| 1. |
- Costa-Climent, Ricardo, et al.
(author)
-
Sustainable profitability of ethical and conventional banking
- 2018
-
In: Contemporary Economics. - Warsaw : University of Economics and Human Sciences in Warsaw. - 2084-0845 .- 2300-8814. ; 12:4, s. 519-530
-
Journal article (peer-reviewed)abstract
- In recent years, social movements have echoed calls for greater social and environmental responsibility. Although financial institutions promote development, consumers have lost confidence in banks. As we enter the Fintech era, banks have the opportunity to use new tools that enable greater transparency for customers. Corporate social responsibility (CSR) plays a key role in increasing social awareness of regulators, society, shareholders, and employees-in short, stakeholders. This study therefore focuses on banks that have designed their activities and investments to contribute to sustainability. The principal contribution of this paper is to show the existence of a range of business models that arise following different responses by different types of banks. These different responses occur because the primary objective of sustainable banks is to meet the needs of stakeholders and contribute to sustainable development, whereas conventional banks simply apply and execute CSR policies. It is possible to differentiate between ethical banks and commercial banks. To ensure economic progress and achieve sustainability, it is fundamental to balance economic profitability with people's social and environmental aspirations.
|
|
| 2. |
- Martinez-Climent, Carla, et al.
(author)
-
Sustainable Financing through Crowdfunding
- 2019
-
In: Sustainability. - : MDPI. - 2071-1050. ; 11:3
-
Journal article (peer-reviewed)abstract
- The phenomenon of crowdfunding has been widely studied, while the sustainability of crowdfunded ventures is attracting growing interest from academia and society. In light of this interest, we conducted bibliometric analysis to study the relationship between crowdfunding and crowdfunded ventures' sustainability orientation. We analyzed the number of publications, type of publications, and most productive countries, journals, and authors. We also analyzed the most cited articles and examined their approach to sustainability and crowdfunding. The results suggested that a sustainability orientation could bring about change in the current financial and environmental system.
|
|
| 3. |
- Costa Climent, Ricardo, et al.
(author)
-
AI-enabled business models for competitive advantage
- 2024
-
In: Journal of Innovation and Knowledge. - : Elsevier. - 2530-7614 .- 2444-569X. ; 9:3
-
Journal article (peer-reviewed)abstract
- Some firms have successfully harnessed artificial intelligence (AI) to create unparalleled wealth, while most around them have failed to do so. This managerial challenge has led to recent calls for research to answer the question of how firms can use AI to create and appropriate economic value. This paper answers that question. The paper reviews the existing research and discusses its merits. This review highlights the need for subsequent conceptual reconfigurations of business model theory, the theory of data network effects, and the theory of situated AI for competitive advantage. The integration of these three theories leads to a novel theory: AI-enabled business models for competitive advantage. This paper contributes to the broad literature on technology management, and more specifically to literature on technology-enabled business models and the use of AI. Several important managerial implications are outlined to help firms ensure successful AI use.
|
|
| 4. |
- Costa Climent, Ricardo, et al.
(author)
-
Business model theory-based prediction of digital technology use : An empirical assessment
- 2021
-
In: Technological forecasting & social change. - : Elsevier. - 0040-1625 .- 1873-5509. ; 173
-
Journal article (peer-reviewed)abstract
- Firms invest heavily in their future use of digital technology to create and appropriate value and thereby survive and prosper. Such decisions regarding the future are part of a firm's foresight, which is a core element of a firm's dynamic capabilities. The contemporary toolbox for generating foresight is dominated by procedural methods, thus ignoring theory-based predictions of the future uses of digital technology. This paper presents the first empirical assessment of business model theory's ability to predict the future uses of digital technology by a given firm. Predictions for a specific niche of hemophilia firms are investigated. Outcomes related to these predictions are then observed. The results show the power of business model theory for deriving such predictions, implying that the managerial toolbox for foresight generation should be extended to include this theory. This study also provides several directions for further development of business model theory to increase its ability to account for value creation and appropriation from the use of digital technology.
|
|
| 5. |
- Costa Climent, Ricardo, et al.
(author)
-
Financing Start-Ups Through Artificial Intelligence
- 2022
-
In: Financing Startups. - Cham : Springer. - 9783030940577 - 9783030940584 - 9783030940607 ; , s. 149-162
-
Book chapter (peer-reviewed)abstract
- The incredible speed with which artificial intelligence (AI) is entering all sectors is forcing companies into a race to link their businesses with AI. This trend is also driving companies, strategists, pioneers, entrepreneurs and researchers to use AI to design new strategies, create new sources of business value and manage innovative forms of financing. This scenario accurately describes the current situation of start-ups. New firms are forced to connect with AI in order to develop, either because of its importance for their products or services or because funders use it to make investment or purchase decisions. Therefore, to define the impact of AI on the financing of start-ups, we must differentiate between two contexts: the financing of technological start-ups based on AI and the use of AI by investors and funders to support the most cutting-edge and profitable start-ups. This chapter begins with an introduction to start-ups, the diversification of financial activities and the coherence of these new models with existing theoretical business frameworks that explain outcomes. Subsequently, it investigates the theory on AI in start-ups, providing real examples of emerging companies that illustrate these two contexts. The chapter provides examples of AI-based start-ups that have already been financed and companies that use AI to support the development of start-ups through either financial investment or logistical support.
|
|
| 6. |
|
|
| 7. |
- Costa-Climent, Ricardo, et al.
(author)
-
Maximizing the benefitsof machine learning:enhancing data networkeffects theory to improvevalue creation andappropriation
- 2023
-
In: ESIC Digital Economy and Innovation Journal. - 2792-8721. ; 2, s. e062-e062
-
Journal article (other academic/artistic)abstract
- The recently proposed theory of Data Network Effects aims toaccount for how user value is created from the use of machinelearning technology. The theory accounts for the uniquelearning ability of machine learning, which uses large data setsto make predictions and enhance decision-making. This paperoffers an assessment of the theory of Data Network Effects,identifying some of its strengths and limitations. Regardingthe strengths, it contributes to the success of companies,accounts for the unique characteristics of ML technologiesand is an advancement of the body of the theory of networkeffects. Their limitations are then transformed into a set ofinterrelated research questions that focus on the relationshipof the use of machine learning and issues such as: valuecapture, a co-evolutionary view, a multi-actor perspective, anddatabase dynamics. This paper outlines a multi-theoreticalapproach to study the value creation and capture enabled bythe use of machine learning technologies.
|
|
| 8. |
- Costa Climent, Ricardo
(author)
-
The Role of Machine Learning in Creating and Capturing Value
- 2022
-
In: International Journal of Software Science and Computational Intelligence. - : IGI Global. - 1942-9045 .- 1942-9037. ; 14:1, s. 1-19
-
Journal article (peer-reviewed)abstract
- TranslatorThe use of machine learning technologies by the world’s most profitable companies to personalise their offerings is commonplace. However, not all companies using machine learning technologies succeed in creating and capturing value. Academic research has studied value creation through the use of information technologies, but this field of research tends to consider information technology as a homogeneous phenomenon, not considering the unique characteristics of machine learning technologies. This literature review aims to study the extent to which value creation and value capture through machine learning technologies are being investigated in the field of information systems. Evidence is found of a paucity of publications focusing on value creation through the use of ML in the enterprise, and none on value capture. This study’s contribution is to provide a better understanding of the use of machine learning technologies in information systems as a social and business practice.
|
|
| 9. |
- Costa-Climent, Ricardo, 1972-, et al.
(author)
-
Using machine learning to create and capture value in the business models of small and medium-sized enterprises
- 2023
-
In: International Journal of Information Management. - : Elsevier. - 0268-4012 .- 1873-4707. ; 73
-
Journal article (peer-reviewed)abstract
- Start-ups have revolutionised many economic ecosystems, becoming innovation pioneers around the world. Most are based on data-driven business models, particularly relying on machine learning technologies. However, not all start-ups that use machine learning technologies manage to create and capture value. The existing literature on the use value enabled by information technologies does not take into account the unique capabilities of machine learning. The theory of data network effects offers a promising explanation of how to create value using machine learning. However, it does not explicitly describe how to capture value using machine learning. In contrast, business model theory explains how companies use technologies to create and capture value, but not specifically through the use of machine learning technology. Therefore, this study aims to improve the theoretical understanding of the key drivers of value creation and capture in start-ups with business models driven by this kind of technology. Statistical techniques are used in a sample of 122 start-ups to explore the theoretical relationships between these two theories. The analysis reveals the link between specific value creation and capture factors of the two theories, such as efficiency, novelty, and performance expectancy. The study also provides evidence of the need to adopt a co-evolutionary perspective of value creation and capture through the use of machine learning.
|
|
| 10. |
- Costa-Climent, Ricardo, et al.
(author)
-
Value creation and appropriation from the use of machine learning : a study of start-ups using fuzzy-set qualitative comparative analysis
- 2023
-
In: The International Entrepreneurship and Management Journal. - : Springer. - 1554-7191 .- 1555-1938.
-
Journal article (peer-reviewed)abstract
- This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.
|
|
