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This study aimed to analyze the role of organizational learning capability and entrepreneurial orientation on frugal innovation and, consequently, operational performance within the Brazilian textile industry.

The sample consisted of 257 valid questionnaires from the textile industry in the state of Santa Catarina, Brazil. Data were analyzed using structural equation modeling using SmartPLS software.

The results showed that organizational learning capability is a strong driver of the operational performance when mediated by frugal innovation. Evidence also showed that relationships between entrepreneurial orientation, organizational learning capability, frugal innovation and operational performance are significant once the direct effect has more power than the indirect effect. Results elucidated different outcomes that are not in accordance with previously seen studies. Moreover, the latter shines a light on a possible interference caused by the COVID-19 pandemic.

The study clarifies the relationship that entrepreneurial orientation and organizational learning capability unleash in frugal innovation and operational performance. It also shows a new situation when looking at the relationship between entrepreneurial orientation, organizational learning capability and operational performance.

This study aims to delve into the interplay between didactic audiovisual translation (DAT) and computer-assisted language learning (CALL), exploring their combined impact on the development of intercultural competence (IC) among learners of English as a foreign language (EFL).

Using a quasi-experimental approach with a quantitative research design, the study analyses the outcomes of a questionnaire answered by 147 students across 15 language centres in Spanish Universities. These participants actively engaged in completing the lesson plans of the Traducción audiovisual como recurso didáctico en el aprendizaje de lenguas extranjeras project, a Spanish-Government funded research initiative aimed at assessing the effects of DAT on language learning.

The current study confirms the reliability of the instrument developed to measure students’ perceived improvement. Beyond validating the research tool, the findings of the current study confirm the significant improvement in intercultural learning achieved through DAT, effectively enhancing students’ motivation to engage in language learning.

The current research solely examines students enrolled in higher education language centres. This paper closes with a CALL for additional research, including participants from other educational stages, such as primary or secondary education. In the broader context of CALL research, this study serves as a valuable contribution by exploring the potential of DAT in fostering IC in EFL settings.

This research confirms the potential of DAT and CALL to promote students’ learning process, as the combination of these approaches not only yields linguistic benefits but also intercultural learning.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

Prior research from transaction costs economics argued that central firms perform better because they have superior access to information to discipline their alliance partners. Central firms may also, however, face higher costs and risks of unintentional learning and weaken their competence through structural inertia. We propose that these costs and risks are influenced by the learning capacities of the firms in the network and can explain different outcomes for focal firm performance.

To test our predictions, we use instrumental variable–generalized method of moments estimation techniques on 15,517 firm-year observations from equity alliance portfolios in the global food industry across a 21-year window.

We find support for our predictions and show that the relationship between network degree centrality and firm performance is negatively influenced by partners’ learning capacity and positively influenced by focal firms’ learning capacity, while firms with low network degree centrality benefit less from their learning capacity.

Future developments in transaction cost economics may consider partner and focal firms’ learning capacity as moderators of the network degree centrality – firm performance relationship.

In alliance decisions, managers must consider that the combination of high network degree centrality and partners’ learning capacity can lead to high costs, risks of unintentional learning, and structural inertia, all of which have negative consequences for performance. In concentrated industries where network positions are controlled by a few large firms, policymakers must acknowledge that firms may face substantial barriers to collaboration with learning-intensive firms.

This study is the first to develop and test a comprehensive transaction cost analysis of the central firm’s unintended knowledge flows and structural inertia in alliance networks. It is also the first to incorporate theoretically and empirically the hazards of complex and unintended information flows on the relationship of network degree centrality to performance in equity alliance portfolios.

This study has two objectives: (1) to identify gaps in the citizen scholar framework by comparing and synthesizing the relevant pedagogical literature and (2) to illustrate how 21st century graduate capabilities can be cultivated through teaching practices using the citizen scholar framework.

We briefly synthesize the relevant citizen scholar pedagogical frameworks and approaches. We use two case studies in two large classroom settings in Ireland and Australia to demonstrate how the citizen scholar attributes can be developed through curriculum design and multiple forms of assessment.

We identify that there is a need for digital literacy to be placed as a fifth attribution cluster in the citizen scholar framework. We also demonstrate that these graduate competencies can be developed at scale by embedding the citizen scholar framework in teaching practices.

We offer a practical implementation mechanism for cultivating 21st century graduate competencies, which will help further embed citizen scholar in pedagogy strategies, thus empowering learning at scale in business education.

The current study makes the first attempt to identify significant attribution gaps in the citizen scholar framework by synthesizing and mapping the relevant approaches. Detailed examples of curriculum design from the two countries also offer new insights into the implementation of a citizen scholar framework.

The study proposes to empirically test a model that illustrates how identical elements (IEs), transfer design and trainer performance as training predictors affect trainees' motivation to improve work through learning (MTIWL) and training transfer (TT) in the Indian context.

An online survey was conducted to validate the study model. The quantitative data collected from 360 executives and managers were analyzed using the covariance-based structural equation modeling (CB-SEM) technique.

The study finds that trainees' MTIWL has a full mediation impact between transfer design, trainer performance and TT. However, a partial mediating impact of MTIWL was found between IEs and TT.

This is the first study that empirically explores the mediating mechanism of MTIWL between IEs, transfer design, trainer performance and TT. This study extends the current understanding of trainees' MTIWL that links the cumulative influence of training predictors to TT.

How an entrepreneurial organisation sets the problem space determines the future venturing mode. However, the factors that contribute to differences in problem space are still not well-developed. Based on sensemaking theory, this study explores the influence mechanism and boundary conditions of organisational sensemaking on effectual problem setting.

The study is based on survey data from 162 entrepreneurial organisations in China. Hierarchical regression is used for analysing the data and testing the hypotheses.

Organisational sensemaking has a negative effect on effectual problem setting and a positive impact on outside-in entrepreneurial marketing capability; outside-in entrepreneurial marketing capability has a negative effect on effectual problem setting; outside-in entrepreneurial marketing capability plays a partially mediating role in the relationship between organisational sensemaking and effectual problem setting. Organisational size negatively moderates the relationship between organisational sensemaking and outside-in entrepreneurial marketing capability. It also negatively moderates the indirect effect of organisational sensemaking on effectual problem setting through outside-in entrepreneurial marketing capability.

First, this study contributes to the research on effectual problem setting by introducing organisational sensemaking as an antecedent. Second, this study extends the literature on organisational sensemaking by showing how organisational sensemaking affects entrepreneurial uncertainty, instead of treating uncertainty as an exogenous shock. Third, this study introduced the outside-in entrepreneurial marketing capability to reveal the mediating mechanism between organisational sensemaking and effectual problem setting.

This study investigates lecturers’ challenges in guiding students’ final scientific work online during the COVID-19 pandemic. It explores the impact of lecturers’ digital technology proficiency on the students’ ability to compile their thoughts and produce scientific work independently.

The study involved 45 lecturers and 140 students. Data was collected through online surveys using the Google Forms application and focus group discussions. The data were analysed qualitatively and interpretively based on the surveys and interviews.

The findings reveal three modes of mentoring: online, mixed, and offline. Many lecturers’ reluctance to use digital technology for mentoring stems from their lack of proficiency, resulting in mixed mentoring methods. This digital inadequacy affects students’ ability to write scientific work independently, as they are not accustomed to self-directed learning. The pandemic has necessitated more independent work from students, with limited physical guidance from lecturers, leading to a decline in the quality of scientific writing.

This paper contains the latest information related to students' scientific writing activities. Student scientific writing activities are disrupted because supervisors do not have the skills to use technology in the remote student mentoring process. Lecturers are not skilled at using technology in carrying out online tutoring assignments.

The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities.

To develop measurement items and constructs, the authors collected 248 responses through an online survey. These responses were then used to establish the structural model and determine discriminant validity through the use of structural equation modeling with SmartPLS 4.0.9.9. Additionally, the authors used SPSS (Version 29) to create graphs and visual representations of the challenges faced and the most commonly used AI tools. These techniques allowed them to explore data and draw meaningful conclusions for future research.

This research shows that AI has a positive impact on higher education, improving learning outcomes and data security. However, issues such as plagiarism and academic integrity can destroy students. The study highlights AI’s potential in education while emphasizing the need to address challenges.

This paper emphasizes the preventive measures to tackle academic challenges and suggests enhancing academic work.

This study examines how AI can be used to personalize learning and overcome challenges in this area. It emphasizes the importance of academic institutions in promoting academic integrity and transparency to prevent plagiarism. Additionally, the study stresses the need for technology advancement and exploration of new approaches to further improve personalized learning with AI.

The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.

In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.

In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.

Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.