با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
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.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
Actualizing big data analytics affordances: A revelatory case study
واقعی سازی هزینه های تحلیلی داده های بزرگ: یک مطالعه موردی الهامی-2020
Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three sociotechnical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.
Keywords: Big data analytics | Affordance theory | Socio-technical approach | Organizational transformation | Organizational benefits | Affordance actualization
Business value of big data analytics: A systems-theoretic approach and empirical test
ارزش تجاری تجزیه و تحلیل داده های بزرگ: یک رویکرد سیستم-تئوری و آزمون تجربی-2020
Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.
Keywords: Big data analytics | Social media analytics | Synergies | Business value of information technology | Market performance | Digital innovation
The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources
سؤالاتی که می پرسیم: فرصت ها و چالش های استفاده از تحلیل داده های بزرگ برای مدیریت استراتژیک منابع سرمایه انسانی-2020
Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.
KEYWORDS Big data analytics | Workforce analytics | Stakeholder | management | Strategic human | capital | Knowledge stars | Human resource | management
Identifying and mapping terrons in Denmark
شناسایی و نقشه برداری از زمینهای وحشی در دانمارک-2020
Spatial assessment of terroir is creating a new possibility for enhancement of high quality agro-food product and to minimize negative environmental effects such as soil degradation and associated risks. The classification and mapping of particular terroir units could be a competitive marketing tool with a major impact on farmers’ incomes. For this purpose, Carré and McBratney (2005) proposed the terron concept to establish combined soil and landscape entities as the first investigative step to identify terroirs. The main objective of the present work was to assemble various environmental factors (i.e. soil, terrain and climate), to identify and then to map terrons in Denmark. First, for representing soil factors, a national soil spectral library was utilized to measure taxonomic distances between 34 Danish reference soil profiles and the Danish national soil profile database (586 soil profiles). Second, the terrain and climate factors for each soil profile location were then compiled as represented by relative slope position, valley depth, valley bottom flatness, vertical distance to the channel network, number of frost days, annual number of growing days, global solar radiation, and precipitation. Third, nine Danish terron classes were established by fuzzy c-means clustering based on an integrated matrix including all soil, terrain and climate factors whereby each terron class is characterized by soil, terrain and climate as a whole entity. Finally, the spatial distribution of Danish terrons was mapped using Cubist regression rules. The results were compared with a soil map derived from the same profile database. We concluded that the map of terrons described natural environment quantitatively and formally in terms of soil, landscape and climatic information better than just a soil class or soil attribute map. Further investigations are needed to discover whether the terron classes give better predictions of landscape-dynamic processes and allow better management options than soil alone. This study also demonstrated several advantages of using soil spectral data and ancillary data to identify and map terrons. The next step will be to validate the terron map by incorporating crop yield data and social factors to delineate natural Danish terroir units.
Keywords: Terron | Vis-NIR | Digital soil mapping | Soils | Terrain | Climate
Disconnect in trade show staffing: A comparison of exhibitor emphasis and attendee prefere
قطع ارتباط در کارکنان نمایشگاه های تجاری: مقایسه تأکید غرفه داران و ترجیح شرکت کنندگان-2020
This research explores how and whether staffing at trade shows by exhibitors is consistent with attendee preferences for staffing in this channel. Using secondary data from 9215 attendees and 885 exhibitors, we observe that relative to attendee preferences, exhibitors significantly understaff with technical personnel, while overstaffing with executive/upper management and sales/marketing personnel. Additional comparisons between attendees who are decision-makers vs. influencers in the purchase process, between attendees from different kinds of firms (B2B vs. B2C, large vs. small firms) and between attendees to trade shows that differ in geographic scope suggest that substantial inconsistencies persist between preferences of attendees and staffing by exhibitors.
Keywords: Trade shows | Exhibitions | Business-to-business marketing | B2B Marketing | Promotions
Value champions in business markets: Four role configurations
ارزش قهرمانان در بازارهای تجاری : پیکربندی چهار نقش -2020
Customer value management has become a key priority in business markets, but many firms struggle to implement it. While the prior literature has considered this primarily as sales responsibility, emerging research suggests that best practice firms employ dedicated value champions to implement customer value management. However, at present, we know little about the characteristics, and the tradeoffs between different value championing approaches in business markets. Based on a discovery-oriented field research and interviews with 59 managers in 11 firms, this study illustrates four alternative role configurations firms use to employ value champions, and unpacks the characteristics and implications of each approach. Collectively, this study advances industrial marketing theory by shedding light on an emergent and contemporary management practice, and offering practical insights into how firms can employ value champions in business markets.
Keywords: Customer value | Organizational champions | Organizational structures | Role configuration | Business markets | Value-based selling
Fostering B2B sales with customer big data analytics
تقویت فروش B2B با تجزیه و تحلیل داده های بزرگ مشتری-2020
This study focuses on the use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from 417 B2B firms. The study also examines whether analytics culture within a firm moderates these effects. The study finds that the use of customer big data significantly fosters sales growth (i.e. monetary performance outcomes) and enhances the customer relationship performance (non-monetary performance outcomes). However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.
Keywords: Big data analytics | Customer analytics | Marketing analytics | Firm performance | Customer relationship management | Big data-enhanced database marketing
Big data analytics for supply chain relationship in banking
تجزیه و تحلیل داده های بزرگ برای رابطه زنجیره تأمین در بانکداری-2020
This paper reports how a commercial bank in Asia uses big data analytic as a tool to explore the internal B2B data to improve supply chain finance and the efficiency of marketing tactics and campaigns. A case study was conducted by analyzing two types of supply chain relationships: (1) supply chain relationships in the credit reports; (2) e-wiring transactions among supply chain companies. The results show that big data analytics is very useful in terms of improving the commercial banks marketing and risk management performances. The case study also set a good example for B2B firms seeking to understand how they could leverage big data analytics to differentiate customer solutions, sustain profitability and generate new business values. Theorical and practical implications are also discussed.
Keywords: Supply chain finance | B2B analytics
Understanding market agility for new product success with big data analytics
درک چابکی بازار برای موفقیت محصول جدید با تجزیه و تحلیل داده های بزرگ-2020
The complexity that characterises the dynamic nature of the various environmental factors makes it very compelling for firms to be capable of addressing the changing customers needs. The current study examines the role of big data in new product success. We develop a qualitative research with case study approach to look at this. Specifically, we look at multiple cases to get in-depth understanding of customer agility for new product success with big data analytics. The findings of the study provide insight into the role of customer agility in new product success. This study unpacks the interconnectedness of the effective use of data aggregation tools, the effectiveness of data analysis tools and customer agility. It also explores the link between all of these factors and new product success. The study is reasonably telling in that it shows that the effective use of data aggregation and data analysis tools results in customer agility which in itself explains how an organisation senses and responds speedily to opportunities for innovation in the competitive marketing environment. The current study provides significant theoretical contributions by providing evidence for the role of big data analytics, big data aggregation tools, customer agility, organisational slack and environmental turbulence in new product success.
Keywords: Big data analytics | Customer agility | Effective use of data | New product success