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The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

The objective of this study is to explore the structural attributes of the pharmaceutical industry before the onset of the COVID-19 pandemic by examining the relationship between inventory and firm performance and developing a taxonomy of pharmaceutical firms based on the earns-turns matrix.

This study examines the inventory–firm performance linkage, considering both total inventory and its discrete inventory components in pharmaceutical firms. In addition, this research develops a new taxonomy of pharmaceutical firms based on the earns-turns matrix. A large panel dataset of firms in the US pharmaceutical industry was collected for the period 2000–2019.

The results reveal that strategic groups identified based on this taxonomy show different levels of profitability and inventory turns in the earns-turns matrix. Most pharmaceutical firms moved from the low-right to the top-left section in the earns-turns matrix, indicating that these firms have generally pursued profitability rather than effective inventory management.

This study explores the structural attributes of the pharmaceutical industry using the earns-turns matrix. This two-dimensional analysis may not, however, capture the full complexity of inventory–firm performance dynamics.

The mapping of strategic groups on the earns-turns matrix provides a useful tool for visual representations of the dynamics of strategic groups in terms of financial performance and inventory management performance. Practitioners can use the earns-turns matrix to benchmark their firm's position against their competitors.

This study broadens the scope of operations management research by introducing the earns-turns matrix as an empirical validation tool for operational and strategic management theories. This study emphasizes the effectiveness of the earns-turns matrix in analyzing strategic groups of pharmaceutical firms.

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.

This study empirically aims to examine the relationship between Bangladesh’s pharmaceutical industry growth and macroeconomic indicators such as the inflation rate, gross domestic product (GDP) growth, foreign direct investment (FDI) inflows, exchange rate and export growth through the long- and short-run relationship.

Using the time series data from 1986 to 2020, this study was developed based on the autoregressive distributed lag (ARDL) framework for co-integration. In contrast, the Toda–Yamamoto Granger Causality approach was also used for finding the direction of causality.

This study used the ARDL bounds test, which found strong co-integration among the variables, indicating a long-term relationship between them. In the long run, inflation, exchange rate and export growth significantly positively influence the pharmaceutical industry’s growth. Surprisingly, an FDI inflow has a negative impact. In the short term, the exchange rate and GDP growth were found to influence the growth of the pharmaceutical industry positively. Bidirectional causality between the growth of the pharmaceutical industry and the exchange rate was also identified using the Granger causality approach.

This paper emphasizes developing the policy as well as making concrete decisions regarding the development of the pharmaceutical industry and economic development in Bangladesh. The results also highlight the necessity for strategic macroeconomic management to support this sector’s long-term development and global competitiveness.

To the best of the authors’ knowledge, this paper is conducted to identify the short- and long-run relationship of pharmaceutical industry development with the economic indicators and progress, where no study has been found on this dimension.

This research paper aims to investigate the impact of pharmaceutical marketing on the prescription behavior of physicians. It further examines whether the use of various promotional techniques including advertisements, sales promotions, personal selling and direct marketing by pharmaceutical companies influences the prescription behavior of doctors. Based on the increasing ethical concerns regarding the promotion of specific drugs for personal gains, the research also investigated the moderating role of ethical ideology in the above relationship.

To accomplish the research goals, quantitative research methods were used. Using the online questionnaire, data were collected from 93 doctors working in government and private hospitals in Pakistan, and structural equation modeling technique was used to analyze the impact of pharmaceutical marketing techniques on the prescription decisions of physicians.

The findings suggested that marketing strategies of pharmaceutical companies positively influences the prescription behavior of doctors. Further, physicians having weak ethical standards are likely to be strongly influenced by the marketing and promotional practices of companies.

The research is pivotal in understanding the perspective of doctors and the ethical considerations that need to be addressed while devising the marketing campaigns by pharmaceutical companies. Further, these findings provide important implications regarding the essential linkage between ethical values and the development of right marketing tools.

To the best of the authors’ knowledge, this research is one of the first few to empirically develop and test the role of moral values followed by physicians when they take prescription decisions. Based on the findings, future researchers are encouraged to further investigate the need of setting boundaries for pharmaceutical companies and restricting their usage regarding the promotional tactics.

This study aims to propose an integrated framework for analyzing the effect of lean and agile innovation on the lean and agile supply chains.

The literature was reviewed and the dimensions of lean and agile supply chain/innovation were extracted. The statistical population included the managers and experts of pharmaceutical companies in Isfahan province. Eight pharmaceutical companies were selected. A researcher-made questionnaire was used to investigate the research variables. The face and content validity of the questionnaire and the data reliability were confirmed. After data collection, the studied companies were positioned in a two-by-two matrix and the associated data of two cells of the matrix, i.e. high lean supply chain/innovation and high agile supply chain/innovation were used for further statistical effect analysis using Smart-PLS.

The research results indicated that with the improvement of lean innovation in pharmaceutical companies, the lean supply chain improved by 97.9%; and with the improvement of agile innovation, the agile supply chain improved by 97.1%.

Considering lean innovation, pharmaceutical companies should deal with the process of conceptualizing innovation, and regarding agility strategy, their focus should be more on generating ideas to improve their agile supply chain. This study was performed during the COVID-19 pandemic and offers appropriate innovation strategies to improve the supply chain of pharmaceutical companies.

The literature review implies that no research has been conducted on the selected and classified variables of this study. Also, using the positioning matrix before statistical analysis distinguishes this paper from similar studies.

The purpose of this study is twofold: first, to determine if pharmaceutical companies can be grouped based on their espoused values, and second, to examine the relationship between these values and company reputation.

A descriptive study design is used with two separate analyses: cluster analysis for grouping the companies; and descriptive data analysis for determining cluster differences.

The findings suggest that there are three value clusters: competent, community and interpersonal, with the community group showing the highest relative reputation, and the interpersonal cluster as the lowest. Brand portfolio composition appears to positively contribute to reputation. The effect of portfolio specialization is based on a company’s closeness to its therapeutic community, which may be influenced by the outward characteristics of its values.

Future research should examine the longitudinal effects of values on reputation combined with case studies.

Regardless of cluster classification, all firms should develop strong ties with their therapeutic communities using both personal and digital/omnichannel strategies.

A company’s values are becoming an important consideration for all customers and stakeholders.

To the best of the authors’ knowledge, this study is the first to systematically examine the activities of leading pharmaceutical firms to link a specific value cluster to company reputation.

This study investigates the role of supply chain knowledge management in enhancing pharmaceutical supply chain resilience.

This study focusses on the Middle East region, where semi-structured online interviews were conducted with 38 professionals from the pharmaceutical supply chain to collect empirical data.

The study reveals that supply chain knowledge management is a crucial value-adding practice that improves pharmaceutical supply chain resilience. Effective supply chain knowledge management enables organisations to develop agility, change, adaptability, problem-solving, response and innovation capabilities that support supply chain resilience. However, challenges related to supply chain management practices, people, processes and technology hinder the effective promotion of supply chain knowledge.

This study reminds managers that knowledge management is critical for building resilience in supply chains.

The study highlights the importance of a resilient pharmaceutical supply chain for organisations and society. The study advocates that effective supply chain knowledge management can help ensure a sustained supply of high-quality pharmaceutical products and services during crises.

The study offers novel insights by examining pharmaceutical supply chain resilience from a knowledge management perspective and highlighting the potential of knowledge capabilities to enable supply chains to recover from crises and adapt to the new normal. This study also highlights the key strategic considerations for managing knowledge effectively throughout the supply chain.

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.

This paper deals with the combined management and design of a sustainable pharmaceutical supply chain network with considering recycling.

This paper first utilizes the analytical hierarchy process to select and rank green manufacturers. Second, the authors proposed a multi-objective nonlinear mathematical model to design a sustainable pharmaceutical supply chain network. The proposed model has been linearized and solved using the LP-metric method using GAMS software.

A real case study has been conducted in Iran. The results show that environmental and social issues can be improved while minimizing total costs.

Given the criticality and importance of drugs in human health and the importance of recycling in today's world, proper management and design of a sustainable drug supply chain are necessary. This study pays special attention to environmental issues by utilizing multi-criteria decision approaches and customer satisfaction.