Big Data in Management Research

Digital computers entered our homes, landed on our desktops, slipped into our pockets, and have seemingly become ubiquitous. At an ever faster pace, these devices have become highly interconnected and interoperable. Consequently, our archives, our work, our actions, and our interactions are increasingly digitalized and stored in databases or made accessible via the Internet. This data, generally characterized by high volume, variety, and velocity (i.e., accumulation rate), has come to be called “Big Data”. As of yet, Big Data has seldom been utilized in management research. Therefore, this dissertation explores the opportunities that Big Data brings for management scholars and describes three distinct projects that show how Big Data can be utilized in management research

Tacit Knowledge Transfer through Global Narratives

A central topic in the knowledge management literature is the distinction between codified and tacit knowledge: the former refers to knowledge that is easily transmittable through formal, systematic language and communicated through blueprints, maps, manuals and similar formats, the latter to disembodied know-how, acquired via direct experience and informally learned behavior and procedures (Howells 2000: 53; Polanyi 1966). In today’s ever-changing and fast-paced global business environment, knowledge management is considered key in attaining a sustainable competitive advantage (Bartlett & Ghoshal 1993: 41; Grant 1996). And since technological innovations have increased the transferability of codified knowledge, the acquisition and dissemination of tacit knowledge has become particularly important. Multinational organizations have struggled with the transfer of tacit knowledge, supposedly because of the lack of physical (i.e. geographic) proximity between employees. Expatriation programs have long seemed to be the only solution

Simplified phenotyping of CYP2D6 for tamoxifen treatment using the N-desmethyl-tamoxifen/ endoxifen ratio

Introduction: CYP2D6 protein activity can be inferred from the ratio of N-desmethyl-tamoxifen (NDMT) to endoxifen (E). CYP2D6 polymorphisms are common and can affect CYP2D6 protein activity and E level. Some retrospective studies indicate that E < 16 nM may relate to worse outcome. Materials and methods: A target NDMT/E ratio was defined as associated with an E level of 15 nM in the 161 patient Test cohort of tamoxifen-treated patients, dichotomizing them into ‘Normal’ (NM) and ‘Slow’ (SM) CYP2D6 metabolizer groups. This ratio was then tested on a validation cohort of 52 patients. Patients were phenotyped based on the standard method (ultrarapid/extensive, intermediate or poor metabolizers; UM/EM, IM, PM) or a simplified system based on whether any variant allele (V) vs wildtype (wt) was present (wt/wt, wt/V, V/V). Comprehensive CYP2D6 genotyping was undertaken on germline DNA. Results: A target NDMT/E ratio of 35 correlated with the 15 nM E level, dichotomizing patients into NM (35; N ¼ 44) groups. The ratio was independently validated by a validation cohort. The simplified system was better in predicting patients without slow metabolism, with specificity and sensitivity of 96% and 44% respectively, compared with the standard method - sensitivity 81% and specificity 83%. Conclusions: The simplified classification system based on whether any variant was present better identified patients who were truly not CYP2D6 slow metabolizers more accurately than the current system. However, as CYP2D6 genotype is not the only determinant of endoxifen level, we recommend that direct measurement of endoxifen should also be considered