External Knowledge Sources Research Articles

Internal versus external knowledge sourcing of organizational rules: an exploratory study of CPGs in a healthcare organization

Abstract In this study, we examine how organizational rules source knowledge. By knowledge sourcing of a rule, we mean the formation of reference ties from the rule to knowledge sources located outside of the focal rule. Rules can source knowledge from sources within the organization (e.g., other rules) and outside (e.g., research publications, policies, standards, etc.). Our theoretical model proposes that knowledge sourcing of rules is driven by inherent incompleteness of rules as a result of bounded rationality of rule makers and rule making process. Incomplete rules can lead to experiences of insufficient rule knowledge, termed “knowledge gaps,” which are shaped by rule dynamics at the levels of individual rules, the rule system, and rule networks. Our theoretical model leads to several hypotheses that we test with longitudinal archival data of clinical practice guideline (CPG) changes in a Canadian healthcare organization. The findings support our theoretical model of incomplete organizational rules which encounter knowledge gaps and close them through internal and external knowledge sourcing. The theoretical and practical implications of the findings are discussed.

A knowledge-augmented neural network model for sarcasm detection

Automatic sarcasm detection from text is one important research task in text mining and natural language processing and has attracted extensive attention from researchers. Most approaches focus on designing various models and features according to the original text, without making use of the knowledge and information from external knowledge source such as Wikipedia, which is publicly available. In this paper, we investigate a knowledge-augmented neural network model that leverages the contextual information of the original text from external knowledge source, for sarcasm detection. We first extract the context from external knowledge source for the original text. Then, the original text and its context are fed sequentially into the embedding layer and encoding layer, to automatically learn high-level semantic representation. Finally, we use the softmax layer to output the classification probability. Based on the Semeval-2018 Task 3 dataset, results show that our proposed model gives 82.79% F1 score, outperforming the existing models and strong neural baselines with significant margins. Experimental analysis also indicates that the contextual information is highly important for sarcasm detection.

A hybrid classification method via keywords screening and attention mechanisms in extreme short text

Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.

Firm survival and innovation: direct and indirect effects of knowledge for SMEs

ABSTRACT This paper explores the effects of innovation on firm survival by using data from a representative survey of small and medium manufacturing firms in the province of Salerno, Italy. We innovate upon the literature by (a) comparing the impact of different sources of internal and external knowledge (including universities) on the probability of firm survival; (b) assessing the mediating impact of the human capital of workers and entrepreneurs on learning from these knowledge sources. Finally, we measure the impact of different types of innovation on firm survival. Our evidence upholds the link between innovation and firm survival, particularly for product and organisational innovation. Results regarding the impact of different sources of knowledge highlight the roles of employee training, the human capital of entrepreneurs and workers and the productivity of university departments providing relevant knowledge. Other elements of external knowledge, such as proximity to the University of Salerno or being in the city of Salerno, are significant facilitators of survival only if mediated through high levels of the human capital of entrepreneurs and workers.

DEVELOPMENT OF A QUESTION ANSWERING CHATBOT FOR BLOCKCHAIN DOMAIN

Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models.

Understanding Regional Branching: Knowledge Diversification via Inventor and Firm Collaboration Networks

The diversification of regions into new technologies is driven by the degree of relatedness to existing capabilities already present in the region. In cases where opportunities for diversification are rather limited, external knowledge that spills over from neighboring regions or from farther away might become an important driver of regional diversification. Despite the relative importance of interregional knowledge flows via collaborative work, we still have a very limited understanding of how collaboration networks across regions might facilitate diversification processes. The present study investigates the diversification patterns of European metropolitan and nonmetropolitan regions into new knowledge domains via technology classes reported in patent applications to the European Patent Office. The findings indicate that externally oriented inventor collaboration networks increase the likelihood that a new technology specialization enters a region, but this external orientation is less important for related diversification than for unrelated diversification. Further, the results demonstrate that interregional collaboration networks help diversification into unrelated technologies if external knowledge sourcing is based on a diverse set of regions and if collaboration is intense within companies located in distinct regions. Within-firm collaborations across regions can compensate for missing related skills in metropolitan and in nonmetropolitan regions alike but are especially important in nonmetropolitan regions. These results provide new evidence about the importance of knowledge flows within multilocation firms in the technological knowledge diversification of regions.

External knowledge and eco-innovation: evidence from small and medium-sized enterprises

Purpose Firms are engaging in eco-innovation as a response to environmental concerns. Due to its complexity, firms are searching, absorbing and using externally acquired knowledge toward implementing eco-innovation. Thus, this study aims to examine how different external knowledge sources affect the eco-innovation of small and medium-sized enterprises (SMEs) in developing economies. Design/methodology/approach The study uses an Ordered Probit regression analysis for 318 developing economy SMEs to examine the impact of vertical flows (suppliers and customers) and horizontal flows (competitors and scientific organisations) on eco-innovations. Findings On the vertical dimension, the results show that external knowledge from suppliers is positively associated with eco-product and eco-process innovations. However, external knowledge from customers is positively associated with eco-product innovation, but not eco-process innovation. On the horizontal dimension, external knowledge from competitors contributes to eco-process innovation, but not to eco-product innovation. Finally, external knowledge from scientific organizations is positively related to eco-product and eco-process innovations. Originality/value First, the study contributes to the external knowledge literature by focusing on developing country SMEs. Second, the methodology used in this study constitutes a novelty as it provides a tool that categorizes firms according to the extent of emphasis they placed on both eco-product and eco-process innovations. Finally, it offers new evidence by revealing that the effects of external knowledge on eco-innovations are differentiated and not equally beneficial to firms.

Searching wide and deep for business model innovation

ABSTRACT To remain competitive, firms must innovate their business models (BMs) to meet the demands of the external environment. Given the severity of external threats or opportunities, managers need to determine the scope of BM innovation (BMI) – how many elements of a BM need to be changed – and the degree of novelty required. However, internal firm knowledge may not be sufficient to conceive of and implement new BMIs. Seeking external knowledge can offer managers diverse perspectives and expertise that should foster BMI, a topic that has been insufficiently addressed in the literature. To address this gap, we investigate whether firms that engage in external knowledge search are more likely to engage in BMI and whether different external knowledge sourcing strategies are associated with different BMI types. Analysing Norwegian firm-level data, we find a close association between a firm’s choice of external knowledge search activity in terms of breadth and depth and the scope and novelty of a BMI. The wider a firm’s search, the wider its BMI scope, and the deeper that search, the more novel its BMI. Our findings contribute new, empirically supported insights on external knowledge search as an important antecedent to BMI. For practitioners, our findings illustrate how different search strategies help firms initiate and implement different types of BMI.

Malaysian SMEs’ Strategic Orientation and Access to Fintech Financing: Moderated by Entrepreneurial Innovativeness

With the advent of Fintech financing, innovativeness plays a significant role in enhancing Small and medium-sized enterprises' (SMEs) access to financing. Three comprehensive constructs, namely, market orientation, learning orientation and technology orientation, are adopted to present a holistic picture of the effect of strategic orientation on Malaysian SMEs’ access to Fintech financing, moderated by entrepreneurial innovativeness. This study adopted a survey research design based on 221 Malaysian SMEs. The results indicate that strategic orientation influence SMEs’ access to Fintech financing. However, the moderating impact of entrepreneurial innovativeness is insignificant in the relationship between strategic orientation and access to Fintech financing. Technological innovation is re-shaping commerce and the economy across all sectors. SMEs need to access external sources of information, knowledge, expertise and technology in order to build their own innovative capability for accessi

Entrepreneurial Absorptive Capacity As Enabler of Knowledge Intensive Entrepreneurship: An Empirical Investigation

This paper builds on Qian and Acs (Small Business Economics, 40, 185–197, 2013) concept of entrepreneurial absorptive capacity and empirically investigates its role in the context of new knowledge intensive entrepreneurship. When it comes to the business formation phase and especially to the creation of knowledge intensive entrepreneurial ventures, knowledge inheritance of new firms seems to be more related to individual attributes. The knowledge intensity of these ventures is very much dependent on the founders’ capability to understand new knowledge, combine their knowledge with external knowledge sources, and exploit it in the context of opportunities they can recognise; that is, their absorptive capacity. This study provides new insights into the concept of absorptive capacity by studying it at the individual level enhancing the way of measuring it. Based on quantitative data from a large-scale survey, we conducted a partial least squares structural equation modelling (PLS-SEM) application, which verified the role of entrepreneurial absorptive capacity in triggering knowledge intensive entrepreneurship. Furthermore, the results imply a weaker but positive indirect effect of absorptive capacity to new venture performance. There is also a statistically significant negative influence of competition intensity and a positive influence of equity funding on the formation of knowledge intensive entrepreneurship. At the policy level this analysis reveals the importance of micro-determinants in fostering knowledge intensive entrepreneurial activities.

Knowledge-based optimization algorithm for the inventory routing problem

AbstractThe Inventory Routing Problem (IRP) is a combinatorial optimization problem that combines routing decisions with inventory management. In this paper, an approach to solving the IRP is studied, which aims at using an external knowledge source (a known good solution or user interaction) to improve the results attained by an evolutionary algorithm solving an IRP instance. The proposed method improves the best solution found by the evolutionary algorithm by modifying schedules for some of the retailers according to those present in the known good solution or to schedules provided by a domain expert. The experiments shown that to improve the optimization results it suffices to perform a few repetitions of the knowledge import procedure. This observation motivates further research on user-interactive optimization algorithms for the IRP, because the number of interactions needed to improve the results can easily be handled by the user.

Knowledge Sources and Manufacturing Enterprise’s Technological Innovation

<p class="MsoNormal"><span style="font-family: Cambria;">This paper uses the questionnaire data of the World Bank</span><span style="font-family: Cambria;">’</span><span style="font-family: Cambria;">s China Enterprise Survey 2012 to establish a Logit model to empirically study the relationship between knowledge sources and technological innovation in Chinese manufacturing enterprises. The results show that although internal and external knowledge has a positive effect on enterprise technological innovation in general, there are significant differences when taking the types of innovation, subject characteristic and the types of cooperation into consideration. </span>From the current development stage of China's manufacturing enterprises, external knowledge sources, external enterprise knowledge subjects, and employee knowledge have a more positive effect on technological innovation, which also shows that the role of technical employees on technological innovation has a significant threshold effect, and only after a certain degree of accumulation can internal technical employees have a more significant positive effect on technological innovation.</p>

Coping with the heterogeneity of external knowledge sources: Corresponding openness strategies and innovation performance

Previous studies on open innovation and strategic management have emphasized the positive impact of external knowledge on firms' innovation performance. However, given that external knowledge is widely distributed across a myriad of heterogeneous sources, it presents challenges for both the openness strategy and knowledge accessibility. When designing a firm's openness strategy, the heterogeneity of these external sources must be considered, choosing a form of openness that aligns with the attributes of the targeted sources. The question remains, which form of openness aligns best with which knowledge source and leads to improved innovation performance? Our regression analysis, based on a sample of 3294 German firms from the Mannheim Innovation Panel (MIP) in 2013 and 2017, reveals that utilizing a search strategy toward market sources of knowledge influences innovation performance more positively than a collaboration strategy. Conversely, only collaboration with science-based sources of knowledge has a significant positive impact on innovation performance. We further delve into the implications of these observed results and describe the practical implications for innovation managers.

The Advance of Multi-Round Dialogue System with Deep Learning

Multi-turn dialog systems have seen significant advances in recent years, driven by various approaches. The Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method have all shown promising results in advancing the state-of-the-art in this field. However, challenges remain in developing models that can handle complex and diverse user inputs and generate responses that are not only informative but also engaging and natural. Further research is needed to address these challenges and advance state of art in multi-turn dialogue systems. This paper reviews four critical methods for improving the quality of multi-turn dialogue systems: Multi-layer Semantic Method, Reinforcement Learning Method, Knowledge Graph Method, and Medicine Knowledge Graph Method. The Multi-layer Semantic Method utilizes multi-layer neural networks to model dialogue context and generate responses with improved coherence and relevance. The reinforcement Learning Method employs a reward-based approach to optimize response generation by training models to maximize long-term dialogue success. The knowledge Graph Method incorporates external knowledge sources, such as knowledge graphs, to enrich the dialogue context and improve response quality. The Medicine Knowledge Graph Method focuses on integrating medical knowledge into dialogue systems for healthcare applications. Each of these methods has demonstrated promising results in enhancing the quality of multi-turn dialogue systems.

Post Method Pedagogy: Opportunities and Challenges in EFL Context of Nepal

The purpose of this article is to familiarize the popular concept, Post Method Pedagogy, that has been emerging in the field of English language teaching and its importance in a developing country like Nepal where English language is regarded as the foreign language. The idea of post-method pedagogy was first put forward by Kumaravadivelu in 1994. It is an attempt to go beyond the quest for the best method best restrictions in language teaching. Pedagogy in this premise is not limited to classroom strategies, institutional materials, curricular objectives, and evaluation measures, but also covers traditionally overlooked areas such as historical, political, and socio-cultural experience. It is a three-dimensional system with three pedagogic parameters: particularity, practicality, and possibility. The parameter of particularity rejects the notion of a perfect method and instead emphasizes that teachers’ critical awareness of environmental factors should be the primary consideration for language pedagogy. Next, the dimension of practicality is presented as a way for teachers to develop in their practice by theorizing from their practice and practicing what they theorize instead of relying on external sources of knowledge. Third, possibility stance emphasizes that, via critical pedagogy, social inequality can be exposed, the status quo can be questioned, individuals have the possibility to reform their identities, and social transformation can take place. The boundaries of the three parameters are somewhat unclear and the characteristic features of these parameters overlap. They shape and are shaped by one another. They interact with each other. However, it can be said that they together constitute the features of ELT classrooms in the Nepalese context with some basic challenges.

Content-based and Knowledge-enriched Representations for Classification Across Modalities: A Survey

This survey documents representation approaches for classification across different modalities, from purely content-based methods to techniques utilizing external sources of structured knowledge. We present studies related to three paradigms used for representation, namely (a) low-level template-matching methods, (b) aggregation-based approaches, and (c) deep representation learning systems. We then describe existing resources of structure knowledge and elaborate on the need for enriching representations with such information. Approaches that utilize knowledge resources are presented next, organized with respect to how external information is exploited, i.e., (a) input enrichment and modification, (b) knowledge-based refinement and (c) end-to-end knowledge-aware systems. We subsequently provide a high-level discussion to summarize and compare strengths/weaknesses of the representation/enrichment paradigms proposed, and conclude the survey with an overview of relevant research findings and possible directions for future work.

External knowledge sourcing, organizational ambidexterity and manufacturing performance: a new insight for dynamic operation management

PurposeThis study intends to examine the relationships between external knowledge sourcing (EKS), organizational ambidexterity (OA), and manufacturing performance (MP) in the context of large manufacturing firms within a dynamic environment setting. The research framework and derived hypotheses are grounded in the knowledge-based view (KBV) and dynamic capability (DC) theories.Design/methodology/approachA self-administered online survey was used in this study to gather data. Respondents were the operation leaders representing large manufacturing firms. The collected data were screened for invalid responses, and hypotheses were tested using structural equation modeling.FindingsThe study reveals that OA and EKS play key roles in achieving a better MP. Likewise, OA also mediates the relationship between EKS and MP.Research limitations/implicationsCross-sectional data were collected from large manufacturing firms within five focus sectors in Malaysia. A similar study can be conducted with more sectors of different contexts to confirm the findings.Practical implicationsKnowledge is critical for the firm to react to environmental dynamism, and the ability to manage it ambidextrously will enable the firm to enhance its performance.Originality/valueThis study offers empirical insights from the perspective of the large manufacturing firms in Malaysia, which are undergoing an Industrial Revolution 4.0 (IR4.0) transformation. This study bridges the knowledge gap by revealing the value that EKS can facilitate MP, exploring OA as the prevalent factor and demonstrating how KBV and DC can be applied in this study.

ROLE OF INNOVATION IN ENTREPRENEURSHIP DEVELOPMENT

The strength of researchers and entrepreneurs is needed in order to overcome technological problems and to develop products with a great competitive advantage. This would achieve international market success. Success is achieved by the support of scientists and research institutions due to the nature of people and their efforts, not because of the innovation policy itself. The assumption is that companies should use external and internal ideas, as internal and external channels, to improve innovation and market potential. Companies promote their own innovative activities by establishing a new business model. Companies cooperate with other participants on the market, exchange ideas and satisfy the needs of consumers in a better way. Companies have to work a lot on improving internal structures of knowledge and technology, i.e., on improving their innovative capacity. By establishing an appropriate knowledge base and considering innovative possibilities, they can intensively deal with connecting to external sources of knowledge and technologies. By combining internal and external sources, they create new value and thus improve their own innovation culture.

A Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks.

This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.

EPPO ontology: a semantic-driven approach for plant and pest codes representation.

The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.