Machiavellianism, Lying, and Motivation as Predictors of Academic Performance in Romanian Engineering Students

Abstract

This study explores the relationship between academic motivation, Machiavellian traits, and lying tendencies among Romanian engineering students, with a focus on how these psychological variables influence academic performance. Data were collected from 706 students using the MUSIC Model of Motivation, the Machiavellianism Scale, and the Lie Attitude Questionnaire. Statistical analysis included Spearman correlations, regression models, and moderation analysis using PROCESS Macro (Model 7). The results showed significant negative correlations between Machiavellianism and all five motivational dimensions (e.g., ρ = −0.259 for Empowerment, p < 0.001), as well as between lying tendencies and motivation (e.g., ρ = −0.206 for Empowerment, p < 0.001). Surprisingly, Machiavellianism had a positive effect on academic grades (β = 0.107, p = 0.043), suggesting strategic academic behavior. Motivation was a stronger predictor of performance among female students. These findings highlight the importance of promoting ethical, intrinsic motivation in university settings and call for thoughtful integration of behavioral variables into academic success models.

1. Introduction

In the current academic context, where student performance is increasingly linked to psychological, motivational, and ethical factors, understanding the mechanisms that influence educational achievement has become essential. This article investigates the complex relationship between academic motivation, Machiavellian personality traits, and lying behaviors, with the aim of analyzing how these variables affect student performance. The study integrates validated theoretical frameworks—namely the 3P model (Presage–Process–Product) and the MUSIC model of motivation—with core concepts from the Dark Triad, offering an original and interdisciplinary perspective on the dynamics of academic success.

Performance is approached not merely as a result of cognitive ability but as the outcome of a combination of psychological, familial, social, and educational factors that significantly impact the personal and professional development of students. Additionally, this research brings added value by addressing differences related to gender and socio-demographic variables, offering relevant directions for educational interventions and university policies aimed at promoting ethics, responsibility, and intrinsic motivation.

Currently, studies address only partially the relationships among performance, motivation, and non-ethical behaviors such as Machiavellianism and lying. Although each of the constructs analyzed—motivation, academic performance, Machiavellianism, and lying—has a consistent individual body of research, their integration into a single study is rare. A search of the Web of Science and Google Scholar confirms the lack of research that simultaneously explores all four constructs within a unified explanatory framework. Moreover, no studies conducted within the Romanian higher education system have addressed this combination yet. In this context, the present study aims to examine the relationship between student motivation, lying tendencies, and Machiavellian traits in order to understand how these psychological variables influence students’ academic attitudes and performance.

The central research question is as follows: to what extent do Machiavellian traits and attitudes toward dishonesty influence academic motivation and, implicitly, university performance among students?

Based on the literature presented above, the aim of this research is to examine the relationship between students’ motivation, tendencies toward lying, and Machiavellian traits in order to understand how these psychological variables influence academic performance and students’ academic attitudes.

Derived from the aim of the study, the following research questions are formulated.

Q1: What is the relationship between students’ academic motivation, their tendency to lie, and Machiavellian traits?

Q2: How do motivation and unethical behaviors influence students’ academic performance?

Q3: What are the factors that influence students’ unethical behaviors, motivation, and performance?

Based on the research questions, the following objectives were established.

Objective 1: Analyze the level of academic motivation in students using the MUSIC model.

Objective 2: Evaluate the general tendency toward lying among students in various contexts.

Objective 3: Investigate students’ Machiavellian traits in terms of deceptive behaviors and intentions.

Objective 4: Explore the relationships between Machiavellian tendencies, lying, motivation, and academic performance.

The working hypotheses are the following:

H1. 

There is a negative correlation between Machiavellian tendencies and students’ academic motivation.

H2. 

Students with stronger tendencies toward lying demonstrate lower levels of academic motivation.

H3. 

Students with high Machiavellian traits are more likely to use lying as a strategy to obtain academic advantages.

H4. 

Academic performance is significantly influenced by the interaction between academic motivation, Machiavellian tendencies, and lying behaviors.

H5. 

There are significant gender-based differences in the relationship between Machiavellian traits, academic motivation, lying behaviors, and academic performance.

The structure of this paper is as follows. The next section provides a literature review regarding academic performance and motivation, as well as unethical academic behaviors, specifically Machiavellianism and lying. Section 3 details the methodological approach, including the instruments, participants, and procedures. Section 4 presents the main results, while Section 5 discusses the findings in relation to prior studies, outlines future research directions, and highlights current limitations. The paper concludes with final reflections and the reference list.

Figure 1 illustrates the conceptual research model developed for this study, highlighting the relationships between academic motivation, Machiavellianism, and lying behaviors as predictors of academic performance, along with the moderating role of gender.

2. Literature Review

2.1. Academic Performance

Organizational and managerial psychology is the science that studies the relationships and interactions between individuals and organizations (Bass, 1965; Dunnette & Kirchner, 1965; Mielu Zlate, 2007). The definition was further elaborated by Furnham, who described organizational psychology as the study of an individual within an organization. Additionally, it concerns itself with both small and large groups, as well as the organization as a whole, i.e., the impact the organization has on the individual (Furnham, 2012). More recently, organizational and managerial psychology has been defined as the science that studies the co-determination and co-evolution of organizations and the activities, statuses, and roles of individuals. This process is examined through both general and specific techniques, with the aim of achieving a functional optimum between the two interrelated variables (Mielu Zlate, 2007).

Academic performance is a core concern in organizational and managerial psychology, as it is one of the main indicators of efficiency and success at the individual, group, and organizational levels. Performance is central to this field, as it directly correlates with organizational goals and management strategies and provides insights into the work environment, organizational culture, and interpersonal relationships while also driving the adaptation of organizational processes to technological changes.

In conceptualizing performance, a distinction is made between behavioral (actional) and outcome-based aspects, with the former referring to what an individual does in a work situation and the latter referring to the consequences of that behavior (Campbell et al., 1990; Campbell & Wiernik, 2015; Mielu Zlate, 2007; Roe, 1999). Only behavior that is relevant to the organization’s goals qualifies as performance. Performance is what an organization hires an individual to do and what that individual does well (Campbell & Wiernik, 2015; Mielu Zlate, 2007). Performance is not defined by the action itself but by processes of judgment and evaluation. Only measurable actions are considered performance. Operationally, performance is the highest level of achievement of proposed objectives (Mielu Zlate, 2007). From the perspective of outcome-based performance, not all results are intrinsic to performance, as other factors or variables may intervene (Mielu Zlate, 2007).

Individual performance refers to how a person manages their resources, especially psychological ones, and the outcomes they achieve. High personal efficiency is achieved when minimal resources yield maximal results (Mielu Zlate, 2007). Over the past 60 years, researchers have focused on identifying the psycho-individual resources that condition performance. Some authors believed that personality factors, especially temperament, are the key to performance, as temperament is stable (Duval & Michaud, 1970). Jean-François Decker suggested that several psychological resources are involved in achieving individual performance (Decker, 1988, 1989). Among the most important ones are motivation, understood as the strong and persistent desire to reach personal or academic goals; beliefs, referring to one’s confidence in success and in the ability to achieve meaningful outcomes; and willpower, defined as the conscious and sustained effort to pursue objectives despite obstacles or distractions. According to Decker, these factors work together to support personal efficiency and determine how effectively individuals manage their inner resources in order to perform at high levels.

This model excludes temperament from the list of psychological factors determining performance due to its innate nature and instead emphasizes regulatory psychological processes like motivation, along with basic character components such as attitude and volitional traits, as central to individual success (Popescu-Neveanu et al., 1995; Zlate et al., 2005).

Other theories claim that performance differences arise from individual differences, specifically declarative and procedural knowledge (Campbell & Wiernik, 2015; Motowidlo et al., 1997), cognitive abilities (Bobko et al., 1999; Schmidt & Hunter, 1998), and Big Five personality traits, namely neuroticism and conscientiousness (Barrick & Mount, 1991), perceived self-efficacy (Bandura, 1986, 2011), and self-esteem. Performance is also influenced by organizational culture, achievement orientation, goals, and feedback (Earley, 2017).

Performance is a key concept in work and organizational psychology, defined in relation to terms such as efficiency, effectiveness, and productivity (Shaw, 1997). Effectiveness refers to the evaluation of performance outcomes, while productivity concerns the ratio between effectiveness and the cost of achieving that level of effectiveness (Campbell & Wiernik, 2015). Some authors argue that efficiency involves achieving set goals with an acceptable use of resources, while effectiveness includes efficiency plus adaptability (Argyris, 1970).

Performance, as an essential concept in organizational psychology, is applicable not only in professional environments but also in academia. Student achievement depends on factors such as motivation, self-directed learning, educational climate, and social relationships, elements that similarly influence organizational efficiency and success.

Academic performance refers to how well a student meets educational standards and is measured in various ways. The student’s assessment is based on the scores or grades achieved in different courses during their college years (Kumar & Tankha, 2021). This is important because higher academic performance can lead to greater career and educational opportunities. Moreover, academic performance develops one’s cognitive skills, problem-solving ability, and adaptability to technological changes among graduates (I. A. Bratu, 2019a; Skinner & Daly, 2010; Stupnisky et al., 2008), impacts self-esteem and personal motivation (Abuawad et al., 2023; Manganelli et al., 2019), and influences mental health (Balogun et al., 2017; Guo et al., 2024). Academic performance can also affect professional and social networking, including online (I. A. Bratu et al., 2022), and it contributes to students’ reputations, particularly when they graduate from prestigious institutions (Arroyo-Machado et al., 2024; Benitez et al., 2020; Erden et al., 2015; Jungblut et al., 2021; Petersen et al., 2014). In the long term, strong academic outcomes shape lifelong learning attitudes (Bingwa & Ngibe, 2021; Braimoh, 2010; Kraft et al., 2017; Santovena-Casal, 2019), influence values and professional ethics (M. L. Bratu & Cioca, 2019; Burns, 2023; Krueger, 2014; Rabi et al., 2006), and correlate with financial stability and higher incomes (Borjas et al., 2020; Furnham & Cheng, 2016; Richardson et al., 2017; Rojas, 2022). Academic performance also impacts the environment through sustainability principles adopted by graduates in their careers and personal lives. Sustainability is becoming increasingly important in all fields, from education and economics (M. L. Bratu & Ionel Cioca, 2019; Cioca et al., 2023; Lungu, 2013) to sports, agriculture, and forestry (I. A. Bratu, 2019b; I. A. Bratu et al., 2024; Liliana, 2017), shaping how we develop and utilize resources for a balanced future.

One of the most comprehensive models providing a logical and structured framework for identifying essential factors that influence academic performance is the 3P modelPresage–Process–Product) (Biggs, 1985; Bonaci et al., 2010; Toni, 2003). This model adopts a systemic perspective, illustrating the learning process from the student’s viewpoint. The model is structured across three levels. The presage level includes personal factors (prior knowledge, abilities, personality, and home background) and contextual ones (subject area, teaching methods, time on task, and task demands). The process level analyzes the complexity of learning, including motives and strategies. The third level, product, refers to performance outcomes: examinations, structural understanding, factual recall, and satisfaction.

To provide a broader understanding of how “academic performance” is treated in the current literature, we conducted a bibliometric analysis using VOSviewer (version 1.6.18), based on data exported from the Web of Science Core Collection. The search query was TS = (“academic performance”), filtered by language (English) and publication year (2020–2025). A total of 20,292 articles were analyzed using the co-occurrence of keywords (author keywords), with a minimum threshold of five occurrences per term. The analysis generated six clusters, aligned with the theoretical model used in our study (Presage–Process–Product (3P)). Each cluster was examined, and representative references were extracted to support their content. Academic performance has been addressed in approximately 49,000 Web of Science (WoS) documents and in about 5.9 million Google Scholar entries. In analyzing 20,292 English-language WoS documents from the past five years, we identified the key factors studied in correlation with academic performance, as shown in Supplementary Figure S1. To analyze the topic of “academic performance”, the bibliometric method of scientific mapping was used with the help of the visualization software VOSviewer. VOSviewer is widely used and has powerful graphical and mapping visualization capabilities (M. L. Bratu et al., 2023).

The terms were grouped into six clusters, which can be aligned with the previously described 3P model (Presage–Process–Product). Cluster 1 (highlighted in red) includes terms such as active learning, course, instruction, system, teaching, environment, technology, application, feedback, and framework. This cluster reflects themes related to the Process component of the 3P model, particularly emphasizing learning strategies (Rochev & Kudelin, 2025). Cluster 2 (highlighted in green) includes terms such as relationship, variable, gender, sample, association, achievement, academic engagement, mediating role, self-efficacy, and positive relationship. These keywords reflect a thematic focus on psychological factors that influence academic behavior, aligning with the Presage component of the 3P model (Vîrgă et al., 2022). Cluster 3 (in blue) groups together keywords like cross-sectional study, health, prevalence, depression, questionnaire, student, population, life, and significant difference. This cluster represents a health-related contextual framework and also corresponds to the Presage dimension of the 3P model (Huang, 2015). Cluster 4 (in yellow) contains keywords such as technique, feature, accuracy, and decision, which indicate a focus on the use of cognitive tools and methods. This set of terms reflects the Process component of the 3P model, particularly in relation to how students approach tasks cognitively (Jin et al., 2024). Cluster 5 (in purple) includes terms such as review, database, bias, and inclusion. These are typically associated with research synthesis and meta-analyses, representing the Product dimension of the 3P model, namely the outcomes of academic inquiry (Braithwaite & Corr, 2016). Cluster 6 (highlighted in light blue) includes terms like instrument, validity, and reliability, which refer to the tools and standards used to measure psychological or educational variables. This cluster aligns with the Process component of the 3P model, emphasizing the role of assessment and measurement tools (Chen & Wei, 2022).

When narrowing the WoS search to focus on factors influencing student academic performance, 227 articles were identified. These articles focus on the following concepts: motivation, perception, career, age, gender, self-efficacy, role, academic environment, relation, degree, personal performance, success, understanding, problem, need, prediction, teaching, and policymakers.

The present study focuses on analyzing student academic performance in relation to motivation and non-ethical behaviors, namely machiavellianism and lying.

2.2. Academic Motivation: The MUSIC Model

Motivation refers to a set of internal psychological phenomena and mechanisms that drive action independent of external factors. Motivation, through its dynamic and tension-generating character, stirs, reshapes, consolidates, and amplifies the individual’s psychological structure (Popescu-Neveanu et al., 1995). The collection of needs that seek satisfaction and drive the individual to fulfill them form the sphere of motivation. Motivation is a physiological and psychological process responsible for the initiation, maintenance, and termination of behavior, as well as the appetitive or aversive value attributed to environmental stimuli upon which that behavior is exerted (Ardeleanu et al., 2006).

Academic motivation refers to the desire and effort that students invest in learning and achieving academic goals. Jones defines academic motivation (Jones, 2009) in a manner consistent with Schunk, Pintrich, and Meece (Schunk et al., 2008) as a process inferred from actions (e.g., choice of tasks, effort, and persistence) and verbalizations (e.g., “I like biology.”) through which goal-directed physical or mental activity is initiated and sustained. Academic motivation is not important in and of itself, but because motivated students tend to engage in activities that help them learn and achieve at high levels in academic settings, According to Jones’ MUSIC Model of Academic Motivation, motivated students are more likely to pay attention during course activities, use effective learning and study strategies, and seek help when needed (Schunk et al., 2008).

The MUSIC Model of Motivation (Jones, 2009, 2018) can be used at any educational level and subject area to (1) design instruction that motivates students, (2) diagnose the motivational strengths and weaknesses of instruction, and (3) research the relationships between factors critical to student motivation. The MUSIC model outlines five key principles that instructors should consider for fostering student motivation. First, students need to feel empowered by having the ability to make decisions about certain aspects of their learning. Second, they should understand the relevance of what they are learning in relation to their short- and long-term goals. Third, it is essential that students believe in their ability to succeed, provided they invest the necessary effort. Fourth, students are more engaged when they are interested in the content and instructional activities. Finally, they need to feel cared for, believing that others in learning environments, such as instructors and peers, are genuinely concerned not only about their academic success but also about them as individuals (Jones, 2018).

MUSIC is an acronym for five key principles: eMpowerment, Usefulness, Success, Interest, and Caring (Jones, 2012).

The first dimension identified by Jones in the MUSIC model, empowerment, refers to the amount of control over the learning process perceived by students. The optimal level of control required for students to feel motivated varies from person to person and also depends on other variables involved in the learning process: skill level, prior knowledge, and independence in academic activity. The core idea is that students perceive they have a certain degree of control over segments of their learning (Jones, 2009).

The concept of empowerment has been thoroughly studied within the framework of self-determination theory (Jones, 2009; Ryan & Deci, 2000). The theory posits that individuals who feel in control show higher levels of involvement and satisfaction, increased motivation, and enhanced decision-making abilities. The additional benefits of a teaching style based on autonomy support include improved understanding of concepts, a positive perception of one’s academic and social competencies, the development of a positive self-image and self-esteem, the stimulation of creativity, the cultivation of well-being, and improved academic performance (Filak & Sheldon, 2008; Jones, 2009; Vallerand & Blssonnette, 1992).

The second dimension of the MUSIC model, Usefulness, refers to the relevance and utility of the studied content, as well as the practical value of the acquired knowledge. Future time perspective theory suggests that students are more motivated when they have long-term goals and perceive their studies as relevant to their career (Jones, 2009; Kauffman & Husman, 2004; Tabachnick et al., 2008), intrinsically committing to academic participation and involvement, with better results.

The expectancy–value model of motivation explains that academic performance is determined both by expectations of success and the utility value of a task (Wigfield & Eccles, 1992, 2000). Studies show that students who perceive courses as useful perform better and are more engaged in academic activities. A course or subject has high utility value for a student if it is either required for graduation or perceived as relevant to their future career.

The third dimension identified by Jones, Success, depends on a student’s belief in their ability to succeed relative to the personal resources invested, especially time and energy. An effective course, one through which students can achieve success, should be supported by adequate academic resources, be challenging, and provide constant feedback.

Perceived personal competence is the core of several motivational theories, including self-efficacy theory (Bandura, 1997) and the expectancy–value theory (Wigfield & Eccles, 2000), which claim that the need to be competent is a fundamental psychological need of human beings (Zimmerman et al., 2017). Students with positive expectations of success tend to choose challenging activities, invest more effort, persevere in the face of difficulties, adapt better to challenging situations, experience greater satisfaction, manage anxiety more effectively, and achieve higher performance.

The fourth dimension of the model, Interest, is based on the theory of Hidi and Renninger, which states that “The potential for interest exists within the individual, but the content and environment define the direction of interest and contribute to its development” (Hidi & Ann Renninger, 2006). Interest represents the attraction to a cognitive activity and conscious engagement in it (Schraw & Lehman, 2001).

Two types of interest have been identified, namely situational interest, which is triggered in a specific context by the environment and is short term, and personal interest, which is activated by internal mechanisms, associated with a specific domain, and long-lasting (Schraw & Lehman, 2001). The effectiveness of teaching depends on the instructor’s ability to integrate methods that foster the development of deep and lasting interest in a course’s content (Jones, 2009).

The fifth motivational dimension identified by Jones is Caring, seen as an essential coordinate of academic performance motivation. Caring is defined by the teacher’s concern for students’ well-being and their ability to treat students with empathy and identify flexible solutions to situations that affect students’ ability to meet course requirements.

Ryan and Deci (Ryan & Deci, 2000) stated that people have a fundamental need to form and maintain interpersonal relationships based on support and care, particularly relationships that determine the sense of belonging, attachment, engagement, connectedness, and community. Supportive relationships in academic environments encourage students to invest more effort, adopt teachers’ values, and reduce anxiety. A supportive environment enhances motivation and self-confidence, leading to improved academic performance.

2.3. Unethical Academic Behaviors: Machiavellianism and Lying

The relationship between Machiavellianism and lying among students has been addressed in a limited number of Web of Science articles; however, there are currently about 15,800 related entries on Google Scholar.

Machiavellianism originates from Niccolò Machiavelli’s political theory, as presented in The Prince (Machiavelli, 2020). It refers to the tendency to manipulate others’ behavior to achieve personal goals. Behaviorally, Machiavellian individuals act in their own interest, even at the expense of others, remain cold and calculated when others are emotional, have a well-formed self-image and high self-esteem, and form alliances with powerful individuals to reach their objectives.

Lying is considered an intentional act of stating an unverifiable or easily refutable condition in order to create confusion, give false hope, influence an action, or induce a particular intellectual, social, or emotional state that serves the liar’s interests in one way or another.

Machiavellianism and lying are considered unethical behaviors because they exclude morality and scruples, relying on unorthodox means such as deceit, falsehood, lack of conscience, cunning, betrayal, and corruption.

Machiavellianism, together with psychopathy and narcissism, forms the three personality traits of the Dark Triad. The core of these traits is the so-called D factor, which defines a person who seeks personal gain regardless of the costs to others and is willing to engage in behavior that violates social, moral, or ethical norms. Such individuals frequently resort to deception, manipulation, lying, and misleading others to maximize personal benefits without being troubled by the consequences of their actions for others (Hurezan et al., 2020). Thus, the common denominator of Machiavellianism and lying is manipulation and the amoral, unethical sphere in which such individuals operate.

In the scientific literature, Machiavellianism and lying among students are studied from the perspective of the Dark Triad in strong positive correlation with procrastination and academic dishonesty (Barbaranelli et al., 2018; Bloodgood et al., 2010; Cheung & Egan, 2021; Esteves et al., 2021; Hidalgo-Fuentes et al., 2022; Lingán-Huamán et al., 2024; Nkundabanyanga et al., 2014; Prince & Wang, 2024; Quah et al., 2012), which includes values, attitudes, techniques, and motives for deviant behaviors (such as entitlement) (Curtis, 2023), including cheating on exams, plagiarism, and falsification. These two traits, as part of the D factor, are not directly dependent on students’ religion or age (Quah et al., 2012; Veríssimo et al., 2022), but they vary indirectly; Machiavellian behaviors are less frequent among religious individuals (Tang & Tang, 2010), and behavioral types differ by gender (Jambrešić et al., 2020; Lingán-Huamán et al., 2024).

Situational factors have also been identified that influence students’ engagement in academic fraud, such as the academic integrity culture, ambiguity around academic fraud, and perceived pressure (Srirejeki et al., 2023). High academic motivation was identified by Rundle et al. as a reason why students refrain from contract cheating (Rundle et al., 2019), whereas low motivation is a key risk factor for academic misconduct (Playfoot et al., 2024). Antisocial behavior, such as rule breaking, has been associated with falsification and both high- and low-risk academic misconduct, while social aggression has been linked to copying, and none of the antisocial behaviors showed an association with plagiarism (Ternes et al., 2019).

In conclusion, Machiavellianism and lying among students, seen as unethical behaviors, generate consequences related to academic dishonesty, particularly in the context of situational factors that increase the likelihood of such manifestations. Ethics courses do not seem to influence the probability of unethical behavior, but increasing motivation and encouraging prosocial behavior may mitigate such tendencies.

Following the analysis of current research focused on the triad of performance–motivation–unethical behavior, it becomes evident that no articles exist in the Web of Science database that address this topic comprehensively. A search of Google Scholar returned articles that only partially cover the topic of this study, either examining the relationship between the Dark Triad and personality or discussing unethical behaviors in relation to motivation. For Romania, there are no published articles on this subject. A search of Scopus-indexed articles identified 33 entries, which also address the topic only partially and do not connect all three elements investigated in this research.

The novelty of this study lies in linking students’ unethical behaviors—Machiavellianism and lying—with academic motivation as described by the MUSIC model, all analyzed in relation to academic performance. As previously mentioned, this combination of variables has not been explored in the existing literature, offering a new perspective on the psychological factors that influence unethical student behavior. Another original aspect is the gender-based approach, which may provide relevant implications for developing strategies to prevent such behaviors. Moreover, the results of this study could contribute to the development of educational strategies aimed at reducing unethical behavior among students, offering an empirical basis for more effective academic policies.

The aim, research questions, and hypotheses of the present study were introduced at the end of the previous section, following the theoretical background presented in this chapter.

3. Materials and Methods

3.1. Participants

The study was conducted between October 2018 and March 2025 among the Faculty of Engineering of Lucian Blaga University in Sibiu, Romania. Population sampling involved all students who participated in the 13 courses and expressed their informed consent, totaling 706 students. The participants were enrolled in various engineering-related programs, including undergraduate specializations such as Engineering and Management in the Mechanical Field, Industrial Engineering and Management, and Transport and Traffic Engineering, as well as graduate programs in Industrial Business Management and European Project Management.

The data were collected through online questionnaires administered via an educational platform used to support the learning process. All students enrolled in the course during the semester had voluntary access to the questionnaires and were invited to participate without incentives or obligations. Out of 1015 students who accessed the platform, 706 submitted valid responses, resulting in a response rate of approximately 69.5%. Data collection took place throughout the semester in which the course was delivered, ensuring natural integration of the survey into students’ learning routines.

This study is part of a larger international research initiative exploring academic motivation and ethical behaviors among university students from various disciplines. Due to the substantial volume of data collected, the present paper focuses on a specific subgroup of Romanian engineering students. This choice was guided by both practical and theoretical considerations. Practically, the authors had direct access to this population, facilitating data collection. Theoretically, engineering education is often characterized by high academic demands, performance-oriented environments, and competitive cultures, which may influence students’ motivational patterns and ethical attitudes. Furthermore, the authors’ broader research agenda is oriented toward developing educational strategies and institutional policies tailored to the needs of future engineers. Therefore, focusing on this subgroup offers meaningful insights while maintaining alignment with the study’s long-term goals.

3.2. Ethical Approval

The study procedure and instruments were approved by the Ethics Committee of Lucian Blaga University in Sibiu, Romania (No. 02-14.07/2022). The project, entitled “Motivation and Academic Performance: Analyzing the Relationship Between Machiavellianism and Lying in Students”, meets the ethical requirements for scientific research, in accordance with the scientific research code of ethics. Prior to the study, students were informed that participation was voluntary and anonymous. They were also informed about the purpose of the study and their right to withdraw at any time.

3.3. Research Instruments

All participants completed the survey individually and online outside of scheduled class hours. Data collection was conducted using a standardized digital format, with identical instructions, item wordings, and response layouts for all respondents. Despite possible differences in educational environments (such as teaching staff or platforms), all students answered the same questionnaires under comparable survey conditions. These instruments targeted psychological constructs (e.g., academic motivation, Machiavellianism, and attitudes toward dishonesty) and not instructional or curricular factors.

To assess potential variations related to the extended data collection period, we conducted preliminary statistical comparisons across academic cohorts. The results showed no significant year-to-year differences in the main variables of interest. Consequently, the full dataset was retained in the final analysis to preserve the sample size and increase statistical power.

To analyze the relationship between Machiavellianism, lying, motivation, and academic performance, three questionnaires were used: the MUSIC Model of Motivation questionnaire, the Machiavellianism questionnaire, and the Lie Questionnaire.

3.3.1. MUSIC Model of Motivation

As described earlier, the MUSIC Model of Motivation questionnaire includes five key principles—Empowerment, Usefulness, Success, Interest, and Caring—measured through 26 items. The questionnaire exists in multiple versions adapted to different education levels and moments (during or at the end of a course).

The MUSIC constructs have been shown to relate to students’ motivation, engagement, domain identification, career goals, course ratings, and instructor ratings (Jones, 2012).

The purpose of the college student version of the MUSIC Inventory is to measure the extent to which students perceive the presence of each MUSIC component in a course. It can be used by instructors to identify strengths and weaknesses in motivational factors affecting students’ engagement (Jones, 2012).

The questionnaire was used with the written consent of Prof. Brett D. Jones and followed the instructions described in the User Guide for Assessing the Components of the MUSIC^® Model of Motivation (Jones, 2012). The inventory title was formulated as generally as possible for the survey of students’ course perceptions. A purpose statement was included explaining the goal of the survey and clarifying that students’ names would not be shared with their instructors. All instructions suggested by the author regarding terms such as “instructor”, “coursework”, and item approach were followed. The items were presented in a random order. Item coding followed the guide’s recommendations. Answer options were displayed vertically.

The interpretation of MUSIC scores followed the guidelines provided by Jones, where average values below 4.0 suggest low motivation, scores of about 4.0–5.0 indicate moderate motivation, and scores above 5.0 reflect high levels of perceived motivation.

The validity evidence for this version of the MUSIC Inventory used with college students is quite good (α = 0.9), as shown by the results of several studies (Jones, 2019). Cronbach’s alpha values were interpreted based on Kline’s criteria (Kline, 2023).

For translation, the guidelines by the International Test Commission were followed (International Test Commission, 2017). The Romanian translation was coordinated by professors from Lucian Blaga University of Sibiu and then back-translated to English by a U.S. professor and verified by Brett Jones. Minor adjustments were made, and the final version was used in the current study.

3.3.2. Machiavellianism Questionnaire

The instrument used to assess Machiavellianism was designed by Peretti, Legrand, and Boniface (de Peretti et al., 2001). The questionnaire consists of 18 items rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The total score is calculated by summing the item scores. Items address various aspects of manipulation, strategic behavior, low emotional competence, and alliance formation. The scale has strong internal consistency (Cronbach’s Alpha α = 0.848).

A key issue concerns how Machiavellianism is measured. Self-reporting scales often fail to fully capture defining traits such as planning, impulse control, and delay of gratification. Therefore, the resulting profiles and their relationships to other psychological dimensions suggest that current instruments may actually assess a mild form of psychopathy or its interpersonal component, rather than a distinct Machiavellian construct (Hurezan et al., 2020).

3.3.3. Lie Questionnaire

The Lie Questionnaire was developed by psychologist Yves-Alexandre Thalmann (Thalmann, 2019). It includes 10 items evaluated on a 5-point Likert scale from strongly agree to strongly disagree. The questions assess lying from social, nonverbal, personal belief, behavioral, and manipulative perspectives. Although the items cover multiple facets—moral, cognitive, and relational (Crowne & Marlowe, 1960; Hart et al., 2019; Paulhus, 1984)—they show good internal consistency (α = 0.728), supporting the treatment of the questionnaire as a unitary scale.

This multidimensional nature is consistent with other validated instruments for assessing dishonest behavior. For example, the Lying in Everyday Situations Scale (LEiSS) developed by Hart et al. (Hart et al., 2019) also includes multiple categories such as antisocial lies, white lies, exaggeration, and prosocial lying. Such structuring supports the relevance of using scales that address diverse types of dishonest behavior within a unitary framework.

The composite score was calculated as the arithmetic mean of all 10 items, with higher scores indicating stronger tendencies toward social distortion and rigid beliefs about lying.

3.4. Data Analysis

To validate hypotheses H1–H5, the collected data were compiled into a centralized database using IBM SPSS Statistics 26. Initially, descriptive statistics (mean, standard deviation, and frequencies) were calculated to provide an overview of the sample characteristics and variable distributions. Reliability analyses (Cronbach’s alpha) were conducted for each of the three instruments used: the MUSIC Model of Motivation, the Machiavellianism Scale, and the Lie Questionnaire. All instruments demonstrated acceptable-to-high internal consistency, with coefficients exceeding the 0.70 threshold.

Normality tests (Kolmogorov–Smirnov and Shapiro–Wilk) indicated that most variables did not follow a normal distribution, justifying the use of non-parametric statistical procedures. Accordingly, Spearman’s rho was applied to test correlations, Mann–Whitney U tests were used for two-group comparisons, and Kruskal–Wallis H tests were used when comparing more than two groups. In addition to statistical significance, measures of the effect size (e.g., R² for regression and ρ for correlations) were reported to indicate the strength of the relationships.

To test hypotheses H1–H3, Spearman’s rho was used to examine the associations between Machiavellianism, tendencies toward lying, and academic motivation. For hypothesis H4, a multiple linear regression analysis was conducted to explore the predictive power of the independent variables (Machiavellianism and lying) on academic performance, with the grade point average (GPA) as the dependent variable. To test H5, a moderated regression analysis (PROCESS macro—Model 7) was performed, introducing gender as a moderator in the interaction between psychological variables and academic performance.

All statistical analyses were performed using a significance level of α = 0.05.

3.5. Research Design

The research employed a quantitative, cross-sectional design based on self-reporting questionnaires. The study integrated five distinct data sources: (1) student responses to the MUSIC model questionnaire; (2) scores on the Machiavellianism Scale; (3) scores on the Lie Attitude Questionnaire; (4) demographic variables (age, gender, marital status, and academic year); and (5) academic performance, measured by self-reported GPAs.

This design allowed for the examination of both bivariate and multivariate relationships between psychological constructs and academic outcomes. Moreover, the inclusion of demographic moderators (e.g., gender and age) enabled the analysis of conditional effects and subgroup comparisons, enhancing the interpretability of results within the broader educational context.

4. Results

4.1. General Presentation of the Data

The final sample consisted of 706 valid responses, collected between 2018 and 2025, from students enrolled in engineering programs. The gender distribution was relatively balanced (52.3% female, 47.7% male), and participants’ ages ranged from 19 to 47 years (M = 22.84, SD = 3.92). Regarding marital status, 76.9% were unmarried, while 23.1% reported being married or in a stable relationship.

The academic year distribution was as follows: 22.8% in year 1, 24.6% in year 2, 26.9% in year 3, and 25.7% in year 4 or master’s students. The mean GPA was 8.54 (SD = 0.93), with values ranging from 6.00 to 10.00.

Descriptive statistics revealed good variability across all psychological constructs. The MUSIC model dimensions showed slightly higher mean scores for Usefulness and Caring and lower scores for Success and Empowerment, indicating a partial motivational imbalance. The Machiavellianism scores were symmetrically distributed, while the Lie Questionnaire showed slightly skewed tendencies, with higher scores among female and married participants.

Tests for reliability and normality, as well as visual inspection through histograms and boxplots, confirmed the appropriateness of non-parametric analyses for most variables. The dataset was deemed suitable for correlational and predictive modeling.

4.2. Objective 1: Analysis of Students’ Academic Motivation Based on the MUSIC Model

Regarding the evaluation of the MUSIC Model of Motivation, descriptive data for each item are provided in Supplementary Figures S2 and S3. These visuals allow for a more nuanced understanding of students’ motivational perceptions.

Supplementary Figure S2 shows the relative frequency of responses for each item in the MUSIC questionnaire. A predominance of agreement (responses 5 and 6) can be observed for the majority of items, indicating a generally high level of academic motivation. However, a few items displayed more balanced distributions, suggesting areas where student motivation is lower or more ambivalent.

Supplementary Figure S3 highlights the relative frequencies on the five MUSIC scales for each participant, following Professor Jones’ guidelines (Jones, 2012), and it offers a visual overview of the variability in the perceived motivation dimensions. The highest aggregate values were observed on the Usefulness scale, followed by Caring, while the Success scale showed the most downward variations, indicating a weaker perception of one’s own academic effectiveness.

Descriptive statistics were calculated for each dimension of the MUSIC model. The results indicated the following mean scores for Empowerment (M = 5.04, SD = 0.28), Usefulness (M = 5.21, SD = 0.30), Success (M = 4.97, SD = 0.34), Interest (M = 5.12, SD = 0.31), and Caring (M = 5.44, SD = 0.26). These scores suggest relatively high levels of perceived motivation among the participants.

Figure 2 presents the distribution of scores for the five dimensions of the MUSIC model—Empowerment, Usefulness, Success, Interest, and Caring—using boxplot diagrams. A relatively consistent median around the value of four can be observed for most dimensions, except for the Success scale, which showed a lower median, indicating a reduced level of students’ confidence in their own success. The Interest scale displayed greater score variability, suggesting individual differences in cognitive engagement. The diagram provides a clear visual understanding of the central tendencies and the dispersion of motivation as perceived by students across each dimension of the model.

The internal reliability of the MUSIC questionnaire was evaluated using Cronbach’s alpha coefficient. The results indicated high consistency for each of the five scales: Empowerment (α = 0.812), Usefulness (α = 0.868), Success (α = 0.857), Interest (α = 0.869), and Caring (α = 0.814). The coefficient for the overall questionnaire (α = 0.947) reflects extremely high coherence of the instrument, supporting the validity of the analysis for each individual scale.

The distribution of scores for the five MUSIC model scales was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. In all cases, the significance values were below the 0.05 threshold (p < 0.001), indicating significant deviations from normality. Consequently, non-parametric tests were used for the analysis of relationships between variables and for group comparisons, appropriate for asymmetric distributions, and ordinal-level data.

The relationship between age and the dimensions of academic motivation was evaluated using Spearman’s rank correlation coefficient, considering the non-normal distribution of data. The results indicated significant positive correlations between age and all five MUSIC model scales: Empowerment (ρ = 0.197, p < 0.001), Usefulness (ρ = 0.108, p = 0.016), Success (ρ = 0.150, p = 0.001), Interest (ρ = 0.220, p < 0.001), Caring (ρ = 0.179, p < 0.001). These coefficients (ρ) also serve as measures of the effect size, indicating small-to-moderate associations between variables, according to Cohen’s guidelines.

These results suggest that as students age, their perceived levels of control, content relevance, confidence in success, cognitive interest, and emotional support from instructors increase significantly. The strongest association was observed between age and interest in educational activity.

Differences in motivation based on marital status were tested using the Mann–Whitney U test, given the non-normal distribution of the data. The results indicated statistically significant differences for two of the five MUSIC model dimensions. For the Interest scale, married individuals obtained significantly higher scores (U = 5673.000, p = 0.011), with a mean rank of 302.65 compared with 239.05 for the unmarried participants. This suggests that married students exhibit greater cognitive engagement and interest in educational activities. Similarly, on the Caring scale, married students perceived a significantly higher level of support, empathy, and concern from instructors (U = 5923.500, p = 0.025), with a mean rank of 295.28 versus 239.61 for the unmarried students.

For the other dimensions (Empowerment, Usefulness, Success), the differences were not statistically significant, although marginal trends were observed for Empowerment (p = 0.066) and Usefulness (p = 0.074), favoring the married individuals.

Differences in academic motivation perception based on the academic year were analyzed using the Kruskal–Wallis test, considering the ordinal nature of the data and the lack of normality. The results indicated statistically significant differences for three of the five dimensions. For the Empowerment dimension, students in the 2022 cohort had the highest scores (H = 11.351, p = 0.010; mean rank = 275.15), while those in the 2024 cohort had the lowest scores (mean rank = 218.08), suggesting a decline in students’ perceived control over their learning as they progress through their academic journeys. Similarly, for the Usefulness scale, perceived relevance of the academic content decreased steadily from the 2021 cohort (mean rank = 266.79) to the 2024 cohort (mean rank = 219.44), with the overall difference reaching statistical significance (H = 8.744, p = 0.033). The most pronounced difference was observed in the caring dimension (H = 43.917, p < 0.001), where students from 2021 reported significantly higher perceptions of support and empathy from instructors (mean rank = 288.66) compared with the students from 2024 (mean rank = 183.12), indicating a substantial drop in perceived relational support over time.

For the Interest scale, a downward trend was observed (H = 6.653, p = 0.084), but it was not statistically significant. No significant differences were recorded for the Success scale (p = 0.854).

4.3. Objective 2: Assessment of General Tendencies Toward Lying Among Students in Various Contexts

Figure 3 presents the average scores obtained by the participants for each item in the questionnaire on attitudes toward lying. The highest scores were recorded for statements addressing the impact on relationships (I2) and detection through nonverbal cues (I4 and I7), reflecting a strong concern with the relational and behavioral dimensions of lying.

Descriptive statistics for the Lie Attitude Questionnaire indicated an overall mean score of 3.86 (SD = 0.51), based on responses from 706 participants, as shown in

Table 1. Descriptive statistics for the Lie Attitude Questionnaire.

Descriptive Statistics
	N	Mean	Std. Deviation
Score	706	3.8622	0.50926
Valid N (listwise)	706

.

Figure 4 illustrates the distribution of scores on the Lie Questionnaire by gender. A higher median was observed among the female participants, as well as a greater dispersion of values compared with the male participants. The visualized differences suggest a stronger attitudinal tendency among women regarding the phenomenon of lying.

The descriptive statistics for the Lie Attitude Questionnaire revealed a higher mean score among female students (M = 3.92, SD = 0.48, n = 399) compared with male students (M = 3.78, SD = 0.53, n = 307), suggesting a stronger disapproval of lying behaviors among women. See

Table 2. Mean scores and standard deviations for the Lie Attitude Questionnaire by gender.

Report
Score
Gender	Mean	N	Std. Deviation	Minimum	Maximum
1	3.9157	399	0.48391	1.70	5.00
2	3.7804	307	0.53054	1.70	5.00
Total	3.8569	706	0.50841	1.70	5.00

for details.

Before applying inferential tests, the distribution of scores obtained from the questionnaire on attitudes toward lying was assessed. The results of the Kolmogorov–Smirnov (p = 0.011) and Shapiro–Wilk (p < 0.001) tests indicated a significant deviation from normality, which led to the use of non-parametric tests in subsequent analyses.

The relationship between age and the total score was analyzed using Spearman’s coefficient. The result indicated a weak but statistically significant positive correlation (ρ(706) = 0.128, p = 0.018), suggesting that as the age increased, the participants tended to express more firmly or elaborately formed attitudes toward lying behaviors. This may reflect a deeper internalization of social norms regarding honesty or greater life experience with such behaviors. This coefficient (ρ) also serves as a measure of the effect size, indicating small-to-moderate associations between variables, according to Cohen’s guidelines.

Differences based on marital status were evaluated using the Mann–Whitney U test. The results indicated a statistically significant difference between the married and unmarried respondents (U = 3479.500, Z = −2.587, p = 0.010). Married individuals obtained significantly higher scores (mean rank = 208.77) than the unmarried ones (mean rank = 162.56), suggesting a more rigid and moralizing attitude toward lying behaviors.

Regarding gender, the Kruskal–Wallis analysis indicated a statistically significant difference between groups (H(1) = 6.256, p = 0.012). The mean rank was higher among the female participants (mean rank = 181.14) compared with the male participants (mean rank = 154.38), indicating a stronger tendency among women to express more evaluative and clearly defined attitudes toward lying behaviors. Although standardized effect sizes were not calculated for the non-parametric tests, the reported mean ranks reflect the magnitude and direction of group differences.

4.4. Objective 3: Investigating Machiavellian Traits in Students from the Perspective of Deceptive Behaviors and Intentions

Respondents’ scores ranged from 25 to 90, with a mean of 58.89 (N = 706). The standard deviation of 10.510 indicates a moderate spread of values around the mean, suggesting that the attitudes measured by the instrument varied significantly, though without extreme outliers. Higher scores indicate a higher level of Machiavellianism, while lower scores reflect a tendency toward cooperation, empathy, and social morality.

Figure 5a illustrates the distribution of individual scores on the Machiavellianism test, showing moderate variability in responses within the sample (N = 706) without extreme patterns. Figure 5b presents a histogram of scores overlaid with a theoretical normal curve, demonstrating an approximately symmetric distribution centered around the mean (M = 58.89).

The Kolmogorov–Smirnov test indicated a significant deviation from a normal distribution for the Machiavellianism scores (p = 0.007), and hence non-parametric tests (Spearman and Mann–Whitney) appropriate for non-normal distributions were used in the inferential analyses.

The Kruskal–Wallis test indicated a significant difference in Machiavellianism scores by gender (H(1) = 4.874, p = 0.027). Analysis of the mean ranks showed that the males (mean rank = 83.88) had significantly higher scores than the females (mean rank = 67.92), suggesting a stronger tendency toward Machiavellian behaviors among male participants, as shown in

Table 3. Kruskal–Wallis test.

Ranks
	Gender	N	Mean Rank
Score	1	399	67.92
	2	307	83.88
	Total	706

. Although standardized effect sizes were not calculated for the non-parametric tests, the reported mean ranks reflect the magnitude and direction of group differences.

5. Discussion

Throughout the analysis, both statistical significance and effect sizes were considered to ensure the robustness and practical relevance of the findings.

5.1. Exploring the Relationships Between Machiavellian Tendencies, Lying, Motivation, and Academic Performance

5.1.1. H1: There Is a Negative Correlation Between Machiavellian Tendencies and Students’ Academic Motivation

To test hypothesis H1, Spearman’s coefficient was used given the non-normal distribution of data. The analysis revealed statistically significant negative correlations between the Machiavellianism scores and all five MUSIC model dimensions. Specifically, Empowerment (ρ = −0.160, p < 0.001), Usefulness (ρ = −0.173, p < 0.001), Success (ρ = −0.166, p < 0.001), Interest (ρ = −0.131, p = 0.001), and Caring (ρ = −0.161, p < 0.001) were all negatively associated with Machiavellian traits, indicating that higher levels of Machiavellianism are linked to lower academic motivation across all measured dimensions, as presented in

Table 4. Spearman’s rank-order correlations between Machiavellianism and MUSIC model dimensions.

			Machiavellianism
Spearman’s rho	Empowerment score	Correlation coefficient	−0.160
		Sig. (2-tailed)	0.000
	Usefulness score	Correlation coefficient	−0.173
		Sig. (2-tailed)	0.000
	Success score	Correlation coefficient	−0.166
		Sig. (2-tailed)	0.000
	Interest score	Correlation coefficient	−0.131
		Sig. (2-tailed)	0.001
	Caring score	Correlation coefficient	−0.161
		Sig. (2-tailed)	0.000

.

These results support hypothesis H1, suggesting that students exhibiting stronger Machiavellian traits tend to have lower academic motivation levels. The strongest negative association was found between Machiavellianism and Empowerment, indicating that manipulative individuals feel less in control of their own learning processes.

The relationship between students’ Machiavellian tendencies and their perception of control over the learning process (Empowerment) is illustrated in Figure 6. The scatterplot highlights a statistically significant negative association, confirmed by Spearman’s coefficient (ρ = −0.259, p < 0.001). As the Machiavellianism scores increased, Empowerment scores tended to decrease, suggesting that students with more pronounced Machiavellian traits feel less autonomous and engaged in academic activities. The regression line emphasizes this downward trend and the consistency of the identified relationship.

5.1.2. H2: Students with Stronger Tendencies Toward Lying Exhibit Lower Levels of Academic Motivation

To test the hypothesis that “students with stronger tendencies toward lying exhibit lower academic motivation”, Spearman correlations were calculated between the scores on the five MUSIC model scales and the score from the questionnaire on attitudes toward lying.

The results revealed statistically significant negative correlations between lying tendencies and each of the five dimensions of academic motivation. Specifically, students with higher inclinations toward dishonesty reported lower scores for Empowerment (ρ = −0.206, p < 0.001), Usefulness (ρ = −0.181, p < 0.001), Success (ρ = −0.133, p = 0.004), Interest (ρ = −0.150, p = 0.001), and Caring (ρ = −0.149, p = 0.002).

These results support hypothesis H2 and suggest that students who hold more permissive attitudes toward lying tend to have lower levels of academic motivation. The strongest negative associations were found for Empowerment and Usefulness, possibly indicating that individuals more prone to dishonest behaviors feel less in control of their learning and perceive educational content as less useful.

To further explore the relationship between lying attitudes and students’ academic motivation (H3), a simple linear regression analysis was conducted using the Lying Attitude Questionnaire score as a predictor for the dimensions of the MUSIC model. The results, presented in

Table 5. Linear regression predicting Empowerment score from Lying attitude score.

PREDICTOR	B (UNSTD.)	B (STD.)	T	P	R2
(CONSTANT)	…	–	…	…
LYING ATTITUDE	−0.13	−0.18	−4.64	<0.001	0.034

, indicate a statistically significant negative effect of lying tendencies on perceived empowerment (F(1, 615) = 21.53, p < 0.001), with an adjusted R² of 0.034. This suggests that stronger permissive attitudes toward lying are modestly associated with a decreased sense of control and autonomy in the academic context.

Figure 7 illustrates the negative relationship between the lying tendency and Empowerment scores. As the score for lying behaviors increased, perceived control over one’s learning (Empowerment) tended to decrease. The regression line indicates a downward trend, supporting hypothesis H2 that inclination toward lying is associated with lower academic motivation.

5.1.3. H3: Students with Stronger Machiavellian Traits Are More Likely to Resort to Lying to Gain Academic Advantages

To test the hypothesis that students with stronger Machiavellian traits tend to lie more frequently, Spearman’s correlation coefficient was calculated between the total Machiavellianism score and the score from the questionnaire on lying. The results showed a statistically significant negative correlation (ρ = −0.164, p < 0.001).

Given that a lower score on the lying questionnaire reflects a more permissive attitude (i.e., greater willingness to lie), this negative relationship suggests that as Machiavellian traits increase, the likelihood of resorting to lying for academic purposes also increases.

Thus, the data support hypothesis H3, confirming that students with higher Machiavellianism scores showed a significantly greater tendency to use lying as an opportunistic strategy.

A simple linear regression analysis was conducted to assess the predictive relationship between Machiavellianism and lying tendencies. The results indicate that Machiavellianism is a significant negative predictor of lying scores (β = −0.157, p < 0.001), accounting for approximately 2.5% of the variance (R² = 0.025), as seen in

Table 6. Linear regression predicting lying attitude score from Machiavellianism score.

PREDICTOR	B	SE	B	T	P	R2
MACHIAVELLIANISM	−0.122	0.031	−0.157	−3.952	<0.001	0.025

.

These findings confirm the hypothesized relationship (H3) and are visually represented in Figure 8. A descending trend can be observed; as the Machiavellianism scores increase, the lie scores tend to decrease, confirming hypothesis H3, stating that students with more pronounced Machiavellian traits more frequently engage in lying to gain academic advantages.

5.1.4. H4: Academic Performance Is Significantly Influenced by the Interaction of Academic Motivation, Machiavellian Tendencies, and Lying Behaviors

To explore how academic motivation, Machiavellian traits, and lying behaviors influence students’ academic performance, a multiple linear regression model was used. The dependent variable was the grade obtained, and the predictors were the five MUSIC model dimensions (Empowerment, Usefulness, Success, Interest, and Caring), the Machiavellianism score, and the lie score.

To explore how academic motivation, Machiavellian traits, and lying behaviors influence students’ academic performance, a multiple linear regression model was used. The dependent variable was the grade obtained, and the predictors were the five MUSIC model scales (Empowerment, Usefulness, Success, Interest, Caring), the Machiavellianism score, and the lie score.

Among all the predictors included, three variables had a statistically significant influence on students’ grades. The Success dimension (β = 0.094, p = 0.001) and the Interest dimension (β = 0.101, p = 0.016) were positively associated with academic performance, suggesting that students who believe in their ability to succeed and who show higher cognitive engagement tend to perform better academically. Machiavellianism (β = 0.125, p = 0.001) also emerged as a statistically significant predictor, albeit with a small effect size. This finding may indicate that certain strategic or goal-oriented behaviors associated with Machiavellianism can positively influence academic outcomes in competitive academic contexts. Although the lie score had a negative standardized beta (β = −0.122), the effect was not statistically significant in the full regression model. The other motivational dimensions (Empowerment, Usefulness, and Caring) also showed limited explanatory power and did not reach statistical significance.

Figure 9 presents the standardized beta coefficients for each predictor. These results only partially support hypothesis H4, emphasizing the role of perceived success and interest as the strongest motivational drivers of academic achievement. The practical impact of Machiavellianism, although statistically evident, remains limited due to the small effect size and should be interpreted with caution.

These results only partially support hypothesis H4, suggesting that academic performance is mainly influenced by perceived success and interest and, to a lesser extent, by Machiavellian traits, with no significant effect from the other dimensions or lying behavior.

5.1.5. H5: There Are Significant Differences Between Students, Based on Gender, in the Relationship Between Machiavellian Traits, Academic Motivation, Lying Behaviors, and Academic Performance

To test hypothesis H5, a multiple linear regression model was constructed with interaction terms between gender (1 = female, 2 = male) and the main predictors.

The model included the following predictors: academic motivation, Machiavellianism, and lying behaviors (reverse scored), along with gender (coded as 1 = female, 2 = male). Additionally, interaction terms were introduced to examine the moderating effect of gender on each of the predictors.

The results showed that the overall model was statistically significant (F(7, 364) = 4.91, p < 0.001), with an adjusted R² of 0.069. The adjusted R² value indicates a small-to-moderate effect size. Within this model, academic motivation was a significant predictor of academic performance (β = 0.13, p = 0.016), indicating a positive relationship between motivation levels and student grades. Additionally, gender had a significant effect on performance (β = −0.76, p = 0.001), with the female students (coded as 1) obtaining, on average, higher academic results compared with the male students.

The interaction between gender and motivation was marginally significant (β = 0.18, p = 0.085), suggesting that the effect of motivation on academic performance was more pronounced for the female students.

Figure 10 supports this conclusion, highlighting that for female students, high academic motivation was more clearly associated with improved performance than it was for male students.

These findings suggest that tailored educational strategies that enhance motivation—especially among female students—may contribute to improved academic outcomes and could inform future interventions in engineering education.

5.2. Theoretical and Practical Implications

5.2.1. Theoretical Contributions

To provide a clearer overview of the empirical findings, we summarize below the outcomes related to each hypothesis tested in this study:

H1. 

Machiavellian tendencies negatively correlate with academic motivation: supported. All five dimensions of the MUSIC model showed significant negative correlations with Machiavellianism, particularly Empowerment and Interest.

H2. 

Students with stronger tendencies toward lying exhibit lower academic motivation: supported. Negative correlations were observed between attitudes toward lying and all five MUSIC scales.

H3. 

Higher Machiavellianism is associated with a greater tendency to lie: supported. A significant inverse relationship was found between the Machiavellian scores and Lie Questionnaire scores.

H4. 

Academic performance is influenced by motivation, Machiavellianism, and lying: partially supported. Success and interest positively predicted academic performance, while Machiavellianism had a small but significant positive effect; however, lying did not significantly predict grades.

H5. 

Gender moderates the relationship between psychological traits and academic performance: supported. Female students showed stronger associations between motivation and academic performance, and gender significantly moderated the effects in the regression model.

Following the analysis of the data obtained, hypotheses H1, H2, and H3, concerning the negative correlations between Machiavellianism, lying, and motivation, were confirmed. Students who exhibited Machiavellian traits and accept lying as an acceptable strategy in society tended to display lower academic motivation, especially on the scales of personal control (Empowerment) and content relevance (Usefulness). The results are consistent with the literature on the Dark Triad, in which dysfunctional traits reduce positive engagement and encourage opportunistic strategies in the educational context (Denovan et al., 2021; Smith et al., 2023; Veres et al., 2020).

The analysis of the MUSIC model dimensions showed generally good academic motivation among the students, but this was imbalanced across the dimensions. The Success dimension recorded lower values, suggesting difficulties in perceiving personal efficacy, a critical aspect for self-regulated learning. This phenomenon can be interpreted through the lens of the self-regulation and expectancy–value theories, according to which motivation decreases when individuals do not believe in the goal or value of their effort (Liu et al., 2021; Sundre & Kitsantas, 2004). Learning motivation is directly correlated with the acquisition of strategies to control one’s own learning behavior, namely strategies described by the Empowerment and Usefulness dimensions (Pascaru, 2013; Sitoiu et al., 2025). Self-regulated learning refers to students’ ability to exert motivational, metacognitive, and behavioral control over their own learning, a process that does not occur in the presence of unethical behaviors (Dragan, 2013).

Socio-demographic variables (gender, age, and marital status) had a significant impact on the analyzed processes. Older and married students exhibited higher levels of motivation (Interest and Caring), suggesting that psychosocial maturity and the assumption of adult roles correlate with deeper educational engagement. This result can be explained by social role theory and the psychological need for belonging and competence (Heiss, 2017; Morgenroth et al., 2015).

The Caring dimension, measuring the teacher’s concern for well-being, empathy, and understanding, varied positively with age but decreased as the year of study increased, meaning that students from the years 2020, 2021, and 2022 felt more understood and protected by teachers. Variations in the Caring dimension can be explained not necessarily by age or academic experience but by the social and educational context specific to each cohort. For example, students in 2021 and partly in 2022 were more exposed to the pandemic context, social restrictions, and online courses, which may have influenced how they perceived teacher support (Caring) or the relevance of educational activities (Usefulness). It is possible that during this period, teacher–student relationships were redefined, generating different perceptions compared with students from later years who returned to the classic face-to-face system (M. L. Bratu et al., 2023; Cioca & Bratu, 2021).

The inverse relationship between Machiavellianism and motivation (especially Empowerment) suggests that manipulative students perceive a reduced sense of control over their learning. This paradox may be explained by their tendency to externalize responsibility and use strategies that avoid genuine effort, based on social instrumentalization (Denovan et al., 2021; Joshi et al., 2024; Veres et al., 2020).

Hypothesis H4 yielded an interesting and unexpected result. Although Machiavellian traits reduced motivation, they correlated positively with academic performance, suggesting that some students may achieve good results through adaptive yet dishonest strategies such as feigning interest, manipulating the teacher, or strategically adapting to evaluation requirements. This supports the idea that performance can be achieved not only through authentic effort but also through surface-level performance, a concept relevant in organizational performance psychology. In the educational context, surface-level performance or pseudo-performance refers to achieving positive academic results through superficial or opportunistic strategies that mask a lack of genuine engagement in the learning process (Butler, 2006). This form of pseudo-performance is often associated with extrinsic motivation, conformism, and Dark Triad personality traits (Paulhus & Williams, 2002).

The gender differences identified in the testing of hypothesis H5—especially the significant effect of motivation on female students’ performance—indicate that women are more receptive to the motivational dimensions of learning, while men, even when exhibiting higher Machiavellian tendencies, may compensate for low motivation through other strategies. Recent research suggests that men and women differ in their mean levels of Dark Triad personality constructs such as Machiavellianism. In general, men reported higher mean scores on subscales representing antagonistic and dominant traits (i.e., achievement, selfishness, assertiveness, immodesty, self-confidence, manipulativeness, callousness, and invulnerability) (Collison et al., 2021; Takikawa & Fukukawa, 2023). Our study results confirm Daniel David’s observation regarding the higher academic commitment of female students compared with male students, indicating that gender differences in motivation and performance persist at the university level, and high motivation is not always accompanied by school satisfaction, suggesting a possible dissociation between engagement and academic well-being, similar to the cultural model identified among Romanian adults, where work is a form of social affirmation but not necessarily a source of satisfaction (David, 2015).

The integration of the three psychological factors analyzed—motivation, Machiavellianism, and lying—within the 3P model (Presage–Process–Product provides a coherent explanation of academic performance, in which motivation and ethics act as background psychological processes (Process), while the final performance (Product) may also be influenced by dysfunctional variables (Presage), such as personality traits from the Dark Triad spectrum (Biggs, 1993; Credé & Kuncel, 2008).

The study results were compared with the psychological profile of Romanians described by Daniel David (David, 2015) regarding the constructs of morality, responsibility, and social conduct, which align with our findings. David highlights the presence of traditional moralism and a strong community orientation, influenced by religion and the role of educational and religious institutions in reinforcing virtues. These contexts explain why married or more mature students in our database exhibited higher levels of caring and interest, traits compatible with their need for belonging and social support. However, our analysis also showed the presence of Machiavellian tendencies among some students, reflecting a conflict between traditional values and individualistic strategies aimed at success, a dissonant adaptation to academic performance requirements. When analyzing the motivation construct from David’s study, it can be seen that Romanians have stronger compensatory effects, competition orientations, and status orientations than Americans but lower independence and perseverance. Compared with Germans, Romanians have stronger commitment, internality, learning desires, status orientations, dominance, optimism, and competition, On the other hand, they scored lower than Germans in terms of perseverance, flexibility, fearlessness, and independence (David, 2015). Even though Machiavellianism is not analyzed, a dissonance is observed between the Romanians’ desire to possess and achieve and the perseverance to act, which can lead to pursuing alternative ways of obtaining what they want.

Our study’s conclusions align with the general psychological profile identified by Daniel David for the Romanian population, especially regarding traits such as cynicism, amorality, and antisocial attitudes, factors that can favor the emergence of Machiavellian behaviors and tendencies toward lying in the academic environment. The low levels of autonomy, interpersonal trust, and self-determination described in the author’s analysis are also reflected in the lower scores for the Empowerment and Success dimensions of the MUSIC model, suggesting that collective psychosocial traits influence not only the general social climate but also students’ educational engagement (David, 2015).

5.2.2. Practical Implications

The practical implications of this study address both pedagogical strategies for teaching staff and broader educational policies at the university level. With respect to instructors, the findings suggest several key directions for improving motivation and ethical engagement among students. Firstly, educators are encouraged to apply psycho-pedagogical methods that explicitly support students’ motivation to learn. Communication styles should emphasize empathy and encouragement, in alignment with the principles of the MUSIC model. Furthermore, the regular updating and careful selection of course content can help stimulate interest and increase its perceived relevance. Assignments, projects, and assessment tools should be transparently designed to ensure clarity and fairness. Finally, instructors are advised to include artificial intelligence tools in the learning process, while simultaneously adapting the structure and objectives of education to support both the intellectual and personal development of students.

At the institutional level, educational policies should promote systemic changes aimed at reinforcing ethical behavior and learner support. This includes the introduction of courses on professional ethics and academic integrity across all degree programs, as well as the integration of student counseling courses that address both psycho-educational and social needs. Additionally, universities are encouraged to develop comprehensive strategies for the responsible use of artificial intelligence in academic contexts. Ongoing pedagogical development for instructors should also be supported through continuous training programs that reflect modern educational challenges and technologies.

A growing concern in the academic world is the way artificial intelligence (AI) is being approached in educational settings. Tools like large language models or AI-based writing assistants have the potential to support learning and help students develop higher-order thinking skills. However, in many institutions—especially in Eastern Europe—the reaction has been predominantly restrictive; instead of encouraging responsible use, the tendency is to limit access or even penalize students who rely on these tools.

This defensive approach can backfire. It may push students toward hidden or opportunistic uses of AI, reinforcing unethical habits instead of promoting transparency and accountability.

Two key risks are particularly pressing. First, there is AI-assisted cheating, where students submit AI-generated work without fully understanding or engaging with it. Second, there is overreliance on AI, which could gradually undermine students’ ability to think critically and manage their own learning.

Even so, the potential of AI in education remains significant. When integrated thoughtfully under the guidance of educators, AI can become a powerful personalized learning tool, one that offers instant feedback, encourages reflection, and supports motivation. Rather than isolating students from such tools, universities should focus on teaching responsible and ethical use, helping students view AI not as a shortcut but as a partner in their academic growth.

5.3. Limitations and Future Research Directions

The following limitations should be considered in the generalization of our findings. Firstly, the study was conducted on a geographically limited sample and composed exclusively of students from a single technical faculty, which reduces the generalizability of the conclusions to the entire student population in Romania. While the findings of this study provide valuable insights into the relationships among academic motivation, Machiavellianism, and dishonest behaviors, certain limitations regarding the sample should be acknowledged. The research was conducted exclusively among Romanian engineering students, a subgroup selected as part of a larger international project. Although this focus allowed for in-depth exploration within a relevant academic context, the results may not be fully generalizable to students from other disciplines or cultural backgrounds. Since constructs such as motivation and ethical behavior can be influenced by the institutional culture, field of study, or socioeconomic context, future studies should include more diverse academic populations to enhance external validity and support cross-disciplinary comparisons.

Secondly, this study used self-reporting instruments. Thus, the results may reflect either an over- or underestimation of these conditions. However, subjective measures are also reliable and valuable, most often being used in the literature to estimate tested variables. Lastly, the cross-sectional design of the study did not allow for causal inferences between the analyzed variables. Future research could include more diverse samples, a longitudinal approach, and mixed methods (quantitative and qualitative) to gain a more nuanced understanding of the relationship between motivation, ethics, and academic performance.

Another limitation concerns the extended data collection period, which spanned several academic cohorts between 2018 and 2025. While all students responded to the same standardized instruments, contextual changes over time—such as the COVID-19 pandemic, shifts to online learning, or the emergence of generative AI tools—may have influenced students’ motivation, ethical attitudes, and academic experiences. This temporal variability may introduce a form of time-based bias, which should be considered when interpreting the results.

Another limitation concerns the lack of control variables for the instructional setting. Differences in teaching staff, platforms, or course formats across academic years may have influenced students’ experiences. Although all participants completed the same standardized instruments, this contextual variability could have introduced uncontrolled variation in the data.

Although the regression model for Hypothesis 5 was statistically significant, its explanatory power was limited (adjusted R² = 0.069). This suggests that the predictors included accounted for only a small proportion of the variance in academic performance. Therefore, the findings should be interpreted with caution, and future research is encouraged to include additional variables that may contribute to a better understanding of academic outcomes.

Although statistical comparisons between cohorts did not reveal significant year-by-year differences, we recognize that unmeasured contextual variables may have influenced student responses. This represents a potential source of bias, and future research should explore whether emerging educational or technological changes have a greater impact over time. Similarly, although no significant differences were found between academic specializations, the diversity of engineering programs may have introduced subtle variations in learning contexts and motivational dynamics. Likewise, although no statistically significant differences were found between the undergraduate and graduate students, the inclusion of both groups may have introduced variability in the motivational and behavioral responses.

6. Conclusions

This study highlights several meaningful connections between academic motivation, Machiavellianism, lying tendencies, and academic performance. First, motivation appears to decrease significantly in the presence of Machiavellian traits and permissive attitudes toward dishonesty. Unethical behaviors, such as lying, were more frequently reported among students with high Machiavellianism scores. While academic performance was positively influenced by students’ perception of success and their interest in learning, it was also—somewhat paradoxically—enhanced by Machiavellian tendencies in certain contexts. Demographic variables such as age, gender, and marital status also played a relevant role; female students, married individuals, and older participants reported higher levels of motivation and demonstrated firmer ethical attitudes. Furthermore, the findings confirm the usefulness of the MUSIC model as a diagnostic and developmental framework for academic motivation. However, the data also suggest that unethical behaviors cannot be addressed solely through formal ethics courses; rather, they require the promotion of an academic culture rooted in autonomy, empathy, and responsibility.

In conclusion, to stimulate authentic performance and reduce unethical behaviors, a complex educational approach is necessary, one that cultivates intrinsic motivation, strengthens students’ autonomy, and promotes prosocial behaviors. Psychopedagogical interventions, combined with an academic culture based on trust and responsibility, can form the foundation for sustainable and ethical education.

As a future direction, we advocate for a shift in institutional policy regarding AI in education, namely away from avoidance and sanctioning and toward guided integration. AI should not be viewed as a threat to academic integrity but as a catalyst for ethical, individualized learning if embedded within transparent pedagogical strategies. Encouraging students to use AI tools for reflection, planning, and revision—rather than substitution—may help reduce dishonest practices and enhance both motivation and performance.

Considering the methodological limitations of the study, future research could expand the analysis by using nationally representative samples, including students from various fields of study, to test the observed relationships. Another direction could be the integration of qualitative methods (e.g., focus groups and interviews) into the research to better understand the cognitive and behavioral mechanisms underlying students’ moral and motivational adaptation strategies. Lastly, expanding the research to include other components of the Dark Triad (narcissism and psychopathy) could provide valuable insights into students’ relationships with academic ethics and performance.