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This study empirically analyzes the direct impacts of soft quality management practices (SQMP) and market orientation ambidexterity (MOA) on product innovation ambidexterity (PIA). It also examines the mediating role of MOA in the relationship between SQMP and PIA.

Following a quantitative research methodology, a well-structured questionnaire is used to collect data from 130 Moroccan certified ISO 9001 firms. The conceptual framework of the study was tested using partial least squares structural equation modeling.

The results show that SQMP have no significant association with PIA while they positively and significantly impact MOA. The study also revealed that MOA has a positive and significant impact on PIA and fully mediates the relationship between SQMP and PIA.

Due to the conceptualization of MOA and PIA as combined ambidexterity, the results of the study might be different in the case of balanced ambidexterity.

To achieve PIA, managers need to be aware that implementing SQMP, while important, is not enough. They must invest sufficient resources to properly implement these practices to support MOA. Consequently, MOA will lead their organization to PIA by establishing the significant impact of SQMP on PIA.

Regardless of the abundant literature on the relationship between quality management (QM) practices and innovation, this study is among the first to examine the impact of SQMP on PIA. Using MOA as a mediator can give a meaningful answer to the indecisive empirical results of the impact of QM on innovation. Furthermore, this research contributes to the scarce literature on relevant studies conducted in developing countries.

While there is a consensus that dynamic capabilities (DC) and organizational agility are two key mechanisms that help firms to survive and cope with the environment’s dynamic change and uncertainties, little is known about their roles in leveraging firms’ preparedness to overcome organizational crises during turbulent contexts such as the COVID-19. The purpose of this study is twofold. This paper first analyzes the direct relationship between DC and organizational agility dimensions (i.e. customer, operational and partnering agilities). Second, this paper investigates the direct link between organizational agility dimensions and a firm’s organizational crisis preparedness.

Based on a survey of Moroccan firms administrated during the early stage of the COVID-19 pandemic, a theoretically derived model was tested using partial least squares structural equation modeling.

The results show that DC are significantly related to the three types of agility and that only customer and operational agility affect organizational crisis preparedness.

This research provides new insights into crisis management literature by introducing and empirically examining the impact of DC and organizational agility in addressing global crises such as the COVID-19 pandemic.

The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment.

A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier.

The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives.

The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.