bluuebunny/crossref_metadata_2025 huggingface dataset:
KRAS Mutations in Duodenal Lavage Fluid after Secretin Stimulation for Detection of Pancreatic Cancer
Authors: Yachida Shinichi , Yoshinaga Shigetaka , Shiba Satoshi , Urabe Makiko , Tanaka Hidenori , Takeda Yohei , Shimizu Akinori , Sakamoto Yuri , Hijioka Susumu , Haba Shin , Ashida Reiko , Kushiyama Yoshinori , Asano Kento , Kobayashi Makiko , Murawaki Yoshiyuki , Onishi Kouji , Yamashita Taro , Kimura Hirokazu , Totoki Yasushi , Kamada Hideki , Isomoto Hajime , Hattori Satoshi , Morizane Chigusa , Ohkawa Kazuyoshi , Kitano Masayuki , Hara Kazuo , Ikezawa Kenji , Hanada Keiji , Matsumoto Kazuya
Published: February 2025

Objective:

Although pancreatic ductal adenocarcinoma (PDAC) is still a devastating disease, the survival rate for surgically removed PDACs has significantly improved in recent years. Early detection is essential in managing PDAC.

Summary Background Data:

The presence of KRAS mutations in PDAC leads to the initial genetic abnormality and offers a significant timeframe for identifying resectable PDACs. A minimally invasive and highly specific PDAC screening test is necessary to prevent the need for invasive follow-up tests.

Methods:

Between July 2021 and March 2023, 169 cases were enrolled in 7 institutions. By administering secretin before esophagogastroduodenoscopy (EGD), the excretion of pancreatic juice into the papillary fluid can be stimulated, creating a resource for testing. Washing fluid was collected using a specialized catheter from control individuals (n=75) and patients with resectable PDAC (n=89) at the initial diagnosis. A highly sensitive technique was employed to study KRAS gene mutations.

Results:

This study obtained an AUC of 0.934 [95%CI: 0.904, 0.964] when using KRAS mutations in duodenal lavage fluid to differentiate between patients with resectable PDAC and healthy controls. The estimated sensitivities were calculated with specificity set at 100%, resulting in a sensitivity of 83.1% [95%CI: 71.7%, 91.2%]. The McNemer test showed a significantly higher sensitivity for KRAS mutations than serum CEA and CA19-9 (P<0.0001).

Conclusions:

We created a method to identify resectable PDACs by analyzing KRAS mutation levels in duodenal fluid collected during EGD with secretin stimulation of pancreatic juice secretion.
Volume Matters
Authors: Nzenwa Ikemsinachi C. , Panossian Vahe S. , DeWane Michael P. , Albutt Katherine H. , Hernandez-Barco Yasmin G. , Fernandez-del Castillo Carlos F. , Lillemoe Keith D. , Warshaw Andrew L. , Fagenholz Peter J. , Luckhurst Casey M.
Published: February 2025

Objective:

To examine the impact of hospital volume on mortality and healthcare utilization in patients admitted with necrotizing pancreatitis (NP).

Background:

Over 20% of patients with acute pancreatitis develop NP, which has been associated with higher rates of procedural intervention, morbidity, and mortality.

Methods:

Adult patients admitted with NP were identified in the 2016-2019 Nationwide Readmissions Database 2016-2019. Hospital volume cutoffs were defined by tertiles of total NP admissions per year (low-volume [<9 admissions/year]; medium-volume [9-25 admissions/year]; high-volume [≥26 admissions/year]). Subgroup analyses were performed for NP patients undergoing procedural intervention. The primary outcome was in-hospital mortality. Multivariable logistic regression models determined the association between clinical outcomes and hospital volume.

Results:

A total of 25,483 patients were identified, 14.3% of whom underwent procedural intervention, with the highest rate of intervention occurring in high-volume hospitals. The most common interventions offered at low- and medium-volume hospitals were open necrosectomy and percutaneous drainage. In contrast, high-volume hospitals had increased rates of minimally invasive surgery and endoscopic management. High-volume centers had the highest mortality rate among all patients (7.3% vs. 6.6% vs. 5.5%, P<0.001) but the lowest among the intervention-only cohort (7.5% vs. 10.4% vs. 12.0%, P<0.001). After adjusting for confounders, high-volume centers had lower odds of mortality in all patients (odds ratio (OR) 0.78, 95% confidence interval (CI) 0.65-0.93) and in the intervention-only cohort (OR 0.64, 95% CI 0.42-0.96). High-volume hospitals were also associated with a shorter hospital stay and lower healthcare costs.

Conclusion:

Management of NP at high-volume hospitals was associated with improved survival and decreased healthcare utilization. As interventional techniques advance, following evidence-based guidelines and implementing clear referral pathways will optimize outcomes for both patients and hospital systems.
Discovery and identification of a novel yeast species, Hanseniaspora drosophilae sp. nov., from Drosophila in Okinawa, Japan
Authors: Seike Taisuke , Takekata Hiroki , Kono Keiko , Sakata Natsue , Kotani Hazuki , Furusawa Chikara , Matsuda Fumio
Published: February 2025

Hanseniaspora, a genus of yeasts in which many species reproduce sexually, has attracted the attention of researchers because of its prevalence in diverse ecological niches. Building on our extensive collection efforts since 2020, three previously unknown yeast strains from wild Drosophila species trapped in ripe bananas in Okinawa, Japan, were isolated. Using a multifaceted approach, including physiological assessments and sequence analysis of the D1/D2 domain of the 26S LSU rRNA gene and the internal transcribed spacer (ITS) region, it was revealed that these strains are novel members of the genus Hanseniaspora. The three strains, JCM 36741T, JCM 36742 and JCM 36748, had identical sequences in their respective D1/D2 and ITS regions, justifying their classification as a single species. Moreover, the new species exhibited a remarkable degree of sequence divergence from its closest relatives, differing by 7 nucleotide substitutions (1.28%) in the D1/D2 domain, 29 nucleotide substitutions and 4 gaps (4.08%) in the ITS regions. These substantial sequence differences highlight the distinctiveness of this novel species in the genus Hanseniaspora. Further analysis revealed physiological characteristics that distinguished the new species from its closest relative, Hanseniaspora hatyaiensis (nom. inval.). These findings culminated in the proposed name Hanseniaspora drosophilae sp. nov., which recognizes the unique ecological niche within the Drosophila microbiota. By uncovering this novel species, this study not only adds to the growing body of knowledge on yeast diversity but also sheds light on the intricate ecological relationships that shape microbial communities. The implications of this discovery extend beyond taxonomic boundaries, inviting further exploration of the evolutionary dynamics and ecological significance of yeast–fly interactions. We propose accommodating H. drosophilae sp. nov. in the genus Hanseniaspora with JCM 36741T as the holotype. The MycoBank accession number is MB 853822.
A genetic map of human metabolism across the allele frequency spectrum
Authors: Zoodsma Martijn , Beuchel Carl , Yasmeen Summaira , Kohleick Leonhard , Nepal Aakash , Koprulu Mine , Kronenberg Florian , Mayr Manuel , Williamson Alice , Pietzner Maik , Langenberg Claudia
Published: February 2025

Genetic studies of human metabolism identified unknown disease processes and novel metabolic regulators, but have been limited in scale and allelic breadth. Here, we provide a data–driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 metabolic traits) measured using protein nuclear magnetic resonance spectroscopy (1H–NMR) across the allele frequency spectrum in ~450.000 individuals. In trans–ancestry analyses, we identify 29,824 locus–metabolite associations mapping to 753 regions with effects largely consistent between men and women and major ancestral groups represented in UK Biobank. We develop a framework for classifying the observed extreme genetic pleiotropy, enabling identification of upstream master regulators of lipid metabolism (proportional pleiotropy), such as ANGPTL3. We establish rare–to–common allelic series by integrating machine–learning guided effector gene assignments with rare exonic variant analyses providing high confidence gene assignments at >100 loci, including less established regulators of lipid metabolism like SIDT2. At 17 such loci we observed phenotypic heterogeneity among variants mapping to the same gene indicating differential metabolic roles of the altered gene product. We identify VEGFA as a potential modulator of HDL–mediated risk for coronary artery disease. Our results demonstrate how rare–to–common genetic variation combined with deep molecular profiling can identify unknown and inform on poorly understood regulators of human metabolism to guide prevention and treatment of diseases.
Training Generalized Segmentation Networks with Real and Synthetic Cryo-ET data
Authors: Purnell Carson , Heebner Jessica , Nguyen Linh , Swulius Michael T. , Hylton Ryan , Kabonick Seth , Grillo Michael , Grillo Stephanie , Grigoryev Sergei , Heberle Frederick A. , Waxham M. Neal , Swulius Matthew T.
Published: February 2025

Deep learning excels at segmenting objects within noisy cryo-electron tomograms, but the approach is typically bottlenecked by access to ground truth training data. To address this issue we have developed CryoTomoSim (CTS), an open-source software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. Using CTS outputs, we demonstrate the effects of key microscope parameters (dose, defocus, and pixel size) on deep learning-based segmentation, and show that including both molecular crowding and diversity within synthetic datasets is key to training cellular segmentation networks from purely synthetic inputs. While very effective as initial models, the accuracy of these networks is currently limited, and real cellular data is necessary to train the most accurate and generalizable U-Nets. Using a co-training approach, we first segment over 100 tomograms from neuronal growth cones to quantify their cytoskeletal distributions and then we build a generalized cellular cryo-ET segmentation network called NeuralSeg that can segment a subset of cellular features in tomograms from all domains of life.
The Impact of Loneliness on Depression, Mental Health Days, and Physical Health
Authors: Akinyemi Oluwasegun , Abdulrazaq Waliah , Fasokun Mojisola , Ogunyankin Fadeke , Ikugbayigbe Seun , Nwosu Uzoamaka , Michael Mariam , Hughes Kakra , Ogundare Temitope
Published: February 2025

Introduction

Loneliness is a significant public health concern and a well-established social determinant of health, affecting both mental and physical well-being. It has been linked to an increased risk of depression, anxiety, cardiovascular disease, and premature mortality. Despite growing awareness, loneliness remains an underrecognized and undertreated factor influencing population health.

Objective

This study examines the impact of loneliness on the likelihood of being diagnosed with depression, as well as its association with self-reported poor mental and physical health days.

Methods

Data were analyzed from the Behavioral Risk Factor Surveillance System (BRFSS) (2016–2023). The primary exposure was self-reported loneliness, captured through the question, “How often do you feel lonely?” with responses ranging from “Always” to “Never.” Main outcomes included depression diagnosis, poor mental health days, and poor physical health days. Covariates included age, race, gender, marital status, education, employment, state, year, metropolitan status, and language spoken at home. Inverse Probability Weighting (IPW) was used to estimate the Average Treatment Effect (ATE), accounting for confounders and state and year fixed effects. Sampling weights ensured national representativeness, and robust standard errors accounted for clustering by state.

Results

Among 47,026 participants, 82.4% reported experiencing some degree of loneliness, with 6.2% feeling “Always” lonely, 8.3% feeling “Usually” lonely, 37.9% feeling “Sometimes” lonely, and 29.9% feeling “Rarely” lonely. In contrast, 17.7% of participants reported “Never” feeling lonely. For further analysis, 2,609 individuals who reported feeling lonely were matched with 2,609 individuals who reported “Never” feeling lonely”, forming a balanced comparison group. The “Always Lonely” population was predominantly White (64.5%) and female (55.0%), with the majority aged 45–64 years. Loneliness was significantly associated with an increased likelihood of depression diagnosis, with a 39.3% percentage-point increase for those reporting Always lonely (ATE = 0.39, 95% CI: 0.34–0.44, p < 0.001). Loneliness was also associated with a 10.9-day increase in poor mental health days (ATE = 10.9, 95% CI: 9.8–11.9, p < 0.001) and a 5.0 day increase in poor physical health days (ATE = 5.0, 95% CI: 3.8–6.1, p < 0.001).

Conclusions

Loneliness is a strong predictor of depression and poor mental and physical health. Interventions addressing social isolation could mitigate the negative health impacts associated with loneliness, improving population health outcomes.
Comparative Genomics Reveals LINE-1 Recombination with Diverse RNAs
Authors: Law Cheuk-Ting , Burns Kathleen H.
Published: February 2025

Long interspersed element-1 (LINE-1, L1) retrotransposons are the most abundant protein-coding transposable elements (TE) in mammalian genomes, and have shaped genome content over 170 million years of evolution. LINE-1 is self-propagating and mobilizes other sequences, includingAluelements. Occasionally, LINE-1 forms chimeric insertions with non-coding RNAs and mRNAs. U6 spliceosomal small nuclear RNA/LINE-1 chimeras are best known, though there are no comprehensive catalogs of LINE-1 chimeras. To address this, we developed TiMEstamp, a computational pipeline that leverages multiple sequence alignments (MSA) to estimate the age of LINE-1 insertions and identify candidate chimeric insertions where an adjacent sequence arrives contemporaneously. Candidates were refined by detecting hallmark features of L1 retrotransposition, such as target site duplication (TSD). Applying this pipeline to the human genome, we recovered all known species of LINE-1 chimeras and discovered new chimeric insertions involving small RNAs,Aluelements, and mRNA fragments. Some insertions are compatible with known mechanisms, such as RNA ligation. Other structures nominate novel mechanisms, such as trans-splicing. We also see evidence that LINE-1 loci with defunct promoters can acquire regulatory elements from nearby genes to restore retrotransposition activity. These discoveries highlight the recombinatory potential of LINE-1 RNA with implications for genome evolution and TE domestication.

GRAPHICAL ABSTRACT
Bayesian Inference of Binding Kinetics from Fluorescence Time Series
Authors: Bryan J. Shepard , Tashev Stanimir Asenov , Fazel Mohamadreza , Scheckenbach Michael , Tinnefeld Philip , Herten Dirk-Peter , Pressé Steve
Published: February 2025

The study of binding kinetics via the analysis of fluorescence time traces is often con-founded by measurement noise and photophysics. Although photoblinking can be mitigated by using labels less likely to photoswitch, photobleaching generally cannot be eliminated. Current methods for measuring binding and unbinding rates are therefore limited by concurrent photobleaching events. Here, we propose a method to infer binding and unbinding rates alongside photobleaching rates using fluorescence intensity traces. Our approach is a two-stage process involving analyzing individual regions of interest (ROIs) with a Hidden Markov Model to infer the fluorescence intensity levels of each trace. We then use the inferred intensity level state trajectory from all ROIs to infer kinetic rates. Our method has several advantages, including the ability to analyze noisy traces, account for the presence of photobleaching events, and provide uncertainties associated with the inferred binding kinetics. We demonstrate the effectiveness and reliability of our method through simulations and data from DNA origami binding experiments.
Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance
Authors: Cubillos Luis H. , Kelberman Madison M. , Mender Matthew J. , Hite Aren , Temmar Hisham , Willsey Matthew , Kumar Nishant Ganesh , Kung Theodore A. , Patil Parag G. , Chestek Cynthia , Krishnan Chandramouli
Published: February 2025

Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements—something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the “true” control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.

Significance Statement

Many researchers believe that brain and muscle synergies represent a fundamental control strategy and could enhance brain-machine interface (BMI) decoding performance. These synergies, extracted through dimensionality reduction techniques, are thought to simplify complex neural data, improving the efficiency and accuracy of BMI systems. In our study, we evaluated brain and muscle synergies in a dexterous finger task. We found that while these synergies effectively compressed high-dimensional data, they did not improve performance through denoising or generalize well across different contexts. Instead, the highest performance was achieved when using all available data, suggesting that synergies, although useful for data compression, may not provide the “true” control space needed to enhance decoder robustness or adaptability in implanted BMI systems.
Structured solvent on a split electron tail: A semiclassical theory of electrified metal-solution interfaces
Authors: Zhang Mengke , Chen Yanxia , Eikerling Michael , Huang Jun
Published: February 2025

We develop a comprehensive semiclassical continuum theory of electronic response and structured solvents at electrified metal-solution interfaces. The approach combines an orbital-free density-functional theory of electrons on the metal side with a statistical field theory of a structured electrolyte solution within a grand canonical framework. The resulting grand potential of the entire electrical double layer (EDL) is a hybrid functional of particle density, including metal electrons and classical solution particles, electric potential, and solvent polarization, referred to as density-potential-polarization functional theory (DPPFT). The DPPFT captures major atomistic phenomena within the EDL, including electron spillover, spatially damped oscillations in solvent polarization and electric potential extending toward the bulk solution, and the layered structure of ions. Based on DPPFT, an EDL model for the Ag(110)-KPF6 aqueous solution interface is parameterized using experimental data for the double-layer capacitance (). The calibrated model is employed to study the influence of electronic, ion, and solvent properties on EDL structure. profiles at different crystal faces and in various electrolyte solutions are rationalized by the model coherently. We reveal that intensified ion layering enhances the capacitance at the potential of zero charge and narrowed ionic peaks in the profile. Contrary to classical models, the DPPFT allows coions to have appreciable densities near the metal surface, with their magnitudes depending on the local solvent structure and the correlation strength between ions and solvent. All in all, the presented framework provides a holistic and computationally efficient approach for atomistic-level modeling of EDLs under constant-potential conditions.

Published by the American Physical Society
2025
Effects of Neutron-Antineutron Transitions in Neutron Stars
Authors: Goldman Itzhak , Mohapatra Rabindra N. , Nussinov Shmuel , Shrock Robert
Published: February 2025

We analyze effects of neutron-antineutron transitions in neutron stars, specifically on (i) cooling, (ii) rotation rate, and (iii) for binary pulsars, the increase in the orbital period. We show that these effects are negligibly small.

Published by the American Physical Society
2025
Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning
Authors: Zhai Zheng-Meng , Moradi Mohammadamin , Lai Ying-Cheng
Published: February 2025

The ever-increasing complexity of modern power grids makes them vulnerable to cyber and/or physical attacks. To protect them, accurate attack detection is essential. A challenging scenario is that a localized attack has occurred on a specific transmission line but only a small number of transmission lines elsewhere can be monitored. That is, full state observation of the whole power grid is not feasible, so attack detection and state estimation need to be done with only limited, partial state observations. We articulate a machine-learning framework to address this problem, where the necessity to deal with sequential time-series data with dynamical memories and to avoid a vanishing gradient has led us to choose the long short-term memory (LSTM) architecture. Leveraging the inherent capabilities of LSTM to handle sequential data and capture temporal dependencies, we demonstrate, using three benchmark power-grid networks, that the complete dynamical state of the whole power grid can be faithfully reconstructed and the attack can be accurately localized from limited, partial state observations even in the presence of noise. The performance improves as more observations become available. Further justification for using the LSTM is provided by our comparing its performance with that of alternative machine-learning architectures such as feedforward neural networks and random forest. Despite the gigantic existing literature on applications of LSTM to power grids, to our knowledge, the problem of locating an attack and estimating the state from limited observations had not been addressed before our work. The method developed can potentially be generalized to broad complex cyber-physical systems.

Published by the American Physical Society
2025
Novel fixed-target serial crystallography flip-holder for macromolecular crystallography beamlines at synchrotron radiation sources
Authors: Gu Do-Heon , Jeong Dong Tak , Eo Cheolsoo , Seo Pil-Won , Kim Jeong-Sun , Park Suk-Youl
Published: February 2025

Synchrotron serial crystallography (SSX) is an emerging method for determining crystal structure at room temperature using synchrotron radiation facilities. Despite the various approaches available, reducing sample consumption, removing mother liquid from crystal solution, soaking small molecules for protein–ligand complex structure and prevention of sample dehydration are still challenging problems to be overcome in SSX. Therefore, we have developed a new flip-type fixed-target SSX sample holder for conventional protein crystallography beamlines based on nylon mesh and kapton film. The potential of the flip-holder was evaluated in an SSX experiment with lysozyme crystals at room temperature. About 19600 diffraction images were collected during 40 minutes using a repetition rate of 10 Hz at the 11C beamline of the Pohang Accelerator Laboratory, and the crystal structure of lysozyme was determined at 1.89 Å resolution. This straightforward flip-holder can be used in synchrotron beamlines for routine crystallography.
How does the use of artificial intelligence affect sustainability rating in Middle Eastern universities?
Authors: Ronaghi Mohammad Hossein , Ronaghi Marzieh
Published: February 2025

Purpose

Artificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes towards intelligent automation. The role of digital transformation in universities and educational institutions has an increasing trend. New business structures and the digitization of processes, other than the advantages they bring about, might have different effects on the environment and sustainability. This study aims to identify the effective factors on AI adoption and the effect of using this technology in educational institutions and universities on their sustainable performance.

Design/methodology/approach

This research is applied using a quantitative approach. Universities selected for the study were ranked by Quacquarelli Symonds (QS). Of the 111 QS listed universities in the Middle East in 2023, 30 universities were randomly selected, and the research questionnaire was emailed to 50 people (administrative, educational and research staff) from each university. Information related to the level of AI technology acceptance and use was collected using a questionnaire among the university staff and faculty members; moreover, their relationship with universities’ sustainable performance scores was assessed. Path analysis and Smart PLS software have been used for data analysis.

Findings

The research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities. Also, the adoption of AI is considered as an effective factor in improving university sustainable performance. Therefore, based on exact data analysis using AI, universities can manage their activities and better control their environmental performance. Also, the use of AI can be effective in the availability to sustainable education in universities and the establishment of social justice in society. Accordingly, to facilitate executive processes and decision-making, policymakers in the field of science and university principals can improve administrative, educational and research processes via investing on AI, in addition to improving environmental activities and sustainable development.

Originality/value

The theoretical contribution of this research, other than designing an AI acceptance model for universities includes evaluating the relationship between using AI and university sustainable performance.
What factors moderate the relationship between the dual network tie strength of MNC’s foreign subsidiary in emerging markets and technological innovation?
Authors: Han Ga-Rog , Lee Jae-Eun
Published: February 2025

Purpose

This study examines the moderating effects of cultural distance and the nationality of foreign subsidiaries’ CEOs on the relationship between internal/external network tie strength and technological innovation. This study focuses on foreign subsidiaries of Korean firms operating in emerging markets, based on the knowledge-based view.

Design/methodology/approach

To test the hypotheses, a survey was administered to 3,840 foreign subsidiaries of Korean firms in emerging markets. Questionnaires were distributed via phone and email, and 282 (7.34%) completed questionnaires were returned. Accordingly, an empirical analysis was conducted, and the hypotheses were tested.

Findings

First, internal network tie strength had a significant negative effect on foreign subsidiaries’ technological innovation. Second, external network tie strength had a significant positive effect on foreign subsidiaries’ technological innovation. Third, as for the moderating effect, cultural distance negatively moderates the significant negative effect of internal network tie strength on the technological innovation of foreign subsidiaries. Fourth, cultural distance positively moderates the positive effect of external network tie strength on foreign subsidiaries’ technological innovation. Fifth, foreign subsidiaries’ CEO nationality (parent country nationals) positively moderates the negative effect of internal network tie strength on foreign subsidiaries’ technological innovations. Sixth, foreign subsidiaries’ CEO nationality (parent country nationals) negatively moderates the positive effect of external network tie strength on foreign subsidiaries’ technological innovation.

Originality/value

Previous studies found a positive relationship between dual network tie strength and technological innovation in foreign subsidiaries from a knowledge-based view. This study’s significance lies in its finding that the varying strengths of foreign subsidiaries’ internal and external network connections can have different impacts on their technological innovation in the context of subsidiaries operating in emerging markets. Additionally, the moderating effects of cultural distance and the nationality of the foreign subsidiary’s CEO were examined. Therefore, this study is significant because it expands the literature on technological innovation in foreign subsidiaries.
From chaos to clarity: how controlled vocabularies shape Indian social science repositories
Authors: Roy Aditi , Ghosh Saptarshi
Published: February 2025

Purpose

The Confederation of Open Access Repositories (COAR) prescribes three types of controlled vocabularies for open access repositories (OARs): access rights, resource type and version type. Interestingly, COAR does not suggest a subject-specific vocabulary for organising content, whereas the subject parameter is one of the most preferred search categories used by information seekers. The purpose of the study is to investigate the use of controlled vocabularies in subject arrangement in OARs.

Design/methodology/approach

The study comprises eight stages. The first is to identify the total number of repositories enlisted in OpenDOAR under the social science domain in India. Next, the samples are selected, followed by data harvesting using MarcEdit OAI-PMH harvester plug-ins. The next step is to process the data and then consolidated controlled vocabulary is constructed by merging six existing thesauri. Then, a similarity matching algorithm is used to determine the usage of controlled vocabularies for subject arrangement. The last step is to evaluate the efficacy of controlled vocabulary in information retrieval.

Findings

The results revealed that subject arrangement differs largely in each repository. The study also showed that the use of controlled vocabularies in OARs for subject arrangement still needs to be standardised to enhance interoperability.

Originality/value

This research reflected that those controlled terms address issues, such as ambiguity, inconsistency and synonym variation, typically found with uncontrolled keywords through standardising subject metadata. This standardisation gives users a more reliable and user-friendly search experience, ultimately improving the discoverability and usability of open access content.
Family governance: understanding the relationship between informal family meetings, formal governance practices and family-centered non-economic goals
Authors: Gimenez-Jimenez Daniela , Calabrò Andrea , Torchia Mariateresa , Åberg Carl
Published: February 2025

Purpose

Stemming from family and business governance arguments, this paper contends that informal family meetings are the baseline “informal governance mechanism” that shapes family-centered non-economic goals. However, it is foreseen that informal family meetings are most effective when family businesses implement formal family governance practices.

Design/methodology/approach

Hypotheses are tested on a sample of 490 family firms using robust ordinary least square estimations.

Findings

The main findings suggest that informal family meetings are positively associated with family-centered non-economic goals and this relationship is fully mediated by the presence of formal family governance practices.

Originality/value

The use of new system theory for developing the hypotheses allows the study to contribute with a more comprehensive understanding of the social systems in family firms, and how interactions between the family logic and the business logic relate.
Professionalization in family businesses: the role of leadership style
Authors: Nasr Arash Khalili , Rashidirad Mona , Asgari Hatamabadi Hamze , Ghasempour Nejati Mobin , Hajli Nick
Published: February 2025

Purpose

This paper investigates the impact of various leadership styles on the professionalization and subsequent performance of family businesses.

Design/methodology/approach

Using a survey method and employing a partial least squares approach to structural equation modeling, we tested our model and analyzed the collected data based on the responses of 216 managers in Iran.

Findings

Our research demonstrates that professionalization mediates the relationship between leadership style and performance. Moreover, our findings show that the participative leadership style is the most effective option for family businesses seeking to achieve professionalization and improve performance.

Research limitations/implications

First, the sample used in this study was drawn from a single country, namely Iran. Second, although we adhered to established practices for measuring financial performance, future research could explore alternative dimensions of performance, including non-financial goals. Third, we did not investigate the impact of different leadership styles on each dimension of professionalization.

Practical implications

These findings provide valuable insights for family business managers seeking to adopt a suitable leadership style to achieve professional management and realize favorable outcomes.

Originality/value

Our study suggests that examining the potential impact of leadership styles on professionalization can provide clarity amidst mixed findings regarding the influence of professionalization on firm performance. Additionally, we challenge the oversimplified categorization of professionalization and argue for a multifaceted view, contending that professionalization comprises various dimensions acting concurrently and potentially mediates the effect of leadership styles on family business performance.
Identifying key drivers of supply chain resilience in Indian pharmaceutical industry for enhanced export performance
Authors: Gera Navneet , Singh Ram
Published: February 2025

Purpose

This paper aims to explore the key drivers of a resilient supply chain for the Indian pharmaceutical industry for enhanced and sustainable export pursuits in a disruptive business time.

Design/methodology/approach

This study literarily identifies the key drivers of supply chain resilience in the Indian pharmaceutical industry and methodologically appraises, analyzes and assesses them to derive a structural model that catalytically acts as facilitators and drivers for enhanced export performance. By reviewing relevant literature, it identifies research and development (R&D), procurement and sourcing, manufacturing, distribution and reverse logistics factors. It collects Likert scale feedback from stakeholders and probes interrelationships among them in achieving the enhanced export performance of India’s pharmaceutical sector.

Findings

The results lucidly identify the key enablers affecting the export performance of India’s pharmaceutical sector. These primarily include developing excellence in manufacturing, reverse logistics and distribution. Furthermore, sourcing and R&D are the antecedents of manufacturing expertise, catalytically contributing to improved export outcomes. Based on the derived structural model, the interrelationships are evaluated to explore causal links in achieving supply chain resilience and simultaneously elucidating the managerial and socio-economic interventions.

Originality/value

Narrating the results, this paper scholarly identifies the key drivers that need to be factored in for enhanced export outcomes of India’s pharmaceutical sector. The paper explains how to achieve supply chain resilience for export performance in India’s pharmaceutical sector.
A model for predicting consumer choice to shop in a new grocery store using the Theory of Planned Behaviour
Authors: Küpper Patrick , Seel Matthias , Kokorsch Matthias
Published: February 2025

Purpose

Gravity models and analogue store approaches are inadequate in predicting purchases in neighbourhood stores. This requires a new theoretical and empirically tested approach.

Design/methodology/approach

We use the Theory of Planned Behaviour (TPB) to determine which factors predict the choice for a new neighbourhood store. We develop a suitable model using a structural equation model with survey data from two cases in which all households in the catchment areas were surveyed both before and after the store opened.

Findings

We find the TPB to be appropriate for predicting store choice. Beliefs about one-stop shopping, social pressure from family members and car availability are most important in explaining the intention to shop in the planned store. These factors also explain the actual shopping in this store after opening.

Originality/value

Our model predicts store choice before a store opens. Using a two-wave survey, we avoid ex-post rationalisation and show that, at least in our cases, quality, price and assortment do not predict store choice.
A comparative exploration of virtual reality’s role in Mandarin intercultural communicative competence development
Authors: Chan Suet Fong , DeWitt Dorothy , Loban Rhett
Published: February 2025

Purpose

Intercultural communicative competence (ICC) is important when different cultural speakers learn Mandarin as a Foreign Language (MFL). The use of virtual reality (VR) has been shown to be effective for improving ICC. Hence, this study investigates a production-based instructional strategy where students use VR to view and create VR environments with cultural elements for learning MFL to determine if this strategy was effective with the current cohort of students (2023). In addition, it would be investigated whether there was a difference in ICC between the 2019 cohort and the 2023 cohort.

Design/methodology/approach

The study employed a quasi-experimental method to assess ICC using the Survey of student’s Intercultural Competence (SSIC) and gauged the improvement within the 2023 cohort. Next, the ICC between the 2023 and 2019 cohorts was compared to determine if there was a significant difference. Data was analysed using paired-samples t-tests and thematic analysis for the open-ended responses.

Findings

There was a significant improvement in ICC after the intervention for the 2023 cohort, which was supported with the open-ended response. However, the t-test results indicated no significant difference in ICC between the 2019 and 2023 cohorts. However, the 2023 cohort seemed to be more motivated, confident and eager to continue using VR. The findings indicate that VR when combined with an appropriate pedagogy could improve students ICC.

Originality/value

The use of VR and this production-based instructional strategy could be used in other languages and could possibly be used to improve linguistic and communication skills.
Unlocking crop insurance: exploring farmers' demand and willingness to pay in the agriculture sector
Authors: Zhllima Edvin , Xhoxhi Orjon , Imami Drini , Skreli Engjell
Published: February 2025

Purpose

This paper examines the willingness to pay (WTP) for insurance premiums and identifies the factors that influence it in fruit, greenhouse vegetables and dairy farming. It evaluates Albanian farmers' WTP for insurance products, emphasizing the key farm characteristics and factors that shape this willingness, offering valuable insights for establishing subsidized insurance.

Design/methodology/approach

The study is based on a structured rapid survey conducted in 2019 with 808 farmers and interviews with key experts and operators in the insurance market. Authors use a double-bounded dichotomous choice contingent valuation technique to gauge WTP for insurance products.

Findings

Results indicate that farmers' WTP ranges between 1.18 and 2.15% of their income from the main crop, depending on the farming activity, with dairy and greenhouse farmers showing a higher WTP than fruit farmers. WTP is positively related to exposure to climate hazards, engagement in modern market channels and contracting.

Research limitations/implications

The study used three different types of farms; thus, the findings are not strictly relevant to one activity. Subsequent research could employ choice experiments to evaluate alternative insurance schemes focusing on one sector.

Originality/value

The paper contributes to the literature by empirically testing various determinants for WTP for which there are no valid findings by using contingent choice analysis to explore WTP for crop insurance.
Working capital management efficiency in large firms during crisis: implications for community-based enterprises
Authors: Roy Prasenjit , Rossi Matteo , Salloum Charbel , Jarrar Hajer , Ghose Biswajit
Published: February 2025

Purpose

This study aims to examine the impact of the COVID-19 pandemic on the working capital management (WCM) efficiency of resilient and nonresilient firms listed on India’s BSE 500 index. It focuses on key WCM components such as cash conversion cycles (CCC), accounts receivable periods, inventory conversion periods and accounts payable periods.

Design/methodology/approach

Panel data from 2012 to 2023 is analyzed using the System Generalized Method of Moments model. This study differentiates between resilient and nonresilient firms based on liquidity stress tests and cash flow performance before and during the pandemic.

Findings

Resilient firms demonstrated superior WCM efficiency, maintaining shorter CCCs, effective receivables and inventory management and stable payables. Nonresilient firms faced significant inefficiencies, including extended CCCs and slower receivables and inventory turnover, exposing gaps in their WCM practices.

Research limitations/implications

This study is limited to the pandemic period. Future research could explore broader timeframes to understand the long-term effects on WCM.

Practical implications

Managers should enhance WCM strategies, focusing on cash flow optimization to strengthen firm resilience during crises.

Social implications

Efficient WCM supports job retention, preserves supplier relationships and stabilizes local economies, contributing to broader community resilience during crises.

Originality/value

This study extends the resource-based view by emphasizing WCM as a critical internal resource that supports firm resilience during economic crises. It contributes new insights into how Indian firms adapted their WCM strategies in response to COVID-19.
Financial inclusion, entrepreneurial activity and the moderating role of human capital: new cross-country evidence
Authors: Pham Thi Thu Tra , Bui Duy Tung , Chu Tuan_Thanh , Nguyen Trinh
Published: February 2025

Purpose

This study aims to reexamine the moderating role of human capital on the effect of extended financial inclusion (FI) for entrepreneurship, using data from the Global Entrepreneurship Monitor for a sample of 42 countries from 2006 to 2017.

Design/methodology/approach

This study distinguished between actual and perceived human capital. Actual human capital was measured through formal education while perceived human capital was captured by self-perceived capabilities for business start-ups. The moderating role of human capital was captured by the interaction terms between FI and human capital to investigate how the effects of FI on entrepreneurship vary with levels of human capital. The estimation used the panel-corrected standard error estimators and the two-step system generalized method of moments estimators.

Findings

Higher levels of formal education decrease the positive effect of extended FI on entrepreneurial activities. Individuals with high levels of self-capability do not leverage FI for entrepreneurial activities as much as those with lower levels of perceived capability. The results are robust to different estimation methods and different forms of actual human capital.

Research limitations/implications

Both financial and human capital matter for new business formation worldwide. The findings suggest that FI policies must account for the decreasing effect in response to high levels of human capital. Future research should explore different measures of entrepreneurial performance, various types of entrepreneurship and entrepreneurship across gender groups to gain deeper insights into strategies for promoting entrepreneurship.

Practical implications

Education strategies should focus on specific types of education, such as entrepreneurship education with financial literacy, rather than traditional academic curriculum, to foster entrepreneurship knowledge, skills and creativity. Likewise, entrepreneurship support schemes should aim to nurture and share appropriate levels of self-efficacy, avoiding excessively high self-efficacy, which is deleterious to the benefits of FI for entrepreneurial activities.

Originality/value

This study offers novel evidence of the decreasing effects of FI on entrepreneurial activities in response to increased actual and perceived human capital.