TY - JOUR
T1 - Organizational intention to adopt big data in the B2B context
T2 - An integrated view
AU - Sun, Shiwei
AU - Hall, Dianne J.
AU - Cegielski, Casey G.
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2020/4
Y1 - 2020/4
N2 - Grounded in the diffusion of innovation theory (DOI), institutional theory, configuration theory, and technology-organization-environment (TOE) framework, the study proposes a model incorporating factors in technological, organizational and environmental contexts that may influence an organization's decision to adopt big data strategies. Using survey data collected from Chinese companies, eight factors in three categories are tested utilizing a structural equation modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The empirical results show that the factors relative advantage, technological competence, technology resources, support from top management, competitive pressure, and the regulatory environment all have a significant impact on the organizational adoption of big data. These findings contribute to the development of a better understanding of precisely how the big data diffusion process across industries functions in B2B practice.
AB - Grounded in the diffusion of innovation theory (DOI), institutional theory, configuration theory, and technology-organization-environment (TOE) framework, the study proposes a model incorporating factors in technological, organizational and environmental contexts that may influence an organization's decision to adopt big data strategies. Using survey data collected from Chinese companies, eight factors in three categories are tested utilizing a structural equation modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The empirical results show that the factors relative advantage, technological competence, technology resources, support from top management, competitive pressure, and the regulatory environment all have a significant impact on the organizational adoption of big data. These findings contribute to the development of a better understanding of precisely how the big data diffusion process across industries functions in B2B practice.
UR - http://www.scopus.com/inward/record.url?scp=85072192620&partnerID=8YFLogxK
U2 - 10.1016/j.indmarman.2019.09.003
DO - 10.1016/j.indmarman.2019.09.003
M3 - Article
AN - SCOPUS:85072192620
SN - 0019-8501
VL - 86
SP - 109
EP - 121
JO - Industrial Marketing Management
JF - Industrial Marketing Management
ER -