Customer Behaviour Environmental psychology.
Dissertation / Primary Research Project
Theme: Customer Behaviour, Environmental Psychology, Service Quality, Hospitality Research
Tools / Frameworks: S–O–R Framework, Mehrabian–Russell Model, SPSS, PROCESS Macro, Regression Analysis, Serial Mediation
Context:
This dissertation examined how the physical environment and perceived service quality influence customer satisfaction and behavioural intentions in a casual-dining restaurant through the emotional responses of pleasure and arousal.
The research was developed in the context of Quattro Bistro, a casual-dining restaurant in Lonavala, India. I was interested in understanding how customers respond to the wider dining experience, not just the food itself. Before a customer forms an opinion about a restaurant, they are already reacting to the space around them: lighting, layout, atmosphere, staff behaviour, comfort, service flow, and the general feeling of the encounter.
The study was grounded in the Stimulus–Organism–Response framework and the Mehrabian–Russell model. These frameworks helped structure the project around a simple but powerful idea: external cues influence internal emotional states, which then shape later responses such as satisfaction, revisit intention, and recommendation.
Figure 1: Conceptual model underpinning this entire study.
Problem:
The core problem was that much of the existing restaurant-environment research is based on western, upscale, or strongly hedonic restaurant settings. This made it unclear whether the same relationships would operate in an Indian casual-dining context, where customers may balance emotional enjoyment with practical service expectations.
The study therefore asked whether physical environment and perceived service quality directly influence customers’ emotional responses, and whether those emotions then influence satisfaction and behavioural intentions.
A further problem was theoretical. Many restaurant-experience models treat physical environment and service quality as separate, parallel stimuli. I wanted to test whether physical environment might also shape perceived service quality itself. In other words, customers may not only feel something because of the environment; they may also use the environment as a cue to judge the quality of the overall service encounter.
My Role:
This was an individual dissertation project. I was responsible for designing the research question, developing the conceptual model, reviewing the literature, building the questionnaire, coordinating data collection with the restaurant, preparing the dataset, conducting the statistical analysis, interpreting the results, and writing the final dissertation.
I also had to manage the practical realities of field research. Data collection depended on voluntary customer participation in a live restaurant environment, which required coordination with restaurant managers and careful planning to maintain response momentum without disrupting service operations.
Method:
The study used a quantitative, cross-sectional field-survey design. Data was collected in the restaurant using a QR-code survey, with customers invited to respond during their dining experience.
The survey measured six main constructs:
Physical Environment
Perceived Service Quality
Arousal
Pleasure
Customer Satisfaction
Behavioural Intentions
The constructs were measured using 7-point Likert-type scales adapted from hospitality and servicescape literature. The data was prepared and analysed in SPSS. This included recoding responses, creating composite variables, checking missingness, testing reliability, examining descriptive statistics, running correlations, and conducting regression analysis.
The main model was tested through a phased regression approach. I also used the PROCESS macro to examine indirect and serial mediation effects, including the extended pathway:
Physical Environment → Perceived Service Quality → Pleasure → Customer Satisfaction → Behavioural Intentions
This allowed me to test whether the physical environment influenced behavioural intentions not only directly, but also through a longer emotional and evaluative chain.
Figure 2: Regression results overlay on the conceptual model showing relationship between the variables.
Output:
The final output was a full dissertation analysing customer behaviour in an Indian casual-dining restaurant through environmental psychology and service-quality theory.
The study found substantial but partial support for the proposed model. Physical environment strongly predicted perceived service quality, but once perceived service quality was included in the model, physical environment no longer had significant direct relationships with arousal or pleasure.
This was one of the most important findings. It suggested that in this setting, customers may not experience the physical environment as a separate direct emotional trigger. Instead, the environment may shape how they evaluate the service encounter, which then influences pleasure, satisfaction, and behavioural intentions.
Perceived service quality emerged as the stronger predictor of emotional states. Pleasure was more important than arousal in predicting customer satisfaction, and customer satisfaction was the strongest predictor of behavioural intentions. The mediation analysis further supported the extended appraisal-style pathway, showing that part of the physical-environment effect operated through perceived service quality, pleasure, and customer satisfaction.
The practical implication was that restaurant managers should not rely only on décor or ambience. Service consistency, responsiveness, confidence, comfort, cleanliness, layout, and the overall coherence of the experience all matter because customers interpret them together.
Skills Applied:
Primary research design
Questionnaire development
Customer behaviour research
SPSS data preparation
Reliability testing
Correlation analysis
Multiple regression
PROCESS macro
Serial mediation analysis
Data cleaning and composite variable construction
Academic writing
Key Insight:
The biggest insight from this research was that customer experience is not always shaped by obvious direct effects.
At the beginning of the project, it seemed reasonable to expect that physical environment would directly influence emotions such as pleasure and arousal. However, the findings suggested a more layered process. In this casual-dining setting, the environment appeared to matter partly because it shaped how customers judged service quality. That service-quality evaluation then influenced pleasure, satisfaction, and behavioural intentions.
This changed how I think about business research. A variable can be important even if its effect is indirect. Some of the most meaningful business mechanisms are not immediately visible; they only become clearer when the relationships between factors are mapped and tested carefully.
Reflection:
This study brought several methodological and practical challenges to the surface.
Data collection was one of the biggest obstacles. Because the project relied on voluntary participation in a real restaurant setting, obtaining a large enough sample for statistical analysis was difficult and time-consuming. This required pre-planning, careful coordination with restaurant managers, informal daily response goals, and regular follow-ups to maintain data-collection momentum.
Access to academic material was another challenge. Many relevant journal articles were behind paywalls, which limited the range of sources I could use and forced me to work carefully with institutional access and the strongest available literature. This did not prevent theoretical development, but it made the literature-review process more selective and deliberate.
The methodology also required trade-offs. I chose not to include a wide range of external control variables in order to preserve interpretability and manage the project within word-count and analytical constraints. This made the model clearer, but it also meant that some potentially important factors, such as demographics, prior expectations, party type, or food-quality perceptions, were not included in the computation.
Another major challenge was using PROCESS instead of a full SEM model. This reduced model complexity and was realistic under the project’s constraints, but it required careful procedural precision. I had to develop technical confidence with SPSS, data cleaning, model formulation, and output interpretation.
Overall, this dissertation developed my ability to plan research, manage primary data, justify methodological choices, and interpret complex statistical relationships. More importantly, it strengthened my interest in the intersection between business, psychology, customer behaviour, and decision-making.
*For a full copy of the thesis please send me an email: samaybhurat@gmail.com
