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1.
《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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Technology advancements in cloud computing, big data systems, No-SQL database, cognitive systems, deep learning, and other artificial intelligence techniques make the integration of traditional ERP transaction data and big data streaming from various social media platforms and Internet of Things (IOTs) into a unified analytics system not only feasible but also inevitable. Two steps are prominent for this integration. The first, coined as forming the big-data ERP, is the integration of traditional ERP transaction data and the big data and the second is to integrate the big-data ERP with business analytics (BA). As ERP implementers and BA users are facing various challenges, managers responsible for this big-data ERP-BA integration are also seriously challenged. To help them deal with these challenges, we develop the SIST model (including Strategic alignment, Intellectual and Social capital integration, and Technology integration) and propose that this integration is an evolving portfolio with various maturity levels for different business functions, likely leading to sustainable competitive advantages. 相似文献
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《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献
4.
Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy. 相似文献
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The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines. 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
7.
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
8.
Felix M. Simon 《The Information Society》2019,35(3):158-169
Data-driven campaigning has been in the spotlight over several years. Yet, we still have a limited understanding of political data analytics companies: how they envision data analytics and voter targeting, their role in electoral processes and what promises they make to their clients. This article focuses on the way in which such issues are conceived of in the marketing rhetoric of the political data analytics industry. Drawing on a sample of 19 political data analytics companies it systematically explores the ways in which data analytics is envisioned and marketed as a powerful tool in electoral processes, exposing a fundamental disconnect between scholarly discourse on the one hand – often critical of the claims of these companies about the efficacy of their methods – and a highly functionary data imaginary on the other hand, actively fostered by the political data-analytics industry and the media. 相似文献
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《Information processing & management》2022,59(2):102852
The key to business success for many companies is the correct use of data to make better, faster and flawless decisions. Companies need to use robust and efficient tools such as business intelligence (BI) as positive catalysts to achieve this goal, which can assist them in mechanizing the tasks of analysis, decision making, strategy formulation and forecasting. In other words, the purpose of using BI in these institutions is to collect, process, and analyze large volumes of data and convert them into effective business value in decision making through the creation of analytical intelligent reporting platforms. Therefore, this study aims to answer the question whether operationalization of BI, Organizational Learning (OL) and Innovation and utilization of their applications can provide financial performance enhancement for these companies. As mentioned above, the statistical population of this research is innovation companies Located in Science Park with 400 staff and according to Morgan table, 196 employees of these companies were picked as statistical case. Info accumulation tool is the questionnaire whose validity and reliability have been measured. Research findings demonstrate that BI and innovation have a critical influence on the companies conduct. But there was no meaningful relationship between OL and financial performance of these companies. 相似文献
10.
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
11.
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
12.
《Information processing & management》2022,59(4):102961
Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions. 相似文献
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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
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Companies are increasingly relying on social media brand communities to interact with consumers and achieve business values. Thus, it is essential to understand how companies can extract value from consumers in social media brand communities. We develop a model clarifying the dual concept of consumer value and illustrating how consumer-perceived value can be transformed into consumer-generated value from a trust transfer perspective. Specifically, we identify three types of consumer-perceived value: utilitarian, hedonic, and social. We capture consumer-generated value in terms of purchase intention and social media word of mouth. Using a two-wave survey, our results strongly support the research model. Specifically, the three types of consumer-perceived values positively affect consumer trust in social media brand communities, which in turn leads to trust in brand and in social media and, thereafter, consumers’ subsequent social media word of mouth and purchase intentions. Our study makes several contributions to the strategic information systems literature concerning leveraging social media brand communities into business strategies. Theoretically, our study expands the understanding of the dual concept of consumer value in social media brand communities through the trust transfer theory. Practically, our study delivers insights for companies into how social media brand communities can be used as a strategic tool for achieving business values. 相似文献
17.
《The Journal of High Technology Management Research》2023,34(2):100456
The Internet of Things (IoT) has sparked a revolution in the manufacturing sector, providing numerous advantages to companies that adopt it. Using IoT, factories can boost productivity, cut expenses, and develop a more sustainable business model. The rise of digital networking and real-time communication are compelling manufacturers to adopt cutting-edge technologies in order to compete in today's fast-paced, international marketplace. The Internet of Things (IoT) to facilitate the virtualization of manufacturing processes and the gathering of real-time data to guarantee seamless supply chain operations. There has been abductive qualitative research done. Case studies of the heavy-duty vehicle sector provided empirical data, while a review of the relevant literature provided the theoretical underpinnings. Information system issues and people and structure issues were cited as barriers to analytics adoption. In this study works on challenges and security of manufacturing. Finally, suitable themes for analysis have been derived using a thematic analysis. The results show that manufacturing firms can benefit from analytics solutions for production activities even if they are not highly automated or complicated. The Internet of Things (IoT) offers numerous opportunities for growth in the business models of manufacturing companies. Businesses can boost efficiency, cut expenses, and develop a more robust business model by implementing IoT. Successfully integrating IoT, however, calls for meticulous preparation and execution. 相似文献
18.
The use of the internet and social media have changed consumer behavior and the ways in which companies conduct their business. Social and digital marketing offers significant opportunities to organizations through lower costs, improved brand awareness and increased sales. However, significant challenges exist from negative electronic word-of-mouth as well as intrusive and irritating online brand presence. This article brings together the collective insight from several leading experts on issues relating to digital and social media marketing. The experts’ perspectives offer a detailed narrative on key aspects of this important topic as well as perspectives on more specific issues including artificial intelligence, augmented reality marketing, digital content management, mobile marketing and advertising, B2B marketing, electronic word of mouth and ethical issues therein. This research offers a significant and timely contribution to both researchers and practitioners in the form of challenges and opportunities where we highlight the limitations within the current research, outline the research gaps and develop the questions and propositions that can help advance knowledge within the domain of digital and social marketing. 相似文献
19.
Universities and companies have decision-making processes that allow to achieve institutional objectives. Currently, data analysis has an important role in generating knowledge, obtaining important patterns and predictions for formulating strategies. This article presents the design of a business intelligence governance framework for the Universidad de la Costa, easily replicable in other institutions. For this purpose, a diagnosis was made to identify the level of maturity in analytics. From this baseline, a model was designed to strengthen organizational culture, infrastructure, data management, data analysis and governance. The proposal contemplates the definition of a governance framework, guiding principles, strategies, policies, processes, decision-making body and roles. Therefore, the framework is designed to implement effective controls that ensure the success of business intelligence projects, achieving an alignment of the objectives of the development plan with the analytical vision of the institution. 相似文献
20.
曾凡斌 《科学．经济．社会》2010,28(4):149-154
整体上市是指一家公司将其主要资产和业务整体改制为股份公司进行上市的做法。辽宁出版传媒选择整体上市不仅有着深刻的政策背景,也是我国传媒资本经营的必然发展。整体上市来源于对剥离上市的扬弃,剥离上市不可避免地引发了大量的关联交易,而不公允的关联交易会给传媒上市公司带来风险。本文以辽宁出版传媒为例,分析了其上市前后的财务资料,并且将其与A股的其他传媒上市公司的财务资料进行对比分析,发现辽宁出版传媒整体上市后,在经营绩效等方面都有一定的提高,产生了有效的市场效应,辽宁出版传媒在资本运营效能上优于传媒板块其他上市传媒公司。本文还对传媒整体上市做出展望,指出传媒整体上市只是为解决自身体制机制问题提供一个平台,整体上市之后传媒企业如何利用资本市场机制改变原有的低效率现象,有效解决公司治理问题,还有待进一步的研究。 相似文献