What Is Leadership Theory: An Essential Guide

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Learn about the most common leadership theories and how to foster leadership in the workplace. With our help, you may become a more effective leader and foster a pleasant work atmosphere.


Understanding leadership is critical for success in every workplace since it shapes individuals’ experiences and drives organisational success. To completely comprehend the concept of leadership, one must first investigate the theories that serve as its foundation.

As firms develop and adapt, the value of competent leadership becomes more apparent. Leaders are accountable for steering their teams to success, fostering innovation, and fostering a healthy work environment. Leadership, on the other hand, is not a one-size-fits-all strategy. There are multiple leadership theories that provide various viewpoints on what constitutes a good leader.

Any organisation’s success is dependent on effective leadership. A good leader can motivate and engage their team, whereas a bad leader can demotivate and disconnect them. A great leader is already familiar on the main drivers to employee motivation, knowing all the measures to do what it takes. The study of what makes a good leader and how they might be improved is known as leadership theory. Businesses can determine the attributes and behaviours that are most effective in their specific setting by knowing different leadership philosophies.

In this article, we will look at the numerous aspects of leadership theory, its historical growth, and its practical consequences in the workplace. By grasping these notions, we can unleash the potential for successful leadership and a thriving work environment.

Historical overview of leadership theory

Early theories of leadership

The study of leadership originated from early research aimed at determining successful leaders’ innate characteristics and actions. These initial theories served as the basis for further investigation and comprehension of leadership.

Trait theory

Trait theory suggests that some inherent qualities make people suitable for leadership roles. It aims to recognise distinct characteristics linked with successful leadership, like self-assurance, honesty, and emotional awareness.

Behavioural theory

Theories on behaviour have caused a shift in emphasis from innate traits to observable actions. These theories propose that leadership can be acquired and honed by learning particular behaviours and skills. The Ohio State and University of Michigan studies are notable instances of initial behavioural theories.

Contemporary theories of leadership

Contemporary leadership theories have evolved from early theories to provide more detailed and contextual insights into effective leadership. They acknowledge the significance of situational factors and the requirement for flexible leadership strategies.

Contingency theory

The Contingency theory highlights the significance of matching a leader’s style with the unique features of the situation to achieve effective leadership. One prominent illustration of this approach is Fiedler’s Contingency Model.

Transformational leadership theory

The theory of transformational leadership emphasises the capacity of a leader to encourage and drive their followers to go beyond their personal interests for the benefit of the group. Such leaders display traits such as charm, intellectual stimulation, personalised attention, and inspirational motivation.

Situational leadership theory

The theory of situational leadership suggests that successful leadership depends on the followers’ skill level and the task requirements. The Situational Leadership Model by Hersey and Blanchard provides a useful structure for adjusting leadership approaches according to particular circumstances.

Trait-based theories of leadership

Effective leadership is influenced by inherent characteristics known as traits. Although success is not solely dependent on these traits, recognising them can aid in identifying potential leaders and designing leadership development initiatives.

Definition and key concepts

The fundamental qualities and abilities linked with effective leadership are the subject of trait-based theories. These characteristics include self-assurance, honesty, empathy, and emotional intelligence.

Common traits associated with effective leaders

  • Self-assurance: Effective leaders strongly believe in their talents and decisions, instilling trust in their team.
  • Integrity: Leaders with integrity uphold strong moral and ethical standards, earning their followers’ trust and respect.
  • Empathy: Understanding and relating to the feelings and experiences of others creates effective interpersonal relationships.
  • Emotional Intelligence: Emotionally intelligent leaders can recognise and regulate their own emotions as well as successfully understand and respond to the emotions of others.

Limitations of trait-based theories

While qualities might provide insight into prospective leadership, they cannot fully explain the complexity of leadership. Contextual elements and acquired behaviours are also important in good leadership.

Behavioural theories of leadership

Behavioural theories, rather than intrinsic features, focus on the acts and behaviours of leaders. They contend that good leadership can be acquired and honed through the practice of specific behaviours and skills.

Definition and key concepts

Behavioural theories investigate leaders’ actions, communication methods such as encouraging others to talk about their day at work and decision-making processes. They serve as a framework for the development of desired leadership behaviours.

Limitations of behavioural theories

Behavioural theories can shed light on leadership behaviours. They may, however, oversimplify the nuances of leadership by ignoring situational and contextual considerations.

Contingency theories of leadership

Contingency theories acknowledge that effective leadership is dependent on a match between a leader’s style and the specific characteristics of the circumstance or environment.

Definition and key concepts

According to contingency theories, only some leadership styles are universally beneficial. Instead, leadership success is determined by aligning the leadership style with the situational demands.

Limitations of contingency theories

Contingency theories are useful for analysing the interaction between leadership styles and conditions. They may, however, oversimplify the complicated dynamics of good leadership.

Transformational leadership theory

Transformational leadership theory emphasises the leader’s ability to inspire and motivate followers to put the communal good ahead of their individual self-interest.

Definition and key concepts

Transformational leaders inspire their followers to embrace a shared vision and exhibit exceptional performance. They create a positive and empowering work environment, stimulating personal growth and development.

Characteristics of transformational leaders

  • Charisma: Transformational leaders have a magnetic and influential presence that inspires and motivates others around them.
  • Intellectual Stimulation: They build an environment of constant progress by encouraging creativity, innovation, and critical thinking among their followers.
  • Individualised Attention: Transformational leaders provide personalised assistance, mentorship, and direction to each follower, considering their individual needs and objectives.
  • Inspirational Motivation: They articulate a compelling vision, impart a sense of purpose, and inspire people to strive for perfection.
  • Training: They are trained professionals who undergo many educational programmes like Leadership training, communication training, conflict resolution training, and motivational training. These training programmes help them build new characteristics that will immensely affect their leadership skills in a positive way.

Benefits of transformational leadership

Transformational leadership promotes high levels of employee engagement, employee feedback, dedication, and satisfaction. It promotes organisational innovation, adaptability, and a positive work environment.

Transformational leadership theory’s limitations

While transformational leadership is highly respected, it may only be appropriate for some situations. The alignment of the leader, followers, and needs of the organisation determines the efficiency of this approach. However, there are great benefits of employee’s feedback, engagement, dedication and satisfaction. Mainly contributing to a much healthier and safer work environment.

Situational leadership theory

According to situational leadership theory, effective leadership depends on adapting one’s style to the developmental level of followers and the demands of the work at hand.

Definition and key concepts

Situational leadership theory emphasises the importance of adapting one’s leadership style to the demands and readiness of followers in a given situation.

Application of situational leadership in the workplace

Situational leadership in the workplace provides a realistic framework for leaders to change their approach based on the individual developmental requirements of their team members. It encourages personal development, liberty, and the creation of a supportive atmosphere.

Limitations of situational leadership theory

While situational leadership theory provides a useful framework, its effectiveness is dependent on correctly measuring follower readiness and selecting the proper leadership style. It necessitates continuous evaluation and correction.

How to promote leadership in the workplace

To promote leadership in the workplace, individuals must be nurtured and developed to take on leadership roles and responsibilities. Several strategies can be used to encourage leadership development:

  • Leadership Development Programmes: Implement comprehensive programmes that provide individuals with the opportunity to improve their leadership skills through training, workshops, coaching, and mentorship.
  • Creating a Leadership Pipeline: Establish a planned career progression route that allows individuals to grow and advance within the organisation, providing a steady supply of skilled leaders.
  • Mentorship and Coaching Programmes: Encourage the formation of formal or informal mentorship and coaching connections between experienced leaders and developing talent to give direction, support, and knowledge transfer. Try prioritising educational training such as Leadership skills training, Motivation training, communication skills training, and decision-making training. These courses help you develop essential leadership skills and traits to become a better leader.
  • Empowering Employees to Lead: Encourage employees at all levels to take initiative, make decisions, and lead initiatives by creating a culture in which they are encouraged to do so. Allow individuals to develop their leadership abilities by providing them with autonomy and trust.

Implications of leadership theory in the workplace

Leadership theory has far-reaching ramifications for organisational HR and management practices. It influences crucial areas such as hiring and selection, performance management, and team dynamics.

  • Processes of Selection and Hiring: Include leadership talents and attributes in the selection criteria to find people with leadership potential. Assess candidates’ behavioural indications and previous experiences to determine their leadership potential.
  • Management and Evaluation of Performance: Align performance evaluation methods with leadership qualities and behaviours. Provide feedback, coaching, and development opportunities to improve leadership skills and progress.
  • Team Dynamics and Collaboration: Promote a shared leadership culture in which team members interact, communicate, and share responsibilities. Encourage different points of view, open debate, and the recognition and appreciation of leadership efforts.
  • Developing a Leadership Culture: Organisations should emphasise the value of leadership at all levels. Create chances for leadership development, reinforce desired leadership behaviours, and recognise and advance good leaders.

The role of leadership theory in human resource compliance

Leadership theory connects with human resource compliance, assuring ethical practises, encouraging diversity and inclusion, and addressing power dynamics in leadership roles.

  • Promoting Inclusive and Diverse Leadership: Leadership theory guides businesses in promoting diversity and inclusion within leadership positions. Motivating diverse leadership perspectives enhances innovation, improves decision-making, and creates a more healthy work environment.
  • Mitigating Bias and Discrimination in Leadership Practices: Human resource compliance should include regular audits of leadership practices to uncover and correct biases or discriminatory behaviours. Training programmes and policies should provide equitable and fair opportunities for leadership positions.
  • Addressing Power Dynamics and Ethical Leadership: Leadership theory emphasises the significance of ethical leadership and the prudent use of power. Organisations should adopt ethical principles, codes of conduct, and accountability procedures to guarantee that leaders operate in the best interests of their followers and the organisation.
  • Compliance with Legal and Regulatory Requirements: Businesses must ensure that their leadership practices meet the legal and regulatory requirements. This also includes anti-discrimination laws, labour regulations, and maintaining transparency in leadership selection and promotion processes.

In Summary

Leadership theory offers useful insights into the multidimensional character of effective workplace leadership. Organisations may foster an atmosphere that nourishes and develops good leaders by understanding leadership’s historical evolution, qualities, behaviours, and situational components. Organisations may unleash the full potential of their workforce through leadership development programmes, inclusive practises, and ethical leadership, resulting in innovation, growth, and success. As we continue to research and develop our understanding of leadership theory, we must apply what we’ve learned to create work environments that empower and inspire individuals to fulfil their full leadership potential. Whether you’re an aspiring leader or a seasoned professional, there’s always room for growth and improvement in your leadership skills.

Meta Description: Unlock the secrets of effective leadership theory. Explore historical evolution, traits, behaviours, and transformative power in the workplace. Empower your leadership skills.

About the Author

Taha Abdul Ghani, a skilled writer at Human Focus, excels in research-driven content creation. With 3 years of experience, his preferred writing niches encompass health and safety, tech, sports, HR compliance, entertainment, lifestyle, and history.

The post What Is Leadership Theory: An Essential Guide appeared first on The 6Q Blog.

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1) input, (2) rationale, and (3) output. Given the prompt, an LLM is able to mimic the triplet demonstration to generate the rationale for any new input. For instance, in a commonsense question answering task, given the input question “Sammy wanted to go to where the people are. Where might he go? Answer Choices: (a) populated areas, (b) race track, (c) desert, (d) apartment, (e) roadblock”, distilling step-by-step provides the correct answer to the question, “(a) populated areas”, paired with the rationale that provides better connection from the question to the answer, “The answer must be a place with a lot of people. Of the above choices, only populated areas have a lot of people.” By providing CoT examples paired with rationales in the prompt, the in-context learning ability allows LLMs to output corresponding rationales for future unseen inputs.

We use the few-shot CoT prompting, which contains both an example rationale (highlighted in green) and a label (highlighted in blue), to elicit rationales from an LLM on new input examples. The example is from a commonsense question answering task.

After the rationales are extracted, in the second stage, we incorporate the rationales in training small models by framing the training process as a multi-task problem. Specifically, we train the small model with a novel rationale generation task in addition to the standard label prediction task. The rationale generation task enables the model to learn to generate the intermediate reasoning steps for the prediction, and guides the model to better predict the resultant label. We prepend task prefixes (i.e., [label] and [rationale] for label prediction and rationale generation, respectively) to the input examples for the model to differentiate the two tasks.

Experimental setup

In the experiments, we consider a 540B PaLM model as the LLM. For task-specific downstream models, we use T5 models. For CoT prompting, we use the original CoT prompts when available and curate our own examples for new datasets. We conduct the experiments on four benchmark datasets across three different NLP tasks: e-SNLI and ANLI for natural language inference; CQA for commonsense question answering; and SVAMP for arithmetic math word problems. We include two sets of baseline methods. For comparison to few-shot prompted LLMs, we compare to few-shot CoT prompting with a 540B PaLM model. In the paper, we also compare standard task-specific model training to both standard fine-tuning and standard distillation. In this blogpost, we will focus on the comparisons to standard fine-tuning for illustration purposes.

Less training data

Compared to standard fine-tuning, the distilling step-by-step method achieves better performance using much less training data. For instance, on the e-SNLI dataset, we achieve better performance than standard fine-tuning when using only 12.5% of the full dataset (shown in the upper left quadrant below). Similarly, we achieve a dataset size reduction of 75%, 25% and 20% on ANLI, CQA, and SVAMP.

Distilling step-by-step compared to standard fine-tuning using 220M T5 models on varying sizes of human-labeled datasets. On all datasets, distilling step-by-step is able to outperform standard fine-tuning, trained on the full dataset, by using much less training examples.

Smaller deployed model size

Compared to few-shot CoT prompted LLMs, distilling step-by-step achieves better performance using much smaller model sizes. For instance, on the e-SNLI dataset, we achieve better performance than 540B PaLM by using a 220M T5 model. On ANLI, we achieve better performance than 540B PaLM by using a 770M T5 model, which is over 700X smaller. Note that on ANLI, the same 770M T5 model struggles to match PaLM’s performance using standard fine-tuning.

We perform distilling step-by-step and standard fine-tuning on varying sizes of T5 models and compare their performance to LLM baselines, i.e., Few-shot CoT and PINTO Tuning. Distilling step-by-step is able to outperform LLM baselines by using much smaller models, e.g., over 700× smaller models on ANLI. Standard fine-tuning fails to match LLM’s performance using the same model size.

Distilling step-by-step outperforms few-shot LLMs with smaller models using less data

Finally, we explore the smallest model sizes and the least amount of data for distilling step-by-step to outperform PaLM’s few-shot performance. For instance, on ANLI, we surpass the performance of the 540B PaLM using a 770M T5 model. This smaller model only uses 80% of the full dataset. Meanwhile, we observe that standard fine-tuning cannot catch up with PaLM’s performance even using 100% of the full dataset. This suggests that distilling step-by-step simultaneously reduces the model size as well as the amount of data required to outperform LLMs.

We show the minimum size of T5 models and the least amount of human-labeled examples required for distilling step-by-step to outperform LLM’s few-shot CoT by a coarse-grained search. Distilling step-by-step is able to outperform few-shot CoT using not only much smaller models, but it also achieves so with much less training examples compared to standard fine-tuning.


We propose distilling step-by-step, a novel mechanism that extracts rationales from LLMs as informative supervision in training small, task-specific models. We show that distilling step-by-step reduces both the training dataset required to curate task-specific smaller models and the model size required to achieve, and even surpass, a few-shot prompted LLM’s performance. Overall, distilling step-by-step presents a resource-efficient paradigm that tackles the trade-off between model size and training data required.

Availability on Google Cloud Platform

Distilling step-by-step is available for private preview on Vertex AI. If you are interested in trying it out, please contact vertex-llm-tuning-preview@google.com with your Google Cloud Project number and a summary of your use case.


This research was conducted by Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Thanks to Xiang Zhang and Sergey Ioffe for their valuable feedback.

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