Technology adoption in Norway : organizational assimilation of big data

2017

As data permeates and drives the digital evolution, the role of Big Data becomes

increasingly essential. Big Data is making its presence known in almost every industry, and

has the potential to not only transform the business world, but society at large. Given that

companies in Norway are still in the early stages of making use of Big Data, studying factors

affecting adoption of Big Data technology in Norway is critical and timely.

Grounded in the Diffusion of Innovation (DOI) theory, Technology Acceptance Model

(TAM), and Technology-Organization-Environment (TOE) framework, an integrative model

is developed for studying factors affecting adoption of Big Data technology in three

aggregated stages of assimilation; initiation, adoption-decision, and implementation. The

model specifies three technological characteristics (relative advantage, complexity, and

security), three intraorganizational factors (organizational size, top management support, and

IT expertise), and three interorganizational factors (competitive pressure, external support, and

privacy) as determinants of assimilation.

The proposed model is tested using survey data collected from 336 executives in

medium to large companies in Norway. Employing a multinomial logistic regression, this

study finds that six predictor variables (relative advantage, complexity, security, top

management support, IT expertise, and competitive pressure) are significant and can

distinguish non-adopters and adopters in the assimilation stages. Of the six factors identified

in the model, three (security, top management support, and competitive pressure) are found to

play a vital role in all stages of Big Data assimilation, while two factors (complexity and IT

expertise) are critical to the implementation and routinization of Big Data technology.

The results indicate that the model is suited for studying organizational adoption of Big

Data technology. Moreover, given the scarcity of research into determinants of adoption in the

Big Data literature, the research model offers a suitable point of departure for future studies

on Big Data adoption. Finally, the findings have important implications for practitioners and

researchers.