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The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

Consumer goods firms often tie up inventory and accounts receivable resources, creating cost and liquidity issues. Dynamic working capital management (DWCM) can mitigate these concerns and enhance operational profitability. The study investigates DWCM's impact on operational efficiency (OE).

The empirical estimation uses pooled ordinary least squares (OLS), random effect and system generalized method moments (GMM) regression analysis of consumer goods firms in Scandinavia from 2005 to 2022 to present the results.

The findings indicate that DWCM has an inverse relationship with operating cost, while positively impacting operating profit. The final outcome demonstrates that DWCM enhances OE. Furthermore, the working capital ratio (WCR) consistently exceeds the cash conversion cycle (CCC) in all models, indicating that prudent management of cash in accounts receivable, inventory and accounts payable leads to higher cost savings and superior performance.

The results suggest that organizations that prioritize the management of the absolute cash committed to inventory, receivables and payables as much as the CCC experience improved OE.

This paper adds to the literature on how DWCM affects OE in the consumer goods sector. It also highlights the impact of time management and cash management in WCM on OE. Additionally, it analyzes how DWCM variables affect operating costs and profits, shedding light on their efficiency impact.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

The purpose of this article is based on the unit patent license fee model in the closed-loop supply chain.

This paper analyzes the impact of the bundling strategy of the retailer selling new products and remanufactured products on the closed-loop supply chain under the condition that the original manufacturer produces new products and the remanufacturer produces remanufacturing products.

The results show that alternative products can be bundled, and in many cases, the bundling of remanufactured products and new products is better than selling alone.

If the retailer chooses bundling, for the remanufacturer, when certain conditions are met, the benefits of bundling are greater than the separate sales at that time; for the original manufacturer, when the recycling price sensitivity coefficient is high, the bundling is better than separate sales.

The paper identifies the influence of socio-economic factors and livelihood training in stimulating micro-entrepreneurship among women self-help group (SHG) members.

The study is based on a sample of 416 women SHG members drawn from all the four districts of Sikkim using cluster sampling procedure. A multivariate binary logistic model is used to find the impact of socio-economic factors, and a Poisson regression has been used to find the impact of training on fostering micro-entrepreneurship. The result is validated using a propensity score matching approach which corrects for the potential self-selection bias in the sample. Subsequently, a covariate adjustment estimator verifies the robustness of the approach.

The study finds that “size of landownership”, “amount of loan borrowed”, “member's age”, “number of earning and dependent members”, “number of years of SHG enrolment” as well as the “district to which the member belongs to” have a statistically significant influence on the graduation of SHG members to micro-entrepreneurs. Furthermore, it is found that members attending the livelihood training programmes had a significantly higher number of microenterprises.

The study differentiates itself by providing empirical evidence on how socio-economic factors and livelihood training stimulate micro-entrepreneurship among SHG women of Sikkim, which has so far remained unexplored. Moreover, advanced econometric method has been used to eliminate the possible self-selection bias involved with training participation and thereby provides reliable and robust results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0070

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

Given the difficulties in finding significant exchange rate exposure in the extant literature, this paper attempts to resolve the so-called “exposure puzzle” by investigating whether currency movements have any significant impact on international industry returns.

This paper utilizes the multivariate Generalized AutoRegressive Conditional Heteroskedasticity (MGARCH) methodology to estimate both symmetric and asymmetric exchange rate exposures for each industry common across 12 countries simultaneously.

The empirical results show that exchange rate exposure is not only statistically significant but also economically important based on the estimation of an asymmetric three-factor exposure model using MGARCH methodology. This is an extremely important finding as it suggests that the “exposure puzzle” may not be a puzzle at all once a better methodology is utilized in the estimation.

Because this study tries to resolve the exchange rate exposure puzzle by focusing on whether exchange rate movements affect ex-post returns as opposed to ex ante expected returns and given the significant exposures with respect to different risk factors found in the study, it is interesting to see if any of these risk factors commands a risk premium. In other words, a natural extension of this study is to test whether any of these risk factors is priced in international industry returns.

The findings of the study have interesting implications for international investors who would like to diversify their portfolios across different industries and are concerned about whether the unexpected movements in the bilateral exchange rates will affect their portfolio returns in addition to its interest rate and world market risk exposures.

The study utilizes the MGARCH methodology, which has not been fully exploited in the exchange rate exposure literature.

The purpose of this study is to examine an integrated model, in which brand equity (BE) mediates the effects of social media usage (SMU) and electronic word of mouth (eWOM) on purchase intentions among Indian consumers of branded apparel.

An online questionnaire was used to collect data from 317 Indian customers of branded apparel, and the data were analyzed using the partial least squares structural equation modeling (PLS-SEM) with the help of SmartPLS version 4.

First, the results indicated that SMU, eWOM and BE significantly impact consumers purchase intention; at the same time, BE is influenced by SMU and eWOM. Second, results confirmed that BE partially mediates the effects of SMU and eWOM on the purchase intentions of consumers of apparel brands.

The study's dataset is limited in its generalizability as it is based on specific responses from Indian consumers of branded apparel via an online survey. The results of this study would help marketers and advertisers create customized advertising campaigns for the people who are most likely to buy their products. Marketers can also use social media to promote the uniqueness or point of difference (PoD) of their apparel brands.

To the best of the authors' knowledge, no study has been conducted on apparel brands in the Indian context that has tested an integrative model, in which BE mediates the effects of SMU and eWOM on the purchase intentions of customers of apparel brands.