Data-Driven Business Models for Life Cycle Technologies: Exemplary Planning for Hybrid Components

2021 ◽  
pp. 488-496
Author(s):  
A.-S. Wilde ◽  
S. Gellrich ◽  
M. Mennenga ◽  
T. Abraham ◽  
C. Herrmann
2015 ◽  
Vol 4 (1) ◽  
pp. 4-24 ◽  
Author(s):  
Julia Selberherr

Purpose – Sustainable buildings bear enormous potential benefits for clients, service providers, and our society. To release this potential a change in business models is required. The purpose of this paper is to develop a new business model with the objective of proactively contributing to sustainable development on the societal level and thereby improving the economic position of the service providers in the construction sector. Design/methodology/approach – The modeling process comprises two steps, the formal structuring and the contextual configuration. In the formal structuring systems theory is used and two levels are analytically separated. The outside view concerns the business model’s interaction with the environment and its impact on sustainability. The inside view focusses on efficient value creation for securing sustainability. The logically deductively developed business model is subsequently theory-led substantiated with Giddens’ structuration theory. Findings – The relevant mechanisms for the development of a new service offer, which creates a perceivable surplus value to the client and contributes to sustainable development on the societal level, are identified. The requirements for an efficient value creation process with the objective of optimizing the service providers’ competitive position are outlined. Research limitations/implications – The model is developed logically deductively based on literature and embedded in a theoretical framework. It has not yet been empirically tested. Practical implications – Guidelines for the practical implementation of more sustainable business models for the provision of life cycle service offers are developed. Social implications – The construction industry’s impact requires it to contribute proactively to a more sustainable development of the society. Originality/value – This paper analyzes the role for the players in the construction sector in proactively contributing to sustainable development on the societal level. One feasible strategy is proposed with a new business model, which aims at cooperatively optimizing buildings and infrastructures and taking the responsibility for the operating phase via guarantees.


2019 ◽  
Vol 46 (8) ◽  
pp. 622-638
Author(s):  
Joachim Schöpfel ◽  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.


2019 ◽  
Vol 2019 ◽  
pp. 36-52
Author(s):  
Bryan Casey

More than a quarter century after civil rights activists pioneered America’s first ridesharing network, the connections between transportation, innovation, and discrimination are again on full display. Industry leaders such as Uber, Amazon, and Waze have garnered widespread acclaim for successfully combatting stubbornly persistent barriers to transportation. But alongside this well-deserved praise has come a new set of concerns. Indeed, a growing number of studies have uncovered troubling racial disparities in wait times, ride cancellation rates, and service availability in companies including Uber, Lyft, Task Rabbit, Grubhub, and Amazon Delivery. Surveying the methodologies employed by these studies reveals a subtle, but vitally important, commonality. All of them measure discrimination at a statistical level, not an individual one. As a structural matter, this isn’t coincidental. As America transitions to an increasingly algorithmic society, all signs now suggest we are leaving traditional brick and-mortar establishments behind for a new breed of data-driven ones. Discrimination, in other words, is going digital. And when it does, it will manifest itself—almost by definition—at a macroscopic scale. Why does this matter? Because not all of our civil rights laws cognize statistically-based discrimination claims. And as it so happens, Title II could be among them. This piece discusses the implications of this doctrinal uncertainty in a world where statistically-based claims are likely to be pressed against data-driven establishments with increasing regularity. Its goals are twofold. First, it seeks to build upon adjacent scholarship by fleshing out the specific structural features of emerging business models that will make Title II’s cognizance of “disparate effect” claims so urgent. In doing so, it argues that it is not the “platform economy,” per se, that poses an existential threat to the statute but something deeper. The true threat, to borrow Lawrence Lessig’s framing, is architectural in nature. It is the algorithms underlying “platform economy businesses” that are of greatest doctrinal concern—regardless of whether such businesses operate inside the platform economy or outside it. Second, this essay joins others in calling for policy reforms focused on modernizing our civil rights canon. It argues that our transition from the “Internet Society” to the “Algorithmic Society” will demand that Title II receive a doctrinal update. If it is to remain relevant in the years and decades ahead, Title II must become Title 2.0.


Web Services ◽  
2019 ◽  
pp. 882-903
Author(s):  
Izabella V. Lokshina ◽  
Barbara J. Durkin ◽  
Cees J.M. Lanting

The Internet of Things (IoT) provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services (IoS). These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. Different business models may support opportunities that generate revenue and value for various types of customers. This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential business opportunities.


This chapter reviews the following key aspects of platform research: platform strategy, dynamic capabilities, and business models. The main platform typologies and basic definitions are described first. It provides a brief summary of the literature relating to arguing platform strategy, platform life cycle, platform building blocks, and business models. A platform strategy categorization taxonomy and platform practical strategies of preventing platform disintermediation are developed. The main types of platform business models are introduced. The multi-sided platform business model pattern (MSP BMP) is designed. MSP BMP is used as a basic conceptual framework and knowledge management tool for describing, analyzing, and interpreting non-price instruments used by digital platforms, especially platform intermediaries.


Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.


Author(s):  
Tasneem Aamir

Digital enterprise transformation focuses on alignment of processes, products, services, business models, and technologies to perceive business value. Digital business integration in an organization utilizes information technology and its tools to drive and manage the life cycle of digital enterprise transformation. It utilizes the practices and approaches of IT governance with modern application tools and APIs. The millennium brought many technological advancements over internet technologies and these technologies operate numerous applications and business services. The span of digital enterprises is expanding and continues to grow with their evolution on a web scale. This chapter is an effort to present understanding about machine learning and automation around businesses intelligence and analytics on a web scale. The chapter provides a brief summary of technologies used in digital enterprise transformation for all the domains of an organization.


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