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Given the overlapping themes and periods in specific subjects within Islamic economics and finance bibliometric research, which may yield similar findings in bibliometric studies, it is essential to document the growth of Islamic economic and financial research using bibliometric methodologies. This study aims to understand better the critical bibliometric review trends and scientific advancements in Islamic economics and finance.

This study uses bibliometric analysis, collecting 46 Islamic economics bibliometric papers from the Web of Science Core Collection from 1975 to 2022. The authors generated top scientific scholars, keyword analysis, citation analysis, content analysis and conclusions for journal development using R Biblioshiny, VOSviewer, ATLAS.ti and Excel.

This study has established a comprehensive bibliometric framework for Islamic economics and finance bibliometric papers, encompassing all critical areas within the discipline and identifying any remaining research gaps. The major significant areas revealed were Islamic social finance and microfinance concerns, which are closely pertinent to the issues of ethics, corporate social responsibility and sustainability, respectively. The authors also identified opportunities for future bibliometric analyses in Islamic economics and finance, which include using more comprehensive databases, refining or broadening search strategies, using advanced techniques and units of analysis and suggesting themes for further exploration.

The study relies merely on the Web of Science Core Collection database, which provides the most in-depth citations by source for the world’s scientific and scholarly research. Future research may consider expanding its scope to include other databases for a broader range of sources. Furthermore, due to the rise of bibliometric studies in Islamic economics and finance, this study also comments on the saturation of bibliometric studies conducted in several similar areas. While researchers bring their unique analytical perspectives to bibliometrics, this study provides a comprehensive view of existing research in Islamic economics and finance, highlighting well-explored topics and those that remain less studied. Thus, this could assist researchers in determining their future research priorities.

Policymakers in Islamic financial and economic institutions, including banking institutions, social, financial institutions and halal institutions, should be impacted by this research when making policies or conducting research. The viability of the current Islamic economic and financial ecosystem will be indirectly maintained and managed by these implications.

This comprehensive meta-analysis in Islamic economics and finance is expected to impact the development and sustainability of the Islamic economic and financial ecosystem, promoting societal welfare through applying Islamic economics and finance.

This pioneering bibliometric analysis of Islamic economics and finance papers aims to offer insights and projections for future research in the field. This research contributes to the literature by examining various aspects, including evaluating literature on trending topics, analyzing papers related to research areas and conducting content analysis of existing bibliometric studies in Islamic economics and finance. It specifically groups these studies around fundamental topics, summarizes findings from contemporary research and identifies emerging research gaps.

This study aims to present recent trends in touchscreen research through scientometric analysis. Devices with touchscreen are powerful tools for performing specialized operations. The touch screens of tablets, smartphones, laptops and television play an important role in teaching, learning and research.

The data was collected from Web of Science database from 2011 to 2021 and analysed using MS-Excel and VOSviewer software. After analysing 389 research papers, the authors identified the high impact journals, collaboration of countries, institutions, authors and growth trend of publications. Analysing the most used keywords, country-wise distribution of publications and research collaboration between institutions will help interpret the research trends in the selected time span.

The publications show an increase in number over the years from 2011 to 2021. Among the countries, USA has the highest number of 127 articles published, followed by England (61) and Canada (30). The results showed that the multiple authorship pattern in touchscreen publication is high when compared to single authors. The institutional analysis indicated that the organizations publishing more than five documents in the area were mostly from United Kingdom, Australia, USA and Korea. Timeline visualizations identified prominent keywords like touchscreen, performance, operant platform, Alzheimer’s disease, etc. in the subject. Interdisciplinary research is dominant in the subject, as seen from the most preferred journals and keywords.

The analysis does not include a comprehensive coverage of the research output, as only Web of Science database from 2011 to 2021 in a 10-year period is included.

The study would benefit stakeholders, including manufacturers and researchers alike, to know the future of touchscreen research.

This study is pertinent to socio-psychological fields because touchscreen technology encourages social connection among older persons and may help foster early literacy skills.

This paper will provide an understanding of the global developments in touchscreen research with recommendations for future research.

Lean Six Sigma 4.0 has brought about a paradigm shift in customization, automation, value creation and digitalization to achieve excellence in human factors, operations and sustainable development. Despite its potential, LSS 4.0 is still in its nascent stage, with researchers striving to identify the key and relevant components of LSS in relation to Industry 4.0. The present study aims to address this knowledge gap through a literature review and subsequently provide a conceptual framework for LSS within the context of digital transformation.

In this study, the authors have conducted a thorough review of reputable articles published between 2011 and 2022, focusing on the integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0). By using appropriate keywords, the authors identified around 85 relevant articles. The main objective of this integrative literature review was to analyze and extract valuable knowledge from the existing literature on LSS and I4.0. Based on the authors’ findings, a conceptual framework was developed.

The review revealed the motivators, building blocks, tools and challenges of LSS 4.0. The conceptual framework delves into the key aspects of LSS 4.0, focusing on the dimensions of people, process and technology, as well as their subdimensions. These subdimensions serve as the building blocks for developing LSS 4.0 capabilities. The proposed framework visually represents the conceptualization and the relationships among its components.

Only a few conceptual approaches to LSS are developed that include the concepts, new roles and elements of I4.0. As a result, this research investigates the gap in current LSS models preceding I4.0 and develops a conceptual framework to provide a novel and comprehensive summary of the new concepts and components driving nascent and current LSS practices in the digital era.

This study offers practical guidance for implementing LSS in the context of I4.0, emphasizing digital transformation. The findings highlight motivators, building blocks, tools, challenges and spread of LSS 4.0 practices, and present a conceptual framework of LSS 4.0. These insights can help organizations enhance their LSS capabilities and achieve excellence in human factors, operations and sustainable development.

This study aims to make a significant contribution to the model-building efforts of researchers focusing on LSS 4.0. By offering practical guidance, the points discussed in this study help enhance the implementation efforts of practitioners and organizations in the context of I4.0, with a specific focus on digital transformation. The guidance provided takes into account the perspectives of people, processes and technology, providing valuable insights for successful integration.

The purpose of this paper is to determine the most efficient hotels in the Indian hotel industry, the competitive positioning of these hotels, and the factors that affect their efficiency change.

This study conducts a two-stage analysis and uses data envelopment analysis (DEA) and Global Malmquist productivity index (MPI) approach in the first stage to calculate the managerial performance of a panel of 63 Indian hotels in 2019–2020 and their efficiency change from 2009–2010 to 2019–2020. Bootstrapped generalized least square (GLS) approach is applied in the second stage to evaluate the impact of contextual variables on efficiency change.

Using the results of the first stage analysis, the authors categorized the 63 Indian hotels into 7 distinct clusters. These clusters represent different levels of competitiveness and pace of growth. The GLS regression reveals a U-shaped relationship between hotel size and efficiency change and a negative relationship between pro social investments and efficiency.

This is the first study in the hotel industry that has used global MPI as a measure of efficiency change in the first stage and GLS in the second stage. In the Indian context, to the best of authors’ knowledge, no such study exists.

The purpose of this study was to examine the factors that influence the information seeking behaviors of ChatGPT users. Specifically, we investigated how ChatGPT self-efficacy, ChatGPT characteristics and ChatGPT utility affect the frequency and duration of information seeking via ChatGPT. We also tested the mediating roles of ChatGPT characteristics and utility in the relationship between ChatGPT self-efficacy and information-seeking behaviors.

This study adopts a quantitative approach and collects data from 403 ChatGPT users using an online questionnaire. The data are analyzed using linear regression and structural equation modeling (SEM).

The linear regression analyses revealed that ChatGPT self-efficacy is positively and significantly related to the information seeking behaviors in ChatGPT. Second, mediation analyses also showed that ChatGPT characteristics and utility significantly mediate the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT independently and sequentially.

This study is the first to investigate the factors and mechanisms that influence information-seeking behaviors in ChatGPT, a new phenomenon in the media landscape. The findings in this study suggest that ChatGPT self-efficacy acts as an important motivator for information-seeking behaviors in ChatGPT and that ChatGPT characteristics and utility provide information regarding potential mechanisms in the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT. The study contributes to the literature on information seeking, self-efficacy and generative AI.

This study is a theory-building effort to extend the risk perception attitude (RPA) framework by considering an organization-related factor. Specifically, this study examined how an organization-related factor, namely safety climate, and individual-related factors, namely risk perception and efficacy belief, jointly influence flight attendants’ occupational health and safety (OHS) information seeking intent.

About 486 flight attendants from an international air carrier, based in Hong Kong, participated in an online survey. Hierarchical regression was employed to examine the three-way interaction of risk perception, efficacy belief, and safety climate.

Results showed that safety climate moderated the effect of efficacy belief on the relationship between risk perception and OHS information seeking intent. For flight attendants who perceived a high safety climate, those who felt more efficacious had a higher intent for OHS information seeking than those who felt less efficacious. In contrast, for flight attendants who perceived a low safety climate, when they perceived a high level of risk, those with low efficacy had a higher information seeking intent than those with high efficacy.

Although the RPA framework has shown its potential to explicate how individual employees engage in OHS information seeking behavior, the framework has largely overlooked the importance of organization-related factors as an influential shaping force of individual employees’ behavior. To fill this research gap, this study extends the RPA framework by examining how safety climate moderates the relationship between risk perception and efficacy belief on flight attendants’ OHS information seeking intent. Safety climate also plays an important role in segmenting audience groups for OHS promotion.

This study aims to develop eco-friendly corrosion inhibitors for aluminum in acidic media by evaluating the corrosion inhibition properties of corn leaf extract (CLE) using response surface methodology (RSM) and experiments.

The RSM was combined with experiments to evaluate the corrosion inhibition properties of CLE on aluminum in acid media.

The effectiveness of the inhibition increased with increasing inhibitor concentration and time but decreased with increasing temperature. The corrosion inhibition mechanism revealed the corrosion process is spontaneous exothermic physical adsorption. Thermodynamic parameters revealed an activation energy between 32.1 and 24.7 kJ/mol, energy of adsorption between −14.53 and −65.07 and Gibbs free energy of −10.12 kJ/mol which indicated the CLE exothermically spontaneously physisorbed. A model was generated to estimate the effect of the process parameters (inhibitor concentration, reaction time and temperature) using the RSM. Optimization of the process factors was also carried out using the RSM. The percentage inhibition efficiency obtained experimentally (85.61%) was closely comparable to 84.89% obtained by the theoretical technique (RSM). The SEM observations of the inhibited and uninhibited Al samples demonstrated that CLE is an effective corrosion inhibitor for aluminum in acid media.

Results herein provide novel information on the possible application of CLEs as effective eco-friendly corrosion inhibitors.

In recent decades, advancements in biosensors technology have made fluorescent biosensor pivotal for biomolecular recognition. This paper aims to provide an in-depth analysis of polymerase chain reaction (PCR) fluorescent biosensor detection technology for identifying Escherichia coli (E. coli), setting the stage for future developments in the field.

The review of literature on PCR fluorescent biosensor detection technology for E. coli over the past decades includes discussions on traditional biological fluorescent detection, quantitative PCR fluorescent detection and digital fluorescent detection technology.

Advancements in fluorescent biosensor technology enable precise measurement of fluorescent signals, and when integrated with microfluidic technology, produce compact, reagent-efficient digital sensor devices.

This paper provides a comprehensive review of recent fluorescent detection technology for pathogenic E. coli, assessing method efficiencies and offering insights to advance the field.

This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time.

This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section.

The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time.

Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

This study begins by outlining the core principles of how flat plate solar collectors (FPSC) operate and the underlying mechanisms of heat transfer. It underscores the pivotal role of fin patterns in enhancing convective heat transfer.

The primary objective is to examine the impact of diverse fin patterns on the thermal performance of FPSCs. The study involves the development of a 3D-CFD model for FPSC using the computational fluid dynamics (CFD) software ANSYS FLUENT. The analysis is conducted for variable mass flow rates (m_f) ranging from 0.01 to 0.05 kg/s with an interval of 0.02 kg/s. Each flow rate is assessed for three distinct fin patterns: straight plate fins, V-shaped fins and wavy fins. Recognizing the variation in solar radiation intensity throughout the day, the analysis is executed at six different time points ranging 10:00 a.m. to 03.00 p.m. with a time interval of 1.00 h.

It is observed that the wavy fin pattern with a mass flow rate of 0.01 kg/s exhibited the highest outlet temperature, showing a significant temperature difference of 12.4 K at noon. Conversely, the V-shaped fin pattern with a mf of 0.05 kg/s has the lowest temperature difference value, measuring only 3 K. The analysis included the calculation of thermal efficiency for each case, revealing that the V-shaped fin pattern at a mf of 0.05 kg/s and a sun radiation intensity of 885.42 W/m² achieved the maximum instantaneous thermal efficiency (η_th) of 34.7%. The study records the outlet air temperature for all of these combinations and presents the data graphically in the paper.

The findings of the simulations indicate that the thermal performance that is η_th and maximum temperature of outlet air of FPSCs can be improved through the utilization of variable fin patterns.