Matthew A. Hawkins and Mohammad H. Rezazade M.
This paper seeks to advance the study of knowledge boundary spanning by approaching spanning as a process that involves four spanning mechanisms.
Abstract
Purpose
This paper seeks to advance the study of knowledge boundary spanning by approaching spanning as a process that involves four spanning mechanisms.
Design/methodology/approach
Building on the insights from practice‐based view of knowledge and knowledge management literature more generally, the authors formalize and articulate two spanning mechanisms (boundary practice and boundary discourse), in addition to two other previously established spanning mechanisms (boundary object and boundary spanner).
Findings
The paper formalizes two further spanning mechanisms and suggests an integrative framework for examining the mutual and compounding effect between the four spanning mechanisms. Building on the suggested framework, the process of spanning is analysed as a time‐based combination of various mechanisms which evolve over time. The framework opens new windows to look at the projective and emergent mode of spanning mechanisms as a duality, rather than a dualism.
Research limitations/implications
Researchers are freed to explore the deployment order of the spanning mechanisms and the conflicting or synergistic effects. Practitioners would benefit from tracing successful spanning processes for replicating in similar contexts to advance collaboration efforts.
Originality/value
Boundary practice and boundary discourse are introduced as well as synthesizing the mechanisms into a coherent framework. Viewing boundary spanning as a process that includes dynamic combination of four spanning mechanisms is a particularly novel insight that can stimulate future research avenues.
Details
Keywords
Nastaran Hajiheydari, Mohammad Soltani Delgosha, Yichuan Wang and Hossein Olya
Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to…
Abstract
Purpose
Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.
Design/methodology/approach
We use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.
Findings
Our findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.
Originality/value
This study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.
Details
Keywords
Mohammad Soltani Delgosha, Nastaran Hajiheydari and Sayed Mahmood Fahimi
In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The…
Abstract
Purpose
In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks.
Design/methodology/approach
To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data.
Findings
The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.”
Originality/value
In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers.