Service Design Research Planning

Defining research objectives is an important step in the service design process. It helps teams ensure they are gathering the right information to inform their design decisions. Take those steps to identify effective research objectives:

• Identify the nature of your study. Exploratory research involves learning more about a subject without any prior assumptions, while confirmatory research is about validating specific assumptions. Depending on the nature of your project, you may need to use one or both of these approaches in your research.
• Clarify your design goals. Consider whether your research will be focused on existing services or if it will be aimed at generating new ideas or concepts.
• Brainstorm research questions. Once you clearly understand the research approach you need to take, start formulating research questions. These should be specific and relevant to the service design goals. For example, you may ask questions like "What are the most common pain points experienced by users of the service?" or "What features of the service are most important to users?"
• Refine research questions. Once you have a list of potential research questions, refine them to ensure they are focused and actionable. Consider whether each question can be answered with the available resources and aligns with the overall service design process.
• Formulate hypotheses. Based on your research questions, formulate hypotheses that can be tested through the research study. For example, you may hypothesize that improving a certain feature of the service will lead to an increase in user satisfaction.

Sampling refers to the process of selecting a smaller group of individuals or units from a larger population. The aim is to use their responses or behaviors to draw conclusions about the larger population.

In probability sampling, each member of the population has an equal chance of being selected. This technique is used in quantitative research to ensure that the sample is representative of the population being studied.

Some probability sampling techniques include:

• Simple random sampling: Randomly selecting participants from a sampling frame
• Systematic random sampling: Selecting every *N-*th person from a flow of people as your participants
• Stratified random sampling: Dividing the population into groups based on specific criteria and randomly selecting participants within these groups
• Cluster sampling: Creating a list of clusters based on specific criteria and randomly selecting some of these clusters, then randomly selecting participants within the selected clusters

Non-probability sampling involves selecting participants based on subjective criteria and is used in qualitative research. These techniques are used to obtain a more in-depth understanding of a particular group of people.

Some non-probability sampling techniques include:

• Convenience sampling: Selecting participants who are readily available — for example, people in a waiting room
• Self-selective sampling: Participants voluntarily choose to participate in a study without specific criteria, for example, by clicking on a link on a company website
• Snowball sampling: Starting with a few participants and asking them to recommend others who meet specific criteria
• Extreme case sampling: Selecting participants who represent extreme or unusual positions, such as early adopters of a new technology
• Emergent sampling: Following new leads during fieldwork as they unfold to flexibly take advantage of new knowledge
• Maximum-input sampling: Selecting participants with a comprehensive overview of an entire experience or system to get a maximum of input from them^[1]

The authors of This is Service Design Doing suggest that using multiple research methods to collect data on the same phenomenon can improve the accuracy and richness of the research. It can also ensure that insights are based on a comprehensive dataset that is robust enough to provide a foundation for design decisions. This is called triangulation.

When opting for triangulation, go for a mix of methods from different categories like:

• Desk research: Using existing information sources such as published literature, reports, and databases to gather information about the topic of interest
• Self-ethnographic approaches: Collecting data about the service and company in order to understand how it impacts the user experience
• Participant approaches: Collecting data directly from the participants through methods such as participant observation, contextual interviews, in-depth interviews, and focus groups
• Non-participant approaches: Collecting data by observing the participants without interacting with them. Examples include non-participant observation, ethnography, and cultural probes.
• Co-creative workshops: Involving participants in the design process through methods such as co-creating personas, journey maps, and system maps.^[2]

Research instruments are tools used to collect data during the research phase. They could be surveys, interview guides, observation checklists, or focus group scripts. These instruments should be carefully designed to ensure that they collect the necessary data and are understandable and easy to use for the research participants. Before using these tools with the actual participants, it is important to pilot-test them with a small sample of users to identify any issues or problems and make necessary adjustments.

Other ways to validate your research tools include:

• Expert review: Ask experts in the field to review your research tools and provide feedback on their validity and reliability.
• Test-retest reliability: Administer the research tools to the same group of participants at two different times to assess the consistency of the results. If the results are consistent, it indicates that the tools are reliable.
• Cognitive interviewing: Conduct cognitive interviews with participants to understand how they interpret the questions and instructions in the research tools and to identify any areas of confusion or misunderstanding.^[3]

After making any necessary changes, the research instruments are ready to be used in the actual research phase.

Quantitative data analysis uses numerical data and statistics to draw conclusions from a large sample size. In service design, it is often used to measure customer satisfaction or evaluate the effectiveness of a new service design.

Examples of quantitative data analysis techniques include:

• Descriptive statistics (e.g., mean, median, mode, standard deviation)
• Correlation analysis
• Regression analysis
• Hypothesis testing

For instance, a service designer may collect survey data from a large sample of customers to determine their level of satisfaction with a particular service. The survey data can then be analyzed using quantitative methods to identify patterns and correlations in the data, which can help the designer to make data-driven decisions about how to improve the service.

Qualitative data analysis, on the other hand, interprets non-numerical data such as interviews, observations, and open-ended survey responses. In service design, qualitative data analysis is often used to gain insights into customer experiences and behaviors.

Examples of qualitative data analysis techniques include:

• Content analysis
• Thematic analysis
• Discourse analysis

For instance, a service designer may conduct in-depth interviews with a small group of customers to understand their needs and expectations for a new service. The interview can then be analyzed using qualitative methods to identify themes and patterns in the data, which can help the designer develop a service that better meets the needs of the customers.

Qualitative data analysis methods are essential in service design as they allow designers to interpret and make sense of the complex and subjective nature of human experiences and behaviors. They provide designers with a structured approach to analyzing and interpreting data gathered from various research methods such as interviews, focus groups, and observations. They include:

• Thematic analysis: This involves identifying and analyzing themes or patterns within qualitative data. Researchers code and categorize data into themes based on their relevance and frequency. For example, in a study on customer satisfaction with a restaurant experience, themes could be identified as food quality, service quality, ambiance, and price.
• Content analysis: This involves analyzing the content of communication to draw inferences and identify patterns and trends. Researchers examine text, images, and other forms of communication to identify themes, patterns, or trends. For example, content analysis might be used to identify common topics and sentiments expressed by users on a particular social media platform.
• Narrative analysis: Here, researchers analyze the language, structure, and content of narratives to identify common themes or patterns. For example, in a study on the experience of cancer patients, narrative analysis could be used to identify common themes like coping strategies, support systems, and quality of life.
• Discourse analysis: Researchers analyze the language, context, and social norms to understand how power and meaning are constructed through discourse. For example, in a study on public perceptions of climate change, discourse analysis could be used to identify how language is used to frame the issue and construct different perspectives.
• Framework analysis: In this method, researchers use predetermined categories or themes to organize the data and identify patterns or themes. For example, in a study on team member satisfaction within a workplace, a framework could be developed with categories like work-life balance, pay and benefits, opportunities for growth, and work environment.^[4]

Visualizing data in service design can help teams understand and organize complex information, identify patterns and gaps in the data, and develop empathy with the people being researched. There are various ways to visualize research data, such as:

• A physical or virtual research wall where all research findings, data, and insights are displayed and organized
• Personas to exemplify different groups of people
• Journey maps to visualize experiences in the service ecosystem
• System maps to show relationships in the service ecosystem
• Key insights to highlight problems or potential solutions
• Research reports for a comprehensive overview

The target audience should be considered when choosing a visualization method, and the purpose of the visualization should be clear. Different audiences will have different needs, and any research outcome that needs to be communicated beyond the team should be easily understood.