Although ethnography is widely used in business research, including in studies of entrepreneurship behavior, rapid ethnography—where fieldwork is undertaken within a short, well-defined timeline—is not as well known or widespread. To compensate for lack of time spent in the field, researchers use “time-deepening strategies” such as multiple and parallel data collection tools, technology-assisted socialization methods, and multiple observers during the fieldwork. We used rapid ethnography alongside quantitative techniques, including a survey, to collect data from Sri Lankan microfinance entrepreneurs. Analysis of the rapid ethnography data allowed us to explain quantitative findings in terms of the social complexities of the microfinance environment. Planning before entering the field; having regular contact with the case organization and respondents before, during, and after data collection; obtaining support from local stakeholders; and using multiple data collection methods simultaneously all contributed to the success of the rapid ethnography technique. The on-site researcher’s ethnic and professional background, which meant she was familiar with the cultural context and business environment and could communicate in the local languages, facilitated access to data collection sites, made it easier to gain gatekeepers’ trust, and enabled her to integrate into the community from which she needed data. However, this same familiarity required her to take deliberate steps to maintain detachment, and reflect on her positionality, that is, her potential assumptions and biases.
By the end of this case, students should be able to
- Understand the use and applicability of rapid ethnography as a data collection technique
- Identify innovative ways of collecting ethnographic data in a short period of time without altering the fundamental ethnographic principles
- Assess their own research position in terms of potential assumptions and biases
- Explain in practical terms how to adopt a reflexive approach in a research context
Project Overview and Context
This project, which led to my PhD, examined how microfinance borrowers in a developing country make business decisions and acquire entrepreneurial expertise. Entrepreneurs who had borrowed from a microfinance institution (MFI) in Sri Lanka completed a survey, were interviewed individually and in groups, completed diaries about their business activities, and were observed working in their businesses and during meetings about loan repayments, all within 2 months.
The types of techniques and the short time span are characteristic of rapid ethnography (RE), “a form of multi-method ethnography involving data collection from numerous sources over a relatively short period of time including interviews, participant observations, document review and sometimes surveys and focus groups” (Baines & Cunningham, 2013, p. 74). Although the fundamental principles of RE are similar to those of conventional ethnography, a number of its elements, for example, planning and preparation, and socialization and rapport-building with participants, are done before researchers go into the field (Baines & Cunningham, 2013; Handwerker, 2001; Millen, 2000). When planning the research, rapid ethnographers collect background data and secondary information to narrow down the research questions (Handwerker, 2001). This information is also used to determine the time period to visit the field so that the researchers can observe relevant activities. In addition, rapid ethnographers use technology-assisted communication such as regular emails, phone calls, and instant messages to build rapport before going into the field (Baines & Cunningham, 2013). Although the actual time spent in the field is often less than 3 months, researchers typically use time-deepening strategies such as multiple and parallel data collection methods and multiple observers (Baines & Cunningham, 2013; Handwerker, 2001; Millen, 2000) to compensate for the limited time. Thus, RE is not an inferior way of collecting data, but an alternative way of studying the research issues within a natural context (Pink & Morgan, 2013).
I became interested in studying how microfinance borrowers make business decisions when I was employed in an MFI in Sri Lanka. Sri Lanka is a lower middle-income developing country in South Asia where around 77% of people live in rural areas, and where very small service-based, manufacturing and agriculture/animal husbandry businesses are common. These businesses are frequently financed by very small loans from a range of financial service providers, including MFIs, which cater to people, particularly women, who often cannot get finance any other way. MFI borrowers form a small, “solidarity” group of peers whose members are responsible for each other’s loans. Microfinance is not a “magic bullet” to reduce poverty or create fast-growing businesses (Augsburg, De Haas, Harmgart, & Meghir, 2015; Banerjee & Duflo, 2011; Bateman, 2010; Karlan & Zinman, 2011). Indeed, solidarity groups can discourage individual borrowers from initiating risky business ventures (Armendáriz & Morduch, 2010). Peer pressure used to ensure loan repayment can also hinder business growth, and some group repayment tactics have been described as abusive and ethically unacceptable (Ali, 2014; Ghatak & Guinnane, 1999). Similarly, because their main concern is loan repayment, MFIs are sometimes said to encourage entrepreneurs to “play safe” (Banerjee & Duflo, 2011). Nevertheless, many people take out microfinance loans and create sustainable businesses. I wanted to understand how these people make decisions at business start-up and later, including the effect of a microfinance loan on their decisions.
It made sense to take advantage of my knowledge of Sri Lanka and my contacts with MFIs and through them, microfinance borrowers, when designing the research. An early decision was to use a survey to ask microfinance borrowers about their business thinking at the business start-up and development stages of their organizations, to see whether and how their thinking changed over time, the extent to which they practiced specific business skills, and so on. The theories used were causation and effectuation in entrepreneurship (Sarasvathy, 2001) and deliberate practice (Ericsson, 2006). Then, I planned to use additional, qualitative data gathered from the following:
- Interviews with and observation of MFI staff (to understand the MFI’s processes);
- Analysis of MFI documents;
- On-site interviews with business owners;
- Observations of business owners at solidarity meetings and elsewhere;
- Diaries kept by business owners;
- My field notes.
My PhD needed to be completed within a limited time: 3 to 4 years. This made RE an attractive technique because it would allow me to gather good data fairly quickly. A number of factors in the research situation made RE feasible for me, including the following.
Familiarity With the Case Organization and Its Senior Management
Having previously worked in the selected MFI, I knew some of the staff personally, and I was broadly familiar with the MFI’s systems, procedures, norms, and jargon. However, as I had left my job at the MFI about 2 years earlier, I was unaware of the most recent policy developments and practices in the MFI, and I had to familiarize myself with them by reading organization manuals and talking with staff at various levels before I could gather data from borrowers. The research would require support from senior management, so it was an advantage that I had previously worked with the MFI’s top management team. The MFI’s senior management were interested in the insights the research might produce for them, and this made it easy for them to support the study.
Freedom From Dependent Relationships
It was an advantage from an ethical perspective that my previous employment had not required contact with borrowers, and I had not supervised any field-level staff in the MFI (i.e., the staff who regularly visit solidarity groups to collect loan payments). If I had previously had contact with borrowers (e.g., negotiating loan matters), or supervised MFI staff, they might have felt obligated to participate because of the power imbalance between an MFI and a borrower, and a supervisor and an employee. This would have been inappropriate because it is important from an ethical perspective that participation in research is voluntary.
Linguistic and Cultural Competence
As I speak Sinhalese, one of Sri Lanka’s local languages, I could get to know research participants quickly and observe their behavior. This was essential due to the short field data collection period. Furthermore, because I was familiar with rural community values and norms due to my upbringing, I could easily integrate into the MFI and the borrower community alike and be sensitive to cultural and institutional practices. For example, because I knew the operational structure of the MFI, I knew the chairperson’s approval was essential before I spoke to staff. In addition, as with virtually any formal event in Sri Lanka, religious observance was common at microfinance meetings and I knew how to participate appropriately.
Method in Action
After gaining ethics approval from the university, I wrote formally to the Chairperson of the MFI requesting permission to collect data. Approval was granted quickly, perhaps because the Chairperson was interested in the research and because I was a former employee. While still in Australia, I used Skype, email, and telephone calls to let my former colleagues know about my project. I introduced myself to those who had arrived since I was employed, briefly explaining my former role in the organization. These introductions and technology-assisted socialization processes helped me later when I interacted with staff members in person and collected key documents.
Field data collection in Sri Lanka took 2 months, most of which I spent in the MFI’s head office and four of its 18 branches. At head office, interacting with staff and collecting document data was relatively easy as the Chairperson had given approval and most staff already knew me. In contrast, at regional and branch level, I had to rely on other staff, some of whom I had never met and who had no particular reason to be interested in the project. I first gave a formal presentation to regional managers to gain their support. Although regional managers did not have any practical role in collecting data, approaching them first helped me to understand and work appropriately within the MFI’s reporting structures. Via the regional managers, I liaised with branch managers to plan when to visit an individual branch. I spent about a week in each branch location, explaining the research to staff and gaining their insights, and visiting clusters (a cluster comprises 10–12 solidarity groups) to meet and observe individual microfinance borrowers.
At the cluster, solidarity group, and individual borrower levels, I relied on the MFI’s field officers to help me start collecting data. In each branch, I visited selected clusters to observe their meetings and speak with borrowers. At the start of the meeting, the MFI field officers would introduce me to cluster leaders and individual borrowers. Without introductions from the field officer’s “familiar face,” it would have been impossible to gather cluster-level data because people’s activities at meetings focused on loans and other business matters. After the introduction, I observed cluster meetings, held discussions with solidarity groups, and conducted focus group discussions to gather data about the MFI–borrower interface.
Introductions from MFI field staff helped me to gain the confidence of gatekeepers such as cluster leaders, and to understand the operational side of the MFI’s activities at branch level. In addition, the field officers helped me with practicalities such as planning my travel to the next location. Nevertheless, working closely with MFI field officers meant ensuring that I was not seen as part of the MFI because, given that borrowers have obligations to the MFI, this might have made borrowers feel compelled to take part in the research. Both the field officer and I emphasized borrowers’ freedom not to take part, to the point of letting people know hownot to participate if that was their preference, and how to keep this decision private. (Borrowers could simply hand in a folded blank survey form, and not volunteer to be interviewed.) I also needed to observe the field officers themselves, and being able to speak Sinhalese helped with this. Indeed, it would have been impossible to understand the nuances of some of the MFI’s loan collection practices without knowing the language, as some officers were reluctant to explain their tactics, some of which were not listed in the MFI operational manual. A typical example was that field officers controlled the time that meetings started. Some insisted on waiting until all borrowers were present before allowing the meeting to begin, which led to those who had arrived on time expressing annoyance with latecomers. Others started the meeting but it would inconvenience latecomers by keeping them waiting outside for a time. A field note makes clear the effect this had:
This cluster had a few people waiting outside the premises without even going inside. When I asked the field staff member what was going on, she did not explain. She said “there’s nothing to be concerned about.” In fact, the field officer was very evasive … However, I overheard another cluster member saying that she feels sorry for them (the latecomers). (Field notes—data collection 2014–2015)
Direct observations combined with knowledge of the language allowed me to interpret facial expressions and concerns people had without disturbing the cluster proceedings.
Individual Data Gathering
At the individual level, I invited some borrowers to be interviewed and to keep a journal to record their daily business and other activities. To select these individuals, I asked field officers to introduce me to borrowers who might like to share their “business story.” While this still entailed a small risk that borrowers would associate me with the MFI and therefore feel obliged to give me information, I mitigated this by ensuring that the field officers only said I had asked to meet each individual. I then immediately explained the project and that I was doing independent research. Field officers were not involved in collecting any of the business stories. I also had to guard against other assumptions. For example, in the initial interviews, microfinance borrowers always focused on the loan from the selected MFI. In such cases, I emphasized that I was interested in all their outstanding loans. Furthermore, whenever I asked participants to discuss any loan repayment difficulties, their first response was always that there were no difficulties. However, on further inquiry, I found that entrepreneurs relied on their family and solidarity groups and sometimes did not have enough income from their businesses to repay MFI loans.
Being able to speak Sinhalese meant I could talk to borrowers without an interpreter. (The one exception was an interview with a Tamil borrower, where I conducted the interview with the help of an interpreter.) I emphasized aspects of myself that participants could relate to. For example, I always discussed my Sri Lankan origins before explaining that I was now studying in Australia. This built rapport quickly, as interviewees’ comments show:
One of my relatives trained me to make cakes. She trained me and later migrated to Australia. She is in Sydney now [pointing and nodding to the researcher]. (Interviewee 2)
I studied to the highest level in school education. I even had the marks to attend the university, like you. However, due to economic difficulties, I could not attend university. (Interviewee 15)
Data From Personal Reflections
During the field data collection, I maintained a reflective journal to record changes in my thinking. The reflective journal also helped me record events and incidents in branch operations, and compare them with the MFI’s operational guidelines. I also recorded many informal discussions I had with staff, some of which gave me information that I doubt would have been shared in formal interviews.
When collecting the data from different branch locations, I sometimes had a gap of a few days between branch visits. I spent these days at the MFI head office reviewing documents and having informal discussions. I also used the time to reflect on the data collection process, listen to the audio recording, and think about what additional questions I should ask in the next interviews.
I began formal, systematic analysis of the data after returning to Australia. To begin with, I translated and transcribed all the interviews and discussions into English, which helped me familiarize myself with the data. Then, key themes arising from the data were coded using NVivo and arranged in tree nodes corresponding to theoretical concepts. This also helped in identifying new themes. In addition, to ensure coding reliability, I conducted a self-audit 1 year after the initial coding. I selected about 10% of interview transcripts and coded them again without looking at the initial codes and compared the two coding structures. I counted the total number of codes in the original structure and calculated the percentage of codes in the self-audit that were different. The results indicated only a marginal difference (4%) suggesting that the initial coding structure was reliable.
I then used these key themes to explain the survey findings and build conceptual models of how MFI borrowers made business decisions. That is, I used the ethnographic data to try to interpret the quantitative results from the survey, building a coherent story from blending both types of results. This approach to reporting findings allowed me to build theory because it showed “the big picture” of the individual entrepreneur’s progress in acquiring expertise. For example, when I developed a conceptual model of entrepreneurial development, the integrated findings not only showed that entrepreneurs used different business decision-making logics at different stages of their business’s development but also revealed how these different logics played out in day-to-day circumstances. They also showed how first-time business owners, and second and subsequent business owners practiced these logics at the business start-up and development phases. Finally, this integrated way of reporting results allowed me to disclose assumptions and biases that were likely to be shaping the research inquiry (Creswell & Millen, 2000), and integrate those reflections into the narrative.
Practical Lessons Learned
Although some traditional ethnographers see RE as a “quick and dirty” method, my project demonstrates that modern technology and time-deepening strategies can be used to collect quality ethnographic data even if the researcher spends less than 3 months in the field, so long as certain factors are managed carefully. First, it is essential to communicate with and get to know the research organization or likely respondents before visiting the field. This can be done using modern technology-assisted communication tools such as email, Skype, or chat sessions. This helps organizations and respondents become familiar with the research and build trust with the researchers. However, in instances where research participants cannot be contacted using modern communication technologies, as with this study’s microfinance borrowers who lived in rural areas, a person whom research participants are familiar with and trust (the MFI field officer in this case) should introduce the researcher.
It also helps if the researchers have at least a basic idea of the selected organization’s internal functions, power relations, and reporting structures. However, this is often difficult in practice as researchers are usually external to the organization. In this study, I had prior knowledge as I had been employed at the selected MFI before beginning my PhD project. Where there is no insider knowledge, the researcher should approach the organization through formal channels. This will facilitate getting approval from the formal gatekeepers.
The success of the RE technique depends on time-deepening techniques such as the use of multiple and parallel data collection methods. In this study, I used a range of techniques: discussions, formal and informal observations, interviews, and daily activity journals and field notes to collect data simultaneously, and all were used to flesh out and interpret the results of my survey. Some rapid ethnographers recommend having multiple researchers at sites, so that the same phenomena can be observed from different perspectives. Furthermore, technology-assisted methods, such as video recordings, email, and social media postings, can be used as supplementary tools to collect data.
The success of the interviews and discussions depends on the ability of the researcher to explain their research and introduce themselves in a manner which participants can understand and which creates trust. This is specially required when conducting research in rural areas or other situations where researcher and participants may initially appear very different from each other. For example, I mentioned my background, education, and hometown details, before explaining I was a researcher in an Australian university. This allowed the participants to link me to things they already knew, and to better understand who I was and what I was doing. The ability to communicate in one of the local languages was an advantage.
Obtaining the support of local agents, such as the MFI field-level staff, even collaborating with them, helps in data collection, particularly in locating and approaching information-rich participants, arranging logistics, and understanding the “research terrain.” Although there is some potential for response bias being created when researchers are introduced by representatives of a particular organization, this can be minimized by asking probing questions and carefully observing the context.
Even in a high-speed situation like RE, researchers should take time to reflect during data collection. Taking a day or two between interviews and discussions allowed me to improve my interview and discussion technique. I had time to identify better ways of asking questions, and think about whether additional information was required to understand a certain phenomenon.
It is also recommended to start translating and transcribing interviews and discussions as soon as a reasonable body of data has been collected. These “pre-analysis” tasks will show quickly whether additional information is needed, if there are missing data, and so on. Starting the data analysis quickly also takes account of the fact that data analysis is an iterative process which evolves with time: Comparing early analyses with later ones reveals where the researcher’s thinking has changed and why—an important aspect of maintaining reflexivity. Finally, starting data analysis early gave me time later to recode a sample of interviews a year after the initial coding, which assured me that my qualitative coding was reliable.
Nevertheless, RE has its challenges. The fundamental principles of RE include (a) reducing the time spent at the field using planning, (b) using technology-assisted communication methods to begin building rapport and becoming familiar with the research site before visiting it, and (c) using multiple methods or multiple observers to compensate for limited time spent in the field and allow quality data to be collected. (I did not use multiple observers, but did use multiple methods: survey, diaries, interviews, focus groups, document analysis, field notes.) As I was already familiar with the organization and the microfinance sector, it was fairly easy to adhere to these principles. Nevertheless, it was challenging not to become complacent as result of familiarity, or to remember that changes had taken place since I worked in the case organization and to take nothing for granted. For example, as I had previously worked with some of the MFI staff, they assumed I was familiar with recent MFI developments and changes and tended to skip over information. On those occasions, I had to remind them I was not familiar with the new initiatives.
It is essential to reflect on your position as a researcher within a familiar context. Reflexivity is about explaining how interpretations and findings are influenced by the researcher’s background, experience, and position in relation to the context (Creswell, 2014). As my supervisors questioned me, clarified issues, and asked for explanations of what was not clear to them, I become aware that not all I knew—or thought I knew—was known to others. I needed to make my tacit assumptions explicit in the interests of in-depth analysis and discussion.
This case study explained my experience of a complex research problem encountered and solved: trying to understand how female microentrepreneurs in Sri Lanka make business decisions and gain entrepreneurial expertise. I assessed the advantages and disadvantages of RE in this process, including when my own ethnic background made me familiar with the cultural context and the organization. In addition, the case described practical strategies that can be used to collect ethnographic data during a short data collection time and showed how to collect quality data without violating ethnographic research principles. Furthermore, this case outlined the potential for RE in entrepreneurship and management research by showing how RE data could be used to explain quantitative findings in a mixed-methods study, lend support to findings that might otherwise seemed more susceptible to “same source” bias, and help develop new theoretical models.
Exercises and Discussion Questions
- What are the advantages and disadvantages of using rapid ethnography techniques in data collection?
- What are some novel ways of collecting quality ethnographic data within a short field data collection time period?
- To what extent do you think the researcher’s familiarity with the cultural and organizational context of the research question contributed to the success of her use of rapid ethnography techniques? Was it sometimes a problem? How?
- How would the researcher have had to change her research approach if she had not already been familiar with the cultural and organizational research context?
- Think about a research question you want to investigate. Now discuss your positionality and how your beliefs and assumptions are likely to shape the research process.
There is a wealth of online resources on ethnography and rapid ethnography, including the articles listed under “Further Reading.” Researchers may find it helpful to include their area of interest in the search, for example, rapid ethnography + academic medicine, human–computer interaction, education, or the type of research being done, for example, rapid ethnography + case study.
The following guide includes an appendix of useful ethnography resources:
Reeves, S., Peller, J., Goldman, J., & Kitto, S. (2013). Ethnography in qualitative educational research: AMEE Guide No. 80. Retrieved from https://www.tandfonline.com/doi/full/10.3109/0142159X.2013.804977
The following chapter, which is available online free of charge, focuses on doing corporate ethnography research quickly.
Isaacs, E. (2013). Chapter 5: The value of rapid ethnography. In B. Jordan (Ed.), Advancing ethnography in corporate environments: Challenges and emerging opportunities (pp. 92–107). New York, NY: Routledge. Retrieved from http://www.izix.com/pubs/Isaacs-RapidEthnography-2013.pdf