External Knowledge Sources Research Articles

A survey of organizational structure and operational practices of elite youth football academies and national federations from around the world: A performance and medical perspective.

Medical and performance units are integral components of player development programmes in elite football academies. Nevertheless, the nature of the operational processes implemented by practitioners within clubs and national federations remains unexplored. The aim of the present study, therefore, was to survey elite youth professional football academies from around the world regarding the operational processes adopted by their medical and performance units. Of the 50 organizations invited, 10 national federations and 25 clubs took part in the survey resulting in a response rate of 70% (95% confidence interval, 56%-81%). The respondents represented three groups: senior club and academy management, performance, and medical staff. The majority (60%-90%) of clubs and national federations reported strategic alignment between senior and academy medical and performance units as well as between academy medical and performance units. Survey responses indicated substantial heterogeneity in the composition and number of medical and performance professionals employed in academies. The majority of respondents agreed their medical and performance departments were effective in utilizing staff knowledge and external sources of knowledge to inform their practice (56%-80%). Performance staff (40%-50%) and physiotherapists (30%-32%) were deemed most influential in injury prevention programmes. During the return-to-play process, the influence of specific practitioners in the medical and performance units was dependent upon the phase of return-to-play. Shared decision-making was common practice amongst performance and medical staff in injury prevention and return-to-play processes. Medical and performance data were generally centralized across the first team and academy in majority (50%-72%) of clubs and national federations. Data were integrated within the same data management system to a higher degree in clubs (68%) vs. national federations (40%). Research and development activity were reported for most academies (50%-72%), and generally led by the head of performance (37%) or team doctor (21%). Research activities were largely undertaken via internal staff (~100%), academic collaborations (50%-88%) and/or external consultants and industry partnerships (77%-83%) in the national federation and clubs. Collectively, these findings provide a detailed overview regarding key operational processes delivered by medical and performance practitioners working in elite football academies.

How to Find the Right Partner? Open Innovation Partner Selection Process

The aim of this paper is to gain an understanding of the partner selection process for open innovation. The inability to choose partners can lead to problems such as opportunistic behavior, the failure of open innovation, and a reluctance to collaborate. Therefore, partner selection is crucial in open innovation. Attention is directed to the determining factors that must be considered in the selection of a partner. The contents of the paper are mainly based on in-depth interviews with experts and practitioners who represent both organizations and individuals as actors in open innovation activities. The results show that complementarity, compatibility, and trust are important considerations in selecting the right partner. The implication of this research is that a managerial approach is needed that supports the company’s ability to find and evaluate external sources of knowledge to support innovation. The originality of this article is to provide an understanding that the existence of an organization in a network helps organizations access external knowledge resources. Involving external parties in the company’s innovation process accelerates innovation. In this case, getting the right partner is very important.

Influence of Team Information Sharing on Collaborative Value Within Ashoka Fellows' Organizations in Africa

Purpose: The study sought to determine the influence of team information sharing on collaborative value within Ashoka Fellows' Organizations in Africa
 Methodology: The study applied pragmatism philosophy to offer several ways to bridge dichotomies in mixed methods approaches to social science. Explanatory sequential mixed-method research design consisting of two distinct phases, namely quantitative and qualitative, was adopted. Both qualitative and quantitative study methods were adopted. In the quantitative study, the target population constituted all the 154 Ashoka Fellows' Organizations working in 19 countries in Africa. Data was collected using a structured questionnaire administered online to the founders (Ashoka Fellows) or the Ashoka Fellows' Organizations' CEOs. One hundred responded by filling out the questionnaire, which translated to a 64.9% response rate. Additionally, qualitative data applied purposive sampling and selected six Ashoka regional team leaders in Africa for in-depth interviews. They all were available for the interviews translating to a 100% response rate. Data analysis techniques combined descriptive and inferential statistics. Statistical Package for Social Sciences and SmartPLS 3 software were used to analyze the collected data.
 Findings: Results revealed that the results confirm that team information sharing significantly influences collaborative value within Ashoka Fellows’ Organizations in Africa. Team Information Sharing has a significant influence on collaborative value witn an R2 = 0.300, chi-square X2 (10, N=100) = 63.010, p<.05, SRMR=0.088, Rms-theta=0.224 and NFI=0.805. The null hypothesis was rejected.
 Unique Contribution to Theory, Practice and Policy: The study recommends that Team members should ensure the team's information exchange is of good quality as the quality of information sharing that develops collaboration must go beyond necessary factual information to a point where team members can transfer their unique understanding. Also, team members should be willing to share information and engage in sequences of interpretation sharing to build on each other's interpretations and collectively make sense. Teams should embrace timely information sharing, which is critical for collaborative value and the use of technology. To ensure quality and timeliness of information shared, teams should consider both investments in information systems as sharing appropriate information allows the linkage of internal and external knowledge sources by facilitating information flow and providing effective data retrieval mechanisms

DUI it yourself: Innovation and activities to promote learning by doing, using, and interacting within the firm

ABSTRACT Implicitly or explicitly, much innovation policy treats investments in research and development (R&D) as the main input to innovation. A large body of literature in innovation studies has challenged this, highlighting the role of external sources of innovation and of innovation based on learning by doing, using and interacting (DUI). Nonetheless, there has been limited empirical research on how firm-internal activities to promote DUI affect innovation, and on how important such activities are relative to internal R&D and to external sources of knowledge. We also know little about how internal DUI activities interact with internal R&D and with external knowledge sourcing. We address these gaps using Norwegian Community Innovation Survey data from 2010. We find that internal DUI is an important driver of new-to-market product innovation. Further, the results show partial substitution effects between internal DUI and internal R&D, as well as between internal DUI and external DUI.

Determining factors for U-I cooperation: a European study

PurposeCompanies need to innovate to remain in the market and be competitive. Thus, success will depend on your internal resources and the external sources of knowledge used. The cooperation between univerity and industry (U–I) allows companies to access resources that, in general, they do not have, allowing them to achieve innovation, competitive advantages, and competitiveness. The purpose of this study is to understand the determinants that influence U–I cooperation in creating knowledge and innovation.Design/methodology/approachThis study analyzes the determinants considered essential for companies to establish cooperation processes with universities. The research uses the last community innovation survey data set, data from 14 countries, and 28,743 observations. The method uses logistic regression.FindingsThe results confirm that the company's size, the innovative capacities associated with R&D, exportation and public funds are essential and significant determinants for the cooperation with universities. On the other hand, the acquisition of machinery and training programs are not a critical factor in establishing cooperation with universities that are not in the same country. The analysis considered companies cooperation with universities of the same country, from the European Union (EU) or other countries outside EU.Originality/valueIn addition to providing substantial theoretical contributions on the subject, this research also provides more information about the importance of U–I cooperation, allowing to characterize companies interested in developing U–I cooperation.

Linking Digital Capacity to Innovation Performance: the Mediating Role of Absorptive Capacity

Digital technologies are considered as factors that accelerate the pace of innovation and increase the firm’s innovation performance. However, few studies have investigated whether this claim is conditioned by other elements that contribute to innovation. Furthermore, firms increasingly rely on external knowledge sources to expand their internal knowledge base for the development of innovations. In this context, absorptive capacity can be considered as an essential organizational capability to embrace adoption of digital technologies and enhance their positive effect on innovation performance. This paper builds on this discussion and studies the contribution of digital capacity on innovation performance, proposing the mediating role of absorptive capacity in the context of the digital transformation. It uses evidence from an extensive Greek survey in 1014 manufacturing firms and analyzes the complex relationships underlying the role of digital transformation to innovation. The contribution of the paper is two-fold: (i) it provides a deeper insight into the underlying mechanisms through which firms can leverage their digital capacity to accelerate innovation, and (ii) it highlights the important mediating role of absorptive capacity in enhancing the positive effects of digitalization indicating that digital capacity is not an unquestionable asset for innovation performance. Accordingly, our results show a positive direct contribution of digital capacity to innovation performance, which is enhanced in the presence of absorptive capacity as a mediator. In fact, the indirect effect of digital capacity to innovation performance through absorptive capacity is stronger. These findings present important policy implications, as there is need for improvement in other innovation-related aspects of the business ecosystem to efficiently address the challenge of digital transformation, such as R&D efforts, training, interaction among actors, and building of communities of practice.

Digital supply chain transformation: effect of firm’s knowledge creation capabilities under COVID-19 supply chain disruption risk

Digital supply chain (SC) transformation has emerged as a way to improve information sharing, better manage demand, and reduce costs in supply chain management. However, the majority of firms are unable to transform their supply chains into digital models. Thus, this study aims to identify the key drivers of digital SC transformation and provide empirical evidence on the extent to which firms’ knowledge creation capabilities could impact firms’ level of digital supply chain transformation. Based on a knowledge-based view, this study proposes that firms’ knowledge creation capabilities – which consist of the capacity to absorb external knowledge and the intent to learn with partners within the SC – are crucial to the successful knowledge transfer required to digitially transform, particularly under the effects of COVID-19 supply chain disruption risks. Survey data collected from 923 Vietnamese firms participating in cross-border trades were analyzed using partial least squares structural equation modeling (PLS-SEM). This study finds that firms’ absorptive capacity and learning intent are critical drivers of their digital supply chain transformation. Moreover, the high uncertainties in external environments are found to substantially accelerate the digital transformation processes and influence the effectiveness of firms’ knowledge creation capabilities in digital SC transformation. Significantly, firms are more inclined toward external knowledge sources to cope with disruption risks in the supply chain. This study contributes a novel approach to better understanding the role of knowledge creation capabilities in responding to supply chain disruption risks and fills a gap in research on drivers of successful digital SC transformation.

Government Support Mechanisms and Open Innovation: An Empirical Look at Korean Manufacturing Firms

Purpose - The purpose of this study is to examine how a broad palette of government support measures and firms' membership in government-developed clusters are related to firms' openness in innovation processes. Design/methodology/approach - Empirically, this study analyzes the Korea Innovation Survey 2018 data on the innovation activities of 1,450 Korean R&D-active manufacturing firms in a three-year period from 2015 through 2017. Findings - The results suggest that firms engage in open innovation to a greater extent--as measured by the breadth of external collaborating partners and of the utilized external sources of knowledge--when they are provided with a broader palette of government support measures and are located in government-developed clusters. However, the effect of diverse government support measures is attenuated for firms located in these clusters. Research implications or Originality - This study contributes to the innovation literature by illuminating how firms' open innovation can be understood in a national innovation system. Moreover, it provides valuable implications for firms seeking to obtain government support and collaborate with others.

Product innovation of domestic firms versus foreign MNE subsidiaries: The role of external knowledge sources

We build upon the knowledge-based view and the open innovation literature to examine product innovation of domestic firms relative to subsidiaries of foreign multinational enterprises, and the mechanisms that domestic firms could use to catch up to foreign subsidiaries for product innovation. We propose three external knowledge sources that will have a positive impact on product innovation, namely, R&D collaborations, inward technology licensing, and external R&D. We hypothesise that the effects would be greater for domestic firms than for foreign subsidiaries. We empirically test our hypotheses using panel data from Spain (2006–2016). We find that domestic firms introduce fewer product innovations than foreign subsidiaries, and that the external knowledge sources are effective mechanisms for domestic firms to reach, or outperform, foreign subsidiaries in terms of product innovation.

Effect of knowledge search depth, user co-creation and moderating factors on the outcomes of service innovations by European public sector organizations

ABSTRACT This study uses econometric methods and survey data for 2,137 European public sector organizations to examine the effects of deeply engaging with external knowledge sources and user co-creation on service innovation outcomes. Drawing on innovation support and constraint theories, we find that the effects of engaging with external knowledge sources and co-creating with users is moderated by innovation support practices, the percentage of employees involved in innovation activities, and constraints. These findings have important implications for the innovation activities of public sector organizations.

Government support versus international knowledge: Investigating innovations from emerging-market small and medium enterprises

We compare how non-financial government support, versus knowledge gained through exporting, supports innovation in Vietnamese small and medium enterprises (SMEs). Analysis of panel data of 1,018 SMEs shows that government support for staff training and technological development quality increases the likelihood of product and process innovation. Resource slack mediates the relationship between these non-financial government support measures and innovation, thus pointing to the importance of underutilized resources to enable the use of external sources of knowledge. Therefore, these policy measures are more efficient for emerging-market SMEs that can incorporate them. The negative moderation effect of exporting in the relationship between non-financial government support and process innovation suggests that policy-makers should limit the use of non-financial support when they design exporting policies for emerging-market SMEs.

Evaluating internal and external knowledge sources in firm innovation and productivity: an industry perspective

Both internal knowledge – investment in internal R&D and information and communication technologies (ICT) as well as external knowledge – knowledge spillovers and active collaboration with partners are rapidly fostering firm productivity and innovation. In this study, we investigate the role of internal and external knowledge in firm productivity and innovation. In addition, we test interactions between investment in R&D and ICT as well as between knowledge spillovers and knowledge collaboration in their association to firm innovation and productivity. We use a recombinant innovation approach and four samples for firms in manufacturing, creative, ICT and science, and professional services industries during 2002–2014 and for pre‐and post‐crisis periods to perform our analysis. In addition to innovation and productivity, we also examine the role of internal and external knowledge as a conduit to the development of innovation internally and the co‐creation of innovation with external partners. Our results lead to managerial and policy implications.

An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching between entities from the web knowledge graph ConceptNet, using semantic knowledge from DBpedia and WordNet, with the ones existing in the knowledge graph. They offer a set of predefined SPARQL templates that directly address the ontology on which their knowledge retrieval framework is built and querying through SPARQL. The framework also features an argumentation component, where the user can argue against the answers of the knowledge retrieval component of the framework under two different scenarios: the missing knowledge scenario, where an entity should be in the answers, and the wrong knowledge scenario, where an entity should not be in the answers. This argumentation dialogue can end up in learning a new piece of knowledge when the user wins the dialogue.

Boosting lesion annotation via aggregating explicit relations in external medical knowledge graph

Predicting a comprehensive set of relevant labels on chest X-ray images faces great challenges towards bridging visual and textual modalities. Despite the success of Graph Convolutional Networks (GCN) on modeling label dependencies using co-occurrence matrix generated from dataset, they still suffer from inherent label imbalance in dataset and ignore the explicit relations among labels presented in external medical knowledge graph (KG). We argue that jointly exploiting both the label co-occurrence matrix in dataset and the label relations in external knowledge graph facilitates multi-label lesion annotation. To model relevant lesion labels more comprehensively, we propose a KG-augmented model via Aggregating Explicit Relations for multi-label lesion annotation, called AER-GCN. The KG-augmented model employs GCN to learn the explicit label relations in external medical KG, and aggregates the explicit relations into statistical graph built from label co-occurrence information. Specially, we present three approaches on modeling the explicit label correlations in external knowledge, and two approaches on incorporating the explicit relations into co-occurrence relations for lesion annotation. We exploit SNOMED CT as the source of external knowledge and evaluate the performance of AER-GCN on the ChestX-ray and IU X-ray datasets. Extensive experiments demonstrate that our model outperforms other state-of-the-art models.

Multilevel Analysis of the Effects of Experiential and Observational Learning on Team Performance

In spite of competing in an increasingly large number of tournaments, do e-Sports players have the ability to learn and improve on a continuous basis? This question is deeply connected with experiential learning theory because players could acquire experience through repetitive participation with diminishing marginal gains. Observational learning serves as a complementary learning mechanism, allowing players to utilize external sources of knowledge (or others) to accelerate their learning process. This study hence advances a dynamic and integrated research model that scrutinizes peer- and rival-based observational learning as moderators of the influences exerted by experiential learning on incremental absorptive capability and team performance. Analyzing data collected from 78,645 teams and 36,422 players involved in e-Sports games, we revealed that peer- and rival-based observational learning steepen the inverted U-shaped relationship between experiential learning and players’ incremental absorptive capability. In addition, peer-based observational learning flattens the U-shaped relationship between experiential learning and team performance, whereas rival-based observational learning steepens the U-shaped relationship between experiential learning and team performance. It is also found that a player’s incremental absorptive capability enhances team performance.

Internal vs. External Knowledge Sourcing of Organizational Rules: An Exploratory Study

In this study, we examine how organizational rules source knowledge from intra- and extra-organizational contexts. By knowledge sourcing of rules, we mean that rules reference knowledge sources located outside of rules. Knowledge sourcing of rules can be captured by formation of citation ties to the referenced knowledge sources within (i.e., other rules) and outside the organization (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 rule system, individual rules, 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. We find evidence that provides overall support to our theoretical model. The theoretical and practical implications of the findings are discussed.

How Organizational Structure Shapes Explorative Innovation in CVC Investment-Relationships

Engaging in exploration is an important source of competitive advantage for established firms. However, exploration is challenging, as it requires the utilization of knowledge outside of firm boundaries. To address this challenge, firms increasingly engage in corporate venture capital (CVC) investments, which allow for assimilation of knowledge held by young ventures. Existing findings on the relationship between CVC and firm exploration remain inconclusive, calling for the investigation of contingency factors. We address this gap by drawing from the knowledge-based view and empirically examining the effect of the organizational structure of CVC units. Further, we consider the interplay of CVCs organizational structure and firms’ diffusion and recombination capabilities for exploration performance. In doing so, the study contributes to our understanding of CVC as external knowledge sourcing mechanism, highlights limitations to this approach, and offers practical insights for corporate investors.

Corpus Statistics Empowered Document Classification

In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited. In this context, for long texts, we proposed Weighted Sparse Document Vector (WSDV), which performs clustering on the weighted data that emphasizes vital terms and moderates the soft clustering by removing outliers from the converged clusters. Besides the removal of outliers, WSDV utilizes corpus statistics in different steps for the vectorial representation of the document. For short texts, we proposed Weighted Compact Document Vector (WCDV), which captures better semantic insights in building document vectors by emphasizing potential terms and capturing uncertainty information while measuring the affinity between distributions of words. Using available corpus statistics, WCDV sufficiently handles the data sparsity of short texts without depending on external knowledge sources. To evaluate the proposed models, we performed a multiclass document classification using standard performance measures (precision, recall, f1-score, and accuracy) on three long- and two short-text benchmark datasets that outperform some state-of-the-art models. The experimental results demonstrate that in the long-text classification, WSDV reached 97.83% accuracy on the AgNews dataset, 86.05% accuracy on the 20Newsgroup dataset, and 98.67% accuracy on the R8 dataset. In the short-text classification, WCDV reached 72.7% accuracy on the SearchSnippets dataset and 89.4% accuracy on the Twitter dataset.

Heterogeneous graph prompt for Community Question Answering

SummaryCompared with general question answering, Community Question Answering (CQA), which has been widely used in various scenarios like E‐commerce and is well welcomed. In order to answer the user's question precisely, many CQA models resort to external knowledge sources such as Wikipedia. The main challenge of the task is knowledge extraction and utilization. Different from the traditional method of designing task‐specific knowledge modules, we propose a graph prompt‐based learning method that directly steers the pretrained language model to solve CQA tasks. Multiple information sources are organized as graph prompts to guide the generation of the model, naturally leveraging the knowledge learned in the pretrained step. Based on the pretrained bidirectional and autoregressive transformers, a large‐scale language model, a comparable performance is achieved with less than 10% of the full‐finetuning time by only optimizing the graph prompt parameters. Experiments on two standard CQA datasets show that compared with traditional sequential initialized prompts, graph prompt achieves 20.47% and 14.89% increments in terms of BLEU and ROUGE‐L scores on quick finetuning and outperforms in few‐shot learning.

Social innovation, goal orientation, and openness: insights from social enterprise hybrids

We empirically examine social innovation and openness through a survey of social enterprise hybrids in the United Kingdom (UK). Social innovation refers to new products, processes, and services that respond to grand challenges. Social enterprises pursue economic, social, and environmental goals but vary in their goal orientation, namely the relative importance ascribed to such goals. We first explore the relationships between commercial, social, and environmental goal orientation and social innovation performance. Next, we consider the moderating impact of openness to external knowledge and ideas on social innovation performance. Our analysis finds positive and significant relationships between commercial and social goal orientation and social innovation performance, but no relationship with environmental goal orientation. In addition, the use of external sources of knowledge and ideas positively strengthens these relationships for both commercial and social goal orientation but not for environmental goal orientation. Our results reveal some important influences on social innovation, openness, and hybrid organizing.