Next Article in Journal
Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information
Previous Article in Journal
The Uneven Short-Run Effects of the COVID-19 Pandemic on Foreign Direct Investment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JRFM
Volume 15
Issue 10
10.3390/jrfm15100469
jrfm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques

by
Reon Matemane
1,*,
Tankiso Moloi
2 and
Michael Adelowotan
2
1
Department of Financial Management, Faculty of Economic and Management Sciences, University of Pretoria, Cnr of Lynwood Road and Roper Street, Hatfield 0028, South Africa
2
Department of Accountancy, School of Accounting, College of Business and Economics, University of Johannesburg, Cnr Kingsway & University Roads, Auckland Park, Johannesburg 2092, South Africa
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2022, 15(10), 469; https://doi.org/10.3390/jrfm15100469
Submission received: 24 August 2022 / Revised: 8 October 2022 / Accepted: 11 October 2022 / Published: 17 October 2022
(This article belongs to the Section Sustainability and Finance)
Browse Figures
Review Reports Versions Notes

Abstract

:
Economic, social and governance (ESG) have become topical subjects amidst the deleterious effects of climate change, inequality and similar pressing challenges facing the people and the planet. The main objective of this study was to rank the importance of both the pillars within the ESG model and the five indicators beneath each pillar for the purposes of executive compensation plans through the Analytical hierarchical process (AHP). It is not known which pillar within the ESG model should be prioritised by companies operating in a developing economy context such as South Africa, and neither is it known which of the available indicators should be prioritised when designing executive compensation plans. AHP and pairwise comparison is employed in prioritising important pillars and indicators. The environmental pillar is identified to be the most important among the three pillars. Indicators that are prioritised mirror both the environmental and socio-economic challenges prevalent in South Africa as an emerging economy. Companies’ boards, remuneration committees, investors and policymakers can use the ESG-based indicators that have been prioritised in this study in designing the executive compensation plans. AHP and pairwise comparison are novel approaches used to prioritise the important pillars within the ESG model and the underlying indicators.

1. Introduction

The majority of available frameworks on ESG focussed on self-regulated reporting and voluntary disclosure (He et al. 2022; Kotsantonis and Serafeim 2019). Because of this, companies mostly follow a tick-box, greenwashing, whitewashing and obfuscation which are common tactics in non-financial reporting terrain (Yu et al. 2020). Companies are, therefore, simply bombarding the users of their reports with a lot of information without making any real efforts to tackle the important and pertinent issues of governance, environmental and social good. Hübel and Scholz (2020) supported this when they lamented the fact that ESG is not yet well established and embedded in organisational processes.
ESG and all its pillars are important for South African companies not only because it has become a topical subject globally but because the issues are equally pertinent in South Africa. On the environmental front, South Africa is notorious for its dominant dependency on fossil fuels for energy which has resulted in it being labelled the 14th largest emitter of greenhouse gases globally (Adebayo and Odugbesan 2021). Therefore, holding the companies accountable through incorporating ESG-based indicators in executive compensation plans can assist in making sure that the private sector minimises its carbon footprint. When considering the social pillar, South Africa is among the most unequal societies in the world, with a Gini-coefficient above 0.6 (Rathbone 2022). Habiyaremye (2022) postulated that the level of inequality is partly ascribed to the past injustices of colonialism and apartheid, where the majority of people, particularly Blacks, were excluded from participating in the mainstream economy. As a result, unemployment, poverty and inequality are the social ills largely seen in the Black majority. The corporate sector still reflects this painful past, with the ownership and management still in the hands of the White minorities (Gyapong and Afrifa 2019; Habiyaremye 2022). In this regard, diversity, inclusivity, equity, and supporting the marginalised and previously disadvantaged people by prioritizing them in procurement and employment are social and governance issues that the corporate sector is expected to contribute to as part of corporate citizenship. Nevertheless, companies seldom incorporate non-financial performance measures in executive compensation plans or when awarding bonuses. Rather, they focus mostly on financial performance measures such as profits, share price and similar metrics (Flammer et al. 2019; Obermann and Velte 2018).
According to Phung et al. (2022), it is not only the non-standardisation of ESG frameworks that is a cause for concern but the fact that there are no incentives for the companies’ executives to really endeavour to make a positive impact on society and the environment. Rather, they are currently incentivised to embark on greenwashing and obfuscation of information when it comes to reporting non-financial information (Yu et al. 2020). In addition to this, executive compensation plans are usually designed to incentivise the maximisation of profits (Ahamed 2022). In most cases, maximisation of profits is pursued at all costs, even if it results in environmental degradation, health hazards and other dangers to humanity (Phung et al. 2022; Porcuna Enguix 2021).
In support of the above, Y.-S. Chen et al. (2006) posited that companies used to deem it unnecessary to embark on initiatives that support greening and preserving the environment. Indeed, companies used to view greening initiatives as a burden and hindrance to overall innovative capabilities (Y.-S. Chen et al. 2006). On the contrary, green products and processes give a company an impetus to surpass its competitors. Such a competitive advantage came about partly due to society being conscious of the climate change risk emanating from pollution and greenhouse gas emissions (Nanath and Pillai 2017; Skordoulis et al. 2022).
Skordoulis et al. (2022) contend that society tends to have a positive perception of companies that act responsibly on environmental issues. Because they make public disclosure on how their activities, processes and products affect the environment, larger companies are more likely to be under pressure to be more innovative in finding ways of reducing the negative environmental impact of their activities (Chithambo et al. 2022). Arguably, this is the reason that studies found that green innovation is more pronounced in larger size companies than the small and medium ones and that the need for companies to be greener will intensify in the foreseeable future (Y.-S. Chen 2008).
Maas (2018) contend that the catastrophic climate change risk and ubiquitous inequality all over the world are some of the challenges facing humanity because of the obsession with profits at all costs. Profit maximisation ought not to be a zero-sum endeavour. It can co-exist with the bigger purpose, including the upliftment of lives and livelihoods of the society and ensuring that companies’ activities result in lesser or no negative impact on the environment (Flammer et al. 2019). All the stakeholders and those who are interested in companies’ affairs, including shareholders, have recently sought to nudge the companies’ executives to deliver on the ESG performance (Krueger et al. 2020). Pertinent ESG issues that the stakeholders have been advocating for in the recent past include pay parity ratios or differential between the executives and average workers, decarbonisation, diversity, inclusivity and equity. Shareholders, in particular, have demanded that the executives should have targets on these issues and be held accountable through executive compensation designs.
The aforesaid ESG issues that the stakeholders demand the executives to be held accountable for have not been investigated and prioritised in the executive compensation literature. Instead, the literature has mainly sought to investigate the relationship between executive compensation and a company’s financial performance. Such investigations have inconclusive and mixed results (Bussin 2015). Some studies found a positive relationship (Alves et al. 2016; Gao and Li 2015), some found a negative relationship (Khan and Vieito 2013; Olaniyi et al. 2017), while some found no relationship at all (Al-Najjar 2017; Cooper et al. 2016). Few studies that attempted to incorporate ESG factors in executive compensation, such as Rath et al. (2020) have merely extended on pay-performance relationship by attempting to establish the moderating effect of ESG disclosure.
This, therefore, means it is not known what the most important pillars within the ESG model are and what the key performance indicators should be within the different pillars of the ESG model that can be used in designing executive compensation plans. Using the analytical hierarchical process (AHP), this study aims to determine which of the pillars within the ESG model are significant and which of the indicators recommended by the panel of experts should be prioritised when designing executive compensation plans. The rest of the paper covers a literature review, methodology, results and conclusion.

2. Literature Review

2.1. Theoretical Underpinning of the Study

In light of the complex nature of the subject, namely, determining the most significant pillars within the ESG model and establishing which of the indicators within each pillar are significant in designing the executive compensation plans; this study adopts a multifaceted theoretical lens. To this end, agency, stakeholder, legitimacy and signalling theory explain the significance of incorporating ESG data in decision-making (Alsayegh et al. 2020). Agency theory postulates that there must be goal congruency between the management and the shareholders (Jensen and Meckling 1976). Executive compensation plans should be designed in a manner that incentivises the management to deliver value to the shareholders. According to Gray et al. (1995), the stakeholder theory is not at odds with agency theory, rather, it is complementary to it. Companies’, through their executives should therefore be accountable, not only to the shareholders but also to all the legal or natural persons, individually or collectively, who are interested in the activities underpinning their value creation (Freeman 2010).
Legitimacy theory suggests that the companies, in their quest to obtain the social licence, endeavour to legitimise their operations through reporting and disclosure of ESG data to the interested stakeholders (Samkin and Deegan 2012). On the other hand, signalling theory postulates that companies disclose ESG information in order to signal their performance on non-financial metrics (Alsayegh et al. 2020; Lee et al. 2022).

2.2. ESG as a Mechanism to Deliver on Sustainable Development Goals

According to Naomi and Akbar (2021), improving companies’ ESG performance can assist a country in delivering on its commitment to sustainable development goals (SDGs). South Africa is among the Paris Agreement signatories and therefore subscribes to the SDGs (Roberts 2016). Prioritising the ESG indicators that should be incorporated in executive compensation plans should therefore cause the JSE-listed companies to improve ESG performance, thereby contributing to the achievement of SDGs. This would in turn, provide a potent incentive to the executives to focus not only on short-term profits and share price increases but also on how their companies’ activities impact the environment and society.
In the South African context, no poverty (SDG 1), gender equality (SDG 5), clean water and sanitation (SDG 6), reduced inequalities (SDG 10), and climate action (SDG 13) are more relevant. This is because South Africa is among the most unequal societies in the world, while it is also plagued by higher unemployment and poverty (Altman 2022; Kollamparambil 2020; Tshishonga 2019). Incorporating ESG factors in executive compensation plans would therefore help South Africa positively contributing to the SDGs.

2.3. Importance of ESG in Decision Making

Park and Jang (2021) contend that investors take companies’ posture on ESG performance very seriously when making resource allocation and investment decisions. This is, therefore, the reason why ESG performance has a direct relationship with companies’ cost of capital. Companies with a better ESG performance would find it easier to raise capital than those with poor ESG performance (In et al. 2019; Nazir et al. 2022; Nekhili et al. 2021).
Nekhili et al. (2021) argue that the lower cost of capital resulting from better ESG performance also means there is a value maximisation that flows to the shareholders. Consistent with the stakeholder theory advocated for in this study, it is not only the shareholders who need a better ESG performance, but all the other stakeholders, such as lenders, customers, employees and society in general, benefits from it. In fact, lenders have also begun to demand better ESG performance from companies. They do this, for example, by demanding that the companies’ projects that they finance do not result in greenhouse gas (GHG) emissions, that the projects are greener or that there are no human rights violations (Henisz and McGlinch 2019; Houston and Shan 2022).

2.4. Prioritisation of ESG-Based Performance Indicators

According to Serafeim (2020), there is no standard composite mix of indicators that can be used in executive compensation design, despite the importance of ESG performance. This is supported by Cavaco et al. (2020), who lamented the dearth of literature on ESG-based indicators in executive compensation. Ranking and prioritising the myriad ESG-based indicators that are available from different frameworks and organisations would therefore assist companies in focusing on those that matter in a developing economy setting such as South Africa. The lack of such a composite mix of ESG-based indicators stems from the proliferation of organisations that attempted to encourage companies to deliver on corporate social responsibility and non-financial performance (Christensen et al. 2022; Lashitew 2021; Widyawati 2021).
This study used the indicators from such organisations, including, the World Bank WDI, Yale University Environmental Performance Index, WHO Global Health Observatory Indicator, ILO Decent Work Indicators, UNSDSN indicators, The 10th Principle Against Corruption, Women’s World Banking Gender Performance indicators, Behind the Brands Score indicators, UNCTAD proposed core SDGs reporting, AAAQ Framework, UN Globa Compact Oxfam Poverty Footprint, Access to Medicine Index, The Women’s Empowerment Principles: Reporting on Progress, Aquastat, Business Call to Action, Quick guide to the Aichi Biodiversity Targets, CDP-Disclosure Insight Action (2017 climate change, forests and water), IUCN Red List, Kepler/Cheveux Inequality Footprint, CEO Water Mandate’s Corporate Water Disclosure Guidelines, Tourism Council Criteria and Suggested Performance Indicators for Hotels and Tour Operators and Development of Guidance on Extended Producer Responsibility. The indicators were then exposed to an international panel of experts through a Delphi inquiry strategy.
Exposing these indicators to a panel of international experts supports Christensen et al. (2022) who contended that there should be a common understanding of what represents a good ESG performance. Such a common understanding can be achieved when there are specific metrics that should be used in evaluating performance (Christensen et al. 2022). The youthful population, the majority of which are unemployed, coupled with poverty, and inequality, are factors that are peculiar to South Africa (Altman 2022; Kollamparambil 2020). Such factors together with the demographic profile of the directors and the senior management of listed companies which is made up of mostly white males because of the apartheid legacy, buttress the need for the prioritisation and ranking of the ESG performance indicators (Mans-Kemp et al. 2022; Viviers and Mans-Kemp 2019).

3. Methodology

3.1. Delphi Inquiry Strategy

A Delphi inquiry strategy was employed in order to subject the indicators identified from the literature to the international panel of experts in the broader field of corporate governance. The panel of experts were unanimous in consensus with regard to the 15 indicators, 5 under each ESG pillar. Using 5 indicators is consistent with the approach followed in the literature by the authors such as Schuster et al. (2020) and Sobota et al. (2017). These authors used a panel of experts to pick 5 indicators in the medical field, a field dominated by the use of Delphi inquiry strategy for determining transition procedures and for emergency care, respectively.
Table 1 summarises the socio-demographic profile of the panel that was invited initially to participate in the study. The majority are based in South Africa (83%), and a significant number possess master’s and/or PhD studies (35%). These experts are mostly in academia and serve as members of the boards. A combination of purposive, convenience and snowballing sampling was used to select the panel of experts. First, the literature was used to identify the experts in academia. Second, companies’ annual reports were used to identify those that are companies’ executives or board members. Lastly, the acquaintances from authors’ relationships and social capital were also used. These specifically include former colleagues and classmates in accounting, finance, auditing, law, and those that occupy similar roles in NGOs, labour unions and institutional investors and are deemed experts in corporate governance and executive compensation roles.
The ranking of the ESG pillars, as well as the indicators beneath them, is done with a view to assisting in prioritizing the indicators for executive compensation plans in the South African corporate sector. Therefore, while the participants identified in the knowledge resource nomination worksheet (KRNW) might not reflect a global representativity, being domiciled in South Africa (83% of the participants) means they understand the contextual setting and the ESG issues peculiar to South Africa. Furthermore, 17% of the participants are from other jurisdictions outside South Africa, and, therefore, are still able to bring an international view to the fore. Of the participants, 95% possessed a postgraduate qualification, 22% of which possessed PhDs. This is a reflection that the study focuses on the subject matter experts. In terms of the field, the participants are well spread across the broad spectrum, ranging from academia to NGOs which also helps in terms of the diversity of views and representativity of interested parties in executive compensation subject. Most academics are well-versed in the theoretical underpinnings of the subject and can therefore provide a different perspective on how the indicators should be ranked (Fauzi et al. 2019). In addition to theory, most of these academics also sit on boards and consult on a similar subject, which further adds a practical dimension to their views on the subject (J. Chen et al. 2019).
On the other hand, the executives and the board members involved in this study are in the coal front of executive compensation plans and the key performance indicators underpinning such plans. They are, therefore, in a position to provide a view of their lived experiences and how truly the suggested ESG-based indicators can best be prioritized when designing executive compensation plans. The participants who are in the audit fraternity provide a different perspective also, especially because executive compensation and associated items such as share options are usually significant line items in the financial statements and would form part of the auditable items when considering materiality (Zolotoy et al. 2021). Institutional investors also represent a significant stakeholder in providing a view on how the ESG-based indicators can be ranked and prioritised. Their sentiments are crucial in the current era of shareholders’ say on pay and shareholders’ activism on the subject (Lozano-Reina and Sánchez-Marín 2020). Finally, labour unions and those in similar NGOs are also critical stakeholders, especially in the South African context where it is not mandatory for the board to have labour representation or co-determination, which is the case in Germany (McIntyre-Mills 2022).
Before any communication was initiated in terms of the surveys, ethical clearance was sought and obtained from the College of Business and Economics’ Ethics Committee in the Department of Accountancy at the University of Johannesburg. In order to obtain consent to participate in the study, an email invitation was then sent out to the panel of experts identified in the knowledge resource nomination sheet (KRNW) based on their profiles. At the same time, a pilot study survey was then sent out to a separate group of experts predominantly in academia (16 in total, 15 from academia and 1 from practice) to provide feedback on the time it takes to complete the first questionnaire, the clarity and the understandability of the instructions and the questions. This group was also requested to provide any other input that they deemed fit to improve the quality of the research instrument. Feedback from the pilot study was obtained within a week, and it was used to improve the research instrument for the first round of surveys. Qualtrics, an online survey software, was used to distribute the questionnaires for both the pilot part of the study as well as the main study consisting of four rounds.
Collectively, all the surveys, first to the fourth, were sent out and responses were received over a period of four months, from May 2021 until September 2021. This was a reiterative process in which the experts in the first round were given a list of indicators obtained from the literature and then asked to decide whether each indicator should be accepted, amended or declined for use in executive compensation plans under each pillar of the ESG model; if they opted to amend or decline the indicator, there were then required to suggest an alternative indicator. Figure 1 below provides a summary of how the research instrument in the first survey captured and sought to achieve.
In the first round, 53 responses were obtained and the indicators that were accepted by more than 50% of the participants and for which participants stated that they were very confident about their choices were carried over to the 2nd round of the study. “Reduction in total kilometers driven with internal combustion engine (ICE) vehicles and initiatives in place to encourage the use of electric vehicles (EV).” and “Mentoring programmes in place by the executives and implementation of the same” were further recommended by the panel of experts in the first round under environmental and governance pillar respectively. These were added to 10 and 21 indicators that were first introduced from the literature in the aforesaid respective pillars. On the other hand, no additional indicator was recommended under the social pillar. As a result, 11, 21 and 33 indicators in environmental, social and governance pillars, respectively, were carried over into the 2nd round of the study. Figure 2 below provides a snapshot of the survey distributed to the panel of experts in the second round.
As depicted in Figure 2, the experts were required to evaluate the indicators in terms of how important they think those indicators are on a five-point Likert scale ranging from not at all important to extremely important. Forty-three responses were obtained in this round. If less than 50% of the experts did not deem an indicator to be important in the 2nd round, such an indicator was eliminated and not carried over to the 3rd round. Consequently, “Reduction in total kilometers driven with internal combustion engine (ICE) vehicles and initiatives in place to encourage the use of electric vehicles (EV’s)” and “Percentage of employees belonging to collective bargaining councils (trade unions)” in environmental and governance pillars were eliminated, respectively, in the 2nd round, since only 45.5% and 34.9% of the participants respectively deemed these indicators as important overall. Therefore only, 10, 21 and 32 indicators in environmental, social and governance pillars respectively were carried over to the third round of survey. Figure 3 below provides a summary of the how the research instrument was structured in the 3rd round.
In the 3rd round, the panel of experts were simply requested to drag and drop the indicators thereby indicating which one should come first in each ESG pillar. This was an important step before conducting the pairwise comparison of the indicators and the pillars, which is necessary for the hierarchical analytical process. In total, 42 responses were received in the 3rd round and the top five indicators were highlighted from this process. Based on the top five indicators highlighted from the 3rd round, the panel of experts were then requested to conduct a pairwise comparison first for the pillars themselves and then for the indicators beneath each pillar. Figure 4 depicts the summary of the survey distributed to the panel in the 4th round.
Overall, 31 responses were obtained in the fourth round. This means overall, 53,43, 42 and 31 experts participated in the first, second, third round and fourth rounds, respectively. The sample size, in all rounds, therefore, comfortably exceeded the range of 7 to 15 experts as recommended by Sourani and Sohail (2015). The fourth round was a necessary step before the analytical hierarchical process, which is discussed below.

3.2. Analytical Hierarchical Process

Analytical hierarchical process (AHP), the methodological approach followed in this study, was then used in order to rank and thus prioritise both the pillars within the ESG model and the 5 indicators under each pillar as picked by the international panel of experts in the 3rd round of Delphi process. AHP is not only a logical step to follow after the Delphi inquiry, but also compatible to it (Shapiro and Koissi 2017). AHP is a multi-criteria decision-making approach employed in solving complex and intricate problems (Alosta et al. 2021; Tuljak-Suban and Bajec 2020). AHP is a systematic approach to thinking by examining parts of the whole system and their linkages. It was developed as a tool for integrating expert judgements. The advantages of the AHP process include the fact that it clearly and concisely communicates the problem, considers different points of view, encourages explicit statements of preference or importance and increases the likelihood of finding an optimal solution. The AHP process structures the problem into a hierarchy and prioritises elements based on judgements (pairwise comparison). The following constitute the chronological order of activities that were carried out as part of the AHP analysis for this study:
  • Pairwise comparison of the pillars in the ESG model;
  • Pairwise comparison of the indicators within each pillar of the ESG model;
  • Estimation of the weights for the pillars and the indicators in point number 1 and point number 2 above, respectively;
  • Computation of consistency ratio, which formed part of validity and reliability assessment.
Steps 1 and 2 were carried out by the experts in this study as part of the 4th round. In making the pairwise comparison, the panel of experts rated the indicators relative to each other in Likert-type response format from extremely less important to extremely more important with some variations in between in accordance with the scale suggested for the pairwise comparison (Saaty 1980). More specifically, the following scales were used:
The responses from the panel of experts, as recorded in Qualtrics (a software used to create and distribute surveys), were then exported to excel and later into SPSS in order to carry out the AHP. The scale values used firstly had to be recoded to obtain the inverse scores applicable in the input matrices needed for analysing the data through the AHP method. Table 2 illustrates how the above scales were recoded for AHP purposes.
Once the recoding was done, the first step in the AHP process was the construction of the pairwise comparison matrices for the pillars and indicators, respectively. Afterwards, the sum of each column was determined. Each number in the matrix was subsequently divided by the appropriate column total to obtain a normalised matrix. Afterwards, the average cross of each row was calculated. The resultant value of each row provides the relative weights or priorities and is the approximate eigenvector.

3.3. Validity and Reliability

In order to validate the results of the AHP, the consistency ratio (CR) is calculated using the formula, CR = CI/RI in which the consistency index (CI) is, in turn, measured through the following formula:
CI = (λmax − n)/n − 1
where:
  • λmax = maximum eigenvalue of the matrix
  • n = number of pillars in the ESG model/number of indicators in the ESG pillars.
The value of RI is related to the dimension of the matrix and was extracted from Table 3. It should be noted that a consistency ratio lower or equal to t 0.10 verifies that the results of the comparison are consistent across all participants (Alonso and Lamata 2006; Golden and Wang 1990; Saaty 1980).

4. Results

As discussed above, the panel of experts unanimously reached a consensus on 5 indicators under each ESG pillar during the 3rd round of the Delphi process. Figure 5 presents these indicators, which in turn were prioritised in the AHP.
According to Altuzarra et al. (2007), priorities or the ranking of each individual element should first be calculated, after which the group priorities must be obtained either through arithmetic mean or geometric mean. Although both arithmetic means and geometric means have been computed in this study, the geometric mean is used for interpretation purposes because it is superior to the arithmetic mean (Krejčí and Stoklasa 2018). The weights, in terms of the geometric mean and arithmetic mean of the pillars within the ESG model (and that of the indicators within each pillar of the ESG model computed according to the AHP process as outlined in the methodology), are presented in Table 4.
Based on the geometric means from Table 5, the environmental pillar is relatively more important, with a geometric mean of 0.397, when compared to social and governance pillars, which obtained geometric means of 0.281 and 0.214, respectively. Therefore, the social pillar is the second most important, and governance is the least important of the three pillars.
Indicator 4 within the environmental pillar is significantly more important in relative terms, with a geometric mean of 0.729. Indicator 4 is “Total weight of hazardous and non-hazardous waste in the current period.” It is followed by indicator 1, “Company’s total consolidated greenhouse gas (GHG) emission for the current year and the cumulative progress towards zero emission”, which obtained a geometric mean of 0.271. This is not very surprising since indicator 1 was also rated the top by the panel of experts during the Delphi inquiry phase. The other three indicators within the environmental pillar were within the same range. Indicator 2, 3 and 5 obtained geometric means of 0.176, 0.147 and 0.116, respectively.
In the social pillar, which has been ranked the second most important after the environmental pillar as discussed above; indicator 1, “Number of initiatives the company has (including policies and practices) to promote economic inclusion when selecting suppliers (forms of economic inclusion may include: suppliers owned by women; suppliers owned or staffed by members of vulnerable, marginalised, underrepresented social groups or youth; previously disadvantaged groups and small and medium sized suppliers).”, obtained the highest geometric mean of 0.276. Indicators 2–4 obtained geometric means of 0.169, 0.108, 0.119 and 0.182 respectively. Indicator 5, “Increase in number of learnerships/internships specifically aimed at the unemployed youth.” is still considered significant, just as in the Delphi inquiry phase, although it achieved a geometric mean of 0.182, which was the second highest value after that of indicator 1.
Finally, indicator 1 in the governance pillar: “The extent to which the composition of governance bodies is diversified in terms of gender, age group, race, and other indicators of diversity.” obtained the highest geometric mean above all the other four indicators. This response is consistent with the rating that the indicator obtained in the Delphi inquiry phase. Indicators 1–5 obtained geometric means of 0.188, 0.179, 0.139 and 0.104 respectively. The second-best performing indicator after indicator 1 in terms of the geometric mean is, therefore, indicator 2, “Number of initiatives (including systems and policies/codes) the company has in addressing workplace harassment (including sexual harassment). Do the initiatives explicitly extend to its supply chain?”.
The AHP results indicate that the environmental pillar should be prioritised above social and governance pillars within the ESG model. The indicators that were flagged as significant in the Delphi inquiry phase are still emerging as significant based on the AHP analysis. A consistency ratio of 0.190 was calculated for the pairwise comparison conducted for the pillars in the ESG model. On the other hand, consistency ratios of 0.147, 0.189 and 0.205 were calculated for the pairwise comparison of the indicators within the ESG pillars. Although these consistency ratios are greater than the 0.1 rule of thumb first suggested by the AHP pioneer Saaty (1980), they are still deemed to be acceptable in the context of this study for the following reasons:
  • The survey was conducted online and there was no direct face-to-face interaction with the experts (Apostolou and Hassell 1993).
  • Excluding the pillars and indicators with consistency ratios above 0.10 is tantamount to squeezing out the richness provided in the data (Apostolou and Hassell 1993).
  • Indeed, in some instances, participants did not rank some of the pillars and indicators. However, upon following up, it was found that the intention was to flag those as equally important and when the data clean-up was conducted, the responses were manually adjusted (Wedley 1993).
Consistency ratios above 0.10 discussed above are still acceptable as the departure from the rule of thumb (that the consistency ratio should not exceed 0.10) was first established by Saaty (1980). The departure from the rule of thumb is well supported by the literature under the circumstances discussed above. The expert and professional judgement of the panel members regarding the relative importance of the pillars and that of the individual indicators within the pillars can, therefore, be relied upon. Figure 6 represents a diagram-based framework that is proposed in this study to be used in designing executive compensation plans.
In an ideal situation and as far as practically possible, all three pillars of ESG and all their underlying 15 indicators, 5 under each pillar, should be incorporated in designing the executive compensation plans for the listed companies. However, due to capacity constraints and other limitations, a company might not be able to implement all the pillars and all the indicators at the same time. Should that be the case, and depending on the company-specific circumstances, as well as the prevailing legislative and regulatory requirements, the environmental pillar should be prioritised over the social and governance pillar, respectively. Within the environmental pillar total weight of hazardous and non-hazardous waste in the current period should be prioritised over all the other indicators in order of priority as per the ranking indicated in the framework. This applies to the indicators in the social (number of initiatives the company has, including policies and practices, to promote economic inclusion when selecting suppliers, forms of economic inclusion may include; suppliers owned by women; suppliers owned or staffed by members of vulnerable, marginalised, underrepresented social groups or youth; previously disadvantaged groups and small and medium sized suppliers) and governance pillars (the extent to which the composition of governance bodies is diversified in terms of gender, age group, race, and other indicators of diversity) alike.
The study, therefore, offers three contributions. First, the practical contribution lies in the developed framework depicted in Figure 6. This framework can be used as a baseline for companies that seek to integrate ESG-based non-financial performance measures in designing executive compensation plans. The framework provides guidance in terms of how the pillars and indicators should be prioritised when designing executive compensation plans. This is particularly important in the South African context where the listed companies have been pilloried by the Public Investment Corporation (PIC), a South African state-owned asset management company, for lacking non-financial performance measures in their executive compensation plans (PIC 2018). Prioritising environmental pillar and the most important indicators therein, namely, waste management and minimization of greenhouse gas emissions, are well spotted in South Africa, a country that has committed to contributing to global efforts of halving the emissions by 2030 and reaching net-zero emissions by 2050 so that overall warming can be curbed to 1.5 °C to prevent the impact of climate change in terms of the Paris agreement (Jegede and Makulana 2019; Preston 2021).
Second, the methodological contribution of this study pertains to the use of Delphi and AHP techniques to solicit experts’ opinions on the appropriate non-financial performance measures that can be incorporated in executive compensation plans alongside conventional financial performance measures. Literature on executive compensation has not sought to engage the opinions of the experts on the subject to solicit their views. Therefore, the indicators recommended and prioritised in this study are not only based on scientific evidence but have also been verified by experts in the field. Finally, the study also offers a theoretical contribution in that it does not only use agency theory, but it incorporates the stakeholder theory and legitimacy theory to understand the complexity surrounding the integration of ESG-based indicators in executive compensation plans and how this can be used to benefit the stakeholders beyond the shareholders and the executives. This is also illustrated in the use of experts from diverse fields representing different stakeholders whose voices ought to be heard if the companies are to obtain a social license to operate (Carlsson and Callaghan 2022).

5. Conclusions

The main objective of this study was to rank both the pillars within the ESG model as well as the 5 indicators under each pillar through the AHP. Such indicators were initially identified from the literature and exposed to a panel of experts through a Delphi inquiry strategy for executive compensation plans. Through the AHP, this study established that the environmental pillar is ranked as the most important, followed by the social pillar, while governance is ranked as the least important. Under the environmental pillar, indicators addressing waste and greenhouse gas emissions are the top two most important. Indicators within the social pillar that are deemed to be the most important, dealing with inclusivity and resolving unemployment crises. Finally, governance pillar indicators also emphasise diversity, equity and inclusivity. The importance of the environmental pillar over all the other pillars within the ESG model, as indicated by the AHP in this study, is consistent with Hřebíček et al. (2011), who argued that all the companies in all the sectors should report and measure how their activities are positively or negatively impacting on the environment. Companies can play a significant role in contributing to the net zero emissions and decarbonisation efforts as advocated for in the Paris Agreement (Hahn et al. 2018), hence the environmental pillar is deemed to be the most critical.
Social pillar indicators are particularly important in South Africa, a country characterised by higher unemployment, poverty and inequality. Therefore, it is hardly surprising that the AHP prioritised indicators that seek to address these challenges; the same applies to those that were flagged as important under the governance pillar, which put an emphasis on inclusivity, equity and diversity. Geza et al. (2022) lamented the higher unemployment among the youths in South Africa, which they suggest companies can contribute to resolving. On the other hand, the dominance of white males in both the boards of listed companies as well as in private companies generally has been highlighted as something that needs to be changed (Adelekan and Bussin 2018; Habiyaremye 2022). The findings of this study are beneficial to the remuneration committees, executive compensation consultants and policymakers as they provide insights in what pillars of the ESG model and which indicators within each pillar should be prioritised in designing the executive compensation plans.

Author Contributions

Conceptualization: R.M. and T.M. Methodology: R.M. and T.M. Software: R.M. Validation: T.M. and M.A. Formal analysis: R.M. Investigation: R.M. Resources: R.M., T.M. and M.A. Data curation: R.M. Writing—original draft preparation: R.M. Writing—review and editing: T.M. and M.A. Visualization: R.M., T.M. and M.A. Supervision: T.M. and M.A. Project administration: R.M. Funding acquisition: R.M. and T.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation (NRF) under the Black Academic Advancement Programme, grant number 120626.

Institutional Review Board Statement

This study involved human participants, and the ethical clearance was obtained from the School of Accounting Research Ethics Committee at the University of Johannesburg (ethical clearance code: SAREC20210415/01).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data for this study is available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Adebayo, Tomiwa Sunday, and Jamiu Adetola Odugbesan. 2021. Modeling CO2 emissions in South Africa: Empirical evidence from ARDL based bounds and wavelet coherence techniques. Environmental Science and Pollution Research 28: 9377–89. [Google Scholar] [CrossRef] [PubMed]
  2. Adelekan, Adeboye, and Mark Bussin. 2018. Gender pay gap in salary bands among employees in the formal sector of South Africa. SA Journal of Human Resource Management 16: 1–10. [Google Scholar] [CrossRef] [Green Version]
  3. Ahamed, Faruque. 2022. CEO Compensation and Performance of Banks. European Journal of Business and Management Research 7: 100–3. [Google Scholar] [CrossRef]
  4. Al-Najjar, Basil. 2017. Corporate governance and CEO pay: Evidence from UK Travel and Leisure listed firms. Tourism Management 60: 9–14. [Google Scholar] [CrossRef] [Green Version]
  5. Alonso, Jose Antonio, and Teresa Lamata. 2006. Consistency in the analytic hierarchy process: A new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14: 445–59. [Google Scholar] [CrossRef] [Green Version]
  6. Alosta, Abdulaziz, Omar Elmansuri, and Ibrahim Badi. 2021. Resolving a location selection problem by means of an integrated AHP-RAFSI approach. Reports in Mechanical Engineering 2: 135–42. [Google Scholar] [CrossRef]
  7. Alsayegh, Maha Faisal, Rashidah Abdul Rahman, and Saeid Homayoun. 2020. Corporate economic, environmental, and social sustainability performance transformation through ESG disclosure. Sustainability 12: 3910. [Google Scholar] [CrossRef]
  8. Altman, Miriam. 2022. Trajectories for South African employment after COVID-19. South African Journal of Science 118: 1–9. [Google Scholar] [CrossRef]
  9. Altuzarra, Alfredo, José María Moreno-Jiménez, and Manuel Salvador. 2007. A Bayesian priorization procedure for AHP-group decision making. European Journal of Operational Research 182: 367–82. [Google Scholar] [CrossRef]
  10. Alves, Paulo, Eduardo Barbosa Couto, and Paulo Morais Francisco. 2016. Executive pay and performance in Portuguese listed companies. Research in International Business and Finance 37: 184–95. [Google Scholar] [CrossRef]
  11. Apostolou, Barbara, and John Hassell. 1993. An empirical examination of the sensitivity of the analytic hierarchy process to departures from recommended consistency ratios. Mathematical and Computer Modelling 17: 163–70. [Google Scholar] [CrossRef]
  12. Bussin, Mark. 2015. CEO pay-performance sensitivity in the South African context. South African Journal of Economic and Management Sciences 18: 232–44. [Google Scholar] [CrossRef]
  13. Carlsson, Liesel, and Edith Callaghan. 2022. The Social License to Practice Sustainability: Concepts, Barriers and Actions to Support Nutrition and Dietetics Practitioners in Contributing to Sustainable Food Systems. Journal of Hunger & Environmental Nutrition 1: 1–19. [Google Scholar]
  14. Cavaco, Sandra, Patricia Crifo, and Aymeric Guidoux. 2020. Corporate social responsibility and governance: The role of executive compensation. Industrial Relations: A Journal of Economy and Society 59: 240–74. [Google Scholar] [CrossRef]
  15. Chen, Jun, Alexandre Garel, and Alireza Tourani-Rad. 2019. The value of academics: Evidence from academic independent director resignations in China. Journal of Corporate Finance 58: 393–414. [Google Scholar] [CrossRef]
  16. Chen, Yu-Shan. 2008. The driver of green innovation and green image–green core competence. Journal of Business Ethics 81: 531–43. [Google Scholar] [CrossRef]
  17. Chen, Yu-Shan, Shyh-Bao Lai, and Chao-Tung Wen. 2006. The influence of green innovation performance on corporate advantage in Taiwan. Journal of Business Ethics 67: 331–39. [Google Scholar] [CrossRef]
  18. Chithambo, Lyton, Venancio Tauringana, Ishmael Tingbani, and Laura Achiro. 2022. Stakeholder pressure and greenhouses gas voluntary disclosures. Business Strategy and the Environment 31: 159–72. [Google Scholar] [CrossRef]
  19. Christensen, Dane M., George Serafeim, and Anywhere Sikochi. 2022. Why is corporate virtue in the eye of the beholder? The case of ESG ratings. The Accounting Review 97: 147–75. [Google Scholar] [CrossRef]
  20. Cooper, Michael, Huseyin Gulen, and Raghavendra Rau. 2016. Performance for Pay? The Relation between CEO Incentive Compensation and Future Stock Price Performance. Working Paper. Salt Lake City: University of Utah. [Google Scholar]
  21. Fauzi, Muhammad Ashraf, Christine Tan Nya-Ling, Ramayah Thursamy, and Adedapo Oluwaseyi Ojo. 2019. Knowledge sharing: Role of academics towards research productivity in higher learning institution. VINE Journal of Information and Knowledge Management Systems 49: 136–59. [Google Scholar] [CrossRef]
  22. Flammer, Caroline, Bryan Hong, and Dylan Minor. 2019. Corporate governance and the rise of integrating corporate social responsibility criteria in executive compensation: Effectiveness and implications for firm outcomes. Strategic Management Journal 40: 1097–122. [Google Scholar] [CrossRef]
  23. Freeman, Edward. 2010. Strategic Management: A Stakeholder Approach. Cambridge: Cambridge University Press. [Google Scholar]
  24. Gao, Huasheng, and Kai Li. 2015. A comparison of CEO pay–performance sensitivity in privately-held and public firms. Journal of Corporate Finance 35: 370–88. [Google Scholar] [CrossRef]
  25. Geza, Wendy, Mjabuliseni Simon Cloapas Ngidi, Rob Slotow, and Tafadzwanashe Mabhaudhi. 2022. The dynamics of youth employment and empowerment in agriculture and rural development in South Africa: A scoping review. Sustainability 14: 5041. [Google Scholar] [CrossRef]
  26. Golden, Bruce, and Qiwen Wang. 1990. An alternate measure of consistency. In The Analytic Hierarchy Process. Edited by Golden Bruce, Edward Wasil and Patrick Harker. New York: Springer, pp. 68–81. [Google Scholar]
  27. Gray, Rob, Reza Kouhy, and Simon Lavers. 1995. Corporate social and environmental reporting: A review of the literature and a longitudinal study of UK disclosure. Accounting, Auditing & Accountability Journal 8: 47–77. [Google Scholar]
  28. Gyapong, Ernest, and Godfred Adjapong Afrifa. 2019. The simultaneous disclosure of shareholder and stakeholder corporate governance practices and their antecedents. International Journal of Finance & Economics 24: 260–87. [Google Scholar]
  29. Habiyaremye, Alexis. 2022. Racial capitalism, ruling elite business entanglement and the impasse of black economic empowerment policy in South Africa. African Journal of Business Ethics 16: 25–41. [Google Scholar] [CrossRef]
  30. Hahn, Tobias, Frank Figge, Jonatan Pinkse, and Lutz Preuss. 2018. A paradox perspective on corporate sustainability: Descriptive, instrumental, and normative aspects. Journal of Business Ethics 148: 235–48. [Google Scholar] [CrossRef]
  31. He, Feng, Hanyu Du, and Bo Yu. 2022. Corporate ESG performance and manager misconduct: Evidence from China. International Review of Financial Analysis 82: 1–15. [Google Scholar] [CrossRef]
  32. Henisz, Witold, and James McGlinch. 2019. ESG, material credit events, and credit risk. Journal of Applied Corporate Finance 31: 105–17. [Google Scholar] [CrossRef]
  33. Houston, Joel, and Hongyu Shan. 2022. Corporate ESG profiles and banking relationships. The Review of Financial Studies 35: 3373–417. [Google Scholar] [CrossRef]
  34. Hřebíček, Jiří, Jana Soukopová, Michael Štencl, and Oldřich Trenz. 2011. Corporate key performance indicators for environmental management and reporting. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59: 99–108. [Google Scholar]
  35. Hübel, Benjamin, and Hendrik Scholz. 2020. Integrating sustainability risks in asset management: The role of ESG exposures and ESG ratings. Journal of Asset Management 21: 52–69. [Google Scholar] [CrossRef]
  36. In, Soh Young, Dane Rook, and Ashby Monk. 2019. Integrating alternative data (also known as ESG data) in investment decision making. Global Economic Review 48: 237–60. [Google Scholar] [CrossRef]
  37. Jegede, Ademola Oluborode, and Azwihangwisi Walter Makulana. 2019. Climate change interventions in South Africa: The significance of Earthlife Africa Johannesburg v Minister of Environmental Affairs (Thabametsi case) [2017] JOL 37526 (GP). Obiter 40: 399–407. [Google Scholar]
  38. Jensen, Michael, and William Meckling. 1976. Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics 3: 305–60. [Google Scholar] [CrossRef]
  39. Khan, Walayet, and João Paulo Vieito. 2013. CEO gender and firm performance. Journal of Economics and Business 67: 55–66. [Google Scholar] [CrossRef]
  40. Kollamparambil, Umakrishnan. 2020. Educational homogamy, positive assortative mating and income inequality in South Africa: An unconditional quantile regression analysis. The Journal of Development Studies 56: 1706–24. [Google Scholar] [CrossRef]
  41. Kotsantonis, Sakis, and George Serafeim. 2019. Four things no one will tell you about ESG data. Journal of Applied Corporate Finance 31: 50–58. [Google Scholar] [CrossRef]
  42. Krejčí, Jana, and Jan Stoklasa. 2018. Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean. Expert Systems with Applications 114: 97–106. [Google Scholar] [CrossRef]
  43. Krueger, Philipp, Zacharias Sautner, and Laura Starks. 2020. The importance of climate risks for institutional investors. The Review of Financial Studies 33: 1067–111. [Google Scholar] [CrossRef]
  44. Lashitew, Addisu. 2021. Corporate uptake of the Sustainable Development Goals: Mere greenwashing or an advent of institutional change? Journal of International Business Policy 4: 184–200. [Google Scholar] [CrossRef]
  45. Lee, Michael, Robyn Raschke, and Anjala Krishen. 2022. Signaling green! firm ESG signals in an interconnected environment that promote brand valuation. Journal of Business Research 138: 1–11. [Google Scholar] [CrossRef]
  46. Lozano-Reina, Gabriel, and Gregorio Sánchez-Marín. 2020. Say on pay and executive compensation: A systematic review and suggestions for developing the field. Human Resource Management Review 30: 1–14. [Google Scholar] [CrossRef]
  47. Maas, Karen. 2018. Do corporate social performance targets in executive compensation contribute to corporate social performance? Journal of Business Ethics 148: 573–85. [Google Scholar] [CrossRef] [Green Version]
  48. Mans-Kemp, Nadia, Suzette Viviers, and Tyler Shiel. 2022. Drivers of board gender diversity in a self-regulatory context. Management Dynamics: Journal of the Southern African Institute for Management Scientists 31: 1–17. [Google Scholar]
  49. Matemane, Reon. 2022. Towards Value Adding Performance: A Metric for Executive Compensation. Ph.D. dissertation, University of Johannesburg, Johannesburg, South African. Available online: https://ujcontent.uj.ac.za/esploro/search/outputs?query=any,contains,reon%20matemane&page=1&scope=Research&institution=27UOJ_INST (accessed on 10 October 2022).
  50. McIntyre-Mills, Janet Judy. 2022. The importance of relationality: A note on co-determinism, multispecies relationships and implications for COVID-19. Systems Research and Behavioral Science 39: 339–53. [Google Scholar] [CrossRef]
  51. Nanath, Krishnadas, and Radhakrishna Pillai. 2017. The influence of green is practices on competitive advantage: Mediation role of green innovation performance. Information Systems Management 34: 3–19. [Google Scholar] [CrossRef]
  52. Naomi, Prima, and Iqbal Akbar. 2021. Beyond sustainability: Empirical evidence from OECD countries on the connection among natural resources, ESG performances, and economic development. Economics & Sociology 14: 89–106. [Google Scholar]
  53. Nazir, Marina, Minhas Akbar, Ahsan Akbar, Petra Poulovo, Ammar Hussain, and Muhammad Azeem Qureshi. 2022. The nexus between corporate environment, social, and governance performance and cost of capital: Evidence from top global tech leaders. Environmental Science and Pollution Research 29: 22623–36. [Google Scholar] [CrossRef] [PubMed]
  54. Nekhili, Mehdi, Amal Boukadhaba, Haithem Nagati, and Tawhid Chtioui. 2021. ESG performance and market value: The moderating role of employee board representation. The International Journal of Human Resource Management 32: 3061–87. [Google Scholar] [CrossRef]
  55. Obermann, Jörn, and Patrick Velte. 2018. Determinants and consequences of executive compensation-related shareholder activism and say-on-pay votes: A literature review and research agenda. Journal of Accounting Literature 40: 116–51. [Google Scholar] [CrossRef]
  56. Olaniyi, Clement Olalekan, Olufemi Bodunde Obembe, and Emmanuel Oluwole Oni. 2017. Analysis of the Nexus between CEO Pay and Performance of Non-Financial Listed Firms in Nigeria. African Development Review 29: 429–45. [Google Scholar] [CrossRef]
  57. Park, So Ra, and Jae Young Jang. 2021. The impact of ESG management on investment decision: Institutional investors’ perceptions of country-specific ESG criteria. International Journal of Financial Studies 9: 48. [Google Scholar] [CrossRef]
  58. Phung, Giang, Hai Hong Trinh, Tam Huy Nguyen, and Vu Quang Trinh. 2022. Top-management compensation and environmental innovation strategy. Business Strategy and the Environment 32: 1–16. [Google Scholar] [CrossRef]
  59. Porcuna Enguix, Luis. 2021. The New EU Remuneration policy as good but not desired corporate governance mechanism and the role of CSR disclosing. Sustainability 13: 5476. [Google Scholar] [CrossRef]
  60. Preston, Brian. 2021. The influence of the Paris agreement on climate litigation: Legal obligations and norms (Part I). Journal of Environmental Law 33: 1–32. [Google Scholar] [CrossRef]
  61. Public Investment Corporation (PIC). 2018. Listed Investmensts 31 March 2017. Environmental Social and Governance Quarterly Report. Available online: http://www.pic.gov.za/wp-content/uploads/2012/04/ESG-listed-quarterly-voting-results-Q4-2016-17.pdf (accessed on 22 August 2019).
  62. Rath, Chetna, Florentina Kurniasari, and Malabika Deo. 2020. CEO Compensation and Firm Performance: The Role of ESG Transparency. Indonesian Journal of Sustainability Accounting and Management 4: 278–93. [Google Scholar] [CrossRef]
  63. Rathbone, Mark. 2022. Economic inequality and trust from a Smithian perspective. Acta Academica 54: 92–112. [Google Scholar] [CrossRef]
  64. Roberts, Debra. 2016. A global roadmap for climate change action: From COP17 in Durban to COP21 in Paris. South African Journal of Science 112: 1–3. [Google Scholar] [CrossRef] [Green Version]
  65. Saaty, Thomas. 1980. The Analytic Hierarchy Process (AHP) for Decision Making. Kobe: AHP, pp. 1–69. [Google Scholar]
  66. Samkin, Grant, and Craig Deegan. 2012. New Zealand Financial Accounting. New York: McGraw-Hill Education Australia. [Google Scholar]
  67. Schuster, Susanne, Katrin Singler, Stephen Lim, Mareen Machner, Klaus Döbler, and Harald Dormann. 2020. Quality indicators for a geriatric emergency care (GeriQ-ED)–an evidence-based delphi consensus approach to improve the care of geriatric patients in the emergency department. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 28: 1–7. [Google Scholar] [CrossRef]
  68. Serafeim, George. 2020. Social-impact efforts that create real value. Harvard Business Review 98: 38–48. [Google Scholar]
  69. Shapiro, Arnold, and Marie-Claire Koissi. 2017. Fuzzy logic modifications of the analytic hierarchy process. Insurance: Mathematics and Economics 75: 189–202. [Google Scholar] [CrossRef]
  70. Skordoulis, Michalis, Grigorios Kyriakopoulos, Stamatiοs Ntanos, Spyros Galatsidas, Garyfallos Arabatzis, Miltiadis Chalikias, and Petros Kalantonis. 2022. The Mediating Role of Firm Strategy in the Relationship between Green Entrepreneurship, Green Innovation, and Competitive Advantage: The Case of Medium and Large-Sized Firms in Greece. Sustainability 14: 3286. [Google Scholar] [CrossRef]
  71. Sobota, Amy, Nishita Shah, and Jennifer Mack. 2017. Development of quality indicators for transition from pediatric to adult care in sickle cell disease: A modified Delphi survey of adult providers. Pediatric Blood & Cancer 64: 1–6. [Google Scholar]
  72. Sourani, Amr, and Mu Sohail. 2015. The Delphi method: Review and use in construction management research. International Journal of Construction Education and Research 11: 54–76. [Google Scholar] [CrossRef]
  73. Tshishonga, Ndwakhulu. 2019. The legacy of apartheid on democracy and citizenship in post-apartheid South Africa: An inclusionary and exclusionary binary? African Journal of Development Studies 9: 167–91. [Google Scholar] [CrossRef]
  74. Tuljak-Suban, Danijela, and Patricija Bajec. 2020. Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study. Symmetry 12: 766. [Google Scholar] [CrossRef]
  75. Viviers, Suzette, and Nadia Mans-Kemp. 2019. Director overboardedness in South Africa: Evaluating the experience and busyness hypotheses. International Journal of Disclosure and Governance 16: 68–81. [Google Scholar] [CrossRef]
  76. Wedley, William. 1993. Consistency prediction for incomplete AHP matrices. Mathematical and Computer Modelling 17: 151–61. [Google Scholar] [CrossRef]
  77. Widyawati, Luluk. 2021. Measurement concerns and agreement of environmental social governance ratings. Accounting & Finance 61: 1589–623. [Google Scholar]
  78. Yu, Ellen Pei-yi, Bac Van Luu, and Catherine Huirong Chen. 2020. Greenwashing in environmental, social and governance disclosures. Research in International Business and Finance 52: 1–23. [Google Scholar] [CrossRef]
  79. Zolotoy, Leon, Don O’Sullivan, Geoffrey Martin, and Robert Wiseman. 2021. Stakeholder agency relationships: CEO stock options and corporate tax avoidance. Journal of Management Studies 58: 782–814. [Google Scholar] [CrossRef]
Jrfm 15 00469 g001 550
Figure 1. The research instrument used in the first survey. Source: Matemane (2022).
Figure 1. The research instrument used in the first survey. Source: Matemane (2022).
Jrfm 15 00469 g001
Jrfm 15 00469 g002 550
Figure 2. The research instrument used in the 2nd survey. Source: Matemane (2022).
Figure 2. The research instrument used in the 2nd survey. Source: Matemane (2022).
Jrfm 15 00469 g002
Jrfm 15 00469 g003 550
Figure 3. The research instrument used in the 3rd survey. Source: Matemane (2022).
Figure 3. The research instrument used in the 3rd survey. Source: Matemane (2022).
Jrfm 15 00469 g003
Jrfm 15 00469 g004 550
Figure 4. The research instrument used in the 4th survey. Source: Matemane (2022).
Figure 4. The research instrument used in the 4th survey. Source: Matemane (2022).
Jrfm 15 00469 g004
Jrfm 15 00469 g005 550
Figure 5. Non-financial performance metric for determining executive compensation. Source: Matemane (2022).
Figure 5. Non-financial performance metric for determining executive compensation. Source: Matemane (2022).
Jrfm 15 00469 g005
Jrfm 15 00469 g006 550
Figure 6. A framework for the prioritisation of ESG pillars and indicators for designing executive compensation plans. Source: Own compilation.
Figure 6. A framework for the prioritisation of ESG pillars and indicators for designing executive compensation plans. Source: Own compilation.
Jrfm 15 00469 g006
Table
Table 1. Socio-demographic information of the participants.
Table 1. Socio-demographic information of the participants.
CharacteristicsFrequencyPercentage
CountrySA9483.2
UK1412.4
US10.9
Others23.5
Total113100
QualificationPhD2522.1
Masters, CA(SA)1715
Masters, CPA10.9
Masters3631.9
CA(SA)1412.4
CFA76.2
Honours76.2
Degree65.3
Total113100
FieldAcademic4741.6
Executive/Board member3430.1
Audit firm1513.3
Institutional investors108.8
Others (NGOs, labour unions and shareholder activists)76.2
Total113100
Source: Matemane (2022).
Table
Table 2. Interpretation of Likert scales used in pairwise comparison.
Table 2. Interpretation of Likert scales used in pairwise comparison.
Likert Scale RatingMeaning
1Extremely less important
3Very strongly less important
5Strongly less important
7Moderately less important
9Equally important
11Moderately more important
13Strongly more important
15Very strongly more important
17Extremely more important
2, 4, 6, 8, 10, 12, 14 and 16Midway between two close scales
Source: Matemane (2022).
Table
Table 3. Recorded scales for AHP.
Table 3. Recorded scales for AHP.
Qualtrics questionnaire code1234567891011121314151617
Recoded values0.1110.1250.1430.1670.20.250.3330.5123456789
Source: Matemane (2022).
Table
Table 4. The value of the Random Consistency Index.
Table 4. The value of the Random Consistency Index.
N12345678910
RI000.580.891.121.240.1.331.401.451.49
Source: Golden and Wang (1990).
Table
Table 5. AHP weights and consistency ratios at pillar and indicator levels.
Table 5. AHP weights and consistency ratios at pillar and indicator levels.
EnvironmentalSocialGovernance
Pillar level
Arithmetic mean0.3970.2810.214
Geometric mean0.4300.3040.265
Ranking123
Consistency ratio0.19
Indicator level
Arithmetic meanGeometric meanRankingConsistency ratioArithmetic meanGeometric meanRankingConsistency ratioArithmetic meanGeometric meanRankingConsistency ratio
Indicator 10.3190.27120.1470.3170.27610.1890.3020.25210.205
Indicator 20.1940.17630.1940.16930.2010.1882
Indicator 30.1690.14740.1230.10850.2040.1793
Indicator 40.8370.72910.1580.11940.1550.1394
Indicator 50.1510.11650.2080.18220.1370.1045
Source: Matemane (2022).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Matemane, R.; Moloi, T.; Adelowotan, M. Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques. J. Risk Financial Manag. 2022, 15, 469. https://doi.org/10.3390/jrfm15100469

AMA Style

Matemane R, Moloi T, Adelowotan M. Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques. Journal of Risk and Financial Management. 2022; 15(10):469. https://doi.org/10.3390/jrfm15100469

Chicago/Turabian Style

Matemane, Reon, Tankiso Moloi, and Michael Adelowotan. 2022. "Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques" Journal of Risk and Financial Management 15, no. 10: 469. https://doi.org/10.3390/jrfm15100469

APA Style

Matemane, R., Moloi, T., & Adelowotan, M. (2022). Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques. Journal of Risk and Financial Management, 15(10), 469. https://doi.org/10.3390/jrfm15100469

Article Metrics

Back to TopTop