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The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

The pandemic induced lockdown lead to supply and manufacturing disruptions that were swiftly dealt with by the Indian Pharma Industry through successful industry-government collaboration. By May 2020 production was back to normal and exports were higher than the same period in May 2019. The case deals with the processes that enabled this to happen, the policy responses and the changes that happened in the period from March 2020 to August 2020.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

This study aims to estimate Pakistan’s export potential in new and existing export products, as well as their potential destination markets.

This study uses a nonparametric approach based on demand, supply and easiness factors for estimating export potential at disaggregated product and destination levels.

A significant number of new export products (extensive margin) and existing products (intensive margin) are identified that have export potential. The estimated unrealized export potential at extensive margins is $2bn and at intensive margins is $5bn. The range of new products included value-added products, semifinished products and intermediate products. Surprisingly, there is high potential to diversify in China and export existing products more intensively in the EU. Moreover, the potential at extensive margins is regional diverse compared to intensive margins.

The methodology used in this paper only provides export potential for short-to-medium term period because the global demand conditions are varying. Also, the mineral and resource-based products cannot be included in the analysis because their exports are heavily dependent on the availability of natural resources.

The findings have important policy implications in terms of providing guidelines for government policies related to industrial development, international trade and export promotion at the product and destination level. Overall, the study reveals that traditional sectors lack room for product diversification. As the existing export incentives favor major industries. To foster diversification, existing incentives must be redesigned to cover new products or sectors. Moreover, China has the greatest potential for product diversification, while Europe has the greatest potential to export current products more intensively. Further research is needed to simulate trade policy scenarios and estimate demand, supply and ease factors in export potential.

This study provides a unique perspective on export potential assessment at disaggregated product and destination levels, reinforcing the importance of redesigning trade policies and export incentives separately for export diversification.

Absorptive capacity, technological collaboration and servitization are analyzed to establish ways to overcome the balance between products and services in manufacturing companies. A fresh perspective is introduced by presenting a framework for innovation strategy, moving beyond product-based R&D.

The hypotheses are tested using data on Spanish firms in the high-tech chemical and pharmaceutical industries through ordinary least squares regression analysis. The sample consists of 112 manufacturing firms included in the Spanish Survey of Business Strategies.

The results show that absorptive capacity facilitates servitization and that technological collaboration moderates the relationship between absorptive capacity and servitization. The synergies between absorptive capacity and technological collaboration for servitization are recognized from the perspective of open innovation as a way of resolving the trade-off between products and services.

Future research should introduce more sources of collaboration by broadening the value chain perspective. Other approaches to innovation may also be considered, including relationships to process innovation.

The results can provide meaningful guidance for companies to determine the key opportunities of servitization driven by absorptive capacity, and the best ways to leverage open innovation and collaboration strategies to exploit such approaches.

This research enriches theories on servitization, open innovation and innovative behavior. Open innovation strategy should be linked to greater servitization activity and should support an open service strategy. This approach is crucial for building innovation capabilities through technological collaboration.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

The devastating acute COVID-19 epidemic crippled the global economy in 2020. Within a month of the COVID-19 epidemic, every industry saw its stock prices plummet the most. Ending the COVID-19 pandemic will need equitable access to safe and effective vaccinations. This study aims to look at the link between COVID-19 vaccination and the stock markets for health and pharmaceutical sector.

The researchers used a mean-adjusted return model and event research approach to figure out how the first dose of the COVID-19 vaccine affects health and pharmaceutical sector stock market returns.

The evidence-based outcome indicates that immunisation announcement affects health and pharmaceutical company returns. Furthermore, the study concludes that the health and pharmaceutical industry is inefficient for a short period of time, but after 41 days, the sector absorbs the noisy information.

Since the outbreak, the development of COVID-19 vaccines has been a key focus of shareholders and investors. This study is unique in that it investigates the effect of the first dosage of SARS-CoV-2 vaccination on equity returns in the health and pharmaceutical industries, and it is likely to make a substantial contribution to the capital market literature on event methodology.