The role of big data analytics and organizational agility in improving organizational performance of business processing organizations

Jamshid Ali Turi, Muddasar Ghani Khwaja, Fareena Tariq, Athar Hameed

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

Purpose: Business processing organizations are continuously focusing on customer knowledge management (CKM) due to the competitive business environment. CKM is being recognized as an essential source for improving organizational performance (OP). This study focuses on understanding CKM and its impact on OP. It also explores the moderating role of big data analytics capability (BDAC) on OP. Moreover, the mediating role of operational and strategic agility on OP was empirically tested. Design/methodology/approach: Positivist research doctrine has been deployed and data was collected using structured survey using cross-sectional approach. The data were collected from 392 employees working in business processing software houses in the emerging market of Pakistan. Structural equation modeling (SEM) was deployed for the estimation of theoretical model. Findings: The study's findings indicate that CKM has no significant impact on OP; although the presence of BDAC moderates the relationship significantly. Moreover, the study recommends that CKM and BDAC to be tested in the project environment, considering organization's operational and technical capabilities. Research limitations/implications: The study proclaims that BDAC can be helpful for organizations to improve their capabilities and output. Likewise, enhancing BDAC reduces failure rates of the projects. Originality/value: This study provides a critical theoretical and practical contribution to project management in business processing organizations. Big data analytics can be of value for diagnostic, predictive and prescriptive analysis in the project management context.

Idioma originalInglés
Páginas (desde-hasta)2081-2106
Número de páginas26
PublicaciónBusiness Process Management Journal
Volumen29
N.º7
DOI
EstadoPublicada - 24 oct. 2023
Publicado de forma externa

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