that impact students’ learning motivation and evaluating strategies to enhance it [71]. Furthermore, in an educational setting, students’ psychological attitudes significantly influence their learning outcomes. Higher levels of enthusiasm enhance their engagement, thus improving their ability to absorb and retain knowledge. Conversely, a lack of academic dedication can hinder the attainment of optimal academic results [43]. Learning motivation, either intrinsic or extrinsic, influences the learning process and determines students learning (mis)behaviour [72].

For example, extrinsic motivation in learning occurs when the learner lacks interest in the subject itself and focuses solely on the rewards or benefits they receive [73]. Put differently, the types of motivational orientation have varying academic outcomes [61]. For example, individuals lacking motivation are more likely to engage in academic dishonesty [74,75]. Furthermore, previous studies showed that individuals influenced by external motivation are more prone to academic misconduct [75,76]. Thus, based on the above, we posit the following:

H1. 

External learning motivation increases academic misconduct.

2.1.2. Personality Traits

Personality traits represent stable patterns of individual behaviours, feelings, and thoughts manifest in interacting with an environment [77]. The five-factor model, or FFM, is the widely used model of personality traits [78]. The FFM classifies individual personality into five main dimensions: Openness to experience—reflects levels of intellectual curiosity, creativity, and preference for diversity and innovation; Conscientiousness—represents tendencies towards self-discipline, responsibility, and goal attainment; Extraversion—encompasses traits of energy, positivity, assertiveness, sociability, and talkativeness.; Agreeableness—indicates a predisposition towards compassion, cooperation, and trust, as opposed to suspicion and hostility; and neuroticism (or emotional stability)—indicates the likelihood of experiencing negative emotions like anger, anxiety, depression, or vulnerability.

Some meta-analyses [79] examined the extent of personality traits and performance and found that three of the Big Five personality traits—conscientiousness, openness, and agreeableness—are significantly associated with positive academic behaviour. Prior research conducted before the pandemic regarding the tendency to engage in academic misconduct provided insights into the relationships between students’ characteristics and their propensity to misbehave [8]. For example, Giluk and Postlethwaite [80] found that conscientiousness and agreeableness are negatively related to cheating. Adeniyi’s [13] research revealed that neuroticism significantly prompted academic dishonesty and conscientiousness had a negative relationship, whereas agreeableness, extraversion, and openness had a positive influence. Additionally, Eshet and Margaliot [81] found that extraversion and emotional stability have a positive impact on academic integrity. Thus, based on the literature, we posit the following:

H2. 

Personality traits correlate with academic misconduct.

2.2. Contextual Characteristics

The COVID-19 pandemic disrupted the educational systems. The initial identification of the outbreak occurred in December 2019 in Wuhan, China [82]. After three years of the pandemic, in May 2023, the World Health Organization (WHO) declared the end of the emergency status [83]. The lockdowns and the changes in the learning environment led to educational challenges like accessibility, adequate learning strategies, and pedagogies, which impacted academic performance and academic integrity rates [29]. Furthermore, the literature attested to a change in studying and learning habits due to the lack of internet connectivity or accessibility, inadequate information and technology equipment, and reduced communication [26].

Learning environments have undergone significant changes, fostering the adoption of innovative teaching methods intertwined with modern technology [84]. In other words, incorporating multimedia technologies into learning environments creates qualitatively different learning experiences in higher education [85]. Examples include the incorporation of information and communication technology [86]. These technological advances have resulted in digitalised learning, such as fully online planned environment (or hybrid or blended) modules combined with traditional face-to-face education [33].

Traditional face-to-face learning is characterised by the use of conventional teaching methods [87], like direct classroom lectures and guiding with immediate feedback in class [85]. Planned online learning includes electronic-based lectures and guiding questions, which may also be tackled on the online platform [85]. Well-planned online learning offers learners enhanced support, responsibility, flexibility, and choice [88]. Planned online learning may be asynchronous (recorded lectures or forums), synchronous (live online meetings and sessions), or both [89]. Asynchronous formats offer the greatest flexibility, allowing students to decide when and how to view lectures. For example, students can watch lecture videos at their convenience and replay them if interrupted. Synchronous is more like in-person classes, offering a framework of fixed lecture times around which students must organise their schedules [90].

Emergency Remote Teaching (ERT) was implemented as an immediate response to the COVID-19 pandemic, transitioning education from face-to-face to online teaching [88], and it was marked by a sudden shift to remote instruction, often without adequate preparation or adaptation [91]. ERT was developed hastily with minimal time and resources [9]. Both professors and students faced stress and challenges during the transition, including concerns about course expectations and miscommunication [92]. Figure 1 depicts the different learning environments and pre-peri and post-COVID-19 timelines in Israel.

The above timeline illustrates the transitions of teaching environments from face-to-face to blended/hybrid due to the COVID-19 pandemic. Initially, the learning environment was face-to-face, and it was predominant until February 2020 (pre-COVID-19 time span). Following the first case of COVID-19 in Israel in February 2020 and the onset of the first lockdown in March 2020, the learning environment changed to emergency remote teaching (ERT), predominantly synchronous (COVID-19 before vaccination time span), and it lasted until December 2020. After the first dose of the vaccine, from January 2021 (COVID-19 after-vaccination time span), emergency remote teaching environments predominated. By April 2021, face-to-face was permitted again, and since May 2023, teaching has transitioned to a blended/hybrid model (post-COVID-19 time span). As of April 2024, blended/hybrid teaching continues (long post-COVID-19 time span).

Research has consistently demonstrated that learning environments significantly impact students’ experiences and outcomes [93], particularly in terms of course enrolment and delivery methods like face-to-face, planned online environments, and emergency remote teaching. Academic integrity breaching (also known as academic misconduct or dishonesty) is a worldwide issue that escalated significantly following the onset of COVID-19 [94]. Prior research revealed that situational variables are strongly associated with academic misconduct [95], such as the shift to online instruction [8]. Eshet et al. [33] observed that, pre-COVID-19, there were higher rates of academic dishonesty in planned online learning compared to face-to-face instruction, while during-COVID-19, higher rates in Emergency Remote Teaching (ERT). Additionally, Maryon et al. [23] found that, during COVID-19, academic misconduct intensified, and Eshet [29] revealed that academic misconduct (plagiarism) rates across various disciplines were higher during unforeseen crises. Thus, based on the above, we posit the following:

H3. 

During crisis times (COVID-19), academic misconduct rates accelerate.

3. Methods

3.1. Sample and Procedure

Data were collected from undergraduate students in five Israeli academic institutions studying for bachelor’s degrees in social sciences (Education, Psychology, Sociology, Criminology, and Management). The questionnaires were distributed to students after the end of the course in five different time spans: (a) pre-COVID-19 (June 2019)—face-to-face learning environment; (b) during COVID-19 before vaccination (June 2020)—emergency remote teaching–learning environment; (c) during COVID-19 after vaccination (June 2021)—emergency remote teaching–learning environment; (d) post-COVID-19 (June 2023)—face-to-face with an optional one day of planned online learning (synchronous modality) environment; and (e) long post-COVID-19 (April 2024)—face-to-face with an optional one day of planned online learning (synchronous modality) environment. The sample consisted of 1040 participants; 11% were male, and 89% were female students. Participants’ average age was 24.84 years with a standard deviation of 3.5 years. The questionnaire was delivered online using Qualtrics XM software (Version Number 8 May 2024). We used a stratified sample based on mandatory courses in statistics, computer usage, and research methods. The average time for filling out the questionnaires was 10 min. About 20% of the participants were excluded from the analysis because their survey instruments were incomplete (less than 100%) or carelessly completed.

3.2. Instruments

Learning motivation contained 8 items that were compiled from the Academic Self-Regulation Questionnaire [96]. They were translated into Hebrew by Peled et al. [40] and had acceptable reliability (α = 0.75). The questionnaire explores two types of motivation: extrinsic and intrinsic motivation. An example of an item is, “I do my homework so that the lecturer will think I am a good student”. The participants responded to these questions using a five-point Likert scale where 1 corresponded to not at all true and 5 corresponded to very true. In this study, the level of reliability found was acceptable (α = 0.72).

Personality Traits contained 10 items from the ten-item personality inventory (TIPI) [97]. They were translated into Hebrew by Peled et al. [40] and had acceptable reliability (α = 0.63). The participants responded to these questions using a five-point Likert scale where 1 corresponded to not at all true and 5 corresponded to very true. Each facet includes one positive and one negative keyed item; an example of an item is “I see myself as Extraverted, enthusiastic”. In this study, the level of reliability found was acceptable (α = 0.63).

Academic misconduct contained 8 items that were compiled from the Academic Integrity Inventory. This part of the survey included items related to perceptions of and intentions of academic misconduct according to integrity culture, cheating frequency, and the likelihood of misconduct [38]. It is based on 5 items with a reliability of 0.75 as measured by Cronbach’s alpha. An example of a question is: “How likely are you to consider turning in work done by someone else as your own”. The survey was translated into Hebrew by Peled et al. [40] and had acceptable reliability (α = 0.75). The participants responded to these questions using a five-point Likert scale where 1 corresponded to largely opposed and 5 corresponded to largely agree. In this study, the level of reliability found was acceptable (α = 0.70).

3.3. Plan of Analysis

Data were analysed through SPSS version 28. Descriptive statistics, Reliability analysis, Phi correlation, Pearson correlations, Forced steps regression, and One-way ANOVA were conducted to analyse the data.

4. Results

Table 1. Mean and standard deviation of Academic Misconduct.

Item	Pre-COVID-19	COVID-19 before Vaccination	COVID-19 after Vaccination	Post-COVID-19	Long Post-COVID-19
1. Plagiarism happens often in my faculty.	2.09 (1.13)	1.84 (0.93)	1.93 (0.99)	2.62 (1.11)	2.41 (1.00)
2. I have personally seen many times another student copying in my faculty.	2.20 (1.20)	1.71 (0.97)	1.92 (1.10)	2.56 (1.18)	2.31 (1.20)
3. My best friend would have condemned me if he had known that I had acted with academic dishonesty.	3.07 (1.31)	3.03 (1.32)	2.34 (1.13)	2.92 (1.19)	2.84 (1.17)
4. An average student at this educational institution would have condemned me if he had known that I had acted with a lack of academic integrity.	2.93 (1.22)	2.99 (1.17)	2.43 (1.06)	2.74 (1.07)	2.83 (1.09)
5. An average student at this educational institution would report if someone had cheated on a test.	2.41 (1.01)	2.79 (1.00)	2.57 (1.10)	2.57 (1.00)	2.77 (0.97)
6. The penalties for lack of academic integrity in this educational institution are severe.	2.96 (0.98)	3.28 (1.07)	2.92 (1.15)	3.15 (1.15)	3.13 (0.97)
7. In my faculty, the students understand the procedures related to academic dishonesty.	3.42 (1.14)	3.89 (0.99)	2.95 (1.18)	3.47 (1.02)	3.54 (1.05)
8. The department where I study supports procedures related to academic misconduct.	3.22 (1.20)	3.32 (1.35)	2.94 (1.21)	3.41 (1.06)	3.25 (1.19)

Note: n = 1090.

presents descriptive statistics (means and standard deviations) for each item on the Academic Misconduct scale throughout the different time spans. Students reported moderate agreement (score 70 to 78 on a scale of 1 to 100) with the statement “In my faculty”, indicating that the students understood the procedures related to academic dishonesty.

There was no multicollinearity between the independent variables (

Table 2. Correlation matrix of the study variables.

Variables	1	2	3	4	5	6	7	8	9	10
1. External Motivation
2. Intrinsic Motivation	0.316 **
3. Extraversion	−0.010	0.014
4. Agreeableness	−0.065 *	−0.202 **	0.051
5. Conscientiousness	0.041	0.125 **	0.159 **	0.267 **
6. Emotional Stability	−0.079 **	0.040	0.175 **	0.318 **	0.351 **
7. Openness to Experiences	0.033	0.149 **	0.277 **	0.124 **	0.272 **	0.217 ***
8. Age	−0.108 **	0.232 **	0.109 **	−0.038	0.109 **	0.047	0.113 **
9. Gender	0.241 **	0.132	0.119 *	0.152 **	0.046	0.126	0.102	0.403 **
10. Grade Point Average	−0.089 **	−0.021	0.043	0.157 **	0.121 **	0.101 ***	0.040	0.040	0.088 **
11. Academic Misconduct	0.002	0.063 *	−0.068 *	−0.238 **	−0.092 **	−0.160 **	−0.036	−0.054	−0.027	−0.103 **

Note: * p < 0.05, ** p < 0.01, *** p < 0.001, Variables 1–8, 10–11 calculated with Pearson correlation. Variable 9 was calculated with Phi correlation.

). Most Pearson correlations were weak, indicating that academic misconduct was a variable comprising a compound of factors.

Table 3. One-way ANOVA between periods for Academic Misconduct.

	Time	n	Mean	Std.	F	Ƞ2
1. Pre-COVID-19	June 2019	256	2.78	0.64	16.41 **	0.06
2. COVID-19 before vaccination	June 2020	319	2.53	0.60
3. COVID-19 after vaccination	June 2021	102	2.96	0.47
4. Post-COVID-19	June 2023	246	2.86	0.60
5. Long post-COVID-19	April 2024	167	2.79	0.56

Note: * p < 0.05, ** p < 0.01. n = 1090.

presents a one-way ANOVA for academic misconduct between periods. A significant difference was found between the five periods (F_(4,1085) = 16.41, p < 0.01). The effect size was medium (Ƞ² = 0.06). Scheffe test showed no significant difference between pre-COVID-19 and long post-COVID-19. That is, 5 years after the COVID-19 outbreak, the level of academic misconduct returned to the same level as before the pandemic.

Steps regression (

Table 4. Forced Steps Regression for Academic Misconduct.

	Variable	B	95% CI for B		SE B	β	R2	ΔR2
			LL	UL
Step 1: Personality Traits							0.07	0.07 **
	Constant	3.71	3.42	4.00	0.15
	Extraversion	−0.03	−0.08	0.01	0.02	−0.05
	Agreeableness	−0.17	−0.23	−0.13	0.03	−0.21 **
	Conscientiousness	−0.01	−0.06	0.05	0.03	−0.01
	Openness	0.02	−0.03	0.07	0.03	0.02
	Emotional Stability	−0.06	−0.11	−0.01	0.02	−0.09 **
							0.14	0.07 **
Step 2: Personality Traits and Learning Motivation	Constant	3.83	3.52	4.14	0.16
	Extraversion	−0.04	−0.08	0.01	0.02	−0.05
	Agreeableness	−0.16	−0.21	−0.10	0.03	−0.18 **
	Conscientiousness	0.02	−0.04	0.08	0.03	0.02
	Openness	0.02	−0.03	0.07	0.03	0.03
	Emotional Stability	−0.08	−0.12	−0.03	0.02	−0.10 **
	External Motivation	0.08	−0.17	−0.08	0.02	0.14 **
	Intrinsic Motivation	−0.13	0.04	0.12	0.02	−0.20 **
Step 3: Personality Traits, Learning Motivation, and Demographics							0.15	0.01 *
	Constant	4.02	3.52	4.52	0.25
	Extraversion	−0.03	−0.08	0.02	0.02	−0.04
	Agreeableness	−0.15	−0.20	−0.09	0.03	−0.17 **
	Conscientiousness	0.02	−0.03	0.08	0.03	0.03
	Openness	0.02	−0.03	0.07	0.03	0.03
	Emotional Stability	−0.08	−0.12	−0.03	0.02	−0.10 **
	External Motivation	0.08	0.04	0.12	0.02	0.15 **
	Intrinsic Motivation	−0.13	−0.17	−0.08	0.02	−0.19 **
	Time Span	0.02	−0.01	0.05	0.01	0.05 ~
	Gender	0.02	−0.11	0.14	0.06	0.01
	Age	−0.01	−0.01	0.00	0.00	−0.06 ~
	Grade Point Average	−0.01	−0.01	0.00	0.00	−0.05

Note: CI = confidence interval; LL = lower limit; UL = upper limit; ^~ p < 0.08; * p < 0.05, ** p < 0.01. n = 1090. Beta values are shown.

) revealed that factors increasing academic misconduct were external motivation (β = 0.154) and periods of COVID-19 progression (β = 0.074). Factors reducing academic misconduct were intrinsic motivation (β = −0.193), the personality trait agreeableness (β = −0.167), the personality trait emotional stability (β = −0.090) and the grade point average (β = −0.079). This confirms hypotheses 1, 2, and 3. The predictive model was found to be significant (F_(13,909) = 8.41, p < 0.01), with an explained variance percentage of 15.7%.

Figure 2 presents the changes in academic misconduct and external motivation throughout the five periods. Both variables showed similar trends, with an overall increase during the pandemic after vaccination took place and a decrease after the pandemic, back to pre-COVID-19 levels.

5. Conclusions and Discussion

Prioritising the consideration of ethical implications is crucial for maintaining the integrity and effectiveness of education delivery [98]. Therefore, our study centred on how contextual variables (a pandemic emergency) affected academic misconduct rates according to personal characteristics (personality traits) and learning motivation. Our research provides valuable insights into the complex dynamics of academic misconduct and underscores the importance of proactive measures to safeguard academic integrity, particularly in times of crisis. The study’s comprehensive approach, across time spans, during and after the COVID-19 pandemic, provides a nuanced understanding of how academic misconduct evolves.

First, this research inquired about the relationship between external learning motivation and academic misconduct. Our findings and the literature [72] confirm H1. This may be due to students being nonchalant in their studies and concerned about obtaining their degrees [73]. In other words, students are less motivated to acquire subject knowledge, and academic misconduct becomes a pathway to completing the assignments [76]. This phenomenon aligns with a key aspect of Self-Determination Theory, specifically goal contents theory, which suggests that extrinsic goals are linked to achievement, meaning the instrumentalities of the task rather than deriving satisfaction from the learning process [62]. Another additional explanation may be the lack of instructors’ technological–pedagogical preparedness [34] and lack of spare time or skill to motivate their students to learn externally. Additionally, the change in studying and learning habits, like home-learning and studying during the pandemic lockdown [26], may have prompted academic misconduct by shortcuts to assessments. Thus, there is a need to develop special learning habits during emergency crises, including academic integrity education and literacy.

Next, this study inquired into the relationship between personality traits and academic misconduct. In line with the literature [40,80], the findings showed that agreeableness and emotional stability reduced academic misconduct. Concerning agreeableness, this behaviour could stem from this personality quality, which is associated with instructors’ compliance, cooperative and collaborative learning related to high academic achievement [99], and their tendency toward pro-social behaviour, thus leading to greater autonomous motivation and adhering to social norms, like physical distancing during COVID-19 [100]; additionally, this trait is negatively related to amotivation [99]. Consequently, this discourages academic misconduct. Concerning emotional stability, this behaviour could stem from withdrawal from negative experiences [101], like academic misconduct, and withstands antisocial behaviour.

Finally, we inquired about the differences in academic misconduct levels before, during (pre and post-vaccination), after, and long after COVID-19. Our results showed that academic misconduct rates were far higher during the pandemic period compared to the pre-post and long post-pandemic. Furthermore, the findings exhibited a bouncing back of academic misconduct rates pre- and long post-pandemic. These findings are consistent with the research literature [102]. This may be due to higher educators’ unpreparedness for online teaching [103], the demand for a myriad of pedagogical challenges [98], the effects of learning methods [75], the lack of the physical presence of instructors [33], and ambiguous ethical narratives [29].

On the one hand, there is concern about the pandemic per se. On the other hand, we transmigrated educational subject content to online platforms and acquired the technological skills for it. During Emergency Remote Teaching (ERT), instructors are often inadequately prepared or able to adapt [91], thus decreasing the quality of instruction and lowering learning motivation [104]. In other words, both professors and students faced challenges during this transition and were concerned about course expectations and miscommunication [92]. Thus, there is a lack of spare time or clear policies for addressing ethical issues like academic misconduct. Another explanation may be due to the transition to abrupt online instruction and the increase in the misuse of online sites for misconduct [8].

Summing up, the results suggest that external motivations and changes in the academic environment during the pandemic significantly influenced academic misconduct. The rise in academic misconduct during the pandemic might be linked to a reduced sense of autonomy, as the constraints and challenges of remote learning could have limited students’ control over their academic experience. In addition, students struggling with academic challenges may have been more susceptible to misconduct, especially if they felt they could not meet the required standards on their own. Furthermore, the impact of social influences, such as peer behaviour and external pressures, underscores the need for a supportive academic environment to foster integrity and discourage dishonest behaviour. Consequently, efforts to enhance intrinsic motivation and provide a supportive academic environment could mitigate such behaviours. Additionally, understanding the role of personality traits can help in designing interventions tailored to student characteristics to promote academic integrity.

We conclude from the above that higher education preparedness for academic integrity during an emergency is still a desideratum and that ethical concerns should not be abandoned but be fully addressed during emergency periods. Eradicating—or at least minimising—academic dishonesty is not an easy task and requires a multi-layered approach involving educators, institutions, and students themselves. Moreover, there may be different viewpoints on what academic integrity involves [17].

Consequently, in line with the literature [59,60], higher education should implement comprehensive practices and policies promoting responsibility and honesty. This could be achieved by breaching physical distances and instructing students and staff on alternative pedagogies during emergencies, such as in small workshops and seminars and ethical modules while teaching subject content. In addition, in line with Self-Determination Theory, lecturers can foster autonomy in various learning environments by giving students more control over their schedules and learning methods. They can also encourage a culture of collaboration and peer support within academic settings to strengthen relationships and reduce competitive pressures that may lead to misconduct. Providing academic support services, such as tutoring and mentoring, can help build students’ confidence in their abilities. Additionally, facilitating open discussions about academic integrity can help understand students’ perspectives and develop policies that resonate with their experiences.

Moreover, technological tools, like artificial intelligence or paraphrasing software [55], could be helpful in promoting integrity awareness. For example, instructors may include tasks that inquire about integrity issues in given content knowledge, including artificial intelligence usage and active classroom discussions. These discussions may address misconduct concerns and their consequences for subsequent professional career development. Finally, instructors could allocate group tasks involving students with pro-social personalities (agreeableness and emotional stability) and intrinsic motivation to serve as social agents in deterring academic misconduct.

Limitations and Future Directions

This study has several limitations. Firstly, as there is a lack of existing literature on the relationship between the research variables, this study adopted an exploratory approach. Data collection relied on subjective self-report questionnaires. Additionally, while our samples were not gender-balanced, they do reflect the general educational gender trends [105]. Nonetheless, we recommend conducting future studies on gender comparisons. Future research could enhance the reliability of the findings by incorporating dyadic data, such as combining self-report measures with objective data obtained through plagiarism detection software. Other future research may include inquiring about the relationship between the different motivations, each of the Big Five Personality Traits, and academic behaviours and time spans.

Next, it is important to acknowledge the limitations associated with a non-longitudinal design. A non-longitudinal approach captures data at a single point in time, which may not fully account for the dynamic and evolving nature of the variables under study. This limits the ability to observe changes or trends over time, potentially overlooking long-term effects or developments. As a result, the findings should be interpreted with caution, particularly when generalising or predicting future outcomes. Future research could benefit from a longitudinal approach to more comprehensively understand the temporal aspects of the phenomena being studied.

Like any empirical research, this study represents a specific framework analysing and reflecting a particular practice based on its data. In essence, our research offers a nuanced theoretical perspective on a broader socio-cultural phenomenon. This suggests that research, theory, and practice could all potentially gain from similar tests focusing on additional contexts utilising different predictors and comparing them with other unpredicted emergencies (i.e., war situations or natural disasters). Additionally, our samples lacked gender balance. Therefore, we suggest conducting future studies to explore gender comparisons and cultural diversity.