TY - JOUR
T1 - Reshaping competitive advantages with analytics capabilities in service systems
AU - Akter, Shahriar
AU - Gunasekaran, Angappa
AU - Wamba, Samuel Fosso
AU - Babu, Mujahid Mohiuddin
AU - Hani, Umme
N1 - Publisher Copyright:
© 2020
PY - 2020/10
Y1 - 2020/10
N2 - Big data analytics capability can reshape competitive advantages for a service system. However, little is known about how to develop and operationalize a service system analytics capability (SSAC) model. Drawing on the resource based view (RBV), dynamic capability theory (DCT) and the emerging literature on big data analytics, this study develops and validates an SSAC model and frames its impact on competitive advantages using a thematic analysis, delphi studies (n=35) and a survey (n=251). The main findings illuminate the varying importance of three primary dimensions (i.e., service system analytics management capability, technology capability and personnel capability) and various respective subdimensions (i.e., service system planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relationship knowledge) in developing overall analytics capabilities for a service system. The findings also confirm the strong mediating effects of three dynamic capabilities (i.e., market sensing, seizing and reconfiguring) in establishing competitive advantages. We critically discuss the implications of our findings for theory, methods and practice with limitations and future research directions.
AB - Big data analytics capability can reshape competitive advantages for a service system. However, little is known about how to develop and operationalize a service system analytics capability (SSAC) model. Drawing on the resource based view (RBV), dynamic capability theory (DCT) and the emerging literature on big data analytics, this study develops and validates an SSAC model and frames its impact on competitive advantages using a thematic analysis, delphi studies (n=35) and a survey (n=251). The main findings illuminate the varying importance of three primary dimensions (i.e., service system analytics management capability, technology capability and personnel capability) and various respective subdimensions (i.e., service system planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relationship knowledge) in developing overall analytics capabilities for a service system. The findings also confirm the strong mediating effects of three dynamic capabilities (i.e., market sensing, seizing and reconfiguring) in establishing competitive advantages. We critically discuss the implications of our findings for theory, methods and practice with limitations and future research directions.
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U2 - 10.1016/j.techfore.2020.120180
DO - 10.1016/j.techfore.2020.120180
M3 - Article
AN - SCOPUS:85087340759
SN - 0040-1625
VL - 159
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 120180
ER -