Verbal Interviews in consumer finance

Verbal interviews are one of the most widely-used methods in qualitative research due to their versatility and ease of execution. They are a standard part of research in both commercial and non-commercial research in consumer finance, such as in the payments sector and by regulators.

A variety of qualitative researchers are trained to conduct interviews, including sociologists, anthropologists, behavioural researchers, and user experience researchers. However, interviews may also be conducted by researchers who are more quantitatively focused, such as economists.

What is Verbal Interviews

  • Qualitative / quantitative
  • Multiple techniques to choose from
  • Face-to-face or remote data collection

Interviews are a standard method in qualitative research. The main types of interviews are structured interviews and unstructured / semi-structured interviews.

In structured interviews, interviewers write questions (the "interview schedule") ahead of time and ask exactly the same questions of each interviewee. The purpose of this consistency is to avoid biasing how the interviewees answer the questions. It means that data is quite uniform, and interviewees' responses can be more readily "coded" (categorised) and analyzed, giving them a quantitative aspect. For a comprehensive guide to structured interviews, see the GAO Guide to Structured Interviews.

In unstructured / semi-structured interviews, interviewers adapt their questions as the interview progresses. This method is generally qualitative because there is variation between the questions asked of each interviewee.

There are also many sub-types of interviews, including life histories, video interviews, place-based interviews, and object-centred interviews.

Strengths

First-person perspective

Topics related to money are always concerned to some extent with numbers, such as bank balances, prices, and interest rates. This means that researchers often need quantitative data. However, while statistics are often valuable in telling us what people do, they might not tell us why people make certain decisions. People's descriptions of their own behaviour can tell us why they behave in certain ways and also what they think motivates their behaviour (see Ethnography in Consumer Finance).

Flexibility of location

Interviews can be carried out with virtually anyone and in many different locations, such as at home, work, school, cafés, over the phone, or over the Internet. In consumer finance research, where the data being collected is often considered personal, choosing an appropriate location can assist in helping interviewees feel comfortable.

Methodological versatility

Interviews are one of the most versatile research methods because different kinds of interviews can be used to achieve specific goals. Unstructured interviews give the interviewee the greatest control over the content.

An interview may begin with a single planned question, with the interviewer generating new questions in response to the interviewee's statements. Life histories often take this format, but the approach can be used for any exploratory research.

For example, asking a person to simply talk about their finances can lead to topics emerging that the interviewer may never have thought of asking about. Moreover, it allows the interviewee to discuss what they feel is important.

In contrast, semi-structured interviews allow deviation from the interview schedule to allow for the discussion of interesting points that emerge while interviews are in process. Structured interviews, in which an interviewer sticks to the questions on the schedule, permit interviews to be directly compared, which is not the case for looser interviews.

How structured an interview is (or isn't) is just part of what makes them work. Props and context help data collection by prompting interviewees to speak on certain topics (see Object-Centred Interviews in Consumer Finance).

Another means of inquiry is cultural domain analysis, which involves asking interviewees to classify items in a list, thus permitting researchers to understand how interviewees order information.

Quantifiable

Structured interviews that apply the same questions to each person in a sample group can work like surveys in providing quantitative data. This is achieved by developing a coding schema for open-ended questions and analysing the data using quantitative software. While interviews are generally more time-intensive to complete than surveys, obtaining data from a representative sample is possible given enough respondents or a smaller group size.

Limitations

Self-reporting bias

Interviews generally depend upon the interviewee to give an accurate account of their own behaviours. However, while people are experts in their own opinions, studies show that we are unreliable at remembering facts. Interviews do not normally include substantial time for observations that could be used to cross-check verbal data. It can therefore be often advantageous to use interviews in conjunction with other methods.

Resource-intensive

Interviews generally require less research time than ethnography, but they still present significant costs in terms of collecting and processing data. Organising interviews and carrying them out is a time-consuming process. In commercial work, recruiters are often used to assist with the organization. Professional interviewers are needed to collect the data, which will then need to be transcribed, coded, and analyzed.

Design complexity

Depending on the research requirements, writing effective interview questions can require high levels of skill. For unstructured or semi-structured interviews, researchers need knowledge of how to adapt questions while the interview is taking place. Structured interviews are intended to produce rigorous results, and questions must be carefully designed. There are many textbooks that discuss the theory and practice of writing interview questions.

Case Study 1 — Learning "good" and "bad" financial behaviours: Semi-structured interviews with Hispanic American college students

Many consumer finance researchers have observed that people often do not appear to manage their money in ways that best suit their interests. This is partly because people may not have the financial skills they need to manage their money well, but it has also been suggested that people learn "good" and "bad" behaviours from those around them. Who do they learn from and how can positive financial behaviours be encouraged?

Marketing professor Kittichai Watchravesringkan wanted to find out how Hispanic American college students acquired financial skills. As he discusses in his chapter in the Handbook of Consumer Finance Research, quantitative and qualitative data from various sources indicates that Hispanic Americans are one of the groups that are most at risk of financial difficulties. However, the data did not demonstrate why this was the case.

Hypotheses have included that Hispanic Americans have lower educational attainment and are reluctant to engage in long-term financial planning. One survey suggested that this demographic group are suspicious of advertising and are reluctant to adopt new products and services that could assist them to manage their finances. Another study suggested that Hispanics have more present-oriented attitudes and are less likely to engage in delayed gratification.

Out of all these possible causes, which are the most important? Underlying all of them is a question of how people learn financial behaviours in the first place: who their "socialization agents" are (family, friends, advertising, etc.). As Watchravesringkan explains,

"Consumer socialization refers to the process by which young consumers develop consumer-related skills, knowledge, values, and attitudes throughout their different life stages via the influence of socialization agents, such as family and peers." (2008, p.275-276)

Studying how behaviours are formed, as well as individuals' motivations, deepens our understanding of why one demographic group might differ from others.

Methodology

To investigate consumer socialisation among Hispanic Americans, the researcher interviewed 11 college students of 20-25 years of age. He focused on college students because Hispanic Americans demonstrate low levels of educational attainment due to financial constraints. All interviewees were either first- or second-generation Hispanic Americans.

Students were selected using a combination of a convenience sample and snowballing techniques. Care was taken to ensure that interviewees represented a range of majors. Monetary incentives were given to interviewees to increase the rate of participation in the study.

Interviews lasted 60-70 minutes and were audiotaped. Interviewees were encouraged to talk about matters that they felt were important. The interviews began with "grand tour" questions to collect general information before turning to financial management specifically. Questions were kept quite broad, focusing on how interviewees learned to manage their finances, their financial values, and their current practices. They included:

  • How do you manage your finances?
  • Are you currently satisfied with the way you handle your finances?
  • How do you learn to develop financial skills?
  • What kind of values may impact your financial management do you believe that you should or should not have?

The interviewer then coded and compared the interviews to spot emergent themes. He showed a selection of their conclusions to the interviewees to attain their responses. Overall, he found that students mostly agreed with the study's conclusions.

Findings

The researchers found that the students' financial behaviour was strongly influenced by their family members. Specifically, interviewees reported that they had learnt the importance of financial management and saving from their fathers. Students also reported being influenced by watching other people get into debt.

In some cases, students watched others become over-indebted and copied their behaviour; in other cases, students learned not to follow the footsteps of people who became over-indebted. To a certain degree, students also learnt from their peers, television, or their religious communities.

Overall, the data suggested that whereas the interviewees learn "good" financial behaviours from their families, they tend to learn "bad" financial behaviours from outside their families (peers and media).

Applications

While this study was small, its findings are potentially useful for further research or program development.

In particular, the suggestion that family members provide a positive overall influence is interesting because it contradicts common assumptions. Many social studies tend to assume that, if a behaviour is specific to an ethnic group, then it must be culturally learned through parent-child transfer.

Instead, the results of this study suggest that inter-group socialisation may not be a problem, but instead may contribute towards improving financial management, such as by developing programs that enhance the influence of families on financial behaviour. As the researchers explain,

The results may aid academic administrators, financial counselors, and consumer educators in gaining a greater understanding of this particular college segment and finding means to develop effective outreach programs geared toward this growing segment. (Watchravesringkan 2008, p.281)

Key to uncovering this insight was that interviews were relatively long and open form. This allowed the students the time and space to cover issues that they felt were relevant to them. In a more structured approach or a shorter time frame, these insights may not have been able to emerge.

Ethics

This study raises various ethical issues that merit consideration. One of these is snowballing, a method of recruiting participants to a study by asking already-enrolled participants to suggest people they know for recruitment. It is especially useful when working with populations that are difficult to access. However, there are some reservations about this method because there is a danger that participants may not feel that they can refuse to assist the researcher. Moreover, it can result in breaches of privacy.

Monetary payments are often used to encourage participation in interviews. Some researchers argue that this is ethical because participants should be compensated for their time. Others raise concerns that financial stress may drive people to participate in research that works against their interests. This is of particular importance in medical research that can have negative physical outcomes for participants. It is less of an issue in research that focuses on opinions, values, and self-reported behaviour (such as Watchravesringkan's study). An article posted at The Hastings Centre describes the ethical issues involved in paying research subjects.

Cultural stereotypes can be reinforced or challenged by research that focuses on a particular social demographic, such as an ethnic group. This is known as "implicit bias" or "in-group bias". Poorly-designed research can sometimes inadvertently reinforce stereotypes, such as when questions are poorly thought through. Watchravesringkan's study, by keeping questions open, maximised the ability of students to share their own perspective and limit bias in the research design.

For more information on this study, see:

Case Study 2 — Interviews for social network analysis: Mobile money in Kenya

An interesting application of interview techniques is in the analysis of social networks. Interviews can be used to collect data on networks, which can be analysed either qualitatively or quantitatively. Essentially, social network analysis is a self-contained mixed method.

Quantitative interview data can be used to map nodes and connections in social networks. The resulting visualisations are an excellent way to see clearly who is connected to whom, and whether a social network is open (loose connections) or closed (close ties among group members).

Qualitative interview data can be used to explain what drives social networks. For example, interviewees can be asked to explain why particular connections exist, how they are maintained, and how they have changed over time. In other words, whereas the quantitative data tells the "what," qualitative data tells the "how."

Social network analysis is particularly useful for studying patterns of circulation, such as remittances, conditional cash transfers, gifts, and other forms of payments. It is handy for analysing mobile money transactions, in which users are often individuals who send and receive money for social purposes as much as for economic ones. It has the advantage of showing not only who is connected to whom, but also in demonstrating how and why money moves across large geographic areas.

Sibel Kusimba and her colleagues conducted a study of mobile money in Kenya, where at least 60% of adults are unbanked. Mobile money was launched in Kenya in 2007 and is widely recognised as the world's most successful mobile money service. It offers person-to-person transfers, a merchant payment service, and a basic means of saving money. In November 2012, a related service called M-Shwari was launched that offers basic savings accounts and microloans.

Kusimba and her team were interested in discovering how rural Kenyans were networked through mobile money and the reasons why they sent money. The researchers wanted to find out whether common assumptions about mobile money—that it empowers individuals, stimulates entrepreneurship, and reflects rural-urban migration patterns—reflect Kenyans' experiences of using mobile money services.

Methodology

Kusimba and her team undertook research in rural Kenya in 2012. They conducted participant observation, research interviews, and survey questionnaires with more than 300 Kenyans, 80% of whom were farmers. The team also conducted interviews with a smaller sample of Kenyans living in Chicago.

The researchers carried out different kinds of interviews to elicit qualitative and quantitative data. "Intercept interviews" were accomplished by walking through the main marketplace. Kusimba "intercepted" people, asked them if they want to answer a few questions, and then saw how much they wanted to talk about the topic. While the resulting sample was not representative, Kusimba notes that it is a good way to canvass general opinions and identify people who are interested in taking part in a more formal study.

Because Kusimba had been working in the geographic area for 20 years, she already had contact with a number of families who were receiving remittances. She, therefore, recruited people for interviews from both this familiar group and her new contacts.

In-depth interviews provided background and contextual information about people's experiences, feelings, social lives, and economic practices. Kusimba notes that the advantage of her in-depth interviews was that the quality of information she received was high. A disadvantage was that the interviews often required a great deal of time and several visits in order to achieve rapport. During interviews, the researchers drew up kinship charts. They asked interviewees to tell them who they had sent money to in the last year, and who had sent them money to draw the networks.

For the quantitative part of the study, the team interviewed between 3-10 individuals from 14 families. Each interviewee was asked to name all of the relatives that they had sent money to, or received money from, in the previous year. Most interviewees had sent money to 5-9 people. Where possible, the researchers then contacted the individuals that had been mentioned and approached them for an interview as well. They entered the resulting matrices into R, statistical computing software that can be used to draw social networks diagrams.

Kusimba and her team chose to ask people to list the names of people they had transacted with rather than the amounts of money they had sent. There were two reasons for this. First, people tended to be inaccurate in recalling quantities of money.

Second, many people did not like to talk about money directly. This was especially the case with men who would organize large ritual ceremonies that could cost up to 26,000 Kenyan shillings. Whereas women would admit that they asked family and friends for financial assistance, men preferred to say that they had collected debts owed to them.

Kusimba notes that, for her research, the primary benefits of social network analysis were:

  • Visualisations help them to clearly see and analyse the connections in a way that is difficult or impossible with interviews
  • They could tell which networks were "open" or "closed"
  • They obtained a sense of who was "brokering" the gaps in networks
  • As SNA is a statistical technique, the networks could be examined in terms of size, number of ties, and other parameters

She explained to us:

Social network analysis is good because it reveals different kinds of social relationships. It also provides quantitative assessments in terms of size and number of ties. These can also become apparent through ethnographic interviews but SNA makes it clearer. We need both because the ethnographic interviews give context. It's also good to follow up SNA and do another study in a few years (or other appropriate time frame) because then you can see the social network change. (personal communication)

Social network analysis has limitations as well as benefits. Like any model, it simplifies reality, collapsing a lot of information about family ties and obligations. People send money for a variety of reasons, including deep kinship ties, social obligation, or as a debt, but these differences are not generally visible in social network models. Whereas social network modelling shows what people do, in-depth interviews demonstrate why they do it.

Methodologically, networks drawn from interview data need to be treated as samples. People forget or intentionally omit their connections for various reasons. Like any other kind of ethnographic information, information needs to be verified wherever possible by talking to the people who an interviewee says they have sent money to or received money from. This sometimes yields contradictory information, but can also improve certainty as to the accuracy of data if different interviewees' accounts agree.

SNA has grown by leaps and bounds over the years. Kusimba notes that collaborations with experts in the method and in consumer science can introduce a range of models and approaches that social scientists might not be aware of. She says that it can also help to have a data scientist as a co-author since this helps with the production of journal articles and also peer-review.

Findings

The team's combination of qualitative and quantitative analysis of social networks resulted in a wide array of discoveries. Many of the findings contradict common assumptions about how mobile money operates as a social and economic tool. Others illuminate how mobile money interacts in the context of rural Kenya.

First, there is the assumption that mobile money is used primarily by individuals conducting person-to-person transfers or using it for their own particular purposes, such as saving money. In contrast, Kusimba argues that it is better to conceptualize mobile money as created by collectives and groups.

Remittances sent by mobile money are used to strengthen social ties, especially among siblings and mothers, and as a way of contributing to social rituals such as funerals, weddings, and coming of age ceremonies. Moreover, a person who receives money will often re-circulate a portion of it to other family members and friends.

Mobile money helps to equalize access to resources within a family, rather than simply contribute to an individual's wealth. Indeed, while remittances are often assumed to flow from urban to rural areas, Kusimba and her team found that money flows in both directions.

Second, promoters of mobile money for development often represent the service as as a tool that empowers people both socially and economically. This is often true, since sending money via a mobile phone can present a significant reduction in economic and transaction costs compared to other kinds of financial services.

However, contrary to the idea that mobile money changes the playing field, Kusimba states that, "For the majority, mobile money is a way of reaching out to traditional economic support networks." Moreover, its functions and uses are sufficiently different from those of mainstream banking that it does not act as a close supplement. Kusimba argues that mobile money is better understood "not as banking but as adjunct to the mobile phone," since the practice of sending and receiving money is closely connected to that of speaking or texting.

Third, mobile money is often seen to benefit women. Mobile money incurs advantages for women because it provides a way to make transactions privately, and this can help women gain some autonomy from their husbands and other men. Mobile money gives individuals more control over their social networks, allowing them to both create and sever connections.

Yet, as Kusimba notes, while women tend receive a large share of remittances, they often view mobile money as something that helps them cope rather than that empowers them. This is because productive wealth are tied up in land and stock, which are predominantly controlled by men.

Fourth, an ethic of generosity places pressure on people to recirculate remittances, and this can be seen as a burden. People grow weary of constant requests for money and may stop answering their phones at times when requests have a high frequency, such as before the beginning of the school year. People avoid storing money on their phones out of fear that it will lead to large purchases or the inability to say no to requests for money.

In fact, Kusimba explains, there is a growing sentiment that Kenyan social life is becoming overly monetized. Aside from the burden of giving, some urban workers will send money to their rural homes rather than return in person to participate in rituals. This is fundamentally altering the structure of social life.

The social network visualizations that the study generated provide a valuable complement to the in-depth interviews. They showed clearly that people's networks are relatively dense and that there are many transactions between the people within them. Family networks are based around siblings and mothers, and show a preponderance of matrilineal ties. Moreover, they are reciprocal networks, with money moving backwards and forwards rather than in one direction. These findings contribute to conversations about families, informal finance, social relationships, and ideas of reciprocity.

Applications

The combination of in-depth interviews with social network analysis has many potential applications. For example, when we think of remittances we often picture urban migrants sending money home to their rural families. However, social network analysis can bring this assumption into question and show the underlying logic of juggling many ties that informs people's money decisions.

Comparison with other areas of the world would be fascinating and may uncover contrasting cultural dynamics around money-sending. This approach is likely to be of interest to policymakers, especially since it emphasizes the importance of groups in mobile money usage.

Ethics

Privacy is always an issue with networks. When an interviewee named a contact, Kusimba and her team generally tried to follow up to see if the named person was also interested in participating in the study. Some follow-ups declined to take part, and it is important not to make them feel pressured into participating.

Some people gave contradictory information in terms of size and frequency of remittances. There were people in the study who sent remittances secretly and did not want them to be a part of the network visualisations. While such problems are anticipated, they mean that interview data on networks is subject to some uncertainty and also entails privacy concerns.

In general, says Kusimba, East Africans are quite open with information, but some of their mobile money use can be illicit. They often omitted information if it could not be verified by at least one other person, usually the recipient/sender, or if the inclusion of personal information had the potential to cause harm to the participant.

For more information on this study, see:

More about Verbal Interviews

Examples in consumer finance research