bluuebunny/crossref_metadata_2025_split huggingface dataset:
Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering
Authors: Szrama Sławomir 
Published: August 2024

Purpose
This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

Design/methodology/approach
The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

Findings
The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

Practical implications
This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Originality/value
Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.
The determinants of product trust in live streaming E-commerce: a hybrid method integrating SEM and fsQCA
Authors: Jiang Hong-Bo , Fan Zou-Yang , Wang Jin-Long , Liu Shih-Hao , Lin Wen-Jing 
Published: August 2024

PurposeThis study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.Design/methodology/approachThis study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.FindingsThe findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.Originality/valueThis study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.
Applications and impacts of blockchain-based solutions in the wine supply chain: a systematic literature review
Authors: Sabbagh Parisa , Morkūnas Mangirdas , Galati Antonino 
Published: August 2024

Purpose
This paper systematically explores the consequences of the adoption of blockchain technology (BCT) in the wine supply chain, aiming to offer a comprehensive overview of its advantages within the dynamic and complex wine sector.

Design/methodology/approach
This study deploys the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method to provide a comprehensive examination of the effects of BCT adoption in the wine supply chain (WSC) by guiding the conduct and reporting of the systematic review.

Findings
The findings identify eight primary areas of advantage in blockchain adoption, illuminating its transformative impact on the wine industry. In addition, an examination of the technical attributes and applications of 17 existing blockchain-based platforms in the wine supply chain contributes valuable insights for strategic decision-making by wineries.

Originality/value
This study, through a systematic literature review, contributes to understand the potential BCT interactions at different stages of the WSC and to highlight some of the lesser known benefits for wineries.
Navigating the digital landscape: communication visibility and entrepreneurial opportunity identification
Authors: Sedighi Mohammadbashir , Hashemi Negin 
Published: August 2024

Purpose
With the advent of digital platforms, entrepreneurs have been able to communicate and share information in visible environments. By focusing on the visibility aspect of digital platform in a resource-constrained economy, this study aims to propose a conceptual model for exploring entrepreneurial opportunity identification. Furthermore, this study delves into the moderating influence of entrepreneurial alertness on opportunity recognition.

Design/methodology/approach
A research model is constructed using the theory of communication visibility to investigate how message transparency and network translucence affect the identification and development of entrepreneurial opportunities, with additional exploration of the moderating influence of entrepreneurial alertness. A questionnaire survey is conducted with 152 Iranian founder-entrepreneurs to test the proposed conceptual model. The research model’s constructs are analyzed using the partial least squares structural equation modeling method.

Findings
The results confirm that communication visibility impacts both aspects of entrepreneurial opportunity identification. In addition, this study reveals the distinctive moderating effects of entrepreneurial alertness on the relationship between message transparency and the creation and discovery of entrepreneurial opportunities, while finding no significant effect on network transparency and opportunity identification.

Originality/value
This paper stands out for its originality in collecting data from an emerging market. Besides, it explores the association between entrepreneurs’ communication dimensions and the recognition of new opportunities at the intersection of digital platforms and entrepreneurship literature. Furthermore, it empirically illustrates the moderating role of entrepreneurial alertness in the relationships between communication visibility and opportunity identification, contributing significantly to existing research.
Deciphering the links: evaluating central bank transparency impact on corruption perception index in G20 countries
Authors: Al Qudah Anas , Hailat Mahmoud Ali 
Published: August 2024

Purpose
This study aims to explore the relationships between GDP growth, education spending, central bank transparency (CBT) and accountability on the corruption perception index within the G20 countries, emphasizing their combined effects and interdependencies.

Design/methodology/approach
Using the central bank transparency index (CBTI) introduced by Dinçer et al. (2019), an analysis spanning from 2002 to 2019 was conducted on selected G20 countries, further refining the results by excluding EU members. Data sources included World Bank statistics and CBTI data. The research deployed the heteroskedastic and contemporaneously correlated panel-corrected standard error model to detail the effects of the aforementioned factors on the corruption index.

Findings
The study revealed no statistical evidence that economic growth had an effect on reducing corruption. Education spending emerged as a potent tool in curbing corruption, especially in EU nations. A strong correlation was identified between CBT and reduced corruption, consistent across G20 countries, regardless of EU affiliation. The insights emphasize the importance of enhancing education spending and CBT in combating corruption. For effective anti-corruption measures, countries are encouraged to invest more in education, amplify internal checks and adopt transparent central bank policies. Further research could delve into cultural, historical and political variables to understand corruption dynamics comprehensively.

Originality/value
This study aspires to address the existing gaps in the literature and provide a substantial contribution to the ongoing discourse and efforts to understand and mitigate corruption within the G20 countries and globally.
Trends, risk factors and interventions for some mental health problems in the US children and adolescents: evidence from the National Survey of Children’s Health, 2016-2022
Authors: Lachaab Mohamed 
Published: August 2024

Purpose
The purpose of this study is threefold: Determine recent trends in several mental health problems in the USA, identify risk factors that may be responsible for the trends and evaluate intervention policies to reduce the consequences of these problems.

Design/methodology/approach
This study used data from the National Survey of Children's Health (NSCH), a nationally representative survey of children under the age of 17 that was conducted between 2016 and 2022. Prevalence rates in the data take into account the probability of selection and nonresponse. Because of the possible correlation in the longitudinal responses in the data, an appropriate extension of the generalized linear models (the marginal models) was used. Marginal models, also known as population-average models, do not require distributional assumptions for the observations, only a regression model for the mean response. The avoidance of distributional assumptions leads to the use of the generalized estimating equations (GEE) method.

Findings
The author found that the odds of children and adolescents experiencing mental health problems in the USA changed over a seven-year period, from 2016 to 2022. Anxiety and depression, in particular, have both increased, with anxiety increasing faster than depression; however, behavioral issues and attention deficit disorder/attention deficit hyperactivity disoder (ADD/ADHD) remained stable until 2020 (the start of COVID-19), when they began to rise. This paper also found a link between increased social media use and increased mental health problems, and bullying has a negative impact on the mental health of children and adolescents.

Originality/value
The NSCH, an annual representative survey, was used in this study to assess mental health problems among children and adolescents in the USA. Marginal models, which enable the capture of potential correlations among observations of the same subject, were used in conjunction with the GEE method. This study differs from previous research, which used other surveys, pre-COVID-19 data points and logistic regressions that assumed independence in repeated observations.
Feeding trust: exploring key drivers, moderators and consequences related to food app usage
Authors: Ravichandran Swathi , Osakwe Christian Nedu , Elgammal Islam Mahmoud Yousef , Abbasi Ghazanfar Ali , Cheah Jun-Hwa 
Published: August 2024

Purpose
This paper aims to utilize an extended involvement-commitment and trust commitment model to examine post-consumption decisions related to food delivery app use.

Design/methodology/approach
A self-administered online survey was used to collect data from food delivery app users in the USA.

Findings
Findings validate a favorable role of perceived app security and menu description on trust in app recommendations. Trust was found to be positively related to involvement, commitment and willingness to provide feedback. The positive moderating role of perceived convenience and rewards and incentives was also confirmed in relation to consumers’ trust in app recommendations, and involvement and commitment

Originality/value
A key contribution of this study includes the development of a comprehensive model to understand postconsumption decisions related to the usage of food delivery apps. To the best of the authors’ knowledge, this study is also the first to unveil the antecedent and moderating factors related to food delivery app users’ willingness to provide feedback, share personal data and to pay more.
Decision-making model in digital commerce: electronic trust-based purchasing intention through online food delivery applications (OFDAs)
Authors: Zaheer Muhammad Asif , Anwar Tanveer Muhammad , Iantovics Laszlo Barna , Manzoor Maryam , Raza Muhammad Ali , Khan Zoia 
Published: August 2024

PurposeThis research aimed to raise awareness about the need for safety measures and features of online food delivery applications (OFDAs) to build electronic trust (e-trust) with augmented purchase intention among customers. Moreover, this study explores the attributes of electronic commerce (e-commerce) and how e-trust influences the purchasing intention of consumers while ordering food through OFDAs including fear of contagious diseases. Determinants of e-commerce in the digital era profoundly impact the performance of enterprises.Design/methodology/approachData from 493 food consumers collected from Federal Capital Territory (FCT) Pakistan, who were regular users of OFDAs. To scrutinize the dataset, confirmatory factor analysis (CFA) was conducted to assess the construct validity in this research study. Structural equation modeling (SEM), which is facilitated by Smart-PLS, was employed to examine the direct, moderation and mediation effects of the proposed model.FindingsResults revealed the positive and significant impact of the e-trust on the purchase intention. Additionally, e-trust acted as a mediating factor in the connection between electronic security (e-security), electronic privacy (e-privacy), usability, electronic payment (e-payment), electronic innovativeness (e-innovativeness) and the buyer's purchase intention. Furthermore, the fear of contagious viruses negatively moderated the e-trust and purchase intention that weakened buying behavior.Originality/valueThis research is primarily centered on enhancing the comprehension regarding safety orientation within the context of an evolving restaurant industry. The findings of this study hold substantial contributions for academics, web developers, application designers, OFDAs, restaurants and other businesses since they indicate the attractiveness of OFDAs in generating feelings of pleasure and boosting users' intentions to keep using the application.
Improving the credibility of case study research in international business studies and beyond: a simple fix for a serious problem
Authors: Buck Trevor , Boussebaa Mehdi 
Published: August 2024

Purpose
The field of international business (IB) formally welcomes and frequently calls for case study research, but the proportion of case study papers appearing in IB journals remains very small. This paper aims to support efforts to redress this imbalance by addressing an overlooked yet critical issue: the (mis)use of tenses when theorizing from case study findings. The authors reveal a pervasive use of the present tense and argue that this leads to decontextualization and, in turn, over-generalization. The paper also suggests ways in which this problem may be avoided in the future, thereby improving the credibility and status of case-based research and helping to de-marginalise it within IB.

Design/methodology/approach
A qualitative content analysis was applied to all (2,627) papers published between 2011 and 2021 in four leading IB journals. In total, 171 case study papers were identified over these 11 years, and a deeper content analysis was then performed to measure the extent of decontextualization/over-generalization implied by the inappropriate use of the present tense in the discussion and theorisation of research findings.

Findings
This study found that, out of 171 case study papers identified, 141 (82.5%) provided at least two instances of over-generalization as implied by the misuse of the present tense. However, some of these papers were found to feature statements that could be claimed to mitigate such inappropriate generalization. These mitigating factors included the repeated use of adverbial phrases denoting context and the use of a “propositional style” that clearly distinguished contextual findings from speculative, decontextualized generalizations. Nevertheless, 71 of the 171 (41.5%) papers still demonstrated inappropriate generalization, even after allowing for mitigating factors.

Originality/value
This study reveals a problematic writing practice and one which has arguably significantly contributed to the “decontextualization” problem critiqued in IB and management studies more broadly. The study also offers further insights into how decontextualization might be avoided, arguing that this problem would be significantly reduced if tenses were used appropriately in discussing and theorizing case study findings. Additionally, the study highlights the continued marginalization of qualitative research methods in IB and reinforces calls to address it.
Effect of process parameters on mechanical and tribological characteristics of FDM printed glass fiber reinforced PLA composites
Authors: S. Rashia Begum , M. Vasumathi , Karupaiah Vigneshwaran , Narayanan Venkateshwaran 
Published: August 2024

Purpose
Additive manufacturing of polymer composites is a transformative technology that leverages the benefits of both composite material and 3D printing to produce highly customizable, lightweight and efficient composites for a wide range of applications.

Design/methodology/approach
In this research work, glass fiber-reinforced polylactic acid (PLA) filament is used to print the specimen via fusion deposition modeling process. The process parameters such as infill densities (40%, 50% and 60%) and raster angle/orientations (0°, 45° and 90°) are varied, and the specimens for tensile, flexural, impact, hardness and wear testing are prepared as per their respective ASTM standards.

Findings
The results revealed that with an increase in infill density, the mechanical properties of glass fiber-PLA specimens increase progressively. Optimal tensile properties and flexural properties are obtained at 0° and 90° raster angle orientations and 60% infill density. Minimum wear rate is achieved at 0° raster angle orientation and it increases at 45° and 90° raster angle orientations.

Originality/value
Using SEM, the microscopic analysis of the fractured specimen was analyzed to study the interface between the fibers and matrix and it indicates the presence of good adhesion between the layers at 60% infill density and 0° print orientation.
Improving fatigue behavior of selective laser melted 316 L stainless steel by ultrasonic-assisted burnishing
Authors: Sayadi Daniyal , Rangrizian Hossein , Khodabandeh Alireza , Khosrojerdi Mohammadreza , Khajehzadeh Mohsen , Razfar Mohammad Reza 
Published: August 2024

Purpose
In this study, two postprocessing techniques, namely, conventional burnishing (CB) and ultrasonic-assisted burnishing (UAB), were applied to improve the fatigue behavior of 316 L stainless steel fabricated through selective laser melting (SLM). The effects of these processes on surface roughness, porosity, microhardness and fatigue performance were experimentally investigated. The purpose of this study is to evaluate the feasibility and effectiveness of ultrasonic-assisted burnishing as a preferred post-processing technique for enhancing the fatigue performance of additively manufactured components.

Design/methodology/approach
All samples were subjected to a sandblasting process. Next, the samples were divided into three distinct groups. The first group (as-Built) did not undergo any additional postprocessing, apart from sandblasting. The second group was treated with CB, while the third group was treated with ultrasonic-assisted burnishing. Finally, all samples were evaluated based on their surface roughness, porosity, microhardness and fatigue performance.

Findings
The results revealed that the initial mean surface roughness (Ra) of the as-built sample was 11.438 µm. However, after undergoing CB and UAB treatments, the surface roughness decreased to 1.629 and 0.278 µm, respectively. Notably, the UAB process proved more effective in eliminating near-surface pores and improving the microhardness of the samples compared to the CB process. Furthermore, the fatigue life of the as-built sample, initially at 66,000 cycles, experienced a slight improvement after CB treatment, reaching 347,000 cycles. However, the UAB process significantly enhanced the fatigue life of the samples, extending it to 620,000 cycles.

Originality/value
After reviewing the literature, it can be concluded that UAB will exceed the capabilities of CB in terms of enhancing the surface roughness and, subsequently, the fatigue performance of additive manufactured (AM) metals. However, the actual impact of the UAB process on the fatigue life of AM products has not yet been thoroughly researched. Therefore, in this study, this paper used the burnishing process to enhance the fatigue life of 316 L stainless steel produced through the SLM process.