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The literature on entrepreneurship implicitly suggests that developed nations' entrepreneurial ecosystems (EE) are positively related to small and medium enterprise performance. However, few theoretical and empirical studies explicitly investigate this relationship in emerging economies. Also, earlier studies have concentrated on the EE–SME performance association in a stable environment without any uncertainties. Hence, the current study analyses the relationship between EE and SME performance in an emerging economy. Entrepreneurial resilience (ER) has been introduced (mediator) to understand the EE–SME performance relationship in the post-pandemic period.

The research sample comprises 359 Indian SMEs. The owners/managers of each SME were contacted to obtain responses. The research employs a multifaceted method Partial Least Squares-structural equation modelling that permits the investigation of several concurrent associations for data analysis.

The findings reveal that EE significantly impacts ER and SME performance. Additionally, ER partially mediates the relationship between EE and SME performance. The findings indicate that post-pandemic India's EE positively influences SME performance directly, as well as indirectly through ER.

The results provide implications for policymakers from economies similar to India's. It suggests that policymakers can enhance ER and, consequently, SME performance during periods of uncertainty by focusing on developing an EE with strong institutional foundations, supportive frameworks and a culture of adaptability.

SMEs and entrepreneurial activities in India are relatively new, but they are rising rapidly. This investigation adds to our knowledge of EE and allows us to comprehend its components in developing countries. This article contributes to the knowledge of India’s SME performance from an EE and ER perspective in the post-pandemic scenario.

Previous research has indicated that entrepreneurial marketing (EM) positively influences small and medium-sized enterprises’ (SMEs) performance. While most studies have examined the relationship in a stable environment, EMs’ effectiveness during environmental instability remains uncertain. Therefore, the study aims to investigate the influence of EM on Indian manufacturing-based SMEs’ performance during the COVID-19-induced environmental instability. Additionally, it explores the mediating role of innovative performance in the relationship between EM and SME performance.

The data were collected by distributing a structured survey questionnaire to 302 owners/managers of SMEs. Hypotheses were tested using structural equation modeling (SEM).

The result indicates that EM significantly impacts both innovation and SME performance. Furthermore, the innovative performance partially mediates the link between EM and SME performance. The findings suggest that even within severely affected sectors (manufacturing) during the pandemic, SMEs can achieve growth and innovation through effective EM practices.

This study validates the theoretical notion that EM remains effective even in unpredictable environments such as the COVID-19 pandemic. The findings offer valuable insights for SMEs seeking innovative strategies to enhance their performance, particularly those in emerging economies.

Prior studies have relied on a single layer of abstraction to analyze the impact of EM. The present study is the first to extend standard construct (EM) conceptualization. Furthermore, it evaluated the efficiency of EM in situations characterized by instability, which is rare in the EM and SME literature.

With enhanced use of humanoids in demanding sectors of industrial automation and smart manufacturing, navigation and path planning of humanoid forms have become the centre of attraction for robotics practitioners. This paper aims to focus on the development and implementation of a hybrid intelligent methodology to generate an optimal path for humanoid robots using regression analysis, adaptive particle swarm optimization and adaptive ant colony optimization techniques.

Sensory information regarding obstacle distances are fed to the regression controller, and an interim turning angle is obtained as the initial output. Adaptive particle swarm optimization technique is used to tune the governing parameter of adaptive ant colony optimization technique. The final output is generated by using the initial output of regression controller and tuned parameter from adaptive particle swarm optimization as inputs to the adaptive ant colony optimization technique along with other regular inputs. The final turning angle calculated from the hybrid controller is subsequently used by the humanoids to negotiate with obstacles present in the environment.

As the current investigation deals with the navigational analysis of single as well as multiple humanoids, a Petri-Net model has been combined with the proposed hybrid controller to avoid inter-collision that may happen in navigation of multiple humanoids. The hybridized controller is tested in simulation and experimental platforms with comparison of navigational parameters. The results obtained from both the platforms are found to be in coherence with each other. Finally, an assessment of the current technique with other existing navigational model reveals a performance improvement.

The proposed hybrid controller provides satisfactory results for navigational analysis of single as well as multiple humanoids. However, the developed hybrid scheme can also be attempted with use of other smart algorithms.

Humanoid navigation is the present talk of the town, as its use is widespread to multiple sectors such as industrial automation, medical assistance, manufacturing sectors and entertainment. It can also be used in space and defence applications.

This approach towards path planning can be very much helpful for navigating multiple forms of humanoids to assist in daily life needs of older adults and can also be a friendly tool for children.

Humanoid navigation has always been tricky and challenging. In the current work, a novel hybrid methodology of navigational analysis has been proposed for single and multiple humanoid robots, which is rarely reported in the existing literature. The developed navigational plan is verified through testing in simulation and experimental platforms. The results obtained from both the platforms are assessed against each other in terms of selected navigational parameters with observation of minimal error limits and close agreement. Finally, the proposed hybrid scheme is also evaluated against other existing navigational models, and significant performance improvements have been observed.