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The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

This study aims to explore the potential of Islamic climate finance in Indonesia and to map Islamic climate finance based on Islamic finance instruments, both commercial and social.

The analysis was conducted in Jakarta, Indonesia, between October 2022 and June 2023. This study adopted a qualitative interpretive approach in two phases. The first phase was desk-based research which focused on document analysis such as official documents, scientific publications, non-governmental organization publications and company reports in Indonesia. This analysis was conducted to identify significant milestones in developing green and eco-friendly finance that used Islamic financial instruments in Indonesia. The second phase consisted of interviews with essential Islamic climate finance project actors, such as green sukuk publishers, zakat and waqf collection agencies, stakeholders, capital market regulators, Shariah supervisory boards and Islamic finance experts.

The main finding of this study is that the development of Islamic green finance in Indonesia can occur through various channels, including greening Islamic capital markets, greening Islamic social finance, Islamic green finance and developing green banking services for the unbanked to support financial inclusion. Green sukuk, or Islamic bonds, are key financial instruments in Islamic green finance. They are used to fund projects in areas such as clean energy, mass transit, water conservation, forestry and low-carbon technology. These green financing initiatives also include socially responsible investments that are designed to improve the lives of people and communities.

First, the availability of data on Islamic green finance practices in Indonesia may be limited, making it difficult to obtain a comprehensive understanding of the current landscape. Second, cultural and religious factors may play a role in the adoption and implementation of Islamic green finance, and these factors may vary across different regions in Indonesia.

The exploration and clustering of Islamic climate finance based on Islamic financial instruments in Indonesia can lead to the development of more sustainable and environmentally friendly practices in the financial industry.

This study serves as a pioneering effort to explore the potential and clustering of Islamic climate finance based on Islamic financial instruments in Indonesia.

This study aims to break through the limitations of previous studies that have focused too much on the individual-level effects of humble leadership. Based on the affective events theory (AET), this study provides to construct an individual-team multilevel model of humble leadership focusing on the followers’ affective reaction and attribution of intentionality.

On the basis of subordinates’ attribution of humble leadership, it is believed that there are actually two motivations for humble leadership: true intention (serve the organizational collective interest) and pseudo intention (serve the leader’s self-interest), to which subordinates have different affective reactions, causing different leadership effectiveness. Thus, this study conducted an extensive review based on the qualitative method and proposed an integrated multilevel model of leader humility on individual and team outputs.

Followers’ attribution of intentionality moderates the relationship between humble leadership and followers’ affective reaction, which also determines followers’ performance (task performance, interpersonal deviant behavior and leader–member exchange); the interaction between team leaders’ humble leadership and collective attribution of intentionality influences team outputs (team outputs, organizational deviant behavior and team–member exchange) through team affective reaction; team humble leadership affects individual outputs through affective reaction and team affective climate plays a moderating role between affective reaction and individual outputs.

This study explores the individual-team multilevel outputs of humble leadership based on the AET theory, which is relatively rare in the current field. This study attempts to incorporate leaders’ motivation (such as attributions of intentionality) into the humble leadership research, by confirming that humble leadership affects affective reaction, which further influences individual-team multilevel outputs.

The accuracy of budgeting is important to fulfilling its various roles. The aim of this study is to examine perceptions of budgeting accuracy in UK universities and to identify and understand the factors that influence them.

A mixed methods research design comprising a questionnaire survey (84 responses, = 51.5%) and 42 semi-structured, qualitative interviews is employed.

The findings reveal that universities tend to be conservative in their budgeting, although previous financial difficulties, the attitude of the governing body and the need to convince lenders that finances are being managed competently might lead to a greater emphasis on a “realistic” rather than cautious budget. Stepwise multiple regression identified four significantly negative influences on perceived budgeting accuracy: the difficulty of forecasting student numbers; difficulties associated with allowing unspent balances to be carried forward; taking a relatively long time to prepare the budget; and the institution’s level of financial surplus. The interviews are drawn upon to both explain and elaborate on the statistical findings. Forecasting student numbers and associated fee income emerges as a particularly challenging and complex issue.

Our regression analysis is cross-sectional and therefore based on correlations. Furthermore, the research could be developed by investigating the views of other parties as well as repeating the study in both the UK and overseas.

Implications for university management follow from the four factors identified as significant influences upon budget accuracy. These include involving the finance department in estimating student numbers, removing or controlling the carry forward of unspent funds, and reducing the length of the budget cycle.

The first study to examine the factors that influence the perceived accuracy of universities’ budgeting, this paper also advances understanding of budgeting accuracy more generally.

As the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists’ intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises.

This study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists’ intention to visit overseas country destinations.

Results show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact; daily new cases in the destination country have a significant negative impact; domestic daily new cases have a significant positive impact; holidays have significant negative impact; daily vaccination of the destination countries has significant positive impact; and domestic daily vaccination have negative significant impact.

First, there is a large uncertainty in studying consumers’ willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs.

First, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control.

First, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people’s willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience.

This study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

The objective of this paper is to synthesize and extend the existing understanding of social health accounting (SHA) literature within the perspectives of social health disclosures (SHAD) and the effect of social health problems on public and private sector accounting.

This study provides a comprehensive and up-to-date systematic literature review (SLR) of past studies on social health within the accounting literature. This is done by employing a three-step SLR research design to investigate a sample of papers, made up of 62 mixed, qualitative and quantitative studies conducted in over 23 countries, drawn predominantly from the extant accounting literature from 2013 to 2023 and published in 25 peer-reviewed journals.

Our SLR offers several findings. First, we find that existing SHA studies apply theories in SHAD studies, but hardly apply them to explain the impact of health problems on business outcomes. Second, we show that the extant studies have focused predominantly on rigorous empirical studies on SHAD, while this is scarce for studies examining the impact of diseases/health problems on both public and private sector accounting. Third, we identify several research design weaknesses, including a lack of primary data analysis, mixed-methods approach and rigorous qualitative studies. Finally, we present directions for future SHA research.

In contrast to the ever-increasing general social and environmental accounting (SEA) research, existing studies examining global health issues and challenges (e.g. diseases, epidemics and pandemics), especially from an accounting perspective are rare. Nonetheless, the past decade has witnessed a steady increase in research on corporate accounting for, and reporting of, health issues; although the emerging literature remains fragmented thereby impeding the generation of useful empirical and theoretical insights for policymakers, practitioners and researchers. Consequently, this paper offers extensive and timely SLR of the existing studies on SHA; critically reviewing past findings published in a wide range of peer-reviewed international journals that discuss the current state of global SHA research, their weaknesses and set future research agenda.

This paper applies novel techniques from the field of operations management to examine the allocation of patrol and investigative personnel to identify which is most effective in improving police performance around homicide clearance.

A panel sample of homicide clearance rates from the 100 largest US cities between 2000 and 2013 were analyzed in two steps: first, a random-effects regression model was performed to locate influential factors; second, optimum analysis was applied to locate the optimal values that yield maximal homicide clearance.

Both patrol and investigative personnel levels have a significant impact on homicide clearance. Maximal clearance can be achieved by allocating departmental personnel to investigative roles.

Given recent trends around “defunding” police and public sector austerity measures, future research should continue to explore the utility of optimum analysis for efficient allocation of policing personnel.

This study provides proof of concept for the use of optimum analysis in policing research.

The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TME

This study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.

The result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.

This study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.

This study aims to investigate the relationship between COVID-19 impacts and auxiliary police officers’ mental health as well as the moderating role of supervisor procedural justice.

Based on the role theory and a police officer survey from China, this quantitative study investigates the relationship between COVID-19 impacts and mental health status among auxiliary police, a rarely examined police type. We also examine the moderating role of supervisor procedural justice.

Auxiliary police officers reported both negative and positive impacts from COVID-19, while the negative impacts were mainly in the work domain, but the positive impacts were primarily in the life area. OLS regression results indicate that negative impacts, especially work-related negative impacts, are significantly related to depression and anxiety, and supervisor procedural justice moderates the relationship between positive impacts and depression and anxiety.

Firstly, we adopted the role theory to examine how public health emergencies affect police officers in their work and life domains. Secondly, we advance the organizational justice literature by assessing whether supervisor procedural justice can moderate the relationship between COVID-19 impacts and their mental health. Thirdly, this research extends the literature on depression and anxiety of auxiliary police officers in China, who attracted less attention in current literature and policies.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.