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General Data Protection Regulation (GDPR) of the European Union (EU) was passed to protect data privacy. Though the GDPR intended to address issues related to data privacy in the EU, it created an extra-territorial effect through Articles 3, 45 and 46. Extra-territorial effect refers to the application or the effect of local laws and regulations in another country. Lawmakers around the globe passed or intensified their efforts to pass laws to have personal data privacy covered so that they meet the adequacy requirement under Articles 45–46 of GDPR while providing comprehensive legislation locally. This study aims to analyze the Malaysian and Saudi Arabian legislation on health data privacy and their adequacy in meeting GDPR data privacy protection requirements.

The research used a systematic literature review, legal content analysis and comparative analysis to critically analyze the health data protection in Malaysia and Saudi Arabia in comparison with GDPR and to see the adequacy of health data protection that could meet the requirement of EU data transfer requirement.

The finding suggested that the private sector is better regulated in Malaysia than the public sector. Saudi Arabia has some general laws to cover health data privacy in both public and private sector organizations until the newly passed data protection law is implemented in 2024. The finding also suggested that the Personal Data Protection Act 2010 of Malaysia and the Personal Data Protection Law 2022 of Saudi Arabia could be considered “adequate” under GDPR.

The research would be able to identify the key principles that could identify the adequacy of the laws about health data in Malaysia and Saudi Arabia as there is a dearth of literature in this area. This will help to propose suggestions to improve the laws concerning health data protection so that various stakeholders can benefit from it.

This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)

This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.

The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.

This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.

Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

This investigation is designed to quantify and appraise the efficiency of resource distribution in the provision of public digital cultural services in China. By acknowledging and incorporating the realities of China's social development, the authors offer recommendations for enhancement derived from the study’s data analysis results. The research zeroes in on the dissection and analysis of the integral elements that structure the provision of public digital cultural services, and it concentrates on the associated data computation. The conclusions drawn herein are expected to serve as a significant point of reference for ongoing academic investigations and practical explorations in affiliated domains.

In this research, the authors utilize a hybrid methodology to meticulously evaluate the efficiency of the components that underpin the provision of public digital cultural services (PDCS) in China. The authors embark on deconstructing the various constituents within the PDCS supply framework, conducting in-depth analyses and providing cogent interpretations of each integral element. Subsequently, the authors deploy the well-regarded SBM super-efficiency model to ascertain the operational efficiency of these components. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.

The provision of PDCS in China as of 2021 had been characterized by overall good efficiency, significant regional disparity and a disconnect between inputs and outputs with weak correlations to economic and demographic data.

In this study, the authors provide an exhaustive deconstruction and interpretation of the public digital cultural services supply system, thereby proposing a framework for evaluating the efficiency of supply element allocation. Additionally, the authors have determined a set of distinct measurable indicators that are readily accessible for open collection. Notably, this analytical and evaluative framework designed for element analysis and measurement may also find application in efficiency evaluation research of the supply systems of other related cultural endeavors.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

The study focuses on influential factors of collaboration on government data security by the Chinese government.

The article explores the case of e-government in the Chinese centralized unitary state system context, using a structured–pragmatic–situational (SPS) approach and the boundary theory as an analytical lens.

The findings indicate that e-government operates in highly interconnected environments where the safe flow of government data requires collaborative and cross-boundary strategies. Any organization is a potential “weakest link”. In addition, collaboration is fragmented by ambiguous accountability and organizational inertia across government departments, resources differences and limited visibility and measurability of security efforts across government levels and conflicts and uncertainties in principal–agent relationships. The solutions for those obstacles are also discussed from the multi-function, multi-level and multi-actor dimensions, respectively. A multi-dimensional overarching security model for the flow of government data is proposed.

The study advances the technology-oriented micro-analysis of previous studies on government data security to cross-organizational revealing at the macrolevel by connecting streams of research in information systems and public administration. These findings will contribute to making the safe flow of government data more resilient in the transformation of e-government.

There is a wealth of value hidden in regional cultural heritage, but its preservation status is not optimistic. This study introduces a method that focuses on the inherent cultural value of regional cultural heritage to preserve it by value construction and release.

Based on the great value of regional cultural heritage due to spatial adjacency and temporal continuity, this paper focuses on its inherent cultural value to explore the preservation path and chooses Shichahai cultural heritage digital resources for a case study. This paper draws lessons from the narrative method of ancient Chinese historiography, constructs a cultural space and tells cultural stories. A linked data organization model for digital resources is created to construct a conceptual cultural space. Then, the space is materialized by linked dataset creation. The authors tell cultural stories discovered from the space, which are presented by various user interfaces using visualization technologies.

A cultural space promotes the development of a fine-grained description of regional cultural heritage and aids in relationship discovery to enhance the value construction ability. Additionally, storytelling via interactive user interfaces is helpful in the utilization and dissemination of knowledge extracted from a cultural space and enhances the value release of regional cultural heritage. In this way, a path with the inherent cultural value of regional cultural heritage as the core is established, and preservation is achieved.

This study focuses on the inherent cultural value of regional cultural heritage and proposes a new path to preserve these resources. This approach will enrich research on the preservation of regional cultural heritage and contribute to the construction and release of its cultural value.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.