Profits Distribution of Operators Led Internet of Things Industrial Value Chain Based on Game Theory

Purpose This study aims to understand their emergence and types of business models in the Internet of Things (IoT) ecosystems. Design/methodology/approach The paper builds upon a systematic literature review of IoT ecosystems and business models to construct a conceptual framework on IoT business models, and uses qualitative research methods to analyze seven industry cases. Findings The study identifies four types of IoT business models: value chain efficiency, industry collaboration, horizontal market and platform. Moreover, it discusses three evolutionary paths of new business model emergence: opening up the ecosystem for industry collaboration, replicating the solution in multiple services and return to closed ecosystem as technology matures. Research limitations/implications Identifying business models in rapidly evolving fields such as the IoT based on a small number of case studies may result in biased findings compared to large-scale surveys and globally distributed samples. However, it provides more thorough interpretations. Practical implications The study provides a framework for analyzing the types and emergence of IoT business models, and forwards the concept of “value design” as an ecosystem business model. Originality/value This paper identifies four archetypical IoT business models based on a novel framework that is independent of any specific industry, and argues that IoT business models follow an evolutionary path from closed to open, and reversely to closed ecosystems, and the value created in the networks of organizations and things will be shareable value rather than exchange value.

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Nondestructive evaluation of the biogenic amine in crayfish (Prokaryophyllus clarkii) by nearinfrared spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211054298 ◽

2021 ◽

pp. 096703352110542

Author(s):

Yan Liu ◽

Chao Wang ◽

Zhenzhen Xia ◽

Jiwang Chen

Keyword(s):

Biogenic Amines ◽

Value Chain ◽

Nondestructive Method ◽

Coefficient Of Determination ◽

Selection Methods ◽

Wavelength Selection ◽

Aquatic Products ◽

Industrial Chain ◽

Quantitative Results ◽

Industrial Value Chain

Biogenic amines are a group of nitrogen substances and widely adopted to assess the food safety, especially for the aquatic products. In China, crayfish ( Prokaryophyllus clarkii) have become one of the most famous aquatic products and form a complete industrial value chain. To ensure the safety of the crayfish industrial chain, a rapid and nondestructive method for determining the biogenic amines of crayfish by nearinfrared spectroscopy coupled with chemometrics was proposed in this study. The quantitative models of histamine, tyramine, cadaverine, and putrescine were built by using the partial least squares (PLS) regression. The spectral preprocessing and the wavelength selection methods were adopted to optimize the models. For histamine, cadaverine, and putrescine in peeled or whole tails, reasonable quantitative results can be obtained by using the optimized models; the coefficient of determination (r2) are 0.88 and 0.90, 0.88 and 0.91, 0.89, and 0.84, respectively. As for tyramine in peeled or whole tails, the results are acceptable and the coefficient of determination (r2) is 0.83 and 0.74, respectively.

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Study on Value Increment of Channel Value Chain Between Manufacturer and Distributor-Based on Game Theory

Lecture Notes in Electrical Engineering - Advanced Manufacturing and Automation VII ◽

10.1007/978-981-10-5768-7_34 ◽

2018 ◽

pp. 313-324

Author(s):

Junwei Ma ◽

Jianhua Wang

Keyword(s):

Game Theory ◽

Value Chain

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Mobile Big Data

Advances in Multimedia and Interactive Technologies - Emerging Perspectives on the Mobile Content Evolution ◽

10.4018/978-1-4666-8838-4.ch008 ◽

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.

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Robust Decentralised Trust Management for the Internet of Things by Using Game Theory

Information Processing & Management ◽

10.1016/j.ipm.2020.102308 ◽

2020 ◽

Vol 57 (6) ◽

pp. 102308 ◽

Cited By ~ 3

Author(s):

Christian Esposito ◽

Oscar Tamburis ◽

Xin Su ◽

Chang Choi

Keyword(s):

Game Theory ◽

Internet Of Things ◽

Trust Management ◽

The Internet ◽

The Internet Of Things

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Innovation Research on Medium and Low-Tech Traditional Industry

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.459.413 ◽

2013 ◽

Vol 459 ◽

pp. 413-417

Author(s):

Heng Tian

Keyword(s):

Value Chain ◽

Innovation Management ◽

Innovation System ◽

The Sustainable Development ◽

Innovation Research ◽

Cultural Innovation ◽

Industrial Value Chain ◽

The Government ◽

Traditional Industry ◽

Resource Advantage

As the resource advantage is gradually weakened, business situation of small and medium-sized enterprises (SMEs) engaged in medium and low-tech traditional industries is becoming more seriously. Industrial innovation and upgrading shall be conducted in order to achieve the sustainable development of traditional industries. Given the characteristic that medium and low-tech traditional enterprises rely on progressive innovation from the external organizations including the government, other enterprises and research institutes, etc., the enterprises should improve their strategic position in the industrial value chain, and change the business mode and growth pattern jointly from the aspects of technical innovation, system innovation, management innovation and cultural innovation, etc., to achieve the transformation from resource advantage to innovation advantage.

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