1 of 18

Redirecting Authoritarian Leadership

A Data-Driven Approach to Fulfilling the Motivational Need for Power

Ryan Hopkins

PSY 790: Capstone in Psychology

Dr. Nick Dominello

May 25, 2025

2 of 18

Introduction: The Impact of Leadership

Leadership significantly shapes workplace culture and employee well-being�

Authoritarian leadership is characterized by control, dominance, and power assertion

Authoritarian Leadership (AL)

Low employee morale

Toxic Workplace

Low employee morale

Kelly & MacDonald, 2019

3 of 18

Understanding the Problem

AL persists despite known negative consequences
Established hierarchical positions make ALs difficult to terminate
Employees tend to fear retaliation for complaining

Huang et al., 2020

Image: OpenAI, 2025

4 of 18

Purpose and Key Questions

Can focusing on performance data satisfy an authoritarian leader’s need for power?

Research Question 1

Explore positive ways to redirect authoritarian leadership.

Goal

Does shifting ALs’ focus from direct employee control to performance metrics improve motivation and morale?

Research Question 2

5 of 18

What the Research Shows

Studies suggest that leadership behaviors can be changed through specific programs (Mason et al., 2014).

Leaders can satisfy their need for autonomy through self-regulated actions (van Tuin et al., 2020)

Using data to guide management can help shift leadership styles (McCarthy et al., 2017 ).

AL can be redirected toward more democratic approaches that better support employee well-being (Ufua et al., 2020).

The well-being of employees can be improved under AL (Asim et al., 2021).

Image: Google, 2025

Let’s explore what the existing literature tells us about leadership and its impact, providing context for our questions.

Several studies suggest that leadership behaviors are not rigid and can be altered through targeted interventions (Mason et al., 2014). For example, research indicates that leaders can satisfy their need for autonomy by managing their own actions (van Tuin et al., 2020). Additionally, we found that using data to guide management can effectively shift leaders towards more desirable styles (McCarthy et al., 2017). These findings lay a strong foundation for our first research question, showing that modifying leadership behavior is achievable using various techniques.

Addressing our second research question of whether shifting authoritarian leaders’ focus to performance metrics improves employee motivation and morale, we find that well-being can actually improve even under challenging authoritarian conditions (Asim et al., 2021). For instance, one study found that by enhancing employees’ psychological connection to their organization, negative effects of authoritarian leadership were buffered, leading to improvements in helping behaviors and productivity. Another study demonstrated that when leaders shifted from rigid autocratic styles to more flexible approaches, employee productivity and morale improved (Ufua et al., 2020).

Collectively, these findings show that leadership behaviors can be constructively redirected, including through data management approaches. They also demonstrate that employee well-being can improve by modifying a leader’s approach or by strengthening employee resilience.

Next, we’ll ground these findings in established psychological theories that help explain the “why” behind these behaviors.

6 of 18

Theoretical Foundation

McClelland’s Motivational Needs Theory	Self- Determination Theory
Achievement Affiliation Power	Autonomy Competence Relatedness

McClelland, 2015

Ryan & Deci, 2000

Image: Google, 2025

Our research is built upon two key psychological theories that help us understand the underlying motivations for behavior, especially in leadership.

First, we have McClelland’s (2015) motivational needs theory. This theory identifies three primary learned needs that drive motivation: Achievement, affiliation, and power. This study focuses on the need for power. Within this context, McClelland distinguishes between personalized power, which emphasizes dominance and self-interest–often seen in authoritarian leaders–and socialize power, which focuses on influencing others toward collective organizational goals. This distinction highlights that satisfying a need for power doesn’t have to be negative.

Diving a bit deeper, we also rely on Ryan and Deci’s (2000) self-determination theory. This theory suggests that optimal motivation and well-being depend on satisfying three innate psychological needs: Competence, autonomy, and relatedness. Our primary focus is competence, which involves mastering tasks, and autonomy, which is the sense of having control and self-direction over one’s own actions. The theory suggests that authoritarian leaders attempts to control others might be a maladaptive way to try and satisfy their unmet needs for autonomy and competence.

Ideally, we would like to boost the leader’s competence and autonomy with an intervention that gives them a greater sense of effectiveness, and ultimately shift them to a more socialized type of power.

Now, let’s look at the gaps in existing literature and see how they can be addressed.

7 of 18

Addressing the Research Gaps

Gaps

Literature does not explicitly address whether metrics-based approaches can satisfy authoritarian leaders’ need for power.
Direct impact of data management interventions on employee motivation and morale under AL is unexplored.

Hypotheses

Metrics-based management fulfills authoritarian leaders’ power needs.
Redirecting authoritarian leaders’ focus to metrics improves employee (a) motivation and (b) morale.

We’ve seen from existing research that leadership styles aren’t fixed and they can be changed, including through interventions that use data to guide actions (Mason et al., 2014; McCarthy et al., 2017). However, some important questions remain unanswered.

A key gap is whether focusing on focusing on performance data can actually fulfill an authoritarian leader’s underlying need for power in a positive way.

Another area to explore is how data-driven modifications in authoritarian behaviors impact employee motivation and morale.

My proposed intervention aims to fill these gaps by first testing if metrics-based management can indeed satisfy the authoritarian leader’s need for power. And, at the same time, we’ll investigate if this shift in focus leads to improvements in employee motivation and morale.

Next, we’ll examine the methods that will be used to test these hypotheses.

8 of 18

Procedure

Intervention

Three phase approach:

Educate - one week
Demonstrate - one week
Apply - two weeks

Study Design

Quasi-experimental, single group pretest-post-

posttest design

Participants

Single facility in the automotive manufacturing industry

5-10 leaders exhibiting authoritarian behaviors

50-100 employees

Grant & Wall, 2009

Chishty-Mujahid, 2016;

Lakens, 2022

Taylor et al., 2005

Alright, let’s now walk through the procedure that will be used to conduct this study.

First, for our study design, I chose a quasi-experimental approach using a single group that will measured at three different intervals: Once before the intervention to get a baseline, immediately after the intervention to evaluate the impact, and again six weeks later to assess if the changes are sustainable. This design is practical for organizational settings, as it allows us to examine potential effects in a real-world context (Grant & Wall, 2009). However, because we can’t randomly assign participants or fully control all variables in a natural setting, it’s important to acknowledge that other factors could also contribute to any observed changes.

For our participants, we’ll be recruiting from a U.S.-based automotive manufacturing company. Authoritarian leadership is prevalent within this industry, making it a relevant context for this study (Chishty-Mujahid, 2016). Five to ten mid- and senior-level participating leaders who exhibit authoritarian behaviors are projected for this study, as well as 50-100 employees from their teams. This sample size is based on established practices to ensure comprehensive data collection when researching small, well-defined populations, within practical resource limitations (Lakens, 2022).

Moving on to our intervention, it’s designed to empower leaders by helping them use a data-management approach to be more effective in their roles. This goal is to provide them with a greater, more constructive sense of control, which we hypothesize will satisfy their underlying need for power (Mason et al., 2014). The intervention is four weeks long and is broken down into three phases that follow a widely used and effective behavior training model (Taylor et al., 2005).

First in the “educate” phase, participants will learn about the benefits of effective data management.

Next, the “demonstrate” phase focuses on how tracking specific data points, such as motivation and morale, can impact productivity and how leaders can improve these areas.

And finally, the “apply” phase provides leaders will receive with guided support to implement these data management principles in their daily roles.

Moving forward, we’ll look at the assessment tools we’ll use to measure the variables and determine whether this intervention is effective.

9 of 18

Employee Assessment Tools

Authoritarian Leadership	Motivation and Morale
Autocratic Leadership Behavior Scale Dimension of the Charismatic Leadership in Organizations (CLIO) questionnaire Rates employee perceptions of autocratic leadership among superiors Inform selection of leaders for intervention	Vigor and Dedication Scales Dimensions of the Utrecht Work Engagement Scale (UWES-9) Vigor scale rates employees’ willingness to exert effort, aligning with motivational construct Dedication scale rates attitudes that reflect enthusiasm and pride, aligning with morale construct

De Hoogh & Den Hartog, 2009

Mills et al., 2012

Here, we’ll take a look at the assessment tools we’ll be using in this study, starting with those for participating employees.

Our first measure will help identify and select leaders for our intervention based on employee perceptions of their authoritarian behaviors. To do this, we’ll use the 5-item Autocratic Leadership Behavior scale, which is a subscale from the Charismatic Leadership in Organizations questionnaire (De Hoogh & Den Hartog, 2009). Employees will rate how often they observe these behaviors from their superiors on a 7-point scale, from “never” to “always.” This scale is well-established and has shown strong reliability and validity in research.

Our second measure is the Utrecht Work Engagement Scale, which we’ll use to assess motivation and morale. Specifically, we’ll focus on the vigor subscale to capture motivation and the dedication subscale to assess morale, as these concepts are closely aligned (Mills et al., 2012; Schaufeli et al., 2002; Wood & Bandura, 1989). Employees will respond to three statements for each subscale, using a 7-point scale with responses ranging from “never” to “always.” The subscales are reliable and accurately measure the distinct aspects of the vigor and dedication showing strong construct validity.

We’ll use the Utrecht Work Engagement Scale to collect data before the intervention, three weeks into the intervention, then six weeks after the last collection. The data will be used to test the second hypothesis, and we’ll also share aggregated, anonymous results with leaders during the intervention to give them actionable feedback on employee well-being.

That covers the tools for the employees. Now, let’s shift to the assessment tools we’ll be using with the leaders.

10 of 18

Leader Assessment Tools

Motivational Need for Power	Power Need Satisfaction
Power Management Inventory (PMI) Personalized power subscale measures dominant behaviors Socialized power subscale measures influence toward collective goals Baseline measure to assess the extent that personalized power is a motivating factor among AL	Basic Psychological Needs Satisfaction Scale (BPNSS) Autonomy and competence subscales are linked to satisfaction of power needs (Deci & Ryan, 2000) Measured at baseline, immediately after intervention, and six weeks post-intervention Determine whether the intervention constructively satisfies power needs

Benson & Owens, 1985

Olafsen et al., 2021

These are the assessment tools we’ll be using specifically with the leaders participating in the study.

First, to understand what extent power is a motivating factor for authoritarian leaders at the beginning of our study, we’ll use the Power Management Inventory. This scale helps assess leaders’ motivational drive in two categories: Personalized power, which is focused on dominance, and socialized power, which is about influencing others toward collective goals (Benson & Owens, 1985). The Power Management Inventory is a well-established tool that has shown good reliability and is effective in distinguishing between these different types of power motives (Henderson, 1993). As mentioned, this will be a baseline measure to understand their initial power motivations.

Next, we’ll use the Basic Psychological Needs Satisfaction Scale to measure whether the intervention was effective in satisfying the leader’s need for power constructively (Olafsen et al., 2021). Based on self-determination theory, we know feeling a sense of autonomy and competence are important for satisfying psychological needs, including those related to power (Ryan & Deci, 2000). Given this theoretical foundation, we’ll use the reliable and valid autonomy and competence subscales to measure power needs satisfaction (Olafsen, et al., 2021).

These tools help us assess both the leaders’ starting motivations and whether the intervention effectively helps to meet those needs in a positive way.

Now, let’s move forward to how we’ll analyze the data we gather using these tools.

11 of 18

Analysis

Hypothesis Testing

Repeated measures ANOVAs

Descriptive Statistics

Identify outliers

Pearson’s Correlation

Establish a link between AL and personalized power needs

Rosnow & Rosenthal, 2014

Sedgwick, 2012

Smaga, 2021

Alright, let’s break down our plan for analyzing the data collected from our participants using the different scales at various times.

First, we’ll start with descriptive statistics. SPSS will be used to generate the mean and standard deviation, which will then be used to generate histograms and boxplots for each of the datasets collected from each assessment. This will help identify any extreme outliers, which will be addressed by using a valid method of trimming equal data points from the opposite end of the data set as well (Rosnow & Rosenthal, 2014).

Next, we’ll conduct a preliminary analysis to check an important underlying assumption of this study: Whether there’s a link between autocratic leadership and leaders’ self-reported need for personalized power. As we’ve covered, power and control are established dimensions of authoritarian leadership (Kelly & MacDonald, 2019). However, this specific link has not been explicitly addressed in literature. We’ll use a Pearson’s correlation test to see if these two variables are significantly related (Sedgwick, 2012).

Then, to test our main hypotheses one and two, we’ll use a one-way repeated measures ANOVA. This is a standard and appropriate method to determine if there are any significant changes in our key outcomes, like leaders’ power need satisfaction and employee motivation and morale, across the three time points where we collected data (Smaga, 2021).

We’ll now shift into what the assumptions and limitations are based on these tests and the overall study.

12 of 18

Anticipated Outcomes

Assumptions	Limitations
Clear correlation - Employee views of autocratic leadership align with leaders’ self-reported personalized power For H1 - Increased and sustained satisfaction of AL power needs through improved autonomy and competence For H2a-b - Increased employee motivation and morale as AL focus shifts to metrics	Study does not explore whether AL style fundamentally changes or if the power motive shifts from personalized to socialized Quasi-experimental design limits internal validity and generalizability

Grant & Wall, 2009

Alright, let’s take a look at what we expect to find from this research and the limitations we need to consider.

First, to help validate our research, we presume to find a clear link between how employees see autocratic leadership and the leaders’ own reported need for personalized power. This assumption is based on power and control being established dimensions of authoritarian leadership as well as personalized power having similar dimensions (Kelly & MacDonald, 2019).

Next, based on previous research showing that leadership behaviors can be modified through interventions (van Tuin et al., 2020) and data-driven approaches (McCarthy et al., 2017), a significant increase in leaders’ power needs satisfaction is expected, specifically by improving leaders’ autonomy and competence.

And for our second hypothesis, we anticipate higher motivation and morale as their leaders shift towards performance metrics instead of direct oversight. This aligns with research that more flexible forms of leadership have resulted in improved employe well-being (Asim et al., 2021; Ufua et al., 2020). The proposed intervention is anticipated to have a similar effect.

While these are the expected outcomes, it is important to point out the limitations of this research as well. One key limitation is that this study won’t explain whether the fundamental authoritarian style has changed, or if it is simply their motives that shifted from personalized dominating motives to more socialized, constructive ones.

Also, because we’re using a quasi-experimental design in a real world setting without random assignment, there are natural limits to our internal validity. Specifically, it’s difficult to rule out other explanations for outcomes, affecting how widely we can generalize our findings to other organizations (Grant & Wall, 2009).

Understanding these limitations helps frame the results appropriately and highlights areas for future research.

13 of 18

Dissemination of Findings

Structure

Focus on connecting results to organizational goals for practical use

For Leaders

Reports, presentation, and discussion linking results to power needs and employee outcomes

For Employees

Presentation and discussions highlighting how their feedback informed the results

Holistic

Multiple communication methods for sustainability and engagement

Collaborative

Interactive workshop to foster leader-employer dialogue and action

Rynes et al., 2001

Wilson et al., 2010

Here, we’ll take a look at how the results will be shared. To maximize the benefits to the organization, we’ll utilize a structured approach that connects the findings directly to the company’s goals (Rynes et al., 2001).

For the leaders who participated, we’ll provide a comprehensive report that clearly links the leaders’ need for power to changes in their satisfaction, and we’ll show how using data management improved motivation and morale. We’ll also have presentations and open discussions. Using different communication methods such as reports, presentations, and open discussions are known to be effective for sharing organizational findings and helping changes stick (Wilson et al., 2010).

When sharing with employees, our priority is transparency and showing how the results are relevant to them. We’ll share summaries, give presentations, and hold facilitated discussions, highlighting how their feedback was used in the study and what the outcomes were. Showing employees that their input matters can help build trust and reduce resistance to change (Brownson et al., 2018).

Finally, we plan to hold an interactive workshop with both leaders and employees. This will be a chance for open dialogue, to promote mutual understanding, and to collaboratively develop plans for sustaining the positive changes. Research suggests that these kind of interactive sessions really help people accept and apply the study’s findings (Wilson et al., 2010).

Next, I want to touch on the ethical considerations for this study.

14 of 18

Ethical Considerations

APA Compliant	Potential Risks	Mitigation Strategies
Informed consent (voluntary participation and withdrawal rights) Anonymous (numerical codes) and confidentiality (aggregated data) Transparency regarding the use of generative AI	Risk of retaliation against employees providing feedback Potential defensiveness from leaders regarding the term “authoritarian leadership”	Using the term “autocratic leadership behaviors” instead of “authoritarian” Framing the study constructively (potential autocratic benefits and goal of socialized power) Highlighting that leaders will also assess their own needs

APA, 2017

APA, 2023

Pizzolitto et al., 2023

The proposed intervention for this study was designed with the APA (2017) ethical guidelines in mind.

Some key measures taken to protect participants include comprehensive informed consent forms that clearly explain everything about the study, including their right to voluntarily participate and withdraw at any time (APA, 2017). We’re also ensuring that participants remain anonymous by using numerical codes instead of names, and all data shared will be aggregated to protect individual identities. It’s also important to be transparent, so I clearly stated my use of AI in structuring the data (APA, 2023).

Despite these precautions, I identified a couple of potential risks that must be acknowledged. Given the hierarchical nature of the manufacturing industry, there’s a potential risk, even if small, that employees could fear retaliation for providing feedback on leadership. Also, the term “authoritarian leadership” can sound negative and might make participating leaders feel defensive.

To address the concerns, I implemented some mitigation strategies. First, recognizing language sensitivity, we’re using the term “autocratic leadership behaviors” in all communications with participants, since it will likely be perceived less connotative than “authoritarian.” Second, we’re framing the study’s purpose constructively, emphasizing that autocratic approaches can have benefits in certain contexts, and highlighting that our goal is to help leaders foster a more positive socialized form of power (Pizzolitto et al., 2023). We’re also making sure to emphasize that leaders will be assessing their own motivational needs, ensuring a balanced perspective in the study.

By combining efforts toward confidentiality, subject sensitivity, and transparent communication, it is believed that we can effectively balance the goal of gaining valuable insights with our ethical obligation to protect the well-being of everyone involved.

15 of 18

Key Takeaways

Advances understanding of authoritarian leadership and satisfying underlying power needs
Significant implications for:

Strategies for workplaces impacted by authoritarian leadership
Enhancing organizational productivity
Promoting a healthier workplace culture

16 of 18

References

1. American Psychological Association. (2017). Ethical principles in psychology and code of conduct 2002, amended effective June 1, 2010, and January 1, 2017). https://www.apa.org/ethics/code/ethics-code-2017.pdf

2. American Psychological Association. (2023). Policy on the use of generative AI in scholarly writing. https://www.apa.org/pubs/journals/resources/publishing-tips/policy-generative-ai

3. Asim, M., Zhiying, L., Nadeem, M. A., Ghani, U., Arshad, M., & Yi, X. (2021). How authoritarian leadership affects employee’s helping behavior? The mediating role of rumination and moderating role of psychological ownership. Frontiers in Psychology, 12, 1–13. https://doi.org/10.3389/fpsyg.2021.667348

4. Benson, P. G., & Owens, W. A. (1985). Power management inventory. In J. V. Mitchell, Jr. (Ed.), The ninth mental measurements yearbook.

5. Brownson, R. C., Eyler, A. A., Harris, J. K., Moore, J. B., & Tabak, R. G. (2018). Getting the word out: New approaches for disseminating public health science. Journal of Public Health Management and Practice, 24(2), 102–111. https://www.jstor.org/stable/48566993'

6. Chishty-Mujahid, N. (2016). The prevalent and persistent virtues of autocratic leadership in the corporate sector: An analysis. Business Review, 11(1), 62–68. https://doi.org/10.54784/1990-6587.1078

7. De Hoogh, A. H. B., Greer, L. L., & Den Hartog, D. N. (2015). Diabolical dictators or capable commanders? An investigation of the differential effects of autocratic leadership on team performance. The Leadership Quarterly, 26(5), 687–701. https://doi.org/10.1016/j.leaqua.2015.01.001

8. Google (2025). Gemini (Image generation with 2.5 Pro version) [Large language model]. https://gemini.google.com/app

17 of 18

9. Grant, A. M., & Wall, T. D. (2009). The neglected science and art of quasi-experimentation: Why-to, when-to, and how-to advice for organizational researchers. Organizational Research Methods, 12(4), https://doi.org/10.1177/1094428108320737

10. Henderson, M. C. (1993). Measuring managerial motivation: The power management inventory. Journal of Nursing Measurement, 1(1), 67–80. https://doi.org/10.1891/1061-3749.1.1.67

11. Huang, Q., Zhang, K., Wang, Y., Bodla, A. A., & Zhu, D. (2022). When is authoritarian leadership less detrimental? The role of leader capability. International Journal of Environmental Research and Public Health, 20(1), 1–17. https://doi.org/10.3390/ijerph20010707

12. Kelly, S., & MacDonald, P. (2019). A look at leadership styles and workplace solidarity communication. International Journal of Business Communication, 56(3), 432–448. https://doi.org/10.1177/2329488416664176

13. Lakens, D. (2022). Methodology and research practice: Sample size justification. Collabra: Psychology, 8(1), 1–28. https://doi.org/10.1525/collabra.33267

14. Mason, C., Griffen, M., & Parker, S. (2014). Transformational leadership development: Connecting psychological and behavioral change. Leadership and Organizational Development Journal, 35(3), 174–194. https://doi.org/10.1108/LODJ-05-2012-0063

15. McCarthy, J., Sammon, D., & Murphy, C. (2017). Changing leadership behaviors: A journey towards data driven culture. Proceedings of the 25th, European Conference on Information Systems (ECIS) [Paper presentation]. Guimarães, Portugal. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1013&context=ecis2017_rip

16. McClelland, D. (2015). Achievement motivation theory. In Organizational Behavior 1 (pp. 46–60). Routledge.

17. Mills, M., Culbertson, S., & Fullagar, C. (2012). Conceptualizing and measuring engagement: An analysis of the Utrecht work engagement scale. Journal of Happiness Studies, 13(3), 519–545. https://doi.org/10.1007/s10902-011-9277-3

18. Olafsen, A. H., Halvari, H., & Frølund, C. W. (2021). The basic psychological need satisfaction and need frustration at work scale: A validation study. Frontiers in Psychology, 12, 1–12. https://doi.org/10.3389/fpsyg.2021.697306

19. OpenAI. (2025). DALL·E 3 [Image generator]. https://chat.openai.com/

20. Pizzolitto, E., Verna, I., & Venditti, M. (2023). Authoritarian leadership styles and performance: A systematic literature review and research agenda. Management Review Quarterly, 73(2), 841–871. https://doi.org/10.1007/s11301-022-00263-

18 of 18

21. Rosnow, R. L., & Rosenthal, R. (2014). Beginning behavioral research (7th ed.). Pearson Education.

22. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037110003-066X.55.1.68

23. Rynes, S. L., Bartunek, J. M., & Daft, R. L. (2001). Across the great divide: Knowledge creation and transfer between practitioners and academics. Academy of Management Journal, 44(2), 340–355. https://www.jstor.org/stable/3069460

24. Schaufeli, W. B., Salanova, M. González-romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:101563093032611.

25. Sedgwick, P. (2012). Pearson’s correlation coefficient. BMJ, 345, 1–2. https://doi.org/10.1136/bmj.e4483

26. Smaga, L. (2021). One-way repeated measures ANOVA for functional data. In Data analysis and rationality in a complex world (pp. 243–251). Springer International Publishing. https://doi.org/10.1007/978-3-030-60104-1_27

27. Taylor, P. J., Russ-Eft, D. F., & Chan, D. W. L. (2005). A meta-analytic review of behavior modeling training. Journal of Applied Psychology, 90(4), 692–709. https://doi.org/10.1037/0021-9010.90.4.692

28. Ufua, D. A., Salau, O. P., Ikpefan, O., Dirisu, J. I., & Okah, E. E. (2020). Addressing operational complexities through re-inventing leadership style: A systemic leadership intervention. Heliyon, 6(7), 1–8. https://doi.org/10.1016/j.heliyon.2020.e04270

29. van Tuin, L., Schaufeli, W. B., van Rhenen, W., & Kuiper, R. M. (2020). Business results and well-being: An engaging leadership intervention study. International Journal of Environmental Research and Public Health, 17(12), 1–18. https://doi.org/10.3390/ijerph17124515

30. Wilson, P. M., Petticrew, M., Calnan, M. W., & Nazareth, I. (2010). Implementation Science, 5(91), 1–16. https://doi.org/10.1186/1748-5908-5-91

31. Wood, R., & Bandura, A. (1989). Social cognitive theory of organizational management. The Academy of Management Review, 14(3), 361–384. https://www.jstor.org/stable/258173