大数据对创新、竞争优势、生产力和决策的影响:文献综述

Nadeem U. Shahid, N. Sheikh
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引用次数: 3

摘要

技术领域的进步使个人和企业能够以前所未有的方式从各种来源收集大量数据(结构化和非结构化)。来自社交媒体、用户生成、互联网、医疗保健、制造业、供应链、金融机构和传感器的数据呈指数级增长。本文的目的是回顾大数据如何驱动和影响创新、竞争优势、生产力和决策支持。方法:对大数据进行全面的文献回顾,并确定大数据分析对创新、竞争优势、生产力和决策支持的影响。所回顾的文献为研究奠定了基础,这是一个基于广泛的文献回顾、案例研究和市场领导者对未来的预测而发展起来的模型。大数据是商业领域的最新流行语。提出了一种新的模型来识别大数据以及创新、竞争优势、生产力和决策支持之间的关系。研究发现:对学术文献和现有案例研究的回顾发现,现有框架与将大数据整合到各种业务和管理职能和目标之间存在差距。有趣的是,文献中有丰富的概念和框架,用于实现业务或管理功能的最终目标,但关于如何将大数据分析集成到这些框架中的问题的文献很少。本文发现缺少一个关键问题,即企业应该执行哪些基本步骤来实施大数据分析并将其集成到现有框架中,以充分利用大数据潜力。研究局限/启示:本研究仅限于对选择性文献的回顾,重点关注对大数据的深入理解。此外,它还关注大数据如何带来创新、竞争优势、生产力和决策支持。虽然有许多其他相关的研究领域可以研究大数据的影响,但不是本研究的一部分。未来的研究可以导致其他相关研究领域的更深入的研究。实践意义:对文献的回顾表明,“大数据”在创新、创造竞争优势、提高生产力和协助数据驱动决策方面发挥着重要作用。企业正在利用客户洞察力来创新以客户为中心的产品和服务,保持竞争,提高各个层面的生产力,每天都做出明智的决策。未来将由更智能的大数据解决方案和见解驱动。原创性/价值:该研究提供的证据表明,大数据是创新的催化剂,创造竞争优势,提高生产力,并协助决策。方法是回顾学术文献和案例研究。它支持开发新模型、实现框架以获得更好的洞察力和模式的需求。大数据的实施方法、框架和治理在实证研究中一直被忽视。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Big Data on Innovation, Competitive Advantage, Productivity, and Decision Making: Literature Review
Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.
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