External Information Sources Research Articles

Drive sustainable business performance with humane entrepreneurship, knowledge management: the mediation of sustainable business advantage and sustainable business model innovation

PurposeThis study evaluates the important role of humane entrepreneurship (HE) on sustainable business advantage (SBA) and sustainable business performance (SBP) by exploring the mediating role of sustainable business model innovation (SBMI) under the influence of knowledge management (KM) in small and medium-sized enterprises (SMEs). Encouraging businesses to implement a combination of KM and HE facilitates the implementation of SBMI that brings value to businesses, communities and the environment in emerging economies.Design/methodology/approachThis study uses an empirical quantitative research method using primary information based on data collected by questionnaires. The study sample resulted in 424 valid responses (66.77% response rate). The target audience of the study is middle and senior managers in SMEs. This study aims to perform structural modeling analysis with the maximum likelihood estimates method to examine structural relationships.FindingsThis study found a positive relationship between KM and HE to SBMI. At the same time, SBMI plays an important role for businesses to achieve SBA and SBP. Similarly, a positive relationship was also found between HE to SBA and SBP. And SBA strongly promotes SMEs to achieve SBP. In addition, the mediating role of SBMI and SBA is clarified to have a positive impact between HE–SBP and SBMI–SBP. HE plays an important role in motivating businesses to innovate sustainable business models, making it easier to gain sustainable competitive advantages, thereby bringing sustainable business results.Practical implicationsThis research helps SMEs realize the importance of KM and HE in SBMI. At that time, SMEs take advantage of internal and external information sources and innovate their business models based on HE mechanisms. Applying HE mechanisms will allow businesses to innovate their business models by combining concerns between stakeholders and the social environment for sustainable development. Therefore, businesses will achieve their SBA toward SBP.Originality/valueThe present literature shows that there are few empirical studies on the mechanism mediating SBMI in the relationship between HE and SBP; and SBA in the link between SBMI and SBP. In addition, the impact of KM and HE on SBMI is also very important in the current context. The study's findings could have huge implications for entrepreneurs and senior managers in SMEs in emerging economies. Help them realize the importance of KM and HE for SBMI, thereby building strategic perspectives that help businesses achieve sustainable competitive advantages and increase SBP for businesses.

How does supply chain collaboration improve innovation performance of SMEs? The roles of absorptive capacity and business environment

There is increasing awareness that customers and suppliers serve as key external information sources for the innovation of small and medium-sized enterprises (SMEs). However, existing research on the influence of customer participation (CP) and supplier participation (SP) remains inconsistent. This study addresses this gap by investigating how CP and SP affect different types of innovation performance of SMEs in China. Furthermore, by addressing the gap regarding the unclear mechanisms of CP and SP in SMEs’ innovation performance, this study explores the mediating role of a firm's absorptive capacity on innovation performance and the conditions under which absorptive capacity will be more effective. Based on a sample of 1861 SMEs in China, the findings highlight that CP positively impacts product innovation performance, with absorptive capacity acting as a partial mediator. Additionally, both CP and SP influence process innovation performance directly or through absorptive capacity. Meanwhile, the business environment moderates the effect of absorptive capacity on product innovation performance. These findings contribute to the theory of open innovation and offer valuable insights for SME managers, entrepreneurs, and scholars in enhancing innovation outcomes.

Individual differences in emotion prediction and implications for social success.

The social world requires people to predict others' thoughts, feelings, and actions. People who successfully predict others' emotions experience significant social advantages. What makes a person good at predicting emotions? To predict others' future emotional states, a person must know how one emotion transitions to the next. People learn how emotions transition from at least two sources: (a) internal information, or one's own emotion experiences, and (b) external information, such as the social cues detected in a person's face. Across five studies collected between 2018 and 2020, we find evidence that both sources of information are related to accurate emotion prediction: individuals with atypical personal emotion transitions, difficulty understanding their own emotional experiences, and impaired emotion perception displayed impaired emotion prediction. This ability to predict others' emotions has real-world social implications. Individuals who make accurate emotion predictions have better relationships with their friends and communities and experience less loneliness. In contrast, disruptions in both internal and external information sources explain prediction inaccuracy in individuals with social difficulties, specifically with social communication difficulties common in autism spectrum disorder. These findings provide evidence that successful emotion prediction, which relies on the perception of accurate internal and external data about how emotions transition, may be key to social success. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Embracing the illusion of explanatory depth: A strategic framework for using iterative prompting for integrating large language models in healthcare education

Healthcare educators are exploring ways to integrate Large Language Models (LLMs) into the curriculum. At the same time, they are concerned about the negative impact on students’ cognitive development. There is concern that the students will not learn to think and problem-solve by themselves and instead become dependent on LLMs to find answers. In addition, the students could start accepting the LLM generated responses at face value. The Illusion of Explanatory Depth (IoED) is a cognitive bias where humans believe they understand complex phenomena in more depth than they do. This illusion is caused when people rely on external sources of information rather than deeper levels of internalized knowledge. This illusion can be exposed by asking follow-up in depth questions. Using the same approach, specifically iterative prompting, can help students interact with LLM’s while learning actively, gaining deeper levels of knowledge, and exposing the LLM shortcomings. The article proposes that educators encourage use of LLMs to complete assignments using a template, that promotes students’ reflections on their interactions with LLMs, using iterative prompting. This process based on IoED, and iterative prompting will help educators integrate LLMs in the curriculum while mitigating the risk of students becoming dependent on these tools. Students will practice active learning and experience firsthand the inaccuracies and inconsistencies in LLM responses.

Decision-making regarding accessing paediatric unscheduled healthcare during the COVID-19 pandemic: a mixed methods rapid review and thematic synthesis

ObjectiveReductions in paediatric unscheduled healthcare utilisation were seen during the COVID-19 pandemic, with concerns around their impact on children’s health. The reasons for these changes are not well described. This...

Statistical learning shapes pain perception and prediction independently of external cues.

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don’t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Mapping Topsoil Behavior to Compaction at National Scale from an Analysis of Field Observations

Soil compaction is one of the most important and readily mitigated threats to soil health. Digital Soil Mapping (DSM) has emerged as an efficient method to provide broad-scale maps by combining soil information with environmental covariates. Until now, soil information input to DSM has been mainly composed of point-based quantitative measurements of soil properties and/or of soil type/horizon classes derived from laboratory analysis, point observations, or soil maps. In this study, we used field estimates of soil compaction to map soil behavior to compaction at a national scale. The results from a previous study enabled clustering of six different behaviors using the in situ field observations. Mapping potential responses to soil compaction is an effective land management tool for preventing future compaction. Random forest was used to make spatial predictions of soil behavior to compaction over cultivated soils of mainland France (about 210,000 km2). Modeling was performed at 90 m resolution. The map enabled us to spatially identify clusters of possible responses to compaction. Most clusters were consistent with known geographic distributions of some soil types and properties. This consistency was checked by comparing maps with both national and local-scale external sources of soil information. The best spatial predictors were available digital maps of soil properties (clay, silt, sand, organic carbon (SOC) content, and pH), some indicators of soil structural quality using SOC and clay content, and environmental covariates (T °C and relief-related covariates). Predicted maps were interpretable to support management recommendations to mitigate soil compactness at the soil–scape scale. Simple observational field data that are usually collected by soil surveyors, then stored and available in soil databases, provide valuable input data for digital mapping of soil behavior to compaction and assessment of inherent soil sensitivity to compaction.

What Motivates Men to Improve Their Health? Understanding the Roles of Self-Esteem and Influential Others in Behaviour Change.

The aim of this study was to examine men's body image confidence, social reflectivity, body image perceptions and external information sources influence regarding body assessments. Data were collected via a cross-sectional survey and found that men have a low motivation toward physical health behaviour changes such as food, alcohol and exercise evaluation, and have generally positive views of their bodies overall. Relationship status, rather than age, defined behavioural and attitudinal differences within the men studied here. Men in this study were largely uninfluenced by celebrities or fashion in developing their own body image perceptions; single males were more likely to turn to friends, the female population generally and societal norms when evaluating themselves. Males in relationships however, weighted their partner's opinion as the greatest influence, followed by their involvement in sport. This study offers an insight into the role of body confidence in male perspectives of the self, which is important for its intrinsic connection to motivations for health behaviours such as body weight management. This has implications for increasing the effectiveness of health-related product and service messaging, and public health messages regarding body weight management for men.

Comparing statistical learning methods for complex trait prediction from gene expression.

Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.

External and internal sources of cognitive group awareness information: Effects on perception and usage

Group selection or group formation is an important but difficult task in learning groups. Group awareness tools collect, transform, and present group awareness information to provide learners, but also teachers with relevant information e.g., about potential learning partners. For these educational tools, the value and usage of the provided information may depend on how it is gathered and where it ultimately comes from. In our study (N = 150), we thus investigate how information from different sources is perceived and ranked. In the study, information about the skills of an anonymous person in a profile was either provided by external sources (teacher assessment, knowledge test result) or internal sources (self-assessment). Results show that information from external sources is perceived as more credible and weighted higher than internal self-assessed information. No difference between information from external personal teacher assessments and external non-personal knowledge tests was found. Hence, it is worth exploring possible effects of other external ratings than teacher assessments to see if these insights are transferable to other contexts.

Application of wireless sensor network based on improved genetic algorithm in English blended learning

Adaptive genetic algorithm is one of the classic algorithms to solve multi-objective optimization problems. In order to make the convergence set to the Pareto boundary more uniform, this article improves how to compare multiple distances in the algorithm, introduces the concept of comparing the difference between single distances, and uses the adaptive and mutation of crossover operators and the minimum distance filtering mechanism. An improved NSGA-II adaptive algorithm is proposed. Among other things, in the machine learning system model, the environment refers to an external source of information, which provides research-related information. This study proved that learning requires systematic organization, similar to short-term memory in information processing models. Find the external environment, then use the methods of thinking analysis, integration, comparison and induction to obtain knowledge, and store the knowledge in a scientific way. With the rapid development of knowledge in modern society, on the basis of the development of English text analysis technology and network technology, the reform of China's education system has reached a new level. Reforming modern teaching methods is an important part of education reform. More and more educators are considering analyzing English texts as one of the important tools to reform teaching methods and improve teaching effects. Among other things, based on the B/S model development technology, the teaching system is researched using production theories and methods, and the teaching system is actually being tested, developed and applied. By comparing the effects of computer network teaching methods and traditional teaching methods, the feasibility and effectiveness of the network teaching system are determined.

The impact of external factors on board decision making in non-profit sport organizations

PurposePressures from non-profit sport organizations’ (NPSOs) external environment influence governance structures and processes. Thus, this study explores the impact of external factors on NPSO board decision making.Design/methodology/approachUsing a sample of six NPSO boards (two national, four provincial/territorial), data were collected via 36 observations, 18 interviews, and over 900 documents. A thematic analysis was conducted via NVivo 12.FindingsResults identified two external factors impacting NPSO board decision making: the sport system structure and general environment conditions. External factors impacted NPSO board decision making in terms of duration, flow, interaction, and scrutiny.Originality/valueResults demonstrate the need for NPSO boards to engage in boundary-spanning activities whereby external information sources from stakeholders are incorporated to make informed decisions. Practically, NPSO boards should harness virtual meetings to continue their operations while incorporating risk management analyses to assess threats and opportunities.

Transfer learning with high dimensional composite quantile regression

Transfer learning has gained wide popularity due to its ability to enhance the performance of target tasks by utilizing external information sources. Despite this, current transfer learning methods often struggle to consistently and effectively handle heterogeneous and/or heavy tail data. To address this issue, we propose a robust transfer learning method based on the composite quantile regression model that can adapt to a variety of data types and integrate multiple quantile information. Additionally, this method can handle situations where the error term variance is infinite. A challenging scenario in transfer learning is that the transferable data set is unknown, so we design two detection algorithms to correctly identify valid transferable data sets. Through numerical simulations and empirical analysis, we verify the good performance of the above algorithms based on the high-dimensional composite quantile regression model.

Safety–Function–Environment Evaluation System for Large-Span Cable-Supported Bridges: Theory and Case Studies

Compared with common bridges, large-span cable-supported bridges contain more components, are located in a more complex environment, and play a more important role in traffic system sustainability. Throughout the service life, it is more necessary to evaluate their safety, functionality, and environmental status. In this study, a comprehensive evaluation system is proposed to fill the gap using advanced sensor-guided structural health monitoring data and probability-based digital twins. Safety evaluation is the basis of the system and can be divided into overall and component levels. The former includes an over-limit analysis of main structural responses and degradation identification of dynamic characteristics. The abnormal areas discovered in this phase and the hot spots prompted by prior information during the design process will be checked in the latter. The functional assessment of this system is mainly based on checklist-type inspection and is often carried out together with appearance inspection and non-main structural component detection. Environmental assessment includes the monitoring and analysis of wind fields, temperature, humidity, foundation scour, and traffic flow and is the source of external information in the aforementioned two modules. The temperature and humidity of the example bridges are basically uniformly distributed along the spatial dimension, fluctuating over a period of one day.

Traditional Ecological Knowledge of the Tengger Tribe and its Influencing Factors in Bromo Tengger Semeru National Park

An understanding of the local traditional ecological knowledge (TEK) of a region is crucial to understand the interactions between indigenous societies and their ecosystems. To understand the TEK of Indonesian indigenous people and its influencing factors, we evaluated the Tengger people’s knowledge and conservation awareness regarding culturally important plants (Javanese edelweiss). We interviewed 641 people from seven Tengger villages located adjacent to Bromo Tengger Semeru National Park (BTS), Indonesia. We developed 31 models to examine factors affecting edelweiss knowledge and calculated the Akaike information criterion values to select the best model. Sex, education, and residency duration were significant predictors of edelweiss knowledge. Men, individuals with higher levels of education, and those who had lived in the area for a longer period of time had higher levels of edelweiss knowledge. Our findings confirm that communication within the community and external information sources, such as media and formal education, are involved in knowledge transmission related to edelweiss. Educational activities for local people can be used to disseminate TEK through formal education in schools and informal education programs conducted by the national park managers.

Top2Label: Explainable zero shot topic labelling using knowledge graphs

Automatic topic labelling aims to generate coherent, interpretable, and meaningful labels to facilitate the interpretation of various topics within a corpus of documents. Typically, we represent a topic as a list of terms and documents ranked according to probability. In our research, we employ Top2Vec for topic modelling. We propose a novel three-phase, zero-shot topic labelling framework leveraging the ConceptNet knowledge graph (a comprehensive semantic network of words and phrases) and pre-trained language models as external sources of information. The first phase enriches the top n words of a given topic (based on probability) by expanding their neighbourhood in ConceptNet, bridging missing connections and information gaps, and subsequently yielding a semantically enhanced set of potential labels through the generation of a sub-graph. The second phase constructs a neighbourhood graph (organized as a subgraph of ConceptNet) for each candidate label, evaluating each node’s semantic similarity to the topic and retaining the best sub-graph according to semantic similarity. In this phase, a one-word label is extracted from the final graph, concisely representing the topic. In the third phase, we use the language model to derive the final labels by taking the optimal graph as input. In the third phase, we utilize the language model to derive the sentence and summary labels using the optimal graph as input. These additional labels offer more comprehensive and contextually rich topic representations, facilitating more profound understanding and interpretation. By harnessing the power of knowledge graphs and language models, our framework expands the knowledge beyond the topic documents, optimizing the discovered topics with more representative terms while preserving the topic information. The proposed zero-shot approach (employing pre-trained language models and the ConceptNet knowledge graph without additional training) alleviates computational burdens. It reduces the cognitive and interpretative load on end-users by generating three types of labels for each topic: a one-word, sentence, and summary. Experimental results demonstrate that our model significantly surpasses unsupervised baselines and traditional topic labelling models while remaining competitive with supervised baselines in topic labelling performance.

Unboxing Thailand’s Startup Ecosystem: The Next Chapter Towards Decentralisation of Innovations

This article reviews the evolution of startup ecosystem development in Thailand from the early 2010s to the beginning of 2020s. We summarise our findings and provide critical analyses about the innovation development in Thailand, using internal and external sources of information about Thailand’s innovation strategies, policies, and case studies. We also include macro-level national policies and changes in the government that affect the foundation of Thailand’s startup ecosystem, including the mechanisms that drive the growth of the ecosystem. We argue for the decentralisation of innovation policies and ecosystems to help navigate through new challenges and uncertainties, for the recovery of socio-economic factors. Different types and examples of startups that are key to the decentralisation of innovations in Thailand are also explained and illustrated. Impediments and caveats of innovation ecosystem development in Thailand are discussed, pointing the directions for future research.

Субъект правосознания: социально-философский анализ

The author turns to the analysis of the problems of the subject of legal consciousness in a socio-philosophical context. The concepts of public and individual consciousness are differentiated in the light of the definition of their attitude to legal reality. The influence of external sources of information (mass media, Internet) on the formation of the subject’s legal consciousness is highlighted. Various points of view of legal theorists on the correlation of social and individual are considered. The author’s understanding of it as a paradigm “subject – his social nature” is outlined. The author emphasizes the substantive and functional separation of natural and positive law, which is positioned as the basis for determining subjectivity within the framework of their applica-tion. The question of the existence of forms of positive law focused on the regulation of social relations in their diversity is outlined. The role of practices as social forms of the existence of law and legal consciousness, as well as hermeneutic spaces and procedures that define the existence of law is substantiated.

Enterprise management system analytical tools

High-quality information and analytical content can be used as a basis for making optimal and informed management decisions by analysing the impact of factors on business efficiency, which will allow us to explore the weaknesses of an enterprise and plan the directions of its strategic development. The study aims to provide a scientific synthesis of theoretical aspects and outline practice-oriented approaches to organising analytical support for the management system at an enterprise. The following methods were used: theoretical aspects were analysed using the generalisation, systematisation and grouping methods; the use of statistical analysis and business process modelling methods for the needs of enterprise management was analysed; the components of Data Science analysis were considered; the main material was presented using the descriptive method. Analytical tools of the enterprise management system were analysed. The emphasis is placed on the procedures of statistical analysis and modelling of business processes, application of Data Science tools and visualisation methods. The external and internal sources of information for the needs of enterprise management are allocated. Varieties of analytical tools that contribute to understanding the essence of problematic issues and determine the search for optimal and reasonable management decisions are presented. The SWOT-analysis matrix as an instrument for optimal combination of information modelling methods, analytical procedures and visualisation of the analysis results is presented. The need to use IT tools to improve the quality of the results of analytical procedures is emphasised. The expediency of developing analytical tools for the needs of optimising the enterprise management system using Data Science technology is substantiated. The advantages of using Big Data for making management decisions are determined. The components of Data Science analysis in the enterprise management system are allocated. The possibility of solving business problems or determining the enterprise development strategy by structuring and visualising a large array of data to identify patterns of enterprise development is emphasised. The tools of analytical support for the needs of organising the system of economic security of enterprise are generalised. The practical value of the work lies in the fact that an algorithm of actions for organising analytical support of the management system at an enterprise is proposed