An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model

This study investigates the use of social media platforms (SMPs) for acquiring supply chain intelligence (SCI) to improve supply chain performance. Given the growth of social media use, there is an urgency for increased understanding of the effectiveness of emerging SMPs. In today's competitive...

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Main Author: Gebremikael, Fesseha
Format: Others
Published: North Dakota State University 2018
Online Access:https://hdl.handle.net/10365/28078
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spelling ndltd-ndsu.edu-oai-library.ndsu.edu-10365-280782021-09-23T17:09:26Z An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model Gebremikael, Fesseha This study investigates the use of social media platforms (SMPs) for acquiring supply chain intelligence (SCI) to improve supply chain performance. Given the growth of social media use, there is an urgency for increased understanding of the effectiveness of emerging SMPs. In today's competitive global environment, supply chain managers need to have a precise understanding about the SMPs that have become one of the premier sources of gaining SCI and in turn foster supply chain performance. Organizations need a methodology for selecting SMPs to remain proactive ahead of their competitors. The evolution of SMPs has caused a paradigm shift in how organizations obtain SCI to increase their revenues, profitability and reputation. The aim of this study is to apply a multi-criteria analysis using the analytic hierarchy process (AHP) to select SMPs. Stage 1 represents the primary goal, the decision maker wishes to gain in executing SMPs; Stage 2 consists of decision criteria; Stage 3 is composed of sub-criteria; and finally Stage 4 represents the SMP alternatives reported in the organizational hierarchy structure. The objective of this model is to rank the SMPs. The model includes key supply chain performance factors in the organization. The hierarchical models are used to breakdown the complex notion of supply chain performance into its constituent parts. The second phase of the hierarchical model consists of the performance indicators of which supply chain performance is composed. Hence, the modeled value is the supply chain performance in the organization. Our results indicate that the top three supply chain performance indicators are quality, assurance of supply and delivery. Meanwhile the top three types of supply chain intelligence are logistics intelligence, product/process intelligence and supply chain visibility intelligence. The top three SMP alternatives are, LinkedIn, Facebook and Twitter. 2018-05-04T19:21:40Z 2018-05-04T19:21:40Z 2018 text/dissertation movingimage/video https://hdl.handle.net/10365/28078 NDSU Policy 190.6.2 https://www.ndsu.edu/fileadmin/policy/190.pdf video/mp4 application/pdf North Dakota State University
collection NDLTD
format Others
sources NDLTD
description This study investigates the use of social media platforms (SMPs) for acquiring supply chain intelligence (SCI) to improve supply chain performance. Given the growth of social media use, there is an urgency for increased understanding of the effectiveness of emerging SMPs. In today's competitive global environment, supply chain managers need to have a precise understanding about the SMPs that have become one of the premier sources of gaining SCI and in turn foster supply chain performance. Organizations need a methodology for selecting SMPs to remain proactive ahead of their competitors. The evolution of SMPs has caused a paradigm shift in how organizations obtain SCI to increase their revenues, profitability and reputation. The aim of this study is to apply a multi-criteria analysis using the analytic hierarchy process (AHP) to select SMPs. Stage 1 represents the primary goal, the decision maker wishes to gain in executing SMPs; Stage 2 consists of decision criteria; Stage 3 is composed of sub-criteria; and finally Stage 4 represents the SMP alternatives reported in the organizational hierarchy structure. The objective of this model is to rank the SMPs. The model includes key supply chain performance factors in the organization. The hierarchical models are used to breakdown the complex notion of supply chain performance into its constituent parts. The second phase of the hierarchical model consists of the performance indicators of which supply chain performance is composed. Hence, the modeled value is the supply chain performance in the organization. Our results indicate that the top three supply chain performance indicators are quality, assurance of supply and delivery. Meanwhile the top three types of supply chain intelligence are logistics intelligence, product/process intelligence and supply chain visibility intelligence. The top three SMP alternatives are, LinkedIn, Facebook and Twitter.
author Gebremikael, Fesseha
spellingShingle Gebremikael, Fesseha
An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
author_facet Gebremikael, Fesseha
author_sort Gebremikael, Fesseha
title An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
title_short An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
title_full An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
title_fullStr An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
title_full_unstemmed An Investigation of the Impact of Social Media Platforms on Supply Chain Performance through Competitive Intelligence using AHP Model
title_sort investigation of the impact of social media platforms on supply chain performance through competitive intelligence using ahp model
publisher North Dakota State University
publishDate 2018
url https://hdl.handle.net/10365/28078
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