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1 – 4 of 4This creates a paradox, since, while AI-generated solutions are crucial to help solve the climate emergency, their very deployment is also adding to the problem. To tackle this…
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DOI: 10.1108/OXAN-DB285037
ISSN: 2633-304X
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Geographic
Topical
Vinicius Andrade Brei, Nicole Rech, Burçin Bozkaya, Selim Balcisoy, Alex Paul Pentland and Carla Freitas Silveira Netto
This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to…
Abstract
Purpose
This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.
Design/methodology/approach
The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.
Findings
The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.
Research limitations/implications
The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.
Originality/value
This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.
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George Okechukwu Onatu, Wellington Didibhuku Thwala and Clinton Ohis Aigbavboa
Nayana Dissanayake, Bo Xia, Martin Skitmore, Bambang Trigunarsyah and Vanessa Menadue
The purpose of this study was to prioritize the appropriate generic contractor selection criteria for Engineering–Procurement–Construction (EPC) projects in the construction…
Abstract
Purpose
The purpose of this study was to prioritize the appropriate generic contractor selection criteria for Engineering–Procurement–Construction (EPC) projects in the construction industry.
Design/methodology/approach
Proceeding from a review of previous studies and validation by a small group of experts, a preliminary set of 16 criteria was first identified. This was followed by three rounds of Delphi surveys: firstly, with 64 experienced participants confirming the relevance of the 16 criteria; secondly, with a reduced subgroup of 47 more experienced participants scoring the importance of each; and finally, providing the opportunity for these 47 to revise their scores in the light of knowing the aggregated results of the previous round.
Findings
The results show the consensus view, of which the most important criteria are ranked as past performance, project understanding, technical attributes, key personnel, health and safety, past experience, time, management, financial, contractual and legal, quality, cost, relationships, environmental and sustainability, organizational and industrial relations, and geographic location.
Originality/value
The findings are useful for both practitioners and academics in making a significant contribution to the body of knowledge of the EPC process. This will assist in providing a better understanding of criteria importance and pave the way to developing an EPC contractor selection model involving the criteria most needed to objectively identify potential contractors and evaluate tenders.
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