Work & Organizational Psychology
Lecture Notes
Week 1
Lecture 1 – Studying behaviour in organizations
Part I – The Foundations of Work and Organizational Psychology
What motivates people at work?
What motivates most: money. support, recognition, progress or clear goals? Early views tended to assume that financial
incentives were the main motivator, reflecting industrial-age thinking where work was primarily about earning a living.
But contemporary research, especially Deci & Ryan’s Self-Determination Theory (2001), shows that people are also
motivated by three deep psychological needs:
• Autonomy – feeling that you have control or choice in what you do.
Example: A software developer who can decide how to structure her tasks feels more engaged than one
micromanaged by her boss.
• Relatedness – feeling connected to others at work.
Example: Employees in collaborative teams, who feel part of something larger, often report stronger
commitment.
• Growth (Competence) – feeling effective and improving one’s abilities.
Example: A nurse who receives ongoing feedback and new training opportunities experiences intrinsic
motivation.
Deci and Ryan argued that satisfying these needs supports internal motivation, while workplaces that ignore them tend
to rely on external control (rewards, punishments, rigid monitoring), which may reduce long-term motivation. This shift
reflects how work in the 21st century has evolved toward knowledge work, creativity, and emotional engagement.
What is Industrial and Organization Psychology
Industrial and Organizational (I-O) Psychology is the scientific study of human behaviour in work settings. It uses
psychological principles to improve both employee well-being and organizational performance.
→ The industrial side focuses on job analysis, personnel selection, performance appraisal, and training: the
“matching people to jobs” aspect.
→ The organizational side looks at motivation, leadership, culture, and teamwork: how people function within
systems and groups.
The field emerged in the late 19th and early 20th centuries, growing out of experimental psychology.
1876–1930: The birth of applied psychology
➢ Early pioneers like Hugo Münsterberg and James Cattell began applying psychological testing to workplace
problems.
➢ During World War I, psychologists developed the Army Alpha and Army Beta tests → early large-scale
intelligence tests used to assign soldiers to suitable roles.
➢ In 1917, Lillian Gilbreth earned the first Ph.D. in industrial psychology. Her research on time and motion studies
aimed to make work more efficient, laying the foundations for what became human engineering or ergonomics
→ fitting work environments to human capabilities.
Example: The layout of a control panel or assembly line can reduce fatigue and errors if designed with human
limits in mind.
1930–1964: The Human Relations movement
The next major influence came from the Hawthorne Studies, which observed workers at Western Electric’s
Hawthorne plant in Chicago. Researchers found that when workers were being studied, regardless of whether lighting
,or pay changed, their productivity increased simply because they felt noticed. This became known as the Hawthorne
Effect, showing that social and psychological factors influence performance as much as physical conditions.
The result was a shift from seeing workers as mechanical parts of a system to recognizing the “emotional world of the
worker.” Psychologists began studying job satisfaction, motivation, and group dynamics, giving rise to modern
organizational psychology.
- Example: Companies started introducing social breaks, feedback sessions, and leadership training to improve
morale and teamwork, not just efficiency.
Contemporary Challenges
In today’s 21st-century workplace, one of the new challenges is hybrid working: combining remote and in-office work.
While hybrid setups increase autonomy and work–life balance, they can also reduce social connectedness and blur
boundaries between work and home. Example: Remote workers might enjoy flexibility but miss informal interactions
that build trust and creativity. Managers must therefore find ways to maintain relatedness and shared purpose across
physical and digital spaces.
Another major topic is culture. Workplaces are no longer limited to one national context; globalization means cross-
cultural collaboration is routine. Understanding cultural differences is crucial for communication, leadership, and
motivation.
Hofstede’s Cultural Dimensions
Dutch researcher Geert Hofstede identified five dimensions that describe how cultures differ in their values and social
structures → Hofstede’s Cultural dimensions. These dimensions are often applied in I-O psychology to predict
workplace behaviour, leadership expectations, and communication styles.
• Individualism vs. Collectivism – whether people define themselves primarily as individuals or as part of a group.
Example: The Netherlands and the U.S. are relatively individualistic; people value personal achievement and
initiative. In collectivist cultures (like China or Japan), harmony and group goals take priority.
• Power Distance – the extent to which inequality and hierarchy are accepted.
Example: In France or many Asian countries, hierarchical authority is respected; in the Netherlands,
relationships are flatter and more informal.
• Uncertainty Avoidance – how comfortable people are with ambiguity or risk.
Example: Belgium tends to score high, meaning employees prefer clear rules and structure. Dutch culture is
more tolerant of flexibility and experimentation.
• Masculinity vs. Femininity – the emphasis on competition and achievement versus cooperation and quality of
life. Example: A masculine culture might reward assertiveness and success (e.g., the U.S.), while a feminine
culture values consensus and work–life balance (e.g., the Netherlands).
• Long-term vs. Short-term Orientation – whether people focus on future rewards and persistence or on
immediate results. Example: East Asian countries often emphasize long-term planning; Western European
nations may lean toward short-term outcomes and adaptability.
These differences shape how employees expect to be led, how decisions are made, and how feedback is given. A Dutch
manager might use open discussion for problem-solving, while a French team could expect decisions to come from
leadership. Recognizing these nuances is central to effective international management and collaboration.
Part II - Research Methods in Industrial and Organizational Psychology
Research Methods and Design
I-O psychology aims to understand and improve work behaviour scientifically = claims must be based on data, not
assumptions. To get reliable evidence, psychologists use systematic research methods that allow them to measure
behaviour, test hypotheses, and draw general conclusions. Good research design is what separates professional science
from intuition or “management fads.”
,a. Experimental designs
In an experiment, researchers manipulate one variable (the independent variable) and observe its effect on
another (the dependent variable). Participants are randomly assigned to different conditions.
• This random assignment helps ensure that any observed difference is due to the manipulation, not
preexisting differences between people.
• Experiments can occur in a laboratory (controlled environment) or in field settings (actual workplaces).
Example: A company wants to know whether flexible work hours improve job satisfaction. Employees are
randomly assigned to either a flexible or fixed schedule. If the flexible group reports higher satisfaction, we can
infer a causal effect.
An experiment can also have mediating variables (aka mechanisms) and moderating variables (influencers):
• A mediator explains how or why an independent variable affects the dependent variable (e.g. anxiety
explains the link: stress (IV) → increases anxiety (Mediator) → reduces performance (DV)).
• A moderator affects the strength or direction of the relationship between IV and DV. Stress (IV) →
performance (DV). But social support (Moderator) changes the effect: high support = stress has little
effect, and low support = stress strongly reduces performance. So, the moderator answers “when” or
“for whom” the IV-DV relationship holds.
In this way, you could have four or more variables in an experiment, using multiple moderators or mediators.
The IV affects both the Mediator and the DV. The Mediator carries part of the IV’s effect to the DV. The
Moderator influences how strong the mediation is (it may strengthen or weaken the indirect path). Example:
Stress (IV) → Anxiety (Mediator) → Performance (DV). But Social Support (Moderator) changes how strongly
anxiety affects performance. → With high support, anxiety has less negative impact.
b. Non-experimental designs
Sometimes, random assignment isn’t possible (e.g. for ethical, logistical, or practical reasons). In non-
experimental designs, researchers simply observe or measure variables as they naturally occur, without
manipulation. Two common forms are:
• Observational designs – watching and recording behaviour directly (e.g., how often managers give
feedback during meetings).
• Survey or questionnaire designs – collecting self-report data about attitudes, satisfaction, or stress.
Example: An HR department might survey employees about their perceived fairness of promotion policies. The
researcher can identify patterns but can’t claim cause-and-effect, because the data are correlational.
c. Quasi-experimental designs
These fall between experimental and non-experimental studies. They involve a treatment or comparison, but
without random assignment. Example: A company introduces a new training program in one branch but not
another. Comparing outcomes between branches can show whether training seems effective, though
differences might partly reflect other factors (like local culture).
, Data Collection Methods
I. Qualitative research
Qualitative methods explore how people experience their work. They use open-ended data such as
interviews, case studies, and document analyses.
→ They’re valuable for understanding meaning, context, and emotion.
→ But they’re less suitable for statistical testing.
Example: Interviewing employees after a merger to understand how they perceive the new company culture.
II. Quantitative research
Quantitative methods collect numerical data, usually through tests, scales, or physiological measures (like heart
rate for stress).
→ These data allow for statistical analysis, hypothesis testing, and generalization.
Example: Measuring job satisfaction scores across 500 employees to identify predictors of turnover.
Often, I-O researchers combine both types to get a fuller picture: qualitative data to explore issues, quantitative data to
test them.
Correlation and Causation
A correlation coefficient (r) shows the strength and direction of a relationship between two variables. It ranges from –
1.00 to +1.00. A positive correlation means as one variable increases, the other also increases (e.g., job satisfaction ↑ →
performance ↑). A negative correlation means as one increases, the other decreases (e.g., stress ↑ → performance ↓).
0.00 means no linear relationship. The larger the absolute value of r, the stronger the association but correlation alone
doesn’t prove causation.
Sometimes we examine how several predictors relate jointly to one outcome. A multiple correlation coefficient (R)
summarizes the overall association between multiple variables and one criterion. Example: Job performance might be
predicted by a combination of cognitive ability, conscientiousness, and experience. The combined R value shows how
well this set predicts performance.
Meta-analysis statistically combines results from many studies to find an overall average effect size. This helps identify
consistent patterns and estimate the “true” relationship across contexts.
Reliability and Validity
Reliability refers to whether a measurement tool gives stable and consistent results.
• Test–retest reliability: the same test produces similar results over time.
Example: If you take a cognitive ability test today and next month, scores should correlate highly.
• Internal consistency: items within a test measure the same underlying construct.
Example: All questions on a job-satisfaction scale should point to the same overall feeling.
High reliability means the measure is dependable, but a measure can be reliable without being valid (e.g., a clock that’s
always 10 minutes slow).
Validity means that a test actually measures what it claims to measure. Two terms are key:
• Predictor: the test or measure used to assess skills or traits (e.g., an interview or personality test).
• Criterion: the outcome you want to predict (e.g., job performance or turnover).
If a predictor correlates strongly with the criterion, it has criterion-related validity → meaning it’s useful for selection or
evaluation. Example: A conscientiousness test is valid for predicting job performance if higher scores consistently link
with better supervisor ratings.
Lecture Notes
Week 1
Lecture 1 – Studying behaviour in organizations
Part I – The Foundations of Work and Organizational Psychology
What motivates people at work?
What motivates most: money. support, recognition, progress or clear goals? Early views tended to assume that financial
incentives were the main motivator, reflecting industrial-age thinking where work was primarily about earning a living.
But contemporary research, especially Deci & Ryan’s Self-Determination Theory (2001), shows that people are also
motivated by three deep psychological needs:
• Autonomy – feeling that you have control or choice in what you do.
Example: A software developer who can decide how to structure her tasks feels more engaged than one
micromanaged by her boss.
• Relatedness – feeling connected to others at work.
Example: Employees in collaborative teams, who feel part of something larger, often report stronger
commitment.
• Growth (Competence) – feeling effective and improving one’s abilities.
Example: A nurse who receives ongoing feedback and new training opportunities experiences intrinsic
motivation.
Deci and Ryan argued that satisfying these needs supports internal motivation, while workplaces that ignore them tend
to rely on external control (rewards, punishments, rigid monitoring), which may reduce long-term motivation. This shift
reflects how work in the 21st century has evolved toward knowledge work, creativity, and emotional engagement.
What is Industrial and Organization Psychology
Industrial and Organizational (I-O) Psychology is the scientific study of human behaviour in work settings. It uses
psychological principles to improve both employee well-being and organizational performance.
→ The industrial side focuses on job analysis, personnel selection, performance appraisal, and training: the
“matching people to jobs” aspect.
→ The organizational side looks at motivation, leadership, culture, and teamwork: how people function within
systems and groups.
The field emerged in the late 19th and early 20th centuries, growing out of experimental psychology.
1876–1930: The birth of applied psychology
➢ Early pioneers like Hugo Münsterberg and James Cattell began applying psychological testing to workplace
problems.
➢ During World War I, psychologists developed the Army Alpha and Army Beta tests → early large-scale
intelligence tests used to assign soldiers to suitable roles.
➢ In 1917, Lillian Gilbreth earned the first Ph.D. in industrial psychology. Her research on time and motion studies
aimed to make work more efficient, laying the foundations for what became human engineering or ergonomics
→ fitting work environments to human capabilities.
Example: The layout of a control panel or assembly line can reduce fatigue and errors if designed with human
limits in mind.
1930–1964: The Human Relations movement
The next major influence came from the Hawthorne Studies, which observed workers at Western Electric’s
Hawthorne plant in Chicago. Researchers found that when workers were being studied, regardless of whether lighting
,or pay changed, their productivity increased simply because they felt noticed. This became known as the Hawthorne
Effect, showing that social and psychological factors influence performance as much as physical conditions.
The result was a shift from seeing workers as mechanical parts of a system to recognizing the “emotional world of the
worker.” Psychologists began studying job satisfaction, motivation, and group dynamics, giving rise to modern
organizational psychology.
- Example: Companies started introducing social breaks, feedback sessions, and leadership training to improve
morale and teamwork, not just efficiency.
Contemporary Challenges
In today’s 21st-century workplace, one of the new challenges is hybrid working: combining remote and in-office work.
While hybrid setups increase autonomy and work–life balance, they can also reduce social connectedness and blur
boundaries between work and home. Example: Remote workers might enjoy flexibility but miss informal interactions
that build trust and creativity. Managers must therefore find ways to maintain relatedness and shared purpose across
physical and digital spaces.
Another major topic is culture. Workplaces are no longer limited to one national context; globalization means cross-
cultural collaboration is routine. Understanding cultural differences is crucial for communication, leadership, and
motivation.
Hofstede’s Cultural Dimensions
Dutch researcher Geert Hofstede identified five dimensions that describe how cultures differ in their values and social
structures → Hofstede’s Cultural dimensions. These dimensions are often applied in I-O psychology to predict
workplace behaviour, leadership expectations, and communication styles.
• Individualism vs. Collectivism – whether people define themselves primarily as individuals or as part of a group.
Example: The Netherlands and the U.S. are relatively individualistic; people value personal achievement and
initiative. In collectivist cultures (like China or Japan), harmony and group goals take priority.
• Power Distance – the extent to which inequality and hierarchy are accepted.
Example: In France or many Asian countries, hierarchical authority is respected; in the Netherlands,
relationships are flatter and more informal.
• Uncertainty Avoidance – how comfortable people are with ambiguity or risk.
Example: Belgium tends to score high, meaning employees prefer clear rules and structure. Dutch culture is
more tolerant of flexibility and experimentation.
• Masculinity vs. Femininity – the emphasis on competition and achievement versus cooperation and quality of
life. Example: A masculine culture might reward assertiveness and success (e.g., the U.S.), while a feminine
culture values consensus and work–life balance (e.g., the Netherlands).
• Long-term vs. Short-term Orientation – whether people focus on future rewards and persistence or on
immediate results. Example: East Asian countries often emphasize long-term planning; Western European
nations may lean toward short-term outcomes and adaptability.
These differences shape how employees expect to be led, how decisions are made, and how feedback is given. A Dutch
manager might use open discussion for problem-solving, while a French team could expect decisions to come from
leadership. Recognizing these nuances is central to effective international management and collaboration.
Part II - Research Methods in Industrial and Organizational Psychology
Research Methods and Design
I-O psychology aims to understand and improve work behaviour scientifically = claims must be based on data, not
assumptions. To get reliable evidence, psychologists use systematic research methods that allow them to measure
behaviour, test hypotheses, and draw general conclusions. Good research design is what separates professional science
from intuition or “management fads.”
,a. Experimental designs
In an experiment, researchers manipulate one variable (the independent variable) and observe its effect on
another (the dependent variable). Participants are randomly assigned to different conditions.
• This random assignment helps ensure that any observed difference is due to the manipulation, not
preexisting differences between people.
• Experiments can occur in a laboratory (controlled environment) or in field settings (actual workplaces).
Example: A company wants to know whether flexible work hours improve job satisfaction. Employees are
randomly assigned to either a flexible or fixed schedule. If the flexible group reports higher satisfaction, we can
infer a causal effect.
An experiment can also have mediating variables (aka mechanisms) and moderating variables (influencers):
• A mediator explains how or why an independent variable affects the dependent variable (e.g. anxiety
explains the link: stress (IV) → increases anxiety (Mediator) → reduces performance (DV)).
• A moderator affects the strength or direction of the relationship between IV and DV. Stress (IV) →
performance (DV). But social support (Moderator) changes the effect: high support = stress has little
effect, and low support = stress strongly reduces performance. So, the moderator answers “when” or
“for whom” the IV-DV relationship holds.
In this way, you could have four or more variables in an experiment, using multiple moderators or mediators.
The IV affects both the Mediator and the DV. The Mediator carries part of the IV’s effect to the DV. The
Moderator influences how strong the mediation is (it may strengthen or weaken the indirect path). Example:
Stress (IV) → Anxiety (Mediator) → Performance (DV). But Social Support (Moderator) changes how strongly
anxiety affects performance. → With high support, anxiety has less negative impact.
b. Non-experimental designs
Sometimes, random assignment isn’t possible (e.g. for ethical, logistical, or practical reasons). In non-
experimental designs, researchers simply observe or measure variables as they naturally occur, without
manipulation. Two common forms are:
• Observational designs – watching and recording behaviour directly (e.g., how often managers give
feedback during meetings).
• Survey or questionnaire designs – collecting self-report data about attitudes, satisfaction, or stress.
Example: An HR department might survey employees about their perceived fairness of promotion policies. The
researcher can identify patterns but can’t claim cause-and-effect, because the data are correlational.
c. Quasi-experimental designs
These fall between experimental and non-experimental studies. They involve a treatment or comparison, but
without random assignment. Example: A company introduces a new training program in one branch but not
another. Comparing outcomes between branches can show whether training seems effective, though
differences might partly reflect other factors (like local culture).
, Data Collection Methods
I. Qualitative research
Qualitative methods explore how people experience their work. They use open-ended data such as
interviews, case studies, and document analyses.
→ They’re valuable for understanding meaning, context, and emotion.
→ But they’re less suitable for statistical testing.
Example: Interviewing employees after a merger to understand how they perceive the new company culture.
II. Quantitative research
Quantitative methods collect numerical data, usually through tests, scales, or physiological measures (like heart
rate for stress).
→ These data allow for statistical analysis, hypothesis testing, and generalization.
Example: Measuring job satisfaction scores across 500 employees to identify predictors of turnover.
Often, I-O researchers combine both types to get a fuller picture: qualitative data to explore issues, quantitative data to
test them.
Correlation and Causation
A correlation coefficient (r) shows the strength and direction of a relationship between two variables. It ranges from –
1.00 to +1.00. A positive correlation means as one variable increases, the other also increases (e.g., job satisfaction ↑ →
performance ↑). A negative correlation means as one increases, the other decreases (e.g., stress ↑ → performance ↓).
0.00 means no linear relationship. The larger the absolute value of r, the stronger the association but correlation alone
doesn’t prove causation.
Sometimes we examine how several predictors relate jointly to one outcome. A multiple correlation coefficient (R)
summarizes the overall association between multiple variables and one criterion. Example: Job performance might be
predicted by a combination of cognitive ability, conscientiousness, and experience. The combined R value shows how
well this set predicts performance.
Meta-analysis statistically combines results from many studies to find an overall average effect size. This helps identify
consistent patterns and estimate the “true” relationship across contexts.
Reliability and Validity
Reliability refers to whether a measurement tool gives stable and consistent results.
• Test–retest reliability: the same test produces similar results over time.
Example: If you take a cognitive ability test today and next month, scores should correlate highly.
• Internal consistency: items within a test measure the same underlying construct.
Example: All questions on a job-satisfaction scale should point to the same overall feeling.
High reliability means the measure is dependable, but a measure can be reliable without being valid (e.g., a clock that’s
always 10 minutes slow).
Validity means that a test actually measures what it claims to measure. Two terms are key:
• Predictor: the test or measure used to assess skills or traits (e.g., an interview or personality test).
• Criterion: the outcome you want to predict (e.g., job performance or turnover).
If a predictor correlates strongly with the criterion, it has criterion-related validity → meaning it’s useful for selection or
evaluation. Example: A conscientiousness test is valid for predicting job performance if higher scores consistently link
with better supervisor ratings.
