scholarly journals The current and future uses of machine learning in ecosystem service research

2021 ◽  
pp. 149263
Author(s):  
Matthew Scowen ◽  
Ioannis N. Athanasiadis ◽  
James M. Bullock ◽  
Felix Eigenbrod ◽  
Simon Willcock
Keyword(s):  
Machine Learning ◽  
Ecosystem Service ◽  
Service Research

Is ecosystem service research used by decision-makers? A case study of the Murray-Darling Basin, Australia

2014 ◽  
Vol 29 (8) ◽  
pp. 1447-1460 ◽  
Author(s):  
Darla Hatton MacDonald ◽  
Rosalind H. Bark ◽  
Anthea Coggan
Keyword(s):  
Ecosystem Service ◽  
Decision Makers ◽  
Service Research ◽  
Murray Darling Basin

scholarly journals Anthropogenic Modifications and River Ecosystem Services: A Landscape Perspective

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2706 ◽  
Author(s):  
Anjana Ekka ◽  
Saket Pande ◽  
Yong Jiang ◽  
Pieter van der Zaag
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Ecosystem Service ◽  
Surface Modifications ◽  
Water Transfer ◽  
Service Research ◽  
Sand Mining ◽  
Land Use Pattern ◽  
Basin Water ◽  
Anthropogenic Modifications

The process of development has led to the modification of river landscapes. This has created imbalances between ecological, economic, and socio-cultural uses of ecosystem services (ESs), threatening the biotic and social integrity of rivers. Anthropogenic modifications influence river landscapes on multiple scales, which impact river-flow regimes and thus the production of river ESs. Despite progress in developing approaches for the valuation ecosystem goods and services, the ecosystem service research fails to acknowledge the biophysical structure of river landscape where ecosystem services are generated. Therefore, the purpose of this review is to synthesize the literature to develop the understanding of the biocomplexity of river landscapes and its importance in ecosystem service research. The review is limited to anthropogenic modifications from catchment to reach scale which includes inter-basin water transfer, change in land-use pattern, sub-surface modifications, groundwater abstractions, stream channelization, dams, and sand mining. Using 86 studies, the paper demonstrates that river ESs largely depend on the effective functioning of biophysical processes, which are linked with the geomorphological, ecological, and hydrological characteristics of river landscapes. Further, the ESs are linked with the economic, ecological, and socio-cultural aspect. The papers show that almost all anthropogenic modifications have positive impact on economic value of ESs. The ecological and socio-cultural values are negatively impacted by anthropogenic modifications such as dams, inter-basin water transfer, change in land-use pattern, and sand mining. The socio-cultural impact of ground-water abstraction and sub-surface modifications are not found in the literature examined here. Further, the ecological and socio-cultural aspects of ecosystem services from stakeholders’ perspective are discussed. We advocate for linking ecosystem service assessment with landscape signatures considering the socio-ecological interactions.

scholarly journals Text mining analysis roadmap (TMAR) for service research

2020 ◽  
Vol 34 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Mohamed Zaki ◽  
Janet R. McColl-Kennedy
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Design Methodology ◽  
Data Sources ◽  
Theory Testing ◽  
Service Research ◽  
Data Types ◽  
Content Type ◽  
Research And Practice ◽  
Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

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.

Recent Advances and Challenges in Ecosystem Service Research

2014 ◽  
Vol 5 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Zhang Yongmin ◽  
Zhao Shidong ◽  
Guo Rongchao
Keyword(s):  
Ecosystem Service ◽  
Service Research ◽  
Recent Advances

scholarly journals Exploring the Gap between Ecosystem Service Research and Management in Development Planning

2014 ◽  
Vol 6 (6) ◽  
pp. 3802-3824 ◽  
Author(s):  
Nadia Sitas ◽  
Heidi Prozesky ◽  
Karen Esler ◽  
Belinda Reyers
Keyword(s):  
Ecosystem Service ◽  
Development Planning ◽  
Service Research

scholarly journals Trends in Ecosystem Service Research: Early Steps and Current Drivers

AMBIO ◽  
2010 ◽  
Vol 39 (4) ◽  
pp. 314-324 ◽  
Author(s):  
Petteri Vihervaara ◽  
Mia Rönkä ◽  
Mari Walls
Keyword(s):  
Ecosystem Service ◽  
Service Research

scholarly journals Where communities intermingle, diversity grows – The evolution of topics in ecosystem service research

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204749 ◽  
Author(s):  
Nils Droste ◽  
Dalia D’Amato ◽  
Jessica J. Goddard
Keyword(s):  
Ecosystem Service ◽  
Service Research
Sign in / Sign up

Export Citation Format

Share Document