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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.

Capital structure is an important factor for the company because it will be directly related to the financial condition of the company. This study aims to determine the effect of asset structure, earning volatility, and financial flexibility on capital structure.

The population in this study was 52 companies in the consumer goods industry sector on the Indonesia stock exchange (IDX) and a sample of 39 companies obtained by purposive sampling method. The research method used in this study is multiple linear regression analysis using Eviews software.

The test results in the study show that asset structure and financial flexibility have a positive effect on capital structure, while earning volatility does not affect capital structure in companies in the consumer goods industry sector on the IDX.

The results of this research can contribute to the addition of knowledge in the field of accounting, especially regarding the capital structure. Company management can use the results of this research as a reference and consideration to find out the factors that affect the capital structure so that company management can still maintain the company's survival and improve company performance.

The results of this study can contribute to the addition of knowledge in the field of accounting, especially regarding capital structure. Company management can use the results of this research as a reference and consideration to determine the factors that affect the capital structure so that company management can still maintain the survival of the company and improve company performance.

This study only uses the variables of asset structure, financial flexibility and earning volatility as independent variables. Further research is recommended to consider the use of other variables that can affect capital structure and if using the same variable is expected to use research objects that have stable or increasing asset and income values, so that asset structure variables and profit volatility can show significant results and influences.

This study is one of the few studies that examines how the effect of asset structure, profit volatility and financial flexibility on capital structure in companies in the consumer goods industry sector on the IDX. Company management must pay attention to the composition of the capital structure as well as possible and make careful planning and the right decisions so as to produce a capital structure that can provide profits.

This paper empirically examines whether sophisticated governance mechanism affects the relationship between earnings management and dividend policy of non-financial firms.

The sample of the study includes non-financial firms listed on the stock exchanges of twenty developed and developing economies from the period 2005–2017. The Generalized Method of Moments (GMM) was applied to estimate the econometric models.

The results confirm the positive association between earning management and the dividend payout ratio of the sample firms. These findings are in line with the signaling theory, which suggests that firms engage in earnings manipulation to signal to the market that they can maintain a smooth dividend distribution. Moreover, findings suggest that board independence, being a mechanism of corporate governance, significantly negatively moderated the relationship between earnings management and the dividend payout ratio of non-financial firms.

The findings provide valuable suggestions to government bodies, regulatory authorities and corporate managers to focus on the effectiveness of governance mechanisms to improve the reliability of financial reports.

These findings imply that the effect of earning management on the dividend payout ratio is less pronounced in firms with more independent directors on the company board.

The study examines the IPO resilience grounded on the firm’s intrinsic factors.

We examine the association of IPO performance and post-listing firm’s performance with issuers' pre-listing financial and qualitative traits using panel data regression.

IPOs floated in the Indian market from July 2009 to March 31, 2022, evince the notable influence of issuers' pre-IPO fundamentals and legitimacy traits on IPO returns and post-listing earning power. Where the pandemic’s favorable impact is discerned on the post-listing year earning power of the issuer firms, the loss-making issuers appear to be adversely affected by the Covid disruption. Perhaps, the successful listing equipped the issuers with the financial flexibility to combat market challenges vis-à-vis failed issuers deprived of desired IPO proceeds.

High initial returns followed by a declining pattern substantiate the retail investors to be less informed vis-à-vis initial investors, valuers and underwriters, who exit post-listing after profit booking. Investing in the shares of the newly listed ventures post-listing in the secondary market can shield retail investors from the uncertainty losses of being uninformed. The IPO market needs stringent regulations ensuring the verification of the listing valuation, the firm’s credentials and the intent of utilizing IPO proceeds. Healthy development of the IPO market merits reconsidering the listing of ventures with weak fundamentals suspected to withstand the market challenges.

Given the tremendous rise in the new firm venturing into the primary market and the spike in IPOs countering the losses immediately post-opening, the study examines the loss-making and young firms IPOs separately, adding novelty to the study.

This study aims to review earned value management (EVM)-relative methods, including the original EVM, earned schedule method (ESM) and earned duration management (EDM(t)). This study then proposes a general implementation procedure and some basic principles for the selection of EVM-relative methods.

After completing an intensive literature review, this study conducts a case study to examine the forecasting performance of project duration using the EVM, ESM and EDM(t) methods.

When the project is expected to finish on time, ESM with a performance factor equal to 1 is the recommended method. EDM(t) would be the most reliable method during a project's entire lifetime if EDM(t) is expected to be delayed based on past experience.

As this research conducts a case study with only one building construction project, the results might not hold true for all types of construction projects.

EVM, ESM and EDM(t) are simple and data-accessible methods. With the help of a general implementation procedure, applying all three methods would be better. The power of the three methods is definitely larger than that of choosing only one for complex construction projects.

Previous studies have discussed the advantages and disadvantages of EVM, ESM and EDM(t). This study amends the available outcomes. Thus, for schedulers or researchers interested in implementing EVM, ESM and EDM(t), this study can provide more constructive instructions.

Online food delivery service has evolved swiftly and stretched the bounds of the catering business. In the gig economy, being a food delivery rider draws employees with the promise of flexibility and independence. To this end, the purpose of this paper is to explore the main themes shared in online reviews by food delivery riders and which of these themes are linked to positive and negative ratings.

The authors used 729 employee reviews posted on the Glassdoor website. In addition, they used content analysis to examine reviews of Uber Eats online food ordering company shared by food delivery riders on an online platform.

The results of this study include seven main themes: “earning,” “customers,” “orders,” “tips,” “car,” “flexible schedule” and “app” (navigation). Positive concepts are associated with “earning,” “orders,” “tips” and “flexible schedule.” Negative themes are linked to the “app” (navigation), “car” and “customers.”

Management should consider online reviews as employees’ opinions and voices. Specifically, management should provide financial support to employees for car maintenance, offer insurance for income stability and arrange training programs to enable them to use several tip-enhancing behaviors.

No research has been conducted using online reviews from an employment search engine to investigate employees’ experiences of online food delivery. To the best of the authors’ knowledge, this study is one of the first attempts using user-generated content from an employment search engine to explore employees’ experiences.

The purpose of the research was to evaluate the relationship between income and the recycled materials used in order to rate the business related to circular repair services under the burden of economic development in the countries of the European Union.

The analytical processes explore data from 2010 to 2020. The countries were divided into clusters according to economic maturity (Human Development Index (HDI), real Gross domestic product (GDP) per capita). Subsequently, the relationships were evaluated through the income indicators (for the 13 population groups), and the circular materials use rate indicator. The three indicators decomposed into five specific metrics were employed. The commonly applied characteristics of the descriptive analysis, Pearson's correlation coefficient and the panel regression analysis were engaged in the investigation.

The results demonstrated the vast disparities between income and circular materials use. In the more economically developed countries, their levels were twice higher as the less-developed countries. However, there is a meaningful positive relationship between them. The greatest attention was paid to the panel regression analysis applied to the relationship between income and circular economy (CE) use. The results showed that in a majority of the cases (different income categories), there is a significant positive relationship. When comparing the outcomes of the regression models between the groups of the countries according to their economic development, a closer relationship was clearly demonstrated in the countries with a lower level of development (Bulgaria, Romania, Croatia, Hungary).

Besides the specific strengths, the study also shows some limitations identified mainly on the data side. The latest data on the consumption of circular materials come from 2020, so they do not cover the period related to the pandemic crisis. It is believed that there may have been some changes in income during the pandemic, and they may have harmed CE. Furthermore, there is to note that some limitations occur in the analytical process. The incompleteness of the data can also be included here as certain data is not available; hence, it was estimated directly by the Eurostat statistical authority.

At the same time, the following are currently considered among the primary barriers: financial restrictions, insufficient infrastructure, weak government support and obstacles on the global market. Consumers, industry leaders and the government are the most influential stakeholder groups in overcoming barriers. Higher demand for repair services will also initiate further development of business activities in this area at various regional levels. Progress in the repair services economy will continue to require extensive efforts in the future. Systematic coordination of activities at multiple levels of government together with manufacturers, designers, educational institutions, community institutions and individuals will be essential.

Socioeconomic characteristics such as sex, age and education represent crucial predictors of consumer behavior. Therefore, the authors would like to focus future research on analyzing these characteristics and examine all the conceptual frameworks of consumer behavior and its positioning in detail within CE and the strategies related to the repair service. Discussing this issue through follow-up research will allow for solving complex transformational and political tasks related to the repair service strategies within CE. It will also inspire the discussion frameworks and multidisciplinary solutions to this issue affecting the fields of human geography, sociological, ethnographic and political sciences.

In less-developed countries, wage change can have a more substantial impact on the development of the CE. Also, a closer relationship between business in the field of repair services with income and the use of circular materials was manifested in the same way.

The purpose of this paper is to investigate the relationship between tax avoidance and earnings persistence in the light of a developing economy, with the main focus on China.

In the analysis, the author conducts a survey on the tax avoidance situation of Chinese listed companies from 2012 to 2020. Then, a multivariate regression analysis is performed in order to analyse the relationship between corporate tax avoidance and earnings persistence.

The findings of the present study show that tax avoidance has a significant positive effect on earnings persistence. However, when the degree of tax avoidance is high, the “risk effect” of tax avoidance exceeds the “value effect”, and tax avoidance will reduce the persistence of earnings. This conclusion is even more prominent when the company is non-state-owned. Further research shows the increase of institutional investors’ shareholding ratio can improve “value effect” of tax avoidance, lessen “risk effect” of tax avoidance, and positively affect the relationship between tax avoidance and earnings persistence.

This study provides evidence for investors to understand the dual effect of tax avoidance on earnings persistence. The results may have implications for regulatory bodies. They can provide a better understanding of the corporate governance role of institutional investors in curbing opportunistic tax avoidance.

This study enriches the research on tax avoidance effects by analysing the impact of tax avoidance on earnings persistence. This study also compensates for the shortcomings of analysing earnings persistence mainly from the perspective of tax differences in the past, and promotes the study of the corporate governance effects of institutional investors under different levels of tax avoidance.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This study examined the factors impeding the implementation of micro-credentials and accepting it as a credible source of earning professional qualifications and certifications necessary for pursuing higher education or other career goals.

The factors were identified by reflecting on the recent literature and Internet resources coupled with in-depth brainstorming with experts in the field of micro-credentials including educators, learners and employers. Two ranking methods, namely Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE) and multi-objective optimization based on ratio analysis (MOORA), are used together to rank the major challenges.

The results of this study present that lack of clear definitions, ambiguous course descriptions, lack of accreditation and quality assurance, unclear remuneration policies, lack of coordination between learning hours and learning outcomes, the inadequate volume of learning, and lack of acceptance by individuals and organizations are the top-ranked and the most significant barriers in the implementation of micro-credentials.

The findings can be used by educational institutions, organizations and policymakers to better understand the issues and develop strategies to address them, making micro-credentials a more recognized form of education and qualifications.

The novelty of this study is to identify the primary factors influencing the implementation of micro-credentials from the educators', students' and employers' perspectives and to prioritize those using ranking methods such as PROMETHEE and MOORA.