Possible negative effects of big data on decision quality in firms: The role of knowledge hiding behaviours

While common wisdom suggests that big data facilitates better decisions, we posit that it may not always be the case, as big data aspects can also afford and motivate knowledge hiding. To examine this possibility, we integrate adaptive cost theory with the resource-based view of the firm. This integration suggests that the effect of big data characteristics (i.e., data variety, volume, and velocity) on firm decision quality can be explained, in part, by data analysts' perceived knowledge hiding behaviours, including evasive hiding, playing dumb, and rationalized hiding. We examined this model with survey data from 149 data analysts in firms that use big data to varying degrees. The findings show that big data characteristics have distinct effects on knowledge hiding behaviours. While data volume and velocity enhance knowledge hiding, data variety reduces it. Moreover, evasive hiding, playing dumb, and rationalized hiding have varying effects on firm decision quality. Whereas evasive hiding reduces firm decision-making quality, playing dumb does not affect it, and rationalized hiding improves it. These results are further validated with applicability checks. Ultimately, these results can explain inconsistent past findings regarding the return on investment in big data and provide a unique look into the potential dark sides of big data.

Automated summary

The characteristics of big data have varying effects on knowledge hiding behaviors among data analysts, with data variety reducing it and data volume and velocity increasing it. Different forms of knowledge hiding also have different impacts on firm decision quality, with some reducing it while others improving it.