Previous studies suggest that paid employment can improve workers’ mental health status by offering a series of manifest and latent benefits (i.e., income, self-achievement and social engagement), which motivates policymakers’ ongoing promotion of labour force participation as an approach to protect women’s mental health status. This study extends the literature by investigating the mental health consequences of housewives’ transition into paid employment across different gender role attitude groups. In addition, the study also tests the potential moderating role of the presence of children in relationships. This study yields two major findings by using nationally representative data (N = 1222) from the United Kingdom Longitudinal Household Study (2010–2014) and OLS regressions. First, from the first wave to the next, housewives who transitioned into paid employment reported better mental health status than those who remained housewives. Second, the presence of children can moderate such associations, but only among housewives with more traditional gender role attitudes. Specifically, among the traditional group, the mental benefits of transition into paid employment are more pronounced among those without children. Therefore, policymakers should develop more innovative approaches to promote housewives’ mental health by considering a more gender-role-attitudes-sensitive design of future labour market policies.

In this era of digital transformation, organisations and individuals have the ability to generate, collect, process, and analyse a vast volume of data to gain benefits and valuable insights. However, despite recognising the competitive advantage of digital transformation in data management, they often face challenges in navigating the transformation process and aligning their existing practices with on-premise technologies. Leadership and governance play a very significant role in digital transformation procedures. The main objectives of this research are 1) to establish a Leadership and Governance Stage Model (LGSM), which provides a standardised, structured, and objective roadmap for organisations to determine their current leadership and data governance maturity stage in digital transformation in data management and 2) to guide organisations in progressing to the next maturity stage by using LGSM as a framework. This research adopts a qualitative approach, utilising a Systematic Literature Review (SLR) conducted over the past ten years, applying Grounded Theory. A total of 14 well-defined Capability Maturity Models (CMMs) of data management were reviewed, summarised, and consolidated. In this way, LGSM is established, comprising six stages: Stages 0, 1, 2, 3, and 4, with Stage 5 representing the most developed and mature stage. The holistic view of this stage model has been summarised at the end of this research, which demonstrates the significance of leadership and governance in digital transformation.

Ineffective data management is a key reason for digital transformation failures. To address this, Capability Maturity Models (CMMs) for data management have been developed over the past decades. However, a Systematic Literature Review (SLR) revealed that most existing models insufficiently address legal compliance—a dimension increasingly critical in light of global regulatory developments such as the GDPR, CCPA, China's PIPL, and the EU AI Act. These frameworks have introduced more stringent, complex, and cross-border obligations that challenge conventional data management practices. Neglecting compliance not only exposes organisations to legal risks and reputational damage but also undermines the ethical and secure handling of data, making it a foundational concern rather than a peripheral one. This study proposes a comprehensive Data Management Stage Model (DMSM), developed and validated through a Design Science approach that integrates Grounded Theory, conceptual mapping, two rounds of expert assessment for model validation, and empirical verification with 10 real-world companies. The model spans six maturity stages (Stage 0 to Stage 5) and ten dimensions, including Data Acquisition, Metadata Management, Data Storage, Data Sharing, Data Analytics, Data Visualisation, Quality Control, Compliance, Operating Infrastructure, and Budget Allocation. These are further organised into subdimensions such as Strategic Planning, Governance, Leadership and Culture, Ability to Perform, Activities Performed, and Process Assessment. This paper places particular emphasis on the Compliance dimension, which encompasses 33 capabilities including cross-border policy alignment and regulatory preparedness, supported by transition tables for practical application. By embedding compliance within a structured maturity framework, the DMSM enables organisations to evaluate and strengthen their legal, ethical, and data security capabilities. It thus serves as both a theoretical contribution and a practical tool to support responsible digital transformation, offering a robust foundation for future research and model refinement.

As digital transformation accelerates, organisations are increasingly investing in data management infrastructures and setting related strategic goals. However, expected returns are often not realised due to ineffective data management practices. To address this, Capability Maturity Models (CMMs) have been developed to assess and enhance data management capabilities across areas such as strategy, policy, leadership, culture, processes, and value-added activities. This study presents a Systematic Literature Review (SLR) covering 28 studies published between 2012 and 2022, with an additional snowballing extension up to January 2024 related to data management maturity, encompassing both fully established Capability Maturity Models (CMMs) and conceptually significant contributions. The studies were evaluated based on two main criteria: (1) the application of a Design Science approach, comprising four key steps—problem definition, comparison of existing models, model development, and evaluation; and (2) the comprehensiveness, clarity, and usability of the models, assessed through four sub-criteria. The analysis identified eleven major shortcomings in the existing literature, including limited synthesis of prior work, lack of scientifically grounded iterative development, insufficient validation and verification, and inadequate coverage of the complete data lifecycle—particularly in relation to legal and ISO compliance. Moreover, critical elements such as a Stage 0 for data awareness, transition tables, and practical self-assessment schemes were often missing. These findings underscore the need for a scientifically rigorous and practically applicable CMM that addresses these gaps and offers a holistic framework for advancing data management maturity.