Is Quantitative Data Collection Feasible in Rural Areas and Small Companies with Limited Users? The Reality Is…
Research methodologies must be adapted to align with the maturity of the company and the characteristics of its user base. Established companies with large user bases and abundant data can effectively leverage both quantitative and qualitative research methods. Their extensive datasets allow for reliable analysis and measurement of metrics such as customer satisfaction, user engagement, and performance indicators.
In contrast, newer companies or those operating in traditional sectors — such as construction materials and agriculture — often face distinct challenges. These industries typically involve smaller, less tech-savvy user bases, making data collection more complex compared to sectors with more digitally literate audiences. Over the past two years (2023–2024), I have experienced these challenges firsthand.
For smaller companies, quantitative research can be less effective due to limitations posed by small sample sizes. With a restricted user base, gathering sufficient data for statistically meaningful analysis becomes difficult. Quantitative data from external sources or from users unfamiliar with the product may lack reliability due to the small sample size. While quantitative methods can be valuable for exploring new product opportunities, evaluating existing products with a small user base through surveys can be ineffective if user participation is low, resulting in insufficient or skewed data.
Additionally, conducting quantitative research in rural areas presents unique challenges. In many rural settings, smartphones and digital technology are not as prevalent. This lack of access to technology makes online surveys impractical. For instance, in my experience with Kitani, collecting survey data from farmers proved particularly difficult. Many farmers did not own smartphones, and those who did were often unfamiliar with online surveys. At a recent Kitani event with 150 participants, over half did not own smartphones.
These conditions complicate the collection of quantitative data. Online surveys, which are a common tool for quantitative research, are ineffective in such environments. Data collection often necessitates in-person visits, which can be both time-consuming and logistically challenging, particularly with larger sample sizes. An alternative approach involves using paper surveys distributed and collected through trusted local leaders. While this method is more practical than visiting each participant individually, it still requires substantial effort to ensure data accuracy and integrity.
In-person visits, while necessary for data collection, can impact the authenticity of responses due to several behavioral science phenomena. For example, social desirability bias may lead participants to provide responses they believe are more socially acceptable rather than their true opinions, due to the presence of researchers. The observer effect and Hawthorne effect can also lead participants to alter their behavior or responses because they know they are being observed, resulting in less authentic feedback. Self-presentation theory suggests that participants might present themselves in a way they think will create a favorable impression, further distorting the data. Additionally, cognitive dissonance theory indicates that participants may change their responses to avoid discomfort associated with conflicting beliefs or behaviors.
To address these issues, combining qualitative insights with quantitative data can offer a more comprehensive understanding. Leveraging existing data, using targeted surveys, and applying advanced sampling techniques can help mitigate sample size limitations. Investing in technology and collaborating with local partners or government agencies can also enhance research efforts and provide additional data sources.
Ultimately, while quantitative data is often desired, trade-offs are inevitable. Effective stakeholder management is crucial as researchers balance the limitations of data collection methods with the need to answer key research questions. Ensuring that research findings are aligned with business objectives will help drive more impactful, data-informed decisions.
