Ready or Not: Exploring Machine Learning Readiness in the Supplier Selection Process
(2021) SMMM20 20211Department of 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:
http://lup.lub.lu.se/student-papers/record/9065883
- author
- Mukhtar, Afra LU and Rashid, Nazish LU
- supervisor
- organization
- course
- SMMM20 20211
- year
- 2021
- 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}}, }