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This paper aims to develop a digital transformation model in hospitals. This study proposes a digital transformation model in hospitals by formulating dimensions and sub-dimensions. The proposed hospital digital transformation model outlines why and how each of dimensions and sub-dimensions are important in the hospital digital transformation model.

This study chose the type of qualitative research using a phenomenology approach. This study used observation techniques and in-depth interviews with 11 informants and conducted group discussion forums with information technology governance experts, a hospital information technology department manager who has an information technology background, and doctor representatives. The data were documented and analyzed using triangulation techniques.

This research provides empirical insights into the dimensions and sub-dimensions of hospital digital transformation models. The findings of the digital transformation dimension in hospitals are 7 dimensions and 37 sub-dimensions, namely, the governance and management dimension which has 8 sub-dimensions; the person has 9 sub-dimensions; strategy dimension which has 5 sub-dimensions; information technology capability has 3 sub-dimensions; the data interoperability dimension has 3 sub-dimensions; the data analytics dimension has 5 sub-dimensions; patient dimensions have 4 sub-dimensions; the findings of the sub-dimensions involved in the digital transformation dimension of the hospital can provide input on the accuracy of the indicators measuring the hospital’s digital transformation.

This research is limited to the qualitative type of phenomenology approach so that future research can test empirically with quantitative methods with techniques through surveys of dimensional and sub-dimensional relationships to hospital digital transformation. The researchers also recommend further assessing the findings of this paper which can develop as a model for measuring the maturity of hospital digital transformation.

This paper covers the implications of developing a hospital digital transformation model that can be used to organize and manage hospital digital transformation.

This paper can be used as a guideline for hospital stakeholders when carrying out digital transformation. This paper can be used as a reference for further research to find, study and develop dimensions and sub-dimensions of digital transformation models.

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

This article presents an exploratory analysis of perceived information needs for managing fixed public sector assets in South-Eastern Europe (SEE).

The analysis was carried out from a user needs perspective. A questionnaire was administered to government officials in SEE. Respondents were queried about what categories of information they need to make or to recommend five typical decisions concerning fixed public sector assets. Respondents also provided assessments on current availability and quality of information on fixed public sector assets, cost-benefit considerations regarding data collection and suitability of information for routine collection and updating.

The findings of this study indicate that the information collected for financial reporting purposes is recognized as insufficient to support decision-making on fixed public sector assets. However, perceived information needs by respondents are still rather basic. The need for more “sophisticated” and “strategic” information is found to be rather low, more so when the cost of collecting and processing such information is considered.

The findings of this study raise concerns about the persistently insufficient attention paid to the potential benefits that well-compiled and appropriately used asset registries could produce. The findings of this study might be useful to governments initiating the identification and measurement of their fixed assets, establishing fixed asset registries and instituting relevant regulation.

This study adds to the scant literature on the management of fixed public sector assets. It provides evidence on the perceived information needs of public sector officials, and it contributes to the ongoing debate on what good public sector asset management is and how it should be pursued.

This study aims to explore the intersection of AI-generated learning materials and active learning strategies in higher education artificial intelligence (AI) is bringing about changes and opening up new possibilities for an improved and more efficient higher education. However, the argument is that its use in education/classroom should be informed by verifiable evidence as well as best practice, which this scholarly work helps build evidence-based research to assess this technology in higher education.

Primary data was collected through structured questionnaire administered online via Google form. Based on the non-probability sampling technique, 300 higher education tutors and students across the UK were purposively targeted, out of which 218 (72.7%) response rate was achieved. Data was analyzed using descriptive statistics with the aid of Statistical Package for Social Sciences, whereby regression, correlation and Chi-square tests were conducted to determine the statistical significance, direction and strength of the relationship between the measured variables.

This study revealed that AI-generated learning materials support active learning strategies that enable students to actively engage in their learning, likewise enabling students to develop deeper understanding of their course content with significantly better knowledge retention, which is critical to the learning process. However, findings further revealed that acceptance/regular use of AI-generated learning materials is still below par across the higher education institutions, and there is major concern that the benefits may not be fully realized due to barriers to adoption.

There are limitations that future studies can improve on, especially in terms of methodology. Pragmatism is a philosophical research stance that integrates quantitative data collection with qualitative data (such as interviews) and will ask in-depth questions to gain holistic quality data for such empirical. Future studies can also improve on the research scope to allow for generalizability of findings and check for potential biases in the data collection, analysis and interpretation processes.

Despite the huge anticipation regarding how AI technology could transform teachers’ roles in higher education, concrete research into AI-generated learning materials and actual impact in facilitating active learning strategies and knowledge retention is currently lacking. This study presents theoretical models on AI acceptance in higher education and explored the Technology, Pedagogical and Content Knowledge framework to inform empirical information on how AI can support active learning strategies and students’ knowledge retention.

This study aims to examine the question “How can we understand the dynamics that give rise to multiplicities of overlapping controls (MoOCs)?” and explore the role of local politics in the emergence of such multiplicities.

Building on assemblage theory, we use an in-depth case study of local government in France where a Socialist and Green coalition (the “Coalition”) replaced a long-standing Communist administration. The Coalition introduced a comprehensive set of new management control systems (MCSs), layered onto pre-existing systems.

The proliferation of new MCSs, together with the persistence of legacy MCSs, gave rise to a MoOC. Linkages between controls constituted potential points of rupture around which the assemblage changed and shifted the nexus of control. Whereas densely populated areas of the assemblage provided sites for conflict in the battle to influence the allocation of resources, some MCSs became isolated or were reterritorialized by political groups seeking autonomy from the new management.

We highlight the material consequences of political contentions around MoOCs that obfuscate, and at times frustrate, the implementation of a programme for government.

We contribute a set of concepts pertaining to the dynamics of MoOCs. We offer an alternative perspective on the disappointment associated with the adoption of new management tools in the public sector, showing how MCSs can become battlegrounds for political contention rather than tools for management improvement. We contribute to literature utilizing assemblage theory for analysing management accounting change.

This chapter reviews the evidence on the role of physicians in shaping inequalities in access to and utilisation of healthcare. The authors examine three types of physician decisions that can influence inequalities in access and utilisation: location decisions, decisions to work in the public and/or private sector, and decisions or behaviours in the doctor–patient encounter. For each, the authors summarise the issues and empirical evidence on possible policies to help reduce inequalities in access. Future research to reduce inequalities should focus on changes to health systems that influence physician decisions, such as health insurance expansions, the public–private mix and financial incentives, as well as physician training and policies for a more diverse physician workforce.