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Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process

Mukhtar, Afra LU and Rashid, Nazish LU (2021) SMMM20 20211
Department of Service Management and Service Studies
Abstract (Swedish)
Title: Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process
Authors: Nazish Rashid and Afra Mukhtar
Supervisor Yulia Vakulenko
Purpose: The aim of this research paper is to gain a better understanding of machine learning
readiness in the supplier selection process. The study aims to analyse the readiness factors that
constitute this and barriers that may hinder this process for companies.
Methodology: This study made use of a qualitative research approach, which includes a single case
study. The case study covers a company interview and a content analysis of company documents.
Findings: The findings reveal that academia relates readiness factors to measure readiness in terms
of adopting machine... (More)
Title: Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process
Authors: Nazish Rashid and Afra Mukhtar
Supervisor Yulia Vakulenko
Purpose: The aim of this research paper is to gain a better understanding of machine learning
readiness in the supplier selection process. The study aims to analyse the readiness factors that
constitute this and barriers that may hinder this process for companies.
Methodology: This study made use of a qualitative research approach, which includes a single case
study. The case study covers a company interview and a content analysis of company documents.
Findings: The findings reveal that academia relates readiness factors to measure readiness in terms
of adopting machine learning in the supplier selection process to the industry. However, this study
is able to identify several other factors related to three contexts at an organisational level.
Furthermore, the findings also validate the barriers found in academia, as well as recognise that
additional barriers might hinder machine learning readiness in the supplier selection process.
Value: The purpose of this research paper was to connect academia with industry practises, as well
as to identify the perception of readiness regarding machine learning technology in the supplier
selection process to highlight the gaps within this field of study.
Research Implications: This study attempts to enhance insights on the current literature regarding
readiness factors and potential barriers for machine learning in the supplier selection process by
adding new readiness factors in the existing TOE framework. In addition to that, the authors
introduced a new aspect to the framework in the form of barriers that should be taken into account.
Practical Implications: The research paper aims to provide valuable insights for procurement
professionals in the retail industry to improve their understanding of how a company is able to
facilitate machine learning readiness in the supplier selection process. (Less)
Please use this url to cite or link to this publication:
author
Mukhtar, Afra LU and Rashid, Nazish LU
supervisor
organization
course
SMMM20 20211
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Supplier selection process, supplier selection factors, artificial intelligence, machine learning, machine learning readiness, machine learning adoption, TOE framework, TOE
language
English
id
9065883
date added to LUP
2021-09-22 11:00:37
date last changed
2021-09-22 11:00:37
@misc{9065883,
  abstract     = {{Title: Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process
Authors: Nazish Rashid and Afra Mukhtar
Supervisor Yulia Vakulenko
Purpose: The aim of this research paper is to gain a better understanding of machine learning
readiness in the supplier selection process. The study aims to analyse the readiness factors that
constitute this and barriers that may hinder this process for companies.
Methodology: This study made use of a qualitative research approach, which includes a single case
study. The case study covers a company interview and a content analysis of company documents.
Findings: The findings reveal that academia relates readiness factors to measure readiness in terms
of adopting machine learning in the supplier selection process to the industry. However, this study
is able to identify several other factors related to three contexts at an organisational level.
Furthermore, the findings also validate the barriers found in academia, as well as recognise that
additional barriers might hinder machine learning readiness in the supplier selection process.
Value: The purpose of this research paper was to connect academia with industry practises, as well
as to identify the perception of readiness regarding machine learning technology in the supplier
selection process to highlight the gaps within this field of study.
Research Implications: This study attempts to enhance insights on the current literature regarding
readiness factors and potential barriers for machine learning in the supplier selection process by
adding new readiness factors in the existing TOE framework. In addition to that, the authors
introduced a new aspect to the framework in the form of barriers that should be taken into account.
Practical Implications: The research paper aims to provide valuable insights for procurement
professionals in the retail industry to improve their understanding of how a company is able to
facilitate machine learning readiness in the supplier selection process.}},
  author       = {{Mukhtar, Afra and Rashid, Nazish}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process}},
  year         = {{2021}},
}