Oil palm (OP), with the scientific title of Elaeis guineensis, stands as one the most promising global oil crops that covers almost 40% of the exported volume of vegetable oils. Palm oils and their downstream harvests have been profoundly imported all over the world. Palm oils are widely consumed by more than three billion people of Asia as a primary dietary constituent. Palm kernel oil has also been popular for non-edible purposes, such as cleaning, sanitizing, cleanings, hygiene, and personal care products, such as cleansing agents, lathers, and surface cleaners (Central Point of Expertise on Timber, 2015). OP is crucial to the economies of the two South East (SE) Asian countries, namely Indonesia and Malaysia, from which it has been exported worldwide in the forms of oil, food additive, and other derivatives (Ommelna et al., 2012). Key locations of global oil palm cultivation are highlighted in (Table 1).

Regardless of the local or global level, the entire supply chain is affected and faces challenges in ensuring logistics and manufacturing operations (Statista, 2020). Businesses have been badly damaged by the pandemic named COVID-19 with its deadly, transmittable, and unpredicted symptoms. This caused severe disorders to human lives and the circular economy (Klande et al., 2019). The global supply chain is faced with considerable risk, and trading and distribution activities have been reduced significantly. The sprawl of COVID-19 affected most countries and territories with an increasing rate of active cases and deaths (Farooq et al., 2021). Though during early-2020, the lockdown initially affected the trade of the vital OP importers, the OP supply chain has been demonstrating resilience to the early shock of lockdown and continued transporting millions of tons of palm oils with minimum disruption from the major importing hubs of South-East Asia and Europe. While OP supply chains of Indonesia were comparatively less affected by the pandemic followed by localized lockdown and insufficient workers, a 10% reduction in palm oil production is predicted in the upcoming years from the previous year (MPOC, 2020).

Sustainability involves the notion of how humans should carry responsibility for the natural environment and future generations (Clark, 2007). Sustainable development seeks to cover the needs of the existing cohort without negotiating the capacity of the impending cohorts (World Commission on Environment Development, 1987). As sustainability is achievable by addressing social, environmental, and financial objectives in the supply chain (Mitchell and Walinga, 2017; Raut et al., 2017), a sustainable supply chain will be better able to minimize the adverse effects of supply chain operations and enhance the performance and value of supply chain management (Vargas et al., 2018). Other benefits of adopting sustainability in the supply chain would be to supplement the brand identity and socially conscious image. It also reduces time and expense associated with potential environmental pollution and other damages. Companies are eventually better off (Bai et al., 2019).

Unfortunately, neither the need to adopt sustainability has been recognized nor the competency required for administering sustainability practices has been adequate by the firms of the developing economies (Luthra et al., 2017). Therefore, it is imperative to adopt sustainability in the supply chain to corroborate various prospects for sustainable performance of the businesses. The COVID-19 pandemic has left unfavorable effects on the sustainability of value chains worldwide (Govindan, 2018) and created substantial detrimental effects on sales revenue, return on investment, procurement strategies, materials supply, logistics service (Bag et al., 2020; Majumdar et al., 2020; Sharma et al., 2020), production, distribution, or logistics (Ivanov, 2020a). Following the interruption and bottleneck of the supply chain caused devastating consequences (Ivanov, 2020b; Karmaker et al., 2021).

It is not surprising that the existing supply chain strategies and practices designed for a regular operation in the supply chain will not be practical and feasible to tackle a global pandemic like COVID-19. The supply chain managers need to reconsider designing their strategies and practices to accommodate the pandemic era. The businesses are wrestling with formulating a supply chain incorporating the features of sustainability (Sharma et al., 2020) and extra-resilient approaches (de Sousa Jabbour et al., 2020) to combat the challenges offered by the COVID-19 consequences. With the gigantic outburst of COVID-19 continuing worldwide, various organizations shift to work-from-culture, laboratories constantly work to innovate vaccines, medications, and healthcare facilities with relentless research.

Adoption by the affected companies in emerging economies of socially distanced working environments, community lockdown, isolation (Majumdar et al., 2020) and adequate personal protective equipment (PPE), ventilators, masks, face shields, and hand sanitizers (Sharma et al., 2020; Karmaker et al., 2021) to prevent the transmission of COVID-19 disease is inadequate. The terrible health hazards, unemployment, and economic turmoil have triggered economic and social sustainability trials during the pandemic (Majumdar et al., 2020). It is a timely need to rethink a viable resistance level of the supply chain (Ivanov and Dolgui, 2020) to halt the disintegration of the economy.

Supply Chain Management (SCM) refers to ensuring a synthesized approach across the supply chain, starting from sourcing raw materials, constructing or assembling a product, dispatching it to retail outlets, and actual consumers' end. The oil millers need to restructure their supply chain practices with “tailor-made indicators to enhance sustainable supply chain management performance, especially in the pandemic era (Kusrini and Maswadi, 2021). The key drivers of SSCM ultimately benefit the stakeholders from seven sectors: oil palm producers, processors or traders, consumer goods manufacturers, retailers, banks/investors, and environmental and social non-governmental organizations (NGOs).

In the research of Kusrini and Primadasa (2018), the weighted key performance indicators (KPI) of the SSCM palm oil industry have been identified by combining literature reviews. It is imperative to improve sustainable supply chain management to remain competitive in the COVID-ridden business environment and enhance performance (Sutawidjaya et al., 2021). Proactively detecting disruptions and quick remedial actions can allow industries to lower the impact of current and future shocks (Farooq et al., 2021). Addressing the consequences of diseases under a unanimous framework in the current timeline can be the first step. Some 26 published literature have been observed relating sustainable supply chain management to formulate the essential indicators to assess the performance of sustainable supply chain management from economic, social, and environmental dimensions, specially catered to the pandemic-ridden business environment.

Evolving from sustainability, sustainable supply chain management (SSCM) aims at managing the supply chain relationship and the flow of materials and information to maximize operational performance and the profitability of the supply chain. Compared with supply chain management, the sustainability feature in the sustainable supply chain management (SSCM) has more multiple dimensions ensuring long-term profitability without adversely affecting natural systems (Seuring and Müller, 2008).

SSC drivers ensure efficient operational and economic performance, contingency planning implementation in uncertain environments, and availing sustainability practices (Tseng et al., 2019; Sajjad et al., 2020; Karmaker et al., 2021). Acknowledging such drivers would help establish the SSC initiatives for sustainable management of the CPO sector. The espousal of SSC would depend on several critical indicators (drivers) that contribute significantly to a successful implementation. Nevertheless, inadequate attention on sustainability drivers to execute the SSC has been exhibited (Munny et al., 2019). Amid the pandemic era, it is crucial to investigate the drivers of sustainability on supply chains that will enhance the post-covid performance of the CPO sector. The essential drivers for SSC well-adapted with pandemic have been mainly derived from the extant literature presented in Table 2.

In Table 2, the ES, SS, and ENS refer to the indicators in the three pillars of sustainability, namely economic, social and environmental, respectively. As an indicator to gauge the organizational improvement (Lim et al., 2015), key performance indicators (a) of sustainable supply chain management (SSCM) of the crude palm oil industry need to be identified. The present study reviews the literature to obtain sustainability KPIs for assessing sustainable supply chain management (SSCM) of CPO mills and proposes the following indicators in Table 3.

Given the dynamic business environment, changing customer needs, and economic, environmental, and social concerns, having a sustainable supply chain (SSC) is vital to produce goods and services in the pandemic era (Bag et al., 2020). It is essential to integrate all the industry actors to implement the value of sustainability properly. A sustainable supply chain management (SSCM) can address socio-environmental issues and improve the performance of the community (Kusi-Sarpong et al., 2018) using appropriate KPI indicators, as suggested by past studies.

A literature screening has been carried out using basic phrases, such as: “drivers and indicators for sustainable supply chain adoption” OR “critical factors/motivators/key indicators” AND “sustainability drivers assessment pandemic” OR “investigate sustainable supply chain drivers” OR “impacts/effects of a pandemic on sustainability and sustainable developments of oil palm sector” OR “scale-up/improving supply chain sustainability in the palm oil industry”. For a literature search, databases like Scopus, EBSCO, and Google Scholar have been utilized. Based on the review of extant literature, some sixteen drivers of SSC have been designated.

Also, followed by an extensive literature review, the indicators and sub-elements of SSCM performance were determined. Based on Table 2, for economic dimensions, a total of 6 sub-elements have been chosen for the study, such as environmental expenses, delivery speed, supply chain expense, market competition, product quality, and adaptability. For social dimensions, 5 sub-elements of social responsibility, health and safety measures, employee development, human rights issues, and customer rights have been determined. Another set consisting of 5 sub-elements have been decided for the study, including energy efficiency, material efficiency, water usage, waste management, and sustainable supplier management. Based on Table 3, for KPI, the sub-elements have been derived as work-in-process, EHS (environment, health & safety) expenses, rate of customer complaint and return, materials usage, acidification potential, and physical load index.

Analysis of sustainability indicators' role in improving SSCM was derived from data analysis undertaken through IBM SPSS statistical software 21.0 to calculate data reliability, frequency distribution, Confirmatory Factor Analysis (CFA) to understand the relationships between the various elements of social, economic, and environmental factors. As suggested by (Urban and Naidoo, 2012), the Kaiser–Meyer–Olkin (KMO) test by SPSS was also used to assess the sampling adequacy and evaluate the correlations among variables. A Kaiser-Meyer-Olkin (KMO) test is used in research to determine the sampling adequacy of data that are to be used for Factor Analysis. It has been introduced Henry Kaiser in 1970 and modified by Kaiser and Rice in 1974 (Kaiser and Rice, 1974). The reliability construct was calculated separately, and data reliability for the diagnosed CFA was assessed. The variables (from section Screening of Literature) determined will be assessed for a diagnosis of CFA to identify the association sustainability indicators and SSCM performance.

As with other statistical methods, the CFA model relies upon assumptions. Procedures such as evaluating distributional characteristics of indicators and examining data for outlying cases should be conducted before CFA techniques are employed. For estimation and model identification purposes, a minimum of three observed variables for each latent variable is recommended as latent factor measured by one variable (e.g., one subscale) is not optimal and may lead to problems with an estimation as well as with construct interpretation (Bollen, 1989). The study did not conduct sensitivity analysis, as it may increase the false sense of security among managers if all pessimistic estimates of the model are optimistic and do not consider the interaction among variables. Hence, no in-depth analysis to check the model's reliability might be considered one limitation of the study.

To verify the suitability of the collected data for factor analysis, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlett's Test of Sphericity value have been investigated. Referring to Table 4, KMO value for Economy Sustainability (ECS), Social Sustainability (SS), Environmental Sustainability (ENS), and Key Performance Index (KPI) are 0.786, 0.786, 0.809, and 0.744, respectively, while Bartlett's Test value is significant (p = 0.000). Therefore, factor analysis in this regard is required.

The internal consistency and reliability of the pilot survey were tested using the Cronbach's α, which is consistent with (Ogunbiyi et al., 2014). An α-value of the range of 0.7 is considered acceptable, and any value above 0.8 is found to be good. In the present study, data reliability with 108 sample sizes can be seen in Table 5. For ECS, the Cronbach's α is 0.776, SS Cronbach's α is 0.767, while ENS Cronbach's α is 0.839, and KPI Cronbach's α is 0.830. Thus, these figures are considered good and acceptable for further study.

Seven hundred ninety-one palm oil mills existed in Indonesia (Ministry of Energy and Mineral Resources,, 2019). Most of the Palm oil mills are situated in the Sumatra area. The demographic background of the respondents shows the percentages of the respondents (Millers) in terms of gender, education, and economic position. The majority of the executive positions are held by males (89.8%), while 10.2% are females. Moreover, most respondents (77.8%) completed their degree levels and have 2 to 3 years (50.9%). An employee with a minimum of 2 years of experience should be considered reliable to provide information about a firm or mill, according to Dustmann and Meghir (2005). Some 501 employees and above (60.2%) were found to be recruited by the surveyed mills to perform their operations (See Table 6).

The mean and standard deviation of the palm oil miller's level of responsiveness to economic sustainability is described in Table 7. The respondents indicated that they have a “neutral” level of economic sustainability practices in these observed variables; “purchase eco-friendly materials (Ec₁)” wherein 49.1%, “minimize waste disposal expenses (Ec₂)” (46.3%), and “minimum time spent on completing an order (Ec₅)” which represent the majority of the respondents (55.6%). About 45.4, 42.6, and 43.5% of the respondents indicated that they have a “high extent” level of economic sustainability practices which include “minimize operating costs (Ec₃)”, “on-time delivery (Ec₄)”, and “able to respond to changes in delivery schedule (Ec₆)”.

The mean and standard deviation of the palm oil miller's level of responsiveness to social sustainability is described in Table 8. The respondents indicated that they have a “neutral” level of social sustainability practices in these observed variables; “demonstrate concern for any social entity (S₁)” wherein 49.1%, “enhance community relationship (i.e., with Government or NGOs) (S₂)” (46.3%), and “enhance employee's job satisfaction (S₅)” which represent the majority of the respondents (55.6%). About 45.4, 42.6, and 43.5% of the respondents indicated that they have a “high extent” level of economic sustainability practices which include “Minimize in employee' work-related injury/ death (S₃)”, “ensure employees' education and training program (S₄)”, and “enhance customer awareness of safe and healthy products (S₆)”.

The mean and standard deviation of the palm oil miller's level of responsiveness to environmental sustainability can be referred to in Table 9. Approximately 39.8 and 32.4% of the respondents agree that they have a “high extent” in “efficient use of renewable energy (rain, air, sunlight, etc.) (En₁)” and “efficient consumption of energy (electricity, etc.) (En₂)”. On the other hand, the majority of the respondents (50%) agreed that they are “neutral” in “evaluating suppliers in terms of quality, price, availability, and speed (En₆)”. About 40.7 and 34.3% of the respondents also agree that they have “neutral” in “reduce pollution due to smoke and waste (En₅)” and “reduce producing of solid waste (En₄)”. 31.5% of the respondents indicate that they have a “low extent” in “minimize using of hazardous and toxic materials (En₃)”.

The mean and standard deviation of the palm oil miller's Key Performance Indicators (KPI) of palm oil mill's sustainable supply chain management can be referred to in Table 10. The majority of the respondents (59.3%) agree that they have a “high extent” of KPI in “materials usage (KPI₅). About 44.4, 52.8, 42.6, and 37.0% also agree that they have a “high extent” in “Work in process (KPI₁)”, “Rate of Customer Complaint and Return (KPI₃)”, Acidification Potential (KPI₅), and Physical Load Index (KPI₆). While 43.5% of the respondents agree they are “neutral” in “EHS (environment, health, and safety).”

The mean and standard deviation distribution of the palm oil miller's level of SSCM against the sustainability indicators are summarized in Table 11. The highest mean of sustainability element is the Environmental Sustainability (4.04, sd: 0.1470), indicating that the respondents mostly tend to have slightly more than “great extent” answers for this particular item. While the rest of the items only indicate that they are “neutral” in demonstrating a considerable concern toward their stakeholders while emphasizing their sustainable improvement in supply chain management of their palm oil companies in the country. The second highest mean is the Key Performance Index (3.76, sd: 0.6073), followed by Economic Sustainability (3.42, sd: 0.5258) and Social Sustainability (3.25, sd: 0.7385).

A Confirmatory Factor Analysis (CFA) model is used to examine the hypothesized relationships between and among the constructs (factors) in the model (Figure 1). A relatively significant result was found concerning criteria implementations. The findings of the present study indicate that:

The Pearson Correlation test is carried out to measure the association between variables. Referring to Figure 1, it can be concluded that EN (Environment) contributes the most in explaining the relationship with dependent variable or KPI (77.3%), followed by SS (Social) (73.2%), and EC (Economic) (70.3%). The convergent validity test results include the coefficients and variance estimates between factors and variable loadings on the factors for each variable (refer to Table 12). Among the EC latent variables, this study found that the path coefficient of the “minimizes waste disposal expenses” obtained the highest value (0.78), followed by “minimum time spent on completing order” (0.76), “minimize operating costs” (0.69), “able to respond to changes in delivery schedule” (0.69), “purchase eco-friendly materials” (0.68), and “on-time delivery” (0.68). Among the SS latent variables, the path coefficient of the “demonstrate concern for any social entity” and “enhance customer awareness of safe and healthy products” scored the highest value (0.70 respectively), followed by “enhance employees' job satisfaction” (0.66), “minimize in employee' work-related injury/ death” (0.65), “ensure employees' education and training program” (0.64), and “enhance community relationship (i.e., with Government, or NGOs)” (0.61).

Among the EN latent variables, the path coefficient of the “efficient consumption of energy (electricity, etc.)” scored the highest value (0.86). This value is followed by “minimize using of hazardous and toxic materials” (0.84), “reduce producing of solid waste” (0.70), “evaluate suppliers in terms of quality, price, availability and speed” (0.69), “efficient use of renewable energy (rain, air, sunlight, etc.)” (0.66), and “reduce pollution due to smoke and waste” (0.56). Among the KPI latent variables, the path coefficient of the “materials usage” scored the highest (0.84), followed by “Acidification Potential” (0.83), “Physical Load Index” (0.78), “Rate of Customer Complaint and Return” (0.71), “EHS (environment, health, and safety)” (0.69), and “Work in process” (0.59).

To govern the best fit of the model, some specific indicators were verified. Cronbach's alpha is used to determine the reliability of the factors of three dimensions. The latent variable of the model contains Cronbach's alpha values for the dimension at 0.897. Therefore, as directed by (Nunnally and Bernstein, 1994), the reliability level for the attitude toward the dimension fulfills the critical value of 0.7.