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The study of the relationship between external auditing services and investment deviation in a French setting has received relatively little research attention thus far. There are insufficient indicators to measure audit quality and then have a measurable link to investment efficiency. This study is motivated by such a research gap as well as the important role of auditing services in assuring investment efficiency. The purpose of this study is to test whether a good audit quality service improves corporate investment awareness in French-listed companies and contributes to establishing a comprehensive analysis framework for inefficient investment and how audit services have become an important tool to reduce the investment deviation of listed companies in France.

Based on a sample of 89 non-financial French firms listed on the Stoxx 600 Index from 2015 to 2021, this study uses feasible generalised least squares (FGLS) regressions to study the relationship between investment deviation and auditing service quality.

After running an FGLS regression model for two firm groups (overinvestment and overinvestment groups) and testing for a set of control variables, especially COVID-19, the findings show a non-linear correlation between audit service and corporate investment deviation. Both underinvestment and overinvestment decisions are negatively and statistically significantly impacted by audit indicators. Furthermore, involving a high-quality specialised auditor may enhance overall monitoring and lead to a lower investment deviation level. Overall, the empirical results show that a high-quality audit service enhances the investment efficiency of French-indexed companies.

This study offers crucial information that audit regulators can use to better appreciate the advantages of high audit quality and to take seriously the policy issues that affect it. Board members are urged to provide excellent audit quality that improves investment efficiency with careful consideration.

This study contributes to the existing audit literature by illuminating the effect of audit quality services on investment deviation to show a deeper understanding of the factors that contribute to the differences in prior studies’ findings in the field of audit quality impacts.

The purpose of this study is to measure the technical efficiency of 20 Algerian insurance companies from 2016 to 2020, by using slacks-based measure (slacks-based measure [SBM] data envelopment analysis [DEA]) model. This research aims to provide a comprehensive assessment of this companies' efficiency, taking into account both the desirable and undesirable outputs.

The study uses a nonoriented, SBM model with the assumption of constant returns to scale to estimate the technical efficiency of commercial insurance companies over a five-year period. The inputs used are labor expense, agent expense and investments, while the outputs included are gross premiums and investment income as desirable outputs and gross claims as undesirable output.

Among 20 insurance companies evaluated, only 5 companies consistently achieved technical efficiency during the study period (Caisse Nationale de Mutualité Agricole [CNMA], MACIR, CARDIF, MUTUALISTE and AGLIC); so they represent the best practices in the Algerian insurance sector, with overall average of the technical efficiency is 81%. However, the reference sets analysis showed that CNMA and AGLIC had high robustness. Also, the results demonstrate the impact of ignoring the undesirable outputs on the accuracy of the assessment.

The sample of the study consists of the active insurance companies in Algeria, based on the Annual Insurance Reports of Algeria; there are 20 companies as shown in the table. The data are taken from the annual reports of insurance companies during the 2020 period, issued by the Algerian Ministry of Finance.

The challenge for insurance company is how to find a balance between reducing claims paid and simultaneously improving the quality of insurance services. In fact, it is observed that studies evaluating their efficiency ignore claims in the analysis process. Therefore, the study highlights the importance of considering undesirable outputs within the DEA framework; this allows for a more accurate assessment of the company's performance and helps in improvement. Furthermore, although the insurance sector plays a crucial role, it has not received enough research attention compared to other financial sectors, especially in Arab and developing countries.

The literature on efficiency assessment in the insurance companies shows a lack of addressing undesirable outputs (such as claims) within the DEA framework; so this study aims at bridging this research gap. Also, the study provides an overview of the efficiency of Algerian insurance companies.

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 purpose of this study is to explore the causal relationship between smart transportation technology innovation and green transportation efficiency.

A comprehensive framework is used in this paper to assess the level of green transportation efficiency in China based on the instrumental variable – generalized method of moments model, followed by an examination of the impact of innovation in smart transportation technology on green transportation efficiency. Additionally, their non-linear relationship is explored, as are their important moderating and mediating effects.

The findings indicate that, first, the efficiency of green transportation is significantly enhanced by innovation in smart transportation technology, which means that investing in such technologies contributes to improving green transportation efficiency. Second, in areas where green transportation efficiency is initially low, smart transportation technology innovation exerts a particularly potent influence in driving green transportation efficiency, which underscores the pivotal role of such innovation in bolstering efficiency when it is lacking. Third, the relationship between smart transportation technology innovation and green transportation efficiency is moderated by information and communication technology, and the influence of smart transportation technology innovation on green transportation efficiency is realized through an increase in energy efficiency and carbon emissions efficiency.

Advancing green transportation is essential in establishing a low-carbon trajectory within the transportation sector.

The purpose of this study is to assess the influence of institutional quality (IQ), fossil fuel efficiency, structural change and renewable energy (RE) consumption on carbon efficiency.

This research uses an econometric approach, more specifically the Autoregressive Distributed Lag model, to examine the relationship between structural change, RE consumption, IQ, fossil fuel efficiency and carbon efficiency in India from 1996 to 2019.

This study finds the positive contributions of variables like fossil fuel efficiency, technological advancement, structural transformation, IQ and increased RE consumption in fostering environmental development through enhanced carbon efficiency. Conversely, this study emphasises the negative contribution of trade openness on carbon efficiency. These findings provide concise insights into the dynamics of factors impacting carbon efficiency in India.

This study's exclusive focus on India limits the generalizability of findings. Future studies should include a broader range of variables impacting various nations' carbon efficiency. Furthermore, it is worth noting that this study examines renewable and fossil fuel efficiency aggregated. Future research endeavours could yield more specific policy insights by conducting analyses at a disaggregated level, considering individual energy sources such as wind, solar, coal and oil. Understanding how the efficiency of each energy source influences carbon efficiency could lead to more targeted and practical policy recommendations.

To the best of the authors’ knowledge, this study addresses a significant gap in the existing literature by being the first empirical investigation into the effects of IQ, fossil fuel efficiency, structural change and RE consumption on carbon efficiency. Unlike prior research, the authors consider a comprehensive IQ index, providing a more holistic perspective. The use of a comprehensive composite index for IQ, coupled with the focus on fossil fuel efficiency and structural change, distinguishes this study from previous research, contributing valuable insights into the intricate dynamics shaping India's path towards enhanced carbon efficiency, an area relatively underexplored in the existing literature.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

The purpose of this study is to evaluate the effect of some internal features that influence the efficiency of non-bank financial institutions (NBFIs) in Bangladesh.

The study selected the top 15 Dhaka Stock Exchange (DSE)-listed NBFIs according to purposive sampling. The study period was from 2016 to 2020. Secondary data were collected from annual reports. The cost-to-income ratio was a dependent variable that was used as a proxy of operational efficiency. The ordinary least square regression technique was applied to measure the impact of firm-specific factors on efficiency.

Results showed that number of employees, branch number, firm size and deposit ratio have a significant effect on efficiency at 5% level. The number of branches and employees showed a negative impact, whereas firm size and deposit ratio showed a positive effect on the firms' efficiency. The deposit ratio is negatively related because deposit interest expenses were more than offset by interest income generation through the conversion of deposits into loans.

The study has practical and policy implications on NBFIs' managers, employees, shareholders, depositors, clients, regulatory authorities and government as efficiency enhancement would bring financial soundness.

This study shed light on some firm-specific factors that can be changed to increase operational efficiency or reduce the cost-to-income ratio. The novelty of the study is that it identified some significant associations between firm-specific factors and the operational efficiency of NBFIs.

The purpose of this study is to evaluate the profit efficiency of decision-making units (DMUs) based on predicted future information to solve the lag problem of improvement benchmarks given by the traditional profit efficiency model.

This paper proposes a two-step profit efficiency evaluation method. The first step predicts the future input and output information of DMUs through the past time-series data, obtaining a likely production possibility set (PPS) and profit frontier for the next period. The second step calculates DMUs' profit efficiency based on the predictions obtained in the first step and provides predictive benchmarking for DMUs.

The empirical results show that the proposed method yields good solutions for the lag problem of benchmarks given in ex-post evaluation, enabling bank managers to use predicted future information to achieve better improvement. Besides, compared with the technical efficiency measure, profit efficiency can better reflect the financial situation of DMUs and give the specific gap between the evaluated and optimal DMU.

For bank managers, the authors' new technique is advantageous for grasping the initiative of development because this technique accounts for the future development of the whole industry and sets forward-looking targets. These advantages can help banks improve in a more favorable direction and improve the asset management ability of banks.

This paper combines the data envelopment analysis (DEA) profit efficiency model with performance prediction and proposes a new two-step profit efficiency model, filling a gap in previous studies.

One of the existing and commonly used solar energy harvesting devices is the parabolic trough solar collector (PTSC). Because of their ability to operate in low and medium temperatures, parabolic trough concentrators are widely used in power generation plants and industrial process heating applications. Therefore, the investigation of how different operating conditions affect these devices’ overall efficiency has received a great deal of attention in the recent decade. This study aims to enhance the thermal performance of the PTSC and reduce the system cost.

In the novel configuration, a noncirculated nanofluid absorbs solar radiation through a glass wall. The base fluid was synthetic oil (5W30), and the nanoparticles used were copper oxide. The heat captured is immediately absorbed by the water circulating inside the copper tube immersed in the nanofluid. ANSYS FLUENT 15.0 was used for carrying out computational fluid dynamics simulations for two models of single and triple copper tubes. The experimental results obtained from a test rig constructed for this purpose were compared with the numerical outcomes of the single copper tube model.

The findings of the simulation demonstrated that performance was superior for the single copper tube model over the triple copper tube model. The numerical findings of the single copper tube model were compared with the experimental results. The numerical and experimental results differed from 3.17% to 5.6%. Investigations were carried out to study the effects of varying the volumetric flow rate of (20, 40, 60 and 80 L/h) and water inlet temperatures of (300, 315 and 330 K) on the effectiveness and performance of the newly developed model. Additionally, two nanofluid volume fractions of 0.05% and 0.075% were used for investigating their effect on the performance of the novel configuration. According to the findings, the highest thermal efficiency of 55.31% was recorded at 0.075% concentration and 80 L/h volume flow rate.

In this study, a novel direct absorption solar collector configuration using a noncirculated nanofluid was designed to enhance the thermal efficiency of PTSC. This new approach makes it possible to boost the thermal performance of the PTSC and lower the system’s cost.

The purpose of the paper is to investigate the association between corporate social responsibility (CSR) and investment efficiency (INE) in Bangladeshi pharmaceutical companies and to explore the moderating role of corporate reputation in this relationship.

The paper employs a two-step method, with stage 1 involving the development of a theoretical model using the literature's strategic framework and stage 2 using structural equation modelling (SEM) to investigate the relationships between variables. The data set used in the analysis includes 296 responses from senior executives/managers and subordinates at Bangladeshi pharmaceutical firms.

The study finds that CSR activities that focus on customers, employees and the community significantly affect INE, as well as the extended stakeholders, and that company reputation moderates this relationship. The effect of CSR on INE differs between well-established companies and business firms with favourable reputations.

The paper contributes to understanding the relationship between CSR and INE in a developing country context and highlights the importance of corporate reputation in this relationship. The findings suggest that companies can enhance their INE through CSR initiatives and that a positive reputation can strengthen this relationship further.

The study adds to the limited literature on CSR and INE in developing countries and provides new insights into the moderating role of corporate reputation in this relationship.