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Although publicly listed firms in Sri Lanka have been increasingly adapting sustainability reporting into their annual reporting practices, a limited number of firms prepare sustainability reports by integrating sustainable development goals (SDGs) into reporting mechanisms. This study attempts to develop an index to monitor firms' sustainability reporting practices based on Global Reporting Institute (GRI) guidelines integrating SDGs.

This paper develops a sustainability score index using the 17 SDGs utilising the results of content analysis of corporate annual reports of a selected sample of 100 firms listed on the Colombo Stock Exchange (CSE). Principal component analysis was employed to examine the reliability of data in the developed index.

Findings show that the developed scoring index is efficient for evaluating the contents of the sustainability reports of Sri Lankan firms. Sustainability reporting practises with regard to the SDGs were observed to have a turbulent period from 2015 to 2019 and the SDGs 12 and 15 were identified to be mostly reported in Sri Lankan corporate sustainability reports.

The results of the study add to knowledge on the monitoring of sustainability reporting practises with reference to SDGs. The study outcomes are useful for the investors, stakeholders, and statutory bodies to measure the sustainable performance of business firms and assess the firm’s commitment towards the global sustainability agenda.

To the best of our knowledge, this is the first study that constructs a sustainability reporting score index integrating SDGs.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

This study aims to investigate the impact of social grants on rural household welfare in a village located in one of the poorest provinces in South Africa – KwaZulu Natal Province. Actually, since the inception of democratic rule, the South African government has turned to social grants to address the issues of poverty, income inequality and to improve household welfare. The coverage of social grants has increased substantially with more than 17 million (about 34% of the population) South Africans being recipients of social grants. Despite having relatively well-developed social security system, poverty levels in rural parts of South Africa remains very high.

This study uses a cross-sectional households survey data conducted in Hlokozi village. A propensity score matching technique, which accounts for non-random selection of households, is applied.

The results reveal that social grants have a significant and positive impact on rural household welfare. Specifically, the nearest neighbour matching estimates suggest that the causal effect for social grants on household welfare is the region of about R5,830. Consistent with the nearest neighbouring method, the results obtained using the Kernel matching method show that social grants are significant in improving rural household welfare.

While there are a number of studies that have shed some light on how social grant reduces poverty in South Africa, there are some gaps. Firstly, only a few studies have interrogated the impact of social grants on household welfare. Secondly, most of these studies have relied on descriptive analysis, and finally, besides poverty being high in rural areas, research on the impact of social grants on rural household welfare remains thin.

This cross-country study aims to investigate from an interdisciplinary perspective the impacts of the accounting regulation's strength and cultural values of long-term orientation (LTO) and indulgence (ND) on board efficacy in developing countries.

Board Efficacy Index scores for 54 developing countries over the period 2007–2016 were employed to ascertain predictors of management's accountability to boards of directors and investors. Two types of explanatory variables – formal and informal – were employed in a pooled Ordinary Least Squares (OLS) analysis.

The research is the first to empirically show that more LTO and ND in a country have significant and positive effects on board efficacy. The findings also show that the strength of auditing and reporting standards (SARS) has a dominant impact on board efficacy, and the SARS' consideration is recommended in future cross-country research on board efficacy.

To restore investor confidence and increase the credibility toward firms, regulatory authorities in developing countries are called upon to integrate compliance with accounting and auditing regulations combined with cultural values in the implementation of good governance practices.

This study contributes to the board efficacy literature in two significant ways. First, the study constructs and empirically tests a conceptual model that integrates both informal factors, the six cultural dimensions of Hofstede et al. (2010), and formal factors, the strength of accounting regulations. Second, conducting a study on a sample not widely used in the literature, over a fairly long period of time, highlights the governance characteristics of this context and strengthens the internal and external validity of the study.

This research addresses the professional logics of street-level managers (SLMs) and bureaucrats (SLBs) working in the Brazilian National Social Security Agency (INSS) through their perceptions of distributive justice and discretion. Since SLMs have the authority to influence SLBs' actions, we investigate whether these two groups hold similar viewpoints.

We integrate the administrative data and survey responses (n = 678) with earlier thematic content analysis (n = 350) in three stages: mean-testing, regression analyses and complementary qualitative analysis, integrated through a mixed-methods matrix.

Whilst no significant differences emerge in distributive justice ideas between groups, SLMs demand wider benefit-granting discretion, praising professionalism whilst adopting managerial posture and jargon.

The study adds to the theoretical discussions concerning SLM’s influence on SLB’s decision-making, suggesting that other factors outweigh it. The finding concerning the managers’ demand for wider discretion asks for further in-depth approaches.

Findings supply valuable insights for policymakers and managers steering administrative reforms, by questioning whether some roles SLMs play are limited to symbolic levels. Further, SLBs’ heterogenous formations might be more relevant to policy divergence than managerial influence and perhaps an underutilised source of innovation.

By approaching street-level management professional logics within a Global South welfare state through a mixed-methods approach, this study offers a holistic understanding of complex dynamics, providing novel insights for public sector management.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

This paper attempts to examine drivers of tourism development by affording a framework that sustains economic growth and protects the local environment. It develops evaluative and predictive models to measure city performance. Further, a strategy-based model of low carbon cities (SMLC) is used to demonstrate possible tourism development scenarios. The model was applied to the city of Surakarta to operationalize city's transformation towards sustainability.

The research methodology is constructed on three interrelated components: theoretical framework, analytical methods and SWOT. First, the authors have initiated this study by an understanding of linkages between planning and tourism. Second, the SMLC has been used to test sustainable tourism in the city of Surakarta. Third, Strength-Weakness-Opportunity-Threat (SWOT) analysis was applied to formulize the recommendations.

When evaluated by the static SMLC model, the city of Surakarta was categorized as an unsustainable touristic city. However, when the dynamic SMLC was applied, the city of Surakarta was categorized as a sustainable touristic city under a high economy scenario. By reason of the methodological and analytical frameworks and the dynamic SMLC, the city of Surakarta could be promoted to a sustainable touristic city after applying opportunity-seeking strategy and policies.

The paper concludes with policy implications to realign city plan and support sustainable tourism development in the city of Surakarta.

This paper attempts to develop a framework for sustainable tourism as it operates in the city of Surakarta by (1) introducing the sustainable touristic city concept, (2) definition and characters, (3) evaluative and predictive models using the SMLC to measure city performance of the city of Surakarta and (4) rigorous and relevant insight into the magnitude of the benefits of tourism.

Indonesia subscribes to rights-based principles of family planning. However, a chasm between principles and practice has long been noted on a global basis, and progress has not been well-documented. This paper aims to assess the extent to which the Indonesian national family planning program has evolved in a manner that is consistent with rights-based principles.

The primary source of data was five Indonesian Demographic Health Surveys undertaken from 1997 to 2017. The analyses were organized around three major categories of family planning-related human rights. Trend analysis and logistic regression were used in analyzing the data.

Indonesian women have considerable autonomy in family planning decision, reporting that family planning decisions were mainly made by themselves or jointly with their spouse. Although contraceptive method awareness and demand for family planning are high, Indonesia fares poorly with regard to informed choice in contraceptive method selection. Access to family planning services is comparatively high as judged by contraceptive prevalence, family planning demand satisfaction and unmet need for family planning. However, significant geographic and socioeconomic inequity were observed on many indicators, with eastern Indonesian provinces consistently lagging behind.

This paper focuses on married couple, as Indonesia has a restrictive policy to limiting access and information of family planning for other groups, unmarried youth in particular.

This paper makes an important contribution to document how effectively the prohuman rights policy orientation toward family planning has been translated into services.

The outbreak of the COVID-19 pandemic has significantly impacted healthcare systems, presenting unforeseen challenges that necessitated the implementation of change management strategies to adapt to the new contextual conditions. This study aims to analyze organizational changes within the total hip replacement (THR) surgery pathway at multiple levels, including macro, meso and micro. It employs data triangulation from various sources to gauge the complexity of the change process and comprehend how multi-level decision-making influenced an unexpected shift.

A multicentric, single in-depth case study was conducted using a mixed-methods approach. Data sources included patient-reported outcome measures specific to the THR pathway and carefully structured in-depth interviews administered to managers and clinicians in two healthcare organizations serving the same population.

Decisions made at the macro level resulted in an overall reduction in surgical activities. Organizational changes at the meso level led to a complete cessation or partial reorganization of activities. Micro-level actions for change and adaptation revealed diverse and fragmented change management strategies.

Organizations with segmented structures may require a robust and structured department for coordinating change management responses to prevent the entire system from becoming stuck in the absorptive phase of change. However, it is important to recognize that absorptive solutions can serve as a starting point for genuine innovations in change management.

The utilization of data triangulation enables the authors to visualize how specific changes implemented in response to the pandemic have influenced the observed outcomes. From a managerial perspective, it provides insights into how future innovations could be introduced.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.