{"id":3858,"date":"2026-03-04T12:50:09","date_gmt":"2026-03-04T12:50:09","guid":{"rendered":"https:\/\/birpub.org\/ijmfs\/?post_type=journal_article&#038;p=3858"},"modified":"2026-03-04T12:50:10","modified_gmt":"2026-03-04T12:50:10","slug":"appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria","status":"publish","type":"journal_article","link":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/","title":{"rendered":"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria"},"content":{"rendered":"\n<p><strong>Stephen, Emmanuel Nse<sup>1<\/sup> &amp; Prof. Basil Eze<sup>2<\/sup><\/strong><\/p>\n\n\n\n<p><strong>Abstract<\/strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><em>This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.<\/em><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong><\/strong><strong><em>Keywords: <\/em><\/strong><em>Artificial intelligence; Project management; Oil and gas sector; Niger Delta; Adoption barriers; Operational performance; Digital transformation<\/em><\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2026\/03\/IJMFS_43129-160-1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of IJMFS_43,129 -160.\"><\/object><a id=\"wp-block-file--media-76299451-f140-4c57-80cd-04342321fdb7\" href=\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2026\/03\/IJMFS_43129-160-1.pdf\">IJMFS_43,129 -160<\/a><a href=\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2026\/03\/IJMFS_43129-160-1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-76299451-f140-4c57-80cd-04342321fdb7\">Download<\/a><\/div>\n","protected":false},"author":1,"template":"","journal_article_cats":[235],"class_list":["post-3858","journal_article","type-journal_article","status-publish","hentry","journal_article_cat-vol-4-no-3"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.1 (Yoast SEO v26.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria - International Journal of Management Foresight and Strategy (IJMFS)<\/title>\n<meta name=\"description\" content=\"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria\" \/>\n<meta property=\"og:description\" content=\"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/\" \/>\n<meta property=\"og:site_name\" content=\"International Journal of Management Foresight and Strategy (IJMFS)\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-04T12:50:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1810\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/\",\"url\":\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/\",\"name\":\"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria - International Journal of Management Foresight and Strategy (IJMFS)\",\"isPartOf\":{\"@id\":\"https:\/\/birpub.org\/ijmfs\/#website\"},\"datePublished\":\"2026-03-04T12:50:09+00:00\",\"dateModified\":\"2026-03-04T12:50:10+00:00\",\"description\":\"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.\",\"breadcrumb\":{\"@id\":\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/birpub.org\/ijmfs\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/birpub.org\/ijmfs\/#website\",\"url\":\"https:\/\/birpub.org\/ijmfs\/\",\"name\":\"International Journal of Management Foresight and Strategy (IJMFS)\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/birpub.org\/ijmfs\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/birpub.org\/ijmfs\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/birpub.org\/ijmfs\/#organization\",\"name\":\"International Journal of Management Foresight and Strategy (IJMFS)\",\"url\":\"https:\/\/birpub.org\/ijmfs\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/birpub.org\/ijmfs\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg\",\"contentUrl\":\"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg\",\"width\":1810,\"height\":2560,\"caption\":\"International Journal of Management Foresight and Strategy (IJMFS)\"},\"image\":{\"@id\":\"https:\/\/birpub.org\/ijmfs\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria - International Journal of Management Foresight and Strategy (IJMFS)","description":"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/","og_locale":"en_US","og_type":"article","og_title":"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria","og_description":"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.","og_url":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/","og_site_name":"International Journal of Management Foresight and Strategy (IJMFS)","article_modified_time":"2026-03-04T12:50:10+00:00","og_image":[{"width":1810,"height":2560,"url":"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/","url":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/","name":"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria - International Journal of Management Foresight and Strategy (IJMFS)","isPartOf":{"@id":"https:\/\/birpub.org\/ijmfs\/#website"},"datePublished":"2026-03-04T12:50:09+00:00","dateModified":"2026-03-04T12:50:10+00:00","description":"This study investigated the role of artificial intelligence (AI) in enhancing project management within the oil and gas sector of the Niger Delta, Nigeria. The research was motivated by the need to evaluate how AI adoption can improve efficiency, safety, and overall performance in an industry facing persistent operational challenges. The study focused on three objectives: to assess the extent of AI adoption in project management practices, to identify barriers hindering effective integration, and to determine performance areas most improved through targeted AI application. A quantitative research design was adopted. The study population comprised 550 staff, with a sample size of 232 determined using Yamane\u2019s formula. A multi-stage sampling technique ensured proportional representation, and the survey data were complemented by secondary information from company and regulatory reports. Findings indicate that AI adoption is progressing but uneven across companies. Shell PLC\u2019s AI-driven risk modeling increased from 25% in 2020 to 50% in 2024, while Dagrow Resources expanded from 5% to 65% over the same period, consistent with survey responses yielding a moderate grand mean of 3.35 on a 5-point Likert scale. Barriers to adoption were also identified. NNPC\u2019s digital readiness improved from 2.5 to 3.6, compared to Shell\u2019s consistently high baseline of 4.2\u20134.5, while regulatory approval timelines averaged over 145 days. Survey evidence reinforced these barriers, producing a high overall mean of 4.15. Performance improvements were most notable in downtime reduction and asset integrity, with Shell reducing downtime from 5% to 18% and Dagrow from 0% to 28%, supported by a grand mean of 4.22 from survey data. Hypothesis testing confirmed that adoption levels remained moderate and that barriers significantly hindered integration. The study concludes that AI has strong transformative potential for project management in Nigeria\u2019s oil and gas sector, but its full realization requires sustained organizational investment and enabling institutional reforms.","breadcrumb":{"@id":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/birpub.org\/ijmfs\/journal_article\/appraisal-of-artificial-intelligence-for-smart-project-management-in-the-oil-and-gas-sector-of-the-niger-delta-nigeria\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/birpub.org\/ijmfs\/"},{"@type":"ListItem","position":2,"name":"Appraisal of Artificial Intelligence for Smart Project Management in the oil and gas Sector of the Niger Delta, Nigeria"}]},{"@type":"WebSite","@id":"https:\/\/birpub.org\/ijmfs\/#website","url":"https:\/\/birpub.org\/ijmfs\/","name":"International Journal of Management Foresight and Strategy (IJMFS)","description":"","publisher":{"@id":"https:\/\/birpub.org\/ijmfs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/birpub.org\/ijmfs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/birpub.org\/ijmfs\/#organization","name":"International Journal of Management Foresight and Strategy (IJMFS)","url":"https:\/\/birpub.org\/ijmfs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/birpub.org\/ijmfs\/#\/schema\/logo\/image\/","url":"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg","contentUrl":"https:\/\/birpub.org\/ijmfs\/wp-content\/uploads\/sites\/5\/2025\/11\/IJMFS-scaled.jpg","width":1810,"height":2560,"caption":"International Journal of Management Foresight and Strategy (IJMFS)"},"image":{"@id":"https:\/\/birpub.org\/ijmfs\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/journal_article\/3858","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/journal_article"}],"about":[{"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/types\/journal_article"}],"author":[{"embeddable":true,"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/users\/1"}],"wp:attachment":[{"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/media?parent=3858"}],"wp:term":[{"taxonomy":"journal_article_cat","embeddable":true,"href":"https:\/\/birpub.org\/ijmfs\/wp-json\/wp\/v2\/journal_article_cats?post=3858"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}