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This research aims to present and empirically validate a conceptual model for social economy organizations that includes organizational commitment, knowledge‐centered culture, and training as critical variables for the success of formal and informal knowledge management practices.

The research is a cross‐sectional study and involved collection of quantitative data. The sample is made up of Portuguese organizations belonging to the social economy sector. The survey consisted of 205 employees. Path analysis was applied, using maximum‐likelihood estimation procedures.

Path analysis results show that the proposed model has a good fit with the data. Training and knowledge management practices are a function of knowledge‐centered culture and organizational commitment.

The research is focused on social economy organizations, therefore generalization of results to other sectors must be cautious. Although a comprehensible model is presented, it does not cover an exhaustive list of critical factors for knowledge management. Additionally, this research is of a cross‐sectional nature, which does not capture dynamic changes.

The research contributes to the existing literature about the critical factors of knowledge management. Social economy organizations that are developing knowledge management practices should attend to the predominant type of organizational commitment and should base their training policies on a knowledge‐centered culture.

Empirical evidence obtained in this study will be of use not only to social economy organizations wanting to become knowledge management ones, but also to researchers wanting to develop a more comprehensive framework of factors that influence (positively or negatively) knowledge management, specifically within social economy organizations.

This research aims to evaluate if knowledge-centered culture (KCC) fosters knowledge sharing equally across employees with different levels of trust propensity, an enduring individual characteristic.

A cross-sectional questionnaire study was conducted with 128 US-based employees.

The authors found that KCC only promoted knowledge sharing in individuals with high levels of trust propensity. For individuals with low levels of trust propensity, KCC had no effect on knowledge sharing.

The authors focused exclusively on trust propensity as a moderator. Future research could analyze the role of other enduring individual differences in the relationship between KCC and knowledge sharing.

A KCC may be inefficient in promoting knowledge sharing in employees with low propensity to trust. Recruitment and selection of individuals with a high propensity to trust is a possible solution to enhance the association between KCC and knowledge sharing in organizations.

By identifying an enduring individual characteristic that shapes the relationship between KCC and knowledge sharing, the authors move toward the development of a contingent view of KCC and show that KCC fosters knowledge sharing differently across employees.

Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues.

PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence.

PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables.

PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

The purpose of this paper is to ascertain the extent to which knowledge management (KM), customer satisfaction (CS) and organizational image (OI) discriminate quality-certified municipalities from non-certified ones (ISO 9001).

An empirical study was carried out involving 81 Portuguese municipalities (40 certified, 41 non-certified), paired in a random sampling procedure. The Knowledge Management Questionnaire (n=1,372 municipality employees), the Customer Satisfaction Questionnaire and the Organizational Image Questionnaire (n=3,096 residents) were applied. Multiple discriminant analysis was performed.

The results indicate that certified and non-certified municipalities are distinct based on a function that considers KM (competitive orientation and formal KM practices), CS (intangible and tangible factors) and OI (favorable image).

The findings need further validation in other countries. However, the results highlight the importance of quality certification for both employees and residents.

The results encourage local public administration organizations to introduce and maintain quality certification.

This research is the only one, to the authors’ knowledge, that simultaneously explores organizational processes of KM, CS and OI in local public administration. The sampling procedure and the information from diverse data sources are unique contributions. The conclusions may aid practitioners and scholars in understanding these organizational phenomena in the context of quality-certified and quality non-certified municipalities.

This is an attempt to better bridge the gap between the mathematical and the engineering/physical aspects of the topic. The authors trace the different sources of non-convexification in the context of topology optimization problems starting from domain discretization, passing through penalization for discreteness and effects of filtering methods, and end with a note on continuation methods.

Starting from the global optimum of the compliance minimization problem, the authors employ analytical tools to investigate how intermediate density penalization affects the convexity of the problem, the potential penalization-like effects of various filtering techniques, how continuation methods can be used to approach the global optimum and how the initial guess has some weight in determining the final optimum.

The non-convexification effects of the penalization of intermediate density elements simply overshadows any other type of non-convexification introduced into the problem, mainly due to its severity and locality. Continuation methods are strongly recommended to overcome the problem of local minima, albeit its step and convergence criteria are left to the user depending on the type of application.

In this article, the authors present a comprehensive treatment of the sources of non-convexity in density-based topology optimization problems, with a focus on linear elastic compliance minimization. The authors put special emphasis on the potential penalization-like effects of various filtering techniques through a detailed mathematical treatment.

The purpose of this paper is to explain and empirically test the dependence of organizational processes related to knowledge on the nature of assumptions operating in processes of human resource management (HRM) in organizations. It concentrates on practices related to training, career development and retention.

This empirical study as a quantitative nature and the sample is made up of 5,306 collaborators in 634 organizations belonging to an economic group in the banking sub-sector. Data were collected through two questionnaires: human resource management practices questionnaire and knowledge management questionnaire – short form. The model was tested by applying univariate and multivariate multiple regression analyses.

Findings provide support for the proposed model and show the predictive capacity of the HRM practices regarding knowledge management (KM) processes, revealing a strong direct relationship between the two constructs. It stands out that the people management practices adopted from an organic and valued perspective possess a particular and distinctive capacity to predict and impact positively on KM processes.

The findings may be used by human resources and KM practitioners interested in the development of organizational knowledge through human resource practices.

The main contribution of this study is to confirm the close relationship of dependency between organizational management processes regarding people and knowledge, showing the positive effect of best practices of HRM on KM processes, as opposed to traditional or transactional practices.

Knowledge and its management and, more recently, strategic knowledge management (SKM) represent major challenges for the organisations of the 21st century. Despite the importance of this knowledge field, and the significant quantity of research that has focussed on this theme, there is a dearth of studies in the literature undertaking a systematic SKM review. This study aims to provide a currently complete and systematic SKM review to stimulate and contribute to furthering this academic field and correspondingly set out an agenda for future research.

This paper sourced the data from the science citation index expanded (SCI-expanded), the social sciences citation index (SSCI) and the arts and humanities citation index (A&H CI) databases. This research spanned references published in journals belonging to the categories of management, business and economics without any chronological filter. The search returned a total of 99 references with their dates of publication between 1995 (1 reference) and 2018 (7 references).

The results included the identification of three core approaches to this study field: SKM models and competitive advantage, SKM implementation and the Systematic SKM approach.

This current study seeks to undertake the mapping of the scientific publications, the intellectual structure and research trends in the SKM field to: identify the fundamental contributions to the SKM research field; determine the research pathways establishing its intellectual structure and the scientific journals with the greatest impact on the field and identify the SKM research field geographical distribution and its collaborative structure.

This study defines a roadmap for an informed research agenda that proposes multiple directions: the usage and development of an innovative theory that may open new paths for future research and theoretical construction; a more sophisticated understanding of the concept and its applicability; approaching the shortcomings interrelated with the content across different levels of analysis; and the application of relevant and appropriate methodologies for operationalizing SKM.

The purpose of this paper is to study the electrochemical properties of electrode material on activated carbon double layer capacitors. It also tries to develop a prediction model to evaluate pore size value.

Back-propagation neural network (BPNN) prediction model is used to evaluate pore size value. Also, an improved heuristic approach genetic algorithm (HAGA) is used to search for the optimal relationship between process parameters and electrochemical properties.

A three-layer ANN is found to be optimum with the architecture of three and six neurons in the first and second hidden layer and one neuron in output layer. The simulation results show that the optimized design model based on HAGA can get the suitable process parameters.

HAGA BPNN is proved to be a practical and efficient way for acquiring information and providing optimal parameters about the activated carbon double layer capacitor electrode material.

The purpose of this paper is to examine the recent horsemeat scandal in European markets. The paper is primarily interested in how this scandal has festered and what perhaps ought to be done to ensure consumers get what they feel they should be getting. It also attempts to identify the lapses that have created the basis for this to happen.

The paper mainly draws on reports in the media and discussions generated following the scandal to assess the issues under consideration. It also looks at the issues surrounding the global supply chain environment and provides solutions on how to strengthen the weak links in the meat supply chain.

The paper finds that the scandal has damaged consumer confidence in the industry's ability to regulate itself. It notes that pinpointing risk has become a difficult struggle as retailers are often inundated with data, and suppliers, for lack of time, have become reluctant to “waste time” completing check-lists and audits. The paper maintains that there is the urgent need for adequate inspection and a means to incentivise the food industry to police itself much better. It recognises that lack of visibility and a lack of direct influence over suppliers further down the supply chain have led to distinct problems within the food industry.

The paper contributes to an ongoing discussion that has been of considerable concern to many consumers. Its importance lies in the fact that it suggests important measures, which, if implemented, could help in ensuring the elimination of fraud in the food chain.

The main objective of this research is to analyze the dynamics of academic research on digital entrepreneurship and innovation, unveiling the main research trends developed in the last decade.

The analysis has been carried out using a dual methodological approach comprising bibliometric methods and an innovative three-level systematic review, involving the analysis of the most used keywords, the co-occurrence network of keywords and a traditional review of the abstracts of the 1713 peer-reviewed articles that make up the database under study, with the aim of revealing and discussing the most important research trends developed in the period analyzed.

The results reveal seven major research trends that underscore the fundamental role of digital platforms in democratizing innovation, enabling a more inclusive and collaborative environment wherein startups, established firms, academia and government entities converge to drive technological advancements and societal progress. Additionally, the pressing need for a holistic understanding of digital technologies' potential to spur economic growth, societal progress and sustainable development is highlighted, as well as the challenges posed by privacy, security and the digital divide.

This research has been developed using an innovative methodology, and contributes to the academic discourse by providing a comprehensive overview of the current state and prospects of digital entrepreneurship and innovation for policymakers, entrepreneurs and researchers, urging a collaborative effort to harness the benefits of digital transformation in an equitable and responsible manner, fostering an environment of innovation that is conducive to the sustainable growth of the digital economy.