A note on the straightforward steps involved in the conduct of participant observation: for MBA students

A note on the straightforward steps involved in the conduct of participant observation: for MBA students (information from perplexity.ai)

My question 1: "As a part-time MBA student doing my 4-month dissertation project, I have a query about the role played by academic theories in the design and analysis of participant observation research. Specifically, how do my choice of about 3 main academic theories affect my data gathering and data analysis (notably, analysis steps involved) in the context of my participant observation, as a middle manager studying my company's organizational climate and staff morale at the present time when the company has newly appointed young CEO, CFO and COOs in order to respond to the intense competition in the marketplace in Hong Kong. Could you offer your advice with this case example to illustrate your underlying ideas."

The reply: "Let's explore how your choice of three academic theories will shape your participant observation research on your company's organizational climate and staff morale during leadership changes. First, academic theories act as lenses guiding what you observe and how you interpret those observations. They influence what questions you ask, what behaviors or interactions you focus on, and how you make sense of your data. For example, if you choose a theory about organizational culture, your data gathering might focus on rituals or norms, while a motivation theory might push you to observe incentives or morale signals.

Could you share which three academic theories you are considering or familiar with? This will help me tailor advice on how they specifically impact your data collection and analysis phases, especially the coding and interpretation of participant observation notes.

The question from the system: How familiar are you with applying academic theories to qualitative research design and analysis, especially participant observation?

My reply: I have some basic ideas on the research method of participant observation, theory-driven analysis as well as the research approach of inductive and deductive reasoning.

In participant observation research, academic theories profoundly shape both data gathering and analysis. Theories act as conceptual lenses that guide what you pay attention to when observing and which aspects of organizational climate and staff morale you focus on. For example, choosing a theory of organizational culture will make you observe rituals, communication styles, and power dynamics, while motivation theories will draw attention to employee incentives, engagement, and morale indicators. This theory-driven approach helps make data collection purposeful rather than random, ensuring you gather relevant data for analysis.

Regarding data analysis, theory influences the coding and interpretation steps. You start with immersion in your observation notes, then generate codes—labels representing behaviors, emotions, or interactions relevant to your chosen theories. You may use open coding to identify emergent themes inductively or apply deductive coding by looking for data that confirm or challenge your theoretical framework. Next, axial coding groups codes into categories, helping to map relationships among concepts, and selective coding focuses analysis on core themes aligned with your research questions. Theories guide you in interpreting patterns, making sense of contradictions, and developing insights about organizational climate under the new leadership.

In your case, as a middle manager observing staff morale during a leadership transition with newly appointed young CEO, CFO, and COOs, choosing about three theories—such as organizational change theory, leadership theory, and employee motivation theory—will affect:

• Data gathering: You observe leadership behaviors, communication flows, employee reactions, and informal interactions related to morale and climate.

• Analysis steps: Your coding will highlight themes like resistance or acceptance of change, leadership styles in action, motivational drivers or barriers, and effects on morale.

• Reasoning approach: You may start deductively testing hypotheses from your theories but remain open inductively to new emergent themes from real-world observations.

This combination of theory-driven participant observation enriched by inductive and deductive reasoning will yield a deep, nuanced understanding of your company's evolving organizational climate and staff morale under competitive pressures in Hong Kong."

My question 2: "Thanks for suggestions. How about any analysis steps to gain some concrete evaluation findings and actionable knowledge so as to make some managerial recommendations in my dissertation report?"

The reply: "To gain concrete evaluation findings and actionable knowledge from your participant observation data for managerial recommendations, consider a structured analysis approach with these key steps:

1. Data Familiarization and Initial Coding: Immerse yourself in detailed observation notes, coding data into meaningful units that relate to organizational climate, staff morale, and leadership change. Use a mix of open (inductive) coding to capture unexpected themes and deductive coding based on your selected theories.

2. Theme Development and Review: Group related codes into broader themes that tell a clear story about your research questions—for example, employee responses to new leadership styles, changes in communication patterns, or morale shifts. Review themes for coherence, distinctiveness, and relevance to your organizational context.

3. Interpretation and Evaluation: Use your theoretical lenses to interpret themes deeply, linking observed patterns to research concepts like motivation, culture, or organizational change. Critically evaluate the implications these findings have on staff morale and organizational climate.

4. Deriving Actionable Recommendations: Translate thematic insights into practical recommendations by asking how observed behaviors, attitudes, or organizational mechanisms can be improved or leveraged for better morale and climate. Frame recommendations with justification from your data (e.g., "Staff report feeling unheard in meetings, suggesting the need for enhanced communication channels").

5. Structured Reporting: Present findings with clear narratives and evidence extracts from your notes, followed by focused, theory-grounded managerial recommendations. Visual tools like conceptual models or diagrams can help communicate complex insights effectively.

This process helps turn rich qualitative data into evidence-based managerially relevant insights, making your dissertation impactful for decision-making in your company’s competitive environment in Hong Kong."

A collection of blog notes on using chatgpt for research purpose.