Psychological Momentum and Inertia: Toward a Model of Academic Motivation

Abstract

Building upon psychological momentum theory, we draw an analogy between motivational constructs proposed herein and the physical principles of mass, inertia, and momentum. From these principles, we derived constructs representing academic inertia in states of both low and high momentum. The sample consisted of 105 African American college students in science, technology, engineering, and mathematics majors. Results of a confirmatory factor analysis (CFA) of a newly developed scale yielded support for two distinct factors reflecting low momentum state inertia (LMSI) and high momentum state inertia (HMSI). The conditional relationship between LMSI and HMSI was then examined with inspiration as a moderating variable. Consistent with our prediction, results indicated that the relationship between LMSI and HMSI was positive and significant at low levels of inspiration, while this slope was not significant at high levels of inspiration. Implications for cognitive-affective factors that may inhibit or facilitate psychological momentum in the context of academic functioning are discussed.

Keywords

psychological momentum inspiration psychological mass science self-efficacy academic procrastination

The term momentum has long been used in sports, politics, and other realms of popular culture as a metaphor for motivation or drive. Commentators routinely observe momentum shifting from one team to another in sporting events and often describe political campaigns as gaining or losing momentum over time. Interest in these manifestations, along with a curiosity about whether psychological phenomena generally adhere to physical principles, has led to the development of psychological momentum theory (PMT; Hubbard, 2010, 2015; Markman & Guenther, 2007; Nevin, Mandell, & Atak, 1983). The theory uses an analogy to describe the relationship that exists between the resistance to change in behavior and the frequency of a given behavior in the presence of a stimulus, with the relationship that exists between mass and velocity in the presence of a force. According to the Newton’s second law of motion, the force applied to an object is proportional to the rate at which its velocity changes. The proportionality constant represents the object’s mass, which is a measure of the object’s inertia. Consequently, PMT postulates that the change in response rate is directly related to the magnitude of the intervention or stimulus.

Notably, PMT does not give a clear analogy for momentum; rather, it defines mass (or inertia) through the response rate (velocity), which can then be associated with momentum. An individual who is not very actively pursuing a given task can be considered to have low momentum, or to be in a low momentum state (LMS), while an active individual would be considered to be in a high momentum state (HMS). Thus, psychological momentum refers to a given state of activity or performance. The objective of any intervention on an individual or group of individuals would be to optimize psychological momentum, which has been characterized in past research by heightened perceptions of self-efficacy (e.g., Iso-Ahola & Dotson, 2014; Jones & Harwood, 2008) and goal progress (e.g., Markman & Guenther, 2007), as well as increased efficiency (Iso-Ahola & Dotson, 2016) and persistence (e.g., Iso-Ahola & Dotson, 2014) in task performance. Psychological momentum has received considerable attention in the sport (e.g., Briki, Hartigh, Markman, Micallef, & Gernigon, 2013) and experimental psychology (e.g., Hubbard, Kumar, & Carp, 2009; Knops, Dehaene, Berteletti, & Zorzi, 2014) literatures, but little is known about how the construct generalizes to other domains. The current research builds upon the literature by proposing academic momentum as a subtype of momentum that merits empirical attention. The analogy is hereby applied to the academic environment to derive psychological analogues of physical properties (e.g., Markman & Guenther, 2007; Nevin & Grace, 2000) by proposing that psychological mass, which is responsible for inertia, may in turn serve as a foundational antecedent of academic momentum. We begin with an introduction to this inertia construct, followed by a brief review of the extant momentum literature.

Inertia and Momentum

Newton’s (1687) first law states that an object remains in its state of rest or in motion, unless it is compelled by forces to change that state. This principle is also referred to as the law of inertia. Newton’s second law explains that a force is necessary to change the state of motion of an object and that the rate at which the velocity changes is proportional to that force with the mass being the proportionality constant. Mass is then the measurable representation of inertia: the larger the mass (or alternatively, the larger the inertia) the more difficult it is to change the state of motion of an object. There is another way to express Newton’s second law through momentum. Momentum is the product of mass and velocity; thus, mass times the rate of change in velocity is equal to the rate at which the momentum changes. Thus, connecting both magnitudes, a force equals the rate of momentum change.

In order to put everything together to set up the basis for the analogy to be introduced next, a force will be equated to any action taken to change the psychological momentum, thus able to modify a given behavior. This “force” may be part of a targeted action (e.g., a workshop on studying best practices, or the insistence by a group of classmates to go partying on weekends) or it could be an environmental condition (e.g., discipline in a sports team or a sexist work environment). In a positive environment, inducing or maintaining positive changes will be easier. For instance, student athletes soon understand that they need to make sure they study for their courses so they adjust their study habits to mimic the rest of the team. A negative environment, on the other hand, will tend to decrease the psychological momentum to improve behavior or performance. For instance, a work environment in which periodic training on avoiding sexual harassment is given will probably be mostly free of sexual harassment; however, inappropriate practices may emerge if training is discontinued. To set the discussion in the context of the current work, we propose the construct of academic inertia, which we conceptually define as the tendency to remain in a status quo state of academic behavior. Thus, a state where the frequency of a given behavior is low will be referred to as a LMS and a state in which the frequency of behavior is high will be referred to as an HMS. Given that inertia affects an object in either a state of rest or a state of motion, one should expect academic inertia to be a factor in both momentum states. Correspondingly, we refer to these constructs hereafter as high momentum state inertia (HMSI) and low momentum state inertia (LMSI).

In the psychological sciences, there are numerous examples of cognitive, affective, and behavioral masses that may inhibit motivation to achieve or maintain a given behavior. According to PMT, the psychological mass is represented by the degree to which the individual ascribes value to a given behavior (Markman & Guenther, 2007). It is important to understand, however, that the perceived value of an end state behavior determines the psychological mass (or inertia) of that behavior and not necessarily that of the current behavior. It is often the case that people may value an activity and desire to act but may lack the emotional resources to put intention into action. For example, an individual with physiological symptoms of depression (e.g., fatigue and exhaustion) may find it difficult to get out of bed and start the day’s activities despite fully intending to do so. More relevant to academic motivation is the situation commonly referred to as “writer’s block.” A student may sit down at the computer with the clear goal of writing a paper for class and yet find it difficult to formulate ideas and actually begin the process of putting words onto the screen.

Consistent with PMT, we argue that the value of an activity the individual wishes to perform is related to the psychological mass of that activity, such that if the individual manages to reach a state where the activity is performed with a high frequency, it will be easier to continue performing the activity (i.e., high inertia or large HMSI). On the other hand, difficulty in implementing a behavior due to insufficient cognitive or emotional resources would be a reflection of psychological mass in the low activity state (i.e., LMSI). The perceived value of the activity thus explains what may seem a contradiction; that is, an individual who finds it hard to acquire a behavior may also find it easy to continue in a high-performance state once reached. We speculate that this kind of connection will exist in academia where the sought activities relate to academic work. The current research is rooted in the assumption that academic work, particularly work within one’s academic major, is considered valuable to students because it has such important implications for their career development. This may not be the case in other areas. For instance, a person who finds it hard to get to the gym may not feel that getting in shape is a priority; thus, the value of the behavior is low. If an individual eventually summons the motivation to go to the gym, he or she may not work out for very long if HMSI is low.

Previously, we explored one aspect of this framework—how psychological momentum can be changed. The counterpart to this concept refers to how momentum is maintained once the expected level of activity is reached. Certainly, behaviors that occur or are reinforced more frequently should tend to continue for a longer period of time. Once established, and in the presence of a larger psychological mass (for instance, when the behavior has high perceived value), the behavior should be sustained beyond the typical period of engagement. Studies consistently indicate that factors such as performance expectancies (e.g., Briki, Doron, Markman, den Hartigh, & Gernigon, 2014), internal causal attributions (e.g., Shaw, Dzewaltowski, & McElroy, 1992), and performance feedback (e.g., Raab, Gula, & Gigerenzer, 2012) are all positively associated with psychological momentum. By acting as proxies for perceived value, these factors act to increase the psychological mass of the psychological state they favor, thus allowing for the HMS to be maintained longer even in the presence of environmental factors that tend to reduce momentum and return behavior to the LMS.

The common factor underlying the depression and writer’s block examples discussed previously is a belief that a given activity is important and worth pursuing. Therefore, the more that value and effort directed at overcoming an academic obstacle increase, the less powerful the effect of resistance to change becomes, and thus, the perceived value of the desired behavioral state acts as a motivation to overcome the LMSI. Psychological inertia in the LMS represents the state that opposes the behavioral change that is sought, but at a certain point, one’s motivation to change reaches a threshold such that change is increasingly likely to the extent that a sufficient degree of effort has been exerted to overcome the block. The motivation that ultimately leads to a change in behavior can therefore be increased, resulting in an HMS. Thus, motivation acts as a force and the value of the desired behavior (HMS) is expected to correlate positively with it.

Current Research

Theorists have long proposed state–trait and trait–trait interactions in theories explaining human behavior. Diathesis-stress (e.g., Beck, 1987) and expectancy-value (e.g., Atkinson, 1957; Trautwein et al., 2012) theories represent two exemplars of interactional models positing synergistic or antagonistic relations between predictor and moderator variables. We follow in this tradition by positing an interactive relationship between two motivational constructs that are metaphorically rooted in concepts of classical physics.

The purpose of the current research was to build on extant momentum literature by introducing a type of momentum unique to the academic domain, while also introducing and examining inertia as a conceptual counterpart to momentum. We submit that the investigation of academic momentum could extend the academic/career development literature in two important ways. First, self-efficacy has long been an important construct of interest to career development researchers (Lent, Brown, & Hackett, 1994); however, efficacy perceptions only reflect social-cognitive dimensions of academic functioning. Career-related intentions and behaviors are typically posited as distinct consequences of these social-cognitive processes (e.g., Lent & Brown, 2013; Lent et al., 1994). Psychological momentum is influenced by social-cognitive factors as well but additionally includes a behavioral dimension that reflects the dynamic feedback process occurring between efficacy perceptions and performance.

Classic conceptualizations of psychological momentum suggest it is a construct that is similar to, yet distinct from, self-efficacy insofar as the latter is an antecedent of the former (Iso-Ahola & Dotson, 2016); therefore, measuring academic inertia in various states of psychological momentum may allow researchers to tap unique sources of variance that reflect heightened perceptions of competence and efficacy expectancies. Second, current models of academic/career development explore general processes by which individuals implement career-related behaviors, but little is known as to how such behaviors are instantiated in specific situations. The study of academic inertia may explain how behavioral movement on academic tasks is engendered, which could further illuminate how and to what degree career-related interests and goals are pursued. However, because academic inertia is a newly proposed construct, there are currently no instruments that are designed to measure it. Thus, we developed and factor analyzed a self-report measure of academic inertia prior to testing our substantive hypothesis.

We examined whether the relationship between these constructs is conditional upon the level of some motivation-relevant moderator variable. Inspiration served as the focal moderator in the current study because research suggests it is reliably associated with change in cognition and affect. Researchers have shown that individuals who feel inspired report increased positive affect (Böttger, Rudolph, Evanschitzky, & Pfrang, 2017; Thrash & Elliot, 2004; Thrash, Elliot, Maruskin, & Cassidy, 2010), creativity (Thrash, Maruskin, Cassidy, Fryer, & Ryan, 2010), and perceptions of goal progress (Milyavskaya, Ianakieva, Foxen-Craft, Colantuoni, & Koestner, 2012), and they tend to perceive inspiration as not being under one’s volitional control (e.g., emanating from an environmental or spiritual influence; Thrash & Elliot, 2004).

We tested these variables among a sample of African American students in science, technology, engineering, and mathematics (STEM) attending an historically black college/university (HBCU). African Americans are vastly underrepresented in the STEM workforce relative to their White and Asian counterparts (National Science Board, 2016). This gap is due in large part to systemic barriers that limit African Americans’ access to quality education and work in STEM, and this has been particularly true since the Great Recession, which has resulted in a widening of the economic disparity gap between Whites and racial/ethnic minorities in the United States (Miller & Horrigan, 2014). These environmental barriers have deleterious consequences for well-being (Chao, Mallinckrodt, & Wei, 2012) that may be internalized as psychological barriers that manifest in the form of inertia in the LMS. The application of inspiration as a force to overcome the LMSI should thus produce positive motivational effects that are evidenced by inertia that is maintained or even enhanced in the HMS. HBCUs in particular are highly effective in promoting academic self-efficacy and achievement among African American students (e.g., Berger & Milem, 2000; Perna et al., 2009); therefore, they should serve as rich environmental sources of inspiration.

We predicted that inspiration would moderate the relationship between LMSI and HMSI such that HMSI would be lowest at low levels of both inspiration and LMSI. At the same time, we expected that scores on HMSI would be high, even at low levels of inspiration, if LMSI is concurrently high. Thus, a small degree of inspiration should be sufficient to strengthen HMSI when LMSI is high. At high levels of inspiration, HMSI will be high whether LMSI is low or high. Overall, then, the slope for the regression of HMSI on LMSI should be positive and significant when inspiration is low, but the two variables should be unrelated when inspiration is high.

As a secondary objective, we wished to begin establishing a nomological network for the two inertia types. Because academic procrastination is conceptually similar to LMSI and self-efficacy is a documented antecedent of HMSI (e.g., Jones & Harwood, 2008), we examined the bivariate relations among these variables in the present study. Specifically, we expected that LMSI and HMSI would correlate positively with academic procrastination and STEM self-efficacy, respectively. All variables in the present study were measured at the STEM domain level.

Method

Participants

The sample consisted of 105 first- and second-year undergraduate students majoring in STEM at an HBCU. Almost all participants identified as African American (102), while three participants identified as multiracial. Most participants identified as female (78), 25 identified as male, 1 participant identified as transgender, and 1 participant did not report his or her gender. Mean age was 19.34 (SD = 1.84) with a range of 18–29, and mean grade point average was 3.17 (SD = .53). In terms of academic status, 57 were first-year students, 47 were second-year students, and 1 participant did not report his or her academic status. Biology was the most frequently represented major (70), followed by engineering technology (14), chemistry (11), computer science (7), and mathematics and physics (3; double major).

Measures

Academic procrastination

Academic procrastination was measured using the Procrastination Assessment Scale–Students (PASS; Solomon & Rothblum, 1984). The PASS is a 44-item questionnaire consisting of two subscales—Areas of Procrastination and Reasons for Procrastination. Within each subscale, participants are asked to rate 6 items measuring the degree of procrastination and 6 items measuring the extent to which participants perceive their procrastination to be problematic. These 6 items include (a) writing term papers, (b) studying for exams, (c) weekly reading assignments, (d) academic administrative tasks, (e) attendance tasks, and (f) general academic activities. In order to map more directly to the inertia and momentum constructs, we measured problematic procrastination using only Items a, b, and c. Participants were asked to respond to the question “To what extent is procrastination on this task a problem for you?” on a 5-point Likert-type scale ranging from 1 (not at all a problem) to 5 (always a problem). The PASS has exhibited good internal consistency in previous research (α = .83; Deemer, Yough, & Morel, 2018) and evidence of concurrent validity through a significant negative relationship with academic performance (Corkin, Yu, & Lindt, 2011).

LMSI and HMSI

The first author developed 9 items that are hypothesized to reflect the two-factor model of inertia within the academic domain of science and engineering (see Table 1). Five LMSI items were developed to represent a state of behavioral inactivity relative to the initiation of particular academic tasks. To adequately represent this construct, items were written to reflect (a) a tendency to remain in a status quo state when academic behavior is at rest and (b) an underlying perception of the importance of the task domains (e.g., studying for exams). We aimed to unify these components by including references to the difficulty associated with increasing one’s motivation in the root of each item (e.g., “I have difficulty…”). We operate on the assumption that students place high value on performance within their major domains of study; therefore, they will generally exert the effort needed to successfully complete difficult academic tasks. The next 4 items were written to represent a desire to continue a given task once it has been perceived to produce successful results, thus representing the effect of inertia in an HMS. This is consistent with PMT’s prediction that current success should increase the likelihood of subsequent success (Iso-Ahola & Dotson, 2014). The task domains were modeled after those reflected in the PASS and include (a) studying for exams and quizzes, (b) course-related reading, (c) course-related writing, and (d) course-related projects. Participants rate the items on a Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Table 1.

Academic Inertia Scale Items.

Low Momentum State Inertia Subscale

1. I find it difficult to get motivated to start studying for ordinary exams and quizzes in my science and engineering courses.

2. I have difficulty getting motivated to start reading required material for my science and engineering courses.

3. It’s hard for me to get motivated to begin studying for very important exams (e.g., midterm and final exams) in my science and engineering courses.

4. I have a hard time getting motivated to start working on required projects (e.g., lab assignments, class presentations) in my science and engineering courses.

5. I have difficulty getting motivated to write term papers in my science and engineering courses.

High Momentum State Inertia subscale

1. Once I start studying and I begin to understand the concepts in my science and engineering courses, I often want to continue studying for as long as possible in order to maximize my learning.

2. After I start a course project in my science and engineering courses and I begin to see progress, I often want to continue working as long as possible so I can maximize my productivity.

3. After I begin a reading assignment in my science and engineering courses and I begin to absorb the information, I often want to keep reading for as long as possible in order to keep the momentum going.

4. After I start writing a term paper in my science and engineering courses and I begin to generate ideas, I want to keep the progress going by writing for as long as I can.

Inspiration

We measured the tendency to be moved motivationally using Thrash and Elliot’s (2003) Inspiration Scale. Participants are asked to rate 4 items (e.g., “I feel inspired”) based on two questions that measure frequency and intensity of inspiration. These questions are “How often does this happen?” and “How deeply or strongly (in general)?” The 4 frequency items are rated on Likert ranging from 1 (never) to 7 (very often), and the intensity items are similarly measured on a Likert-type scale ranging from 1 (not at all) to 7 (very deeply or strongly). Frequency and Intensity subscale scores can be computed separately, or all 8 items can be summed to yield an overall inspiration score. We used overall inspiration scores in the present study. Thrash and Elliot obtained Cronbach’s α coefficients ranging from .89 to .96 for the subscale and total scale scores. Their work yielded strong evidence of concurrent validity, as inspiration was found to correlate positively and significantly with constructs such as openness to experience, creativity, positive affect, and optimism.

STEM self-efficacy

STEM self-efficacy was measured using Fantz, Siller, and DeMiranda’s (2011) Engineering Self-Efficacy Scale. Because we were interested in measuring self-efficacy at a level broader than the engineering domain, we adapted the items by replacing the term “engineering” with “STEM.” An example of an original item includes “I’m confident I can understand the basic concepts in my engineering classes” while the adapted version is “I’m confident I can understand the basic concepts in my STEM classes.” Items are rated on a 7-point Likert-type scale ranging from −3 (strongly disagree) to +3 (strongly agree). However, in order to ensure consistency with other measures in the study, we rescaled the items prior to collecting data such that scores ranged from 1 (strongly disagree) to 7 (strongly agree). Fantz et al. provided evidence of concurrent validity through a significant positive correlation with current semester grade point average. Their work also revealed a Cronbach’s α coefficient of .83, thus supporting the measure’s internal consistency.

Procedure

To be eligible for inclusion in the study, participants were required to be at least 18 years of age and currently majoring in a STEM discipline. As this study is part of a broader longitudinal project, an additional requirement was that all participants were to be either in their first or second year of study at the university. A research assistant attended all biology, chemistry, computer science, engineering technology, mathematics, and physics classes that are typically taken by first- and second-year students and recruited participants via in-class solicitation. Participants were also recruited by posting flyers across campus that provided a brief description of the nature of the study. Students who agreed to participant reported to a computer lab where they provided written consent and completed an online survey. Upon completing the survey, participants were thanked for their cooperation and given a US$20 gift card as remuneration.

Results

CFA and Descriptive Statistics

Participants provided complete data for the substantive variables. We began by performing a CFA on the inertia and momentum items to determine whether they reflect the hypothesized two-factor structure. Although the current sample may appear to be too small to conduct a CFA, Monte Carlo simulation research suggests that a minimum sample size of 90 is needed to yield a proper solution with unbiased parameter estimates for a two-factor CFA model with 8 indicators and standardized factor loadings set at .80 (a model similar to the current one; Wolf, Harrington, Clark, & Miller, 2013). Items 1–5 were specified to load on LMSI, and Items 6–9 were specified to load on HMSI. Standardized factor loadings ranged from .68 to .93 for LMSI and .77 to .92 for HMSI (see Table 2). With the exception of the root mean square error of approximation (RMSEA) value, the model fit indices suggested an acceptable fit to the data, χ²(26) = 72.64, p < .001, comparative fit index (CFI) = .93, RMSEA = .13 (90% confidence interval [CI] = [.10, .17]), Tucker Lewis Index (TLI) = .91, and standardized root mean square residual (SRMR) = .05. Because RMSEA is in part a function of N (Steiger & Lind, 1980), the elevated RMSEA value and wide CI may be due to the low sample size in the current study (MacCallum, Browne, & Sugawara, 1996). The inertia and momentum items were summed as subscale scores and subsequently used in the hierarchical regression analysis.

Table 2.

Descriptive Statistics and Standardized Factor Loadings for the Academic Inertia Scale Items.

Variable/Item	M	SD	Skewness^a	Kurtosis^b	Range	Loading
LMSI
Item 1	2.80	1.37	.00	−1.18	1–5	.82
Item 2	2.64	1.35	.24	−1.10	1–5	.93
Item 3	2.50	1.35	.31	−1.06	1–5	.86
Item 4	2.42	1.32	.38	−0.97	1–5	.76
Item 5	2.56	1.45	.34	−1.22	1–5	.68
HMSI
Item 1	3.48	1.35	−.44	−0.95	1–5	.77
Item 2	3.61	1.31	−.47	−0.97	1–5	.92
Item 3	3.55	1.26	−.44	−0.83	1–5	.91
Item 4	3.62	1.28	−.48	−0.83	1–5	.84

Note. LMSI = Low Momentum State Inertia subscale; HMSI = High Momentum State Inertia subscale.

^aStandard error = .24. ^bStandard error = .47.

Descriptive statistics, correlations, and reliability estimates for the regression model variables are presented in Table 3. Participants scored above the scale midpoints of HMSI, inspiration, and self-efficacy and below the midpoints of LMSI and academic procrastination. LMSI was positively associated with academic procrastination (r = .49, p < .001) and negatively related to self-efficacy (r = −.25, p = .012). HMSI was positively and significantly associated with both inspiration (r = .44, p < .001) and self-efficacy (r = .28, p = .004).

Table 3.

Zero-Order Correlations, Descriptive Statistics, and Reliability Estimates for the Study Variables.

Variables	1	2	3	4	5
1. Academic procrastination	—
2. Inspiration	−.11	—
3. LMSI	.49***	−.12	—
4. HMSI	−.06	.44***	.09	—
5. STEM self-efficacy	−.29**	.54***	−.25*	.28**	—
6. M	2.50	4.98	2.58	3.56	5.90
7. SD	1.01	1.40	1.17	1.17	0.82
8. α	.82	.96	.91	.92	.93

Note. LMSI = low momentum state inertia; HMSI = high momentum state inertia.

*p < .05. **p < .01. ***p < .001.

Hierarchical Regression Analyses

To test the hypothesis that inspiration moderates the relationship between LMSI and HMSI, we entered LMSI and inspiration on Step 1 with gender and STEM self-efficacy as statistical control variables. Only binary gender cases were used in the analysis. This was followed by entry of the LMSI × Inspiration product term on Step 2. The predictors were centered at their means prior to conducting all analyses (Cohen, Cohen, Aiken, & West, 2003). Given the small sample size and the fact that the data were previously subjected to a CFA, we set α at .01 for the regression analysis to reduce the risk of committing Type I error. Results are presented in Table 4. The Step 1 model was significant, F(4, 98) = 6.94, p < .001, as the predictor variables explained 22.1% of the variance in HMSI. LMSI was shown to be unrelated to HMSI (β = .16, p = .08), whereas inspiration was a significant positive predictor of this outcome (β = .41, p < .001). The LMSI × Inspiration product term entered on Step 2 accounted for an additional 9.1% of the variance in HMSI, ΔF(1, 97) = 12.80, p < .001 (β = −.32), thus supporting our hypothesis. A plot of the two-way interaction is shown in Figure 1. Results of a post hoc test of the simple slopes indicated that LMSI was a significant positive predictor of HMSI at low, t(97) = 4.01, p < .001 (b = 2.73), and moderate, t(97) = 2.65, p = .009 (b = 1.10), levels of inspiration. The LMSI-HMSI slope was nonsignificant at high levels of inspiration, t(97) = −.96, p = .34 (b = −.52).

Table 4.

Results of Hierarchical Regression Analysis Predicting High Momentum State Inertia With Inspiration as a Moderator.

Predictor	B	SE	β	p
Step 1
Gender	−0.35	.99	−.03	.73
STEM self-efficacy	0.49	.53	.10	.35
LMSI	0.77	.43	.16	.08
Inspiration	1.92	.51	.41	.00
Step 2
LMSI × Inspiration	−1.63	.45	−.32	.001

Note. LMSI = low momentum state inertia.

Figure 1.

Plot of the interaction between low momentum state inertia and inspiration in predicting high momentum state inertia.

Discussion

The physics concept of momentum has been used as an analogy for motivation in numerous areas of psychological inquiry such as experimental and sport psychology. However, the metaphor has never been applied to the realm of academic motivation despite its intuitive applicability. The current study addressed this issue by introducing and empirically examining two distinct inertia constructs that reflect different momentum states. We obtained evidence to support the hypotheses that the relationship between inertia in a LMS and its counterpart in an HMS is generally conditional upon the influence of a moderating factor that can be construed as a force in analogical terms.

In support of our first hypothesis, the relationship between LMSI and HMSI was positive when inspiration was low, but this relationship was not significant when inspiration was high. HMS levels did not vary significantly between low and high LMS when participants reported being highly inspired. Rather, it appears that only a modest amount of inspiration is needed when LMS is high. This underscores our assertion that motivation is a key underpinning of LMS, as we drew upon the operational definition of psychological mass as reflecting one’s perception of the value, or importance, of a task (Markman & Guenther, 2007). Given that physical principles hold that mass is a representation of inertia, our findings suggest that high levels of LMSI reflect motivational potential that can be tapped for adaptive purposes if paired with the proper triggering stimulus. The fact that scores on the STEM self-efficacy measure were relatively high (i.e., above the median) lends support to the notion that when faced with increasing difficulty with task initiation, students who feel confident in their ability to meet the demands of the task are likely to exert effort needed to begin making progress. Of course, additional research is needed to examine more explicitly the relations between LMSI and other factors that are akin to psychological mass, such as achievement orientation, effort expenditure, and task value (e.g., intrinsic value, utility value; Eccles, 2005). Such research would illuminate the conditions under which students match tasks that are difficult to initiate with commensurate levels of effort, thus allowing moderating influences such as inspiration to facilitate HMSI more efficiently.

As alluded to previously, students who experience writer’s block have difficulty making progress in terms of performance despite the fact that they may be making frequent and deliberate attempt to overcome the block. In light of this high underlying motivation, only a small force of inspiration would be needed to ease the difficulty associated with initiating an academic task. It is important to note, however, that the Inspiration Scale (Thrash & Elliot, 2003) only measures frequency and intensity of inspiration; thus, it remains to be determined exactly what participants were inspired by. Future qualitative research on particular sources of inspiration would complement quantitative approaches reflected in measures such as the Inspiration Scale. One potentially profitable area of investigation includes faculty and/or peer mentors who act as role models for students to emulate. It would be particularly useful to examine the motivational impact that same-race mentors have on their mentees and compare this with the motivational impact that mentors of a different race have.

Our secondary aim of the current study was to begin developing a nomological network for the inertia constructs. HMSI was found to be positively associated with both inspiration and STEM self-efficacy, suggesting that it is an approach-based form of motivation that is capable of energizing and directing behavior. This form of inertia may not actually lead to enhanced academic performance in the short term, but it is the perception that one is performing well along with the expectation that one will continue to perform well that maintains the behavior. Perceived competence and efficacy expectancies are thus key components of HMSI that are instrumental in promoting academic persistence and long-term performance. To this end, exploring potential areas of convergence with established theories of career development (e.g., social-cognitive model of career self-management; Lent & Brown, 2013) and optimal motivation constructs (e.g., intrinsic motivation; Deci & Ryan, 2000) would be fruitful. Investigating these linkages is an important direction for future research because attainment of an HMS may foreshadow the long-term persistence that is so critical to retention of students in STEM. In contrast, evidence of the construct validity of LMSI was demonstrated through a positive correlation with academic procrastination and negative correlations with both inspiration and STEM self-efficacy. LMSI and academic procrastination likely share in common a number of self-regulatory and situational characteristics. Low self-efficacy (e.g., Klassen, Krawchuk, & Rajani, 2008; Wäschle, Allgaier, Lachner, Fink, & Nückles, 2014) and perceived task difficulty (Schraw, Wadkins, & Olafson, 2007; Solomon & Rothblum, 1984) are antecedents of academic procrastination and these characteristics have been identified as either central to, or a correlate of, LMSI in the present study.

The near zero bivariate relationship between LMSI and HMSI suggests that not only are moderating variables required to trigger movement from a low momentum to HMS but also that there may be intervening variables that transmit this energization of behavior. The weak correlation between the two variables indicates that their manifestations likely do not occur closely together in time. This makes conceptual sense when one considers that it may take a considerable length of time for students to move from a state of academic inactivity (i.e., just beginning a study or homework session) to a state in which they feel as if they are making significant progress in learning. In light of the negative relation of LMSI with self-efficacy, it may be that the experience of an impasse during one’s studies engenders decreased confidence in one’s ability to achieve a task goal and with this a concomitant rise in avoidance motivation. Students whom become too discouraged by lack of progress or overly concerned with failure are at greater risk of postponing their academic work. Of course, there also exists the possibility of transitioning from an avoidance orientation to an approach orientation in the event that students perceive that they are understanding the course material and reaping some reward for their effort. Thus, constructs such as procrastination, self-efficacy, and mastery approach/avoidance goals (Elliot & McGregor, 2001) are potential mediating variables that warrant further investigation in this model. Similarly, research which focuses on identifying factors other than inspiration that trigger movement from low to HMSs would be profitable. Self-efficacy would appear be an ideal moderator given that antecedents of self-efficacy (Bandura, 1997) are known to have a galvanizing effect on behavior. For instance, receiving encouraging words from a peer or professor or observing a peer who appears to be having success with his or her academic work may provide sufficient impetus to propel a student beyond a troubling impasse.

Although our findings suggest that academic inertia demonstrates promise as a motivation construct, certain limitations in the current study should be noted. First, we measured LMSI and HMSI as individual difference variables, but clearly these constructs operate as a function of situational factors as well. Whether an individual can enter an HMS likely depends on a host of situational characteristics such as task interest, perceived competence, task complexity, and internal (e.g., fatigue) or external factors (e.g., distracting stimuli in the environment) that may inhibit performance. We note, however, that the current study’s focus was on introducing the model and exploring the importance of inspiration as a factor in the facilitation of academic motivation and behavior. Nonetheless, future research would do well to further explore the task-dependent nature of academic inertia. Second, it is possible that the data were influenced by self-selection bias in that students who frequently experience high or low momentum states may have been more likely to participate upon learning that the survey comprised measures of these motivational constructs. Finally, while the study sheds light on the factors that foster HMS and LMS among African American students, the sample lacked diversity in terms of gender and academic major representation as most participants identified as women and biology majors. Because African American women tend to endorse stronger multiculturalist inclusive attitudes than their male counterparts (Fhagen-Smith, Vandiver Worrell, & Cross, 2010), racial identity is a factor that may need to be controlled for in similar studies in the future.

In sum, the present findings offer support for a model of motivation that complements existing research on PMT that had heretofore been limited to the realms of experimental and sport psychology. The conceptual model developed here also extends the academic/career development literature in important ways. Whereas models of academic/career development often tend to focus on macrolevel processes such as how career interests are shaped over time, the current model offers insight into how behavior is instantiated in specific academic situations. The current model also incorporates the measurement of a behavioral dimension of academic behavior that may elucidate the role of social-cognitive processes (e.g., self-efficacy, competence feedback) during periods of generative academic activity. From a practical standpoint, it appears that inquiries into the influence of academic inertia may also hold promise for the development of interventions aimed at helping students overcome academic and career obstacles.

Footnotes

Authors’ Notes

The data presented and views expressed in this article are solely the responsibility of the authors.

Acknowledgment

Pedro A. Derosa acknowledges the Larson #1 Professorship made available through the State of Louisiana Board of Regents Support Fund.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grant HRD-1661201 from the National Science Foundation.

References

Atkinson

J. W.

(1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359–372. doi:10.1037/h0043445

Bandura

(1997). Self-efficacy: The exercise of control. New York, NY: Freeman.

Beck

A. T.

(1987). Cognitive models of depression. Journal of Cognitive Psychotherapy: An International Quarterly, 1, 5–37.

Berger

J. B.

Milem

J. F.

(2000). Exploring the impact of historically Black colleges in promoting the development of undergraduates’ self-concept. Journal of College Student Development, 41, 381–394.

Böttger

Rudolph

Evanschitzky

Pfrang

(2017). Customer inspiration: Conceptualization, scale development, and validation. Journal of Marketing, 81, 116–131. doi:10.1509/jm.15.0007

Briki

Doron

Markman

K. D.

Den Hartigh

R. J. R.

Gernigon

(2014). Differential reactions of virtual actors and observers to the triggering and interruption of psychological momentum. Motivation and Emotion, 38, 263–269. doi:10.1007/s11031-013-9372-3

Briki

Hartigh

Markman

Micallef

J-P.

Gernigon

(2013). How psychological momentum changes in athletes during a sport competition. Psychology of Sport and Exercise, 14, 389–396. doi:10.1016/j.psychsport.2012.11.009

Chao

R. C.-L.

Mallinckrodt

Wei

(2012). Co-occurring presenting problems in African American college clients reporting racial discrimination distress. Professional Psychology: Research and Practice, 43, 199–207. doi:10.1037/a0027861

Cohen

West

S. G.

Aiken

L. S.

(2003). Applied multiple correlation/regression analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum Associates.

10.

Corkin

D. M.

S. Y.

Lindt

S. F.

(2011). Comparing active delay and procrastination from a self-regulated learning perspective. Learning and Individual Differences, 21, 602–606. doi:10.1016/j.lindif.2011.07.005

11.

Deci

E. L.

Ryan

R. M

. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268. doi:10.1207/S15327965PLI1104_01

12.

Deemer

E. D.

Yough

Morel

S. A.

(2018). Performance-approach goals, science task preference, and academic procrastination: Exploring the moderating role of competence perceptions. Motivation and Emotion, 42, 200–213. doi:10.1007/s11031-017-9649-z

13.

Eccles

J. S.

(2005). Subjective task value and the Eccles et al. model of achievement-related choices. In Elliot

A. J.

Dweck

C. S.

(Eds.), Handbook of competence and motivation (pp. 105–121). New York, NY: Guilford Press.

14.

Elliot

A. J.

McGregor

H. A.

(2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501–519. doi:10.1037//0022-3514.80.3.501

15.

Fantz

T. D.

Siller

T. J.

DeMiranda

M. A.

(2011). Pre-collegiate factors influencing the self-efficacy of engineering students. Journal of Engineering Education, 100, 604–623. doi:10.1002/j.2168-9830.2011.tb00028.x

16.

Fhagen-Smith

P. E.

Vandiver

B. J.

Worrell

F. C.

Cross

W. E

. (2010). (Re)examining racial identity attitude differences across gender, community type, and socioeconomic status among African American college students. Identity: An international journal of theory and research, 10, 164–180. doi:10.1080/15283488.2010.495907

17.

Hubbard

T. L.

(2010). Approaches to representational momentum: Theories and models. In Nijhawan

Khurana

(Eds.), Space and time in perception and action (pp. 338–365). Cambridge, England: Cambridge University Press.

18.

Hubbard

T. L.

(2015). The varieties of momentum-like experience. Psychological Bulletin, 141, 1081–1119. doi:10.1037/bul0000016

19.

Hubbard

T. L.

Kumar

A. M.

Carp

C. L.

(2009). Effects of spatial cueing on representational momentum. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 666–677. doi:10.1037/a0014870

20.

Iso-Ahola

S. E.

Dotson

C. O.

(2014). Psychological momentum: Why success breeds success. Review of General Psychology, 18, 19–33. doi:10.1037/a0036406

21.

Iso-Ahola

S. E.

Dotson

C. O.

(2016). Psychological momentum—A key to continued success. Frontiers in Psychology, 7, 1–7. doi:10.3389/fpsyg.2016.01328

22.

Jones

M. I.

Harwood

(2008). Psychological momentum within competitive soccer: Players’ perspectives. Journal of Applied Sport Psychology, 20, 57–72. doi:10.1080/10413200701784841

23.

Klassen

R. M.

Krawchuk

L. L.

Rajani

(2008). Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination. Contemporary Educational Psychology, 33, 915–931. doi:10.1016/j.cedpsych.2007.07.001

24.

Knops

Dehaene

Berteletti

Zorzi

(2014). Can approximate mental calculation account for operational momentum in addition and subtraction? The Quarterly Journal of Experimental Psychology, 67, 1541–1556. doi:10.1080/17470218.2014.890234

25.

Lent

R. W.

Brown

S. D.

(2013). Social cognitive model of career self-management: Toward a unifying view of adaptive career behavior across the lifespan. Journal of Counseling Psychology, 60, 557–568. doi:10.1037/a0033446

26.

Lent

R. W.

Brown

S. D.

Hackett

(1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79–122. doi:10.1006/jvbe.1994.1027

27.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149. doi:10.1037/1082-989X.1.2.130

28.

Markman

K. D.

Guenther

C. L.

(2007). Psychological momentum: Intuitive physics and naïve beliefs. Personality and Social Psychology Bulletin, 33, 800–812. doi:10.1177/0146167207301026

29.

Miller

J. S.

Horrigan

J. B.

(2014). STEM urgency: Science, technology, engineering, and mathematics in an increasingly unequal and competitive world. Washington, DC: Joint Center for Political and Economic Studies.

30.

Milyavskaya

Ianakieva

Foxen-Craft

Colantuoni

Koestner

(2012). Inspired to get there: The effects of trait and goal inspiration on goal progress. Personality and Individual Differences, 52, 56–60. doi:10.1016/j.paid.2011.08.031

31.

National Science Board. (2016). Science and engineering indicators 2016 (NSB-2016-1). Arlington, VA: Author.

32.

Nevin

J. A.

Grace

R. C.

(2000). Behavioral momentum and the law of effect. Behavioral and Brain Science, 23, 73–130. doi:10.1017/S0140525X00002405

33.

Nevin

J. A.

Mandell

Atak

J. R.

(1983). The analysis of behavioral momentum. Journal of the Experimental Analysis of Behavior, 39, 49–59. doi:10.1901/jeab.1983.39-49

34.

Newton

. (1687). Philosophiae naturalis principia mathematica.

35.

Perna

Lundy-Wagner

Drezner

N. D.

Gasman

Yoon

Bose

Gary

(2009). The contribution of HBCUs to the preparation of African American women for STEM careers: A case study. Research in Higher Education, 50, 1–23. doi:10.1007/s11162-008-9110-y

36.

Raab

Gula

Gigerenzer

(2012). The hot hand exists in volleyball and is used for allocation decisions. Journal of Experimental Psychology: Applied, 18, 81–94. doi:10.1037/a0025951

37.

Schraw

Wadkins

Olafson

(2007). Doing the things we do: A grounded theory of academic procrastination. Journal of Educational Psychology, 99, 12–25. doi:10.1037/0022-0663.99.1.12

38.

Shaw

J. M.

Dzewaltowski

D. A.

McElroy

(1992). Self-efficacy and causal attributions as mediators of perceptions of psychological momentum. Journal of Sport & Exercise Psychology, 14, 134–147. doi:10.1123/jsep.14.2.134

39.

Solomon

L. J.

Rothblum

E. D.

(1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31, 503–509. doi:10.1037//0022-0167.31.4.503

40.

Steiger

J. H.

Lind

J. C.

(1980, May). Statistically based tests for the number of factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.

41.

Thrash

T. M.

Elliot

A. J.

(2003). Inspiration as a psychological construct. Journal of Personality and Social Psychology, 84, 871–889. doi:10.1037/0022-3514.84.4.871

42.

Thrash

T. M.

Elliot

A. J.

(2004). Inspiration: Core characteristics, component processes, antecedents, and function. Journal of Personality and Social Psychology, 87, 957–973. doi:10.1037/0022-3514.87.6.957

43.

Thrash

T. M.

Elliot

A. J.

Maruskin

L. A.

Cassidy

S. E.

(2010). Inspiration and the promotion of well-being: Tests of causality and mediation. Journal of Personality and Social Psychology, 98, 488–506. doi:10.1037/a0017906

44.

Thrash

T. M.

Maruskin

L. A.

Cassidy

S. E.

Fryer

J.W.

Ryan

R. M.

(2010). Mediating between the muse and the masses: Inspiration and the actualization of creative ideas. Journal of Personality and Social Psychology, 98, 469–487. doi:10.1037/a0017907

45.

Trautwein

Marsh

H. W.

Nagengast

Lüdtke

Nagy

Jonkmann

(2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interaction modeling study. Journal of Educational Psychology, 104, 763–777. doi:10.1037/a0027470

46.

Wäschle

Allgaier

Lachner

Fink

Nückles

(2014). Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction, 29, 103–114. doi:10.1016/j.learninstruc.2013.09.005

47.

Wolf

E. J.

Harrington

K. M.

Clark

S. L.

Miller

M. W.

(2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73, 913–934. doi:10.1177/0013164413495237