The Generation of Qualitative Data in Information Systems Research: The Diversity of Empirical Research Methods

Abstract

This paper investigates the concept of data collection in information systems qualitative research. In this text, I replace the term "data collection" with "data generation" to emphasize that the researcher arranges situations that produce rich and meaningful data for further analysis. Data generation comprises activities such as searching for, focusing on, noting, selecting, extracting, and capturing data. This paper analyzes and compares a repertoire of empirical research methods for generating qualitative data. It describes and visualizes (through a common data-generation template) 12 research methods: interviewing, questionnaire study, document study, artifact study, observation study, participant observation, intervention study, practice-based design study, lab-based design study, focus group study, test study, and self-reporting. I compare these data-generation methods according to 1) the researcher's role in data generation, 2) data generation's influence on everyday life reality, 3) each data-generation method's relationship to everyday life reality, 4) what parts/mediators of everyday life reality each data-generation method addresses, 5) the expected value of generated data and 6) possible shortcomings in generated data. As a basis for investigating data generation, I ontologically clarify (based on a practice-theoretical perspective) the empirical landscape of information systems (the kinds of phenomena and sources of data that exist). A concluding discussion contains 1) analyses concerning relationships between data-generation methods and compound research methods/strategies such as case study research, action research, and design science research and 2) the role of interpretation in data generation versus data analysis.

Empirical Study of the Occurrence of Domestic Violence in Banggai Regency, Central Sulawesi Province

“…This research is empirical in nature. Data collection by observation, interviews and documentation (Goldkuhl, 2019). Then the data was analyzed using qualitative descriptive.…”

Problem resolution with business analytics: a task-technology fit perspective

“…We collected data through semi-structured interviews. Interviews are regarded as one way to generate data that relies on the interviewees' valid knowledge and their ability to express such knowledge appropriately (Goldkuhl, 2019). We employed purposeful sampling, involving the use of pre-determined criteria, to select interviewees (Matavire and Brown, 2013).…”

School principals’ experiences of ethical leadership during times of accountability : complexities, dilemmas and dynamics.

“…This thinking is further supported by Mthembu (2018) who emphasises that knowledge is not just collected but, instead, it is socially constructed. Similarly, Goldkuhl (2019) cautions us on the usage of the concept of data collection in qualitative research as problematic since it may presuppose that researchers simple pickup ready-at-hand data to use in their research. This researcher further mentions that qualitative scholars cannot simply gather already existing data out of everyday life to enrich their research (Goldkuhl, 2019).…”

“…It is against this background that adopting data collection in relation to this interpretive qualitative multiple case study seemed to be inappropriate. I believe data collection leans more towards the positivist paradigm because quantitative researchers collect data only at one point in time (for example a survey investigation) (Goldkuhl, 2019). As a result, the positivist paradigm yields objective epistemology and a realism ontology (Kivunja & Kuyini, 2017;Scotland, 2012).…”