{"id":{"repo_id":"middlesex","oai_identifier":"oai:repository.mdx.ac.uk:368400"},"canonical_url":"https://search.dev.ndltd.org/etd/middlesex/oai:repository.mdx.ac.uk:368400","repository":{"repo_id":"middlesex","name":"Middlesex University","base_url":"https://repository.mdx.ac.uk/oai2"},"display":{"title":"Reifegradmodellbasierte Implementierung von Self-Service Business Intelligence in deutschen Klein-, Mittel- und Großun-ternehmen – diskriminanzanalytische Untersuchung tech-nischer, organisatorischer und kultureller Unterschiede","abstract":"Self-Service Business Intelligence (SSBI) promises to overcome the divide between IT and business and to enable data-driven decision-making. In practice, however, many companies struggle to harness this potential due to technical, organizational, and cultural barriers. Existing Business Intelligence maturity models focus mainly on technological aspects and offer little guidance for SSBI-specific challenges. This lack of orientation leaves companies without practical support and highlights the absence of a research-based, empirically supported model for SSBI implementation, resulting in low success rates despite high demand. This dissertation develops a theoretically grounded and empirically validated maturity model tailored to SSBI. It integrates technical, organizational, and cultural dimensions and provides a criteria catalogue for self-assessment together with stage-specific recommendations to support companies in implementing and advancing SSBI. The research design combines a systematic literature review with an empirical study of 442 German companies across manufacturing, trade, and services. The review identifies shortcomings in existing maturity models and forms the basis for a new model with three main dimensions and 15 sub-dimensions. A quantitative survey (CAWI) and discriminant analysis validate the model by revealing systematic differences in implementation measures across maturity stages. The findings highlight three critical drivers of SSBI maturity: user support (organizational), data democratization (cultural), and data access (technical). Companies at higher maturity stages demonstrate strong knowledge sharing and user autonomy, while organizations at lower stages benefit from structured assessment and tailored recommendations to progress step by step. The results confirm that successful SSBI requires balanced development across all three dimensions. By integrating theoretical insights and empirical evidence, this dissertation presents a five-stage maturity model (Beginners, Advanced, Specialists, Strategists, Visionaries). It closes a significant research gap and provides a reliable instrument for both academia and practice to systematically enhance SSBI implementation, strengthen data-driven decision-making, and improve long-term competitiveness through a maturity-based implementation concept for SSBI.","abstract_html":"Self-Service Business Intelligence (SSBI) promises to overcome the divide between IT and business and to enable data-driven decision-making. In practice, however, many companies struggle to harness this potential due to technical, organizational, and cultural barriers. Existing Business Intelligence maturity models focus mainly on technological aspects and offer little guidance for SSBI-specific challenges. This lack of orientation leaves companies without practical support and highlights the absence of a research-based, empirically supported model for SSBI implementation, resulting in low success rates despite high demand. This dissertation develops a theoretically grounded and empirically validated maturity model tailored to SSBI. It integrates technical, organizational, and cultural dimensions and provides a criteria catalogue for self-assessment together with stage-specific recommendations to support companies in implementing and advancing SSBI. The research design combines a systematic literature review with an empirical study of 442 German companies across manufacturing, trade, and services. The review identifies shortcomings in existing maturity models and forms the basis for a new model with three main dimensions and 15 sub-dimensions. A quantitative survey (CAWI) and discriminant analysis validate the model by revealing systematic differences in implementation measures across maturity stages. The findings highlight three critical drivers of SSBI maturity: user support (organizational), data democratization (cultural), and data access (technical). Companies at higher maturity stages demonstrate strong knowledge sharing and user autonomy, while organizations at lower stages benefit from structured assessment and tailored recommendations to progress step by step. The results confirm that successful SSBI requires balanced development across all three dimensions. By integrating theoretical insights and empirical evidence, this dissertation presents a five-stage maturity model (Beginners, Advanced, Specialists, Strategists, Visionaries). It closes a significant research gap and provides a reliable instrument for both academia and practice to systematically enhance SSBI implementation, strengthen data-driven decision-making, and improve long-term competitiveness through a maturity-based implementation concept for SSBI.","abstract_has_math":false,"creators":["Staisch, A."],"institution":"Middlesex University / KMU Akademie & Management AG","degree_name":"DBA","degree_level":"DBA thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T03:03:11Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:repository.mdx.ac.uk:368400"],"render_values":[{"text":"oai:repository.mdx.ac.uk:368400","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Staisch, A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["Middlesex University Research Repository"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Business School","Business and Law"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Middlesex University / KMU Akademie & Management AG"]},{"key":"dc:relation","label":"Dc Relation","values":["https://repository.mdx.ac.uk/item/368400"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://repository.mdx.ac.uk/item/368400"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["DBA thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["DBA"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:repository.mdx.ac.uk:368400"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Self-Service Business Intelligence (SSBI) promises to overcome the divide between IT and business and to enable data-driven decision-making. 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