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Purpose: This study examines the integration of smart agriculture and supply chain management systems within Turkey’s agricultural sector under Industry 5.0.

Need for the study: With global challenges like the COVID-19 pandemic and climate change, ensuring safe food production and accessibility is critical. However, there is a gap in understanding the readiness and awareness of Industry 5.0 technologies in agriculture and logistics. This study aims to fill this gap by investigating the adoption and implications of smart agriculture and logistics in Turkey.

Methodology: Drawing on secondary data from regulators, farmers, and supply chain experts, this study employs coding methods, particularly theoretical coding, to develop a framework for assessing the sector’s readiness for smart technologies and Industry 5.0 awareness.

Findings: This study reveals insights into the adoption and impacts of smart agriculture and supply chain systems in Turkey. It identifies factors shaping institutional logics within the sector and explores how Industry 5.0 technologies influence these logics. Additionally, it offers theoretical insights into Turkey’s agricultural future in the Industry 5.0 era.

Practical implications: Practically, this study informs policymakers, regulators, farmers, and supply chain stakeholders about Industry 5.0 technology readiness and awareness in Turkey’s agricultural sector. It guides strategies for smart technology adoption, improving productivity, food safety, accessibility, and sustainability. Furthermore, it contributes to institutional logics literature, shedding light on the independent logics driving organizational settings in smart agriculture and supply chain management.

The current study aims to investigate how young smartphone users in Bangladesh relate their brand experiences to brand loyalty. In addition, we want to visualize the direct and mediating effects of brand satisfaction, brand love and brand advocacy in our model.

The researchers examined the hypotheses by employing structural equation modeling (SEM) in AMOS and Decision Analyst STATS, version 2.0, with a sample size of 470 Bangladeshi smartphone users. The authors constructed the conceptual model by drawing upon both theoretical and empirical foundations. The researchers obtained data by utilizing an adopted and self-administered pre-structured questionnaire distributed via an online platform.

The results showed that brand experience greatly influences brand satisfaction, love, advocacy and loyalty, all of which have a significant impact on users’ brand loyalty across the country. The findings also suggested that the function of brand satisfaction as a critical mediator in the link between brand experience and brand loyalty was significant.

This experiment contributes to the body of knowledge by focusing on emotional brand attachments like brand satisfaction, love and advocacy and proposing that they can mediate experience and loyalty in the mobile market. The study also helps managers and executives better understand the primary drivers of smartphones, which are essential for generating and sustaining consumers’ happiness and loyalty in today’s highly competitive consumer market.

This research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.

Leveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.

The results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.

This research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.

This study focuses on the unsolved problem of the mechanism underlying the effect of big data analytics capabilities (BDAC) on organizational learning (OL) by shedding light on the roles of knowledge management (KM) and data-driven decision-making (DDDM) as mediators. Practitioners and academics have considered the underlying mechanism through which BDAC enhance OL.

This study proposes a framework by reviewing existing management literature and collecting data from 343 respondents. The model was tested using structural equation modeling and the bootstrapping method.

The results indicate that BDAC enhances OL, KM and DDDM. Furthermore, KM enhanced the DDDM and OL. In addition, DDDM enhances the OL. Finally, we verified that KM and DDDM mediated the link between BDAC and OL.

This study provides new insights into the mechanism underlying the effect of BDAC on OL by elucidating the mediating roles of DDDM and KM.

This study investigates the crucial link between sustainable practices and organizational development, focusing on sustainable knowledge management (SKM), green innovation (GI) and corporate sustainable development (CSD) in diverse Pakistani organizations.

This study employs a comprehensive research methodology involving advanced statistical techniques, such as confirmatory factor analysis, structural equation modeling and hierarchical linear modeling. These methods are instrumental in exploring the complex interrelationships between SKM, GI, moderating factors and CSD.

This research generates significant findings and actively contributes to sustainable development. The following sections (Sections 4 and 5) delve into the specific findings and in-depth discussions, shedding light on how industry regulation, organizational sustainability priorities, workplace culture collaboration and alignment between green culture and knowledge management practices influence the relationships between SKM, GI and CSD. These findings provide valuable insights for the research community and organizations striving for sustainability.

The study’s findings have practical implications for organizations seeking to enhance their sustainability efforts and embrace a socially and environmentally conscious approach to organizational growth.

This study contributes to the literature on sustainable practices and organizational development. Researchers and business people can learn a lot from it because it uses advanced econometric models in new ways and focuses on the link between knowledge management, GI and sustainable corporate development.