{"id":63655,"date":"2026-04-30T01:42:45","date_gmt":"2026-04-30T01:42:45","guid":{"rendered":"https:\/\/jums.academy\/v10i4-3\/"},"modified":"2026-04-30T10:21:14","modified_gmt":"2026-04-30T10:21:14","slug":"v10i4-3-2","status":"publish","type":"page","link":"https:\/\/jums.academy\/en\/v10i4-3-2\/","title":{"rendered":"v11i1"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-background-position:left top;--awb-border-sizes-top:0px;--awb-border-sizes-bottom:0px;--awb-border-sizes-left:0px;--awb-border-sizes-right:0px;--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-padding-top:20px;--awb-padding-bottom:20px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#000000;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-unboxed\" id=\"accordion-63655-1\"><div class=\"fusion-panel panel-default panel-b316da499a23a8947 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_b316da499a23a8947\"><a aria-expanded=\"false\" aria-controls=\"b316da499a23a8947\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-1\" data-target=\"#b316da499a23a8947\" href=\"#b316da499a23a8947\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">All published articles sorted by issues<\/span><\/a><\/h4><\/div><div id=\"b316da499a23a8947\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_b316da499a23a8947\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<ul style=\"list-style-type: none;\">\n<li><a href=\"https:\/\/jums.academy\/en\/v10i4-3-2\/\">Junior Management Science, Volume 11, Issue 1, April 2026<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i4\/\">Junior Management Science, Volume 10, Issue 4, December 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i3\/\">Junior Management Science, Volume 10, Issue 3, September 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i2\/\">Junior Management Science, Volume 10, Issue 2, June 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i1\/\">Junior Management Science, Volume 10, Issue 1, March 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i4\/\">Junior Management Science, Volume 9, Issue 4, December 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i3\/\">Junior Management Science, Volume 9, Issue 3, September 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i2\/\">Junior Management Science, Volume 9, Issue 2, June 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i1\/\">Junior Management Science, Volume 9, Issue 1, March 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i4\/\">Junior Management Science, Volume 8, Issue 4, December 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i3\/\">Junior Management Science, Volume 8, Issue 3, September 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i2\/\">Junior Management Science, Volume 8, Issue 2, June 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i1\/\">Junior Management Science, Volume 8, Issue 1, March 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i5\/\">Junior Management Science, Volume 7, Issue 5, December 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i4\/\">Junior Management Science, Volume 7, Issue 4, September 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i3\/\">Junior Management Science, Volume 7, Issue 3, July 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i2\/\">Junior Management Science, Volume 7, Issue 2, June 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i1\/\">Junior Management Science, Volume 7, Issue 1, March 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i4\/\">Junior Management Science, Volume 6, Issue 4, December 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i3\/\">Junior Management Science, Volume 6, Issue 3, September 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i2\/\">Junior Management Science, Volume 6, Issue 2, June 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i1-2\/\">Junior Management Science, Volume 6, Issue 1, March 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i4\/\">Junior Management Science, Volume 5, Issue 4, December 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i3\/\">Junior Management Science, Volume 5, Issue 3, September 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i2\/\">Junior Management Science, Volume 5, Issue 2, June 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i1\/\">Junior Management Science, Volume 5, Issue 1, March 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i4\/\">Junior Management Science, Volume 4, Issue 4, December 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i3\/\">Junior Management Science, Volume 4, Issue 3, September 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i2\/\">Junior Management Science, Volume 4, Issue 2, June 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i1\/\">Junior Management Science, Volume 4, Issue 1, March 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i4\/\">Junior Management Science, Volume 3, Issue 4, December 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i3\/\">Junior Management Science, Volume 3, Issue 3, September 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i2\/\">Junior Management Science, Volume 3, Issue 2, June 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i1\/\">Junior Management Science, Volume 3, Issue 1, March 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i3\/\">Junior Management Science, Volume 2, Issue 3, December 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i2\/\">Junior Management Science, Volume 2, Issue 2, September 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i1\/\">Junior Management Science, Volume 2, Issue 1, June 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v1i2\/\">Junior Management Science, Volume 1, Issue 2, December 2016<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v1i1\/\">Junior Management Science, Volume 1, Issue 1, June 2016<\/a><\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-title title fusion-title-1 sep-underline sep-solid fusion-title-text fusion-title-size-five\" style=\"--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-sep-color:#000000;\"><h5 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;--fontSize:18;--minFontSize:18;line-height:1.38;\"><\/h5><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-builder-row fusion-builder-row-inner fusion-row\"><div class=\"fusion-layout-column fusion_builder_column_inner fusion-builder-nested-column-0 fusion_builder_column_inner_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-1\"><h4><strong>Junior Management Science, Volume 11, Issue 1, April 2026<\/strong><\/h4>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-2 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-title title fusion-title-2 fusion-sep-none fusion-title-text fusion-title-size-four\" style=\"--awb-margin-bottom:-5px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;\"><h4 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;--fontSize:26;line-height:1.43;\"><h4><strong>Junior Management Science, Volume 11, Issue 1, April 2026<\/strong><\/h4><\/h4><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-3 fusion_builder_column_1_5 1_5 fusion-one-fifth fusion-column-first\" style=\"--awb-bg-size:cover;width:20%;width:calc(20% - ( ( 4% ) * 0.2 ) );margin-right: 4%;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-image-element in-legacy-container\" style=\"--awb-caption-title-font-family:var(--h2_typography-font-family);--awb-caption-title-font-weight:var(--h2_typography-font-weight);--awb-caption-title-font-style:var(--h2_typography-font-style);--awb-caption-title-size:var(--h2_typography-font-size);--awb-caption-title-transform:var(--h2_typography-text-transform);--awb-caption-title-line-height:var(--h2_typography-line-height);--awb-caption-title-letter-spacing:var(--h2_typography-letter-spacing);\"><span class=\" fusion-imageframe imageframe-none imageframe-1 hover-type-none\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"212\" height=\"300\" title=\"Cover\" src=\"https:\/\/i0.wp.com\/jums.academy\/wp-content\/uploads\/2026\/04\/Deckblatt-pdf.jpg?resize=212%2C300&#038;ssl=1\" alt class=\"img-responsive wp-image-63723\"\/><\/span><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-4 fusion_builder_column_4_5 4_5 fusion-four-fifth fusion-column-last\" style=\"--awb-bg-size:cover;width:80%;width:calc(80% - ( ( 4% ) * 0.8 ) );\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-2\"><ol>\n<li><a href=\"#A1\" data-mce-href=\"#A1\"><strong>Christopher Dudel<\/strong>, Runtime-Sensitive Learned Operator Selection in ALNS: Testing Improvements to Adaptive Operator Selection while Optimizing Runtime, 1-26<\/a><\/li>\n<li><a href=\"#A2\" data-mce-href=\"#A2\"><strong>David Schopp<\/strong>, Die Rolle von Corporate Digital Responsibility im digitalen Controlling, 27-42<\/a><\/li>\n<li><a href=\"#A3\" data-mce-href=\"#A3\"><strong>Leonie Terhardt<\/strong>, Textauswertung in der Finanzanalyse: Eine Ereignisstudie zum Sentiment in Earnings Calls, 43-73<\/a><\/li>\n<li><a href=\"#A4\" data-mce-href=\"#A4\"><strong>Moritz Praetz<\/strong>, The Impact of Biodiversity Risk on Banks&#8217; Credit Default Swap Spread Changes, 74-106<\/a><\/li>\n<li><a href=\"#A5\" data-mce-href=\"#A5\"><strong>Sebastian Schmidt<\/strong>, Kausale Inferenz Unter Anwendung Von Double Machine Learning: Oregon Health Insurance Experiment, 107-138<\/a><\/li>\n<li><a href=\"#A6\" data-mce-href=\"#A6\"><strong>Susanne Rautzenberg<\/strong>, The Dark Side of Employer Branding \u2013 Aesthetic Labour and Employer Attractiveness in the Beauty and Cosmetics Industry, 139-163<\/a><\/li>\n<li><a href=\"#A7\" data-mce-href=\"#A7\"><strong>Justus Scharfst\u00e4dt<\/strong>, Understanding Participation Trends in the Avani 5G Patent Pool: A Descriptive Analysis, 164-180<\/a><\/li>\n<li><a href=\"#A8\" data-mce-href=\"#A8\"><strong>Philipp Iversen<\/strong>, Generative AI-Enabled Music Generation in Marketing and Consumer Response, 181-194<\/a><\/li>\n<li><a href=\"#A9\" data-mce-href=\"#A9\"><strong>Ege \u00d6zkul<\/strong>, Predicting Stock Market Trends Using Convolutional Neural Networks: A Deep Learning Approach, 195-226<\/a><\/li>\n<li><a href=\"#A10\" data-mce-href=\"#A10\"><strong>Felix Achim Bautz<\/strong>, Relative Performance-Messung als Instrument der Unternehmenssteuerung, 227-245<\/a><\/li>\n<\/ol>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-5 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div style=\"width: 2px; height: 15px;\" data-mce-style=\"width: 2px; height: 15px;\"><br><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div style=\"width: 2px; height: 15px;\" data-mce-style=\"width: 2px; height: 15px;\"><br><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-6 fusion_builder_column_1_6 1_6 fusion-one-sixth fusion-column-first fusion-column-no-min-height\" style=\"--awb-padding-top:10px;--awb-padding-bottom:10px;--awb-bg-size:cover;width:13.3333%; margin-right: 4%;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-button-wrapper fusion-aligncenter\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-1 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/JUMS_Volume11_Issue1_2026.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Read Issue<\/p>\n<\/span><\/a><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-7 fusion_builder_column_1_2 1_2 fusion-one-half fusion-column-no-min-height\" style=\"--awb-bg-size:cover;width:48%; margin-right: 4%;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div style=\"width: 2px; height: 2px;\" data-mce-style=\"width: 2px; height: 2px;\"><br><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-8 fusion_builder_column_1_6 1_6 fusion-one-sixth fusion-column-no-min-height\" style=\"--awb-padding-top:10px;--awb-padding-bottom:10px;--awb-bg-size:cover;width:13.3333%; margin-right: 4%;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-button-wrapper fusion-aligncenter\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-2 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_self\" href=\"https:\/\/jums.academy\/en\/submit\/\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Submit your thesis now<\/p>\n<\/span><\/a><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-9 fusion_builder_column_1_6 1_6 fusion-one-sixth fusion-column-last fusion-column-no-min-height\" style=\"--awb-padding-top:10px;--awb-padding-bottom:10px;--awb-bg-size:cover;width:13.3333%;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-button-wrapper fusion-aligncenter\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-3 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_self\" href=\"https:\/\/jums.academy\/en\/newsletter\/\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Keep up to date<\/p>\n<\/span><\/a><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-10 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div style=\"width: 2px; height: 15px;\" data-mce-style=\"width: 2px; height: 15px;\"><br><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div style=\"width: 2px; height: 15px;\" data-mce-style=\"width: 2px; height: 15px;\"><br><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-11 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-3\"><p><a name=\"A1\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Runtime-Sensitive Learned Operator Selection in ALNS: Testing Improvements to Adaptive Operator Selection while Optimizing Runtime<\/h5>\n<p>Christopher Dudel, Technical University of Munich (Bachelor\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 1-26<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-2\"><div class=\"fusion-panel panel-default panel-76931e76de473bb76 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_76931e76de473bb76\"><a aria-expanded=\"false\" aria-controls=\"76931e76de473bb76\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-2\" data-target=\"#76931e76de473bb76\" href=\"#76931e76de473bb76\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"76931e76de473bb76\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_76931e76de473bb76\"><div class=\"panel-body toggle-content fusion-clearfix\">We propose and test two variations of the Adaptive Large Neighborhood Search (ALNS) meta-heuristic: First, we add time sensitivity to the operator selection scheme to optimize the ALNS for both solution quality and runtime. We reward comparatively slow operators with reduced rewards for finding improvements. This ensures that the meta-heuristic is slowed down less by operators which consistently find good solutions but take long to do so. Secondly, we replace the Adaptive Layer with a Learned Operator Selection Policy trained via Deep-Q Learning. The training takes both solution quality and operator runtime into account. We test our algorithms against classic ALNS as well as random operator selection. We perform an analysis of how operator portfolios affect performance. Our chosen problem domain is the Capacitated Vehicle Routing Problem with 100 to 400 customer nodes.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-4\"><p><em>Keywords: adaptive large neighborhood search; vehicle routing; optimization; logistics; deep learning.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-5\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp1-26\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp1-26<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-4 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Dudel.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-5 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Dudel_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-6 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp1-26\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite Article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-12 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-6\"><p><a name=\"A2\"><\/a><\/p>\n<h5 style=\"text-align: left;\">The Role of Corporate Digital Responsibility in Digital Controlling<\/h5>\n<p>David Schopp, Heinrich Heine University D\u00fcsseldorf (Bachelor\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 27-42<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-3\"><div class=\"fusion-panel panel-default panel-84d65a1a8efb934c8 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_84d65a1a8efb934c8\"><a aria-expanded=\"false\" aria-controls=\"84d65a1a8efb934c8\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-3\" data-target=\"#84d65a1a8efb934c8\" href=\"#84d65a1a8efb934c8\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"84d65a1a8efb934c8\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_84d65a1a8efb934c8\"><div class=\"panel-body toggle-content fusion-clearfix\">Digitalization is fundamentally changing controlling and, at the same time, increasing the demands for responsible handling of data, technologies, and automated decision-making systems. Against this backdrop, this thesis examines the extent to which CDR influences the design and implementation of digital controlling processes. The aim of the thesis is to systematically present the influence of ethical, data protection and technological aspects on digital controlling and to identify practical starting points for the integration of CDR. Methodologically, the thesis is based on a literature analysis of relevant scientific articles as well as regulatory and practical sources. Building on the domains of digital controlling according to Keimer and Egle, the effects of CDR on the areas of data, technologies, processes, methods and competencies are analyzed. The focus is particularly on data governance, data protection, artificial intelligence and big data, as well as the changing role of the controller. The results show that CDR as a framework concept permeates all domains of digital controlling and significantly expands the requirements for controlling systems and controller competencies. The responsible use of digital technologies requires transparent data structures, ethically reflective analysis methods and the strategic anchoring of CDR in corporate management. This work thus contributes to closing an existing research gap and illustrates that CDR is not only a normative obligation but can also create sustainable added value for companies and stakeholders.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-7\"><p><em>Keywords: corporate digital responsibility; digital controlling; artificial intelligence; data protection.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-8\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp27-42\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp27-42<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-7 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Schopp.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-8 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp27-42\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-13 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-9\"><p><a name=\"A3\" class=\"mce-item-anchor\"><\/a><\/p>\n<h5 style=\"text-align: left;\" data-mce-style=\"text-align: left;\">Text Analysis in Financial Analysis: An Event Study on Sentiment in Earnings Calls<\/h5>\n<p>Leonie Terhardt, Heinrich Heine University D\u00fcsseldorf (Master\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 43-73<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-4\"><div class=\"fusion-panel panel-default panel-b1168cb3f42d7ab32 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_b1168cb3f42d7ab32\"><a aria-expanded=\"false\" aria-controls=\"b1168cb3f42d7ab32\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-4\" data-target=\"#b1168cb3f42d7ab32\" href=\"#b1168cb3f42d7ab32\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"b1168cb3f42d7ab32\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_b1168cb3f42d7ab32\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<p>This thesis examines the influence of sentiment in earnings calls on financial market reactions. The objective is to analyse the extent to which the tone used in presentations and Q&amp;A segments affect abnormal returns, and how differences in investors\u2019 information environments shape these effects. The study is based on transcripts from companies in the S&amp;P Composite 1500, whose tone is recorded using finance-specific lexicons and evaluated within an event-study framework. The results suggest that sentiment in the Q&amp;A section in particular triggers significant immediate market reactions, with analysts&#8217; questions having the greatest influence. Conversely, the sentiment of the presentation assumes significance during the drift period and frequently results in subsequent corrections. In addition, investors show increased sensitivity to verbal signals when it comes to companies with a higher degree of uncertainty. The thesis contributes to the literature by demonstrating that context-specific sentiment measurement provides valuable insights for the evaluation of qualitative company information. Furthermore, it highlights the limitations of generic text lexicons.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-10\"><p><em>Keywords: sentiment; earnings calls; text analysis; event study; financial market reactions.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-11\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp43-73\" data-mce-href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp43-73\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp43-73<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-9 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Terhardt.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Read issue<\/p>\n<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-10 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Terhardt_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Read Appendix<\/p>\n<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-11 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp43-73\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">\n<p>Cite article<\/p>\n<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\" data-mce-style=\"width: 2px; height: 20px;\"><br><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-14 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-12\"><p><a name=\"A4\"><\/a><\/p>\n<h5 style=\"text-align: left;\">The Impact of Biodiversity Risk on Banks\u2018 Credit Default Swap Spread Changes<\/h5>\n<p>Moritz Praetz, Technical University of Munich (Master\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 74-106<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-5\"><div class=\"fusion-panel panel-default panel-e0f798c2ee7273706 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_e0f798c2ee7273706\"><a aria-expanded=\"false\" aria-controls=\"e0f798c2ee7273706\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-5\" data-target=\"#e0f798c2ee7273706\" href=\"#e0f798c2ee7273706\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"e0f798c2ee7273706\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_e0f798c2ee7273706\"><div class=\"panel-body toggle-content fusion-clearfix\">This paper explores the impact of biodiversity risk on banks\u2019 credit risk using a news-based biodiversity index and CDS of 39 global banks between 2015 and 2023. Using a linear OLS regression, this paper finds evidence for a significant positive relationship between biodiversity news and CDS spread changes, where negative news leads to increasing CDS prices. Furthermore, cross-sectional analyses are conducted to test for heterogeneity. Using the Kunming Declaration in 2021 as an external shock, this paper finds evidence that the relationship persists for the period after the Kunming Declaration, suggesting no significant effect of biodiversity risk before. Further tests reveal no significant impact of a country\u2019s state of biodiversity. In contrast, since the Kunming Declaration, the relationship is stronger for banks which openly disclose biodiversity risks. Banks located in the USA, the only UN nation which is not a member of the CBD, experience a weaker effect of biodiversity news on CDS spread changes. These results show that banks are subject to biodiversity-related credit risks, where expectations of new policies and regulation following the Kunming Declaration significantly affect banks\u2019 CDS spreads.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-13\"><p><em>Keywords: biodiversity; banking; credit default swaps; credit risk.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-14\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp74-106\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp74-106<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-12 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Praetz.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-13 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Praetz_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-14 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp74-106\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-15 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-15\"><p><a name=\"A5\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Causal Inference Using Double Machine Learning: Oregon Health Insurance Experiment<\/h5>\n<p>Sebastian Schmidt, University of Hamburg (Master\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 107-138<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-6\"><div class=\"fusion-panel panel-default panel-ea75492b465903ec6 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_ea75492b465903ec6\"><a aria-expanded=\"false\" aria-controls=\"ea75492b465903ec6\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-6\" data-target=\"#ea75492b465903ec6\" href=\"#ea75492b465903ec6\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"ea75492b465903ec6\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_ea75492b465903ec6\"><div class=\"panel-body toggle-content fusion-clearfix\">This study applies advanced methods of causal inference, specifically the Double  Machine Learning (DML) framework, to estimate the causal effects of public health  insurance on individual health outcomes using data from the 2008 Oregon Health  Insurance Experiment (OHIE), a randomized controlled trial. DML integrates modern  machine learning with the econometric principles of causal identification to obtain <br \/>unbiased treatment effect estimates in high-dimensional data. Interactive Regression Models (IRM) and Interactive Instrumental Variable Models (IIVM) are employed to estimate effects of Medicaid coverage on perceived health, number of doctor visits, <br \/>satisfaction, access to medical services, and quality of care. The results indicate small but positive causal effects of Medicaid coverage on perceived health and healthcare utilization, statistically insignificant effects on satisfaction and access to medication, and a slightly negative effect on perceived quality of care. The findings highlight the potential of Double Machine Learning as a robust framework for causal analysis in empirical research.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-16\"><p><em>Keywords: causal inference; double machine learning; oregon health insurance experiment.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-17\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp107-138\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp107-138<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-15 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Schmidt.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-16 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp107-138\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-16 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-18\"><p><a name=\"A6\"><\/a><\/p>\n<h5 style=\"text-align: left;\">The Dark Side of Employer Branding \u2013 Aesthetic Labour and Employer Attractiveness in the Beauty and Cosmetics Industry<\/h5>\n<p>Susanne Rautzenberg, Vienna University of Economics and Business (Master\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 139-163<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-7\"><div class=\"fusion-panel panel-default panel-ffa776cb4a10f115e fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_ffa776cb4a10f115e\"><a aria-expanded=\"false\" aria-controls=\"ffa776cb4a10f115e\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-7\" data-target=\"#ffa776cb4a10f115e\" href=\"#ffa776cb4a10f115e\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"ffa776cb4a10f115e\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_ffa776cb4a10f115e\"><div class=\"panel-body toggle-content fusion-clearfix\">This thesis examines the impact of Aesthetic Labour on employer branding within the cosmetics industry, with a focus on Generation Z (Gen Z). Aesthetic Labour refers to the employment of workers whose appearance reflects the company\u2019s brand image, thereby shaping perceptions of<br \/>employer attractiveness. The central research question is how such practices influence Gen Z\u2019s perceptions of employer attractiveness in the beauty sector.<\/p>\n<p>Through semi-structured interviews with twelve current and former Gen Z employees of well- known cosmetics firms, this study uncovers the dual nature of employer branding in the industry.<\/p>\n<p>Externally, a polished, glamorous image attracts applicants, while internally, aesthetic norms may foster subtle pressures on employees to conform, creating tensions despite otherwise supportive work cultures. The findings reveal that Aesthetic Labour enhances employer attractiveness by creating a desirable yet sometimes unattainable image. This study contributes to the understanding of Aesthetic Labour\u2019s paradoxical role in employer branding. It highlights the need for cosmetics firms to balance proven branding strategies with inclusive and diverse employment practices to meet the evolving demands of the modern workforce.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-19\"><p><em>Keywords: aesthetic labour; employer branding; cosmetics industry; generation z.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-20\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp139-163\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp139-163<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-17 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Rautzenberg.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-18 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_Rautzenberg_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-19 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp139-163\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-17 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-21\"><p><a name=\"A7\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Understanding Participation Trends in the Avani 5G Patent Pool: A Descriptive Analysis<\/h5>\n<p>Justus Scharfst\u00e4dt, Technical University of Munich (Bachelor\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 164-180<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-8\"><div class=\"fusion-panel panel-default panel-2596ec8babb2f3ff5 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_2596ec8babb2f3ff5\"><a aria-expanded=\"false\" aria-controls=\"2596ec8babb2f3ff5\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-8\" data-target=\"#2596ec8babb2f3ff5\" href=\"#2596ec8babb2f3ff5\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"2596ec8babb2f3ff5\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_2596ec8babb2f3ff5\"><div class=\"panel-body toggle-content fusion-clearfix\">Patent pools play a critical role in adapting to new technology standards by simplifying the licensing of Standard-Essential Patents (SEPs). The Avanci 5G pool provides a licensing platform for vehicle manufacturers to access essential technologies for connected vehicles. Despite the growing importance of these pools, there is limited research on the dynamics of participation and its implications for different stakeholders. This study provides a descriptive analysis of the Avanci 5G pool, particularly focusing on the development of the SEP and licensee coverage since its inception. It uses a mixed-methods approach and combines multiple data sources for the coverage estimates. My findings reveal substantial growth in both SEP and licensee coverage but highlight a significant gap in the participation of Asian automakers, potentially due to perceived anti-competitive risks. Policy makers and relevant stakeholders should aim to foster a competitive and innovative environment in SEP licensing.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-22\"><p><em>Keywords: patent pool; licensing; connected vehicles; innovation; anti-competitive risks.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-23\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp164-180\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp164-180<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-20 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Scharfst\u00e4dt.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-21 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Scharfst\u00e4dt_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-22 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp164-180\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-18 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-24\"><p><a name=\"A8\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Generative AI-Enabled Music Generation in Marketing and Consumer Response<\/h5>\n<p>Philipp Iversen, Technical University of Munich (Bachelor\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 181-194<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-9\"><div class=\"fusion-panel panel-default panel-c5c011ddb324934ad fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_c5c011ddb324934ad\"><a aria-expanded=\"false\" aria-controls=\"c5c011ddb324934ad\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-9\" data-target=\"#c5c011ddb324934ad\" href=\"#c5c011ddb324934ad\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"c5c011ddb324934ad\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_c5c011ddb324934ad\"><div class=\"panel-body toggle-content fusion-clearfix\">Generative AI is revolutionizing the marketing industry by producing high-quality, cost-effective and time-efficient content. This study investigates the potential of AI-generated music in digital advertising. Two studies, a survey and a real-world A\/B test, evaluate the different songs on chosen criteria: Overall, Melodiousness, Creativity, Naturalness, Correctness and Prompt following for the survey, and click-through rates (CTR) for the field experiment. The survey results show that AI-generated music can be comparable in quality to human compositions, even scoring significantly higher in the categories Prompt Following and Melodiousness. However, AI music showed significantly worse results in the category Creativity. The field experiment revealed no statistically significant difference in CTR between advertisements using AI-generated and royalty-free music, demonstrating that AI music can be a good substitute in supporting roles. This research underlines the possibility for AI-generated music to be used in hyper-personalized advertising, while addressing challenges related to perceived creativity and copyright. The findings contribute to understanding AI\u2019s disruptive potential in marketing and offers practical insights to integrate AI tools effectively.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-25\"><p><em>Keywords: AI-generated music; music in advertising; AI consumer response.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-26\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp181-194\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp181-194<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-23 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Iversen.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-24 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Iversen_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-25 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp181-194\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-19 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-27\"><p><a name=\"A9\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Predicting Stock Market Trends Using Convolutional Neural Networks: A Deep Learning Approach<\/h5>\n<p>Ege \u00d6zkul, Technical University of Munich (Master\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 195-226<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-10\"><div class=\"fusion-panel panel-default panel-adc8d0e2a0e98e05f fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_adc8d0e2a0e98e05f\"><a aria-expanded=\"false\" aria-controls=\"adc8d0e2a0e98e05f\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-10\" data-target=\"#adc8d0e2a0e98e05f\" href=\"#adc8d0e2a0e98e05f\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"adc8d0e2a0e98e05f\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_adc8d0e2a0e98e05f\"><div class=\"panel-body toggle-content fusion-clearfix\">Technical analysis aims to predict stock returns based on price and volume patterns and has seen growing adoption of machine learning methods. However, most approaches rely on hand-crafted features. This paper investigates whether deep learning applied to stock chart images can predict future returns without manual feature engineering. Building on Jiang et al. (2023), this study applies Convolutional Neural Networks (CNNs), a computer vision architecture, to predict stock returns from price charts and extends their approach by implementing Vision Transformers, specifically the Class-Attention in Image Transformer (CaiT). Stock prices, volumes, and moving averages are encoded into images, which are used to train models that classify future stock returns as either positive or negative. Results show that both CNN and CaiT models outperform traditional technical indicators such as momentum and reversal strategies when applied to US stocks. Moreover, combining the two models yields incremental predictive power. An investment strategy based on their joint predictions achieves higher returns and Sharpe ratios than either model alone.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-28\"><p><em>Keywords: technical analysis; stock return prediction; deep learning; convolutional neural networks; vision transformers.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-29\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp195-226\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp195-226<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-26 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_\u00d6zkul.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-27 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/MA_\u00d6zkul_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-28 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp195-226\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-20 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-30\"><p><a name=\"A10\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Relative Performance Measurement as an Instrument of Corporate Management<\/h5>\n<p>Felix Achim Bautz, Heinrich Heine University D\u00fcsseldorf (Bachelor\u2019s thesis)<br \/>Junior Management Science 11(1), 2026, 227-245<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-63655-11\"><div class=\"fusion-panel panel-default panel-1121bf968f03dd702 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_1121bf968f03dd702\"><a aria-expanded=\"false\" aria-controls=\"1121bf968f03dd702\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-63655-11\" data-target=\"#1121bf968f03dd702\" href=\"#1121bf968f03dd702\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"1121bf968f03dd702\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_1121bf968f03dd702\"><div class=\"panel-body toggle-content fusion-clearfix\">Stabilizing and improving organizational performance is a central challenge in management control. Behavior-based results controls is a key mechanism in this<br \/>context. The lack of a generally effective and accepted incentive system requires a deeper examination of the underlying mechanisms beyond surface-level system features. Relative Performance Measurement (RPM) is considered a dynamic tool to align performance and motivation with strategic goals. However, its use is associated with risks such as demotivation and dysfunctional behavior. This study analyzes the effects of RPM in the context of competition and social comparison within the social system of the organization. Based on agency theory (AT), the economic rationale of RPM is derived using tournament theory. Its limitations are discussed using the example of forced-ranking practices. The perspective is extended by social comparison theory, which shows how task-related, individual, and contextual variables, as well as system design, influence the effectiveness of RPM in different settings. The study contributes to the integration of economic and behavioral approaches in performance measurement (PM) and provides practical implications for the targeted design of RPM systems in management control.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-31\"><p>Keywords: tournament theory; social comparison theory; management control; agency theory; relative performance measurement<\/p>\n<\/div><div class=\"fusion-text fusion-text-32\"><p><em>DOI:\u00a0<a href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp227-245\">https:\/\/doi.org\/10.5282\/jums\/v11i1pp227-245<\/a><\/em><\/p>\n<\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-29 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2026\/04\/BA_Bautz.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read issue<\/span><\/a><\/div><div class=\"fusion-button-wrapper fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-30 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v11i1pp227-245\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite Article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4896,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-63655","page","type-page","status-publish","hentry"],"jetpack_shortlink":"https:\/\/wp.me\/P7lBbr-gyH","jetpack-related-posts":[],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/63655","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/users\/4896"}],"replies":[{"embeddable":true,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/comments?post=63655"}],"version-history":[{"count":35,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/63655\/revisions"}],"predecessor-version":[{"id":64156,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/63655\/revisions\/64156"}],"wp:attachment":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/media?parent=63655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}