scholarly journals How machine learning activates data network effects in business models: Theory advancement through an industrial case of promoting ecological sustainability

2021 ◽  
Vol 131 ◽  
pp. 196-205
Author(s):  
Darek M. Haftor ◽  
Ricardo Costa Climent ◽  
Jenny Eriksson Lundström
Keyword(s):  
Machine Learning ◽  
Network Effects ◽  
Business Models ◽  
Ecological Sustainability ◽  
Data Network

Capturing Value in Platform Business Models That Rely on User-Generated Content

2021 ◽  
Author(s):  
Hemang Subramanian ◽  
Sabyasachi Mitra ◽  
Sam Ransbotham
Keyword(s):  
Network Effects ◽  
Business Models ◽  
Switching Costs ◽  
Service Level ◽  
User Generated Content ◽  
Organizational Boundaries ◽  
Considerable Uncertainty ◽  
Current Usage ◽  
Significant Interest ◽  
Using Data

Business models increasingly depend on inputs from outside traditional organizational boundaries. For example, platforms that generate revenue from advertising, subscription, or referral fees often rely on user-generated content (UGC). But there is considerable uncertainty on how UGC creates value—and who benefits from it—because voluntary user contributions cannot be mandated or contracted or its quality assured through service-level agreements. In fact, high valuations of these platform firms have generated significant interest, debate, and even euphoria among investors and entrepreneurs. Network effects underlie these high valuations; the value of participation for an individual user increases exponentially as more users actively participate. Thus, many platform strategies initially focus on generating usage with the expectation of profits later. This premise is fraught with uncertainty because high current usage may not translate into future profits when switching costs are low. We argue that the type of user-generated content affects switching costs for the user and, thus, affects the value a platform can capture. Using data about the valuation, traffic, and other parameters from several sources, empirical results indicate greater value uncertainty in platforms with user-generated content than in platforms based on firm-generated content. Platform firms are unable to capture the entire value from network effects, but firms with interaction content can better capture value from network effects through higher switching costs than firms with user-contributed content. Thus, we clarify how switching costs enable value for the platform from network effects and UGC in the absence of formal contracts.

Transforming Digital Enterprises Towards Web Scale With Machine Learning

2021 ◽  
pp. 241-260
Author(s):  
Tasneem Aamir
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Life Cycle ◽  
Business Models ◽  
It Governance ◽  
Business Services ◽  
Enterprise Transformation ◽  
Modern Application ◽  
Digital Enterprise ◽  
Present Understanding

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.

Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach

2019 ◽  
Vol 240 ◽  
pp. 118162 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Parveen Fatemeh Rupani ◽  
Mohammad Mobin Rupani ◽  
Hesam Kamyab ◽  
Weilan Shao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ecological Sustainability ◽  
Learning Approach ◽  
Machine Learning Approach

Accountable algorithms? The ethical implications of data-driven business models

2020 ◽  
Vol 31 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Christoph F. Breidbach ◽  
Paul Maglio
Keyword(s):  
Machine Learning ◽  
Business Models ◽  
Analytical Framework ◽  
Data Driven ◽  
Service Research ◽  
Future Research ◽  
Cost Models ◽  
Ethical Challenges ◽  
Content Type ◽  
Ethical Implications

PurposeThe purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.Design/methodology/approachThis study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.FindingsThe resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.Practical implicationsFuture research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.Originality/valueAt a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

scholarly journals A Networked Analysis and Engineering Framework for New Business Models

2019 ◽  
Vol 11 (21) ◽  
pp. 6018 ◽  
Author(s):  
Wolfgang Vorraber ◽  
Matthias Müller
Keyword(s):  
Network Effects ◽  
Business Models ◽  
Design Science ◽  
Science Research ◽  
Research Process ◽  
Practical Case ◽  
Analysis Tool ◽  
Sustainable Business ◽  
New Business ◽  
Sustainable Business Models

New technological possibilities and paradigm shifts from product-centered to service-centered offerings are one of the main drivers of business models. Business ventures today are more and more networked. Often, various partners are needed to deliver a service or product to frequently cross-linked customers with sometimes bi- or even multi-variant roles. Furthermore, business models are embedded in socio-technical systems where different kinds of needs and values of all actors, including social, ecological, technical and economic values, have to be balanced. The resulting complex network of actors, needs and values requires continuous management in order to create and operate viable and sustainable business models. This paper proposes a multi-layer framework to analyze existing business models as well as to shape new business ventures in a networked and values-based way and to support the identification of tacit network effects within business ecosystems. Based on an existing multi-layered analysis toolkit, focusing on legal and business dynamics aspects, an enhanced visualization and analysis tool is proposed that focuses especially on ethical, social and environmental aspects to foster the creation of (strongly) sustainable business models. The research process to create the presented approach followed the Design Science Research paradigm by applying argumentative-deductive analysis (ADA) and first applications in real-world case studies. A practical case from an international Open Source Software (OSS) project serves as an example to illustrate this values-based visualization and analysis layer and its benefits for managers and decision makers in the area of business model and information system management.

scholarly journals The Role of Artificial Intelligence and Data Network Effects for Creating User Value

2020 ◽  
Author(s):  
Robert Wayne Gregory ◽  
Ola Henfridsson ◽  
Evgeny Kaganer ◽  
Harris Kyriakou
Keyword(s):  
Artificial Intelligence ◽  
Network Effects ◽  
Data Network ◽  
User Value

Signs of epistemic disruption: Transformations in the knowledge system of the academic journal

First Monday ◽  
2009 ◽  
Author(s):  
William W. Cope ◽  
Mary Kalantzis
Keyword(s):  
Peer Review ◽  
Social Relations ◽  
Network Effects ◽  
Business Models ◽  
Impact Analysis ◽  
Knowledge System ◽  
Knowledge Systems ◽  
Breaking Point ◽  
Academic Journal ◽  
Knowledge Cultures

This article is an overview of the current state of scholarly journals, not (just) as an activity to be described in terms if its changing processes, but more fundamentally as a pivotal point in a broader knowledge system. After locating journals in what we term the process of knowledge design, the article goes on to discuss some of the deeply disruptive aspects of the contemporary moment, which not only portend potential transformations in the form of the journal, but possibly also the knowledge systems that the journal in its heritage forms has supported. These disruptive forces are represented by changing technological, economic, distributional, geographic, interdisciplinary and social relations to knowledge. The article goes on to examine three specific breaking points. The first breaking point is in business models—the unsustainable costs and inefficiencies of traditional commercial publishing, the rise of open access and the challenge of developing sustainable publishing models. The second potential breaking point is the credibility of the peer review system: its accountability, its textual practices, the validity of its measures and its exclusionary network effects. The third breaking point is post-publication evaluation, centred primarily around citation or impact analysis. We argue that the prevailing system of impact analysis is deeply flawed. Its validity as a measure of knowledge is questionable, in which citation counts are conflated with the contribution made to knowledge, quantity is valued over quality, popularity is taken as a proxy for intellectual quality, impact is mostly measured on a short timeframe, ‘impact factors’ are aggregated for journals or departments in a way that lessens their validity further, there is a bias for and against certain article types, there are exclusionary network effects and there are accessibility distortions. Add to this reliability defects—the types of citation counted as well as counting failures and distortions—and clearly the citation analysis system is in urgent need of renewal. The article ends with suggestions towards the transformation of the academic journal and the creation of new knowledge systems: sustainable publishing models, frameworks for guardianship of intellectual property, criterion-referenced peer review, greater reflexivity in the review process, incremental knowledge refinement, more widely distributed sites of knowledge production and inclusive knowledge cultures, new types of scholarly text and more reliable use metrics.

scholarly journals ASSESSMENT OF TRENDS IN THE APPLICATION OF DIGITAL MARKETING

10.23856/3303 ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 28-35 ◽  
Author(s):  
Inta Kotane ◽  
Daina Znotina ◽  
Serhii Hushko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Applied Research ◽  
Business Models ◽  
Digital Marketing ◽  
The Internet ◽  
Sources Of Information ◽  
Scientific Publications ◽  
Method Analysis ◽  
Descriptive Method

One of the conditions for the future development of companies is the identification and use of digital capabilities. In recent years, the environment in which we live and work has changed radically. If the emergence of the Internet was revolutionary in the way we communicate and obtain information, currently the availability and mobility of technologies affects consumers' habits and promotes the transformation of classic business models. Aim of the study: to explore and learn about the development trends of digital marketing. Applied research methods: monographic descriptive method, analysis, synthesis, statistical method. The study based on scientific publications, statistics and other sources of information. The results of the study show that in 2019 digital marketing tools are most actively used: artificial intelligence / augmented reality / machine learning; video marketing; chatbots, virtual assistants.

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