Interpretative Analysis Research Articles

DISCOURSE ANALYSIS OF PROPER NAMES OF UKRAINIAN EARLY MODERN PERIOD

specific character of proper names discursive implementa- of proper names discursive implementa- of proper names discursive implementa- proper names discursive implementa- discursive implementa- tion of Ukrainian proper names in the early modern period of the 17th century. The research task is to char- acterize proper names as a resource of persuasiveness in the preaching discursive practice of the 17th cen- tury; to determine the discursive polyplanarity of proper names in early modern Ukrainian sermons; to an- alyze proper names in terms of text generation and cognitive processes of the speaker (knowledge, mem- ory reproduction, analogy); to reveal the principles of combining proper names and creating images (met- aphors, similes) in Ukrainian early modern practice; to establish the influence of ideology on the discourse and creation of the imagery of agionyms for the designation of the names of the Rus saints. Research methods: discourse analysis (to characterize the process of text generation in the preach- ing discursive practice of the 17th century, which is conditioned by historical sociocultural processes), ono- mastic discourse analysis (based on the analysis of proper names in discourse) taking into account axiolog- ical and ideological representations of discourses, contextual and interpretive analysis (two stages, contex- tualization and interpretation, are covered), linguistic and cultural interpretation (to characterize baroque conceptism and determine secondary meanings in the preaching discursive practice). Proper names are an active resource of text creation in the early modern sermon, which is analyzed in accordance with the traditional episteme and sociocultural conditions of the Ukrainian early modern of the second half of the 17th century, the requirements of institutional discourse, the type of discursive practice, and the author’s instructions. Baroque conceptism with the creation of new meanings contributed to the memorization and perception of the presentation, activation of attention, emotions of recipients, influence on the consciousness of Ruthenianas. The preaching practice of this period acquires certain signs of epistemic discursive transformations, which explain beliefs and partly knowledge that acquire symbolic meaning in the preaching activity to the discourse of faith. The encyclopedic nature of the author, the creation of new images, and persuasiveness accumulate in the preaching of multi-layered proper names that synthesize different cultural layers in the preacher’s updated interpretation. The onomastic discourse analysis not only studies the imagery of historical discourses, but also extrapolates the study of proper names to the peculiarities of ancient text generation and the cognitive processes of the speaker. Processing of information by the author is considered in the aspects of processing foreign language sources (analysis of methods of adaptation of borrowed proper names), reproduction of information from memory (inaccurate spelling of proper names), creation of analogies, images (metaphors, similes). The creation of images by analogy determines baroque syncretism, which combines theonyms and mythonyms, biblical anthroponyms and mythonyms, Christian theonyms and pagan theonyms. New fields of knowledge and secondary meanings of astronomical names (names of constellations, stars) become one of the resources of the baroque imagery of the sermon, its axiological representation. Sociocultural and ideological conditions actualize sermons in honor of the Rus saints in the Ukrainian preaching discourse. The comparison of these agionyms with biblical anthroponyms (parallels from the Old Testament), names of ancient philosophers, poets (Sophocles), names of world history (Alexander the Great) is characterized.

Increased destination patronage: can data privacy, safety, and security do the magic?

ABSTRACT The study explores data privacy, assurances, and destination imagery for data protection from the perspectives of destination managers and international tourists. The study utilised concurrent mixed methods to address the first study’s propositions anchored on the social contract theory via interpretative data analysis. The second study was grounded on the optimism and pessimism theory, and the hypotheses were tested using structural equation modelling. The study revealed six assurances and also unearthed destination managers’ understanding of using symbols such as elephant, bird, dog, baby, and jewelry to define the protection of tourists’ data. The results of the hypotheses showed that data privacy, safety, and security are significant predictors of destination patronage from an international tourist’ perspective. The study adds to the data privacy literature by exploring symbols or images to describe data protection within the social contract theory perspective, which is considered a new contribution to hospitality and tourism literature.

Natural Language Processing-Based Deep Learning to Predict the Loss of Consciousness Event Using Emergency Department Text Records

The increasing adoption of electronic medical records (EMRs) presents a unique opportunity to enhance trauma care through data-driven insights. However, extracting meaningful and actionable information from unstructured clinical text remains a significant challenge. Addressing this gap, this study focuses on the application of natural language processing (NLP) techniques to extract injury-related variables and classify trauma patients based on the presence of loss of consciousness (LOC). A dataset of 23,308 trauma patient EMRs, including pre-diagnosis and post-diagnosis free-text notes, was analyzed using a bilingual (English and Korean) pre-trained RoBERTa model. The patients were categorized into four groups based on the presence of LOC and head trauma. To address class imbalance in LOC labeling, deep learning models were trained with weighted loss functions, achieving a high area under the curve (AUC) of 0.91. Local Interpretable Model-agnostic Explanations analysis further demonstrated the model’s ability to identify critical terms related to head injuries and consciousness. NLP can effectively identify LOC in trauma patients’ EMRs, with weighted loss functions addressing data imbalances. These findings can inform the development of AI tools to improve trauma care and decision-making.

STYLISTIC MEANS OF IMPLEMENTING THE IDEA OF THE WORLD’S IRIDESCENCE IN PH.K. DICK’S “UBIK” IN THE CONTEXT OF TRANSLATION TRANSFORMATIONS

The article deals with the formation means of the idea of the world’s iridescence or illusiveness, its stylistic and translation aspects within the textual framework of Ph.K. Dick’s novel “Ubik” and its translat- ed version in Ukrainian. The relevance of the study is defined by general scarcity of current enquiries highlighting stylistic means of moulding the idea of the world’s iridescence in science fiction as well as translation methods of delivering it from English to Ukrainian. The purpose of this scientific inquiry is to identify the stylistic devices that play a role in the delinea- tion of ways in which reality may be reduced to tatters in comparison with a coherent state of things and to pinpoint the translation techniques of rendering the related contexts from English into Ukrainian. The top- icality of the study is determined by the paucity of relevant research focusing on the kernel of the prevail- ing theme in Dick’s “Ubik”, namely illusiveness of reality, and translation techniques and transformations that help adequately deliver the significant sequences from English into Ukrainian. It is argued that the idea in question being the core of the prevailing theme in Dick’s “Ubik” is at- tained by means of incorporating several ontological planes represented by mental constructs, or reali- ties, focalised through the perceptions and personalities of different characters of the novel. Contextu- al and text interpretation analyses enabled the authors to determine and interpret those semantic domi- nants that underpin the formation of the world’s iridescence effect in the novel, namely: “alternate reali- ties”, “dream as a variant of reality”, “changes in time and space”, “changes of forms of consciousness and existence”. It has been proven these dominants’ diegetic interrelation. The idea of the world’s iridescence in the novel is brought about by the literary work’s fluctuating plot convolutions stemming from seemingly incoherent planes of different sections of the novel: a mosaic of the primary diegesis together with several hypo-diegeses comes across as a kind of an ontic hazy maze building up to an overall effect of the world’s fluidity and oscillation. The method of stylistic analysis was applied to interpret stylistic phenomena inherent in the novel in relation to their contribution to the studied idea as those were found functioning in the relevant textu- al microcontexts and enhancing the aforementioned semantic dominants. It was esti mated that expres- estimated that expres- sive means and devices of stylistic syntax constitute four fifths of the whole researched corpus of units, thus playing a major role in the formation of the idea of the world’s illusiveness (those include aposiopesis, rhetoric questions, asyndeton, and nominative sentences) while expressive means and stylistic devices of stylistic semasiology prove much less numerous, accounting for a fifth of the units analysed (presented by metaphors, epithets, similes, antitheses). Among the typical translation methods, instrumental in the rel- evant textual sequences in “Ubik”, a share of lexical and semantic transformations equals 60.2% of the re- searched sets of translation counterparts, with modulation and specification being the dominant ones and logical synonymy, calquing and lexical compensation trailing behind. Grammatical transformations com- pose nearly two fifths (39.8 %) of the researched body of units, the most efficient ones being substitution and word order change. The results of the study enabled understanding the interaction mechanism of semantics of the leit- motif of a literary work, its stylistic representation and translation solutions of its delivery from English into Ukrainian. We envisage the prospects for further research in studying linguistic aspects of cross-cultural conveying symbolic significance of recurring narrative elements.

Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks

Abstract Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not designed to process data from sensor networks with irregular spatial distribution. In this study, we focus on anomaly detection in environmental sensor networks using graph neural networks, which can represent sensor network structures as graphs. We investigate its performance on two datasets with distinct dynamics and resolution: commercial microwave link (CML) signal levels used for rainfall estimation and SoilNet soil moisture measurements. To evaluate the benefits of incorporating neighboring sensor information for anomaly detection, we compare two models: graph convolution network (GCN) and a graph-less baseline: long short-term memory (LSTM). Our robust evaluation through 5-fold cross-validation demonstrates the superiority of the GCN models. For CML, the mean area under receiver operating characteristic curve for the GCN was 0.941 compared to 0.885 for the baseline-LSTM, and for SoilNet, it was 0.858 for GCN and 0.816 for the baseline-LSTM. Visual inspection of CML time series revealed that the GCN proficiently classified anomalies and remained resilient against rain-induced events often misidentified by the baseline-LSTM. However, for SoilNet, the advantage of GCN was less pronounced, likely due to inconsistent and less precise labeling. Through interpretable model analysis, we demonstrate how feature attributions vividly illustrate the significance of neighboring sensor data, particularly in distinguishing between anomalies and expected changes in signal level in the time series.

Interpretable ECG analysis for myocardial infarction detection through counterfactuals

In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into clinical practice. This study addresses this issue by investigating the use of counterfactual explanations to improve model interpretability for clinicians, particularly in differentiating healthy subjects from myocardial infarction patients. Utilizing the PTB-XL dataset, we developed a methodology for systematic feature extraction and refinement to prepare for counterfactual analysis. This led to the creation of the Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method, designed to improve the practicality of counterfactual explanations in a clinical setting. The validity of our approach was assessed using custom metrics that reflect the diagnostic relevance of counterfactuals, evaluated with the help of two cardiologists. Our findings suggest that this approach could support future efforts in using ECGs to predict patient outcomes for cardiac conditions, achieving interpretation validity scores of 23.29 ±1.04 and 20.28 ±0.99 out of 25 for high and moderate-quality interpretations, respectively. Clinical alignment scores of 0.83 ±0.12 for high-quality and 0.57 ±0.10 for moderate-quality interpretations underscore the potential clinical applicability of our method. The methodology and findings of this study contribute to the ongoing discussion on enhancing the interpretability of machine learning models in cardiology, offering a concept that bridges the gap between advanced data analysis techniques and clinical decision-making. The source code for this study is available at https://github.com/tanyelai/vcce.

Influence of quartz types on mechanical parameters in the early silurian Longmaxi shale, Sichuan Basin

Quartz is one of the most abundant minerals in shales, occurring predominantly in three forms: biogenic quartz (BQ), detrital quartz (DQ) and clay-transformed quartz (CTQ). The content of quartz is typically correlated with the fracability of shale gas reservoirs. However, the origins of quartz and its influence on mechanical properties remain not fully elucidated. To address this gap, this study examines shale samples from LM1-LM8 graptolite zones in well W202 of the Weiyuan shale gas field, the Sichuan Basin as the research object. After systematic analysis of organic geochemistry, mineral composition, cathodoluminescence observation, major/trace elements, and log interpretation and calculation of shale mechanical parameters, the genetic mechanism of quartz was analyzed and the percentage of quartz of different origins and the influence on rock mechanical properties were evaluated quantitatively. The results indicate that the three types of quartz (BQ, DQ and CTQ) were variably present in the Longmaxi shale across different graptolite zones. The LM1-LM3 graptolite zones exhibit the highest abundance of BQ, with contents ranging from 58.3 to 79.6%, with an average of 65.7%. DQ predominates in the LM4-LM5 and LM6-LM8 graptolite zones, with average contents of 38.7% and 34.9%, respectively. The BQ content is marginally higher in the LM4-LM5 zone compared to the LM6-LM8 zone, averaging 12.7% and 6.1%, respectively. The three types of quartz exert a dual influence on Young’s modulus, and a single linear effect on Poisson’s ratio. An increase BQ content correlates with a decreased in Poisson’s ratio, whereas increased contents of DQ and CTQ correlate with an increased Poisson’s ratio. With the highest Young’s modulus and the lowest Poisson’s ratio, the LM1-LM3 shale is the most favorable interval for hydraulic fracturing. Furthermore, BQ is positively correlated with the Brittleness index (BI), whereas the contents of DQ and CTQ are negatively correlated with BI. When the quartz content falls between 52% and 85%, the ratio of DQ to BQ is optimal for fracture development coinciding with high BI values, which are most conducive to fracturing stimulation.

A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification

Histological assessment is essential for the diagnosis and management of celiac disease. Current scoring systems, including modified Marsh (Marsh–Oberhuber) score, lack inter-pathologist agreement. To address this unmet need, we aimed to develop a fully automated, quantitative approach for histology characterisation of celiac disease. Convolutional neural network models were trained using pathologist annotations of hematoxylin and eosin-stained biopsies of celiac disease mucosa and normal duodenum to identify cells, tissue and artifact regions. Biopsies of duodenal mucosa of varying celiac disease severity, and normal duodenum were collected from a large central laboratory. Celiac disease slides (N = 318) were split into training (n = 230; 72.3%), validation (n = 60; 18.9%) and test (n = 28; 8.8%) datasets. Normal duodenum slides (N = 58) were similarly divided into training (n = 40; 69.0%), validation (n = 12; 20.7%) and test (n = 6; 10.3%) datasets. Human interpretable features were extracted and the strength of their correlation with Marsh scores were calculated using Spearman rank correlations. Our model identified cells, tissue regions and artifacts, including distinguishing intraepithelial lymphocytes and differentiating villous epithelium from crypt epithelium. Proportional area measurements representing villous atrophy negatively correlated with Marsh scores (r = − 0.79), while measurements indicative of crypt hyperplasia positively correlated (r = 0.71). Furthermore, features distinguishing celiac disease from normal duodenum were identified. Our novel model provides an explainable and fully automated approach for histology characterisation of celiac disease that correlates with modified Marsh scores, potentially facilitating diagnosis, prognosis, clinical trials and treatment response monitoring.

Deep learning via CNN for identification of blue core phenomenon in helicon plasma discharge

Based on deep learning image recognition techniques, a convolutional neural network model for discharge mode recognition of helicon plasma was trained. The accuracy of the model was evaluated using functions such as F1-scores and the confusion matrix. The final recognition accuracy was more than 98.18% after 30 iterations. Interpretable analysis was done using methods such as gradient-weighted class activation mapping to verify the model's robustness as well as repeatability. The model identification results were compared with Langmuir probe diagnostic results. It was found a good fit between the model and the probe results, corroborating the correctness of the model. The present model can well identify the critical power of entering W mode in the discharge process of helicon plasma. As the discharge database expands, it has great potential for recognizing the higher-order discharge modes based on deep learning.

The Lived Experience of Internationally Mobile Students

In response to calls for more individualized research on international student mobility (ISM), this article reports on a hermeneutic phenomenological study into the lived experience of two participants, one from Inner Mongolia and one from Ecuador, who migrated internationally for higher education. Within a constructivist epistemological framework, this study reveals how these participants describe and interpret their international mobility experience across four semi-structured interviews over a ten-year period. Four key themes were identified through interpretive data analysis: (1) study abroad as self-initiated and self-fulfilling; (2) key moments shaping the experience (10-year lens); (3) self-actualization via international student mobility; and (4) complex perceptions relating to “home.” A key contribution of this research is substantiating how individuals display agency within the context of ISM, both in their practical responses to circumstances and in their growing sense of self-actualization.

Geoscientist-driven machine learning-assisted fault interpretation: A case study from Atlanta Field to demonstrate the impact of fault labeling on the fault prediction cube

The energy industry is undergoing a digital transformation, leveraging cloud capabilities and artificial intelligence technology to overcome the challenges of conventional geoscience workflows. Machine learning (ML) methodologies have been developed and applied to various geophysical and geologic workflows, such as seismic processing, imaging, seismic interpretation, and petrophysical analysis. The traditional seismic interpretation approach requires manual interpretation of faults line by line at a fixed increment. Consequently, depending on the geologic complexity and size of the area of interest, the traditional approach can be a time-consuming and repetitive task. We focus on applying ML to seismic data for fault interpretation using a 2D convolutional neural network methodology to accelerate the fault interpretation process. This approach has the potential to significantly reduce the time and effort required to interpret faults, depending on the seismic data quality and geologic complexity, which is demonstrated by our study in the Atlanta Field, a postsalt asset in Brazil's northern Santos Basin. The basin has high structural complexity due to the salt tectonics, which required additional labeling interpretation to capture different fault patterns and train the algorithm to predict faults within the seismic volume. Additionally, the study provides the impact of the initial fault interpretation label on the ML result, such as in the fault extension and fault dip. Overall, the ML approach reduced the total effort from two weeks to four days, representing a time improvement of 60% from interpretation to structural framework workflow.

Predictive Modeling and Interpretative Analysis of Risks of Instability in Patients with Myasthenia Gravis Requiring Intensive Care Unit Admission

Predictive Modeling and Interpretative Analysis of Risks of Instability in Patients with Myasthenia Gravis Requiring Intensive Care Unit Admission

A IMIGRAÇÃO JAPONESA EM PORTO VELHO

This article aims to present the trajectory of Japanese immigrants to the Amazon region, more specifically to Porto Velho, Rondônia, at the beginning of the 20th century. This migration is analyzed as the result of a treaty signed between Brazil and Japan, considering the perspective of national interests on both sides. The article will examine the problems and difficulties faced by the families who arrived in the region, recount their history, and recognize the community's contribution to the city's development. The study seeks to contextualize the relationships between the governments involved, shedding light on issues relevant to contemporary debates about migratory rights in Brazil, the integration of foreign communities, and the fight against discrimination. This work presents accounts based on the commemorative edition celebrating 50 years of the September 13th Colony (1954-2004), provided by the Japanese-Brazilian Cultural Association of Rondônia-Nikkey. The methodology employed includes bibliographic procedures such as literature review, scientific articles, family accounts, normative interpretation, and reflective analysis. This research intends to contribute to the preservation of memory and the construction of a more complete and in-depth understanding of the history of Japanese immigration in Rondônia, thereby strengthening a more inclusive and plural society.

Orality in Song and the Advancement of Traditional History: The Experiences of Saidu Faru

Since antiquity, orality has been the only genre through which thoughts and ideas are commuted and disseminated by heart, either as songs or traditional historical antecedents to the nascent and generation unborn. It is usually in the form of traditional history by which information is collected, commuted and disseminated in song form through the mouth. Albeit, songs of variegated types have tremendously been instrumental to a plethora of transformations evident in all human global societies. This is a similitude of the invaluable multivariate songs sung by Mal. Saidu Faru- an African iconic song composer from Faru village in Talata Mafara in the present Zamfara State, Nigeria. Historically, some of his songs to some extent have indelibly contributed positively to the remoulding and repositioning of human life, psyche, and characters, and especially in strengthening, the governance, religion (Islam), cultural heritage and political advancement of northern Nigeria society. The nature of philosophical songs soothes the human mind, bringing solace and redirection of hopes through religious proclivity that has culminated in the serene ambience that makes Mal. Saidu Faru songs were indispensable. Based on the foregoing prism, the paper adopted the historical paradigm of data collection (primary and secondary sources) and equally used qualitative content analysis for interpretation. The paper recommended that Mal. Saidu Faru songs should be listened to, especially to derive the invaluable benefits in remoulding human character, repositioning societal malaises toward the creation of peace, and harmonious relations, as quintessential to good governance in Nigerian society.

Prediction of axial compressive capacity and interpretability analysis of web perforated Σ-shaped cold-formed steel

The Σ-shaped cold-formed steel with web opening (ΣSCFSWWO) is a critical component in modern structural systems, and accurate calculations of its axial compressive capacity are essential for ensuring the overall stability and safety of structures. However, traditional finite element analysis (FEA) and experimental methods are often inefficient and require complex modeling techniques for capacity calculations. Moreover, the interaction between section design parameters and structural capacity remains unclear. This study integrates FEA, experimental methods, and machine learning to propose a novel approach for predicting capacity and analyzing parameter interpretability. A dataset comprising 1000 numerical simulation results and 260 experimental data points was established. Machine learning techniques, including BP (Backpropagation) neural networks, RBF (Radial Basis Function) neural networks, Decision trees, Random Forests, and XGBoost algorithms, were employed to develop predictive models for capacity. The SHapley Additive exPlanation (SHAP) method was utilized for interpretability analysis. Results indicate a good correlation between FEA and experimental outcomes, effectively simulating the mechanical behavior of the components. Comparative analysis reveals that the XGBoost model demonstrates the highest predictive accuracy and generalization capability. SHAP analysis elucidates the influence of various input parameters on axial compressive capacity predictions and clarifies the interactions among these parameters. This research provides a novel reference for calculating the axial compressive capacity of ΣSCFSWWO, and the proposed methodologies can be extended to other similar components.

Epistemic Emotions in Learning: Using Qualitative Inquiry to Explore Implications for Veterinary Educators in Responding to Student Emotions in Their Classrooms.

Veterinary students frequently experience heightened emotions which can stimulate or compromise learning. The impact of student emotions on educators, or the ways that educators can respond to these is less well known. This has potential impacts for educators' own emotional responses, and for educators' effectiveness in supporting learning. To better support educators in facilitating student learning, this study sought to further understand how students' epistemic emotions are experienced by educators. We explored the experiences of educators from three international veterinary schools, using iterative interpretive analysis of workshop discussions designed and implemented for the purpose of the study. Analysis revealed that veterinary educators experience a range of emotions in the course of teaching their students, arising from events, such as emotional topics or clinical situations; receipt of grades; and the experience of uncertainty, e.g., in teaching methods or open-ended tasks. The educators' responses to these included feeling overwhelmed and anxious - wanting to help facilitate student learning but lacking the tools to do so. Consequently, educators felt unable to engage effectively with students, and learning was deactivated. This could occur even when students were interested and curious. Educators' responses were particularly challenged by time and assessment pressures (needing to remain on topic and teach to learning outcomes). Strategies for responding to student emotions and to support development of educator emotional intelligence have been generated, which include a need for institutional recognition of the time resources necessary for educators to reflectively learn from complex situations experienced in their classrooms.

Изложба „Истории“ на Лаура Димитрова и Стефан Алтъков

The text is an interpretive analysis of the artworks of the artists Laura Dimitrova and Stefan Altkov, created in the period 2022–2023 and presented in a general exhibition “Stories” in various gallery spaces in the country. The exhibition takes on a different look in each exhibition area, it is rearranged and its individual components and details are added, removed or replaced in order to achieve a spatial arrangement of the “narrative”. The artworks are characterized by a unique plastic language for each of the artists, individual preferences of means of expression and materials, but the exhibition acts as a dynamic and comprehensive work of visual arts in the spirit of postmodernism of the XXI century.

Machine learning the in-medium correction factor on nucleon-nucleon elastic cross section

Abstract The nuclear equation of state cannot be directly measured in the heavy-ion collision experiments; it is usually inferred from the comparison between transport model simulations and experimental measurements. The in-medium correction factor (F , which is defined as the ratio of the cross section in the nuclear medium to that in free space) on the nucleon-nucleon elastic cross section is one of the important inputs of the transport model, and its magnitude is still debated. The advent of Machine Learning (ML) has profoundly influenced the way scientists study the natural world and has provided a new paradigm that merges traditional investigation with advanced datadriven techniques. The aim of this work is to present a ML-based method to study the in-medium correction factor F . The ultra-relativistic quantum molecular dynamics (UrQMD) transport model is used to simulate 132Sn + 124Sn collisions at beam energy of 0.27 GeV/nucleon with different impact parameters. The value of F is randomly selected from 0.4 to 0.8 for the simulation of each event. Several observables simulated by the UrQMD model with different F , which are thought to be probably sensitive to the in-medium nucleon-nucleon cross sections, are fed into the LightGBM (Light Gradient Boosting Machine, which is a modern decision tree-based ML algorithm) to establish the mapping between the observables and F . The mean absolute error (MAE), which is the absolute difference between the true and the predicted F , is about 0.080 and 0.021 by using event-by-event and 40-event summed observables, respectively. It indicates that ML can recognize information about the F factor. Furthermore, according to the results of Shapley Additive exPlanations (SHAP), an interpretability analysis method in ML, features that have the greatest effect on the F are identified. ML combined with the transport model may open a new venue to study the F factor. In addition, the interpretability analysis of the ML algorithm may also offer valuable insights for subsequent research.

Internal corporate venturing: riding the relational rollercoaster through sensemaking and sensegiving reconfiguration

PurposeInternal corporate venturing is a vehicle for firms to realize strategic and financial goals through entrepreneurial ventures. Prior research presents a strategic process in which individual managers make rational choices based on their formal roles and top-down corporate objectives. Recent work has challenged this by adopting a relational approach using a macro-level perspective highlighting cultural and institutional logics. This study augments and develops this relational approach by contributing a micro-level perspective by focusing on managers engaged in developing ventures in large organizations. The data show how internal corporate venturing (ICV) actors use discursive practices to make sense of their relationship contexts and develop interpretive repertoires to give sense to their decisions and shape their future strategies. The data illustrate how corporate venturing actors make sense of their uncertain experience and develop insider-outsider strategies by balancing three competing interpretive repertories, which form the basis of strategies supporting an entrepreneurial future in an organizational context.Design/methodology/approachForty-two interviews were conducted with ICV actors, including senior directors of corporate venturing units in multinational corporations and their venture project leaders. The authors conducted a micro-level study through an interpretive sensemaking analysis of managers' “talk.” Interviews are considered through three lenses: “functional talk” (why they said it), “interpretive themes” (what they said) and “interpretive repertoires” (how they said it).FindingsThe perceived challenges experienced by the participants through their relationships were identified. Participants emphasized balancing project and organizational role risk in pursuing venture development, leading to a perceived dependent trust relationship between supporters. Three interpretive repertories were identified through which participants positioned their explanations of their relationship contexts in ICV. Participants used these to discursively frame their corporate venturing practices and position their future strategies.Originality/valueA new framework of corporate venture sensemaking and sensegiving reconfiguration is provided to explain how managers discursively resolve conflicting relationship pressures while maintaining personal positioning. The paper shows how conflicting interpretive repertoires and personal interpretations are generated through a discursive practice comprising sensemaking and sensegiving reconfiguration processes to shape their future strategies. The paper contributes to theory by explicating the relational perspective of ICV at the micro-level and demonstrates how this is influenced by the discursive practices of managers leading the ICV activity.

Measuring the Barriers in Adoption of IFRS for SMEs in Developing Economies: An ISM‐MICMAC Approach

ABSTRACTPresent study explores various macroeconomic determinants, which have a direct or indirect impact on the implementation of international financial reporting standards (IFRS) for small and medium enterprises, especially in emerging economies like India. Based on the identification of variables/measures from existing literature, the study further used multi‐criteria decision‐making approach's interpretive structural modeling (ISM) and MICMAC analysis using driving and dependence powers to frame hierarchy‐based model of barriers in IFRS adoption. The paper has developed an interrelationship among 11 identified variables using ISM methodology and found that a full set of IFRS (Factor 1) forms the base of the completely proposed model, as per expert input, with all other factors relating to it either directly or indirectly. While common law system (2), taxation compliances (5), and professional education (4) are weak factors, which depend upon strong factors, the study found strong factors, that is, full IFRS (Factor 1), institutional factors (Factor 8), access to foreign capital (Factor 3), and complexity (Factor 6). Additionally, four factors, such as implementation costs or cost benefit analysis (Factor 9), inadequate infrastructure (Factor 11), firm size and internationalization (Factor 7), and perception of accountants (Factor 10), are identified as linkage variables. Further, using dependent and driving powers of identified variables were analyzed with the help of MICMAC analysis. The present work will be helpful to academicians, practitioners, and various concerned stakeholders in the relevant field.JEL Classification: M 40, M 41, M 48