A Round-Table Discussion of “Big” Data in Qualitative Organizational Communication Research

Abstract

The forum guest editor Ryan Bisel in this issue takes on the topic of big data and presents a round table that grew out of a conference panel. Five scholars engage in a discussion of the social and cultural trend of big data and implications to qualitative organizational communication research. The contributors respond to questions and delve into a number of issues, from theoretical, to institutional, to operational, to practical, by sharing thoughts and experiences about definition, assumptions, theory building, execution at every stage of a big data project and reflections beforehand and afterward. Opening Remarks The phrase "big data" refers to a trend in corporate and academic circles to utilize increasingly available stores of structured and unstructured

A modified peer rating system to recognise rating skill as a learning outcome

“…For academic-oriented majors, who by definition have limited job prospects in their disciplines, a case for career preparation is harder to make. However, the era of big data heralds a need to process a large quantity of data in order to inform treatment decisions or service choices (Bisel, Barge, Dougherty, Lucas, & Tracy, 2014).…”

Qualitative Research Ethics in the Big Data Era

“…We should anticipate that other funding agencies and research institutions will do the same. However, as mentioned earlier, these trends privilege the persistent idea that studies with more respondents are better than studies with fewer respondents (Bisel et al, 2014), and that openness provides a means not only to transparency, but also to reliability. This may incentivize qualitative studies with larger sample sizes that effectively strip their data of its context to protect participants or aggregate analysis across multiple studies and sites.…”

Making sense of Big Data – can it transform operations management?

“…If BD is to be exploited to assist in decision-making it needs to be analysed and utilised with care. Bisel et al (2014) indicate that there is an assumption that the size of a data-set is seen by many researchers and analysts as a proxy for quality, and yet as Lazer et al (2014) stress, quantity is not a substitute for quality. The volume and variety of BD make assessing its veracity challenging.…”

“…Sampling and hypothesis-building is traditional data analysis, not BD, despite the constant stream of largely unstructured data created from multiple sources and in a range of formats which causes the BD analytics challenge (Bisel et al, 2014, Chen et al, 2012, Tinati et al, 2014. Case 2's data, whilst not quite unstructured, lacked a standardised company format.…”