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1.
《International Journal of Information Management》2016,36(5):748-758
The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. 相似文献
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Surabhi Verma Som Sekhar Bhattacharyya Saurav Kumar 《Information processing & management》2018,54(5):791-806
Research on the adoption of systems for big data analytics has drawn enormous attention in Information Systems research. This study extends big data analytics adoption research by examining the effects of system characteristics on the attitude of managers towards the usage of big data analytics systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Technology Acceptance Model, with further validation by a survey of 150 big data analytics users. Results of this survey confirm that characteristics of the big data analytics system have significant direct and indirect effects on belief in the benefits of big data analytics systems and perceived usefulness, attitude and adoption. Moreover, there are mediation effects that exist among the system characteristics, benefits of big data analytics systems, perceived usefulness and the attitude towards using big data analytics system. This study expands the existing body of knowledge on the adoption of big data analytics systems, and benefits big data analytics providers and vendors while helping in the formulation of their business models. 相似文献
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Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions. 相似文献
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Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics. 相似文献
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While the use of big data tends to add value for business throughout the entire value chain, the integration of big data analytics (BDA) to the decision-making process remains a challenge. This study, based on a systematic literature review, thematic analysis and qualitative interview findings, proposes a set of six-steps to establish both rigor and relevance in the process of analytics-driven decision-making. Our findings illuminate the key steps in this decision process including problem definition, review of past findings, model development, data collection, data analysis as well as actions on insights in the context of service systems. Although findings have been discussed in a sequence of steps, the study identifies them as interdependent and iterative. The proposed six-step analytics-driven decision-making process, practical evidence from service systems, and future research agenda, provide altogether the foundation for future scholarly research and can serve as a step-wise guide for industry practitioners. 相似文献
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The mechanism of business analytics affordances enhancing the management of cloud computing data security is a key antecedent in improving cloud computing security. Based on information value chain theory and IT affordances theory, a research model is built to investigate the underlying mechanism of business analytics affordances enhancing the management of cloud computing data security. The model includes business analytics affordances, decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and the management of cloud computing data security. Simultaneously, the model considers the role of data-driven culture and IT business process integration. It is empirically tested using data collected from 316 enterprises by Partial Least Squares-based structural equation model. Without data-driven culture and IT business process integration, the results suggest that there is a process from business analytics affordances to decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and to the management of cloud computing data security. Moreover, Data-driven culture and IT business process integration have a positive mediation effect on the relationship between business analytics affordances and decision-making affordances of cloud computing data security. The conclusions in this study provide useful references for the enterprise to strengthen the management of cloud computing data security using business analytics. 相似文献
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Understanding how the application of big data analytics (BDA) generates business value is a persistent challenge in information systems (IS) research. Improving understanding of how BDA realizes business value requires unpacking theories to study the phenomenon. This study unpacks the task-technology fit (TTF) theory toward generating new and improved insights into the business value of BDA. Extant studies on TTF have mainly focused on traditional IT which is different from digital technologies like BDA that are malleable and dynamic. While TTF has primarily focused on how the technology meets task requirements, this study contends that tasks can also be structured to fit the functionality of technology. This study proposes a 2 × 2 matrix framework to explain how BDA and tasks interact. The framework indicates how the reconfigurability of tasks and the editability of BDA impact the fit between tasks and BDA. Future research should explore how the fit between tasks and BDA changes over time. 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100465
Businesses have begun using IT apps for a variety of reasons in recent years. The rapid advancement of new technologies has opened up vast prospects for businesses to digitise their operations, enhance their use of information systems, and compete more effectively in the global marketplace. Information technology (IT) businesses can benefit greatly from Big Data analytics due to the depth and breadth of their data analysis. Big data can be used to examine IT departments in the following ways: performance analysis, forecast maintenance, security analysis, and resource analysis. When it comes to boosting their business's dependability, speed, quality, and effectiveness, most companies rely on big data. Companies can gain a competitive edge thanks to the massive amounts of data that big data is able to collect, store, and manage. Big data analytics is being used by a growing number of businesses to make sense of their mountain of data. In this paper, we examine the ways in which IBM, TCS, and Cognizant use big data within their operations. Long-term planning strategies and business intelligence practises are also suggested in this research as means of protecting personal information. 相似文献
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《Information processing & management》2022,59(1):102762
Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness. 相似文献
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ABSTRACTHigher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. We argue that there are five crucial questions about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students' privacy and associated rights, including (but not limited to) autonomy interests. We address information access concerns, the intrusive nature of information-gathering practices, whether or not learning analytics is justified given the potential distribution of consequences and benefits, and issues related to student autonomy. Finally, we question whether learning analytics advances the aims of higher education or runs counter to those goals. 相似文献
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《Information processing & management》2022,59(1):102736
Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability. 相似文献
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Business analytics (BA) becomes increasingly important under rapidly changing business environment. A research challenge is that BA use is not fully understood. We tackle this challenge from the perspective of dynamic capability by using an empirical model with the emphasis on BA use in customer relationship management (CRM). Based on 170 samples from firm-level survey, we analyze the nomological linkage from IT competence to CRM performance. The results show data management capability fully mediates between IT competence and BA use, while customer response capability partially mediates between BA use and CRM performance. 相似文献
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Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big data-based practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm. 相似文献
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介绍了大数据的概念与特点,举例说明大数据应用的优势;论述了大数据给图书馆带来的影响及挑战,指出图书馆应该顺应大数据时代潮流,运用大数据分析来提升图书馆的服务;认为控制硬件成本、重视用户隐私及数据、人才培养等问题是大数据时代图书馆发展需要解决的问题. 相似文献
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Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques. 相似文献
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Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
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《Information processing & management》2020,57(6):102318
We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models. 相似文献