The Next “G” On ESG: The Strategic Thinking of Businesses Towards Supply Chain Fraud

Supply Chain Management is in the core of businesses’ operational activities worldwide. Its main purpose is the proper management of resources and the assurance of the sustainable operation of the economic entities. However, Supply Chain Management is exposed to breaches related to the code of conduct as well as fraud. Integrating the principles of Environmental, Social and Corporate Governance (ESG) can help build a healthy, sustainable, and resilient supply chain. The purpose of the research is twofold and refers to: (i) highlight those factors of the ESG that contribute to the decrease and mitigation of the fraud in supply chain and (ii) the business strategies that can be developed from businesses and can be based on ESG factors. In this context, a log-log model of multiple linear regression was proposed. Secondary data were extracted from the Thomson Reuters database. The model was based on 681 observations concerning companies operating in Europe. The results have led to the conclusion that the existence of policies related to human resources and technology contribute signicantly to tackling supply chain fraud. Regarding the rst factor, Human Resource is important to feel safe and their rights should be protected by companies. Securing their rights can lead individuals to their commitment to the work environment, as well as to their protection from threats and violations. Finally, the role of technology is fully consistent with transparency in the supply chain. For this reason, the adoption of reliable solutions and technologies, which turn to the green economy, offer visibility and optimization of processes.


Introduction
Driven by globalization and customers' demands, Supply Chain Management (SCM) plays a key role in creating competitive advantage for businesses. So, it is vital to develop a complete and responsive supply chain that meets customer's requirements and ensures both market share and pro tability (Gardas, Raut, and Narkhede 2019). SCM re ects not only the ow of materials, but also the ow of information between members in the supply chain (Yang, Fu, and Zhang 2021). The availability of information has been increasing exponentially over the last decade. The explosion of this available information and the various changes in the business environment have provided the opportunity for improvements and changes in the supply chain (Zhang, Chen, and Chen 2021). Therefore, companies and organizations are called upon to rede ne their business models and focus on the optimal dissemination of information. Proper and effective information sharing helps ght supply chain fraud and can enable the company to meet the ercest competition, succeed in a more complex business environment and increase their e ciency and effectiveness (Köhler et al. 2021;Shi and Geng 2021).
In recent years a rising number of corporations focus not only on a supply chain that will ensure economic bene ts for them, but to a sustainable supply chain management that adhere to social and environmental standards too (Thorlakson, Hainmueller, and Lambin 2018). The aim of companies is to create a cascade of sustainable practices that ows smoothly throughout the supply chain (Bhutta et al. 2021). The ESG factors is not an exception to this rule. Without that standards, and proper strategies to implement into daily supply chain activities, supply chain risks would materialize a lot more often than they do (Gillan, Koch, and Starks 2021a;Ragazou 2021). ESG factors serve as a guideline for environmentally, socially, and ethical supply chain and they apply as much internally, as they do externally (Broadstock et al. 2021).
In this paper, we highlight: (i) the ESG factors that plays a key role in managing fraud in supply chain management and (ii) business strategies that can be developed to base don these factors. Our results identify workforce, resource use and environmental innovation as the strongest factors in mitigating fraud in supply chain management. We contribute to the sustainability performance of supply chain management debate by showing that an increase in each of these factors will increase ESG score, while ESG is crucial for ensure transparency and sustainability in supply chain management. So, businesses should develop strategies, oriented to the ESG factors that highlighted from the research, to manage fraud and achieve sustainability.
This article is organized as follows. Section 2 highlights the transition from supply chain management to sustainable supply chain management, the link between sustainable supply chain management and ESG and nally, the role of ESG in ensuring transparency in Sustainable Supply Chain Management. In Section 3 is de ned the study method which includes research design, data collection and framework analysis, while in Section 4 the qualitative study details and results are presented. Lastly, we conclude with Section 5.

Supply Chain Management moving to Sustainability
Sustainability is a dynamic process based on three "pillars": the economy, society and the environment and emerged around 1987, where it was introduced as one of the key concepts in the management of the production process. According to the WCED (World Commission on Environment and Development), sustainability refers to the protection of the environment and natural resources, as well as ensuring social and economic welfare for present and future generations (WCED 1987).
The needs of society in economic, social, and environmental level have forced organizations to integrate in their strategy the concept of sustainable development, but also to develop this kind of strategies for achieving a competitive advantage in the global market (Fritz et al. 2021;Silva and Figueiredo 2020).
Businesses have linked sustainability with different operational departments, such as administration and Supply Chain Management too (Moshood et al. 2021). The supply chain is characterized by several weaknesses and challenges, which cannot be solved and addressed only by individual efforts, but by a set of actions based on cooperation. Some of the best practices that can be adopted by companies in supply chain are the development of more accurate forecasting and scheduling systems, close cooperation with suppliers and customers, real-time monitoring of the chain with the use of technology and ensuring a high degree of exibility (Khan et al. 2021). All these methods and practices can lead to a Sustainable Supply Chain Management for businesses globally.
Given the complexity of the supply chain, fraud is not a surprise for businesses, but one of the biggest threats they should face immediately. Often covering an extensive network of third parties around the world, including agents, intermediaries, resellers, distributors and partners, results in systems becoming recipients of misconduct (Manning 2018). Usually, this risk occurs in communities that are less strict in law enforcement or do not systematically monitor inappropriate behavior (van Ruth et al. 2018; Yan et al. 2020). Also, several different business policies and procedures, codes of conduct and information systems used by each third party are intertwined in the supply chain, creating a prime environment for fraud (Ryan 2016).
Taking measures to differentiate companies for reducing the phenomenon of fraud in supply chain is one of the main ways to react towards that. Also, the adoption of best practices such as the integration of ESG factors contributes to mitigate the risk of fraud in the supply chain. Environmental, social, and corporate governance (ESG) practices determine a company's strategy, business model and behavior as these practices are related to sustainability (Saygili et al. 2021). The three aspects of ESG practices encompass a wide range of concepts, including environmental factors such as renewable energy and waste management, social factors such as community involvement and labor management, and governance factors such as business ethics and danger management. ESGs have been the subject of increasing debate and research on company performance, productivity, industry trends and the impact on sustainable investment strategies (Gillan et al., 2021a;Yang et al., 2021a). This growing attention has also been shown in the appearance and popularity of sustainability reports published by companies, as well as various indicators and ratings. Understanding the raison d'être of ESG factors is essential to objectively assess the importance that is attached to sustainable business practices over time.
Mainly, ESGs transforms decision making process as well as the composition of workforce and highlights new needs in terms of data management process to ensure transparency in supply chain. In terms of transparency, this is a priority for businesses and the supply chain (Yang et al., 2021a). Reporting on the impact of ESGs and business risk, which are referred to as "essential", is increasingly important for business stakeholders and especially for business investors. This is because ESG transparency is directly related to business performance. The volume and type of data that companies need to disclose will continue to grow. This trend is expected to accelerate as technology evolves. These advances will produce more information about the operations and impact of business (Gillan, Koch, and Starks 2021b). Businesses will need new data management capabilities that will enable the collection, management, analysis and reporting of ESG data from the immediate activities of supply chain partners (Lööf, Sahamkhadam, and Stephan 2021). In the past, companies used data collection tools to reduce supply chain fraud, that today can be characterized as less e cient. Businesses now use tools based on advanced technology, which helps to integrate and streamline data to provide the transparency that is required. ESG data management platforms have tools that automate data management (Yu and Luu 2021). They are constantly analyzing data that identi es compliance issues and monitors progress, which makes reporting and monitoring much more effective.
Fraud in supply chain management remains a threat for the business world. However, constant vigilance and strong internal controls help to reduce fraud and detect these "red ags" as soon as it possible.

The Dataset
Data on ESG were retrieved from the Re vitiv Eikon platform powered by Thomson Reuters. Re nitiv Eikon is an open-technology solution for academics who would like to exam deeply the ESG performance and capacity of businesses in different sectors globally, as provides access to industry-leading data, insights, and exclusive and trusted news (Re nitiv Eikon, 2021; Gaganis et al., 2021). The database of Thomson Reuters captures and calculates ESG measures, of which a subset of the most comparable and material per industry, power the overall company assessment and scoring process (Milner, Ham, and Hur 2014; Re nitiv Eikon 2021). These are grouped into different categories that reformulate the three pillar scores and the nal ESG score, which re ects the company's ESG performance, commitment and effectiveness based on publicly reported information. Based on the objective of this study, ESG score is used as the dependent variable (Landis and Skouras 2021; Re nitiv Eikon 2021).
The category scores are rolled up into three pillar scores -environmental, social, and corporate governance (Achim and Borlea 2015; Paltrinieri et al. 2020). The ESG pillar score is a relative sum of the category weights, which vary per industry for the environmental and social categories (Mohammad and Wasiuzzaman 2021).
A company's ESG scoring is the numerical expression of the way that its performance is perceived over a wide range of environmental, social and governance (ESG) issues. An ESG score can be characterized as a tool that: (i) helps businesses to be alert regards the continuous changes in the market and (ii) motivate them to reconsider their corporate strategy by setting in the core ESG performance. There are many reasons besides understanding why a business needs to know its ESG score. One of the most critical refers to the rapid growth of ESG investments, with investors looking for portfolios of sustainable assets. With a reliable link between strong performance on key ESG issues and nancial performance, ESG score is used by institutional and independent investors to identify companies that may offer good returns. Executives equate a good ESG score with healthy earnings (Giannarakis, Konteos, and Sariannidis 2014). In terms of ESG score reliability, the more reliable an ESG rating is, the more consistently it is calculated and reported, the greater the impact it will have on long-term performance, by managing ESG risks and opportunities, encouraging impact investment and pushing corporate governance to create a more sustainable business. The Re nitiv Eikon ESG scoring is calculated on a scale between 0.0 to 1 and can provide comparable scores for businesses across sectors and regions. The calculation of the scores was de ned by Thomson Reuters as following (Re nitiv Eikon 2021): A relative percentile ranking is only applied if a numeric data point is reported by a company, while all the companies in an industry group report that respective data point. Each measure has a polarity indicating whether a higher value is positive or negative. For instance, more water recycled is positive, but more emissions are negative (Landis and Skouras 2021; Re nitiv Eikon 2021). Percentile rank scoring methodology is adopted to calculate scores of the categories included in the three pillars of ESG.

The Variables
The main objective of the study is to highlight the factors that are related to ESG score and contribute to the prediction of fraud on supply chain. Regression analysis was used in this study, as the common tool to use for forecasting and prediction (Topliss and Costello 1972). Speci c, a multiple log-log regression model was developed to determine if exists a relationship between at least two or more explanatory variables (Clifford et al. 2013;DeFries and Fulker 1985). Furthermore, the rst step of creating a multiple regression model is to choose the factors (Pan et al. 2021; Wilkie and Galasso 2021). The factors that were chosen in the current study was ESG score, Resource Use Score, Emissions Score, Environmental Innovation Score, Workforce Score, Human Rights Score, Management Score, Policy Water E ciency Score, Policy Environmental Supply Chain Score, where the ESG score is the dependent variable. Both the dependent variable and the independents are listed and fully described in Table 1.
Global complexity is increasing as supply chains become more interconnected, economies grow and develop, weather patterns change, and societies experience and acquire more sophisticated technology. ESG factors is an attempt to capture more of this complexity in business decision-making and to assess potential for continued viability in a world that increasingly requires more sustainable outcomes. Based on that point, dependent variable ESG score was selected as it plays a key role on the prediction of fraud in supply chain.
The independent variables were de ned as in Table 1 The choice was due to the strong relationship between these factors and the ESG score. In addition, this group of indicators are part of the three main pillars of ESG and present the highest contribution on the prediction of the supply chain from disruptions (Alda 2021). Firstly, the way that a company uses the resource to achieve a better performance and capacity leads to the improvement and sustainability of its supply chain (Alonso-Fradejas 2021; Tseng, Bui, and Lim 2021). So, the role of Resource Use Score plays a vital role in the study. Moreover, organizations are progressively thoughtful and responsive to the carbon emission in today's world, which relates to their organizational operations (Molthan-Hill et al. 2020; Tseng et al. 2021). In their main priorities is to calculate their Carbon Footprint, which is called as CFP, because they want to maintain and reduce it (Firoozi Nejad et al. 2021). This can be act as the initial step for any rm to maintain carbon emissions and create an e cient environmental management system and as a result a sustainable supply chain. Emissions Score which measures a company's commitment to and effectiveness in reducing environmental emission in the production and operational processes was selected as independent variable in this study based on the above statement (Magerakis and Habib 2021). Following to the Emissions Score, Environmental Innovation Score was included too, as independent variable, because it re ects a company's capacity to reduce the environmental costs and burdens for its customers, and thus creating new market opportunities through new environmental technologies and processes or eco-designed products (Fuente, Ortiz, and Velasco 2021). Management Score was selected for the development of the model of the current study and measures a company's commitment to and effectiveness in following best practice corporate governance principles, while Workforce score was included too (DasGupta 2021). Workforce is vital for any business and is one of the most important assets of it. Providing a range of growth opportunities to employees can positively impact wellbeing (Rajesh and Rajendran 2020). Purpose is one of the most powerful drivers of engagement. An engaged employee will feel as though they are contributing towards something that matters to them. Similarly, promoting sustainable behavior at work can indirectly impact on wellbeing (Sakun et al. 2020). For example, by encouraging sustainable travel (eg walking and cycling) this can improve workforce's health, reduce stress and decrease air pollution. Based on that, workforce Score was selected as independent variable in this study as it measures a company's effectiveness towards job satisfaction, healthy and safe workplace, maintaining diversity and equal opportunities, and development opportunities for its workforce (Dor eitner, Kreuzer, and Laschinger 2021). Also, Human Rights Score was added and measures a company's effectiveness in respecting the fundamental human rights conventions. Lastly, policy factors were selected for the model like the Policy Environmental Supply Chain Score and the Policy Water E ciency Score (Naffa and Fain 2020). The rst re ects a company's efforts to include in the supply chain measures to reduce their environmental impact, while the second re ects a company's policy to improve its water e ciency by using various forms of processes/mechanisms/procedures and a system or a set of formal documented processes for e cient use of water and driving continuous improvement. Resource Use Score Independent Resource Use Score highlights the ability of a businesses to rationally manage their materials and energy and to focus on solutions that are more environmentally friendly, thus improving supply chain management.

Emissions Score
Emission Score represents the degree of commitment and e ciency of a company, in terms of reducing its environmental emissions that come of its production and operation processes.

Environmental Innovation Score
Environmental Innovation Score re ects a company's ability to reduce its environmental footprint as well as its customer burdens, thus creating new market opportunities through new environmental technologies and processes or eco-friendly products.

Management Score
Management Score measures a company's commitment and effectiveness in terms of the best practices of corporate governance principles.
Workforce Score Workforce Score measures the effectiveness of a company in terms of Human Resources. This rating expresses employee's satisfaction with the work, the implementation of safety and quality systems, respect for diversity of individuals, while it ensures equal development opportunities.

Human Rights Score
Human Rights Score measures a company's effectiveness in respecting fundamental human rights principles.

Policy Environmental Supply Chain Score
Supply Chain Environmental Policy Score highlights all the actions of the company to integrate in the supply chain measures and practices, regarding the reduction of their environmental impact.

Policy Water E ciency Score
Policy Water E ciency Score indicates the intention of businesses to improve water e ciency by adopting various forms of processes and systems.

Descriptive Statistics
In  The maximum scores observed on Resource Use Score, Emissions Score, Environmental Innovation Score, Workforce Score and Management Score were exceeded 99 points, which was supposed to be close to the maximum possible score. This indicates that variables are compatible with the scoring method. As for the minimum score, Emission Score, Environmental Innovation Score, Management Score and Human Rights Score were far lower than the minimum scores for the other variables of the model. Regards the average, that of Resource Use Score and Workforce Score, as well as, Human Rights Score, Policy Water E ciency Score and Policy Environmental Supply Chain Score were very close. Environmental Innovation Score had the lowest average score of the independent variables but had the second highest standard deviation in scores.

Multiple linear regression analysis
The main purpose of the multiple linear regression analysis is to investigate the relationship between a dependent variable (in the current research the dependent variable is ESG score) and two or more independent variables in the following form: y = β o + β 1 χ 1 + β 2 χ 2 + …+ β n χ n + ε i In the above equation the terms of β0 ... βn are called as the coe cients of the regression and their estimation is based on a record of observations. This is done by curve tting based on the least square method with the aim of minimizing the difference between the observed and estimated values. The predictors should have little or no correlation with each other. For example, the correlation coe cient should be less than 0.7 to evade from problems like multicollinearity. The last term of the equation is εi and is mainly referred to as the residual. Also, residuals can be used about testing the general signi cance (F-test) of the equation and the signi cance of each regression coe cient (t-test). For obtaining valid results from the above tests, the residual εi should be distributed independently, with a mean of zero and a constant variance of σ2. This is described by a residual analysis and can also lead to the elimination of the data outliers. Another way of estimating the interpretive power of a linear model is the coe cient of determination, which is called R2. This coe cient measures which part of the variance of the dependent variable can interpret by independent variables Essentially, it is a simpler coe cient that measures the ability of a set of factors to interpret a phenomenon.
However, a regression model will have unit changes between the χ and y variables, where a single unit change in χ will coincide with a constant change in y. Taking the log of both variables will effectively change the case from a unit change to a percent change. This is especially important when using medium to large datasets, as happens in the current research. Usually, logarithmic transformation is a convenient means of transforming a highly skewed variable into a more normalized dataset. In theory, we want to produce the smallest error possible when making a prediction, while also considering that we should not be over tting the model. Over tting occurs when there are too many dependent variables in play that it does not have enough generalization of the dataset to make a valid prediction. By using the logarithm of one or more variables this can improve the t of the model by transforming the distribution of the features to a more normally shaped bell curve. Based on the reasoning above, the second-order model was adopted as the following: log(y) = β o + β 1 logχ 1 + β 2 logχ 2 + …+ β n logχ n + ε i Moreover, by taking the logarithm of both dependent variable and all independent variables and creating a log-log functional form can contribute to overcome the problem of non-linearity. A log-log function is suitable when a unit percentage change in one of the independent variables is expected to respond to a percentage change in dependent variable. Also, another problem that can occur in multiple regression analysis is that of the imperfect multicollinearity (Shrestha 2020). Perfect multicollinearity occurs when an independent variable is a perfect linear relationship of one or more independent variables and is something that can happen very rare. However, the occurrence of severe imperfect multicollinearity is more common (Mans eld and Helms 1982). When severe imperfect multicollinearity occurs, there is a linear functional relationship between two of more independent variables, which is so strong that the estimation of coe cients of the variables in the regression model is signi cantly affected (Haitovsky 1969). The VIF test examines the degree to which an independent variable can be explained by the other independent variables in the model. The VIF test re ects the degree to which multicollinearity has increased the variance of the estimated coe cient (Jou, Huang, and Cho 2014). If the VIF value range between 1-10, then there is no multicollinearity. On the other side, if the VIF <1 or VIF > 10, then there is the problem of multicollinearity (Dias Curto and Castro Pinto 2011; Jou et al. 2014). In the current study, results from VIF test that displayed in Table 3 shows no sign of multicollinearity in the regression model. Regards  Table 4 are summarized the results of the model.  percentage. The rest of independent variables, except of Policy Water E ciency Score, have a positive impact on ESG (dependent variable) and affect it between a range of 6.9 to 13.2 percentage. As for the Policy Water E ciency Score, this variable has an inverse relationship, which means that a unit percentage increase of this variable will affect negatively the dependent.
Related to the variable Workforce, this is the one that affects mostly the dependent variable ESG Score.
This means that companies considering, mostly issues related to their Workforce. Corporate's workforce contributes to a strong governance that ensures the smooth operation and prospects of companies.

Discussions And Conclusions
The purpose of this article is to explore those ESG factors that mitigate fraud in supply chain management and the strategies that can be developed from companies based on these factors. Indicators of ESG can be characterized as a "signal" to attract investment interest. However, the role of technology, and that of green technology, is critical in mitigating fraud in SSCM too.
In our research, the variable that highlights the importance of green technology in order to ensure transparency in SSCM is that of Environmental Innovation Score (Zhang et al. 2017). There are two main reasons that every business in the world want to mitigate fraud. The rst one is referred to the legislation as it requires better and more accurate detection of products at all stages of the supply chain. But beyond that, companies want to meet their internal needs and the requirements of their customers to ensure the quality and safety of products (DuHadway, Carnovale, and Hazen 2019). Blockchain technology (BCT) is one of the emerging technologies in the eld of supply chain management, as it can ensure a wellorganized supply chain as well as security and transparency about it. Based on fraud avoidance, BCT enables authentication, con dentiality, privacy and data access control as well as ensuring integrity of services. It also serves to integrate other green technologies such as Internet of Things (IoT), enhance security, consensus mechanism for dynamic data storage, data transparency and protection, reliability and cost management (Dai, Wang, and Vasarhelyi 2017). Moreover, technologies related to data collection and processing, such as portable terminals, tablets, barcode readers, wireless networks and RFID technology, can have a critical role in the new supply chain trends to ensure transparency. These systems offer great improvement in terms of productivity, error reduction, and large volume data management with high security and low cost (Mishra et al. 2018).
Therefore, it is imperative for businesses to create long-term resilience and exibility in their supply chain, so that they can meet their future challenges that will arise and transform them into opportunities. At the same time these new demands are created by customers due to digitalization (Boyson, Corsi, and Paraskevas 2021; Wisetsri et al. 2021). To achieve this, a holistic approach is required regards supply chain management and business operations. To ensure the smooth operation and transparency of supply chain, businesses must utilize technology and develop a strong digital framework. To achieve that an agile strategic approach in supply chain management will be an ideal method (Geyi et