A: Foundations of Organizational Behaviour

Question 1

1. Describe the Hawthorne studies and explain what the “Hawthorne effect” means.

Answer 1

The Hawthorne Studies were a series of behavioural experiments conducted at the Western Electric Hawthorne Works Plant between 1924 and 1933. These studies were among the first to systematically explore the factors that influence worker productivity and satisfaction. Initially, the research aimed to understand the relationship between lighting levels and worker productivity. However, as the studies progressed, the researchers, including Elton Mayo and Fritz Roethlisberger from Harvard Business School, expanded their focus to other variables, such as rest breaks, working hours, and financial incentives.

The Hawthorne Effect emerged from these studies, describing the phenomenon where individuals alter their behaviour in response to being observed. This effect was particularly evident when workers increased their productivity not because of the experimental manipulations (like changes in lighting) but because they knew they were being watched. The Hawthorne Effect highlighted the impact of social factors, such as attention and group dynamics, on worker behaviour and performance. The studies also provided insights into motivation, job satisfaction, resistance to change, group norms, and effective leadership within organizations.

Question 2

2. Briefly describe each of the following types of research designs: case studies, correlational studies, and experiments. Highlight the strengths and weaknesses of each, using examples from organizational behaviour.

Answer 2

Case Studies:
Description: Case studies involve an in-depth, contextual analysis of a single event, situation, or individual. In organizational behaviour, this might involve a detailed examination of a company’s approach to leadership or a particular incident within an organization.
Strengths: Case studies provide a deep, comprehensive understanding of complex issues, often revealing insights that might be missed in broader surveys or experiments.
Weaknesses: The findings from case studies are often not generalizable to other situations or organizations because they focus on a single instance. There is also a risk of researcher bias, as the interpretation of data can be subjective.
Example: A case study could explore the leadership style of a successful CEO and how it influences company culture, providing rich detail but limited generalizability.

Correlational Studies:

Description: Correlational studies examine the relationship between two or more variables to determine whether they are statistically associated. Unlike experiments, these studies do not involve manipulation of variables.
Strengths: Correlational studies are useful for identifying relationships and making predictions. They are often easier and more ethical to conduct than experiments.
Weaknesses: They cannot establish causality; it’s impossible to know from correlation alone whether one variable causes the other.
Example: In organizational behavior, a correlational study might examine the relationship between job satisfaction and employee turnover rates, finding that as satisfaction decreases, turnover increases. However, this does not prove that low satisfaction causes turnover; other factors might be involved.

Experiments:

Description: Experiments involve the manipulation of one or more independent variables and the measurement of their effects on dependent variables. Participants are usually randomly assigned to different conditions to control for confounding variables.
Strengths: Experiments are the gold standard for establishing causality, allowing researchers to determine the direct effects of specific variables. They have strong internal validity, particularly when conducted in controlled settings.
Weaknesses: Experiments can lack external validity if the settings are too artificial, and it can be difficult or unethical to manipulate certain variables (e.g., employee motivation).
Example: An experiment in organizational behavior might manipulate goal-setting techniques (specific vs. vague goals) to observe their impact on employee productivity, demonstrating a direct causal link between the type of goal and performance.

Question 3

3. What does reliability mean? Validity? Provide examples of each.

Answer 3

Reliability refers to the consistency of a measure. A reliable measure yields the same results under consistent conditions. There are several types of reliability:
Internal Consistency: Measures whether items on a test are consistent with one another. For example, if a job satisfaction survey is reliable, all items that measure satisfaction should produce similar results.
Inter-Rater Reliability: Measures the degree of agreement among different raters. For example, if multiple managers rate an employee’s performance and their ratings are similar, the measure has high inter-rater reliability.
Test-Retest Reliability: Measures consistency over time. For example, if an employee’s job performance is measured at two different points in time using the same method, and the scores are similar, the measure has high test-retest reliability.

Validity refers to the accuracy of a measure or the extent to which it measures what it is intended to measure. Types of validity include:
Construct Validity: Ensures the measure accurately reflects the concept it is intended to measure. For example, if a test claims to measure job satisfaction, it should accurately assess the various dimensions of satisfaction at work.
Content Validity: Ensures the measure covers the full range of the concept. For example, a leadership assessment should cover all relevant aspects of leadership, such as decision-making, communication, and team management.
Criterion Validity: Ensures the measure correlates with an outcome it is supposed to predict. For example, a cognitive ability test used in hiring should predict job performance effectively if it has high criterion validity.

Question 4

4. Describe five facts about job performance and discuss what they mean.

Answer 4

Performance is Not Static: Job performance can fluctuate over time due to various factors, including changes in motivation, work environment, and personal circumstances. This suggests that performance evaluations should consider trends over time rather than single-point assessments.

Supervisor Ratings May Not Reflect Actual Performance: Supervisor ratings can be influenced by biases, political factors, and external circumstances beyond the employee’s control. This means that performance appraisals might not always accurately reflect an employee’s true contribution, highlighting the need for objective measures.

Performance Distributions Are Asymmetric: In many organizations, a small number of employees contribute the majority of the work. This implies that identifying and supporting top performers can be crucial for organizational success.

Performance Trends Matter: Employees whose performance is improving are often rated more favorably than those with stable or declining performance, even if their overall performance is similar. This suggests that potential for growth is highly valued in performance evaluations.

Gender Differences in Performance Consequences: The same behaviors can have different consequences for men and women in the workplace. For example, women may be penalized more than men for not engaging in helping behaviors, which highlights the importance of recognizing and addressing gender biases in performance evaluations