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

Abstract

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.

The Psychology behind Knowledge Hiding in an Organization

“…KH takes place among coworkers (Aljawarneh and Atan 2018), team members (Babic et al 2019;Singh 2019), and even between supervisors and subordinates (Butt 2019) for various reasons (Afshar-Jalili et al 2020). Studies have found that KH is not only a hindrance to knowledge sharing (Qureshi and Evans 2015;Liu et al 2020a) but is also detrimental to organizations, as it can affect a firm's decision-making quality (Ghasemaghaei and Turel 2021), idea implementation (Li et al 2020), organizational performance, team performance (Chatterjee et al 2021), and creativity (Bogilovic et al 2017;Fong et al 2018;Peng et al 2019), in addition to employees' turnover intention (Serenko and Bontis 2016) and organizational citizenship behaviors (Arain et al 2020). KH also affects the internal and external stakeholders in an organization by affecting, for example, employees' abilities to solve customer problems, relationship building with customers (Chaker et al 2020), creativity (Rhee and Choi 2017), and job (Chatterjee et al 2021;Wang et al 2018b;Jahanzeb et al 2020) and innovation performance (Khoreva and Wechtler 2020).…”

Assessing the Relationship Between Technostress and Knowledge Hiding—A Moderated Mediation Model

“…Meanwhile, Ghasemaghaei and Turel (2021) argue that data analysts are often overwhelmed when dealing with big-volume data, finally turning to seek a less stressful work environment. This effect can be attributed to the ACT, which indicates the reduced capacity of individuals to handle less-relevant tasks when performing self-relevant complex tasks (Cohen, 1980).…”

“…Knowledge hiding, generally as an undesirable employee behavior, has attracted an increasing amount of attention due to its negative impacts on both employees and organizations, and it has been proposed that big data can have a negative influence on decision quality, especially via the mediating effect of employees' knowledge hiding behavior (Ghasemaghaei & Turel, 2021). However, the mechanism of how "big data" influences knowledge hiding, which is important for enterprises to further understand how employees feel and respond when facing technostress, and develop practical actions to alleviate these negative impacts in advance, remains unclear.…”

The need for compelling problematisation in research: The prevalence of the gap‐spotting approach and its limitations

“…In the second article, Ghasemaghaei and Turel (2021) note that while common wisdom suggests that big data facilitate better decisions, it may not always be the case, as big data can also afford and motivate knowledge hiding. To examine this possibility, they integrated adaptive cost theory with the resource‐based view of the firm and suggested 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' knowledge hiding behaviours, including evasive hiding, playing dumb and rationalized hiding.…”