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Decide for the future by looking back: The perceived impact of predictive analytics to support decision making in the construction industry

Uppström, Johan LU and Nordh, Daniel LU (2018) INFM10 20181
Department of Informatics
Abstract
Based on data from the past, predictive analytics can give a competitive advantage for companies when it is used to forecast the future. Specifically, predictive analytics can be used to aid decision makers in strategic planning. As the construction industry faces challenges with both more costly and complicated projects, predictive analytics can support decision makers in the industry in the most critical parts of a construction project. The purpose of our study was to identify the perceived impact of predictive analytics to support decision making in the construction industry. The research was conducted by using a qualitative research strategy interviewing managers from six midsize and large Swedish construction companies. We used... (More)
Based on data from the past, predictive analytics can give a competitive advantage for companies when it is used to forecast the future. Specifically, predictive analytics can be used to aid decision makers in strategic planning. As the construction industry faces challenges with both more costly and complicated projects, predictive analytics can support decision makers in the industry in the most critical parts of a construction project. The purpose of our study was to identify the perceived impact of predictive analytics to support decision making in the construction industry. The research was conducted by using a qualitative research strategy interviewing managers from six midsize and large Swedish construction companies. We used concepts on how IT may impact decision making and sorted these into the decision-making process, containing the phases of intelligence, design and choice. Our findings show that construction companies perceived extensive impact of predictive analytics to support decision making in the intelligence, and reasonably impact the choice phase. However, our findings reveal that the construction companies perceive that predictive analytics barely impact the design phase. Our findings contribute to IS research since the impact of predictive analytics on decision making for a specific industry has been identified. (Less)
Please use this url to cite or link to this publication:
author
Uppström, Johan LU and Nordh, Daniel LU
supervisor
organization
course
INFM10 20181
year
type
H1 - Master's Degree (One Year)
subject
keywords
Predictive analytics, Business intelligence, Construction industry, Decision making process, Decisions
report number
INF18-003
language
English
id
8950032
date added to LUP
2018-06-18 14:57:15
date last changed
2018-06-18 14:57:15
@misc{8950032,
  abstract     = {{Based on data from the past, predictive analytics can give a competitive advantage for companies when it is used to forecast the future. Specifically, predictive analytics can be used to aid decision makers in strategic planning. As the construction industry faces challenges with both more costly and complicated projects, predictive analytics can support decision makers in the industry in the most critical parts of a construction project. The purpose of our study was to identify the perceived impact of predictive analytics to support decision making in the construction industry. The research was conducted by using a qualitative research strategy interviewing managers from six midsize and large Swedish construction companies. We used concepts on how IT may impact decision making and sorted these into the decision-making process, containing the phases of intelligence, design and choice. Our findings show that construction companies perceived extensive impact of predictive analytics to support decision making in the intelligence, and reasonably impact the choice phase. However, our findings reveal that the construction companies perceive that predictive analytics barely impact the design phase. Our findings contribute to IS research since the impact of predictive analytics on decision making for a specific industry has been identified.}},
  author       = {{Uppström, Johan and Nordh, Daniel}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Decide for the future by looking back: The perceived impact of predictive analytics to support decision making in the construction industry}},
  year         = {{2018}},
}