literature review on motivation in the workplace

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How motivational leaders inspire loyalty and drive performance.

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In today’s evolving work environment, leadership is no longer about simply issuing directives and overseeing tasks. Instead, effective leadership demands a dynamic blend of emotional intelligence, trust-building, and innovation. Motivational leaders understand that success lies in cultivating an atmosphere where employees feel valued and empowered to perform at their highest potential. The shift to more compassionate and adaptive approaches is helping reshape the modern workplace, ensuring that loyalty and high performance are the outcomes of authentic engagement and shared purpose.

The Shift from Authority to Compassion

Traditional leadership often relied on a top-down, authoritative model where decisions were made in isolation and then passed down to the team. However, this model has proven less effective in fostering long-term loyalty or high performance in today's workforce. Compassionate leadership, where leaders prioritize understanding their team members' motivations, challenges, and personal circumstances, has emerged as a powerful alternative.

Kenneth-Maxwell Nance

Kenneth-Maxwell Nance, founder of Leadership’s Grand Theory (LGT) and LGT Academy, emphasizes that "great leaders are not just commanders; they are connectors. Empathy helps to bridge the gap between leadership and followership, creating a collaborative environment where every team member feels seen and valued." Nance, whose career spans military and corporate sectors, has seen firsthand how empathetic leadership creates a foundation for stronger relationships, fostering both loyalty and performance.

According to Jennifer Garvey Berger, leadership expert and author of Unlocking Leadership Mindtraps , empathy is crucial because it allows leaders to "navigate complexity by seeing the world from others’ perspectives, helping them make more nuanced and informed decisions." This balance of empathy and strategy is essential for leaders aiming to inspire long-lasting commitment from their teams.

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One of the cornerstones of modern leadership is trust. Leaders who listen actively, provide transparent communication, and involve their teams in decision-making processes build a culture of trust that enhances engagement. When employees trust their leaders, they are more likely to remain loyal to the organization and to go above and beyond in their roles.

Nance’s Leadership’s Grand Theory advocates for trust as a critical element in leadership effectiveness. “Trust is the currency of leadership,” says Nance. “When team members feel that their leader has their best interests at heart, they are more likely to stay engaged and committed, even during challenging times.” Nance’s leadership approach has been shaped by his work with multinational organizations and his belief that trust-building is a continuous process that leaders must nurture.

Recognition as a Driver of Performance

Recognition is a powerful motivator in the modern workplace. Leaders who actively acknowledge and reward their employees for their contributions create a culture of recognition that encourages ongoing excellence. This culture not only boosts morale but also reinforces the actions and behaviors that align with the organization’s values and objectives.

“Recognition isn't just about handing out awards,” says Nance. “It's about creating an environment where employees feel their contributions are truly appreciated and where they can see how their work makes a difference.” In his leadership development work, Nance has shown how regular acknowledgment of team efforts leads to higher engagement and increased productivity.

Resilience and Adaptability in Leadership

Resilience has become a defining trait of modern leaders, particularly in navigating the unprecedented challenges brought on by global events such as economic shifts and technological disruption. Leaders who demonstrate resilience not only manage through crises but also inspire their teams to remain focused and perform under pressure.

Nance’s Leadership’s Grand Theory incorporates resilience as a core component of leadership, providing leaders with tools to remain adaptable and responsive during times of uncertainty. “Resilient leadership,” he notes, “is about being able to bounce back from setbacks and maintain focus on long-term goals, while also supporting your team in doing the same.” His work has helped leaders across industries develop the resilience needed to drive sustained performance, even in the face of adversity.

Encouraging Innovation through Collaboration

Innovation is often the product of a collaborative and inclusive work environment. Leaders who foster a culture of openness and psychological safety enable their teams to share ideas freely, experiment, and collaborate on new solutions. This collaborative spirit drives innovation and can be a key factor in enhancing overall performance.

Nance’s emphasis on collaboration is central to his leadership philosophy. “Innovation thrives in environments where diverse perspectives are welcomed and encouraged,” he explains. “Leaders must create spaces where individuals feel safe to contribute new ideas without fear of failure.” Nance’s experience in both the military and corporate worlds has shown that the most innovative solutions often come from teams that feel empowered to think outside the box.

In agreement, Amy Edmondson , professor of leadership at Harvard Business School , asserts that psychological safety is the foundation of innovation. When leaders foster an environment where employees feel safe to speak up, they unlock a wellspring of creativity and collaboration.

Modern leaders inspire loyalty and drive performance by cultivating empathy, trust, recognition, resilience, and collaboration. It is clear that effective leadership in the modern workplace is about more than authority—it’s about building meaningful connections, fostering trust, and nurturing a culture of continuous growth and innovation.

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Work Motivation: The Roles of Individual Needs and Social Conditions

Thuy thi diem vo.

1 Department of Business Administration, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Da’an District, Taipei City 106335, Taiwan; wt.ude.tsutn.liam@31880701d (T.T.D.V.); wt.ude.tsutn.liam@nehcwc (C.-W.C.)

Kristine Velasquez Tuliao

2 Graduate Institute of Human Resource Management, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City 320317, Taiwan

Chung-Wen Chen

Associated data.

The data that support this study are publicly available.

Work motivation plays a vital role in the development of organizations, as it increases employee productivity and effectiveness. To expand insights into individuals’ work motivation, the authors investigated the influence of individuals’ competence, autonomy, and social relatedness on their work motivation. Additionally, the country-level moderating factors of those individual-level associations were examined. Hierarchical linear modeling (HLM) was used to analyze data from 32,614 individuals from 25 countries, obtained from the World Values Survey (WVS). Findings showed that autonomy and social relatedness positively impacted work motivation, while competence negatively influenced work motivation. Moreover, the individual-level associations were moderated by the country-level religious affiliation, political participation, humane orientation, and in-group collectivism. Contributions, practical implications, and directions for further research were then discussed.

1. Introduction

Work motivation is considered an essential catalyst for the success of organizations, as it promotes employees’ effective performance. To achieve an organization’s objectives, the employer depends on the performance of their employees [ 1 ]. However, insufficiently motivated employees perform poorly despite being skillful [ 1 , 2 ]. Employers, therefore, need their employees to work with complete motivation rather than just showing up at their workplaces [ 3 ]. Work motivation remains a vital factor in organizational psychology, as it helps explain the causes of individual conduct in organizations [ 4 ]. Consequently, studies on the factors that encourage work motivation can contribute to the theoretical underpinnings on the roots of individual and practical social conditions that optimize individuals’ performance and wellness [ 5 ].

Several decades of research have endeavored to explain the dynamics that initiate work-related behavior. The primary factor examining this aspect is motivation, as it explains why individuals do what they do [ 6 ]. The basic psychological needs have represented a vital rationalization of individual differences in work motivation. Psychological needs are considered natural psychological nutrients and humans’ inner resources. They have a close relationship with individual conduct and have a strong explicit meaning for work performance [ 7 , 8 ]. Different needs are essential drivers of individual functioning due to the satisfaction derived from dealing with them [ 9 ]. In addition to individual-level antecedents, the social context has also been regarded to have implications for work motivation. Social exchange and interaction among individuals accentuate the importance of work motivation as something to be studied with consideration of contextual factors [ 10 ].

Significant contributions have been made to the socio-psychological perspective of work motivation ( Table 1 ). However, current literature shows three deficiencies. First, over 150 papers utilize the key approaches of psychological needs to justify motivational processes in the workplace [ 11 ], which justifies the vital role of psychological needs in interpreting individual work motivation. The association between psychological needs and work motivation has often been implicitly assumed; however, the influence of psychological needs on work motivation has been inadequately tested [ 8 ]. The verification of the extent and the direction of influence will provide a better understanding of, and offer distinct implications for, the facilitation of work motivation. In examining the influence of psychological needs on work motivation, this paper mainly focuses on the intrinsic aspect of motivation. The study of Alzahrani et al. (2018) [ 12 ] argued that although intrinsic motivation is more efficient than extrinsic motivation, researchers have mostly neglected it.

Several investigated predictors of work motivation in general and intrinsic motivation in particular.

Second, there is no study examining the country-level moderating effects of social conditions and national cultures on individual relationships between psychological needs and work motivation. Pinder (2014) [ 20 ] argued that contextual practices could influence variables at the individual level. Culture is a crucial factor influencing motivation [ 15 , 16 , 17 , 18 ]. Researchers (e.g., [ 19 ]) have further suggested that both the proximal social situations (e.g., workgroup) and the distal social situations (e.g., cultural values) in which humans operate influence their need for satisfaction and their motivation type. Intrinsic motivation interacts with prosocial motivation in judging work performance [ 21 ]. By including the social conditions in the framework, prosocial motivation is considered. Prosocial motivation refers to the desire to help and promote the welfare of others [ 22 , 23 ]. The study of Shao et al. (2019) [ 24 ] proposed that prosocial motivation promotes employee engagement in particular organizational tasks. Researchers often consider prosocial motivation as a pattern of intrinsic motivation [ 23 ]. This implies that when intrinsic motivation is investigated, prosocial motivation should be examined together to obtain a comprehensive understanding.

Third, there are few studies using a considerable number of cross-national samples to investigate factors influencing work motivation. A cross-cultural analysis makes the findings more objective by minimizing individual bias towards any particular culture. Therefore, the examination of the study is crucial to expanding insights on the influence of social situations on the individual associations between psychological needs and work motivation.

2. Literature Review and Hypothesis Development

2.1. work motivation: a conceptual background.

Work motivation is considered “a set of energetic forces that originate both within as well as beyond an individual’s being, to initiate work-related behavior, and to determine its form direction intensity and duration” [ 20 ]. Nicolescu and Verboncu (2008) [ 25 ] argued that work motivation contributes directly and indirectly to employees’ performance. Additionally, research (e.g., [ 26 ]) has postulated that work motivation could be seen as a source of positive energy that leads to employees’ self-recognition and self-fulfillment. Therefore, work motivation is an antecedent of the self-actualization of individuals and the achievement of organizations.

Literature has identified several models of work motivation. One of the primary models is Maslow’s (1954) [ 27 ] need hierarchy theory, which proposes that humans fulfill a set of needs, including physiological, safety and security, belongingness, esteem, and self-actualization. Additionally, Herzberg’s (1966) [ 28 ] motivation-hygiene theory proposed that work motivation is mainly influenced by the job’s intrinsic challenge and provision of opportunities for recognition and reinforcement. More contemporary models also emerged. For instance, the study of Nicolescu and Verboncu (2008) [ 25 ] has categorized the types of motivation into four pairs, including positive-negative, intrinsic-extrinsic, cognitive-affective, and economic-moral spiritual. Additionally, Ryan and Deci [ 29 ] focused on intrinsic motivation and extrinsic motivation.

With the existence of numerous factors that relate to work motivation, this paper mainly focuses on intrinsic motivation. Previous research found that emotional intelligence and interpersonal relationship quality predict individuals’ intrinsic motivation [ 14 ]. Additionally, the study of Lin (2020) [ 13 ] argued that personal factors, including age, gender, educational level, living setting, health status, and family support, impact people’s intrinsic motivation. To understand more about intrinsic motivation, the authors examined individuals’ psychological needs. Fulfillment of the basic needs is related to wellness and effective performance [ 7 ]. Since intrinsic motivation results in high-quality creativity, recognizing the factors influencing intrinsic motivation is important [ 5 ].

Although a significant number of important contributions have been made regarding intrinsic motivation, self-determination theory is of particular significance for this study. Self-determination theory (SDT) postulates that all humans possess a variety of basic psychological needs. One of the primary crucial needs is the need for competence [ 30 , 31 ], which makes individuals feel confident and effective in their actions. Additionally, the need for autonomy [ 32 ] is one of the important psychological needs, which makes people satisfied with optimal wellness and good performance obtained as a result of their own decisions. Moreover, SDT proposed the crucial importance of interpersonal relationships and how social forces can influence thoughts, emotions, and behaviors [ 33 ]. This means that the psychological need for social relatedness [ 34 ] also plays a significant role in human’s psychological traits. Individuals need to be cared for by others and care for others to perceive belongingness. The need for relatedness can motivate people to behave more socially [ 35 ].

Prior research (e.g., [ 36 ]) has explored self-determination theory and related theories as approaches to work motivation and organizational behavior. The study of Van den Broeck et al. (2010) [ 37 ] emphasized grasping autonomy, competence, and relatedness at workplaces. This paper contributes to the exhaustive understanding of intrinsic work motivation influenced by further examining the impact of these three factors on work motivation as well as the moderating effects of social contexts.

2.2. Main Effect

2.2.1. individuals’ competence and work motivation.

Competence is “the collective learning in the organization, especially how to coordinate diverse production skills and integrate multiple streams of technologies” [ 38 ]. The study of Hernández-March et al. (2009) [ 39 ] argued that a stronger competence was commonly found in university graduates rather than those without higher education. Competence has been considered a significant factor of work motivation that enhances productivity and profits. Harter’s (1983) [ 40 ] model of motivation proposed that competence enhances motivation because competence promotes flexibility for individuals [ 41 ]. Likewise, Patall et al. (2014) [ 42 ] indirectly argued that competence positively affects work motivation. Individuals become more engaged in activities that demonstrate their competence [ 6 ]. When people perceive that they are competent enough to attain goals, they generally feel confident and concentrate their efforts on achieving their objectives as soon as possible for their self-fulfillment.

Individuals’ competence positively relates to their work motivation.

2.2.2. Individuals’ Autonomy and Work Motivation

Autonomy is viewed as “self-determination, self-rule, liberty of rights, freedom of will and being one’s own person” [ 43 ]. Reeve (2006) [ 44 ] argued that autonomy is a primary theoretical approach in the study of human motivation and emotion. Autonomy denotes that certain conduct is performed with a sense of willingness [ 30 ]. Several researchers (e.g., [ 45 ]) investigated the positive relationship between individuals’ autonomy and work motivation. When humans are involved in actions because of their interest, they fully perform those activities volitionally [ 36 ]. Dickinson (1995) [ 46 ] also proposed that autonomous individuals are more highly motivated, and autonomy breeds more effective outcomes. Moreover, when individuals have a right to make their own decisions, they tend to be more considerate and responsible for those decisions, as they need to take accountability for their actions. Bandura (1991) [ 47 ] has argued that humans’ ability to reflect, react, and direct their actions motivates them for future purposes. Therefore, autonomy motivates individuals to work harder and overcome difficulties to achieve their objectives.

Individuals’ autonomy positively relates to their work motivation.

2.2.3. Individuals’ Social Relatedness and Work Motivation

The psychological need for social relatedness occurs when an individual has a sense of being secure, related to, or understood by others in the social environment [ 48 ]. The relatedness need is fulfilled when humans experience the feeling of close relationships with others [ 49 ]. Researchers (e.g., [ 34 ]) have postulated that the need for relatedness reflects humans’ natural tendency to feel associated with others, such as being a member of any social groups, or to love and care as well as be loved and cared for. Prior studies have shown that social relatedness strongly impacts motivation [ 50 , 51 , 52 ]. Social relatedness offers people many opportunities to communicate with others, making them more motivated at the workplace, aligning them with the group’s shared objectives. Marks (1974) [ 53 ] suggested that social relatedness encourages individuals to focus on community welfare as a reference for their behavior, resulting in enhanced work motivation. Moreover, when individuals feel that they relate to and are cared for by others, their motivation can be maximized since their relatedness need is fulfilled [ 54 ]. Therefore, establishing close relationships with others plays a vital role in promoting human motivation [ 55 ]. When people perceive that they are cared for and loved by others, they tend to create positive outcomes for common benefits to deserve the kindness received, thereby motivating them to work harder.

Individuals’ social relatedness positively relates to their work motivation.

Aside from exploring the influence of psychological needs on work motivation, this paper also considers country-level factors. Previous research (e.g., [ 56 ]) has examined the influence of social institutions and national cultures on work motivation. However, the moderating effects of country-level factors have to be investigated, given the contextual impacts on individual needs, attitudes, and behavior. Although social conditions provide the most common interpretation for nation-level variance in individual work behaviors [ 57 ], few cross-national studies examine social conditions and individual work behaviors [ 56 ]. Hence, this paper investigates the moderating effects, including religious affiliation, political participation, humane orientation, and in-group collectivism, on the psychological needs-work motivation association.

A notable theory to explain the importance of contextual factors in work motivation that is customarily linked with SDT is the concept of prosocial motivation. Prosocial motivation suggests that individuals have the desire to expend efforts in safeguarding and promoting others’ well-being [ 58 , 59 ]. It is proposed that prosocial motivation strengthens endurance, performance, and productivity, as well as generates creativity that encourages individuals to develop valuable and novel ideas [ 21 , 60 ]. Prosocial motivation is found to interact with intrinsic motivation in influencing positive work outcomes [ 21 , 61 ]. However, there are few studies examining the effects of prosocial motivation on work motivation [ 62 ].

Utilizing the concept of prosocial motivation and examining it on a country-level, this paper suggests that prosocial factors promote basic psychological needs satisfaction that reinforces motivational processes at work. Therefore, prosocial behaviors and values may enhance the positive impact of individuals’ basic psychological needs, including competence, autonomy, and social relatedness, on work motivation.

2.3. Moderating Effects

2.3.1. religious affiliation.

Religions manifest values that are usually employed as grounds to investigate what is right and wrong [ 63 ]. Religious affiliation is considered prosocial because it satisfies the need for belongingness and upholds collective well-being through gatherings to worship, seek assistance, and offer comfort within religious communities. Hence, religious affiliation promotes the satisfaction of individuals’ psychological needs, which directs motivation at work and life in general. Research (e.g., [ 64 ]) has argued that religious affiliation is an essential motivational component given its impact on psychological processes. The study of Simon and Primavera (1972) [ 65 ] investigated the relationship between religious affiliation and work motivation. To humans characterized by competence, autonomy, and social relatedness, attachment to religious principles increases their motivation to accomplish organizational goals. Religious membership will increase the influence of psychological needs on work motivation. The tendency of individuals affiliated with any religion to be demotivated is lower compared to those who are not. Individuals with religious affiliations also tend to work harder as the virtue of hard work is aligned with religious principles. Accordingly, religious affiliation may enhance the positive association between individuals’ psychological needs and work motivation.

2.3.2. Political Participation

Political participation, indicated by people’s voting habits, plays a crucial role in ensuring citizens’ well-being and security [ 66 ]. Political participation encourages shared beliefs and collective goals among individuals [ 67 ]. The communication and interaction among people help them grasp the government’s developmental strategies, motivating them to work harder. Political participation is a collective pursuit that makes societal members feel more confident, socially related, and motivated at work to achieve communal targets. Increased political participation reinforces effective public policy to enhance its members’ welfare, congruent with the perspectives of prosocial motivation. The prosocial values and behaviors derived from political participation satisfy human needs and interact positively with intrinsic motivation. Therefore, political participation may strengthen the positive influence of individuals’ competence, autonomy, and social relatedness on work motivation. Conversely, poor political participation is perceived as a separation from the society that may lead to demotivation. In a society with poor political participation, an individualistic mentality is encouraged, thereby decreasing the desire to pursue cooperative endeavors.

2.3.3. Humane Orientation

GLOBE characterizes humane orientation as “the degree to which an organization or society encourages and rewards individuals for being fair, altruistic, generous, caring, and kind to others” [ 68 ]. Research (e.g., [ 69 , 70 ]) has argued that a high humane orientation encourages members to develop a strong sense of belonging, commit to fair treatment, and manifest benevolence. The desire to help others or enhance others’ well-being indicates prosocial values and behaviors [ 71 , 72 ]. Since humane orientation is correlated with philanthropy and promotes good relations, this cultural value may enhance work motivation. Fairness, which is derived from a humane-oriented society, is one of the most vital influences on work motivation [ 1 ]. Moreover, altruism, promoted by humane-oriented societies, encourages individuals to sacrifice individual interests for shared benefits. Altruism then encourages attachment to others’ welfare and increases resources needed for prosocial behaviors such as work [ 73 , 74 ]. Members of humane-oriented countries view work in a positive light—it is an opportunity for them to perform altruistic behaviors and engage in collective actions. Therefore, people are more likely to work harder for common interests in humane-oriented societies. In such conditions, individuals with competence, autonomy, and social relatedness will be more motivated to work. By contrast, a less humane-oriented society gives prominence to material wealth and personal enjoyment [ 75 ]. Although this may be perceived as a positive influence on the association between psychological needs and work motivation, such an individualistic mindset works against the prosocial factors that further motivate individuals.

2.3.4. In-Group Collectivism

House et al. (2004) [ 68 ] defined in-group collectivism as “the degree to which individuals express pride, loyalty, and cohesiveness in their organizations or families”. Collectivistic cultures indicate the need for individuals to rely on group membership for identification [ 76 ]. High collectivism enhances equity, solidarity, loyalty, and encouragement [ 77 , 78 ]. Humans living in a collectivist culture are interdependent and recognize their responsibilities towards each other [ 79 ]. In-group collectivism transfers the concepts of social engagement, interdependence with others, and care for the group over the self (e.g., [ 79 , 80 , 81 ], thereby motivating individuals to work harder for the common interests. Oyserman et al. (2002) [ 82 ] have further argued that individualistic values encourage an independent personality, whereas collectivistic values form an interdependent one. Therefore, in-group collectivism is a prosocial value that emphasizes the importance of reciprocal relationships and encourages people to work harder to benefit the group. By contrast, low collectivism promotes individual interests and personal well-being while neglecting the value of having strong relations with others [ 70 ]. Considering that in-group collectivism promotes individuals’ prosocial behaviors of individuals, people who are competent, autonomous, and socially related to collective societies are less likely to be demotivated at the workplace. Consequently, in-group collectivism may intensify the positive influence of individuals’ competence, autonomy, and social relatedness on their work motivation.

(a–d): The positive relationship between individuals’ competence and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

(a–d): The positive relationship between individuals’ autonomy and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

(a–d): The positive relationship between individuals’ social relatedness and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

3.1. Sample

The data came from the seventh wave (2017–2021) of the World Values Survey (WVS) [ 83 ], which examines humans’ beliefs and values. This survey is performed every five years to explore changes in people’s values and perceptions. Face-to-face interviews, or phone interviews for remote areas, were conducted by local organizations. Almost 90 percent of the world’s population is represented in the WVS. At least 1000 individuals were selected as respondents to exhibit each nation’s population. Further information regarding the WVS can be reached at the WVS website ( http://www.worldvaluessurvey.org , accessed on 14 October 2021).

The samples of this study were based on the availability of national-level data for the moderators and individual-level data for the measures of independent and dependent variables. Respondents without answers on the individual measures and corresponding country-level data were excluded from the analysis. The final data included 32,614 respondents in 25 countries aged 18 and above. The 25 countries included Argentina, Australia, Brazil, China, Colombia, Ecuador, Egypt, Germany, Greece, Guatemala, Hong Kong, Indonesia, Iran, Japan, Kazakhstan, Malaysia, Mexico, New Zealand, Philippines, Russia, South Korea, Taiwan, Thailand, Turkey, and the USA.

3.2. Dependent Variable

Consistent with previous researchers (e.g., [ 84 ]), the authors used four items to gauge individual work motivation, namely “Indicate how important work is in your life”, “People who do not work turn lazy”, “Work is a duty towards society”, and “Work should always come first, even if it means less spare”. The first item was measured on a scale from 1 to 4, in which lower scores indicate a higher level of work importance. The other three items were gauged on a scale from 1 to 5 (1 indicating strongly agree and 5 indicating strongly disagree). The scores for each item were reverse coded, and the mean scores were computed so that higher scores indicate greater work motivation.

3.3. Independent Variables

The independent variables of this study include individuals’ competence, autonomy, and social relatedness. First, people’s competence was measured by the item “What is the highest educational level that you attained” on a scale from 0 to 8, in which higher scores indicate a higher level of educational attainment. The authors used the item to gauge individual competence, as a capacity for learning is highlighted in the examination of competence [ 39 ]. Second, a scale from 1 to 10 was utilized to measure the item “How much freedom of choice and control”, which represented individual autonomy (1 indicating no choice at all and 10 indicating a great deal of choice). The authors used the item to gauge people’s autonomy as this item indicates the degree to which individual can make their own decisions. Finally, the individual’s social relatedness was gauged by twelve items, representing twelve types of organizations where individuals are active/inactive members or do not belong. The twelve items were measured on a scale from 0 to 2 (0 indicating do not belong, 1 indicating inactive member, and 2 indicating active member). The mean score of the twelve items represents the individual’s social relatedness. The membership in organizations represents social relatedness, as this indicates the reciprocal relationship between the individual and the organization through their mutual rights, responsibilities, and obligations towards each other [ 85 ].

3.4. Moderators

The four country-level moderators in this study were religious affiliation, political participation, humane orientation, and in-group collectivism. Similar to prior research (e.g., [ 86 ]), the authors used the percentage of the country’s population with religious affiliation obtained from Pew Research Center 2015 [ 87 ]. Secondly, the index of voter turnout collected from the International Institute for Democracy and Electoral Assistance [ 88 ] was utilized to gauge political participation. Voting habits are an indicator of an individual’s presence in their country’s life, and a nation with a high index of voter turnout illustrates its substantial degree of political participation [ 89 ]. Finally, two cultural values, including humane orientation and in-group collectivism, were obtained from the GLOBE study [ 68 ]. The authors used scores on cultural practices as the moderators for this study because they indicate the actual behaviors as “the way things are done in this culture” [ 68 ].

3.5. Control Variables

Several individual-level and country-level elements related to the dependent variable were considered control variables. The effects of gender, marital status, age, and income level were accounted for, as these four variables are basic personal factors that may impact individual’s motivation [ 90 ]. Gender (1 indicating male and 0 indicating female) and marital status (1 indicating married and 0 indicating other status) were dummy coded. Moreover, age was measured in years, while income level was gauged using a scale from 1 representing the lowest group to 10 representing the highest group. Along with the above individual-level controls, education and family strength were treated as country-level control variables. Education and family are primary institutions that shape individuals’ motivation [ 91 , 92 ]. Similar to prior researchers (e.g., [ 93 ]), education was computed as two-thirds of the adult literacy rate attained from the UNESCO Institute for Statistics 2020 [ 94 ] and one-third of the mean years of schooling obtained from the Human Development Report 2020 [ 95 ]. This score is commonly approved as representing access to education in a country [ 42 ]. Regarding family strength, the score was quantified by the ratio of divorces to marriages per 1000 members of the population consistent with previous researchers (e.g., [ 93 ]). The data was obtained from the United Nations Demographic Yearbook [ 96 ].

3.6. Measurement and Analysis

To perform the descriptive statistics, cross-level correlations, scale reliability, confirmatory factor analysis, convergent validity, and discriminant validity, the authors utilized SPSS software.

The framework of this study considers independent variables, dependent variables, and moderators at different levels. Thus, the authors used a hierarchical linear model (HLM) [ 97 ] to test the hypotheses. HLM was defined as a “complex form of ordinary least squares (OLS) regression that is used to analyze variance in the outcome variables when the predictor variables are at varying hierarchical levels” [ 98 ]. This technique evaluates the impacts of higher-level outcomes on lower-level ones while preserving an appropriate degree of analysis [ 99 ]. HLM has been employed in several cross-level studies (e.g., [ 100 , 101 ]).

Table 2 presents a matrix of correlations and sample statistics from the individual-level to country-level variables. Table 3 and Table 4 report convergent and discriminant validity test results, respectively. Finally, Table 5 illustrates results for hypotheses testing using HLM. Three models are presented in the table: those of individual-level main effects and control variables (Model 1), those of country-level main effects (Model 2), and country-level moderating effects (Model 3).

Descriptive statistics, cross-level correlations and scale reliability a,b,c .

a   n = 32,614 level 1; n = 25, level 2. b * p < 0.05, ** p < 0.01. c The reliability found in the parentheses is expressed as Cronbach’s alpha for scales with ≥four items.

Convergent validity.

Discriminant validity—Fornell and Larcker’s criterion.

* p < 0.05.

HLM results: (The DV is work motivation) a,b .

a , n = 32,614 level 1; n = 25, level 2. b , †, p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

For the confirmatory factor analysis, previous research (e.g., [ 102 , 103 , 104 ]) suggested that analysis of each variable requires at least three items. Factor analysis using statistical software will provide imprecise results if there are fewer than three items per variable [ 105 ]. Therefore, the authors only performed Confirmatory Factor Analysis (CFA) for social relatedness and work motivation.

To assess the measurement, convergent and discriminant validity were tested. Composite Reliability (CR) and Average Variance Extracted (AVE) were performed to illustrate convergent validity. The study of Hair et al. (2019) [ 106 ] suggested that CR is required to be above a threshold of 0.7. On the other hand, the AVE value should be higher than a threshold of 0.5 [ 107 ]. As shown in Table 3 , CR is acceptable while AVE is slightly lower than a threshold of 0.5. Despite the limitation of AVE, the acceptable result of the discriminant validity is achieved. The discriminant validity was tested using Fornell and Larcker (1981)’s criterion [ 107 ]. This proposes that the square root of the AVE of any latent variable should be higher than its correlation with any other construct. The result of the discriminant validity test indicates that all the two latent constructs have a square root of AVE higher than its correlation with the other construct, as presented in Table 4 .

The authors argued that individuals’ competence (H1), autonomy (H2), and social relatedness (H3) positively relate to their work motivation. However, the findings only supported H2 (β2 = 0.036, p < 0.001) and H3 (β3 = 0.042, p < 0.001). In contrast, the findings presented that H1 was also significant, but in the opposite direction compared with our original prediction. The result suggests that individuals’ competence negatively relates to their work motivation.

In Hypotheses 4a–d, we proposed that higher levels of religious affiliation (4a), political participation (4b), humane orientation (4c), and in-group collectivism (4d) strengthen the relationship described in H1. However, the results only demonstrated support for the two hypotheses, H4c (γ13 = 0.032, p < 0.001) and H4d (γ14 = 0.042, p < 0.001). In contrast, the findings presented that H4a was also significant, but opposite our initial prediction. This different result proposes that a higher level of religious affiliation weakens the association between individuals’ competence and work motivation.

In Hypotheses 5a–d, the authors argued that the higher levels of religious affiliation (5a), political participation (5b), humane orientation (5c), and in-group collectivism (5d) enhance the positive relationship between individuals’ autonomy and their work motivation. However, the results only supported the two hypotheses H5b (γ22 = 0.012, p < 0.05) and H5c (γ23 = 0.012, p < 0.1), while H5a and H5d were not significant.

In Hypotheses 6a–d, the authors argued that the higher levels of religious affiliation (6a), political participation (6b), humane orientation (6c), and in-group collectivism (6d) enhance the positive relationship between individuals’ social relatedness and their work motivation. However, the results only supported H6c (γ33 = 0.019, p < 0.01). In contrast, the findings indicated that H6d was also significant, but in the opposite direction compared to our initial hypothesis. The different result suggests that higher in-group collectivism weakens the positive association between individuals’ social relatedness and work motivation. Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 represent the significant moderators of the associations examined.

The association between competence and work motivation at different levels of humane orientation.

The association between competence and work motivation at different levels of in-group collectivism.

The association between autonomy and work motivation at different levels of political participation.

The association between autonomy and work motivation at different levels of humane orientation.

The association between social relatedness and work motivation at different levels of humane orientation.

Regarding the statistical results of the control variables, gender, marital status, and age consistently indicated significant positive relationships with work motivation across three models. On the other hand, family strength indicated a significant negative association to work motivation only in Model 1.

5. Discussion

The study’s objective was to examine the influence of individuals’ competence, autonomy, and social relatedness on their work motivation, as well as the impact of country-level moderators, including religious affiliation, political participation, humane orientation, and in-group collectivism on their relationships. Seven primary findings are crucial in this research. First, people’s autonomy and social relatedness positively relate to their work motivation. This result is in line with the findings of prior researchers (e.g., [ 45 , 52 ]), postulating that humans’ autonomy and social relatedness breeds work motivation. The study of Theurer et al. (2018) [ 108 ] argued that, among motivational elements, autonomy had been found to greatly predict positive work motivation. When people feel they have enough control over their activities, they are more confident and motivated to work. Along with autonomy, humans’ social relatedness promotes communal benefits, thereby motivating people to work harder for their organization. Second, the association between individual competence and work motivation is moderated by cultural values, including humane orientation and in-group collectivism. The findings are consistent with the viewpoints of prior researchers (e.g., [ 69 , 70 , 77 , 78 ]), namely that a society with higher levels of humane orientation and in-group collectivism strengthens altruism, solidarity, loyalty, and the encouragement of individuals, which results in work motivation. Consequently, there will be an increase in the differences in individuals’ competence and work motivation if they live in a society with greater humane orientation and in-group collectivism. Third, political participation and humane orientation moderate the relationship between individual autonomy and work motivation. These results are in line with the investigations of prior researchers (e.g., [18,45), which found that social circumstances and cultural practices promote people’s motivation. Accordingly, the differences in individuals’ autonomy based on their work motivation will be enhanced if they belong to nations with higher political participation and humane orientation. Fourth, the association between social relatedness and work motivation is moderated by humane orientation. Accordingly, in a humane-oriented society, the differences in individuals’ social relatedness based on their work motivation will be strengthened.

The remaining findings were contrary to the original propositions. Pinder (2014) [ 20 ] argued that it is possible to find that contextual practices can influence variables at the individual level in the opposite prediction in motivation research. Fifth, individuals’ competence negatively influences their work motivation. This finding proposes that more competent individuals are less motivated at work. One possible interpretation of this opposite result is that, when the majority of the organization members recognize individuals’ competence, these individuals may perceive that it is not necessary to devote most of their time and energy to work anymore. These individuals may believe that no matter how unwillingly they perform, they are still competent enough because of their prior achievements. Additionally, competent individuals recognize that they have already sacrificed their enjoyment of life for their previous successes; therefore, they tend to offset this by investing their valuable time in other aspects. This is consistent with other researchers’ investigations (e.g., [ 109 ]), which found that low-skilled individuals are more often compelled to engage in regular work activities and are more easily motivated than others. By contrast, highly competent individuals tend to be motivated by challenging tasks and improving themselves through further education. Sixth, the relationship between competence and work motivation is negatively moderated by religious affiliation. This finding suggests that religious affiliation weakens the association between individuals’ competence and work motivation. One possible explanation for this finding is that strong religious beliefs are the foundation for virtuous living [ 110 ]. Individuals with religious affiliation usually employ religious principles to guide their behavior, regardless of their competence. In other words, both competent and incompetent individuals tend to be more motivated at the workplace if they are affiliated with any religion, thereby diminishing the influence of competence in work motivation. Seventh, the relationship between social relatedness and work motivation is negatively moderated by in-group collectivism. This result proposes that a higher degree of in-group collectivism weakens the association between individuals’ social relatedness and work motivation. One possible explanation for this is that, under an in-group collective society, people put more weight on mutual relationships and encourage acts that may build up the solidarity of groups. Since in-group collectivism is viewed as a social attachment in which people emphasize the group over the self (e.g., [ 79 , 80 , 81 ]), individuals are fairly conscious of their responsibility to the group regardless of their social relatedness. Both socially related and unrelated individuals belonging to in-group collective cultures tend to work harder for common goals. Accordingly, the influence of individuals’ social relatedness on their work motivation is reduced.

6. Limitations and Future Research

Despite its significant contributions, this study has its limitations. The use of secondary data represents the fact that the data collection process was beyond the authors’ control. However, the collection of cross-national data is time-consuming and costly. The authors used the available data but strove for the efficient use of multilevel data. The secondary data also limited the measurement of individual-level factors based on the available data. Moreover, it is quite complex to gauge an individual’s work motivation appropriately, since personal work motivation may not be one-dimensional. Nevertheless, the authors made efforts to employ the measurements utilized by prior research. Moreover, it is complicated to measure social factors such as political participation. There are challenges in investigating social contexts due to the absence of direct measurements [ 111 ]. This compels the authors to identify substitute measurements for this study. Finally, this study covered 25 samples from 25 countries with different characteristics. Despite the attempt of this study to include the most relevant social conditions in the framework, the influence of other national differences and cultural sensitivities were not considered.

This paper directs further research considering that several frameworks and approaches should be employed to better examine motivation [ 112 ]. First, as some of the results were opposite to the original propositions based on the theoretical foundations employed, combining different concepts and approaches is necessary to enhance perspectives of psychological needs and social issues. For instance, the relationship between competence and work motivation can be further investigated by employing other theories to understand their association better. Similarly, the moderating effects of social contexts such as religious affiliation and in-group collectivism should be further examined to obtain a more in-depth comprehension of the roles of contextual circumstances and cultural values in individual-level relationships. Additionally, self-determination theory and the concept of prosocial motivation may be used to explore motivation towards specific behavior in organizations, such as organizational citizenship and proactive behaviors. Organizational context, such as rewards, training, and culture, can be considered as part of the framework to enhance the conception of work motivation.

7. Conclusions

This study has utilized a multilevel framework to examine the influence of psychological needs and social context on work motivation. Through this research, a deeper understanding of the roles of competence, autonomy, and social relatedness, as well as social situations and cultural values on work motivation, is achieved. The contrary findings call for integrating other concepts and approaches towards a more comprehensive knowledge of work motivation.

Along with the theoretical contribution, the study’s findings offer practical implications. The satisfaction of psychological needs promotes self-motivation, which creates positive outcomes. Hence, organizations can provide programs and activities to promote employees’ autonomy and social relatedness as this will enhance their work motivation. Employee empowerment can be advocated by encouraging them to make their own decisions at the workplace, providing constructive criticisms rather than instilling the fear of failure. Additionally, managers should encourage solidarity, support, and mutual care among employees. Putting more weight on employees’ fulfillment of needs will further increase employees’ motivation, thereby diminishing costs related to stress or turnover [ 50 ]. To establish a novel mechanism towards promoting work motivation in the entire nation, the government should pay attention to the political structure and conditions that encourage citizens’ participation. Additionally, a culture of humane orientation should be promoted in the workplace and society so that solidarity, kind assistance, and altruism among communities as well as among individuals can be strengthened. For instance, teamwork should be encouraged for employees to help each other overcome difficulties at the workplace or share responsibilities with their colleagues. This will motivate people to work harder for collective goals, contributing to the development of organizations.

Author Contributions

Conceptualization, T.T.D.V. and K.V.T.; data collection, T.T.D.V.; methodology, T.T.D.V. and K.V.T.; formal analysis, T.T.D.V. and K.V.T.; resources, K.V.T. and C.-W.C.; writing-original draft, T.T.D.V. and K.V.T.; writing-review, editing & proofreading, T.T.D.V., K.V.T. and C.-W.C.; visualization, K.V.T.; supervision, K.V.T. and C.-W.C.; project administration, K.V.T. All authors have read and agreed to the published version of the manuscript.

This paper does not receive funding from any individuals or organizations.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Employee Motivation in Contemporary Academic Literature: A Narrative Literature Review

• Articles in this Issue

Published Online : May 11, 2024

Page range: 185 - 201, received : sep 01, 2023, accepted : feb 21, 2024, doi: https://doi.org/10.2478/orga-2024-0013, keywords elton mayo , employee motivation , motivation factor , motivation theory, © 2024 petra jarkovská et al., published by sciendo, this work is licensed under the creative commons attribution-noncommercial-noderivatives 4.0 international license..

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Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

• Review Article
• Open access
• Published: 05 May 2021
• Volume 34 , pages 39–71, ( 2022 )

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• TuongVan Vu   ORCID: orcid.org/0000-0001-6700-2439 1 , 2 ,
• Lucía Magis-Weinberg 3 ,
• Brenda R. J. Jansen 4 ,
• Nienke van Atteveldt 1 , 2 ,
• Tieme W. P. Janssen 1 , 2 ,
• Nikki C. Lee 1 , 2 ,
• Han L. J. van der Maas 4 ,
• Maartje E. J. Raijmakers 1 , 2 ,
• Maien S. M. Sachisthal 4 &
• Martijn Meeter 1 , 2  

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The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

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Introduction

In most countries, motivation for school clearly declines throughout school time (Martin, 2009 ; OECD, 2016 ; Scherrer & Preckel, 2019 ) with individual heterogeneity in changes depending on specific motivation constructs across academic domains (Gaspard et al., 2020 ; Scherrer & Preckel, 2019 ). Given this undesirable decline and the fact that motivation can be targeted by interventions, motivation has long been a central focus of educational psychology. The influence of motivation on achievement is well-documented (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; Valentine et al., 2004 ). Yet the reverse relation is also often found, as achievement can affect motivation through experiences of success or failure (Garon-Carrier et al., 2016 ; Guay et al., 2003 ; Jansen et al., 2013 ). A common view is that both the “motivation → achievement” and “achievement → motivation” links exist and that motivation and achievement influence each other in a reciprocal manner over time (Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Möller et al., 2009 ).

Researchers have been studying this reciprocal relationship between motivation and achievement for at least 20 years (Marsh et al., 1999 ). However, further insights into the nature of the relationship are currently lacking; features such as the direction of causality, behavioral mediating pathways, possible effect of the time scale, and generalizations to different motivation constructs and population groups are currently not well understood. These issues are important not just from a scientific viewpoint, but also from a practical viewpoint. To be able to design the most effective interventions aimed at improving achievement and motivation, we need to improve our understanding of the reciprocity to identify the best timing, duration, content, and appropriate target variables of such interventions, as well as other contextual factors contributing to their success.

Our objective is to summarize the current understanding of motivation-achievement interactions (drawing mainly from the academic motivation literature) and to identify the theoretical and methodological challenges that could inspire further advances to understand such specific features of this reciprocal relationship. While an exhaustive review of the literature is beyond the scope of the current paper (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ), we start with a summary of how influential theories of academic motivation have conceptualized reciprocity between motivation and achievement, and the types of empirical evidence that have been found in support of the reciprocal relationships. In our current understanding, we have found areas of consensus, but have also identified sizable gaps. This leads to a recommended research agenda for future empirical studies on the reciprocal relations between motivation and academic achievement and suggestions on how these insights could inform future interventions.

Reciprocal Relations in Theories of Academic Motivation

Commonalities between theories.

Individual differences in academic achievement are partly the result of differences in motivation for learning (Arens et al., 2017 ; Burnette et al., 2013 ; Eccles & Wigfield, 2002 ; Guay et al., 2003 ; Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Robbins et al., 2004 ; Seaton et al., 2014 ). This robust finding has spawned a wealth of theories on academic motivation and how to stimulate it. These theories differ in both substance and focus, but also have many common elements. Figure 1 represents an attempt to synthesize, for the purposes of this paper, some of the commonalities of well-established theories that have had an impact in the field of academic motivation (leaning strongly on the seminal review of Eccles & Wigfield, 2002 and adding theories that have gained traction since). Our goal is not to comprehensively review and synthesize the existing theories (although this is an urgent task, Koenka, 2020 ), but rather to illustrate how the commonalities between the theories suggest a framework in which the reciprocal relationships between motivation and achievement can be studied and understood.

The motivation-achievement cycle, a summary model of motivation-achievement interactions, capturing some of the commonalities within prominent theories of academic motivation. Blue boxes denote motivation constructs, green (dotted) arrows behavioral intermediaries (quality of learning and quantity of learning), and yellow boxes and arrows denote achievement-related constructs (flow and perceived performance). Gray arrows denote outside influences that are themselves not part of motivation-achievement interactions (e.g., cultural and social influences that affect both expectancies and values)

Motivation has up to 102 definitions (Kleinginna & Kleinginna, 1981 ), but is often seen as a condition that energizes (or de-energizes) behaviors. In many theories, motivation results from what can be called an appraisal of the behavior that one is motivated to perform (the word appraisal is rarely used with regard to motivation, but the processes described are akin to those captured in the emotion literature). In that appraisal, two elements are combined (Eccles & Wigfield, 2002 ): the value attached to the behavior and its outcomes, and the expectancy of the likelihood of certain outcomes of the behavior. These two sides, expectancy and value, are explicit in expectancy-value theory (Eccles & Wigfield, 2002 , 2020 ), attribution theory (Graham, 2020 ; Weiner, 2010 ), control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ), and Dweck’s integrative theory (Dweck, 2017 ).

Many other theories focus either on the value attached to behavior or on expectancies. Theories on the values side of the ledger (goal theories, flow theory, self-determination theory, individual differences theories, and interest theories) focus on interest, goals, needs for relatedness, competence, and autonomy. Theories on the expectancy side, notably self-efficacy theory, control theories, social-cognitive self-regulation theories, and the process-oriented metacognitive model, focus on how students’ beliefs (or perception ) about their competence and efficacy (i.e., academic self-concept, see below), expectancies for success or failure, and sense of control over achievement affect motivation. Different constructs have been studied that tap into these beliefs underlying one’s expectancies, such as academic self-concept, self-efficacy, locus of control, and perceived control.

A motivation construct frequently used to study the reciprocal motivation-achievement relationship is academic self-concept (hitherto, ASC, discussed in further details in section “Different motivation constructs” below) which is how individuals evaluate their ability specifically in an academic domain (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Shavelson et al., 1976 ). ASC is a component distinct from physical, social, and emotional self-concepts within the multidimensional, hierarchical model of self-concept (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ). ASC is itself also multidimensional and usually measured by the Self Description Questionnaire (Marsh et al., 1999 ; Marsh & O’Neill, 1984 ); its academic subscales tap into general academic self-concept, math self-concept, and verbal self-concept. Much empirical research on motivation-achievement interactions operationalizes motivation as ASC in a certain academic domain, most often in mathematics and verbal subjects such as language and reading (Guay et al., 2003 ; Seaton et al., 2014 ); for meta-analyses and reviews, see Burnette et al. ( 2013 ), Eccles and Wigfield ( 2002 ), Marsh and Craven ( 2006 ), Marsh and Martin ( 2011 ), and Robbins et al. ( 2004 ).

It is worth noting that many theories posit that beliefs about the self (including self-concept and self-esteem and mindset/implicit theory of self attributes) are important causes of human behavior and learning (Bandura, 1997 ; Carver & White, 1994 ; Deci & Ryan, 2000 ; Molden & Dweck, 2006 ). Although the idea that ASC or other beliefs about the self affect achievement has been challenged (see the discussion in Marsh & Craven, 2006 ), there has also been much empirical research in support of it (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; and the meta-analyses of Huang, 2011 ; Valentine et al., 2004 ). One suggested pathway is that positive self-beliefs can lead to self-affirmative, self-regulatory, academic behaviors (or achievement behaviors , see below) such as exerting effort, demonstrating persistence, and selecting goals that are conducive to the achievement of academic goals.

Another pathway for beliefs about the self to act as a causal agent on academic achievement, according to self-worth theory (Covington, 2000 ), is that students with positive beliefs about themselves assign high and positive values to academic activities. Academic activities are then viewed as important, intrinsically interesting, of high expected utility and of low cost, which leads to high achievement (Valentine et al., 2004 ). Also, in self-determination theory, feelings of competence are a precursor of intrinsic motivation, again leading to a higher value being assigned to academic activities if one feels competent. This would then lead to behaviors that support later achievement. A recent study of more than 30,000 college students found that need for competence (relative to need for autonomy and relatedness) is the strongest predictor of perceived learning gains (Yu & Levesque-Bristol, 2020 ).

An appraisal of values and expectancies leads to the decision to engage (Cleary & Zimmerman, 2012 ; Kuhl, 1984 ; Schunk & DiBenedetto, 2020 ). According to the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ; Schunk & DiBenedetto, 2020 ), students first identify values and expectancy of learning activities, then engage in self-regulatory processes (self-instruction, attention focusing, task strategies, etc.). Following their performance, students conduct self-evaluations, infer causal attributions, and make adaptive or maladaptive attributions of their successes and failures. This account stresses the importance of metacognition, where students who can monitor their learning processes can then maintain their engagement in the learning cycle.

The appraisal of values and expectancies can also trigger academic emotions, such as pride in achievement, hope, boredom, and enjoyment. Control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ) describes how such emotions codetermine what are termed achievement behaviors —behaviors that are conducive to the achievement of academic goals. In line with dominant theories of emotion (e.g. Frijda, 1988 ; Lazarus, 1999 ), Pekrun ( 2006 ) assumed that an appraisal of control of the learner and the value of learning activities lie at the basis of academic emotions. For example, if a learner values an academic outcome and believes it is somewhat under his or her control, he or she may feel the emotion of hope. While it is not certain that the same kinds of appraisal lie at the basis of both motivation and academic emotions, it would seem plausible and parsimonious. Indeed, Pekrun ( 2006 ) suggested that this is the case, though he cautioned that more research is needed.

Figure 1 may raise the question of what actually distinguishes motivation from emotions, since both seem to result from an appraisal of the situation, and both energize or de-energize certain behaviors. This is a valid question, and Kleinginna and Kleinginna ( 1981 ) already noted that a sharp line between motivation and emotion is difficult to draw (also see Berridge, 2018 ). Emotions will typically be more temporary than motivation, but this is a fuzzy distinction. Emotions and motivation may also interact. Emotions may for example make a learner assign more or less value to academic activities, or may change the learner’s expectations around their chances of success or failure, which then changes the appraisal that underlies motivation. Literature showing that emotions and academic achievement also form reciprocal relationships over time has recently emerged (Putwain et al., 2018 ).

Pathways from Motivation to Achievement and Vice Versa

While it is generally accepted that motivation affects achievement, it is not completely clear how . Theoretically, two routes can be discerned (see Fig. 1 ). The first is the quantity (frequency and intensity) of academic behaviors aimed at achievement (such as effort, persistence, etc.) (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Marsh et al., 2016 ; Pinxten et al., 2014 ; Plant et al., 2005 ; Trautwein et al., 2009 ). As a second route, higher levels of motivation could also be associated with higher quality of academic behaviors; for example, by adopting effective learning strategies, adaptive meta-cognitive strategies, spaced practice, elaboration, retrieval practice, interleaving, dual coding, and so on. Several theories of academic motivation support the idea that higher motivation leads to higher quality behaviors. Both intrinsic motivation (self-determination theory, Deci & Ryan, 2000 ) and interest (interest theories, Alexander et al., 1994 ) have been linked to deeper learning (Alexander et al., 1994 ; Schiefele, 1999 ; Scott Rigby et al., 1992 ). Positive academic motivations have also been suggested to facilitate creative learning strategies (control-value theory, Pekrun, 2006 ), and incremental implicit beliefs (growth mindset) to facilitate mastery-oriented strategies (Burnette et al., 2013 ).

Effects of achievement on motivation may also take two routes. The first is through perceived achievement. Many theories, such as self-efficacy theory (Bandura, 1997 ), expectancy-value theory (Eccles & Wigfield, 2002 ), control theories (Skinner, 1995 ), and attribution theory (Weiner, 2010 ) explicitly suggest that past achievement leads a learner to experience feelings of self-efficacy and perception of control. What matters most in this regard is the learner’s own evaluation of this outcome, for which we use the term perceived performance in Fig. 1 . High perceived performance will thus change the expectancies of learners (i.e., make them trust that good outcomes are attainable), but it may also alter the value attached to learning activities. For example, in self-determination theory, the feeling of competence (strengthened by positive perceived achievement) is a basic need that increases the intrinsic value of learning.

The second route from achievement to motivation is central to flow theory (Csikzentmihalyi, 1990 ). An activity in which the learner is holistically immersed can generate a feeling of flow, which is rewarding in its own right and alters the value attached to the academic behaviors.

External Factors Affecting Motivation, Effort, and Achievement

Figure 1 suggests a positive feedback loop, with motivation feeding achievement, and achievement feeding motivation—an idea that is alluded to in some theories (Cleary & Zimmerman, 2012 ; Eccles & Wigfield, 2002 ; Schunk & DiBenedetto, 2020 ). Most explicit in this regard is the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ) where the pathway between self-regulation and achievement is a cyclical feedback loop. Schunk and DiBenedetto ( 2020 ) suggest an iterative process between perceived progress, self-efficacy, and goal pursuit. Bandura’s social cognitive theory also stresses the reciprocity of the interactions between behavioral, environmental, and personal factors (Bandura, 1997 ). Crucially, this raises the question of how such a positive feedback loop could get started, and how, once started, it could lead to any other outcome than either perfect motivation and achievement, or negative motivation and failure. The answer to those questions may rest in the external influences on motivation and achievement. These are indicated in Fig. 1 by the gray arrows:

Extrinsic rewards and requirements tied to achievement, e.g., schools or parents, may change the value attributed to academic behavior, and so change motivation. Although this has been described in self-determination theory as potentially detracting from intrinsic motivation (Deci & Ryan, 2000 ), it may also jolt a motivation-achievement cycle that would otherwise not start (Hidi & Harackiewicz, 2001 ). Supporting autonomy and creating relatedness are other ways in which external actors can increase the value attached to learning, increasing motivation and achievement (Deci & Ryan, 2000 ).

Cultural norms (described in control theories and control-value theory, Pekrun, 2006 ; Skinner, 1995 ), social learning, and verbal persuasion by others (social cognitive theory, Bandura, 1997 ) can alter the expectations, values, and attributional processes of learners (expectancy-value theory, attribution theories, Eccles & Wigfield, 2020 ; Graham, 2020 ), and therefore keep a motivation-achievement cycle going that would otherwise falter or not start up.

Effort is not only a result of the learner’s motivation but also of outside requirements (e.g., deadlines and exams set by the educational institution, Kerdijk et al., 2015 ). Such outside requirements can lead to achievement in the absence of strong motivation.

Quality of learning is not only affected by motivation but also by the abilities of the learner and the quality of teaching, instructions, and study materials. Thus, achievement can increase in the absence of stronger motivation, because of better support for learning.

Perceived achievement is not only determined by true achievement but also by elements of educational design, such as the form in which feedback is given (e.g., a grade that either accentuates the ranking of the student or the degree to which the study material was mastered, or feedback on effort instead of performance, De Kraker-Pauw et al., 2017 ). Perceived achievement is also subject to interpretative, comparison, and attributional processes (described in attribution theories, Graham, 2020 ; Weiner, 2010 ). This means that true high achievement can still fail to support motivation (e.g., when a sibling performs even better), or low achievement can be viewed in such a way so as to not be detrimental for motivation.

Such external factors are not only important for a complete causal understanding of motivation-achievement interactions (i.e., highly relevant for educational researchers) but also because they offer entry points for interventions that enhance motivation, achievement, or both (i.e., highly relevant for educators).

What Avenues for Empirical Research Have Been Explored?

Figure 1 shows that theories of academic achievement imply a reciprocal relationship between motivation and achievement. A comprehensive review of studies is beyond the scope of this manuscript (see narrative reviews and meta-analyses) (Huang, 2011 ; Marsh & Craven, 2006 ; Scharmer, 2020 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ), but we will review the kinds of evidence that have been brought to bear in support of such reciprocal relationships. Analyzing this evidence allows future directions on the field to be charted.

The earliest support for the relationship between motivation (focusing specifically on self-concepts and other self beliefs) and academic achievement comes from cross-sectional and correlational studies, reviewed by Hansford and Hattie ( 1982 ). These studies established a relationship between self-concepts and academic achievement, but no causal paths. Subsequent work set out to investigate the causal and temporal ordering of the effects using structural equation models (SEMs) and longitudinal data (e.g., Marsh et al., 1999 ). To date, the majority of evidence for the reciprocal relationship between self-concept and achievement has come from such time-series or cross-sectional data collected at schools, to which various SEMs have been fitted (see Marsh & Craven, 2006 for a narrative review and Huang, 2011 for a meta-analysis of such studies).

More recent studies showcase impressive efforts of researchers to use large sample sizes and longitudinal data of up to six waves, allowing changes in motivation and achievement of students to be tracked across their school career (e.g., Marsh et al., 2018 ; Murayama et al., 2013 ). A recent meta-analysis (Scharmer, 2020 ), which includes such studies that were published between 2011 and August 2020, showed that overall, the pooled effect of achievement on motivation was twice (β = .12) the pooled effect of motivation on achievement (β = .06), though both are what is conventionally considered a small effect. These findings are in line with Valentine and DuBois ( 2005 ) who found that academic achievement had a stronger effect on self-belief than vice versa. In contrast, Huang ( 2011 )’s meta-analysis found a slightly larger effect of self-concept on achievement than the other way around. Valentine and DuBois ( 2005 )’s findings were also more similar to Scharmer’s ( 2020 ) in terms of the size of the effects (achievement on self-belief: β = 0.08; self-belief on achievement: β = 0.15). Huang ( 2011 ), however, found considerably larger ranges of effects overall (achievement on self-concept: β = 0.19–0.25; self-concept on achievement: β = 0.20–0.27).

There have also been interventions and randomized controlled field studies in which either self-concept or other motivation constructs were manipulated (e.g., Savi et al., 2018 ; Vansteenkiste et al., 2004 ), thereby allowing for causal inferences. The meta-analysis of these studies by Lazowski and Hulleman ( 2016 ) showed that, while interventions targeting motivation usually led to positive outcomes on achievement (medium effect size; average Cohen’s d of 0.49), it did not matter which theory was at the basis of the intervention— all theories of motivation performed about equally well. However, experimental studies that look at the reverse causal path, manipulating achievement (or the perception of achievement) to affect motivation, are scarce. One example is an intervention study by Betz and Schifano ( 2000 ) where students were ensured of successful completion of a task followed by affirmation of their accomplishments with applause and verbal praise. This resulted in an increase in self-efficacy (a motivation construct highly related to ASC, Bong & Skaalvik, 2003 ). Nevertheless, to the best of our knowledge, few studies have done both: combining experimental manipulation and longitudinal design to investigate reciprocal motivation-achievement relations (an exception that we are aware of is Bejjani et al., 2019 which will be discussed later).

Research Agenda

The overview given above suggests that empirical evidence for reciprocal relations between motivation and achievement exists. However, several features of such relationships are still poorly understood. Also, some doubts about the robustness of the effects have recently surfaced (which we discuss in detail in section “Choice of appropriate statistical models” below). In other words, there are still unanswered theoretical and empirical questions about the reciprocal relationship between motivation and academic achievement. Below, we outline these issues and a research agenda for future research that can answer these remaining questions. These are organized into questions pertaining to theoretical lacunae, methodological challenges, and questions about the scope of theories and the generalizability of empirical results.

Theoretical Lacunae

Multiple motivation constructs.

First, as we presented above, many motivation theories have implicitly or explicitly conceptualized the relationship between a plethora of motivation constructs and achievement as reciprocal. However, to date, a large amount of empirical research on reciprocal motivation-achievement interactions has mainly studied ASC (Arens et al., 2019 ; Brunner et al., 2010 ; Chen et al., 2013 ; Dicke et al., 2018 ; Gottfried et al., 2013 ; Grygiel et al., 2017 ; Guay et al., 2003 ; Guo et al., 2015 ; Möller et al., 2011 ; Niepel et al., 2014a , 2014b ; Retelsdorf et al., 2014 ; Viljaranta et al., 2014 ; Walgermo et al., 2018 ; for meta-analyses and reviews, see Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ) . This raises the question of whether findings generalize to other motivation constructs that are related yet could also have a distinctive reciprocal relationship with academic achievement.

Moreover, although the studies involving ASC were groundbreaking attempts to show reciprocal relations, there are several reasons why future studies should contemplate using different motivation constructs other than ASC. First and foremost, ASC and achievement are highly intertwined, as items in ASC questionnaires usually ask students to report on their achievement (e.g., “I get good marks in most academic subjects,” “I learn quickly in most academic subjects” (Marsh & O’Neill, 1984 ). Fulmer and Frijters ( 2009 , p. 228) in their critique of how motivation is measured in educational psychology also made the point that “self-report measures confound the measurement of motivation with other variables, such as ability and attention.”

Second, a meta-analysis investigating mean-level changes of a number of important motivation constructs concluded that the decline in motivation shows non-trivial differences across these constructs (Scherrer & Preckel, 2019 ). An important implication of this finding is that more attention should be paid to differentiation among multiple motivation constructs in future empirical studies.

Third, ASC might also be less malleable than other motivation constructs since general self-concept is relatively stable—especially for those at lower levels (Scherbaum et al., 2006 ). Research into the Big-Fish-Little-Pond phenomenon (i.e., students in high-achieving classes having lower ASC than those with comparable aptitude in regular classes) suggests that domain-specific ASC (more so than general ASC) is influenced by social comparison (Fang et al., 2018 ; Marsh et al., 2018 ). Nevertheless, it may be hard to manipulate ASC in a randomized controlled trial (although it has been indirectly done by affirming general self-esteem and personal values, Cohen et al., 2009 ). Other motivation constructs that can be modified through external influences (e.g., situational interest, perceived control, etc.) might yield useful guidance for designing interventions.

Furthermore, the heavy focus on ASC may reflect an emphasis on a cognitive, intrapsychological theoretical view of motivation while losing sight of social, contextual, historical, and environmental factors that arguably also play important roles (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ). Last but not least, ASC is mainly self-reported and, despite the availability of well-constructed measures, it suffers from all the caveats inherent to self-report measures (see section “Alternatives to self-reports” below).

Given that there are other well-studied motivation constructs such as achievement goals, self-efficacy, interest, and intrinsic motivation (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ), further research with multiple non-ASC motivation constructs included as concomitant predictors of academic achievement is therefore much needed. In recent investigations of the reciprocal relationship between motivation and achievement, motivation constructs other than ASC have started to be included (e.g., self-efficacy in Grigg et al., 2018 ; Schöber et al., 2018 ; achievement goals in Scherrer et al., 2020 ; intrinsic motivation in Hebbecker et al., 2019 ; and interest in Höft & Bernholt, 2019 ). Yet, these studies are still small in number. Twenty-four out of 41 studies included in the meta-analysis of Scharmer ( 2020 ) still used ASC as the main motivation construct of interest.

Behaviors as Mediating Factors in the Motivation → Achievement Link

As mentioned above, theories of academic motivation imply several pathways through which motivation influences achievement and vice versa (see Fig. 1 ). For the motivation → achievement link, the rationale is that motivation leads to active and effortful commitment to learning (e.g., E. Skinner et al., 1990 ), implying that motivation constructs that are beliefs about competence and efficacy influence achievement by inducing self-regulatory, academic behaviors. In a similar vein, the volition theory of motivation (Eccles & Wigfield, 2002 ; Kuhl, 1984 ) posits that motivational beliefs only lead to the decision to act. Once the individual engages in action, volitional processes are required and determine whether the intention is fulfilled. Thus, self-regulatory processes theoretically mediate the link between beliefs and accomplishment of the task.

However, there is a relative paucity of empirical research and especially longitudinal studies that include measures of such regulatory processes. Usually, when studies found reciprocal relations between ASC and other motivation constructs and achievement, they left unanswered which pathways mediate the link between such beliefs and achievement (Marsh & Martin, 2011 ). To our knowledge, initial attempts to study mediating processes in longitudinal designs (Marsh et al., 2016 ; Pinxten et al., 2014 ; Trautwein et al., 2009 ) yielded mixed findings with regards to the role of effort in the relationship between ASC and academic achievement. This may be due to the fact that there are multiple operationalizations and evaluations of the construct effort (Massin, 2017 ), which may have varying relations with academic achievement. Specifically, Marsh et al. ( 2016 ) and Pinxten et al. ( 2014 ) measured subjective effort—i.e., students were asked to rate their own effort expenditure. Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., 2020 ). Subjective effort, as opposed to objective effort, might therefore have a very different relation to motivation and achievement.

In non-longitudinal studies looking at the relations between academic motivation and achievement, the evidence on behavioral mediators also shows differentiation related to how effort is measured. When effort is measured as quality of learning (e.g., selecting adaptive goals, adopting higher-quality learning strategies, etc.), there is some evidence for a positive link between academic achievement and effort (Trigwell et al., 2013 ). However, when effort is measured as a quantity of learning (such as study time, practice time, time-on-task, persistence, etc.), this relationship seems either weak or only significant after controlling for quality of learning (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Plant et al., 2005 ) or even negative (the labour-in-vain effect, Koriat et al., 2006 ; Nelson & Leonesio, 1988 ; Undorf & Ackerman, 2017 ). This provides suggestions for future attempts to parse the mediating factors in the motivation → achievement link in reciprocal relations between these two constructs. It is most fruitful to measure subjective and objective measures of quantity and quality of learning (and use triangulation of methods, as strongly suggested by Scheiter et al., 2020 ) and compare their effects on academic achievement.

Irrespective of what operationalization is chosen, it is important to note that it is not trivial to evaluate and conceptualize effort (see extensive discussions in Baars et al., 2020 ; Scheiter et al., 2020 ). Is effort the allocation of cognitive control, i.e., mental effort (Kool & Botvinick, 2018 ), or the intention to think deeply, regardless of the amount of time spent (Haynes et al., 2016 ), or a preference for thinking hard (Beck, 1990 ), a decision process rather than a capacity or resource that is physically limited (Gendolla & Richter, 2010 )? Yet, only by measuring regulatory processes that mediate the motivation → achievement pathway, we can make progress in understanding the underlying mechanism of mutual influences between motivation and achievement.

Mutualistic Perspective and the Network Approach

Next, studies have typically investigated relations between one or a small number of motivation constructs (e.g., ASC and interest, Walgermo et al., 2018 ). The discussion above and Fig. 1 show that multiple motivation constructs are linked to academic achievement, which may also all be mutually related. Like many topics in psychology, there is a huge overlap in terms and variables in the literature on motivation and achievement; the same construct may have different names, or different constructs go under the same name (this is known as Jingle-Jangle fallacies; e.g., Marsh et al., 2003 ). One possible solution to the Jingle-Jangle fallacies with regard to motivation was proposed by Marsh et al. ( 2003 ), who presented a factor model with two higher-order factors (dubbed learning and performance ) that explained relations between motivation constructs. In this approach, assumptions on the number of factors and factor structure are necessary.

The network approach is different; it does not assume an a priori structure of motivation factors. Instead, it uses the (bidirectional) partial correlations between variables in empirical data and in doing so clusters of variables which can be interpreted as constructs may emerge. The idea of a network of mutual relations to model psychological constructs was introduced by van der Maas and colleagues (van der Maas et al., 2006 , 2017 ) as an explanation for the positive correlations (the positive manifold) between intelligence sub-test scores. This led to a productive area of research with applications in many areas of psychology (Dalege et al., 2016 ; Robinaugh et al., 2020 ; Sachisthal et al., 2019 , 2020 ; Zwicker et al., 2020 ). The general hypothesis in psychological network models is that correlations between observed behaviors, such as cognitive functions, psychopathological symptoms, and attitudes (or, motivation constructs ), are not due to unobserved common causes, but to a network of interacting psychological, social, and/or biological factors. These observed behaviors are the nodes in the network and the partial correlations are the edges.

An example of how such a network approach can be applied to the area of motivation can be found in a study of interest in science (Sachisthal et al., 2019 ). This study included measures of students’ value of science, their science engagement, and achievement. The correlations between these measures were modeled as a network, within which clusters of variables emerged. These can be seen as empirically derived constructs, replacing the at times arbitrary theoretical separation between (motivation) constructs. Given that in motivation research many constructs with considerable overlap exist (Anderman, 2020 ; Hattie et al., 2020 ), such empirically derived concepts may prove especially relevant.

Within this network, variables with the strongest direct relationships can be identified. A positive change in a central variable should lead to a positive change throughout the network and these central variables may differ between contexts. For example, enjoyment emerged as the central node in the network of Dutch students, whereas engagement behaviors emerged as central in the network of Colombian students and therefore different approaches for increasing science interest are advised for the two countries (Sachisthal et al., 2019 ). Central variables may be efficient intervention targets as interventions informed by network analyses have been shown to be highly effective as these central variables were later shown to be predictive of subsequent behaviors (e.g., Sachisthal et al., 2020 ). Moreover, further support for this assumption comes from a recent study by Zwicker et al. ( 2020 ) who identified guilt as the central node in the network of attitude and environmental behaviors. They then successfully manipulated guilt which increased willingness to engage in such behaviors.

In sum, these works exemplify how network approaches can be used (1) to model distinctive but highly related motivation and achievement constructs simultaneously and map their relations and (2) to derive hypotheses about which included constructs may be efficient targets for interventions (see Borsboom, 2017 , for an overview). Moreover, the fact that network analyses found different central variables in different populations also showcases how such an approach can flexibly capture interactions between motivation factors in real life. Last but not least, at a more abstract level, a mutualistic network approach can potentially solve the question of the mechanisms of the impact of motivation on achievement (also raised in Hattie et al., 2020 as an important avenue for future research). Specifically, how clusters of motivation constructs, behavior, and achievement interact with one another can be modeled, and how reciprocal relations between them arise over time. This can only be achieved when multiple motivation constructs are measured in one single study (as argued above in section “Multiple motivation constructs”).

Time Scale of the Interactions (Short vs. Long Cycle)

Another gap in the literature that we identified is that much research on the reciprocity between motivation and achievement has been done with data collected at large time intervals, which reflect changes that happen over months or years (e.g., Harackiewicz et al., 2008 ; Marsh et al., 2005 , 2016 ; Nuutila et al., 2018 ). For example, it is common for studies to include data collected per academic semester or year (e.g., Gottfried et al., 2013 ); sometimes, other time intervals have been used, such as weeks (e.g., Yeager et al., 2014 ). However, theories of motivation such as self-determination theory or expectancy-value theory are not formulated with an explicit time scale, and the interactions they describe seem framed in terms that suggest that the effects of motivation constructs happen without delays (i.e., on a time scale of seconds). Recent accounts of motivation are situated ones, which also call attention to fine-grained, moment-to-moment fluctuations that occur during learning engagement (Schunk & DiBenedetto, 2020 ). This raises the question how such fast dynamics can be captured if constructs are measured with large time lags in between.

It is possible that there are interactions between motivation and achievement at both short and long timescales, and that these are qualitatively different. We will refer to these hypothetical interactions at different time scales as short (or fast) and long (or slow) cycles between motivation and achievement. Some constructs may change in slower cycles (e.g., achievement goal orientation, mindset, academic self-concept) than others (e.g., autonomy, or even faster: emotions). In research focusing on interest and achievement emotions, for instance, a stable, so-called trait level (e.g., individual interest) is often distinguished from a shorter, task-dependent state level (e.g., situational interest) (see Hidi & Renninger, 2006 ; Renninger & Hidi, 2011 for interest; Pekrun, 2006 for achievement emotions). Nesselroade’s ( 1991 ) model of within-person psychological change also distinguishes between state and trait. The former is rapid and potentially more easily reversed than the latter. Developmental processes are thought to underlie trait constructs, for instance suggesting that the repeated experience of a positive state (i.e., enjoyment) will lead to a positive trait value. While it has been suggested that reciprocal relations play a more central role on the trait level—e.g., explaining the stronger relations between emotion antecedents and emotions (Bieg et al., 2013 ), studies investigating reciprocal relations are still missing at a state (or task) level . Furthermore, the difference between slow and fast change is also more salient for certain constructs than for others. For example, in one rare study where the within-task changes in multiple motivation constructs was studied, researchers found that while students’ self-efficacy generally grew throughout the progress of a task, interest did not (Niemivirta & Tapola, 2007 ). This suggests that when studies do not consider fast vs. long cycles of constructs, the effects of a faster changing variable on a slowly changing variable can be missed.

The remedy to these problems is to consider using data collected at either diverse time intervals or with theoretically informed time intervals to capture the ebbs and flows of the relations between constructs over time and their corresponding short and long cycles (Duff et al., 2015 ; McNeish & Hamaker, 2019 ). In addition, special attention should be paid to “short cycles”—especially since fast-changing constructs may be more effective targets for interventions. Intensive longitudinal designs can help uncover potential “real-time” causal variance attributable to a construct that would be missed when it is measured at relatively lengthy intervals such as one academic semester or year (McNeish & Hamaker, 2019 ). This may also help when developmental trajectories are characterized by non-linear trends that cannot be captured by low-frequent measurements (McNeish & Hamaker, 2019 ). A deliberate choice of time intervals and the use of non-questionnaire measures will also be helpful in this respect (see section “Alternatives to self-reports” below).

A related but distinguishable issue is the stability of the reciprocal relation between motivation and achievement. Whether or not reciprocal effects of motivation and achievement are stable across school careers is a question with significant theoretical and practical consequences (Marsh et al., 2018 ). Two recent studies found motivation declines to be associated with particular academic stages, for example some constructs such as achievement goal orientation specifically dropped in the transition to secondary school (Scherrer et al., 2020 ). The Scherrer et al. ( 2020 ) data are however among the first longitudinal attempts that can reveal how such declines could potentially impact the reciprocity between motivation and achievement. Theoretically, one could assume that the impact of motivation on achievement is low early in a new environment (e.g., a school transition) where learners experience considerable uncertainty regarding their competence and academic standing (Eccles et al., 1993 ; Valentine et al., 2004 ). When the learning environment is stable, the impact of achievement on subsequent motivation might be more substantial. Some support for such a pattern is provided in Scherrer et al. ( 2020 ) who found the reciprocal effects only in later time points and not in earlier time points after transition into secondary school. However, these studies were not designed specifically to test the transition vs. non-transition contrast, prompting the need for subsequent longitudinal studies that focus on the effect of school transition (to our knowledge, Rudolph et al., 2001 is among the first but only has two waves of data).

Methodological Challenges

When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980 ; Holland, 1986 ; Marsh et al., 2018 ; Mega et al., 2014 ). In almost every study investigating reciprocal motivation and achievement relations, the need for experimental designs in which either motivation or achievement is manipulated is raised as a suggestion for future research (Marsh et al., 2016 , 2018 ; Mega et al., 2014 ; Pinxten et al., 2014 ). The term “effect” in many existing studies is used only in “conventional statistical sense and standard path analytic terminology, as representing a relation that is not necessarily causal” (Marsh et al., 2018 , p. 268).

Research that aims to establish causality in the reciprocal relationship between motivation and achievement would need to meet three preconditions. The first precondition of causality is order , that is “x must precede y temporally” (Antonakis et al., 2010 , p. 1087). Causality of reciprocal effects requires both orders (x precedes y, y precedes x), as well as alternations of x and y (x precedes y, which is again followed by x). The pale blue (with solid outline) squares in Fig. 2 show this alteration of measurements of motivation and achievement. The top pale blue rectangle starts with motivation, whereas the bottom starts with achievement. The second precondition is correlation : “x must be reliably correlated with y (beyond chance)” (Antonakis et al., 2010 , p. 1087).

Representation of three types of study designs that can investigate the relationships between motivation and academic achievement. (1) The gray box shows that to establish that motivation causes academic achievement (top) or vice versa (bottom), experimental manipulation is needed, intervening on the predictor at time point 1, which influences the outcome at time point 2 and so on. The straight thin arrows are the cross-lagged relations and the curved arrows the autoregressive relations. (2) The light blue boxes (top and bottom) illustrate the types of design where reciprocity but not necessary causal effects between motivation and achievement can be established. (3) The green boxes (top and bottom) show the type of design that can investigate both reciprocity and causality between motivation and achievement (i.e., a study where experimental manipulation is included and reciprocal relationships are measured). t time-point, M motivation, A achievement

Several studies with high quality and quantity of longitudinal data meet these two pre-conditions (e.g., Arens et al., 2017 ; Bossaert et al., 2011 ; Chamorro-Premuzic et al., 2010 ; Chen et al., 2013 ; Collie et al., 2015 ; Dicke et al., 2018 ; Grygiel et al., 2017 ; Hebbecker et al., 2019 ; Höft & Bernholt, 2019 ; Marsh et al., 2016 , 2018 ; Miyamoto et al., 2018 ). In these studies, autoregressive paths (the curved arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of the same variable at the next time point) and cross-lagged paths (the straight arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of a different variable at a later time point) are found. In other words, autoregressive paths represent the direct effects of variables on themselves over time and cross-lagged paths the direct effects of two variables on each other over time. Such cross-lagged paths show the reciprocity between the variables but not necessarily causality in these relations (Usami et al., 2019 ). Correlation between different variables, measured at different time points, is a necessary but not sufficient requirement of causality in mutual relations. Establishing causality of reciprocal effects requires the experimental manipulation of at least one of the two variables.

Importantly, to our knowledge, no studies of the mutual relations between motivation and achievement also satisfy the third precondition of causality, that is the manipulation of x has an effect on y at a later time point, followed by (a) repeated measure(s) of x (and y) (Antonakis et al., 2010 ). In Fig. 2 , manipulation is indicated by the thick arrow. In the upper panel of Fig. 2 , the manipulation of motivation affects achievement in the gray (with dash outline) part of the figure. If the manipulation is followed by an alteration of the variables with cross-relations, the findings would support causality of motivation in reciprocal relations between motivation and achievement. We searched for such studies in meta-analyses of interventions (Harackiewicz et al., 2014 ; Lazowski & Hulleman, 2016 ; Sisk et al., 2018 ), in the latest meta-analysis of longitudinal studies (Huang, 2011 ) and Scharmer ( 2020 ). We encountered two studies that contained both an experimental manipulation of a motivation construct and subsequent multiple, alternate measurements of motivation and performance. Cohen et al. ( 2009 ) found that structured writing assignments to prompt African American students to reflect on their personal values (i.e., self-affirmation interventions) resulted in improved academic achievement (GPA), as well as self-perception and an increased rate of remediation, in the following school year for low-achieving African Americans. Yeager et al. ( 2019 ), in a large-scale mindset intervention, also had more than one wave of manipulated motivation and measurement of achievement. Although the authors discuss the role of a recursive process Yeager & Walton, 2011 ) , neither of these interventions modeled reciprocal effects between motivation and performance (Cohen et al., 2009 ; Yeager et al., 2019 ).

In the lower panel of Fig. 2 , the arrow indicates manipulation of achievement. A manipulation of achievement that affects motivation, which is again cross-related to achievement, would support a causal effect of achievement in reciprocal relations between achievement and motivation. However, it is hard to manipulate achievement independently from motivation. For example, manipulations of instruction, modeling, practice, and self-correction may all affect achievement, but they may do so partly by making the material more appealing, raising motivation at the same time or before achievement is raised. New manipulations are needed that raise, for example, perceived performance without raising performance per se, as a way to circumvent such issues. For causal inferences, experiments would ideally include (double-blinded) random assignment, which is possible in the lab but poses important practical problems in the classroom (cf. Savi et al., 2018 ). In sum, future research with the types of studies that can investigate both reciprocity and causality between motivation and achievement would be highly valuable.

Choice of Appropriate Statistical Models

Although the existence of the reciprocal relationship between motivation and performance is generally agreed upon, there are also empirical works that fail to establish such a relationship (Fraine et al., 2007 ) or cast doubts on the robustness of the reciprocal effects (Burns et al., 2020 ; Ehm et al., 2019 ). Such studies most importantly also point out that the choice of sophisticated statistical models to investigate such relationships can have implications for the conclusion drawn (e.g., Burns et al., 2020 ; Ehm et al., 2019 ). Ehm et al. ( 2019 ) specifically found that although a cross-lagged panel model (CLPM) supported reciprocal motivation-achievement relations, other models did not—such as the random-intercept CLPM, which Hamaker et al. ( 2015 ) showed to be more effective than CLPM in explicitly modeling within- and between-individual changes across time. In addition, as Usami et al. ( 2019 )—in their comprehensive unified framework of longitudinal models—demonstrated, it is important to identify the existence of third time-varying or time invariant variables (such as stable traits) that can have a causal effect on the longitudinal relationship but are yet accounted for in a model. Substantial knowledge about such confounders will help researchers select the correct statistical model. Again, this issue is closely related to the short vs. long cycle of the constructs discussed above.

Alternatives to Self-Reports

Most studies investigating reciprocal relationship between motivation and achievement have measured motivation through questionnaires probing ASC (e.g., the Academic Self-Description Questionnaire by Marsh & O’Neill, 1984 ). Despite their evident psychometric benefits, self-reports (including questionnaires) of motivation suffer from many inherent caveats. Fulmer and Frijters ( 2009 ) list several that are relevant. First of all, questionnaires are subjective and rely on the assumption that motives are consciously accessible, declarative, and communicable to other people, while as discussed above, motivation arises from partially inaccessible and non-declarative cognition and emotions. Students may also differ in their capacity to reliably answer the questions (e.g., consider alexithymia—a psychological trait that is characterized by difficulties with verbalization of one’s own emotions and psychological introspection, Lumley et al., 2005 ). Second, the lack of rigor in the conceptualization of motivation constructs often becomes apparent when using questionnaires (we discuss concrete issues related to ASC in the Different Motivation Constructs section). This is closely related to the Jingle-Jangle Fallacies discussed in Marsh et al. ( 2003 , p. 192). Third, questionnaires might not measure reliably motivation constructs that are not trait-like and subject to temporal and situational fluctuations (e.g., situational interest) (also see our discussion of this point in Time scale of the relations section above). In practice, self-reports cannot be sampled with high frequency during learning (see process-oriented measures below). Fourth, questionnaires are problematic from a developmental perspective because, across age groups, there might be varying factor structures in empirical data. Furthermore, some children may be too young to process some motivation constructs. Finally, self-reports are sensitive to demand characteristics and a tendency to give socially desirable answers (e.g. students who are familiar with the implicit theory of intelligence might tend to report that they endorse a growth mindset, Lüftenegger & Chen, 2017 ).

Most recent discussions of motivation-achievement interactions emphasize the need for alternative methods to self-report questionnaires. These alternatives include experience sampling, daily diaries, think-aloud protocols, observations, and structured interviews (Eccles & Wigfield, 2020 ). These alternatives have their strengths, but some limitations remain, such as the subjective nature of these measures and a possible high demand on research participants’ cognitive resources when a large number of measures are administered during a session. In addition, some demand frequent small breaks during a task to report internal states, which may interfere with the flow of the task.

Several alternative methods are available to observe and measure motivation or engagement “online” during learning, for example by using frequent choices of learners or video observations (Järvenoja et al., 2018 ). With the development of new technologies, it is now also possible to collect such data longitudinally on a large scale. For example, MathGarden, an online math learning tool, provides access to math learning data of thousands of students. Motivation is indexed by the frequency and length of voluntary, self-initiated practice, and can be linked to learning and performance (Hofman et al., 2018 ). Other promising process-oriented measures are eye-tracking and facial emotional expressions (D’Mello et al., 2008 ; Grafsgaard et al., 2014 , 2011 ; Nye et al., 2018 ; van Amelsvoort & Krahmer, 2009 ).

Another process-oriented approach uses physiology for high-frequency and non-interfering measures of motivational states. We will briefly discuss the use of autonomic nervous system (ANS) and central nervous system (CNS) measures. ANS techniques can be used to measure arousal , which is defined as higher activation of the sympathetic relative to the parasympathetic system. Motivated and effortful behavior is accompanied by increased arousal, and thus ANS techniques can provide an index of motivation. Popular techniques are electrodermal activity (EDA), electrocardiograms (ECG), and impedance cardiography (ICG). Sympathetic arousal measured with EDA has been associated with emotion, cognition, and attention (Critchley, 2002 ). Sympathetic arousal can also be measured with pre-ejection period (Tavakolian, 2016 )—which is the time in between “the electrical depolarization of the left ventricle and the beginning of the ventricular ejection” (Lanfranchi et al., 2017 , p. 145). One shared challenge with EDA and ECG is that arousal is a “fuzzy” construct, meaning many things, yet nothing specific (Mendes, 2016 ). A common factor that elicits EDA is subjective salience or motivational importance . Pre-ejection period is often used as an index for effort mobilization in studies investigating motivational intensity theory (Brehm & Self, 1989 ). Suppression of parasympathetic activity, which can be measured as reduction in high frequency heart rate variability, has been associated with effortful control (Spangler & Friedman, 2015 ) and emotion regulation (Beauchaine, 2015 ), but a recent meta-analysis supports a more general role in top-down self-regulation (Holzman & Bridgett, 2017 ).

A CNS measure of motivational states can be provided by electroencephalography (EEG). Higher mental effort/workload has been associated with attenuated parietal alpha activity (Brouwer et al., 2012 , 2014 ; Fink et al., 2005 ), higher frontal theta activity (Cavanagh & Frank, 2014 ; Klimesch, 2012 ), and a higher theta/alpha ratio. Another useful EEG index of motivation is asymmetrical frontal activity, which has been proposed to index motivational direction . Approach and avoidance motivation are respectively related to greater left and right frontal activity (Kelley et al., 2017 ).

It should be noted that none of these process-oriented measures has currently been established as reliable enough to replace verbal reports. A standard conclusion is that “more research is needed” (Holzman & Bridgett, 2017 ). A constructive way forward, which Fulmer and Frijters ( 2009 ) and Scheiter et al. ( 2020 ) strongly advocate, is to triangulate multiple methods, including self-reported and process-oriented measures. Given that physiological measures are relatively new, triangulation can help establish their reliability and validity. For example, EEG could be measured along with behavioral process-oriented task measures of effort. This allows testing whether fluctuations in theta and alpha activities are due to subjective effort mobilization and not due to other processes such as emotional arousal. Such triangulation studies can point the way to reliable online measures of motivation that do not rely exclusively on self-reports.

Measuring Achievement

While achievement is a less-fraught construct than motivation, it still presents its own challenges. First, achievement is nearly always bound to a specific domain, for example mathematics (Arens et al., 2017 ) or reading skill (Ehm et al., 2019 ; Sewasew & Koester, 2019 ). It is unclear whether findings generalize from one domain to others. It is possible that there are quantitative or even qualitative differences between domains in how motivation and achievement interact, for example as a function of the feeling of flow that is or is not associated with performance within the domain.

A second aspect of achievement that may affect results is the type of measurement used. Achievement can be measured using standardized tests and grades in schools (Arens et al., 2017 ; Marsh et al., 2016 ), but for example also through teacher or self-assessment (Chamorro-Premuzic et al., 2010 ). These tend to vary substantially in reliability and validity and yield different results (e.g., stronger reciprocity for school grades than for test scores; Marsh et al., 2016 ). Moreover, in longitudinal studies, it is often difficult to assess whether performance at different moments in time truly reflects the same skill. For example, studies of reading skill may assess basic letter decoding skills in a first wave, and complex reading comprehension in the last (Sewasew & Schroeders, 2019 ). Such changes in tested skills are likely to lead to a lower stability of scores, and skew estimates of change over time. This consideration would speak for designs (discussed above) with shorter periods between measurement waves, where the same measures can be used in different waves.

A third aspect of achievement which may be important is that achievement can be construed as mastery of skills, which usually grows over time, or as performance relative to peers, which by definition cannot grow for all students. Studies typically use raw test scores as a dependent measure to assess this (Huang, 2011 ; Scharmer, 2020 ), which reflect mastery of skills. What is communicated to students, on the other hand, tends to be performance relative to peers (e.g., rankings or grades, which tend to be age-normed either explicitly or implicitly). This implies that perceived performance (see Fig. 1 ) will be based on relative performance, and not on the absolute achievement that researchers tend to study.

Scope of the Theories and Generalizability of Findings

Studies investigating motivation-achievement interactions have often studied the development of these processes separately during childhood, adolescence, and early adulthood. It is therefore unclear whether results can be generalized across developmental stages. Furthermore, as in many subfields of psychology, the majority of research in this area has been conducted in Western, educated, industrialized, rich, and democratic (WEIRD) societies (Henrich et al., 2010 ), where, for example, rates of schooling are much higher than other places (e.g. the Global South). Here, we outline considerations of generalizability across developmental stages and ethnic and sociocultural settings.

Generalization Across Developmental Stages

Childhood and adolescent development is characterized by rather different trajectories for academic achievement (with a general pattern of improvement with age) than for academic motivation (with a general pattern of decrease during adolescence, as well as diversification in sources of motivation) (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). As a result, we can speculate that the reciprocal relationships between motivation and achievement will change with age. Below, we first highlight findings on changes in motivation across development, and next describe the consequences of developmental differences on reciprocal relations between motivation and achievement, as a function of age, developmental, and academic stages (such as puberty or school grade).

The way in which value guides goal pursuit transforms profoundly from childhood to adolescence to adulthood (Davidow et al., 2018 ), and is reflected in changes in reward sensitivity and cognitive control. At the individual level, motivational beliefs related to competence, control and agency, intrinsic and extrinsic motivation, and subjective task value undergo significant changes throughout the lifespan (Wigfield et al., 1998 , 2019 ). Social cognitive accounts often postulate that the development of more sophisticated cognitive capacities with age allows adolescents to improve performance but also to be more aware of their own abilities and those of their peers (Dweck, 2000 , Scherrer and Preckel, 2019 ). As children go through school, previously held optimistic beliefs on competency become more realistic or even pessimistic (Fredricks & Eccles, 2002 ; Jacobs et al., 2002 ; Scherrer & Preckel, 2019 ; Watt, 2004 ). A meta-analysis by Scherrer and Preckel ( 2019 ) found a small but significant overall decrease in several motivation constructs including academic self-concept, intrinsic motivation, mastery, and performance-approach achievement goals over the course of elementary and secondary school. However, for several other constructs, including self-esteem, academic self-efficacy, and performance-avoidance achievement goals, there was no consistent developmental trend across empirical studies. Overall, this heterogeneity in developmental patterns of various motivation constructs suggests that the reciprocal interactions with achievement may also follow different trajectories across development and still need to be investigated.

Beyond the individual level, social influences on learning and motivation within the family, peer, and school contexts (see Fig. 1 ) also play a significant role in the changes in motivation and achievement (Nolen & Ward, 2008 ; Wigfield et al., 1998 ). Sensitivity to social context continues to develop through childhood and adolescence, transforming through the different school stages (Ladd et al., 2009 ). Broadly speaking, motivation for academic activities decreases between childhood and adolescence, and motivation reorients toward social and novel situations (Crone & Dahl, 2012 ). According to the stage-environment fit account, the decline in academic motivation in adolescents is driven by a mismatch between their newly developed needs and their social settings (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). Specifically, the transition to middle and high schools is usually accompanied by changes in peer relationships, friendship, and teacher-student relationships, an increase in normative and performance-focused evaluation and a decrease in perceived autonomy. Adolescence is especially characterized by heightened social influences on motivation (Casey, 2015 ): social interactions become increasingly important and peer affiliation motivation peaks (Brown & Larson, 2009 ).

Indeed, peer relationships show a stronger influence on academic self-concept for seventh graders, compared to fifth graders (Molloy et al., 2011 ). As children transition into middle school, there is increased competition for grades and typically a larger pool of peers that serve as a reference group (Molloy et al., 2011 ). During adolescence, same-aged peers in school can motivate academic achievement to a larger extent, and a stronger focus on performance rather than mastery goals is sometimes empirically observed (Maehr & Zusho, 2009 , but see Scherrer et al., 2020 ; Scherrer & Preckel, 2019 where meta-analytic findings point to declines in both mastery and performance goals).

In sum, individual developmental changes in self-concept, self-regulation, social influence, and the values attributed to certain academic goals suggest that reciprocal motivation-achievement relations from one age group cannot be readily generalized to other ages (Marsh & Martin, 2011 ). Qualitative and quantitative differences in the reciprocal relationship between motivation and achievement thus seem plausible, but the lack of developmentally appropriate measures complicates comparisons across different stages (Fulmer & Frijters, 2009 ). Populations of different ages have distinct motivation factor structures (Rao & Sachs, 1999 ) and young children do not yet have the cognitive and memory capacity to process some motivation constructs and contextual references (Fulmer & Frijters, 2009 ).

Taken together, it is critical to understand how changes in motivation interact with changes in abilities, and affect behavior across different age groups and school career. The literature would greatly benefit from an integration of research across a broader age range, and identifying continuities and discontinuities in the reciprocal relationship between motivation and performance across development. One way to do this is to leverage accelerated longitudinal designs, with multiple measurements of cohorts with different starting ages and differentiation between multiple motivation constructs (Guay et al., 2003 ; Marsh & Martin, 2011 ; Scherrer & Preckel, 2019 ).

Generalization Across Sociocultural Settings

The reciprocal relationship between motivation and achievement may also take different shapes across contexts, as students belong to different ethnic, gender, socioeconomic (SES), and cultural groups. However, the majority of current research on the reciprocal relations between motivation and academic achievement has suffered from what can be considered a sampling bias problem (Pollet & Saxton, 2019 ), i.e., conducted using homogenous samples in terms of ethnicity (Marsh & Martin, 2011 ) and cultural background (Henrich et al., 2010 ). In the meta-analysis by Valentine et al. ( 2004 ), which showed that samples from non-Western countries tended to have larger effect sizes than those from Western countries, there were only four non-Western samples out of a total of 60 samples. In her meta-analysis of Scharmer ( 2020 ), 90% of samples were collected in WEIRD countries (Australia, USA, and Western Europe, with fully half using German samples). This is problematic, given that even within WEIRD samples, motivation of students from different groups (e.g., African Americans vs. European Americans) is influenced by different factors, and may contribute differently to their academic achievement (Cohen et al., 2009 ). Ten years later, the remark of Marsh and Martin ( 2011 ) thus still stands that it is premature to conclude that the reciprocal relationship between motivation and achievement is universal.

Demonstrating this across diverse populations is important for three reasons. Firstly, even the same motivation construct might contribute differently to achievement across groups. For example, Chiu and Klassen ( 2010 ), using PISA data and a very large and diverse sample ( N participant = 88,590, N country = 34), found a positive link between mathematics self-concept and mathematics achievement, but this relationship was moderated by cross-country differences in cultural orientations (specifically, degree of egalitarianism, rigidity in gender roles, aversion to uncertainty). As mentioned above, Sachisthal et al. ( 2019 ) also showed that across populations different motivation constructs are central in the network of constructs.

Second, it is not unlikely that different groups have diverging motivation constructs. For instance, general self-concept is conceptualized differently across cultures (Becker et al., 2012 ; Taras et al., 2010 ; Vignoles et al., 2016 ). Thus, the extent to which academic self-concept contributes to a general sense of self likely differs across groups (Hansford & Hattie, 1982 ). Chen and Wong ( 2015 ) also found that Chinese students assigned different meanings to performance-approach and performance-avoidance goals than what is usually found in Western populations. As a result, interventions may need to target different factors in different sociocultural settings.

Finally, there might be culture-dependent or population-specific pathways connecting the relationship between motivation and achievement. For example, culture is likely to have a strong influence on attributional processes (see extensive theoretical discussion in Graham, 2020 ; empirical data in Chiu & Klassen, 2010 ) and implicit theory of intelligence (W. W. Chen & Wong, 2015 ). Chiu and Klassen ( 2010 ) found that calibration of mathematics self-concept (i.e., the degree to which judgments of one’s competence in a domain accurately reflect actual performance) was positively related to mathematics achievement. However, this link was significantly stronger in places where the prevailing culture was more egalitarian or more tolerant of uncertainty.

Such findings suggest differences between sociocultural contexts are not just gradual but also likely to be qualitative. This would threaten the generalizability of findings (Henrich et al., 2010 ). Note that many of the empirical studies cited in this section are non-longitudinal. Reciprocal relationships between motivation and achievement may look different from what we currently know when representative samples are included. It is thus highly relevant for future motivation research to increase ethnic, and other group diversity in their studies. This can be done by better sampling within geographical boundaries (Pollet & Saxton, 2019 ) and by reaching out to under-researched territories such as in Africa, Middle East, Southeast Asia, Central Asia, and South America.

Diversifying study populations can be tough, but is essential for new understanding of human universals and specifics in motivation. For example, collecting experimental data across countries offers alternative perspectives to experimental set-ups and findings, which subsequently prompt researchers to rethink the constructs of interest and their operationalizations (Vu et al., 2017 ). Nevertheless, there are innovative solutions to overcome practical difficulties, including collaborating with researchers who reside in places where certain specificity and universality in motivation constructs can be expected (as outlined in some of the examples above) and making use of networks of researchers such as Psychological Science Accelerator to get access to multiple laboratories and populations across the world ( https://psysciacc.wordpress.com/ ).

Discussion and Conclusions

We have summarized theories of motivation and analyzed these specifically with regards to how they conceptualize reciprocal interactions between motivation and achievement. This led to a summary of pathways between motivation and achievement, depicted in Fig. 1 . The common denominator between theories suggested reciprocal positive influences of motivation on achievement and vice versa, which has been supported by much previous research. We reviewed the strengths of the underlying data, but also limitations and gaps in the evidence. This led to a research agenda consisting of the following recommendations for future studies on the relationship between motivation and performance: (1) include multiple motivation constructs (on top of ASC), (2) investigate behavioral mediators, (3) consider a network approach, (4) align frequency of measurement to expected change rate in intended constructs and include multiple time scales to better understand influences across time-scales, (5) check whether designs meet the criteria for measuring causal, reciprocal inferences, (6) choose an appropriate statistical model, (7) apply alternatives to self-reports, (8) consider various ways of measuring achievement, and (9) strive for generalization of the findings to various age, ethnic, and sociocultural groups.

One of the hardest problems to solve is the lack of studies that allow for firm causal inferences. Most studies contain sophisticated statistical analyses of longitudinal data. While impressive, the underlying data remains correlational in nature and susceptible to explanations in terms of the presence of a (time-varying or time-invariant) third variable (or variables) that is not accounted for in a model, yet does have a causal effect on the outcomes. Usami et al. ( 2019 ) outline three assumptions that need to be checked when making causality inferences in the context of longitudinal designs. These are the assumptions of consistency, of positivity after controlling for confounders, and of no unobserved confounders (see full the discussion in Usami et al., 2019 ). In our view, the trickiest is the third assumption: “the relation between x and y must not be explained by other causes”(Antonakis et al., 2010 , p. 1087; Usami et al., 2019 ). There seems to be no way to conclusively rule out the presence of such confounders. Substantial knowledge about potential confounders and their characteristics, and using that knowledge to select the most appropriate cross-lagged model, is necessary.

We argued that the strongest support for causal claims on motivation-achievement relations would be studies manipulating either motivation or achievement at one time point and studying the effects on motivation-achievement interactions across subsequent time points. Such studies do not yet exist to our knowledge. Many studies do show effects of manipulations affecting motivation thereby having an effect on achievement, but these studies have not looked at longitudinal interactions. The other pathway (i.e., achievement → motivation) has not been studied extensively, because of difficulties identifying manipulations that would directly affect achievement but not motivation.

A way to work around this problem is to manipulate perceived achievement, instead of true achievement (our lab study, manuscript in preparation). In this experiment, participants perform a learning task that lasts an hour. Their motivation and achievement are measured at multiple consecutive time points. Halfway through the experiment, a manipulation of perceived feedback is introduced: participants receive rigged feedback that their achievement has dropped to below peer average. The causal relations between motivation and achievement can be examined because manipulated perceived achievement leads to corresponding changes in motivational beliefs, to changes in motivational behaviors and eventually, to changes in actual achievement across multiple consecutive time points. Another example of manipulation of achievement can be found in Bejjani et al. ( 2019 ) where they used a feedback manipulation (a competence-threatening IQ score) to study its effect on subsequent motivation and learning.

Furthermore, we have argued that motivation can best be seen as a constellation of highly related, multidimensional constructs, and manipulations of motivation may directly or indirectly influence achievement and vice versa. An innovative method to study the motivation-achievement relationship can be a network approach, where observational and interventional data are used to estimate a causal graph. The idea is that to estimate causal relations, one variable can be manipulated at a time, and its effects on other variables can be observed. The network approach is also beneficial in the classroom context where there are many variables to take into account which cannot be independently manipulated (Yeager & Walton, 2011 ).

Our discussion of various theories of motivation in education showed how densely motivation and performance are interlinked. They can best be seen as a cycle of mutually reinforcing relations. While a cycle suggests a closed loop, we list several options for outside intervention, which are represented by the gray arrows in Fig. 1 . Some of these are well-researched practical interventions, such as autonomy support and training in helpful attributions (Hulleman et al., 2010 ). Others are excellent avenues for future research. For example, designing how feedback reaches the learner offers opportunities for motivation support. Research has shown how to provide negative feedback in a way that does not lower a learner’s motivation (Fong et al., 2019 ), how peer comparison can be harnessed for motivation (Mumm & Mutlu, 2011 ), or how feedback can be given without giving away that errors have been made (Narciss & Huth, 2006 ). It is our impression that this research has so far not reached all classrooms.

In conclusion, this view of a cycle between motivation and achievement, as shown in Fig. 1 , has intuitive appeal and fits well with theories of academic motivation. However, empirical evidence for a cycle is far from complete. The research agenda we have presented contains important challenges for future research aimed at elucidating how motivation and achievement exactly interact, and whether a cycle and a network of constructs are good ways of conceptualizing these interactions. As academic motivation typically drops considerably in adolescence and may be lower for some groups (e.g., through the effects of SES, stereotype threat, and the likes), such evidence is necessary for gaining knowledge on how to best intervene in the cycle, and bring out the best in each student.

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Acknowledgements

We would like to thank Sibel Altikulaç, Abe Hofman, and Simone Plak who participated in our Expert Workshop in Motivation-Performance Cycle in Math Learning in Amsterdam in June 2019 where we discussed ideas for this paper. We would like to thank Milene Bonte, Wouter van den Bos, Camila Bosano, and Bruce McCandliss who are members of the advisory board for the Jacobs Foundation project of which the subproject to write this manuscript is a part. We also want to thank Asmar Isilak who helped with the first database search for empirical studies on the motivation-achievement cycles in learning .

This work was supported by the Jacobs Foundation Science of Learning pilot grant to Nienke van Atteveldt and Brenda R. J. Jansen [project number 2019 1329 00]. Nienke van Atteveldt was also supported by a Starting Grant from the European Research Council (ERC, grant #716736). The funders had no role in study design, decision to publish, or preparation of the manuscript.

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Vu, T., Magis-Weinberg, L., Jansen, B.R.J. et al. Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda. Educ Psychol Rev 34 , 39–71 (2022). https://doi.org/10.1007/s10648-021-09616-7

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ORIGINAL RESEARCH article

How does work motivation impact employees’ investment at work and their job engagement a moderated-moderation perspective through an international lens.

• 1 Independent Researcher, Netanya, Israel
• 2 Graduate School of Career Studies, Hosei University, Tokyo, Japan

This paper aims at shedding light on the effects that intrinsic and extrinsic motivation, as predictors, have on heavy work investment of time and effort and on job engagement. Using a questionnaire survey, this study conducted a moderated-moderation analysis, considering two conditional effects—worker’s status (working students vs. non-student employees) and country (Israel vs. Japan)—as potential moderators, since there are clear cultural differences between these countries. Data were gathered from 242 Israeli and 171 Japanese participants. The analyses revealed that worker’s status moderates the effects of intrinsic and extrinsic motivation on heavy work investment of time and effort and on job engagement and that the moderating effects were conditioned by country differences. Theoretical and practical implications and future research suggestions are discussed.

Introduction

Our world today has been described by the acronym VUCA (volatile, uncertain, complex, and ambiguous). In this rapidly changing world, organizations and individuals need to engage in continuous learning. To achieve a competitive advantage, organizations need to develop organizational learning, which can be achieved by acquiring learning individuals. From the latter’s viewpoint, it is getting more necessary for workers to learn continuously to enhance and maintain their employability. As shown in previous research, the number of people engaging in lifelong learning has significantly increased ( Corrales-Herrero and Rodríguez-Prado, 2018 ).

In such an era, an organization needs to acquire and retain learning individuals. However, it is not an easy task because they might have turnover intentions, even when they are motivated to work. Since learning individuals enhance their skills continuously and have a “third place” for new encounters (e.g., school), they are likely to find other attractive job opportunities. Therefore, it is valuable for us to explore how motivation affects learning individuals’ attitudes and behavior. However, to the best of our knowledge, researchers have not addressed this issue.

Recently, researchers and practitioners have paid increasing attention to employees’ job engagement (JE) ( Bailey et al., 2017 ). Previous studies suggested that engaged workers are likely to achieve high performance and have low intention to leave ( Rich et al., 2010 ; Alarcon and Edwards, 2011 ). However, JE does not necessarily represent workers’ favorable attitude ( van Beek et al., 2011 ). In the case of working individuals, their appearance of being “highly engaged” can be caused by time constraints or impression management motive.

Recognizing the ambiguous nature of “engaged workers,” this study also focuses on a relatively new construct called heavy work investment (HWI). People high in HWI are apparently similar to those high in JE. However, as will be discussed later, these two constructs are distinct. By focusing on both engagement and HWI, we can reveal the underlying mechanism of how motivation affects the learning individuals’ engagement.

To address these issues, we analyzed quantitative data which include both learning individuals (hereafter called “working students”) and non-student workers. The choice of employees who are students as opposed to “regular” employees was based on arguments presented in the conservation of resources (COR) theory ( Hobfoll, 1989 , 2011 ). It will be elaborated further in this paper.

Besides, since the contexts of lifelong learning and work in an organization can affect the focal mechanism, we collected data from two countries—Israel and Japan—and conducted a between-country comparative analysis. As we will discuss below, these two countries widely differ in cultural dimensions, as suggested by Hofstede (1980 , 2018) . We limit the scope of the research to Israel and Japan to concentrate on a specific issue which was not investigated in previous studies, especially in a comparison between these two countries (to the best of our knowledge). The sample and analysis of this study can provide insightful implications because these two countries are widely different in their national cultural contexts.

Work Motivation

A general definition of motivation is the psychological force that generates complex processes of goal-directed thoughts and behaviors. These processes revolve around an individual’s internal psychological forces alongside external environmental/contextual forces and determine the direction, intensity, and persistence of personal behavior aimed at a specific goal(s) ( Kanfer, 2009 ; Kanfer et al., 2017 ). In the work domain, work motivation is “a set of energetic forces that originate within individuals, as well as in their environment, to initiate work-related behaviors and to determine their form, direction, intensity and duration” (after Pinder, 2008 , p. 11). As mentioned, work motivation is derived from an interaction between individual differences and their environment (e.g., cultural, societal, and work organizational) ( Latham and Pinder, 2005 ). In addition, motivation is affected by personality traits, needs, and even work fit, while generating various outcomes and attitudes, such as satisfaction, organizational citizenship behaviors (OCBs), engagement, and more (for further reading, see Tziner et al., 2012 ).

Moreover, work motivation, as an umbrella term under the self-determination theory (SDT), is usually broken down into two main constructs—intrinsic versus extrinsic motivation ( Ryan and Deci, 2000b ). On the one hand, intrinsic motivation is an internal driver. Employees work out of the excitement, feeling of accomplishment, joy, and personal satisfaction they derive both from the processes of work-related activities and from their results ( Deci and Ryan, 1985 ; Bauer et al., 2016 ; Legault, 2016 ). On the other hand, extrinsic motivation maintains that the individual’s drive to work is influenced by the organization, the work itself, and the employee’s environment. These can range from social norms, peer influence, financial needs, promises of reward, and more. As such, being extrinsically motivated is being focused on the utility of the activity rather than the activity itself (see Deci and Ryan, 1985 ; Legault, 2016 ). However, this does not, by any means, point that extrinsic motivation is less effective than intrinsic motivation ( Deci et al., 1999 ).

Furthermore, the SDT ( Ryan and Deci, 2000b ) argues that each type of motivation is on opposite poles of a single continuum. However, we agree with the notion that they are mutually independent, as Rockmann and Ballinger (2017) wrote:

“…there is increasing evidence that intrinsic and extrinsic motivations are independent, each with unique antecedents and outcomes … in organizations, because financial incentives exist alongside interesting tasks, individuals can simultaneously experience extrinsic and intrinsic motivation for doing their work.” (p. 11)

Literature-wise, the intrinsic–extrinsic outlook of motivation lacks coherent research, and to the best of our knowledge, most of the past research addressed the intrinsic part (e.g., Rich et al., 2010 ; Bauer et al., 2016 ). As such, we would align with the approach to distinguish the two work motivations as was reviewed in this section and consequently treat it as a predictor in our research.

Job Engagement

Work engagement is typically defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” ( Schaufeli et al., 2002 , p. 74). As such, engaged employees appear to be hardworking ( vigor ), are more involved in their work ( dedication ), and are more immersed in their work ( absorption ) (see also Bakker et al., 2008 ; Chughtai and Buckley, 2011 ; Taris et al., 2015 ). JE was initially proposed as a positive construct ( Kahn, 1990 ), and empirical studies revealed that a high level of JE leads to positive work outcomes. For example, recent studies exhibited its positive effect on individual job performance and adverse effect on turnover intention ( Breevaart et al., 2016 ; Owens et al., 2016 ; Shahpouri et al., 2016 ; Kumar et al., 2018 ). Therefore, employees’ JE has been regarded as one of the performance indicators of human resource management.

In terms of antecedents and predictors, it is broadly accepted that JE may be affected by both individual differences (e.g., Sharoni et al., 2015 ; Latta and Fait, 2016 ; Basit, 2017 ) and environmental/contextual elements (e.g., Sharoni et al., 2015 ; Basit, 2017 ; Gyu Park et al., 2017 ; Lebron et al., 2018 ) (see also Macey and Schneider, 2008 ) or even an interaction between these two factors (e.g., Sharoni et al., 2015 ; Hernandez and Guarana, 2018 ).

Intrinsic/Extrinsic Motivation and JE

To the best of our knowledge, only a few papers examined the association between work motivation and JE. For instance, Rich et al. (2010) tested a model in which both intrinsic motivation and JE were tested “vertically,” meaning they were both mediators (in the model) rather than two factors in a predictor–outcome relationship. This offers a further incentive to examine the association between (intrinsic/extrinsic) work motivation and JE.

Because JE is “…driven by perceptions of psychological meaningfulness, safety, and availability at work” ( Hernandez and Guarana, 2018 , p. 1), a vital notion behind work motivation is the perception of the job as a place for fulfilling different needs: extrinsic needs, such as income and status, and intrinsic needs, such as enjoyment, and personal challenge. This perception, very likely, bolsters the association between the employees’ drive to work and the workplace or the work themselves, increasing the involvement and the amount of work they put into their work (i.e., JE). These assumptions lead us to hypothesize the following:

H1: Intrinsic motivation positively associates with JE.

H2: Extrinsic motivation positively associates with JE.

Heavy Work Investment

Fundamentally different from being immersed or involved at work (e.g., JE), employees usually invest time and energy at their workplace with various manifestations, which ultimately barrel down to the concept of HWI. This umbrella term encompasses two major core aspects: (1) investment of time (i.e., working long hours) and (2) investment of effort and energy (i.e., devoting substantial efforts, both physical and mental, at work) ( Snir and Harpaz, 2012 , Snir and Harpaz, 2015 ). These dimensions are, respectively, called (a) time commitment (HWI-TC) and work intensity (HWI-WI). Notably, many studies deal with the implications of working overtime (e.g., Stimpfel et al., 2012 ; Caruso, 2014 ). However, to the best of our knowledge, empirical studies regarding the investment of efforts at work as an indicator of HWI (e.g., Tziner et al., 2019 ) are scarce. Therefore, the current research addresses both of the core dimensions of HWI (i.e., time [HWI-TC] and effort [HWI-WI]).

In reality, HWI consists of many different constructs (e.g., workaholism and work addiction or passion to work) but conclusively revolves around the devotion of time and effort at work (see Snir and Harpaz, 2015 , p. 6). HWI is apparently similar to JE, but these two constructs are distinct. As shown in previous studies, the correlation between workaholism—one component of HWI—and JE is generally weak, and engaged individuals can be not only high in HWI but also low in HWI ( van Beek et al., 2011 ).

For HWI’s possible predictors, Snir and Harpaz (2012 , 2015) have differentiated between situational and dispositional types of HWI (based on Weiner’s, 1985 , attributional framework). Examples of situational types are financial needs or employer-directed contingencies (external factors), while dispositional types are characterized by individual differences (internal factors), such as work motivation.

Intrinsic/Extrinsic Motivation and HWI

As previously mentioned, employees may be driven to work by both intrinsic and extrinsic forces, motivating them to engage in work activities to fulfill different needs (e.g., salary, enjoyment, challenge, and promotion). Ultimately, these two mutually exclusive elements would translate into the same outcome—increased investment at work. At this juncture, however, we cannot say what type of work motivation (intrinsic/extrinsic) would more tightly link to either (1) the heavier devotion of time (HWI-TC) or (2) the heavier investment of effort (HWI-WI) at work. Consequently, we hypothesize further the following:

H3: Intrinsic motivation positively associates with both HWI-TC and HWI-WI.

H4: Extrinsic motivation positively associates with both HWI-TC and HWI-WI.

It is important to emphasize that, again, HWI and JE are mutually independent constructs. Nevertheless, HWI points at two different investment “types”—in time and effort. Theoretically, we see that although both aspects of investment are, probably, linked to JE, we may also conclude that these associations would differ based on the type of investment. For example, while workers may allegedly spend a great deal of time on the job, in actuality, they may not be working (studiously) on their given tasks at all, a situation labeled as “presenteeism” (see Rabenu and Aharoni-Goldenberg, 2017 ). However, exerting more effort at work, by definition, means that one is more engaged, to whatever extent, in work (e.g., investing more effort, basically, means investing time as well, but not vice versa). In other words, while we expect that JE will be positively related to dimensions of HWI (one must devote time and invest more effort to be engaged at work), we also assume that JE will be more strongly correlated with the effort dimension, rather than time . As such, we hypothesize the following:

H5a: JE positively associates with HWI-TC.

H5b: JE positively associates with HWI-WI.

H5c: JE has a stronger association with HWI-WI than with HWI-TC.

The purpose of H5a–H5c is to differentiate JE from HWI-WI and HWI-TC, as they may have some overlaps. However, they are still stand-alone constructs, which is the reason the current research gauge them both and correlate them, though they are both outcome variables (an issue of convergent and discriminant validity).

Worker’s Status—Buffering Effect

An organization or a workplace is usually composed of several types of employees, albeit not all of them exhibit the same attitudes and behaviors at work. For example, temporary workers report greater job insecurity and lower well-being than permanent employees ( Dawson et al., 2017 ). Another example is of students (i.e., working students vs. non-student employees). The motivators and incentives needed to drive corporate/working students differ from others. They are, for instance, more interested in salary, promotion, tangible rewards in their job, and other such benefits ( Palloff and Pratt, 2003 ).

Furthermore, capitalizing upon the COR theory ( Hobfoll, 1989 , 2011 ), the main argument is that employees invest various resources (e.g., time, energy, money, effort, and social credibility) at work. The more resources devoted, the less will remain at the individual’s disposal, and prolonged state of depleted resources without gaining others may result in stress and, ultimately, burnout. As such, a worker who is also a student will, by definition, have fewer resources at either domain (work, social life, or family), as opposed to a worker who does not engage in any form of higher education at all. Working students are under severer time constraints than non-student employees because they face “work–study conflict.” Therefore, compared to non-student workers, working students have difficulty in devoting so much time and physical as well as psychological effort to work. Specifically, working students with a low level of motivation may take an interest in studies and thus not be likely to devote much effort to work. However, motivated working students will maintain their effort through effective time management because they highly value their current work. Thus, JE and HWI of working students will depend on their motivation to a greater degree than non-student workers. Ergo, we posit that the associations between intrinsic/extrinsic motivation and HWI and JE are conditioned by the type of worker under investigation.

For the current study, the notion of working students versus non-student employees would be gauged, as not much attention was given to distinguishing both groups in research. Usually, samples were composed of either group distinctively, not in tandem with one another. Hence, we hypothesize the following, based on our previous hypotheses:

H6: Worker’s status moderates the relationship between intrinsic motivation and HWI-TC, HWI-WI, and JE, such that the relationship will be weaker for working students than for non-student employees.

H7: Worker’s status moderates the relationship between extrinsic motivation and HWI-TC, HWI-WI, and JE, such that the relationship will be weaker for working students than for non-student employees.

Country Difference—Buffering Effect

Worker’s status’ moderation of the links between intrinsic/extrinsic motivation to HWI and JE, as mentioned above, does not appear in a vacuum. This conditioning may also be dependent on international cultural differences. That is to say, we assume that we would receive different results based on the country under investigation because the social, work, cultural, and national values differ from one country to another. Firstly, culture, in this sense, may be defined as “common patterns of beliefs, assumptions, values, and norms of behavior of human groups (represented by societies, institutions, and organizations)” ( Aycan et al., 2000 , p. 194). As mentioned, countries differ from one another in many aspects. The most prominent example is the cultural/national dimensions devised by Hofstede (1980 , 1991) . Different countries display different cultural codes, norms, and behaviors, which may affect their market and work values and behaviors. As such, it is safe to assume that work-related norms and codes differ from one country to another to the extent that working students may exhibit or express certain attitudes and behaviors in country X, but different ones in country Y. The same goes for non-student (or “regular”) workers, as well.

In this study, we examine the case of Israel’s versus Japan’s different situation and cultural perspectives in the work sense. Japan’s culture is more hierarchical and formal than the Israeli counterpart. Japanese believe efforts and hard work may bring “anything” (e.g., prosperity, health, and happiness), while in Israel, there is much informal communication, and “respect” is earned by (hands-on) experience, not necessarily by a top-down hierarchy. Japanese emphasize loyalty, cohesion, and teamwork ( Deshpandé et al., 1993 ; Deshpandé and Farley, 1999 ). Compared to Israeli, Japanese employees are more strongly required to conform to the organization’s norm and dedicate themselves to the organization’s future. Such cultural characteristics may affect the working attitudes and behavior of working students. Specifically, in Japan, working students try to devote as much time as possible even if they are under severe time constraints caused by the study burden. Moreover, sometimes, they experience guilt because they use their time for themselves (i.e., study) rather than for the firm (e.g., socializing with colleagues). Thus, they engage in much overtime work as a tactic of impression management ( Leary and Kowalski, 1990 ) to make themselves look loyal and hard working.

In addition, in Israel, there is high value to performance, while in Japan, competition (between groups, usually) is rooted in society and drives for excellence and perfection. Also, Israelis respect tradition and normative cognition. They tend to “live the present,” rather than save for the future, while Japanese people tend to invest more (e.g., R&D) for the future. Even in economically difficult periods, Japanese people prioritize steady growth and own capitals rather than short-term revenues such that “companies are not here to make money every quarter for the shareholders, but to serve the stakeholders and society at large for many generations to come” (for further reading, see Hofstede, 2018 ).

In Hofstede’s use of the term, some aspects of these cultural differences can be summarized as Japan being higher in power distance, masculinity, and long-term orientation than Israel ( Hofstede, 2018 ). These cultural differences led us to formulate the following hypotheses:

H8: Country differences condition the moderation of worker’s status on the relationship between intrinsic motivation and HWI-TC, HWI-WI, and JE, such that the effect of worker’s status suggested in H6 will be weaker for Japanese than for Israelis.

H9: Country differences condition the moderation of worker’s status on the relationship between extrinsic motivation and HWI-TC, HWI-WI, and JE, such that the effect of worker’s status suggested in H7 will be weaker for Japanese than for Israelis.

It is important to note, however, that H8 and H9 are also developed to increase the external validity of the research and its generalizability beyond a single culture, as Barrett and Bass (1976) noted that “most research in industrial and organizational psychology is done within one cultural context. This context puts constraints upon both our theories and our practical solutions to the organizational problem” (p. 1675).

Figure 1 portrays the overall model.

Figure 1. Research model. Worker’s status: 1 = working students, 2 = non-student employees. Country: 1 = Israel, 2 = Japan. HWI-TC = time commitment dimension of heavy work investment. HWI-WI = work intensity dimension of heavy work investment.

Materials and Methods

For hypothesis testing, this study conducted questionnaire-based research using samples of company employees who also engage in a manner of higher education (i.e., working students) and those who do not (i.e., “regular” or non-student employees). Since working students in both countries do not concentrate in specific age groups, industries, or functional areas, participants were recruited from various fields. Moreover, to reduce the impact of organization-specific culture, we collected data from various companies rather than from a specific company, in both countries.

Participants

The research constitutes 242 Israeli (70.9% response rate) and 171 Japanese (56.6% response rate) participants, from various industries and organizations. The demographical and descriptive statistics for each sample are presented in Table 1 . The table also contains the result of group difference tests, pointing at some demographic differences between Israeli and Japanese samples. Therefore, the following analyses include these demographics as control variables to control their potential influence on the research model and reduce the problem that would arise from said differences between the two countries.

Table 1. Demographical and descriptive statistics for the Israeli ( N = 242) and the Japanese ( N = 171; in parenthesis) samples.

The items of the questionnaire were initially written in English and then translated into Hebrew and Japanese, utilizing the back-translation procedure ( Brislin, 1980 ).

Work motivation was gauged by the Work Extrinsic and Intrinsic Motivation Scale (WEIMS; Tremblay et al., 2009 ), consisting of 18 Likert-type items ranging from 1 (“Does not correspond at all”) to 6 (“Corresponds exactly”). Intrinsic motivation had a high reliability (α Israel = 0.92, α Japan = 0.86; e.g., “…Because I derive much pleasure from learning new things”) as did extrinsic motivation (α Israel = 0.73, α Japan = 0.75; e.g., “…For the income it provides me”).

HWI (see Snir and Harpaz, 2012 ) was tapped by 10 Likert-type items ranging from 1 (“Strongly disagree”) to 6 (“Strongly agree”), five items for each dimension, namely, time commitment (HWI-TC; e.g., “Few of my peers/colleagues put in more weekly hours to work than I do”) and work intensity (HWI-WI; e.g., “When I work, I really exert myself to the fullest”), respectively. HWI-TC had a high reliability (α Israel = 0.85, α Japan = 0.92) as did HWI-WI (α Israel = 0.95, α Japan = 0.91).

JE was gauged by the Utrecht Work Engagement Scale-9 (UWES-9; Schaufeli et al., 2006 ) consisting of nine Likert-type items ranging from 1 (“Strongly disagree”) to 6 (“Strongly agree”). The measure had a very high reliability (α Israel = 0.95, α Japan = 0.94; e.g., “I am immersed in my work”).

For the Israeli sample, a pencil-and-paper research survey was distributed to 341 total potential participants in two universities and one college. One of the authors provided the questionnaire in several courses (MBA and management, human resource management, psychology, and more), at the end of each class session. Those wishing to participate replied affirmatively and were included in the total sample. We assured the anonymity and discretion of the participants and the data derived from the research and included a conscious consent question at the beginning of the survey asking for their agreement to participate. No incentives were given whatsoever to the participants for their cooperation. A total of 341 surveys were distributed, yet only 242 came back filled, and all of them were valid to use as data in the research.

For the Japanese sample, the data were collected by using the online questionnaire system of Google spreadsheet. Invitation messages were sent to the potential respondents via email or SNS messenger with the link of the questionnaire. One of the authors contacted 189 full-time workers who participated in one or more of the following (1) strategic management and organization management classes of a Japanese private university, (2) human resource management course in an educational service company, or (3) one-off lectures conducted by the author. All of them were non-student workers, and ultimately, 97 of them answered the questionnaire in full (51.3% response rate). As for the working students, the same author reached out to three graduate schools through personal networks. Then, he asked the liaison of each school to list up working students and send them the questionnaire link by email or SNS messenger. In total, the link was sent to 113 working students (in said three universities), and 74 completed the questionnaire (65.5% response rate). Thus, the overall response rate was 56.6%.

Data Analyses

The data were analyzed utilizing the SPSS (v. 23) software package and PROCESS macro for SPSS (v. 3.3). PROCESS is an add-on macro for the SPSS and SAS software packages written by Andrew F. Hayes. It is a modeling tool based on ordinary least squares (OLS) and logistic regressions for basic and complex path analyses with strong algorithms and modular capabilities and can handle simultaneous moderation and mediations effects (including moderated-moderation effects).

The choice of PROCESS (over SEM) is based on methodological and mathematical reasons. To elaborate, holistic testing of the entire model (see Figure 1 ) via SEM will result in 15 different observed variables (including the interaction effects) and a two-group comparison, and abundant regression lines would result in a high number of degrees of freedom. It would also require a considerably higher sample size to meet the mathematical conditions for SEM. However, we should note that one of the limitations of PROCESS is the inability to test models with more than one dependent variable ( Y ) or more than one independent variable ( X ), and as such it is required to test the model (see Figure 1 ) separately—one for each predictor–criterion linkage.

Control Variables

As per Table 1 , we can see some differences between the two countries, and as such, we included them as covariates in the moderated-moderation analyses. In other words, in these analyses, we controlled for the effects of job position, age, number of children, tenure, and also gender and marital status. This is relevant for Tables 4 –6 . Evidently, the inclusion of control variables has increased the predictive capacity and goodness of our results. Gender is a dichotomous closed question with options of (1) male or (2) female. Age is an open question: “what is your age (in years)? ______.” Marital status is a closed question with options of (1) single, (2) married, (3) divorced, or (4) widowed. Number of children is an open question: “How many children do you have? ______.” Tenure is an open question: “what is your tenure at work (in years)? ______.” Job position is a dichotomous closed question with options of (1) non-managerial or (2) managerial.

Common Method Bias

Harman’s one-factor test ( Podsakoff et al., 2003 ) was used to assess the degree to which intercorrelations among the variables might be an artifact of common method variance (CMV). The first general factor that emerged from the analysis accounted only for 35.19% of the explained variance in the Israeli sample and 37.27% in the Japanese sample. While this result does not rule out completely the possibility of same-source bias (CMV), according to Podsakoff et al. (2003) , less than 50% of the explained variance accounted for by the first emerging factor indicates that CMV is an unlikely explanation of our investigation findings.

First, we explored descriptive statistics and associations between the variables. These results are displayed in Tables 2 , 3 , for each sample.

Table 2. Pearson correlation matrix for working students ( below the diagonal; n = 77) and non-student employees ( above the diagonal; n = 165), means and standard deviations in the Israeli sample ( N = 242).

Table 3. Pearson correlation matrix for working students ( below the diagonal; n = 97) and non-student employees ( above the diagonal; n = 74), means and standard deviations in the Japanese sample ( N = 171).

As shown in Table 2 , we found the following regarding the Israeli sample:

- JE positively correlates with HWI-TC for working students, r (77) = 0.55, p = 0.000, and for non-student employees r (165) = 0.30, p = 0.000 (supporting H5a, in Israel).

- JE positively correlates with HWI-WI for working students, r (77) = 0.76, p = 0.000, and for non-student employees r (165) = 0.77, p = 0.000 (supporting H5b, in Israel).

These differences in correlation coefficients are in line with our H5c, meaning JE has stronger links to HWI-WI as opposed to HWI-TC. Ergo, in order to gauge whether these differences are statistically significant, we used Fisher’s Z transformation and significance test. For working students, the difference is indeed significant ( Z = 2.31, p = 0.021) and is also for the non-student employees’ group ( Z = 6.41, p = 0.000). This supports H5c, in Israel.

Moreover, as shown in Table 3 , we found the following regarding the Japanese sample:

- JE positively correlates with HWI-TC only for non-student employees, r (74) = 0.30, p = 0.001, but is non-significant for working students, r (94) = 0.15, p = 0.146 (partially supporting H5a, in Japan).

- JE positively correlates with HWI-WI for working students, r (94) = 0.72, p = 0.000, and for non-student employees, r (74) = 0.62, p = 0.000 (supporting H5b, in Japan).

These differences in correlation coefficients are in line with our H5c, meaning JE has stronger links to HWI-WI as opposed to HWI-TC. Ergo, in order to gauge whether these differences are statistically significant, we used Fisher’s Z transformation and significance test. For working students, the difference is indeed significant ( Z = 5.12, p = 0.000) and is also significant for the non-student employees’ group ( Z = 2.48, p = 0.013). This supports H5c, in Japan.

To test the rest of our hypotheses (i.e., H1–H4 and H6–H9), we utilized the PROCESS macro for SPSS using model no. 3 for moderated moderation (95% bias-corrected bootstrapping with 5,000 resamples). The results from the analyses are presented in Tables 4 –6 . However, it is important to note that we also used heteroscedasticity-consistent standard error (SE) estimators, as suggested by Hayes and Cai (2007) , to ensure that the estimator of the covariance matrix of the parameter estimates will not be biased and inconsistent under heteroscedasticity violation.

Table 4. Moderficients and confidence intervals (CIs) for predicting HWI-TC.

Table 5. Moderated-moderation regression coefficients and confidence intervals (CIs) for predicting HWI-WI.

Table 6. Moderated-moderation regression coefficients and confidence intervals (CIs) for predicting job engagement (JE).

Firstly, the findings that are shown in Tables 4 –6 support H1 – H4 , meaning both intrinsic motivation and extrinsic motivation relate positively to HWI-TC, HWI-WI, and JE, in all samples (Israel and Japan). Additionally, the interaction effects (most of them) are significant, which is the most important part of any moderation analysis (see Appendix in Shkoler et al., 2017 ). Figures 2 –7 portray moderation effects.

Figure 2. Interaction effects of Intrinsic Motivation × Worker’s Status × Country in predicting HWI-TC. HWI-TC, time commitment dimension of heavy work investment.

Figure 3. Interaction effects of Extrinsic Motivation × Worker’s Status × Country in predicting HWI-TC. HWI-TC, time commitment dimension of heavy work investment.

Figure 4. Interaction effects of Intrinsic Motivation × Worker’s Status × Country in predicting HWI-WI. HWI-WI, work intensity dimension of heavy work investment.

Figure 5. Interaction effects of Extrinsic Motivation × Worker’s Status × Country in predicting HWI-WI. Notes . HWI-WI = work intensity dimension of heavy work investment.

Figure 6. Interaction effects of Intrinsic Motivation × Worker’s Status × Country in predicting job engagement.

Figure 7. Interaction effects of Extrinsic Motivation × Worker’s Status × Country in predicting job engagement.

Figures 2 –7 display surprising findings:

(1) The behaviors of the correlations (for instance, between intrinsic motivation and JE or HWI-TC) are different between the two countries, in general, such that means and correlations are both higher in the Israeli sample as opposed to the Japanese one.

(2) The behaviors of the correlations (for instance, between intrinsic motivation and JE or HWI-TC) are different between the two groups of worker status, in each country on its own , such that (a) working students, in Israel, exhibit stronger links to the outcome variables (i.e., HWI-TC, HWI-TC, and JE) as opposed to non-student employees; (b) however, in most cases, these associations were not so different between said groups, in the Japanese sample.

(3) The behaviors of the correlations (for instance, between intrinsic motivation and JE or HWI-TC) are different between the two groups of worker status when comparing each country, such that (a) working students, in Israel, exhibit stronger links to the outcome variables as opposed their Japanese counterparts; (b) however, in most cases, these associations were not so different between non-student employees (in Israel vs. Japan).

(4) The only analysis in which points 1–3 above do not apply is when using intrinsic motivation to predict HWI-WI (again, in a moderated-moderation model). It suggests that intrinsic motivation’s impact on the increased effort at work changes based on neither worker status nor the country/culture.

These findings support our hypotheses H6–H9: (1) worker status does moderate the links between work motivation and the outcome variables (HWI-TC, HWI-TC, and JE), and (2) county/cultural differences can moderate said relationships as well. Still, more importantly, they work as a conditioning moderator on the previous moderation (i.e., moderated moderation) in all of the analyses done.

The aims of the current paper were (1) to shed light on the relationship between intrinsic/extrinsic motivation and HWI of time (HWI-TC) and effort (HWI-WI) and JE, (3) to assess convergent and discriminant properties of JE in relation to HWI-TC and HWI-WI, and (4) to gauge the moderation effects of both worker status (working students vs. non-student employees) and country/culture (Israel vs. Japan) on said relationships (point 1) in a moderated-moderation analysis type. Our research hypotheses were supported to a great extent. The findings are summarized in Table 7 .

Table 7. Results of hypothesis testing.

Theoretical Implications

Our research adheres to the very few studies that have tested and validated Snir and Harpaz’s (2015) HWI conceptual model between its various predictors (i.e., intrinsic/extrinsic motivation) with regards to specific moderators (e.g., worker’s status and country/culture). Our findings supported the model (see Snir and Harpaz, 2015 , p. 6) and contributed to its incremental validity. Apart from realizing parts of the model’s structure and processes, we have also shown that the moderation effects suggested in the model may be conditioned by other moderators as well (in our study, country/culture differences), leading to more need for further research.

Although it is not the main focus of the current research, we have established some convergent and discriminant validity relationship between JE and HWI. Specifically, JE has a high convergent validity with HWI-WI, yet low convergent-borderline-discriminant validity with HWI-TC, increasing the need for exploring these issues further.

We have provided more evidence as to the critical role of culture in differentiating model and relationship behaviors. Our findings regarding the between-country differences found in the moderating effects of workers’ status supported our hypotheses, suggesting that compared to Israeli workplaces, those in Japan, indeed, put much emphasis in loyalty and cohesion. Japanese working students show similar work behavior (i.e., JE and HWI) as non-student workers. Attitudes, norms, and behavioral codes accepted in a country X may be quite different in country Y, not only in the general society but at the workplace as well. Concerning the workers’ status, it seems plausible that employees’ differing perceptions of the work context may affect their “readiness” to translate a drive to work to an actual HWI of JE, alone or in conjunction with cultural perceptions as well.

Furthermore, our findings on between-country differences have important insights for research in organizational learning. Employees’ continuous learning is essential for organizations to be competitive in the current and future VUCA world. Therefore, an organization needs to provide employees with opportunities to learn and support, which enables them to manage their work–study conflict effectively. However, as suggested in the results of the Japanese sample, it may be possible that cultural norms restrain workers from dedicating their time to learning. In addition to the effects of organization-level human resource development climate ( Chaudhary et al., 2012 ), we also need to consider the effects of national-level culture in the examination of organizational learning practices and their consequences.

Practical Implications

If JE is an organizational goal toward which many workplaces strive, their respective managers may very well need to enhance employees’ work motivation (such as offering more rewards or challenge), thus increasing the employees’ propensity for translating that motivation into actual HWI or JE.

The moderation effects emphasize the need for smart and careful management in workplaces with international employees, as we notice how different Israel is from Japan, for example. Managers and even service-givers must pay attention to these cultural differences when doing work with or for an entity (e.g., country, organization, or group) from outside the providing side’s national boundaries.

Besides, the stronger associations between work motivation and JE or HWI in Israeli sample (see Figures 2 –7 ) suggest that working students virtually actuate more of their working drives into the behavioral expressions of their drives to work, thus investing heavier in them. That may be so because working students are keener on proving themselves to the organization toward the end goal of being recruited as permanent employees (supported by the results in Israel, as opposed to Japan). Hence, those who have less occupational security are more likely to translate their drive to work into actual HWI and JE. Nevertheless, in today’s economy, in which “occupational sense of security” appears to be declining, it seems plausible that in the future the moderated association between motivation and HWI, found in our paper, will diminish in strength or even dissipate entirely. This argumentation finds support in recent publications (e.g., Neuner, 2013 ; Koene et al., 2014 ; Weil, 2014 ). Perhaps working students are also more susceptible to organizational incentives (i.e., intrinsic or extrinsic), as opposed to their non-student counterparts (i.e., “regular” employees).

On the other hand, Japanese workers showed relatively weak relationships between work motivation and JE or HWI. These findings suggest that the Japanese workplace norm restrains working students from putting much effort to study, and thus, they work long hours for managing impression or making up for their “violation” of the workplace norm. Such workplace derives from traditional Japanese culture which emphasizes loyalty and dedication to the employer ( Blomberg, 1994 ), and even modern companies in Japan expect employees to dedicate most of their life to the organization, resulting in much overtime work of Japanese workers ( Franklin, 2017 ; Pilla and Kuriansky, 2018 ; Mason, 2019 ). Therefore, to encourage employees’ continuous learning and associating organizational learning, managers in Japanese firms need to reconstruct the workplace norm such that working students will not feel guilty by studying outside of their organization.

Limitations and Future Research Directions

While our study has strength in the newness of findings and the use of an international sample, we should mention its limitations. First, our data are cross-sectional and single sourced. It limits the generalizability of the research and does not let us see if the findings are stable across time. Although it may not be a major limitation, our research was not focused on a specific industry, sector, or type of workers (e.g., high-tech, low-tech, services, or marketing and sales). While this bolsters the external validity of the research, it limits the construct validity of the results.

In our model, we included only individual differences as predictors and only contextual elements as moderators. As such, we recommend using a mix of said variables, such as “place” in the model, as predictors and moderators, so as not to be limited to one direction of explanations. For Snir and Harpaz’s (2015) model of HWI (p. 6), we only validated a part of it but did not include HWI as a mediator, but only as an outcome. Thus, we recommend using the full model to shed light on its possible processes, beyond predictor–outcome relationships. In addition, we urge researchers to investigate and identify more potentially interesting and relevant moderators, as we showed in our model (i.e., country/culture differences).

To expand our understanding of cultural difference, we recommend replicating our study in other countries with cultural similarities or differences to the ones used in the research, to broaden the generalizability and validity of our findings. As we noted previously, In Hofstede’s use of the term, Japan is higher in power distance, masculinity, and long-term orientation than Israel. Thus, this study might reveal the moderating effects of both these cultural dimensions and the worker’s status. However, this study only includes two countries, which might limit the generalizability of the results. Therefore, we suggest scholars worldwide to not only replicate our research in other countries but to also consider other cultural dimensions to generalize and expand our findings. Furthermore, in future international comparative studies, researchers can explore why and how each country’s cultural and institutional components influence the differences that would exist between countries.

Concerning our findings regarding convergent and discriminant validity between JE and HWI, we also encourage more research to be done in order to provide a clearer picture regarding these validity issues we raised in the current study.

We suggest conducting longitudinal studies incorporating other potential moderator variables (such as work ethic and gender) or mediators (as previously mentioned) and further investigating processes—which we enumerated in the discussion section—as likely to connect work motivation to JE, HWI, and potential outcomes.

It is also safe to assume that the associations we discovered in the research would be dependent on which industry we focus on (e.g., high-tech, low-tech, marketing, or service), and as such, we would also suggest incorporating this element in future research.

Finally, we suggest that future research compare the effect of intrinsic and extrinsic motivation on various kinds of behavior using the same sample. Although this study is one of few studies that investigate the effect of both types of motivation in one study, it assumed that they result in similar attitude and behavior. As Ryan and Deci (2000a) argued, these two types of behavior can lead different kinds of behavior since their sources are different—that is, intrinsic motivation derives from one’s free choice, but extrinsic motivation is promoted by external controls. Therefore, future research can include various kinds of behavior in a model and explore whether these two types of motivation lead to a different behavior and why.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The procedure of this study was approved by the Ethics Committee of Hosei University Graduate School of Career Studies. The committee approved that this study does not contain ethical flaws like leaking of private information and inhumane questions in the questionnaire. All subjects gave written informed consent regarding the purpose of research, that of data collection, and the privacy protection method. The current study was correlational, based on a survey, and not a manipulation on subjects. At the beginning of each questionnaire, we explained the general goal of the research. Informed consent was obtained from all individual participants included in the study. We ensured anonymity and discretion of the results and also ensured that the subjects know they could leave the participation at any time they choose.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : intrinsic and extrinsic motivation, heavy work investment, job engagement, work status, moderated moderation, cultural differences

Citation: Shkoler O and Kimura T (2020) How Does Work Motivation Impact Employees’ Investment at Work and Their Job Engagement? A Moderated-Moderation Perspective Through an International Lens. Front. Psychol. 11:38. doi: 10.3389/fpsyg.2020.00038

Received: 27 July 2019; Accepted: 07 January 2020; Published: 21 February 2020.

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*Correspondence: Or Shkoler, [email protected]

† These authors have contributed equally this work

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