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Amid the growing global emphasis on sustainable agriculture, organizations and governments face a pressing need to equip farmers with the knowledge and tools necessary for the adoption of sustainable farming practices, aligning with the Sustainable Development Goals (SDGs). However, understanding the complex relationship between training programs and the adoption of sustainable practices among small-scale farmers remains a critical challenge. Taking a human resource approach, this paper attempts to understand the interrelationships between training effectiveness, farmers’ psychological and demographic characteristics in explaining the adoption of sustainable farming practices.

We employed a multi-stage random sampling method and administered a structured questionnaire to collect data from 331 small farmers who were part of a government-led, large-scale intervention aimed at training them in sustainable farming practices.

Our research findings not only emphasize the critical role of HR approach through training but also underscore its importance in the broader mission of aligning with the SDGs. Specifically, we demonstrate that sustained exposure to training, intrinsic motivation to acquire knowledge, and the innovative capacity of farmers collectively enhance the effectiveness of training programs, thereby contributing significantly to the widespread adoption of sustainable farming practices in line with SDGs.

Drawing from self-determination theory, training effectiveness literature, and the call for improved alignment with the SDGs, this study presents a model that explains how psychological characteristics, combined with the quality and quantity of training influence the adoption of sustainable farming practices among small-scale farmers.

This study explores the impact of intellectual capital on the adoption of supply chain analysis by manufacturing companies. The authors also examine the potential role of supply chain analytics in supply chain innovation and agility.

Data were gathered from 268 managers and directors of Jordanian companies. The hypotheses were tested using the Smart PLS software.

The results reveal that human, structural and social capital significantly impact supply chain analytics. Moreover, the findings show that supply chain analytics significantly affect supply chain innovation and agility. In other words, cultivating intellectual capital is crucial for utilizing supply chain analysis to enhance performance in terms of innovation and agility.

This study adds to the literature on the determinants of the adoption of supply chain analytics and its function in establishing the dynamic capabilities of businesses, including supply chain innovation and agility.

Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.

Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.

The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.

Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.

The purpose of this literature review paper is to explore the concept of organization agility and its relevance in today's dynamic business environment. By conducting an in-depth review of existing academic and industry literature on organization agility, this study aims to identify the key factors that influence an organization's agility and the benefits and drawbacks associated with fostering agility.

Through the technique of bibliometric analysis, we provide the growth trajectory of the field by identifying the publication trends, prominent authors and countries and most prolific journal publishing in the concerned domain. We also provide the intellectual structure of the organization agility research by identifying the prominent themes that have been worked upon till date. In addition, with the backing of the theories, contexts, characteristics and methodology (TCCM) framework, we identify the most frequently applied theories, constructs and methods in organization agility research and provide new avenues for future research by analyzing the most frequently used theories, methods, constructs and research contexts.

With the ever-increasing ambiguity and need for change (why), organization agility serves as the organization's backbone. It acts as a springboard for the organization, an anchor point that remains constant while other functional aspects constantly fluctuate and change. Organization agility can be defined (what) as the ability of organizations to quickly respond to market needs by sensing, renewing, adapting and succeeding in a turbulent market. To summarize, organizational agility matters at three fundamental aspects (where): strategic level or the market capitalizing level, internal operational level and individual level.

This paper is unique in the sense that it is the first comprehensive literature review in the field of organization agility research to use a hybrid methodology (bibliometric review with TCCMs).

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

The main goal of this research is to analyze and assess the barriers to Digital Transformation (DT) of the healthcare Supply Chain (SC) in developing countries and evaluate strategies to overcome such barriers. The DT has been related not only to the development of SC performance but also to the expansion of its resilience capabilities in a healthcare setting to overcome the results of unavoidable risk events and return to its previous or new state, which has at least the same or better conditions, after the occurrence of an unpredicted event.

Assessment of the barriers and determination of the importance or effectiveness of proposed strategies to handle these barriers are conducted through a hybrid Multi-Criteria-Decision-Making method consisting of an Analytical Hierarchy Process and multi-attributive border approximation area comparison (MABAC) under picture fuzzy (PiF) environment.

Based on the outcomes of the research, the most important barriers for DT in healthcare SC functions were the organizational and technological issues, including Low support from top managers (O1), Lack of digital culture (O2), and Info/data safety and privacy (T4). To handle these barriers, the prior strategy was selected as “Making breakthroughs and incentives of top managers for the DT in the industry (ST3)”.

The study may be extended to include developed countries and or experts from developed countries. Through this, the study will be generalized to a global level. The most common qualitative criteria, which include subjectivity, are considered. The research may be enhanced by including quantitative criteria in the decision-making process. The barriers related to DT for healthcare SC were considered. Thus, the study may be detailed by looking at the problem in terms of operational failure and determining the digital technology that can overcome such operational failures. The precedence or importance of any barrier may fluctuate with the upgradation of technological tools and improvements.

In terms of practical contribution, this research provides real-world suggestions to facilitate digital transformation in their SCs. Specifically, this study increases the awareness of healthcare and other industries' managers about obstacles that prevents digital transformation in SC and provides managerial path to relieve the effects of barriers.

To the authors' best knowledge, this is the first study to examine barriers of DT required for the promotion of resilient healthcare SCs in this concept. In addition, no previous research applied PiFSs-based AHP and MABAC integration to assess barriers and develop strategies for the DT of healthcare SC. The findings may be references for governmental institutions, policymakers, decision-makers, and stakeholders to develop proper strategies for a successful DT in healthcare SC resilience and the healthcare industry.