ترجمه مقاله نقش ضروری ارتباطات 6G با چشم انداز صنعت 4.0
- مبلغ: ۸۶,۰۰۰ تومان
ترجمه مقاله پایداری توسعه شهری، تعدیل ساختار صنعتی و کارایی کاربری زمین
- مبلغ: ۹۱,۰۰۰ تومان
abstract
Effective management of organizational resources in big data initiatives is of growing importance. Although academic and popular literatures contain many examples of big data initiatives, very few are repeated in the same organization. This suggests either big data delivers benefits once only per organization or senior managers are reluctant to commit resources to big data on a sustained basis. This paper makes three contributions to the Special Issue's theme of enhancing organizational resource management. One is to establish an archetype business process for big data initiatives. The second contribution directs attention to creating a dynamic capability with big data initiatives. The third identifies drawbacks of resource based theory (RBT) and it's underpinning assumptions in the context of big data. The paper discusses lessons learnt and draws out implications for practice and business research. The paper's intellectual and practical contributions are based on an in-depth case study of the European ICT Poles of Excellence (EIPE) big data initiative and evidence from the extant literature.
Conclusions
Based on empirical data, literature and discussion three conclusions can be drawn: first, the archetype business process for big data initiatives provides a framework for effective resource management. The big data business process enables organizations to identify capabilities and roles required to ensure successful outcomes. Effective business processes overcome obstacles that prevent big data initiatives from being repeatable. Second, relationships between big data and dynamic capabilities is important because big data processes need to morph over time as organizations reconfigure or develop new capabilities to achieve results from big data insights. Therefore, big data processes cannot be allowed to ossify. Third, theoretical weaknesses of VRIN in the context of big data require further investigation to establish RBT's relevance to provide deeper insights into big data. The lasting contribution from this paper would be a greater number of big data initiatives successfully implemented, repeatedly, in the same organization.