Methods of data collection
According to Kahn and Cannell (1957) « an interview is a purposeful discussion between two or more people ». Interviews may be used in order to gain understanding about a research topic by collecting information. More specifically through the usage of interviews one can gather reliable and valid data that is relevant to specific research questions and objectives. There are three main types of interviews: structured interviews, semi structured interviews and unstructured interviews.
Structured interviews involve predefined questions and follow a predefined sequence while asking the questions. Due to this structured interviews are also refereed to as interviewer administrated questionnaires. The main advantage of structured interviews are their replicable nature as well as an increased comparability of the results (Saunders et al., 2009).
Semi structured interviews involve a set of predefined questions and themes but the sequence and questions might be changed from interview to interview. Further new questions might be added during the process of the interview (Saunders et al., 2009).
Unstructured interviews involve no predefined set of questions and promote a more open and free discussion but within a frame of a predetermined research field (Saunders et al., 2009). In-depth interviews are a form of unstructured interviews which offer the opportunity to capture rich, descriptive data about people’s behaviours, attitudes and perceptions, and unfolding complex processes.
Private banking customers
For conducting the interviews with the private banking customers, the author has chosen to apply a semi structured approach. Before each interview, the interviewee was asked whether it is allowed to record the interview, for the purpose of creating interview transcripts as a source for the thesis. One interviewee did not want to be recorded thus, in order to capture the answers the author took handwritten notes.
At the beginning of the interview, a set of initial questions was asked in order to understand the kind of relationship the interviewee has with the bank. The following part of the interview was based on the initial answers. An interview guide was used in order to navigate through the conversation. The interviews did not involve preconceived theories or ideas in order to receive unbiased answers from the participators.
The Swedish banking customers were interviewed with the aim to explore their experiences, views, beliefs and motivation about topics relevant for the research. The interviewees were selected randomly with the only criteria of being a Swedish banking customer and belonging to the millennial demographic cohort identified as being born after 1990 and before 2005. The reason for choosing only millennials is that millennials are overall more motivated towards the adoption of new technologies and therefore act as early adopters within the digital financial market (Ashoka and Vinay (2017), Hussain and Wong (2015)). Five interviews were performed (see table 1).
Content analysis describes different techniques used to systematically analyse qualitative data such as spoken or written communication (Cole, 1988). It can be used to evaluate various types of media such as print media (e.g.: magazines, articles, newspapers), visual media (e.g.: movies, videos or television) or content on the internet. Within research, content analysis is used as a technique to analyse qualitative data in a systematic and objective manner to quantify phenomena (Elo and Kyngas, 2008). It allows researchers to concentrate qualitative data into content related categories with the goal to cluster related statements, phrases or words which share the same meaning (Cavanagh, 1997).
The outcome of a content analysis is a summarised and general description of the investigated phenomena. Hereby Elo and Kyngas (2008) differentiate between two types of terms used when describing the outcome – « concept » and « category ». The term concept is used in a more specific context when the aim is to built a new theory whereas category describes a broader approach.
For the analysis of the interviews, the author has followed the scheme identified by Elo and Kyngas (2008) for creating a content analysis. Elo and Kyngas (2008) state that conducting a content analysis involves the three phases: preparation, organising and reporting.
During the preparation phase the author read through the material and derived the core elements from the interviews. These elements or « unit of meanings » can be single words or whole statements and may contain more then one meaning relevant for the topic (Elo and Kyngas, 2008). The choice of the unit of meaning has an impact on the analysis process. Identifying units of meanings with too many meanings may result in making the analysis process too complex (Catanzaro, 1988). However the choice of elements which are defined too narrowly could also negatively impact the analysis process leading to fragmentation (Graneheim and Lundman, 2004). Further Graneheim and Lundman (2004) specified that while conducting a content analysis it is important to state whether latent content such as the notice of non verbal communications, laughter, silence or other types of subtext is to be included into the analysis. The rules regarding the inclusion of latent content for the analysis are specified within the Rules for transcription section below.
1.1. Problem definition
1.3. Research questions
2. Theoretical framework
2.1. Development of artificial intelligence
2.2. Current popularity of artificial intelligence
2.3. Development of chatbots
2.4. Artificial neural networks
2.5. Prerequisites within Sweden for utilising innovative digital technologies .
2.6. The Core/Context mode
3.1. Research approach
3.2. Methods of data collection
3.3. Data analysis
4.1. Case description
4.2. Customer interviews summary (Appendix A)
4.3. Expert interview summary (Appendix B
5.1. Customer interviews: Report
5.2. Customer interviews: Analysis .
5.3. Application of the Core/Context model on Swedbank
7.1. Results discussion .
7.2. Methods discussion
7.3. Further research
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Artificial intelligence in banking A case study of the introduction of a virtual assistant into customer service