Integration Of External Knowledge Research Articles

Effects of alliance portfolio breadth and depth on exploratory and exploitative innovation: Evidence from Indian high-tech sectors

Firms increasingly pursue collaborative innovation using alliance portfolios (APs) to enhance innovation outcomes. AP breadth and depth are two important dimensions of collaborative innovation. AP breadth enhances a firm’s access to a variety of external knowledge whereas AP depth helps integration of external knowledge. This study examined how AP breadth and depth, separately and jointly, affect firm innovation. Drawing primarily on the organisational learning perspective, we suggested that AP breadth enhances a firm’s exploratory innovation but lowers its exploitative innovation. However, AP depth improves a firm’s exploitative innovation but diminishes its exploratory innovation. Further, we suggested that the interaction effect of AP breadth and depth is negative for exploratory innovation whereas it is positive for exploitative innovation. An empirical investigation conducted on a longitudinal dataset of Indian firms from high-tech sectors broadly supported our theoretical arguments.

GPT for medical entity recognition in Spanish

AbstractIn recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.

BERT for Twitter Sentiment Analysis: Achieving High Accuracy and Balanced Performance

The Bidirectional Encoder Representations from Transformers (BERT) model is used in this work to analyse sentiment on Twitter data. A Kaggle dataset of manually annotated and anonymized COVID-19-related tweets was used to refine the model. Location, tweet date, original tweet content, and sentiment labels are all included in the dataset. When compared to the Multinomial Naive Bayes (MNB) baseline, BERT's performance was assessed, and it achieved an overall accuracy of 87% on the test set. The results indicated that for negative feelings, the accuracy was 0.93, the recall was 0.84, and the F1-score was 0.88; for neutral sentiments, the precision was 0.86, the recall was 0.78, and the F1-score was 0.82; and for positive sentiments, the precision was 0.82, the recall was 0.94, and the F1-score was 0.88. The model's proficiency with the linguistic nuances of Twitter, including slang and sarcasm, was demonstrated. This study also identifies the flaws of BERT and makes recommendations for future research paths, such as the integration of external knowledge and alternative designs.

Sentiment interpretability analysis on Chinese texts employing multi-task and knowledge base.

With the rapid development of deep learning techniques, the applications have become increasingly widespread in various domains. However, traditional deep learning methods are often referred to as "black box" models with low interpretability of their results, posing challenges for their application in certain critical domains. In this study, we propose a comprehensive method for the interpretability analysis of sentiment models. The proposed method encompasses two main aspects: attention-based analysis and external knowledge integration. First, we train the model within sentiment classification and generation tasks to capture attention scores from multiple perspectives. This multi-angle approach reduces bias and provides a more comprehensive understanding of the underlying sentiment. Second, we incorporate an external knowledge base to improve evidence extraction. By leveraging character scores, we retrieve complete sentiment evidence phrases, addressing the challenge of incomplete evidence extraction in Chinese texts. Experimental results on a sentiment interpretability evaluation dataset demonstrate the effectiveness of our method. We observe a notable increase in accuracy by 1.3%, Macro-F1 by 13%, and MAP by 23%. Overall, our approach offers a robust solution for enhancing the interpretability of sentiment models by combining attention-based analysis and the integration of external knowledge.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Activation triggers: reconceptualization and review

Purpose- Businesses depend more on knowledge than other factors. Many academics have described the idea of absorptive capacity as a skill for handling outside knowledge. Since organizational phenomenon is a complex , the issue of absorptive capacity as a strategic and dynamic capability still merits further research consideration. Previous research shows that some of the variables are beyond the firm's control and moderate the effect of knowledge source and experience on absorptive capacity. The intensity of the triggers may have an impact on the firm's investment in absorptive capacity capability. The purpose of this study is to review and identify endogenous and exogenous contingencies that function as activation triggers on absorptive capacity based on the previous literature. Methodology- The theoretical model, hypothesis, and measurement indicators for the study's variables are described in this paper. Findings- The study of previous research shows that learning organization structure, business environment, organizational leadership, learning culture, management review, and competitive strategy play roles as moderator activation triggers in this study. According to the findings, businesses face complex and unpredictable business environment changes, as well as significant knowledge gaps. Decentralized and dynamic structures with participation in decision-making have a moderating effect on the capacity to absorb knowledge and make it easier for businesses to absorb new ideas. The results show that organizational culture fosters the development of fresh perspectives and encourages the integration of external knowledge to maximize the benefits of complementary knowledge sources. Also, the competitive strategy defines the scope and context of perception and learning. Other activation triggers that require more investigation include information technology, stakeholders, professional conduct, and research and development units. Conclusion- Managers can explore a variety of strategic alternatives and forge competitive advantages for their companies by using activation triggers as a group of variables to upgrade existing talents and develop new capabilities of absorptive capacity. Keywords: Absorptive capacity, activation triggers, endogenous and exogenous contingencies, knowledge. JEL Codes: D83, L1, O32

The impact of the collaborative innovation network embeddedness on enterprise green innovation performance

As the market environment becomes increasingly competitive, enterprises that rely solely on internal research and development innovation are no longer sufficient to meet the demands of competition. Consequently, enterprises have broken down organizational boundaries and shifted from closed innovation to open collaborative innovation. The flow of knowledge across organizations facilitates the acquisition of heterogeneous resources from partners, promotes the integration and configuration of internal and external knowledge, thereby enhances the competitiveness of enterprises. However, some scholars argued that collaborative innovation does not always achieve win-win outcomes, and the existence of substitution effects between enterprises and their partners may hinder the innovation level of the focal enterprise. Therefore, based on the resource-based theory and the network embeddedness theory, this study constructs a theoretical model to investigate the effects of network embeddedness, network experience and partner diversity on enterprise green innovation performance in the Chinese green collaborative innovation network. The impact of network embeddedness on innovation performance is examined from two dimensions: structural embeddedness and relational embeddedness. The moderating effects of network experience and partner diversity are analyzed to further explain this phenomenon. Using Chinese green patent data from 2000 to 2015 as the research object, the collaborative innovation network of enterprises is constructed, and the network characteristic variables are calculated using social network analysis methods. Finally, negative binomial regression and robustness tests are conducted using STATA software. The research findings provide managerial implications for Chinese enterprises to achieve competitiveness and sustainable development.

The impact of value co-creation orientation on radical service innovation: Exploring a serial mediation mechanism

By integrating the dynamic capability perspective and service-dominant logic (SDL), this study empirically investigates the distinct roles of internal and external knowledge integration capabilities in the relationship between value co-creation orientation and radical service innovation. Based on survey data from a sample of 200 service innovation projects from IT and Internet-related industries in China, this study uses structural equation modeling (SEM) and a bootstrapping approach to test the hypothesized relationships in the conceptual model. The results show that external knowledge integration capability mediates the relationship between value co-creation orientation and radical service innovation. In addition, internal and external knowledge integration capability jointly play a serial mediating role between value co-creation orientation and radical service innovation. These findings contribute to the growing literature on value co-creation in radical service innovation, and offer service managers meaningful implications that reveal how to convert multiple sources of knowledge resources into superior radical service innovation.

Absorptive capacity in family firms: a systematic literature review

AbstractTwo-thirds of all businesses worldwide are family firms. For these family firms, absorptive capacity (AC) is key to acquiring, assimilating, transforming, and exploiting new, external knowledge for purposes of value creation. In this study, we conduct a literature review to obtain an overview of the existing research on AC in family firms. Drawing on 27 articles, we show that because of the family members’ induced influence on the family firm, the findings from previous studies based on non-family businesses cannot be applied to family businesses. In fact, we demonstrate that this influence can even create ambiguous effects on the integration of AC into the firm. The results of our literature review indicate that on the one hand, family members promote the integration of external knowledge, that is, they utilize their family-specific resources. On the other hand, tapping on such family-specific resources can simultaneously cause family firms to isolate themselves and restrict the firm’s access to external knowledge. Our analysis also reveals that research on AC in family firms is mostly conducted on a conceptual level and that the few empirical studies usually draw on simple, one-dimensional constructs. Thus, we encourage more future empirical research to draw on the multidimensional constructs of AC and family influence.

An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis

Opinion Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers. SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers’ choices and businesses’ reputation. Aspect Based Sentiment Analysis (ABSA) is the task of categorizing text by aspect and identifying the sentiment attributed to it. Implicit Aspect Identification (IAI) is a subtask of ABSA. This paper aims to empirically investigate how external knowledge (e.g. WordNet) is integrated into SVM model to address some of its intrinsic issues when dealing with classification. To achieve this research goal, we propose an approach to improve Support Vector Machines (SVM) model to deal with IAI. Using WordNet (WN) semantic relations, we suggest an enhancement for the SVM kernel computation. Experiments are conducted on three benchmark datasets of products, laptops, and restaurant reviews. The effects of our approach are examined and analyzed according to three criteria: (i) kernel function used, (ii) different experimental settings, and (iii) SVM behavior towards Overfitting and Underfitting. The research finding of our work is that the integration of external knowledge (e.g. WordNet) is experimentally proved to be significantly helpful to SVM classification for IAI and especially for addressing Overfitting and Underfitting that are considered as two of the main structural SVM issues. The empirical results demonstrate that our approach helps SVM (i) improve its performance for the three considered kernels and under different experimental settings, and (ii) deal better with Overfitting and Underfitting.

Effects of sustaining innovation and knowledge integration ability on innovation performance

Along with the changes in economic environment and upgrading industries, medical industry has become the lifeline of current economic survival. The added value of products is promoted through the sustaining innovation of knowledge-based production and the proper application of information technology to make the growth and advance of enterprises. The samples of this study are mainly employees of medical industry in southern Taiwan. The questionnaires are distributed and collected by email. A total of 322 valid samples were received, and the sample retrieval rate in this study was 64%. The research results conclude significantly positive effects of 1.sustaining innovation on knowledge integration ability, 2.knowledge integration ability on innovation performance, and 3.sustaining innovation on innovation performance. According to the results, suggestions are proposed, expecting to assist medical industry in promoting the external knowledge resource absorptive power and integration ability to create the sustaining innovation and competitiveness in medical industry.

Knowledge Informed Machine Learning using a Weibull-based Loss Function

Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are reviewed with the goal of helping the reader understand the domain. In addition, a knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets, for remaining useful life (RUL) prediction. Specifically, knowledge is garnered from the field of reliability engineering which is represented through the Weibull distribution. The knowledge is then integrated into a neural network through a novel Weibull-based loss function. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set. However, the Weibull-based loss function is less effective on the IMS data set. The results, shortcomings, and benefits of the approach are discussed in length. Finally, all the code is publicly available for the benefit of other researchers.

Multigenerational product innovation process in electronic information industry: a qualitative study

PurposeDeveloping multigenerational products through iteration helps enterprises cope with environmental uncertainty and maintain lasting competitive advantages in a globally competitive environment. From the knowledge integration perspective, this study aims explore how enterprises achieve multigenerational product innovation.Design/methodology/approachAn exploratory case study design is used to explore the multigenerational product innovation process.FindingsThis study proposes a model of the multigenerational product innovation process and shows that knowledge integration includes two important parts: the internalization of external knowledge and intergenerational knowledge integration. This study further conclude that multigenerational innovation has three main channels: rapid iteration, continuous reflection and coordination.Originality/valueFirst, this study presents a theoretical model of the process of multigenerational product innovation, including triggers, knowledge integration, learning systems and outcomes, and further enhances our understanding from product innovation to multigenerational product innovation. Second, this study contributes to the literature on the multigenerational product innovation process by revealing the knowledge integration process of the internalization of external knowledge and intergenerational knowledge integration.

ВПЛИВ КОНЦЕПЦІЇ ВІДКРИТИХ ІННОВАЦІЙ НА ЗАКОНОДАВСТВО У СФЕРІ ІННОВАЦІЙНОЇ ДІЯЛЬНОСТІ ТА ІНТЕЛЕКТУАЛЬНОЇ ВЛАСНОСТІ УКРАЇНИ

The paper offers a comprehensive look at the dialectics of Open Innovation and legal problems of proper protection of intellectual property rights, since only theoretical and methodological aspects received fragmented attention in the research literature. The author examines in detail the content of the «Open Innovation 2.0» concept. Thus, the use of Open Innovation can take various forms, from internal and external licensing of intellectual property rights, spin-off and spin-out models, co-development, research joint ventures to crowdsourcing of Open Innovation, which is characteristic of the Open Innovation process «outside-in». The main connection between these forms is that Open Innovation involves the exchange of objects of intellectual property rights between different subjects of innovation activity for the purpose of joint creation and commercialization of objects of intellectual property rights. The author came to the conclusion that it is necessary to distinguish Open Innovations from Open- source software, which is not associated with restrictions on further modification, but with the preservation of information about the original authorship and the changes made. The role of the concept of Open Innovation in promoting the development of the Patenting Institute, which determines, in particular, its function as a catalyst for technological cooperation under the terms of license agreements, is considered. The author identified the need for a thorough analysis of theoretical and practical problems regarding the prerequisites for the development of Open Innovation in Ukraine, the peculiarities of the implementation of the legal basis of the concept of Open Innovation in Ukrainian legislation in the field of innovation activity and Intellectual Property, mechanisms of co-creation of subjects of innovation activity with a vector for the integration of internal and external technological knowledge precisely from the standpoint of Intellectual Property Law.

POPTric: Pathway-based Order Preserving Triclustering for gene sample time data analysis

Exploratory analysis of high throughput gene sample time (GST) data has an impact in biomedical and bioinformatics research. Mining gene expression pattern in such three dimensional data facilitate in understanding hidden biological knowledge as well as underlying complex gene regulatory mechanism. In particular, we propose a novel semi-supervised Pathway-based Order Preserving Triclustering (POPTric) algorithm which records gene expression pattern under subset of sample across subset of time points. Nowadays, integration of external knowledge has gaining popularity in the field of gene expression data analysis. Our algorithm is able to identify different types of triclusters such as additive, multiplicative, and additive-and-multiplicative triclusters. POPTric at first identifies biclusters and then it discovers triclusters from the 3D data. We apply our algorithm in artificial datasets as well as breast cancer, HIV datasets, and compare with other state-of-the-art methods. To establish the biological significance of proposed algorithm we perform Gene Ontology enrichment analysis. We also identify hub genes from the tricluster which are previously known to be associated with breast cancer.

Knowledge Integration and Disruptive Innovation Product Development: Case Studies of Company A

Because of the energy crisis, companies started developing electric vehicles. Company A, as a new entrant, successfully developed a disruptive innovation of the world’s first high-performance, zero emissions, and two-wheeled electric scooter. As disruptive innovation is frequently discovered in the combination of different industrial technologies, company A is comprised of high-tech and traditional industry resources and knowledge. This study aims to explore the process of heterogeneous industry knowledge integration in the traditional motorcycle manufacturing industry by a case study of company A. Following qualitative research method, this study concludes four main findings in product development: (1) organizational knowledge integration is identified to mitigate the impact of functional conflict; (2) design validation knowledge integration is identified to set the standard for pioneer product strategy; (3) internal and external engineering knowledge integration is identified to realizes the goal of pioneer product strategy; (4) product validation knowledge integration is identified to coordinate the flexible planning process.

Friend or foe? Co-opetition and entrepreneurial networking

We study the formation of an entrepreneurial network in an environment, in which entrepreneurs who are contesting with each other for the development of a new venture have the possibility to collaborate. On the one hand, such bilateral knowledge collaborations are beneficial because they allow the integration of external knowledge. On the other hand, external knowledge collaborations reduce an entrepreneur’s incentive to invest in her internal knowledge. We analyze this trade-off and show that if the knowledge transfer between collaborating partners is complete, the only stable entrepreneurial network is one with exactly one collaboration of each entrepreneur. If, however, knowledge transfers are only partial, entrepreneurial networking becomes more important and entrepreneurs form more knowledge collaborations. Moreover, internal or external knowledge spillovers reduce the incentives to form knowledge collaboration. These results have several practical implication for entrepreneurs and managers of small- and medium-sized enterprises (SMEs) in their pursuit to better understand factors that influence knowledge collaborations with competitors and to devise their co-opetition strategy.

Open-Innovation Practices: Diversity in Portuguese SMEs

This study aimed to explore the diversity of open-innovation practices that are adopted in Portuguese SMEs considering the outside-in, inside-out, and coupled paradigms. A quantitative study was carried out considering a sample of 187 Portuguese SMEs. The findings revealed that these organizations favored the adoption of the outside-in paradigm. The inside-out model was the least relevant, especially for smaller companies (i.e., small and micro-companies). The most adopted outside-in practices were the integration of external knowledge from suppliers and clients; in the inside-out model, licensing processes were more important; while in the coupled model, joint ventures and network consortiums stood out. The increase in the innovation capacity of these organizations was highlighted as the most relevant benefit, while the lack of resources and difficulties in integrating knowledge emerged as challenges. This study is especially relevant for the establishment of public-support policies that promote the involvement of Portuguese SMEs in open-innovation processes.

Open Innovation Application to Digital Transformation of Healthcare Services

The pandemic of COVID-19 urged healthcare ecosystem to expedite the deployment and use of digital healthcare services. Digital healthcare services by nature require the integration of diverse external and internal knowledge provided by multiple stakeholders of the healthcare ecosystem. Thus, open innovation could significantly contribute to value-based digital healthcare performance. This article illustrates how different open innovation strategies contribute to different types of value created by digital healthcare services. Healthcare providers, policymakers and insurance companies while seeking to utilize digital healthcare services need to leverage between different strategies of open innovation (e.g., inside-out or inside-in). While orchestration of the strategies might provide the optimal results, special attention needs to be given to inside-in open innovation processes.

Bridging the gap: integrating external knowledge from open innovation platforms

While external knowledge has the potential to benefit a firm’s innovation activities, research shows that the positive effects of a more open model of innovation do not come naturally. This paper draws on the project level to explore the organizational factors that hamper or impede the integration of external knowledge sourced from an open innovation platform and to suggest interventions to overcome these barriers. While open innovation is mainly discussed as a concept that resides at the level of the organization, this paper draws on the project level to contribute to a multi-level understanding of open innovation and to offer a deeper understanding of the challenges project teams face, when integrating external knowledge. To investigate occurring barriers, four cases of external knowledge integration within a multinational corporation are analyzed. The results show that due to the external nature of the knowledge, an additional effort of project teams is required such as forming alliances with key individuals and changing negative attitudes towards external sources to overcome organizational resistance. Theoretical as well as practical implications are discussed.