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1.
This study investigates how Big Data Analytics (BDA) can be leveraged to support a city’s transformation into a smart destination. We conduct an in-depth case study of a city-in-transformation and adopt the perspective of technology affordances to uncover the varying opportunities enabled by BDA to facilitate the attainment of smart tourism goals. Our findings unveil three types of BDA affordances and demonstrate how these affordances are actualized in a cascading manner to enable informed decisions and a sustainable development of smart tourism. Implications are presented for future investigation of the affordances of BDA in smart tourism, as well as for policy makers and practitioners who engage in the development of innovative tourism services for the smart citizens. 相似文献
2.
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
3.
Big data analytics (BDA) adoption is a game-changer in the current industrial environment for precision decision-making and optimal performance. Nonetheless, the determinants or consequences of its adoption in small and medium enterprises remain unclear, hence the objective of this study. Data analysis of 171 Iranian small and medium manufacturing firms revealed that complexity, uncertainty and insecurity, trialability, observability, top management support, organizational readiness, and external support affect significantly on BDA adoption. The findings confirm the strong impact of BDA adoption in small to medium-sized enterprises, marketing and financial, performance enhancement. Understanding the drivers of BDA adoption helps managers to employ appropriate initiatives that are vital for effective implementation. The results enable BDA service providers to attract and diffuse BDA in small to medium-sized enterprises. 相似文献
4.
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
5.
《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
6.
Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes
We introduce Big Data Analytics (BDA) and Sentiment Analysis (SA) to the study of international negotiations, through an application to the case of the UK-EU Brexit negotiations and the use of Twitter user sentiment. We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA. Through SA on a corpus of 13,018,367 tweets on defined Brexit hashtags, we illustrate how SA can provide a platform for decision-makers engaged in international negotiations to grasp collective preferences. We show that BDA and SA can enhance decision-making and strategy in public policy and negotiation contexts of the magnitude of Brexit. Our findings indicate that the preferred or least preferred Brexit outcomes could have been inferred by the emotions expressed by Twitter users. We argue that BDA can be a mechanism to map the different options available to decision-makers and bring insights to and inform their decision-making. Our work, thereby, proposes SA as part of the international negotiation toolbox to remedy for the existing informational gap between decision makers and citizens’ preferred outcomes. 相似文献
7.
Although organizational factors related to big data analytics (BDA) and its performance have been studied extensively, the number of failed BDA projects continues to rise. The quality of BDA information is a commonly cited factor in explanations for such failures and could prove key to improving project performance. Using the resource-based view (RBV) lens, data analytics literature, business strategy control, and an empirical setup of two studies based on marketing and information technology managerial data, we draw on the dimensions of the balanced scorecard (BSC) as an integrating framework of BDA organizational factors. Specifically, we tested a model –from two different perspectives– that would explain information quality through analytical talent and organizations' data plan alignment. Results showed that both managers have a different understanding of what information quality is. The characteristics that make marketing a better informer of information quality are identified. In addition, hybrid (embedded) type analyst placements are seen to achieve better performance. Moreover, we add greater theoretical rigour by incorporating the moderating effect of the use of big data analytics in companies. Finally, the BSC provided a greater causal understanding of the resources and capabilities within a data strategy. 相似文献
8.
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
9.
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
10.
A longitudinal study of the actual value of big data and analytics: The role of industry environment
Despite the popularity of big data and analytics (BDA) in industry, research regarding the economic value of BDA is still at an early stage. Little attention has been paid to quantifying the longitudinal impact of organizational BDA implementation on firm performance. Grounded in organizational learning theory, this study empirically demonstrates the impact of BDA implementation on organizational performance and how industry environment characteristics moderate the BDA-performance relationships. Using secondary data regarding BDA implementation from 2010 to February 2020, we find that BDA implementation has a significant impact on two types of business value creation: operational efficiency and business growth. Furthermore, the impact of BDA on operational efficiency is amplified in less dynamic and complex environments, while the BDA-business growth relationship is more pronounced in more dynamic, complex, and munificent environments. Collectively, this study provides a theory-centric understanding of BDA’s economic benefits. The findings offer insights to firms about what actual benefits BDA implementation may generate and how firms may align the use of BDA with the industry environments they are operating in. 相似文献
11.
《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
12.
《Information processing & management》2022,59(1):102758
Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures. 相似文献
13.
14.
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
15.
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
16.
The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors. 相似文献
17.
Andrea De Mauro Marco Greco Michele Grimaldi Paavo Ritala 《Information processing & management》2018,54(5):807-817
The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace. 相似文献
18.
《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
19.
《International Journal of Information Management》2017,37(6):750-760
People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream). 相似文献
20.
《International Journal of Information Management》2016,36(4):645-652
The internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. On the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, data-information processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top. 相似文献