The study checks the opportunities and challenges in using different competition models between downstream channel partners, namely, comparable channel power, distinct channel power and crisis cartel during production cost disruption. We employed a supply chain consisting of a manufacturer-modern trade outlet-e-tailer, and different game theoretic models like Nash, Stackelberg and Collusion games were used to analyse the pre-disruption and disruption cases. The research revealed that the downstream channel partners could enhance profitability during disruptions by engaging in crisis cartels or operating under the competitor's leadership, surpassing pre-disruption levels. However, models involving channel leadership and comparable channel power yield lower profits for downstream partners during disruption. Surprisingly, none of the models offer improved profitability for the manufacturer during production cost disruptions, and both the crisis cartel and channel leadership models prove detrimental to the manufacturer's profits. Similarly, none of the models could improve the consumer surplus of customers. Additionally, we extend the basic model by analysing the impact of customer channel preference and the price elasticity of demand during production disruptions. We found that the channel preference coefficient plays a crucial role in determining the profitability of all supply chain partners. Furthermore, the price elasticity of demand significantly affects pricing strategies for the modern trade outlet and e-tailer but does not influence the manufacturer.

Purpose-Cogeneration plants powered by bagasse offer an efficient way to meet the thermal and electrical demands of sugar mills. However, configuring input settings in real time to meet fluctuating power targets remains a complex operational challenge. This study aims to develop a flexible, data-driven, surrogate-based inverse optimization framework to help managers adjust process parameters efficiently and effectively. Design/methodology/approach-This framework integrates machine learning and metaheuristics. First, an XGBoost surrogate model was trained using three years of SCADA data with a 60/20/20 train-validation-test split from a 4 MW sugar-mill cogeneration unit. Second, the authors formulated an inverse optimization problem to compute the optimal operational settings for a given power target. Third, three metaheuristic solvers - the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) - are benchmarked for speed, accuracy and robustness. Optuna-based hyperparameter tuning was conducted for both the surrogate model and the GA solver to enhance performance. Findings-The XGBoost model achieved high predictive performance (Test RMSE = 48.5 kW, Test R-2 = 0.987). The PSO balanced speed and accuracy for most targets, the GA offered consistent reliability and the DE showed strength at mid-range targets. Statistical significance testing (Wilcoxon signed-rank test, p < 0.01) confirmed the observed performance differences between solvers. Optuna-based tuning improved solver performance and reduced the worst-case error by over 60%. Practical implications-Plant managers can apply this framework to generate accurate real-time recommendations for input parameters based on desired power outputs. Its adaptability makes it particularly suitable for plants operating under varying load conditions. This framework reduces dependency on manual tuning and provides a scalable solution for dynamic operational control. Originality/value-This study uniquely integrates machine-learning surrogates with inverse optimization for cogeneration, offering a scalable alternative to traditional modeling. Unlike earlier studies, it incorporates long-term SCADA data, statistical validation and optimizer tuning in a unified decision-support framework. By incorporating a three-way data split and solver hyperparameter tuning, the framework enhances both generalization and optimization reliability, addressing the gap in real-time operational decision support.

The volatility of bunker fuel prices significantly impacts the maritime industry due to increasing regulatory complexities and market uncertainties. This study focuses on addressing these challenges by identifying bunker price prediction as a critical area of impact for improving operational efficiency and decision robustness. A comprehensive optimization model has been proposed to determine optimal ship speed and bunkering strategies both within and outside Emission Control Areas (ECAs). The traditional two-stage method involves training predictive models for bunker price, with the predictions being used as input parameters to solve the optimization problem. However, the loss function in this two-stage method does not consider the effect of predictions on the downstream decision-making problem. Therefore, this study adopts an integrated framework, Decision-Focused Learning (DFL) that unifies prediction and optimization processes into a cohesive approach. Unlike traditional sequential approaches, this method embeds the predictive model directly within the optimization process. It is comparatively more capable of handling data-driven optimization problems than traditional two-stage methods. In this study, computational experiments show that how DFL outperforms traditional methods, achieving 1.29% lower Normalized Regret, which translates to predictions resulting in approximately $22, 730 higher profit for liner services. These findings offer valuable managerial insights into improving efficiency and sustainability in liner shipping operations by making better decisions under real world uncertainties.

In the rapidly expanding Q-commerce sector, which promises delivery to customers within 10--15 min, even the slightest disruption can impose substantial harm on the growth and reputation of businesses. This study examines the optimization of dark store resilient portfolio, i. e., dark store selection and protection against disruptions, alongside strategic allocation of risk mitigation inventory at the protected dark stores. We develop a mixed integer linear programming (MILP) model to minimize costs linked to protection and prepositioning of inventory at dark stores as well as costs related to pick-up and delivery of customers' orders and stockouts. Additionally, we incorporate risk assessment measures such as value-at-risk and conditional value-at-risk in the MILP model to evaluate a Q-commerce company's risk-averse decisions, and compare risk-neutral with risk-averse strategies for analysing impact of disruption risks in the operations of dark stores. The findings suggest that protecting key dark stores and allocating more customer orders to these protected dark stores would maintain continuity in customer order fulfilment during disruptive events. The results further highlight the quantity of risk mitigation inventory that needs to be prepositioned at the protected dark store for better inventory decisions under disruption risks. Moreover, we also observe that dark stores exhibiting higher disruption risk are usually not selected for order fulfilment unless protected, emphasizing the importance of operational resilience. Finally, this study uncovers various managerial insights for Q-commerce companies in effectively implementing a resilient strategy, thereby enabling decision-makers to improve the overall performance of dark stores.

The modern food supply chain (FSC) faces pressing challenges, including food fraud, safety and security issues, food waste and sustainability concerns. Simultaneously, consumers are becoming increasingly interested in understanding the origins and pathways of their food. To tackle these challenges, digitisation and traceable FSCs are vital. However, numerous obstacles hinder the widespread adoption of traceability in digital FSCs. This study identifies and explores interconnections between barriers to traceability adoption in digital FSCs through an integrated DELPHI and Fuzzy DEMATEL approach. The findings highlight that Education and Training Gaps (B13), Data Integration Challenges (B14), Data Silos (B10), Environmental Sustainability Concerns (B16) and Short-Term Focus (B17) are among the most influential barriers, impacting several other challenges. To mitigate these barriers, the study proposes a nine-pillar framework. The insights derived from this research can support government agencies, policymakers and agro-food industries in advancing traceability adoption across digital FSCs.

Purpose - The integrated production and routing problems in a supply chain, commonly referred to as the production routing problem (PRP), combine lot-sizing and vehicle routing problems. PRP involves determining production schedules, quantities to produce and distribution plans to efficiently meet customer demands. This research aims to analyze the research background, identify current trends, track evolutions and propose future research directions for PRP. Design/methodology/approach - This study employs descriptive analysis and a systematic literature review (SLR) to explore PRP literature. Software such as R Studio and Excel was used for the analysis. An SLR of 90 articles (1994-2023) was conducted, classifying the literature into themes and sub-themes, providing an evolution of PRP research and introducing a diagnostic framework for a firm's readiness. Findings - Existing PRP review papers cover only 25% of the literature. This study fills the gap by offering a comprehensive review, categorizing PRP research into themes such as nature of demand, problem structure, solution methodologies, mathematical formulations, software tools and applications. The review also identifies sub-themes, including vehicle routing problem variants, environmental and reverse logistics, inbound logistics, multiple objectives and multi-echelon. The study also traces the evolution of PRP literature and solution methodologies. Originality/value - This study makes a significant contribution by identifying current trends, mapping the literature into various themes and sub-themes, introducing a diagnostic framework to assess firm readiness for PRP adoption and presenting PRP evolution from 1994 to 2023. It also outlines future research directions and theoretical and managerial implications for scholars and practitioners in the field of PRP.

Effective management of healthcare waste (HCW) imposes a great challenge to all countries. Specially in the developing countries, it is often mixed with municipal waste, adversely affecting the health and safety of the medical personnel, general public and environment. Healthcare waste management (HCWM) basically deals with segregation, collection and storage, routing and transportation, treatment and safe disposal of HCW, while obeying some national legislation. In every stage of HCWM, there are several alternative choices/strategies to be evaluated against a set of conflicting criteria. Numerous multi-criteria decision-making (MCDM) methods have appeared to resolve the issue. This article reviews 101 articles available in Scopus and other scholarly databases on applications of MCDM techniques in solving HCWM problems. Those articles are classified into six groups: (a) selection of the most effective HCW treatment technology, (b) identification of the best HCW disposal site, (c) assessment of the best-performing healthcare unit adopting ideal HCWM strategies, (d) selection of third party logistics providers, (e) identification of HCWM barriers and (f) evaluation of specific HCWM plans. It is observed that the past researchers have mostly preferred to apply MCDM tools for solving HCW treatment technology selection problems, whereas analytic hierarchy process, decision-making trial and evaluation laboratory and best-worst method and fuzzy set theory have been the mostly favoured MCDM tool, criteria weight measurement techniques and uncertainty model, respectively. The outcomes of this article would help the healthcare personnel/policymakers in unveiling the current status of HCWM research, exploring extant research gaps and challenges and providing future directions leading to sustainable environment.

This paper presents a stochastic MILP model for the fair distribution of profits in a supply chain under ripple effect with risk-neutral primary suppliers and a robust manufacturer. The robustness is understood as the mean-risk fairness that aims at equitably efficient business-as-usual and worst-case performance of the manufacturer. The objective is to equitably optimise conditional profit-at-risk and expected profit of the manufacturer as well as expected profits of all primary suppliers. In addition, the backup suppliers are considered andrecovery supply portfolios optimised for each disruption scenario. To coordinate production across the entire supply chain, a collaborative partnership is applied enforcing the manufacturer's expected service level to be not less than the expected service level of each primary supplier. The findings indicated that if the manufacturer aims at equitably efficient maximisation of average and worst-case profit under collaborative partnership, the associated expected profits of all primary suppliers may also converge to their respective collaborative maxima, and the more reliable is supply chain environment, the closer to their maxima are the supplier's profits. The findings also demonstrated that the fair distribution of supply chain profits under collaborative partnership simultaneously enforces coordinated production of parts by primary suppliers and products by the manufacturer.abstract & iquest;Please edit the abstract down to no more than 200 words.

Purpose-Like all other service industries, evaluation of service quality in health-care units is a complex decision-making task involving multiple stakeholder groups with varying interest, conflicting qualitative criteria and competing health-care units. The past researchers have already attempted to solve this problem while integrating different uncertainty models with various multi-criteria decision-making (MCDM) tools. This paper aims to propose application of an MCDM method for evaluating service quality of health-care units in uncertain environment. Design/methodology/approach-This paper presents application of an integrated approach combining grey numbers with combined compromise solution (G-CoCoSo) method for appraising service quality of six Urban Primary Health Centers (UPHCs) in Kolkata, India, based on the opinions of three different stakeholder groups (health-care service recipients, medical officers and health-care administrators) against six subjective criteria (tangibles, responsiveness, service, assurance, empathy and hygiene). A sensitivity analysis is also performed to investigate the effect of varying values of lambda on the ranking performance of G-CoCoSo method. Findings-Based on the collective judgments of the three stakeholder groups expressed in grey numbers, tangibles is identified as the most important criterion, followed by responsiveness. On the other hand, assurance criterion has the least importance. The G-CoCoSo method singles out H3 as the best UPHC, followed by H1. On the contrary, H5 appears as the worst performing UPHC. The results of sensitivity analysis prove that this integrated method is insensitive to changing values of lambda. Similarly, a comparative study against other grey integrated state-of-the-art MCDM methods validates its solution accuracy. Originality/value-To the best of the authors' knowledge, G-CoCoSo is used for the first time in this paper to solve a health-care service quality evaluation problem demonstrating satisfactory results. It would assist both the health-care professionals and patients in identifying the relative strengths and weaknesses of each of the UPHCs under consideration.

Additive manufacturing (AM) is a disruptive technology that enables the fabrication of intricate geometries layer-by-layer by discretizing the given geometry into multiple slices. Overhangs are regions of these slices where the surface projection exceeds the underlying horizontal support. AM techniques, like material extrusion (MEX), require explicit support structures, which are added to ensure proper printability and dimensional stability. Although supports provide part balancing to avoid material sagging, they should be minimised as they increase the overall material usage, print time and associated costs. Limited studies have been done on the self-supporting capacity of thin-walled AM structures. This research presents a novel analytical model based on the beam bending principle to determine the material's limit to self-sustain overhangs. The model determines this limit in terms of an overhang angle (from the vertical) using part geometry, process parameters and material properties. It is found that the overhang angle has an inverse square root relation with an apparent number of layers, which can be linearly approximated as a function of the number of layers. The model is further extended to incorporate buckling effects in the extruder fibres. Analytical results showed that overhangs as high as 75o are possible without any external supports, as against the conventional 45 degrees limit. The presented model can alleviate the AM process by increasing the printing efficiency and reducing material wastage.

Additive Manufacturing (AM) is a disruptive technique that enables solid physical realization of a given 3D model, via layer-by-layer deposition in a rapid manner. Various AM techniques have been developed to suit different material, geometric, and property needs. Motion systems are crucial for any advanced manufacturing technique, as they define the kinematic capabilities of the overall system, such as range of travel, feed speed, acceleration, and working volume. While the majority of the machine tools and AM systems are based on serial kinematic motion systems, parallel machines are also gaining popularity because of their precision and design. The scissor mechanism is a parallel kinematic system that offers a compact design with an extensive motion range and distributed loading. Although they are extensively used in material handling and lifting operations, limited research has been done on the incorporation of scissor-based motion systems for advanced manufacturing. This research presents a novel arrangement of scissor kinematics, retrofitted with a rotary table, to provide an overall tri-axial hybrid motion system, suitable for machine tools or AM applications. The system achieves two Parallel Translations (2PT) along Y and Z directions and a rotation about the Z-axis using a Serial Rotary table (1SR). Analytical design and computational models are prepared to validate the proposed design. A design case study is presented to demonstrate the potential application of the triaxial scissor system in sheet-based Electron Beam Additive Manufacturing (EBAM).

Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring-including integration, interoperability, and scalability issues-and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively.

This paper aims to quantify and integrate the Flood Exposure Index (FEI) and the Flood Sensitivity Index (FSI) to develop the Flood Multi-dimensional Vulnerability Index (FMVI), focusing on Kingston upon Hull, the UK's second most flood-vulnerable city. The research applies Geographic Information Systems (GIS) to map flood risk parameters spatially and employs the Analytical Hierarchical Process (AHP) to incorporate expert judgement more useful for in weighting the vulnerability parameters. A novel concept of Impact Percentiles is introduced using the Index of Multiple Deprivation (IMD), comparing localised deprivation scores against national averages and maxima to quantify relative multi-dimensional vulnerabilities. Assessing relative multidimensional vulnerability is superior because it contextualises vulnerability by enabling comparisons across regions or populations, making it more useful for prioritisation, policy decisions, and resource allocation. The analysis reveals acute deprivation across six Lower Layer Super Output Areas (LSOAs) in Kingston upon Hull and estimates relative disadvantage. Critical datadriven insight is provided into the intersection of physical flood exposure and socio-economic sensitivity. This data-driven research approach emphasises the ease and significance of integrating geospatial and socio-economic insights from a range of Impact Percentiles to prioritise high deprivation areas for policy interventions, enabling nuanced flood risk assessments, mitigation, and adaptation strategies.

Column Generation (CG) is a well-established methodology for tackling large-scale real-world optimization problems. Nevertheless, as problem sizes increase, challenges like long-tail effects and degeneracy become more prevalent. Various strategies for stabilizing dual variables have demonstrated their effectiveness in mitigating these challenges. Generally, numerical tests are employed to identify the best parameter values for stabilized CG using different configurations for the same problem. This study introduces an innovative approach using machine learning (ML) to predict the best algorithm configuration, eliminating the need for extensive numerical experimentation. The core objective of this study is to predict optimal dual variables to generate improved bounds in the Restricted Master Problem of stabilized CG. By and large, this comprehensive approach represents a robust and flexible framework, optimizing algorithm configurations and expediting the convergence of the CG model. Extensive computational experiments confirm the efficacy of our ML-based approach inaccurately predicting optimal dual variables and outperforming conventional methods. The practical utility is exemplified in optimizing workforce scheduling, demonstrating significant reductions in computational time across problem instances. This real-world application highlights the remarkable benefits of the smart approach in enhancing the efficiency and effectiveness of CG-based optimization solutions.

With the global shift towards a carbon-neutral supply chain (CNSC), blockchain technology (BT) is becoming increasingly significant. The food supply chain (FSC) significantly generates carbon emissions. This study evaluates how the integration of blockchain technology (IBT) is feasible to attain a CNSC. This study also finds the nexus among the sustainable development goals and how they behave between IBT and CNSC. This study presented a new framework based on the resource-based view and dynamic capability, which was tested using structural equation modeling (SEM). A comprehensive online survey was conducted utilizing a questionnaire that gathered responses from 200 individuals employed in the agricultural and food sectors. The finding reveals that the implementation of disruptive BT has a beneficial impact on the FSC by reducing emissions, ensuring safety, improving supply chain performance, minimizing food waste, and boosting consumer trust. Nonetheless, two variables, namely enhance supply chain performance, and build consumer trust, do not contribute to achieving a CNSC, as they enhance operational efficiency and trust, which might not directly result in a decrease in carbon emissions. The study enriches the literature on IBT in FSC to attain a CNSC while making the supply chain network more transparent, agile, and sustainable. It also challenges conventional wisdom by revealing factors that do not lead to a CNSC and guides policymakers to develop strategies to attain a CNSC.

The agri-food supply chain is a complex network enclosing various stakeholders, from farmers to consumers, with multifaceted interactions and dependencies. Traditional supply chain management approaches often need help adapting to dynamic environments and optimising decision-making processes. Deep reinforcement learning is employed by integrating value-based and policy-based models, enhanced by advanced learning techniques, to tackle these challenges. This paper explores applying Deep Reinforcement Learning (DRL) approaches, including Q-learning, Deep Q-Learning (DQL), and the Actor-Critic method, to optimise the efficiency of the agri-food supply chain. The actor-critic model significantly enhances decision-making processes across various supply chain stages by improving efficiency and increasing profit margins. A specific scenario of sugar processing and distribution is incorporated, considering real-world scenarios to validate our model. DRL methods optimise sugar production, storage and distribution, ensuring timely deliveries and enhancing profitability. The models address fluctuating demand and transportation logistics challenges, resulting in a more streamlined and responsive sugar distribution network. The findings reveal that Actor-Critic and DQL methods significantly outperform traditional Q-learning considering product profitability, offering unique advantages in handling complex state-action spaces.

The telecom industry is a key driver of operational efficacy across societal and economic domains. Within the telecom industry, mobile technology is growing fastest. The increasing number of mobile subscribers supported by correspondingly large number of mobile towers is creating a massive impact on the environment by substantially raising the greenhouse gas emissions, which are expected to rise with the integration of network components aimed at improving efficiency in next generation technologies. Hence, adoption of green technologies in the telecom industry is the need of the day. Research in green telecom adoption is rather limited, particularly in the context of emerging economies such as India. However, interestingly, India presents an interesting case to study as it is the world's second-largest telecom market with over one billion subscribers. Therefore, this study is taken up, which identifies the factors of green technology adoption by telecom service providers in India. First, open-ended semi-structured interviews were conducted with 10 telecom experts in India. The analysis was done using content analysis. The findings reveal 10 factors and 24 subfactors of green technology adoption. Further, by development of causal-loop diagram, the study identifies 30 interrelationships among subfactors, thereby offering a holistic perspective aiding in informed investment decision towards implementing green telecom technology. This study's main contribution lies in proposing the interrelationships among factors for increasing the green technology adoption.

Ensuring traceability in the perishable food supply chain (PFSC) is crucial for safeguarding consumer rights, food quality, and safety. Blockchain technology (BT), with its decentralized and immutable attributes, offers significant potential to enhance this traceability. However, its widespread adoption faces considerable roadblocks due to industry regulations and operational obstacles. This study aims to identify and analyze these roadblocks for BT adoption in the food industry to support strategic decision-making. Through a comprehensive literature review and expert discussions, 14 key roadblocks to blockchain adoption were identified, and a Grey DEMATEL integrated ANP methodology was applied. Findings reveal that the three most significant roadblocks to BT-based traceability adoption are a 'data security concern: lack of technological maturity and acceptance' (prominence value 4.219), 'threat to data privacy' (4.035), and 'lack of digital infrastructure' (3.971). Addressing these top three roadblocks in order of importance is crucial for accelerating BT adoption. This study provides theoretical contributions to methodological technologies and offers practical insights for professionals to overcome these roadblocks, thereby enhancing food security and safety within global PFSCs through robust, end-to-end encrypted traceable systems. For the successful implementation of blockchain technology strong rule and regulation against data security, creating centre or excellence and standardisation is advisable.

PurposeIn the health-care supply chain, selection of the most appropriate suppliers plays a decisive role while providing the right quantity of medical supplies at right time and least possible cost maintaining the desired quality level. The main aim of health-care supplier selection is to meet the quality standards, minimize risk, ensure consistent delivery, optimize operating costs, effective inventory management, efficiency improvement and develop long-term relationships through collaboration and communication. This paper aims to propose application of an integrated decision-making tool for solving a pharmaceutical supplier selection problem in the Indian health-care scenario.Design/methodology/approachIn this paper, the performance of 25 pharmaceutical suppliers is assessed treating nine real-time financial metrics for the year 2023-24 as the evaluation criteria. The relative significance of those metrics (criteria) are estimated using criteria importance through intercriteria correlation (CRITIC) method, while the considered pharmaceutical suppliers are ranked from the best to the worst using multi-attributive border approximation area comparison method. Thus, this paper designs an integrated tool to ease out solution of a pharmaceutical supplier selection problem.FindingsThe CRITIC method picks out inventory turnover ratio and return on assets as the most and least important criteria. Application of the proposed integrated decision-making tool helps in partitioning all the 25 pharmaceutical suppliers into upper and lower approximation areas, with identification of suppliers S11 and S20 as the best and worst choices, respectively. The suppliers located in the upper approximation area can act as the benchmarks to the underperformers. It would also assist in highlighting the relative strengths and weaknesses of each of the suppliers under consideration.Originality/valueBeing an easy-to-implement, simple and objective decision-making approach, it provides consistent and reliable results to the said supplier selection problem. It would remain unaffected due to the changes in the measurement units displaying the criteria values of the alternative suppliers and type of the criteria formulation. Besides health care, its application can also be extended to choose suppliers in other industries, such as automobile, textile, manufacturing, chemical and construction. It can be fused with different uncertainty models to cater subjectivity in the decision-making process.

PurposeThis study attempts to understand the main characteristics of inclusive leadership tweets in the context of team behavior. Further, we explore what the different content, topics and sentiments found in inclusive leaders' tweets are. Organizational practices matter, but when it comes to inclusion, individual's local and interpersonal experiences make all the difference - and herein lies the essential role of leaders at every level of the organization (Nishii and Leroy, 2022).Design/methodology/approachThis study extracted tweets from X (formerly Twitter) using the Tweepy library. Tweepy is a Python package for accessing the Twitter Application Programming Interface that allows developers to access Twitter content, such as tweets, retweets and timestamps. A Python script was used to search for all the tweets related to the keyword inclusive leadership. All texts met our criteria was extracted and stored in a comma-separated values file. We collected over 86,435 tweets between February 2023 and January 2024; however, after preprocessing, we were left with 37,108 unique tweets, which were further analyzed by data preprocessing, word frequency, word co-occurrence and sentiment analysis.FindingsThe results of this study highlight essential components of inclusive leadership and establish its positive impact on team performance and innovation. The study also found counterintuitive results on impact of leaders' tweets on team voice.Practical implicationsOrganizations can attune their tweets to the significance of inclusive leadership to increase their brand equity and reputation. Policymakers in the diversity, equity and inclusion space can share tweets and content reiterating inclusive leaders' critical role in fostering equity, inclusion and belongingness. Such practices also enhance employer branding; hence, it becomes crucial for leaders to drive the narrative on inclusion to attract talent.Originality/valueThis paper contributes to inclusive leadership literature in multiple ways. First, to the best of our knowledge, this is the first study that utilizes the Twitter Analytics framework to provide descriptive, content and network outcomes that focus on how inclusive leadership is perceived on Twitter. This study helps leaders understand the impact of their tweets in employees.