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The study aims to add to the body of knowledge of open source tangible product management (also called open design). The objective is also to develop a guideline for efficient open source tangible product development and adoption.

The exploratory research design using secondary data (like newspapers, magazines, research articles, bogs, papers, etc.) is used to analyze open source tangible product design challenges and enablers. The success stories of Open Source Software projects (OSS) were studied for identification of critical success factors and further their relevancy was tested in the two popular cases of open source drug discovery (malaria and tuberculosis)

Open innovation has become a part of competitive strategy of current businesses. It requires an efficient intellectual property protection regime for its implementation. However, in a market dominated by proprietary benefits, the open source technology development can serve as remedy for innovation needs of neglected sectors. The OSS literature revealed managing two classes of factors, namely technology sponsor level factors and environmental factors for efficiency and effectiveness. The case study analysis in the context of applicability of these OSS critical factors showed their limitations in open source tangible products, and highlighted understanding additional challenges and remedies.

Open source innovation is a collaborative effort involving inputs from various/diverse players, hence monitoring the effort and motivation level of the contributors is a cumbersome task. Only the information that is available online and in print media is taken as research inputs in this work. Also the data taken were from two case studies; a lot more case studies in the open design domain can progress the theory. The implications of this study are far-reaching in the areas where profit motivated proprietary efforts lack in addressing societal need. It provides guidelines for addressing those unmet needs by developing products in a collaborative way without intellectual property hurdles.

The essence of open design is becoming more vital, and there is a pressing need to build theory to support it, which still is elusive and dispersed. The study fills the gap using secondary data and case study approach.

The purpose of this study is to examine the state of the literature on knowledge sharing in open source software (OSS) development communities by examining the existing research and identifying the knowledge gaps and opportunities that can inform areas for future research.

A systematic literature review was conducted of literature published between January 2011 and February 2023. A total of 24 papers were identified and reviewed.

The findings reveal that the literature on knowledge sharing in OSS development communities from developing countries are limited. Additionally, there exists a limited focus on the development of frameworks to support knowledge sharing in OSS communities. The transient nature of OSS development contributors’ results in knowledge loss; thus, knowledge retention needs further investigation.

This study only included papers whose titles, keywords or abstracts included the search keywords “knowledge sharing” and “Open Source Software”. While the keywords were carefully applied, when applying the search, it cannot be ruled that some relevant studies might have been missed. The study was also limited to conferences and journal papers published in English. Despite the limitations, the study provides a systematic review of knowledge sharing in OSS communities and presents findings that can be useful to researchers and practitioners interested in this area.

The study provides a systematic literature review of published papers and identifies themes and future research areas on knowledge sharing in OSS communities. Additionally, this review offers insights into future research avenues for theory, content and context on knowledge sharing in OSS development communities.

This research aims to develop a conceptual framework that explores the influence of open-source software (OSS) on the sustainability of library services within the context of academic libraries in Bangladesh.

The study uses a comprehensive research methodology that includes literature review and analysis to construct a robust conceptual framework. This study investigates the various dimensions of OSS adoption and its impact on library service sustainability.

The research findings reveal the critical factors and mechanisms through which OSS can positively affect the sustainability of library services. This study identifies key drivers and challenges associated with the adoption of open-source solutions in the context of Bangladesh academic libraries.

The framework developed in this research offers practical insights for academic libraries in Bangladesh seeking to adopt OSS solutions. This study guides how to leverage these technologies to enhance the sustainability of library services in a cost-effective and efficient manner.

This study contributes to the academic literature by presenting a novel conceptual framework tailored to the unique context of Bangladesh academic libraries. This study adds value by addressing the specific challenges and opportunities related to OSS adoption and its implications for library service sustainability in this region.

The heterogeneous character of Industry 4.0 opens opportunities for studies to understand the difficulties and challenges found in the transformation process of manufacturers. This article aims to present a critical analysis of the modernization process of an Industry 3.0 automated cell into a fully autonomous cell of Industry 4.0. The objective is to elucidate the difficulties found in this transition process and the possible ways to overcome the challenges, focusing on the management perspective.

For this, the needed steps for the technology transition were defined and the main I4.0 enabling technologies were applied, such as the application of machine learning algorithms to control quality parameters in milling.

The main challenges found were related to the obsolescence of the equipment present in the cell, challenges in data integration and communication protocols, in addition to the training of people who work actively in the project team. The difficulties faced were discussed based on similar studies in the literature and possible solutions for each challenge.

This understanding of possible barriers in the modernization process, and the step-by-step defined for this transition, can be important references for professionals working in manufacturing industries and researchers who aim to deepen their studies in this important and disruptive stage of world industrialization.

This study explores how introducing a permissioned blockchain in a supply chain context impacts accountability relationships and the process of rendering an account. The authors explore how implementing a digital transformation impacts the governance of network transactions.

The authors mobilize 28 interviews and documentary analysis. The authors focus on early blockchain adopters to get an insight into how implementing a permissioned blockchain can transform information sharing, coordination and collaboration between business partners, now converted into network participants.

The authors suggest that implementing a permissioned blockchain impacts accountability across three levers, namely through the ledger, through the code and through the people, where these levers are interconnected. Blockchains are often valued for their ability to enable transparency through the visibility of transactions, but the authors argue that this is an incomplete view. Rather, transparency alone does not help to satisfy a duty of accountability, as it can result in selective disclosure or obfuscation.

The authors extend the conceptualizations of accountability in the blockchain literature by focusing on how accountability relationships are enacted, and accounts are rendered in a permissioned blockchain context. Additionally, the authors complement existing work on accountability and governance by suggesting an integrated model across three dimensions: ledger, code and people.

Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets.

Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively.

Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.

The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The purpose of this paper is to revisit previous design principles and guidelines for online makerspaces in public libraries (Kim et al., 2020) and expand the design principles with more updated implications and examples from the literature published during and after the COVID-19 pandemic.

The authors reviewed recently published papers about online transitions of makerspaces, especially during and after the COVID-19 pandemic, summarized their implications and deduced applicable design principles and guidelines.

This paper proposes updated design principles and guidelines based on four key areas: Program and service design; Tools and materials; Facilitation; and Logistic support. These updated design principles considered a wider range of patrons that public library makerspaces may serve, the digital divide issues and logistic concerns that should be addressed beyond the scope of a single makerspace.

This paper compiles various lessons learned and strategies regarding online makerspaces and maker programming for public libraries and provides helpful design principles and guidelines for the continued use of online components for makerspace services and programs.

The model of pay-for-knowledge incentivizes individuals with financial rewards for sharing their expertise, facilitating a transactional exchange between knowledge providers (sellers) and seekers (buyers). While this model is effective in promoting paid contributions, its influence on free knowledge exchanges remains ambiguous, creating uncertainty about its overall impact on platform knowledge ecosystems. This study aims to explore the mechanim of how knowledge payment influences free knowledge contribution. Based on relational signaling theory, this study posits that a buyer’s payment for knowledge acts as a positive relational signal in the buyer–seller relationship and examines how the signaling effect varies across different social contexts through attribution theory.

This paper empirically tests the hypotheses by analyzing a data set comprising 630 instances from 359 unique knowledge sellers on Zhihu, a prominent knowledge-sharing platform in China. This paper use zero-inflated negative binomial models to conduct this analysis.

The findings reveal that when buyers pay for knowledge, this action positively influences sellers to contribute knowledge for free. However, the strength of this influence is moderated by the platform’s social functions: appreciation feedback tends to weaken this effect, while social network ties enhance it.

Prior research has predominantly focused on the financial incentives of pay-for-knowledge and its spillover effects on unpaid users’ activities. This study shifts the focus to the social dimensions of pay-for-knowledge, arguing that buyer-initiated knowledge payments signal buyers’ commitment to foster reciprocal relationships with sellers. It expands the literature on the relationship between knowledge payment and contribution, moving beyond financial incentives to include social factors, thus enriching our understanding of the interplay between paid and free knowledge activities. Additionally, the empirical evidence supports the efficacy of pay-for-knowledge in promoting both free and paid contributions within knowledge-sharing platforms.

The purpose of this study is to conduct a bibliometric analysis to discover the published information on the subject of “Library Automation Software.”

This study is based on bibliometric research to analyze the growth of literature on the subject area “Library Automation Software.” The data were retrieved from the leading database Web of Science to analyze the scholarly publications on the topic during 2001–2022. To analyze the bibliometric data on the subject, the VOSviewer, Biblioshiny and MS Excel software were used.

Results manifest that the topic of “Library Management Systems” has been greatly used by several authors as a title to publish their research work. The USA emerged as the most productive country. The most used keywords to retrieve literature on the topic were “Library Automation,” “Library Management Systems,” “KOHA,” “Integrated Library Systems” and “Open Source Software.”

To the best of the authors’ knowledge, this is the first bibliometric study in Pakistan that has been conducted to measure the productivity of literature at the international level on library automation.