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International Journal of Managerial Finance

ISSN : 1743-9132

Article publication date: 3 April 2017

The purpose of this paper is to study the status of studies on capital structure determinants in the past 40 years. This paper highlights the major gaps in the literature on determinants of capital structure and also aims to raise specific questions for future research.

Design/methodology/approach

The prominence of research is assessed by studying the year of publication and region, level of economic development, firm size, data collection methods, data analysis techniques and theoretical models of capital structure from the selected papers. The review is based on 167 papers published from 1972 to 2013 in various peer-reviewed journals. The relationship of determinants of capital structure is analyzed with the help of meta-analysis.

Major findings show an increase of interest in research on determinants of capital structure of the firms located in emerging markets. However, it is observed that these regions are still under-examined which provides more scope for research both empirical and survey-based studies. Majority of research studies are conducted on large-sized firms by using secondary data and regression-based models for the analysis, whereas studies on small-sized firms are very meager. As majority of the research papers are written only at the organizational level, the impact of leverage on various industries is yet to be examined. The review highlights the major determinants of capital structure and their relationship with leverage. It also reveals the dominance of pecking order theory in explaining capital structure of firms theoretically as well as statistically.

Originality/value

The paper covers a considerable period of time (1972-2013). Among very few review papers on capital structure research, to the best of authors’ knowledge; this is the first review to identify what is missing in the literature on the determinants of capital structure while offering recommendations for future studies. It also synthesize the findings of empirical studies on determinants of capital structure statistically.

  • Literature review
  • Meta-analysis
  • Capital structure
  • Pecking order

Kumar, S. , Colombage, S. and Rao, P. (2017), "Research on capital structure determinants: a review and future directions", International Journal of Managerial Finance , Vol. 13 No. 2, pp. 106-132. https://doi.org/10.1108/IJMF-09-2014-0135

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determinants of capital structure literature review

  • > Journals
  • > Journal of Financial and Quantitative Analysis
  • > Volume 58 Issue 6
  • > Determinants of Capital Structure: An Expanded Assessment

determinants of capital structure literature review

Article contents

Determinants of capital structure: an expanded assessment.

Published online by Cambridge University Press:  09 December 2022

  • Supplementary materials

Using a standardized methodology, we empirically evaluate 55 proposed determinants of capital structure in terms of statistical significance, economic significance, and identification. We find that robust and economically important determinants of debt ratios are relatively few in number. Nevertheless, because each determinant relates to one of five market imperfections—taxes, distress costs, information asymmetry, agency costs, or supply frictions—we draw conclusions from the evidence as a whole regarding the explanatory power of different capital structure theories. We find greater support for pecking order theory and supply-related theories, with less support for traditional tradeoff theory and agency theory.

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For generously sharing data, we thank Ramin Baghai, Lucian Bebchuk, Efraim Benmelech, Adam Bonica, Filipe Campante, Quoc-Anh Do, John Graham, Barry Hirsch, Gerard Hoberg, Jay Li, David Macpherson, Stephen McKeon, Lalitha Naveen, Renana Peres, Gordon Phillips, Alessio Saretto, Matthew Serfling, Jared Smith, Robert Tumarkin, Philip Valta, Ekaterina Volkova, Jing Wang, Jin Xu, and Ayako Yasuda. For helpful comments, we thank an anonymous referee, Lee Biggerstaff, François Derrien, Brad Goldie, John Graham, Ben Iverson, Jonathan Karpoff, Jared Smith, Serafeim Tsoukas, Ekaterina Volkova, and David Yin. For excellent research assistance, we thank Greg Adams, Troy Carpenter, Spencer Crawford, Paige Nelson, Marshall Ringwood, and Logan Smith.

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  • Volume 58, Issue 6
  • Toshinori Fukui (a1) , Todd Mitton (a2) and Robert Schonlau (a3)
  • DOI: https://doi.org/10.1017/S0022109022001405

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Research on capital structure determinants: a review and future directions

Research output : Contribution to journal › Review Article › Research › peer-review

Purpose: The purpose of this paper is to study the status of studies on capital structure determinants in the past 40 years. This paper highlights the major gaps in the literature on determinants of capital structure and also aims to raise specific questions for future research. Design/methodology/approach: The prominence of research is assessed by studying the year of publication and region, level of economic development, firm size, data collection methods, data analysis techniques and theoretical models of capital structure from the selected papers. The review is based on 167 papers published from 1972 to 2013 in various peer-reviewed journals. The relationship of determinants of capital structure is analyzed with the help of meta-analysis. Findings: Major findings show an increase of interest in research on determinants of capital structure of the firms located in emerging markets. However, it is observed that these regions are still under-examined which provides more scope for research both empirical and survey-based studies. Majority of research studies are conducted on large-sized firms by using secondary data and regression-based models for the analysis, whereas studies on small-sized firms are very meager. As majority of the research papers are written only at the organizational level, the impact of leverage on various industries is yet to be examined. The review highlights the major determinants of capital structure and their relationship with leverage. It also reveals the dominance of pecking order theory in explaining capital structure of firms theoretically as well as statistically. Originality/value: The paper covers a considerable period of time (1972-2013). Among very few review papers on capital structure research, to the best of authors’ knowledge; this is the first review to identify what is missing in the literature on the determinants of capital structure while offering recommendations for future studies. It also synthesize the findings of empirical studies on determinants of capital structure statistically.

Original languageEnglish
Pages (from-to)106-132
Number of pages27
Journal
Volume13
Issue number2
DOIs
Publication statusPublished - 2017
  • Capital structure
  • Literature review
  • Meta-analysis
  • Pecking order

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

  • 10.1108/IJMF-09-2014-0135

Other files and links

  • Link to publication in Scopus

T1 - Research on capital structure determinants

T2 - a review and future directions

AU - Kumar, Satish

AU - Colombage, Sisira

AU - Rao-Melancini, Purnima

N2 - Purpose: The purpose of this paper is to study the status of studies on capital structure determinants in the past 40 years. This paper highlights the major gaps in the literature on determinants of capital structure and also aims to raise specific questions for future research. Design/methodology/approach: The prominence of research is assessed by studying the year of publication and region, level of economic development, firm size, data collection methods, data analysis techniques and theoretical models of capital structure from the selected papers. The review is based on 167 papers published from 1972 to 2013 in various peer-reviewed journals. The relationship of determinants of capital structure is analyzed with the help of meta-analysis. Findings: Major findings show an increase of interest in research on determinants of capital structure of the firms located in emerging markets. However, it is observed that these regions are still under-examined which provides more scope for research both empirical and survey-based studies. Majority of research studies are conducted on large-sized firms by using secondary data and regression-based models for the analysis, whereas studies on small-sized firms are very meager. As majority of the research papers are written only at the organizational level, the impact of leverage on various industries is yet to be examined. The review highlights the major determinants of capital structure and their relationship with leverage. It also reveals the dominance of pecking order theory in explaining capital structure of firms theoretically as well as statistically. Originality/value: The paper covers a considerable period of time (1972-2013). Among very few review papers on capital structure research, to the best of authors’ knowledge; this is the first review to identify what is missing in the literature on the determinants of capital structure while offering recommendations for future studies. It also synthesize the findings of empirical studies on determinants of capital structure statistically.

AB - Purpose: The purpose of this paper is to study the status of studies on capital structure determinants in the past 40 years. This paper highlights the major gaps in the literature on determinants of capital structure and also aims to raise specific questions for future research. Design/methodology/approach: The prominence of research is assessed by studying the year of publication and region, level of economic development, firm size, data collection methods, data analysis techniques and theoretical models of capital structure from the selected papers. The review is based on 167 papers published from 1972 to 2013 in various peer-reviewed journals. The relationship of determinants of capital structure is analyzed with the help of meta-analysis. Findings: Major findings show an increase of interest in research on determinants of capital structure of the firms located in emerging markets. However, it is observed that these regions are still under-examined which provides more scope for research both empirical and survey-based studies. Majority of research studies are conducted on large-sized firms by using secondary data and regression-based models for the analysis, whereas studies on small-sized firms are very meager. As majority of the research papers are written only at the organizational level, the impact of leverage on various industries is yet to be examined. The review highlights the major determinants of capital structure and their relationship with leverage. It also reveals the dominance of pecking order theory in explaining capital structure of firms theoretically as well as statistically. Originality/value: The paper covers a considerable period of time (1972-2013). Among very few review papers on capital structure research, to the best of authors’ knowledge; this is the first review to identify what is missing in the literature on the determinants of capital structure while offering recommendations for future studies. It also synthesize the findings of empirical studies on determinants of capital structure statistically.

KW - Capital structure

KW - Leverage

KW - Literature review

KW - Meta-analysis

KW - Pecking order

UR - http://www.scopus.com/inward/record.url?scp=85015743903&partnerID=8YFLogxK

U2 - 10.1108/IJMF-09-2014-0135

DO - 10.1108/IJMF-09-2014-0135

M3 - Review Article

AN - SCOPUS:85015743903

SN - 1743-9132

JO - International Journal of Managerial Finance

JF - International Journal of Managerial Finance

  • DOI: 10.1504/ajaaf.2015.072226
  • Corpus ID: 167620775

Capital structure determinants: a literature review

  • Asheesh Pandey , M. Singh
  • Published 6 October 2015

9 Citations

Determinants of capital structure: a literature review, a study on analysis of i-reits and their capital structure, measurement matters – a meta-study of the determinants of corporate capital structure, determinants of capital structure in banking sector: a bangladesh perspective, capital structure management by share repurchase for companies in emerging markets, a panel data analysis on the capital structure determinants of construction companies listed in s&p bse 500, determinants of european telecom operators' capital structure, determinants of capital structure in nigerian oil and gas sector, dr. adepoju adeoba asaolu determinants of capital structure in nigerian oil and gas sector, 44 references, determinants of capital structure in india (1990-1998): a dynamic panel data approach, an empirical analysis of capital structure determinants: evidence from the indian corporate sector, determinants of corporate capital structure in nigeria, a study on determinants of capital structure in india, factors affecting capital structure decisions: empirical evidence from selected indian firms, the determinants of corporate capital structure: evidence from japanese manufacturing companies, determinants of capital structure: evidence from banking sector in albania, determinants of capital structure of a-reits and the global financial crisis, determinants of capital structure: comparison of empirical evidence for the use of different estimators, determinants of capital structure: a case study of listed companies of nepal, related papers.

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Firms’ capital structure during crises: evidence from the united kingdom, 1. introduction, 2. materials and methods, 4. conclusions and discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

VariablesDefinitionSource
Dependent variables
Accounting measurements
ROAReturn on asset: measures how a firm efficiently uses its assets and generates gains. =(Net Income − Bottom Line + ((Interest Expense on Debt-Interest Capitalized) × (1 − Tax Rate)))/Average of Last Year’s and Current Year’s Total Assets × 100.LSEG
ROEReturn on equity: measures how well a firm makes returns from its equity.
=((Net Income − Bottom Line − Preferred Dividend Requirement)/Average of Last Year’s and Current Year’s Common Equity × 100).
LSEG
Market measurement
LNQThe natural logarithm of Tobin’s Q. Tobin’s Q is long-term financial performance expectations that stem from shareholder-related financial performance. =LN ((The market value of equity + book value of assets − book value of equity—deferred taxes)/book value of assets).Authors’ calculation
Explanatory variables
CSR measurements
ESGA relative sum of the environmental, social, and governance pillars. LSEG
EThe weighted average relative rating of resource use, emission, and innovation related to company reports on their environmental activities.LSEG
SThe weighted average relative rating of the workforce, human rights, community, and product responsibility is interrelated to companies’ social commentary.LSEG
GBased on company governance details, the weighted average relative rating of management, shareholders, and CSR strategy is formed. provides more information on each pillar.LSEG
Capital structure measurements
SDAThe short-term debt ratio. =(The portion of debt payable within one fiscal year consisting of the current portion of long-term debt and sinking fund obligations of preferred stock or Debentures)/the book value of total assets.Authors’ calculation
LDAThe long-term debt ratio is = (all interest-bearing financial obligations, not including amounts demanded within one fiscal year. It is shown net of premium or discount)/the book value of total assets.Authors’ calculation
TDAThe total debt ratio represents all interest-bearing and capitalized lease obligations. =(Sum of long and short-term debt)/the book value of total assets. Authors’ calculation
Control variables
MBGrowth = market value of the company/total shareholder’s equityAuthors’ calculation
PPETATangibility represented by fixed assets (Property + plant + and equipment)/the book value of total assetLSEG
CRThe current ratio measures firm liquidity.
=current assets (Denotes cash and other assets, sold, or estimated to turn to cash, sold or consumed within one fiscal year or one operating cycle)/current liabilities (represents a debt or other requirements that the firm assumes to fulfill within one fiscal year).
LSEG
LNTAThe natural logarithm of the firm’s total assets and measures the size.Authors’ calculation
BetaSystematic risk (beta) measures a stock’s volatility concerning the overall market volatility.
=percent changes of end price between 23 and 35 consecutive months related to the local market index.
LSEG
COVID-19The dummy variable takes the value of one for the period 2020–2021 and zero otherwise.Authors’ calculation
Industry dummiesBasic Materials (D1), Consumer Discretionary (D2), Consumer Staples (D3), Energy (D4), Health Care (D5), Industrials (D6), Technology (D7), and Telecommunications (D8). These are based on the London Stock Exchange ICB Industry classifications. Each dummy takes the value of one if it is related to a specific industry and zero otherwise.Authors’ calculation
ESG Pillar SubcategoriesScoreDefinition
EnvironmentResource use Reflects a company’s performance and capacity to reduce the use of materials, energy, or water and find more eco-efficient solutions by improving supply chain management.
Emissions reductionMeasures a company’s commitment to and effectiveness in reducing environmental emissions in its production and operational processes.
Innovation Reflects a company’s capacity to reduce its customers’ environmental costs and burdens, thereby creating new market opportunities through new environmental technologies and processes or eco-designed products.
SocialWorkforceMeasures a company’s effectiveness in providing job satisfaction and a healthy and safe workplace, maintaining diversity and equal opportunities, and development opportunities for its workforce.
Human rightsMeasures a company’s effectiveness in terms of respecting fundamental human rights conventions.
CommunityMeasures a company’s commitment to being a good citizen, protecting public health, and respecting business ethics.
Product responsibilityReflects a company’s capacity to produce quality goods and services, integrating the customers’ health and safety, integrity, and data privacy.
GovernanceManagementMeasures a company’s commitment to and effectiveness in following best practice corporate governance principles.
ShareholdersMeasures a company’s effectiveness in the equal treatment of shareholders and the use of anti-takeover devices.
CSR strategyReflects a company’s practices to communicate that it integrates economic (financial), social, and environmental dimensions into its day-to-day decision-making processes.
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIIABLESDASDASDALDALDALDATDATDATDA
L.SDA0.645 ***0.642 ***0.656 ***
(0.00248)(0.00292)(0.00213)
L.LDA 0.798 ***0.595 ***0.790 ***
(0.00446)(0.00544)(0.00390)
L.TDA 0.719 ***0.552 ***0.760 ***
(0.00440)(0.00552)(0.00472)
*** *** ***
) ) )
*** *** ***
)
*** ***
) ) )
*** *** ** *** *** *** *** ** ***
) ) ) ) ) ) ) ) )
*** ** *** *** *** * ***
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat16,44820,26738,34562,08111,63781,73243,53835,42645,351
Prob > F000000000
AR (1)0.02120.03220.02191.98 × 10 6.73 × 10 1.82 × 10 1.34 × 10 1.18 × 10 1.47 × 10
AR (2)0.7910.9460.8490.01270.4070.01210.1070.3030.0225
Hansen0.1160.1910.1500.3090.2660.2210.04940.1250.102
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.645 ***0.636 ***0.657 ***
(0.00251)(0.00267)(0.00230)
L.LDA 0.794 ***0.597 ***0.789 ***
(0.00446)(0.00601)(0.00462)
L.TDA 0.732 ***0.561 ***0.763 ***
(0.00483)(0.00540)(0.00481)
*** ** ***
) ) )
*** *** ***
) ) )
*** *** ***
) ) )
*** *** *** *** *** *** ** ***
) ) ) ) ) ) ) ) )
*** *** *** *** *** *** ***
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat19,64615,37035,07737,52714,06454,49138,85021,92545,269
Prob > F000000000
AR (1)0.02080.03210.02231.90 × 10 7.50 × 10 1.73 × 10 1.25 × 10 1.10 × 10 1.34 × 10
AR (2)0.8940.9490.7180.01600.3870.009840.08790.2800.0225
Hansen0.1160.1370.1910.2720.1880.2340.06850.1170.0970
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.695 ***0.736 ***0.782 ***
(0.00278)(0.00240)(0.00348)
L.LDA 0.874 ***0.725 ***0.903 ***
(0.00286)(0.00451)(0.00330)
L.TDA 0.872 ***0.802 ***0.895 ***
(0.00444)(0.00468)(0.00305)
*** *** ***
) ) )
*** *** ***
) ) )
**
) ) )
*** ** *** ** *** ***
) ) ) ) ) ) ) ) )
*** *** *** *** *** *** *** ***
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat27,16123,819120,683279,318164,378388,153121,309114,797215,227
Prob > F000000000
AR (1)7.46 × 10 3.84 × 10 4.78 × 10 1.82 × 10 1.10 × 10 1.86 × 10 1.26 × 10 5.38 × 10 1.97 × 10
AR (2)0.3570.4050.2650.1130.6910.07920.1500.3110.0719
Hansen0.2990.3540.2710.3780.1280.2880.2280.2110.280
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.637 ***0.629 ***0.669 ***
(0.00217)(0.00258)(0.00198)
L.LDA 0.794 ***0.618 ***0.789 ***
(0.00367)(0.00508)(0.00350)
L.TDA 0.719 ***0.586 ***0.764 ***
(0.00395)(0.00423)(0.00396)
ROA−3.82 × 10 *** −1.13 × 10 *** −4.43 × 10 ***
(2.09 × 10 ) (2.24 × 10 ) (3.01 × 10 )
ROE −2.59 × 10 *** −4.70 × 10 *** −1.96 × 10 ***
(7.09 × 10 ) (1.38 × 10 ) (1.41 × 10 )
LNQ −3.15 × 10 *** −1.13 × 10 ** −3.07 × 10 ***
(2.24 × 10 ) (5.64 × 10 ) (5.47 × 10 )
E−4.08 × 10 ***−1.65 × 10 9.86 × 10 ***−1.97 × 10 ***2.13 × 10 −2.11 × 10 ***−2.14 × 10 ***1.40 × 10 ***−1.12 × 10 ***
(9.60 × 10 )(1.10 × 10 )(9.60 × 10 )(2.45 × 10 )(2.99 × 10 )(2.16 × 10 )(3.10 × 10 )(4.52 × 10 )(2.50 × 10 )
COVID-190.00410 ***0.00663 ***0.00342 ***−0.0124 ***−0.00506 ***−0.0142 ***−0.00657 ***0.00161−0.00930 **
(0.000560)(0.000587)(0.000369)(0.000993)(0.00140)(0.00129)(0.00150)(0.00145)(0.00131)
*** *** *** *** *** *** **
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat20,44327,02442,84675,99925,52592,94256,05556,45962,621
Prob > F000000000
AR (1)0.02140.03260.02201.76 × 10 5.11 × 10 1.54 × 10 1.42 × 10 8.98 × 10 1.35 × 10
AR (2)0.8590.8260.9660.04950.2990.03540.09520.1990.0168
Hansen0.2180.3310.2940.3800.3510.2830.06870.2480.109
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.641 ***0.633 ***0.663 ***
(0.00228)(0.00270)(0.00202)
L.LDA 0.795 ***0.620 ***0.788 ***
(0.00399)(0.00535)(0.00431)
L.TDA 0.724 ***0.589 ***0.761 ***
(0.00376)(0.00469)(0.00413)
ROA−4.61 × 10 *** −6.02 × 10 ** −5.59 × 10 ***
(2.19 × 10 ) (2.95 × 10 ) (3.09 × 10 )
ROE −2.38 × 10 *** −8.35 × 10 *** −2.93 × 10 ***
(5.93 × 10 ) (1.45 × 10 ) (1.26 × 10 )
LNQ −3.48 × 10 *** −1.59 × 10 *** −3.87 × 10 ***
(2.15 × 10 ) (4.87 × 10 ) (5.16 × 10 )
S−4.90 × 10 ***−1.60 × 10 3.32 × 10 ***−1.45 × 10 ***1.38 × 10 −1.80 × 10 ***−2.32 × 10 ***−2.17 × 10 −1.61 × 10 ***
(1.06 × 10 )(9.94 × 10 )(9.40 × 10 )(2.44 × 10 )(3.05 × 10 )(3.26 × 10 )(3.47 × 10 )(3.59 × 10 )(3.14 × 10 )
COVID-190.0004250.00279 ***−0.000385−0.0111 ***−0.00543 ***−0.0132 ***−0.0119 ***−0.00269−0.0141 ***
(0.000655)(0.000623)(0.000571)(0.00135)(0.00140)(0.00171)(0.00172)(0.00170)(0.00180)
** *** *** *** *** *** ***
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat23,58919,30758,25864,23826,54884,37867,06774,87669,728
Prob > F000000000
AR (1)0.02100.03230.02221.77 × 10 5.15 × 10 1.64 × 10 1.66 × 10 6.89 × 10 1.36 × 10
AR (2)0.2400.2680.7570.02720.2020.02120.1100.1720.0205
Hansen0.1280.2160.1190.3930.2700.3000.04630.1120.0815
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.696 ***0.734 ***0.778 ***
(0.00256)(0.00211)(0.00265)
L.LDA 0.876 ***0.740 ***0.904 ***
(0.00272)(0.00380)(0.00294)
L.TDA 0.869 ***0.808 ***0.894 ***
(0.00357)(0.00330)(0.00232)
ROA−2.63 × 10 *** −1.17 × 10 *** −2.93 × 10 ***
(1.96 × 10 ) (3.09 × 10 ) (3.52 × 10 )
ROE −1.24 × 10 *** −5.18 × 10 *** −9.66 × 10 ***
(3.23 × 10 ) (1.11 × 10 ) (8.00 × 10 )
LNQ −1.13 × 10 *** −4.71 × 10 −5.58 × 10
(2.55 × 10 ) (5.47 × 10 ) (5.78 × 10 )
G−1.56 × 10 **1.24 × 10 *−4.44 × 10 ***5.65 × 10 ***1.25 × 10 ***1.24 × 10 ***1.41 × 10 9.83 × 10 ***4.33 × 10 ***
(7.21 × 10 )(6.36 × 10 )(7.54 × 10 )(1.18 × 10 )(1.47 × 10 )(1.65 × 10 )(1.47 × 10 )(1.59 × 10 )(1.54 × 10 )
COVID-19−0.00261 ***0.000318−0.000765−0.00388 ***0.00425 ***−0.00519 ***−0.00626 ***0.00102−0.00695 ***
(0.000589)(0.000492)(0.000569)(0.000868)(0.000910)(0.00116)(0.00139)(0.00142)(0.00136)
*** *** *** ** * *** **
) ) ) ) ) ) ) ) )
ControlsYESYESYESYESYESYESYESYESYES
SectorsYESYESYESYESYESYESYESYESYES
ConstantYESYESYESYESYESYESYESYESYES
F-Stat41,46232,389132,931404,937250,876535,344174,330194,306516,418
Prob > F000000000
AR (1)7.10 × 10 3.65 × 10 5.57 × 10 1.77 × 10 8.83 × 10 1.74 × 10 1.35 × 10 5.33 × 10 1.96 × 10
AR (2)0.3770.1420.2090.06190.3740.04430.1020.1270.0471
Hansen0.2490.3600.2460.4550.2210.5470.2360.2690.195
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MinMeanMaxSkewnessKurtosisStd. Dev.p25Medianp75
SDA00.06180.7223.517.30.1150.00240.01950.0628
LDA00.1551.061.948.080.1910.003560.08580.251
TDA00.2282.113.4420.60.2920.02510.1650.321
ROA−198−6.5343.8−3.6719.832.2−9.172.136.95
ROE−365−11.6107−3.3818.261.2−17.32.4713.2
LNQ−0.06914.457.45−0.7965.151.263.834.525.18
ESG2.34489.5−0.08392.3721.729.444.858.8
E1.1940.291.90.262.0525.318.738.258.8
S3.24593.90.09052.12527.443.963.3
G3.850.495.6−0.2631.9726.129.653.871.7
MB−82915.955,943745,5167510.821.73.47
PPETA00.250.9521.13.420.2450.0460.1780.384
CR02.9236.34.7628.84.980.951.562.76
LNTA5.7311.717.70.1242.592.69.7611.613.3
Beta−1.440.6162.760.114.230.6880.220.61
COVID-1900.5041−0.014610.5011
Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)
(1) SDA1.000
(2) LDA0.133 ***1.000
(3) TDA0.602 ***0.797 ***1.000
(4) ROA−0.067 ***−0.024 *−0.115 ***1.000
(5) ROE−0.146 ***0.015−0.038 ***0.766 ***1.000
(6) LNQ−0.149 ***−0.100 ***−0.059 ***−0.205 ***−0.039 ***1.000
(7) ESG−0.048 **0.109 ***0.077 ***0.079 ***0.143 ***−0.0271.000
(8) E−0.044 **0.155 ***0.120 ***0.089 ***0.158 ***−0.0300.559 ***1.000
(9) S−0.037 *0.146 ***0.111 ***0.0290.058 ***−0.086 ***0.566 ***0.519 ***1.000
(10) G−0.0150.041 **0.0280.041 **0.100 ***−0.059 ***0.387 ***0.324 ***0.334 ***1.000
(11) MB−0.006−0.012−0.011−0.009−0.0070.035 **0.014−0.051 **−0.081 ***0.0291.000
(12) PPETA0.125 ***0.199 ***0.164 ***0.121 ***0.058 ***−0.226 ***−0.071 ***−0.051 **0.0130.015−0.0171.000
(13) CR−0.201 ***−0.162 ***−0.203 ***−0.0170.0060.070 ***−0.115 ***−0.113 ***−0.080 ***−0.080 ***0.001−0.160 ***1.000
(14) LNTA−0.143 ***0.216 ***0.024 *0.483 ***0.394 ***−0.288 ***0.473 ***0.608 ***0.529 ***0.374 ***−0.023 *0.168 ***−0.186 ***1.000
(15) Beta−0.078 ***0.098 ***0.0050.026 *−0.043 ***−0.0100.160 ***0.191 ***0.221 ***0.173 ***0.021−0.0110.032 **0.231 ***1.000
(16) COVID-19−0.0070.029 **0.022 *0.032 **0.022 *−0.012−0.030−0.028−0.063 ***0.0170.0130.035 ***0.063 ***0.031 **0.169 ***1.000
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.710 ***0.729 ***0.727 ***
(0.00306)(0.00296)(0.00299)
L.LDA 0.850 ***0.683 ***0.839 ***
(0.00349)(0.00617)(0.00382)
L.TDA 0.800 ***0.663 ***0.833 ***
(0.00563)(0.00771)(0.00501)
*** *** ***
) ) )
*** *** ***
) ) )
*** *
) ) )
*** *** *** *** *** *** ***
) ) ) ) ) ) ) ) )
) ) ) ) ) ) ) ) )
MB1.80 × 10 ***4.06 × 10 ***6.46 × 10 ***3.46 × 10 **6.31 × 10 ***−8.47 × 10 1.58 × 10 ***0.000105 ***−4.04 × 10 ***
(8.85 × 10 )(5.11 × 10 )(7.72 × 10 )(1.58 × 10 )(2.74 × 10 )(1.36 × 10 )(2.37 × 10 )(6.83 × 10 )(1.24 × 10 )
PPETA0.0670 ***0.0549 ***0.0503 ***0.0706 ***0.210 ***0.0809 ***0.164 ***0.288 ***0.132 ***
(0.00351)(0.00323)(0.00324)(0.00604)(0.00979)(0.00580)(0.00976)(0.0133)(0.00776)
CR−0.000386 ***−0.000386 **−0.000398 **0.00192 ***0.00366 ***0.000922 ***0.00325 ***0.00329 ***0.00143 ***
(0.000147)(0.000154)(0.000166)(0.000281)(0.000422)(0.000318)(0.000365)(0.000405)(0.000300)
LNTA0.0004370.000797*−0.00319 ***0.00553 ***0.00946 ***0.00579 ***0.00864 ***0.0120 ***0.00281 ***
(0.000462)(0.000425)(0.000414)(0.000693)(0.00138)(0.000669)(0.00123)(0.00153)(0.00103)
Beta−0.000611−0.000201−0.000854 **−0.00209 **−0.00918 ***−0.00400 ***−0.00805 ***−0.00969 ***−0.00775 ***
(0.000425)(0.000363)(0.000339)(0.000992)(0.00119)(0.000896)(0.00108)(0.00137)(0.00104)
D1 Basic Materials−0.0194 ***−0.0159 ***−0.0186 ***−0.0384 ***−0.0910 ***−0.0470 ***−0.0733 ***−0.114 ***−0.0643 ***
(0.00161)(0.00152)(0.00202)(0.00284)(0.00583)(0.00348)(0.00484)(0.00748)(0.00458)
D2 Consumer Discr.
D3 Consumer Staples−0.0124 ***−0.00987 ***−0.00810 ***−0.0158 ***−0.0317 ***−0.0196 ***−0.0330 ***−0.0513 ***−0.0267 ***
(0.00166)(0.00150)(0.00168)(0.00291)(0.00578)(0.00297)(0.00417)(0.00685)(0.00366)
D4 Energy−0.00867 ***−0.00625 ***−0.00631 ***−0.0218 ***−0.0196 **−0.0147 **−0.0439 ***−0.0467 ***−0.0259 ***
(0.00209)(0.00183)(0.00148)(0.00483)(0.00866)(0.00690)(0.00812)(0.0133)(0.00901)
D5 Health Care−0.00852 *−0.00931 **−0.00922 **−0.0101 **−0.00461−0.0114 ***−0.0213 ***−0.0254 **−0.0177 **
(0.00464)(0.00407)(0.00408)(0.00422)(0.00846)(0.00395)(0.00668)(0.0108)(0.00729)
D6 Industrials−0.00473 ***−0.00496 ***−0.00303 ***−0.0169 ***−0.0279 ***−0.0179 ***−0.0258 ***−0.0386 ***−0.0208 ***
(0.000752)(0.000696)(0.000781)(0.00126)(0.00200)(0.00114)(0.00321)(0.00385)(0.00255)
D7 Technology−0.0114 ***−0.00916 ***−0.0146 ***−0.0144 ***−0.0210 **−0.0136 ***−0.0278 ***−0.0430 ***−0.0288 ***
(0.00272)(0.00236)(0.00260)(0.00371)(0.0103)(0.00418)(0.00691)(0.0127)(0.00663)
D8 Telecom0.00982 **0.00716 ***0.0103 ***−0.00948 **−0.00230−0.0121 **−0.00701−0.007150.00241
(0.00381)(0.00264)(0.00378)(0.00444)(0.00756)(0.00515)(0.00545)(0.00764)(0.00532)
Constant0.00152−0.002700.0716 ***−0.0473 ***−0.0952 ***−0.0429 ***−0.0781 ***−0.115 ***0.00994
(0.00600)(0.00580)(0.00540)(0.00851)(0.0186)(0.00848)(0.0168)(0.0210)(0.0133)
F-Stat18,09521,85433,13962,46121,92641,51321,56623,82626,951
Prob > F000000000
AR (1)4.60 × 10 2.72 × 10 4.99 × 10 1.97 × 10 1.19 × 10 1.94 × 10 1.71 × 10 2.07 × 10 1.73 × 10
AR (2)0.4630.4930.3650.02230.2710.02100.03800.1930.0194
Hansen0.4380.3770.08520.4160.1100.1530.1050.2490.0839
(1)(2)(3)(4)(5)(6)(7)(8)(9)
VARIABLESSDASDASDALDALDALDATDATDATDA
L.SDA0.701 ***0.719 ***0.747 ***
(0.00270)(0.00277)(0.00284)
L.LDA 0.848 ***0.703 ***0.838 ***
(0.00409)(0.00565)(0.00399)
L.TDA 0.804 ***0.696 ***0.837 ***
(0.00507)(0.00669)(0.00439)
ROA−2.93 × 10 *** −2.93 × 10 *** −5.16 × 10 ***
(2.35 × 10 ) (2.99 × 10 ) (4.94 × 10 )
ROE 1.68 × 10 *** −1.34 × 10 *** −2.68 × 10 ***
(4.30 × 10 ) (1.14 × 10 ) (1.01 × 10 )
LNQ −2.93 × 10 *** −1.20 × 10 ** −2.50 × 10 ***
(2.47 × 10 ) (5.28 × 10 ) (6.37 × 10 )
ESG−3.84 × 10 ***1.82 × 10 3.83 × 10 ***−7.01 × 10 ***−1.65 × 10 −0.000113 ***−9.43 × 10 ***1.09 × 10 −8.54 × 10 ***
(1.23 × 10 )(1.10 × 10 )(8.33 × 10 )(2.58 × 10 )(2.68 × 10 )(2.45 × 10 )(2.69 × 10 )(2.81 × 10 )(2.56 × 10 )
COVID-190.00243 ***0.00348 ***0.00275 ***−0.0158 ***−0.00822 ***−0.0203 ***−0.0139 ***−0.000469−0.0199 ***
(0.000676)(0.000575)(0.000541)(0.00163)(0.00126)(0.00156)(0.00186)(0.00166)(0.00164)
*** *** *** *** ** *** ** *** ***
) ) ) ) ) ) ) ) )
MB2.11 × 10 ***4.60 × 10 6.55 × 10 ***−5.77 × 10 ***5.24 × 10 ***−8.56 × 10 ***6.73 × 10 ***9.36 × 10 ***−1.24 × 10 ***
(7.88 × 10 )(4.52 × 10 )(6.56 × 10 )(1.46 × 10 )(2.62 × 10 )(1.16 × 10 )(1.42 × 10 )(5.29 × 10 )(1.01 × 10 )
PPETA0.0617 ***0.0547 ***0.0439 ***0.0794 ***0.182 ***0.0906 ***0.161 ***0.242 ***0.128 ***
(0.00269)(0.00255)(0.00202)(0.00597)(0.00827)(0.00565)(0.00774)(0.0109)(0.00624)
CR−0.000663 ***−0.000608 ***−0.000614 ***0.00239 ***0.00308 ***0.00133 ***0.00326 ***0.00273 ***0.00141 ***
(0.000129)(0.000147)(0.000163)(0.000273)(0.000315)(0.000299)(0.000306)(0.000321)(0.000268)
LNTA0.00130 ***0.00114 ***−0.000789 ***0.00394 ***0.0109 ***0.00370 ***0.00768 ***0.0130 ***0.00301 ***
(0.000352)(0.000333)(0.000294)(0.000661)(0.00104)(0.000639)(0.00101)(0.00110)(0.000798)
Beta−0.000550−0.000576−0.000614 *−0.00214 **−0.00755 ***−0.00298 ***−0.00825 ***−0.00867 ***−0.00751 ***
(0.000457)(0.000393)(0.000348)(0.000947)(0.00108)(0.000897)(0.000963)(0.00110)(0.000895)
D1 Basic Materials−0.0180 ***−0.0155 ***−0.0156 ***−0.0423 ***−0.0828 ***−0.0500 ***−0.0746 ***−0.103 ***−0.0625 ***
(0.00143)(0.00135)(0.00133)(0.00271)(0.00456)(0.00309)(0.00423)(0.00571)(0.00415)
D2 Consumer Discr.
D3 Consumer Staples−0.0119 ***−0.0106 ***−0.0101 ***−0.0148 ***−0.0308 ***−0.0192 ***−0.0325 ***−0.0468 ***−0.0259 ***
(0.00139)(0.00122)(0.00132)(0.00327)(0.00475)(0.00310)(0.00391)(0.00564)(0.00332)
D4 Energy−0.00749 ***−0.00684 ***−0.00234−0.0239 ***−0.0245 ***−0.0151 **−0.0468 ***−0.0433 ***−0.0259 ***
(0.00191)(0.00170)(0.00162)(0.00485)(0.00697)(0.00621)(0.00719)(0.0112)(0.00695)
D5 Health Care−0.00842 **−0.00933 **−0.00641 **−0.00896 **−0.00756−0.0115 ***−0.0224 ***−0.0236 ***−0.0201 ***
(0.00393)(0.00372)(0.00276)(0.00431)(0.00575)(0.00397)(0.00640)(0.00796)(0.00596)
D6 Industrials−0.00495 ***−0.00519 ***−0.00314 ***−0.0152 ***−0.0267 ***−0.0178 ***−0.0252 ***−0.0349 ***−0.0215 ***
(0.000654)(0.000657)(0.000593)(0.00114)(0.00173)(0.00125)(0.00302)(0.00302)(0.00204)
D7 Technology−0.00996 ***−0.00862 ***−0.00802 ***−0.0149 ***−0.0198 **−0.0172 ***−0.0313 ***−0.0311 ***−0.0250 ***
(0.00243)(0.00215)(0.00177)(0.00373)(0.00801)(0.00373)(0.00637)(0.00998)(0.00560)
D8 Telecom0.00782 **0.00579 **0.000564−0.00997 **−0.0100−0.00892 *−0.00679−0.0126*0.00239
(0.00319)(0.00225)(0.00321)(0.00446)(0.00697)(0.00512)(0.00504)(0.00653)(0.00537)
Constant−0.0100 **−0.00915 **0.0280 ***−0.0259 ***−0.115 ***−0.0116−0.0646 ***−0.135 ***0.00841
(0.00450)(0.00429)(0.00386)(0.00864)(0.0139)(0.00891)(0.0137)(0.0152)(0.0104)
F-Stat25,64022,36043,47679,85752,24559,17419,21353,58129,484
Prob > F000000000
AR (1)5.22 × 10 3.00 × 10 5.16 × 10 1.85 × 10 1.02 × 10 1.79 × 10 1.64 × 10 1.51 × 10 1.67 × 10
AR (2)0.2240.1480.3800.05640.2340.06570.04930.07570.0281
Hansen0.3880.4320.3190.4270.1380.2290.1810.3200.164
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Alhajjeah, D.; Besim, M. Firms’ Capital Structure during Crises: Evidence from the United Kingdom. Sustainability 2024 , 16 , 5469. https://doi.org/10.3390/su16135469

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Review on the effects of biochar amendment on soil microorganisms and enzyme activity

  • Soils, Sec 1 • Soil Organic Matter Dynamics and Nutrient Cycling • Research Article
  • Published: 21 June 2024

Cite this article

determinants of capital structure literature review

  • Xinxin Jin 1 , 2 , 3   na1 ,
  • Tongxin Zhang 2   na1 ,
  • Yuetong Hou 2 ,
  • Roland Bol 3 ,
  • Xiaojie Zhang 2 ,
  • Min Zhang 4 ,
  • Jun Meng 1 ,
  • Hongtao Zou   ORCID: orcid.org/0000-0001-6772-5338 2 &
  • Jingkuan Wang 2  

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The multiple benefits of biochar use as a soil amendment has garnered global attention. Biochar addition is a crucial factor to improve soil biomass, soil enzyme activities, microbial biomass and improve soil nutrient utilization rate. However, the precise mechanism of effects of biochar addition on microbial community structure and diversity, as well as enzyme activity, remains unclear, especially for biochar obtained from different pyrolysis temperatures and variable quantities in which it is applied to soil.

Materials and methods

We compiled and summarized the existing literature on the impacts of biochar on microorganisms and enzymes, with a specific on articles published over a five-year period (2018–2022). This review provides a comprehensive review of the relevant literature on enzyme activity, microbial diversity, community structure and abundance following biochar amendment in soil, and further elucidates the underlying mechanisms of biochar-induced effects on various factors.

Results and discussion

The impact of biochar on soil microorganisms could be categorized into three aspects: (1) biochar, due to its porous structure and high surface area, functions as a sanctuary for soil microorganisms; (2) biochar provides essential elements such as carbon (C) and nitrogen (N) sources to soil microorganisms, and finally (3) biochar improves the survival conditions of soil microorganisms by modifying soil pH, CEC, aggregation, and enzyme activity. Importantly, biochar produced at lower pyrolysis temperatures provides valuable C and N for soil microorganisms. Whereas biochar obtained at higher pyrolysis temperatures contains much less active C and N. However, it still contributes to microbial nutrition through diverse mechanisms, e.g., nutrient immobilization and increased nutrients residence time through its bonding with soil labile C.

Conclusions

This review found that the type of source material and pyrolysis temperature were the primary determinants in the impacts of biochar on soil microbial abundance, community structure, and diversity.

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This study was supported by the National Key Research and Development Program of China (2022YFD1500600), the Guiding Fund of the Central Government for Local Science and Technology Development, China (2023JH6/100100056), the Science and Technology Plan Project of Shenyang, China (22–317-2–08), the Earmarked Fund for Modern Agroindustry Technology Research System, China (CARS-01–51). Partial support was provided by the Doctoral Research Start-Up Funding of Shenyang Agricultural University (880418059, 880418058).

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Xinxin Jin and Tongxin Zhang these authors contributed equally to this paper.

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Postdoctoral Station of Crop Science and Tillage, Agronomy College, Shenyang Agricultural University, Shenhe District, NO 120 Dongling Road, Shenyang, 110866, Liaoning Province, China

Xinxin Jin & Jun Meng

Key Laboratory of Arable Land Conservation in Northeast China, Ministry of Agriculture and Rural Affairs, College of Land and Environment, Shenyang Agricultural University, P. R, Shenyang, 110866, China

Xinxin Jin, Tongxin Zhang, Yuetong Hou, Xiaojie Zhang, Na Yu, Hongtao Zou & Jingkuan Wang

Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Wilhelm-Johnen-Straße, 52428, Jülich, Germany

Xinxin Jin & Roland Bol

College of Engineering, Shenyang Agricultural University, Shenyang, 110866, China

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• Biochar provides nutrients such as carbon and nitrogen sources for soil microorganisms.

• Biochar improve soil enzyme activity by improving the quantity and reproductive capacity of microorganisms.

• Soil microorganisms and enzymes mutually influence and constrain each other.

• Biochar type and formation pyrolysis temperature control on soil microbial community and enzymes activity

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Jin, X., Zhang, T., Hou, Y. et al. Review on the effects of biochar amendment on soil microorganisms and enzyme activity. J Soils Sediments (2024). https://doi.org/10.1007/s11368-024-03841-7

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Session 9A: Percolator: Theory and Praxis of Liberatory Justice in Public Service Organizations: Rewards, Challenges, and the Way Forward
08:30 , , and
08:30 important intermediate mechanisms in the causal linkage between PBF and student success. A few studies have examined PBF-driven shifts in spending patterns in public institutions, finding only marginal to null average treatment effects on financial priorities of public four-year institutions (Rabovsky, 2012; Kelchen & Stedrak, 2016; Hu et al., 2022). However, changes in institutional processes often take time and financial priorities of incentivized institutions may evolve over time as institutions learn and adapt to their changing state funding environments (Heinrich & Marschke, 2010; Mizrahi, 2020). This study examines the dynamic shifts in institutional spending in public four-year institutions subject to PBF policies and by minority-serving institution (MSI) status. The study leverages institution-level data from IPEDS and a comprehensive state-level PBF dataset and employs event study analysis. Understanding the dynamic changes in institutional spending over multiple periods may provide information on why PBF policies continue to yield limited improvements in college completion outcomes. Evidence on the dynamic shifts in institutional spending may also enable states to better design and implement performance incentives that induce desirable institutional changes and improve student outcomes ultimately. 

       

 

 

 

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Our analysis employs multivariate regression and other methods, considering variables for major threat categories, consequences of terrorist attacks, and terrorist funding potential through irregular trade. Additional control variables include political, power, and demographic factors. This approach provides a more comprehensive assessment of terrorism risks and funding allocation efficiency.

08:30 and

The latter has become especially salient in public administration, with social equity being elevated as a core public service value and the demonstrated performance benefits of a(n) (effectively managed) diverse workforce. More historically, public organizations have sought to be demographically representative institutions, with recognizable implications for responsiveness among street-level bureaucrats, especially in arenas with administrative discretion (Keiser et al., 2002).

The objective of this paper is to address these policy-salient concerns by examining what qualities of public sector jobs are most attractive across age groups, as well as race and gender. To do so, we utilize a large-scale pre-registered conjoint experiment that allows us to make valid inferences on the impact of our independent variables on job attractiveness.

Our contributions are two-fold: first, we compare the simultaneous effects of a range of variables on job attractiveness whereas previous work has examined them in isolation; and secondly, we devote specific attention to comparing differences in the needs and work values of individuals across age groups. The findings highlight what matters the most in how job seekers self-select into differing organizational/policy domains, professional contexts, as well as job characteristics. The paper ends with a discussion of the findings and future work to advance this area of research.

08:45 and

This study focuses on one such stressed organizational context -child welfare services- and uses the job demands-resource model to unpack the reform needed to motivate and engage child welfare caseworkers. By doing so, it builds on the literature of how work engagement in public sector contexts, especially highly stressed ones, may be differently affected by clusters of job demands and resources. Using an explanatory sequential mixed method approach, the study first identifies the clusters of job demands and job resources that are antecedents of high satisfaction and overall work commitment in child welfare caseworkers. This is done by analyzing secondary survey data from the second cohort of the National Survey of Child and Adolescent Well-Being(NSCAW II).

This analysis is followed by in-depth interviews with current child welfare caseworkers to understand the relative importance of the identified job demand and resource clusters. Additionally, the interviews will add richness to the study by unpacking the personal experiences of caseworkers in the post-pandemic public sector human service work environment. The study, therefore, will provide useful insights to better inform the design and implementation of human resource policy reforms in the public sector.

09:00 and

In this paper, we develop a model for understanding where organizations fall on the continuum of preventing exclusion to promoting inclusion in their DEIA work. Preventing exclusion is associated with legal compliance, internal processes, and diversity inputs while promoting inclusion is associated with creating equitable environments where individuals feel a sense of belonging.

We test this model using survey data from veteran serving organizations (VSOs) participating in 18 AmericaServes networks across the United States (n=1,000) and individual surveys of veterans utilizing services (n=2,731). We propose that how organizations define and do DEIA work has profound impacts for whether historically marginalized groups access and utilize services. We conclude with guidance for organizations to develop and implement substantive and systematic DEIA work.

This work is funded by USAA and done in partnership with the D’Aniello Institute for Veteran and Military Families.

09:15

Specifically, this study explores the impact of organizational inclusion and justice on the behavioral pathways that employees strategically choose in response to harassment experiences and their willingness to report such incidents. The findings reveal diverse effects on behavioral choices: Enhanced justice significantly predicts both the willingness to report incidents and turnover intention, though it is not significantly associated with changes in assignment or transfer. Inclusion, conversely, exhibits nuanced effects across behavioral strategies, significantly predicting the willingness to report but demonstrating positive associations with turnover intention and transfer.

Qualitative data further confirm that organizational inclusion and justice play a crucial role in reshaping policies to protect victims, although mixed perspectives exist among employees regarding their behavioral choices when addressing harassment experiences. The study highlights the substantial impact of organizational inclusion and justice as proactive measures in curbing misconduct within highly bureaucratic settings. However, it underscores the necessity for delicate management strategies to ensure effectiveness in addressing workplace harassment.

08:30 and
08:44 , and

To address the gaps, we employ a quasi-experimental method, regression discontinuity (RD) design, based on school performance data and ratings from New York City public schools from 2007 to 2013. We find that performance signals affect overall turnover, but only at the lower end of performance ratings. Compared with schools earning a C grade, schools earning a D grade have higher levels of teacher turnover. Moreover, teachers from different racial groups respond to low-performance signals differently. Compared to their counterparts in schools that earned a C, white teachers in D schools are more likely to transfer to higher-rated schools. In contrast, Black teachers in D schools are more likely to exit NYC schools to join other districts or leave the profession entirely. This study deepens our understanding of employee turnover under performance regimes and shows an unintended effect of performance management: performance regimes drive minority teachers away and worsen the lack of representation.

08:58 , and

This case study analyzes interviews with 23 CoC representatives, a survey of 114 CoCs (33% response rate), and HUD performance data. We find limited evidence that funding levels are associated with reported measures of performance. Broadly, our data show that governance complexities and environmental constraints violate many of the principal-agent assumptions embodied within performance management doctrine. At the same time, interviews suggest that some CoCs use HUD reporting requirements for varied purposes, including catalytic and discursive capacities (Musso and Weare, 2019; Moynihan, 2008; Nathan 2008). Overall, CoCs are building performance management systems capacities, but still face challenges regarding sustainable organizational culture. Impediments to performance include both internal organizational factors and external factors such as lack of housing, limited funding, and regulatory restrictions. Overall, the evidence supports a more cooperative and discursive model for capacity building rather than a top-down view of performance management governance in networked grant-in-aid systems.

09:12 and

Our paper contributes to the collaborative performance literature. We argue that to understand shared data use during the implementation phase, we need to examine groups’ engagement with performance practices during the earlier planning and coordination phases using a temporal view. We also submit that the three mechanisms constitute broader theoretical streams that call for theorizing about specific causal pathways within them. We identify and examine three lower-level mechanisms that can help explain collective data use: ambiguity reduction, formality-informality complementarity, and identity creation.

To develop and illustrate our arguments, we employ a mechanism-based case study. This approach relies on the use of explanatory narratives, and it is particularly appropriate if the unit of the analysis is a social, interactive process. As our case, we selected the Citizen Security Plan in Jamaica (2020-2023). The Plan is an initiative that aims to combine addressing crime and safety issues with efforts of community development. It was selected because it requires government to collaborate; it relies on the use of goals and data; and it allowed us to observe changes across project phases.

08:30 and

This study conducts a nationwide survey of 50,000+ faculty at public postsecondary education institutions to assess what factors impact their awareness of student homelessness. We will conduct exploratory factor analysis to investigate a myriad of personal backgrounds, professional experience, university engagement, and campus resource item variables. We hypothesize that faculty with personal experience with homelessness, those in human service and social work fields, and those who frequently engage with their university resources are more likely to have increased awareness of student homelessness. Data collection was completed in December 2023.

08:45

To accomplish this, I will employ a two-way fixed-effect model using data sourced from the U.S. Census Bureau, Georgia Department of Education, Governor’s Office of Student Achievement, and National Center for Education Statistics (NCES). The dataset spans the school years from 2011 to 2019, with dependent variables of financial outcomes (total expenditure, instructional expenditure, fixed cost) and student outcomes (Georgia Milestones scores, graduation rate, school safety index).

09:00
09:15
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To study this, we investigate disaster resilience planning in a rural county in the Southeast of the US exposed to several natural disasters, including tornadoes, ice storms, and strong winds. The county is characterized as having a high level of social vulnerability compared to the rest of the US (US Federal Emergency Management Administration, 2023). The empirical base includes data from observations of local government public meetings, content analysis of relevant planning documents, and interviews with collaborative partners. The data are analyzed using social network analysis methods, including descriptive and inferential techniques. The findings have implications for public management theory and practice in resilience planning.

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Ordinal representation pertains to altering the order of representation among bureaucrats when cardinal representation cannot be improved. For instance, in an organization with four bureaucrats where two are female and two are male regarding gender representation, cardinal representation cannot be enhanced. To address the question of whether ordinal representation holds significance in coproduction, this study examines the ordinal effects of gender representation on individuals’ decisions to coproduce.

By employing two distinct policy areas—recycling and emergency preparedness—the study randomizes the order of female officials in a setting with two males and two females, where gender representation cannot be enhanced in a cardinal manner. Both experiments failed to consistently identify evidence of the ordinal effects resulting from placing females in different orders on citizens’ overall willingness to coproduce. However, the results revealed a pattern indicating that the gender of the chief leader influences an increase in the willingness of others of the same gender—and simultaneously decreases the willingness of their gender counterparts—to participate in coproduction.

08:45
09:00 , and
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This paper addresses the widening academic gap and examines the role of public education in fostering academic equity. This study responds to the call for a more holistic understanding of what perpetuates academically successful youth from historically inequitable backgrounds by linking the individual with their greater environment (McCoy & Bowen, 2015). Specifically, we ask, “what individual and institutional factors promote equitable access to higher education across marginalized student identities?” We propose a two-level, intersectional public education equity framework.

The framework is tested using data from surveys conducted among 1,400+ high school seniors and 50 guidance counselors in ten public high schools in the United States. The findings reveal misalignments between schools and individuals regarding perceptions of protective factors for social equity, indicating significant variations in the factors believed to impact access to higher education. Additionally, the study identifies certain risk factors for academic inequity, such as homelessness, first-generation status, lack of school resources, and financial constraints, which can be mitigated through protective factors such as societal expectations, family support, mentorship programs, and peer norming.

08:45

Research on place-based incentives has primarily focused on single incentive programs, concentrating on property values or job creation as desired outcomes. Few studies have compared multiple place-based investments or evaluated the combination of investments and resulting changes in equitable access to capital for neighborhood residents. This paper contributes to existing research by analyzing several programs—Community Development Block Grant (CDBG), Neighborhood Opportunity Fund (NOF), New Market Tax Credit (NMTC), Property Tax Abatement (PTA), Small Business Improvement Fund (SBIF), and Tax Increment Financing (TIF)—and how the related investments alter the racial composition of neighborhoods as a result of home loan approvals. In doing so, this paper offers a better understanding of and policy prescriptions for enhancing social equity when redeveloping and revitalizing local communities in need.

09:00 , and

We explore these tensions by drawing on quality rating data from England's Care Quality Commission to compare service quality across health and social care organizations that are government-run, CICs "spun-out" of the state, or privately-founded CICs. Specifically, we use ordered logit regression models to compare over 2,000 quality ratings of these three types of providers across five dimensions: safe, effective, caring, responsive, well-led, plus an overall rating. We draw on a 'publicness' theoretical framework to explore whether and to what extent public or private ownership, as well as the loss of public ownership through the ‘spin-out’ of public services into independent social enterprises, impacts quality. Our initial results show that overall, both types of social enterprise CICs performed better than government-run services, whilst non-spin-out CICs performed best on caring and responsive and spin-out CICs performed best on safe and effective dimensions.

09:15 and
08:30 and
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Understanding this is important, first, because officials often have a more comprehensive view of local financial health than objective financial indicators can capture alone. Compared to information contained in financial statements, commonly used for indicators, local practitioners possess broader relevant data and a nuanced understanding of what it means in the local context. Second, local government officials, i.e. individuals positioned within a network of government and community actors, ultimately make local investment, policy, and programmatic decisions. As such, when it comes to understanding policy outputs, their perceptions of their municipality’s financial condition arguably matter more than objective measures.

Drawing from open system theory and the literature on perceived organizational outcomes, this research aims to explore whether public managers holding positions in different city departments have systematically different views on financial health. This research examines survey data from city officials in 273 Kansas cities with populations over 500. The survey, conducted between September 2023 and January 2024, targeted professionals in five positions—City Administrator and Directors of Public Works, Planning and Finance. Through descriptive and empirical analysis, this research illuminates how perceived local financial conditions in influence the decisions and fiscal responses across different organizations.

09:00 and
09:15 , , , and
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Building upon previous works on policy designs of net energy metering, which have gained less attention despite its prevalence, we explore different tariff designs and create indexes encompassing various NEM tariffs. Using panel data (about 200 investor-owned utilities in 50 states from 2013 to 2021), we evaluate how different tariff designs have affected the penetration of distributed solar. By studying the correlation between policy designs and the adoption of DERs, our study contributes to policy design literature, understanding how various policy designs affect policy outcomes and how to design policies for other distributed resources.

10:30 and

Strategic management is often touted as an approach for integrating strategy formulation and implementation in response to environmental challenges. As one of the popular approaches used by the public sector, strategic management is often touted as a means for effective public service delivery. However, it is unclear whether current strategic management approaches are up to the task of addressing climate-related threats to the sustainability of public services at the local level where problems are fundamentally transboundary and require coordination across typical silos. We address this gap by asking: What manager-led processes drive resource-constrained cities to adapt their capabilities to the accelerating impacts of climate change? Using a novel mixed methods approach combining survey, text analysis of planning documents, and interviews, we examine how resource-constrained cities in Indiana integrate their capabilities and planning in response to climate change in the context of GSI.

10:45
11:00 and

While regulatory competition suggests a state would relax its enforcement on an entity when its corporate siblings (entities that belong to the same company) in other states have been penalized for violations, regulatory learning theory, predicts otherwise. When an entity’s corporate siblings become violators, it tarnishes the reputation of the whole company and indicates possible wrongdoing of the focal entity itself, prompting regulators to increase scrutiny on the focal entity.

We test the two competing theories using a facility-level panel dataset of Clean Air Act enforcement actions. Preliminary results show a mixed pattern. While regulators increase enforcement on a facility when its same-industry siblings located in the same state become high priority violators (regulatory learning dominates), they relax enforcement on the focal facility if the same-industry violator siblings are in competitor states (regulatory competition dominates).

10:15
10:30 , , and
10:45

Public Administration scholars must pay attention to this restructuring and its impacts to agency adjudication practices. This working systematic review of the Federal Administrative Judiciary will analyze distinct approaches employed by legal and public administration scholars to explore the conceptual and very practical tension between judicial independence and bureaucratic discretion. As the first systematic review regarding this topic, I expect to chronicle the development of these positions within the federal government by exploring institutional collaboration and influences. And finally, I hope to identify topics that may bolster comprehension of administrative adjudication in the USA.

This presentation is relevant to the overall theme of “Bringing Theory to Practice”. As a heavily applied social science, public administration scholars focusing in management must attend to the legal discourse, particularly regarding judicialized employees. ALJs are in such a position within an agency to provide a unique bridge between public administration and the legal discipline. With the ongoing restructuring of their position, there are ample opportunities for practice to also inform theoretical innovation.

10:15 and
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10:57 and

The aim of our review is twofold. First, given the potential of relational contracting as an alternative to traditional contracting in complex situations, we aim to examine how relational approaches may or may not be a viable alternative to traditional transactional approaches. Second, we aim to contribute to the existing literature by developing an integrative framework of relational contracting as a way of managing buyer-supplier relationships in public procurement. Using ASReview Lab, an open-source machine learning software, we identify, collect, and assess relevant articles on this topic. Based on the findings, we develop an integrative framework of relational contracting in public procurement and present a research agenda to tackle theoretical and empirical lacunas in research into relational contracting.

10:15 , , , and

While previous literature discusses how politics and power structure shape global public policy and governance transformation, there is a notable gap in understanding grassroots-based practices that explore innovative narratives, actors, and strategies to establish a community of practices for poverty reduction in the global south. To address this research gap, we outline a processual, multilevel, network-centric perspective by investigating two community-based poverty reduction cases in Africa and China.

Our findings reveal that development narratives, actors’ networks, and pragmatically evolutionary practices constitute the three key pillars for building a community of practice focused on poverty reduction in the global south. The paper contributes to the literatures on the role of action research in poverty reduction in the global south, aligning with the first priority of the Sustainable Development Goals (SDGs). Meanwhile, it highlights the significance of knowledge network in the formulation and implementation of public policies. The study also bridges the knowledge gap between development theory and practical applications in poverty reduction in the global south.

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Our mixed method study takes place in the Puget Sound Basin of Washington state, where we gather data on stakeholder perceptions from approximately 48 CGRs working on ecosystem recovery. We use an exploratory sequential design, starting with interviews to generate a list of indicators with which stakeholders evaluate usefulness of scientific information. We then draw on this list to develop a survey sent to approximately 800 stakeholders. Our initial data show that scientific information is considered most useful when it comes from a reputable source and is produced transparently. Unexpectedly, less valuable indicators of usability included peer-review and co-production with information users. Our study contributes to CGR theory on knowledge management, identifying qualities that may enhance likelihood that information influences joint decisions. It also offers policy implications for information producers, suggesting ways to enhance information’s usability for practitioners in ecosystem recovery.

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Our quantitative study investigates the link between learning organization and job satisfaction and the mediating role of psychological safety in a policing context. We use the dimensions of learning organization questionnaire (DLOQ) developed by Watkins and Marsick (1997), Edmonson’s (1999) instrument for measuring psychological safety and the short index of job satisfaction (Sinval & Marôco, 2020). The participants in our study are experienced German police officers selected for future leadership positions.

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Seeking to identify effective and efficient outreach methods, in 2023 the Northeast Ohio Regional Sewer District conducted a field experiment involving 56,000 households in the Cleveland, Ohio metropolitan area. Using a conjoint design, the experiment randomly assigned households to a control condition or one of up to 56 combinations of treatments. Treatments included black and white postcards, color postcards, letters from the utility, letters from a community organization, English-only messages, bilingual messages, and multiple mailings. Some mailings framed assistance in terms of dollar value, while others expressed benefits as percentage discounts.

Results indicate that direct mail significantly increased CAP inquiries, and that a single, simple black-and-white postcard was the most cost-effective medium. Surprisingly, messaging variables did not drive significantly different response rates. The study is a model of university-government collaboration, and its findings provide unprecedented evidence about direct mail as a means of reducing learning burdens for public assistance programs.

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To understand data and data skills in city governments, this research proposal uses survey results from local government chief administrators in the census west region of the United States. The findings of this exploratory research suggest that 1) a data-skills gap exists in local government, 2) data skill expertise contributes indirectly to a chief administrator’s satisfaction in their organization’s overall data skills, and 3) data capture, curation, and analysis skills have smaller skill gaps compared to data communication and application skills. The findings provide important insight into the data skill needs of local governments and help identify important research questions for local governments and the acquisition of data skills.

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To answer this question, we employ a conjoint experiment on high-level directors in local government to determine their interest in applying to management positions given different job characteristics. Our conjoint survey experiment asks respondents to make four discrete choices between paired job descriptions. These job descriptions vary in characteristics of the work of city managers including the flexibility of the schedule, after-hours commitments, paid time off, perceived stability of the position, and requirements for public engagement. The data is then analyzed considering the respondent characteristics, position, mentorship, and family life considerations to more comprehensively explore the propensity of women to seek out next-level managerial roles based on these job requirements. This paper disentangles the question of whether women would be more interested in applying to city management roles if the position was designed differently. This study offers local governments recommendations for rethinking the nature of the city manager role.

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Our findings indicate that self-motivation emerges as a significant factor positively influencing innovative behavior among national civil servants. Individuals who exhibit a strong internal drive and intrinsic motivation are more likely to engage in innovative practices, contributing to a culture of creativity within the public sector. Peer trust also emerges as a noteworthy factor associated with enhanced innovative behavior.

Surprisingly, institutional support, often considered as a key determinant in fostering innovation, was not found to have a significant impact on innovative behavior in our study. Similarly, the presence of competition among organizations within the public sector was not found to significantly influence innovative behavior among national civil servants. This nuanced finding invites a deeper exploration of the nature of competition and its implications for fostering innovation within the unique dynamics of national civil service environments.

The implications of these findings are substantial for public sector leaders and policymakers:.recognizing the importance of cultivating self-motivation and fostering peer trust can serve as a strategic approach to promote innovative behavior among civil servants.

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We argue that team leadership assignments are gendered in ways that will disadvantage women. Whereas men are likely to be given leadership assignments that are conducive to continuing career progress within their organizations, women are likely to be given leadership assignments that hamper their progress. One reason for this is *structural*: Women and men begin their careers in different types of teams, and consequently accumulate early-career experiences that delimit their future leadership opportunities in divergent ways. A second reason is *aspirational*: Women who are candidates for open leadership positions will be inclined to doubt their qualifications, to be skeptical of their leadership capabilities, and to experience anxiety about assuming formal team-level leadership responsibilities. And a third reason is *stereotypical*: Organizational stakeholders who have input into promotion decisions will harbor differing expectations about women's and men's leadership potential, expectations that will tend to be more negative when it comes to women's leadership capacities.

We test these expectations using longitudinal, individual-level personnel data on United States federal employees.

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However, in contrast to employees, politicians can be conceived as organizational outsiders. Instead, we therefore argue that political considerations affect how politicians assess and value performance measures. Specifically, we hypothesize that (a) politicians will perceive performance information featuring high and low performance signals differently, but also that (b) political ideology in terms of being aligned/opposed to the measured public services and (c) being affiliated/in opposition to the ruling political coalition will affect their perceptions.

To test these hypotheses, we conducted a pre-registered survey experiment among political candidates for Danish regional councils charged primarily with governing health care services (n=885). Respondents were randomly exposed to either no information or true performance information (high/low) about their own region’s health care system. They were then asked to evaluate the validity, legitimacy, and usefulness of the information, and whether they wanted to receive additional information. The results have potentially important practical implications concerning when political decision-makers are willing to trust and use performance information and policy evidence.

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The model captures three dimensions at which legitimacy can be created or undermined: the political articulation of public interests (input), the administrative implementation process (throughput), and the results achieved for citizens (output). A comprehensive review of the literature will be structured along the ITO model. Initial findings suggest that results for 1) input and 2) output are mixed, while they are most promising regarding 3) throughput legitimacy.

First, while performance systems can increase political control, they are modest regarding strengthening minority interests. The management literature laments that a stronger results focus has not been accompanied by more resource autonomy, but such an increase in control is not a problem from a legitimacy perspective. At the same time, though performance systems can be pluralist in nature, evidence suggests they often reinforce existing power differentials.

Second, research documents that performance systems improve outcomes, but gains may not be necessarily equitable. Third, they can enhance the evidence base for decision making, and bias here is less of an issue from a democratic perspective if it reflects political values. Performance systems create process legitimacy if they capture citizen feedback, structure interactions between government and civil society, and increase citizen trust.

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In this paper, we revisit the relationship between politics and administration, emphasizing how politics can influence agency performance even in the most professional and high performing agencies.

We describe the mechanisms by which political alignment or misalignment influence performance. We detail how presidents work to 1) change outputs by directly influencing agency capacity (e.g., budget and personnel levels) and 2) change outputs without directly targeting capacity by using the tools of the administrative presidency to let capacity idle, reorient capacity, or diminish capacity indirectly.

We test these relationships using newly created measures of agency performance for 139 U.S. federal agencies during the 2000-2022 period. The new measures combine dozens of subjective and objective measures of performance that vary across agencies and time. We conclude with the implications of our findings for future research focusing on the intersection of both politics and management.

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determinants of capital structure literature review

Chemical Society Reviews

Reactive capture and electrochemical conversion of co 2 with ionic liquids and deep eutectic solvents.

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* Corresponding authors

a Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, OH, USA E-mail: [email protected]

b Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

c Koç University TÜPRAŞ Energy Center (KUTEM), Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

d Department of Materials Science and Technology, Faculty of Science, Turkish-German University, Sahinkaya Cad., Beykoz, 34820 Istanbul, Turkey

e Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA

f Conn Center for Renewable Energy Research, University of Louisville, Louisville, KY 40292, USA

g Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA, USA

h Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA

i Department of Chemistry, University of California, Davis, Davis, CA 95616, USA

j Department of Mathematics, Computer Science, & Engineering Technology, Elizabeth City State University, 1704 Weeksville Road, Elizabeth City, NC 27909, USA

k Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA

l Koç University Surface Science and Technology Center (KUYTAM), Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

Ionic liquids (ILs) and deep eutectic solvents (DESs) have tremendous potential for reactive capture and conversion (RCC) of CO 2 due to their wide electrochemical stability window, low volatility, and high CO 2 solubility. There is environmental and economic interest in the direct utilization of the captured CO 2 using electrified and modular processes that forgo the thermal- or pressure-swing regeneration steps to concentrate CO 2 , eliminating the need to compress, transport, or store the gas. The conventional electrochemical conversion of CO 2 with aqueous electrolytes presents limited CO 2 solubility and high energy requirement to achieve industrially relevant products. Additionally, aqueous systems have competitive hydrogen evolution. In the past decade, there has been significant progress toward the design of ILs and DESs, and their composites to separate CO 2 from dilute streams. In parallel, but not necessarily in synergy, there have been studies focused on a few select ILs and DESs for electrochemical reduction of CO 2 , often diluting them with aqueous or non-aqueous solvents. The resulting electrode–electrolyte interfaces present a complex speciation for RCC. In this review, we describe how the ILs and DESs are tuned for RCC and specifically address the CO 2 chemisorption and electroreduction mechanisms. Critical bulk and interfacial properties of ILs and DESs are discussed in the context of RCC, and the potential of these electrolytes are presented through a techno-economic evaluation.

Graphical abstract: Reactive capture and electrochemical conversion of CO2 with ionic liquids and deep eutectic solvents

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determinants of capital structure literature review

S. Dongare, M. Zeeshan, A. S. Aydogdu, R. Dikki, S. F. Kurtoğlu-Öztulum, O. K. Coskun, M. Muñoz, A. Banerjee, M. Gautam, R. D. Ross, J. S. Stanley, R. S. Brower, B. Muchharla, R. L. Sacci, J. M. Velázquez, B. Kumar, J. Y. Yang, C. Hahn, S. Keskin, C. G. Morales-Guio, A. Uzun, J. M. Spurgeon and B. Gurkan, Chem. Soc. Rev. , 2024, Advance Article , DOI: 10.1039/D4CS00390J

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    potentially impact the capital structure decisions. Testing a wide range of determinants will possibly give a broader understanding of what determines the capital structure in listed Swedish firms. Modigliani and Miller (1958) are known to be among the first to publish research about capital structure and firm value (Baker and Martin, 2011, p. 2).

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