Accountable algorithms? The ethical implications of data-driven business models. (5th May 2020)
- Record Type:
- Journal Article
- Title:
- Accountable algorithms? The ethical implications of data-driven business models. (5th May 2020)
- Main Title:
- Accountable algorithms? The ethical implications of data-driven business models
- Authors:
- Breidbach, Christoph F.
Maglio, Paul - Abstract:
- Abstract : Purpose: The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service. Design/methodology/approach: This 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. Findings: The 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 implications: Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically. Originality/value: At 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 theAbstract : Purpose: The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service. Design/methodology/approach: This 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. Findings: The 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 implications: Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically. Originality/value: At 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. … (more)
- Is Part Of:
- Journal of service management. Volume 31:Number 2(2020)
- Journal:
- Journal of service management
- Issue:
- Volume 31:Number 2(2020)
- Issue Display:
- Volume 31, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 31
- Issue:
- 2
- Issue Sort Value:
- 2020-0031-0002-0000
- Page Start:
- 163
- Page End:
- 185
- Publication Date:
- 2020-05-05
- Subjects:
- AI -- Big data -- Business model -- Ethics
Service industries -- Management -- Periodicals
658.005 - Journal URLs:
- http://info.emeraldinsight.com/products/journals/journals.htm?id=josm ↗
http://www.emeraldinsight.com/ ↗ - DOI:
- 10.1108/JOSM-03-2019-0073 ↗
- Languages:
- English
- ISSNs:
- 1757-5818
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5064.010600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22084.xml