Precise marketing of precision marketing value chain process on the H group line based on big data

2018 ◽  
Vol 35 (3) ◽  
pp. 2837-2845
Author(s):  
Bo Zhang ◽  
B. Zhang
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Chain Process

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

Big Data in Cultural Tourism – Building Sustainability and Enhancing Competitiveness

2021 ◽  
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Value Chain ◽  
New Technologies ◽  
Cultural Tourism ◽  
End Users ◽  
Ethical Implications ◽  
Tourism Sector ◽  
Cultural Experiences ◽  
Selection Of

The use of big data is becoming increasingly important across the tourism sector and the value chain. With this publication, UNWTO intends to provide a baseline research on using big data by tourism and culture stakeholders, in order to improve the competitiveness of cultural tourism and reinforce its sustainability. The study sets the basis to connect tourism, culture and new technologies for mutual benefits, while calling for a reflection on the ethical implications for policymakers, businesses and end-users. The selection of case studies illustrates the most frequent case-scenarios of the use of big data in cultural tourism within destinations, compiled during the research. As the new technologies are facing ever-evolving scenarios, their use will be harnessed by the tourism sector in its endeavour to innovate and provide new cultural experiences.

scholarly journals The mediating effect of big data analysis on the process orientation and information system software to improve supply chain process in Saudi Arabian industrial organizations

2021 ◽  
pp. 135-142 ◽  
Author(s):  
Ahmed Faek Elgendy
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Information Systems ◽  
Data Analysis ◽  
Saudi Arabian ◽  
Big Data Analysis ◽  
Chain Process ◽  
Process Orientation ◽  
Industrial Organizations ◽  
Supply Chain Process

This study aims to investigate the nature of the relationship between Big Data Analysis as a mediator in Process Orientation (PO) and Information Systems Programming (ISP) to supply chains processes in Saudi Arabian industrial organizations. A stratified random sample of 357 managers and employees working in 37 industrial companies in Saudi Arabia was tested. The study relied on the descriptive and analytical research methodology. The results indicated that there is a significant indirect effect of Big Data Analysis (Planning, Procuring, Manufacturing, Delivering) as the mediator on Process Orientation and Information Systems Programming (ISP) and (PO) to improve supply chain process as well as organizational effectiveness. The researcher made a number of recommendations for the Saudi Arabian manufacturing firms to develop analytical capabilities in managers in order to utilize big data analysis as a tool to increase efficiency and effectiveness in the organizational system. A wide spread awareness program about the benefits to adopt big data analysis and management information systems may be adopted to ensure an efficient supply chain system.

Design of big data-driven framework based on manufacturing value chain

2021 ◽  
Author(s):  
Jingwen Song ◽  
Aihui Wang ◽  
Ping Liu ◽  
Daming Li ◽  
Xiaobo Han ◽  
...  
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Data Driven

Innovation Platforms

2018 ◽  
pp. 72-95
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Model ◽  
Value Chain ◽  
Analytical Techniques ◽  
Human Interaction ◽  
Business Relationship ◽  
Potential Relationship ◽  
Relationship Of ◽  
Cross Fertilization

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.

Mobile Big Data

2016 ◽  
pp. 138-158
Author(s):  
Shivom Aggarwal ◽  
Abhishek Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Value Chain ◽  
Mobile Technologies ◽  
Complex Concept ◽  
Current Trends ◽  
Mobile Big Data ◽  
Challenges And Opportunities ◽  
New Challenges ◽  
Data Ecosystem

Mobile technologies have given rise to tremendous amounts of data in real-time, which can be unstructured and uncertain. This growth can be attributed as Mobile Big Data and provides new challenges and opportunities for innovation. This chapter attempts to define the concept of Mobile Big Data, provide description of various sources of Mobile Big Data and discuss SWAI (Sources Warehousing Analytics Insights) model of Big Data processing. To understand this complex concept, it is important to visualize the Big Data ecosystem, respective players. Moreover, mobile computing, Internet of things, and other associated technologies have been discussed in light of marketing and communications based applications. The current trends in Mobile Big Data and associated value chain help us understand where the next frontiers of innovation are and how one can create value. This is linked to the future aspects of the Mobile Big Data and evolution of technologies from now onwards.

scholarly journals DIGITAL TRANSFORMATION IN THE AGRI-FOOD SECTOR – OPPORTUNITIES AND CHALLENGES

2018 ◽  
Vol XX (2) ◽  
pp. 98-104 ◽  
Author(s):  
Katarzyna Kosior
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Sustainable Use ◽  
Strategic Decision ◽  
Digital Data ◽  
Food Sector ◽  
Agriculture Sector ◽  
Food And Agriculture ◽  
Use Of Resources ◽  
Productivity And Competitiveness

The aim of the paper is to discuss the major opportunities and challenges that emerge in the agri-food sector as a result of digitization processes. Digital technologies with Big Data and the Internet of Things are widely considered promising new tools for both increasing productivity and competitiveness in the agri-food sector and ensuring a more sustainable use of resources. Knowledge and insights derived from ever-increasing volumes and a variety of digital data may help to optimize farm production processes, improve risk management, predict market trends and enhance strategic decision-making capabilities. Yet, advanced data analytics has also the disruptive power to reshape the whole string of markets within the agriculture value chain. Digitization may fundamentally change the relations between technology and input suppliers, farms, traders, processing units, retailers and consumers. The first evidence shows that farm data markets suffer from specific drawbacks and limitations which may constrain the transformative potential of Big Data in the food and agriculture sector. The major concerns raised relate to farm data ownership and privacy issues, market power of major agriculture technology providers and uneven distribution of benefits accruing from digitization.

Sign in / Sign up

Export Citation Format

Share Document