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This meta-analytical review aims to clarify the relationships between three bundles of human resource management (HRM) practices—competency-enhancing, motivation-enhancing and opportunity-enhancing—and organizational innovation by addressing two questions: (a) Which types of HRM bundles are most strongly related to different forms of innovation (i.e. process and product innovation)? And (b) Which mechanism provides a stronger explanation for the positive effects of HRM bundles on innovation?

Based on data from 103 studies, a meta-analysis was conducted to quantitatively summarize existing HRM–innovation studies at the organizational level.

The results showed that the competency-enhancing bundle was more positively related to product innovation than the motivation-enhancing and opportunity-enhancing bundles. The opportunity-enhancing bundle was most strongly associated with process innovation. The authors further found that knowledge management capability (KMC) and employee motivation mediated the positive relationship between the three HRM bundles and innovation outcomes. In comparing the two mechanisms, this review suggests that KMC better explains both the impact of the competency-enhancing HRM bundle on product innovation and the effect of the opportunity-enhancing bundle on process innovation.

Based on behavioral and knowledge management perspectives, this study takes a sub-bundle approach to providing an integrative review by comparing the direct effects and mediating paths of HRM bundles on product and process innovation.

Drawing on the conservation of resources (COR) theory, this study examined the mediating role of psychological availability in the relationships between principals' individual-level and group-level authentic leadership and individual teachers' wellbeing, that is, job satisfaction, life satisfaction and emotional exhaustion.

The authors conducted a three-wave online questionnaire survey among 266 teachers from 52 schools in China. Multilevel structural equation modeling (MSEM) was used to analyze the hypothesized relationships among the study variables.

The principals' group-level and individual-level authentic leadership were both positively associated with individual teachers' psychological availability, which in turn was positively related to their job satisfaction and life satisfaction, and negatively related to their emotional exhaustion.

School administrations should elevate the levels of principals' authentic leadership by selecting and developing authentic principals to increase teacher wellbeing.

Differing from prior research that has focused on the effect of authentic leadership at either group-level or individual-level, this study simultaneously investigated the dual-level effects of principals' authentic leadership. Moreover, psychological availability was found to mediate the dual-level effects of principals' authentic leadership on teachers' job satisfaction, life satisfaction and emotional exhaustion.

This study aims to develop a green, economical and efficient ultrasonic-/microwave assisted extraction (UMAE) process for the extraction of anthocyanins.

After optimizing the extraction conditions by response surface methodology, three assays including DPPH, ABTS·+, FRAP were applied to analyze the antioxidant activity of the extracted anthocyanins. The stability under different temperatures, reductant concentrations and pHs was also discussed. The components of anthocyanins in blueberry were analyzed by HPLC-QTOF-MS2.

The optimal extraction parameters were ultrasonic power of 300 W, microwave power of 365.28 W and solid–liquid ratio of 30 (g/mL). The possible structures can be speculated as Delphinidin-3-O-galactoside, Delphinidin, Petunidin, Delphinidin-3-O-glucoside, Petunidin-3-O-glucoside, Cyanidin-3-O-glucoside. The results demonstrated that the UMAE can improve the yield of anthocyanins in shorter extraction time with higher activity.

The present study may provide a promising and feasible route for extracting anthocyanins from blueberries and studying their physicochemical properties, ultimately promoting the utilization of blueberry anthocyanins.

This paper contributes to the literature as follows. First, it examines if option and stock compensations raise creditor's risk, and which one is more important than the other. Second, it explores if CEO's compensation interacts with CEO overconfidence to raise creditor's risk. Third, it investigates how banks use different loan terms to alleviate their credit risk.

This study used advanced regression analysis and use of generalized methods of moment methodology.

The results show that option compensation is more important than stock compensation in raising credit risk; option compensation interacts with CEO overconfidence, giving rise to a much higher credit risk; and covenant usage is more important than other loan contract terms in mitigating credit risk given that covenant use could not be substituted away by using other loan contract terms such as increasing interest rate, reducing principal or shortening loan duration. This paper has practical implications for credit markets.

The main implication is that hand-collect data are available up to 2010.

It informs creditors the potential sources of loan risk emanating from option rather than stock incentives; it informs creditors that option incentive interacts with CEO overconfidence rendering the credit risk bigger than expected, and it informs creditors the importance of using covenants vis-à-vis other loan contract terms for mitigating compensation and overconfidence risk.

Banks are alerted to the risk due to the interaction between overconfidence and compensations, implying that overconfident managers remunerated with options compensations are more risky than overconfident managers who are not remunerated as such.

This paper is original: (1) The authors show that option compensation is more risky than stock compensation from viewpoint of creditors. This has not been assessed. (2) Interaction between managerial compensation and managerial overconfidence has not been assessed before. (3) Use of different loan contract terms to alleviate risk from overconfident managers (who are prone to over investment but who are innovative according to the literature) has not been evaluated.

The purpose of this paper is to analyze the mechanism of the role of government subsidies on corporate environmental investment and explore how specific characteristics of firms affect corporate environmental responsibility.

This paper examines the relationship between government subsidies and corporate environmental investment and models with a sample of 78,854 industries. The authors measure the corporate environmental investment by the natural logarithm of the volume of waste gas treatment facilities.

The results show the positive effect of government subsidies on corporate environmental investment. In addition, state ownership positively regulates the relationship between government and corporations, but the relationship between them is negatively regulated by the slack resources.

When people are increasingly concerned about corporate social and environmental responsibility, clarifying the link between government subsidies and corporate environmental investments can help policymakers formulate policies and allocate limited resources.

This study uses the resource-based view as a theoretical framework to reveal the mechanism of action between government subsidies and corporate environmental responsibility, enriching the previous literature that explores the issue based on the legitimacy perspective.

This study examines academic librarians' perceptions and attitudes toward Internet of Things (IoT) applications in Hong Kong academic libraries and the problems and possible improvements in using IoT technologies to strengthen library services.

This qualitative research used video conferencing software for semi-structured, one-on-one interviews. Participants were given introductory material about the IoT and asked to complete an interview. The data were analyzed using inductive theme clustering for this study.

The analysis identified three themes: perception about applying IoT technology to the library, problems and improvements in using IoT. Participants were generally optimistic about the potential benefits of IoT for improving library operations and providing personalized services. However, they also expressed concerns about privacy and security, errors and extra efforts for information literacy training. They suggested improvements such as incorporating facial recognition technology, advanced RFID technology and collections identification technology to enhance user experience.

Most studies examined users' views rather than librarians' on IoT applications, which few studies cover, especially in East Asia.

This study aims to investigate the relationship between chief executive officer (CEO) power and the level of tax avoidance of Tunisian listed companies. It also examines the moderating role of institutional ownership in this association.

The sample comprises 306 firm-year observations of companies listed on the Tunis Stock Exchange during the 2013–2020 period.

The results indicate that CEO power reduces tax avoidance levels. Moreover, the relationship between CEO power and tax avoidance is more pronounced in the presence of institutional ownership, suggesting that CEOs act less opportunistically when monitored by institutional investors, which results in a reduction in tax avoidance.

This study suggests that CEO power and institutional shareholders’ influence are important factors in determining firms’ avoidance behavior. This study has significant implications for shareholders and regulatory bodies. Indeed, shareholders apprehend the impact of appointing a powerful CEO on tax avoidance practices. This study may also provide regulators with new insights into the influence of CEO power dimensions and institutional ownership on tax aggressiveness.

This study fills the gap in the accounting literature by investigating how CEO power may impact tax avoidance behavior and provides empirical evidence on the moderating impact of institutional ownership on this relationship in an emerging economy context characterized by a weakly protected investor setting.

Over the years, the significance of retrofitting has gained much attention with the unveiling of its different applications, such as energy retrofit and deep retrofit, to enhance the climate-resilience of buildings. However, no single study comprehensively assesses the climate-resilience of retrofitting. The purpose of this study is to address this gap via a systematic literature review.

Quality journal studies were selected using the PRISMA method and analysed manually and using scientometrics. Three dimensions of climate-resilience, such as robustness, withstanding and recovery, were used to evaluate the contribution of retrofit measures for achieving climate-resilient houses across four climate zones: tropical, arid, temperate and cold.

Most passive measures can enhance the robustness of residential buildings but cannot verify for withstanding against immediate shocks and timely recovery. However, some passive measures, such as night-time ventilation, show excellent performance over all four climate zones. Active measures such as heating, ventilation and air conditioning and mechanical ventilation with heat recovery, can ensure climate-resilience in all three dimensions in the short-term but contribute to greenhouse gas emissions, further exacerbating the long-term climate. Integrating renewable energy sources can defeat this issue. Thus, all three retrofit strategies should appropriately be adopted together to achieve climate-resilient houses.

Since the research is limited to secondary data, retrofit measures recommended in this research should be further investigated before application.

This review contributes to the knowledge domain of retrofitting by assessing the contribution of different retrofit measures to climate-resilience.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.