Environmental innovations and sustainability practices of manufacturing firms in Uganda

Abstract

Purpose

The purpose of this study is to investigate the pivotal role of environmental innovations in driving sustainability practices within medium and large manufacturing firms operating in Uganda.

Design/methodology/approach

Using a cross-sectional and quantitative methodology, data were collected through a questionnaire survey involving 208 manufacturing companies. The smart partial least squares path modelling technique was used for the analysis.

Findings

The analysis unveils significant and positive associations. Specifically, product innovation exhibits a robust and affirmative relationship with sustainability practices. Similarly, the correlation between process innovation and sustainability practices emerges as statistically significant. Moreover, the findings underscore the noteworthy and constructive predictive influence of environmental innovation on sustainability practices.

Practical implications

These empirical results present substantial implications for theoretical frameworks and practical applications. From a policy perspective, the findings emphasise the importance of incentivising eco product and eco process innovations as potential drivers of eco-friendly practices. On the managerial front, strategic resource allocation and the adoption of integrated environmental innovation strategies are advocated, with the ultimate goal of enhancing sustainable business approaches within Uganda’s manufacturing subsector.

Originality/value

To the best of the authors' knowledge, this study represents the inaugural attempt to investigate the role of environmental innovations in elucidating sustainability practices within a least developed country. Notably, while all dimensions demonstrate significance, it is noteworthy that product innovation emerges as the more substantial contributor to the promotion of sustainability practices.

Citation

Alinda, K., Tumwine, S. and Kaawaase, T.K. (2024), "Environmental innovations and sustainability practices of manufacturing firms in Uganda", Asia Pacific Journal of Innovation and Entrepreneurship, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/APJIE-08-2023-0164

1. Introduction

In recent times, the global landscape has become fraught with a pressing challenge, prominently characterised by environmental pollution and climate change, as underscored by the United Nations Environmental Programme (UNEP, 2023). The degradation of the Earth’s natural environment and the subsequent ecological imbalances have witnessed a noticeable escalation. This situation has amplified the appeals emanating from academia, practitioners, policymakers and social movements, all advocating for a departure from conventional, unsustainable practices (Martin et al., 2021; Whiteman et al., 2013). Moreover, human activities, primarily characterised by the release of greenhouse gases, have undeniably played a central role in driving global warming. This is substantiated by empirical evidence revealing that the global surface temperature has risen by approximately 1.1°C above pre-industrial levels during the period of 2011–2020 (UNEP, 2023). The escalation of global greenhouse gas emissions persists, underscored by a complex interplay of historical and ongoing factors stemming from unsustainable energy consumption, land utilisation and changes, varied lifestyles, consumption patterns and production practices. These factors exhibit disparities not only across different regions but also within and between countries, highlighting the multifaceted nature of this global challenge (UNEP, 2023).

Thus, effectively addressing this challenge necessitates a concerted global effort and an unwavering commitment from businesses spanning various sectors. Moreover, governments worldwide are intensifying their efforts to compel firms to adopt sustainability practices (SPs). However, it is evident that not all firms exhibit equal dedication to their environmental obligations, with some encountering difficulties in achieving comparable levels of SPs (Balasubramanian and Shukla, 2020). To this end, we pose a pivotal question with potentially profound implications:

Q1.

Can the concept of environmental innovation (EI) offer valuable insights into deciphering the disparities witnessed in firms’ dedication and contributions to SPs?

At the heart of this discussion lies the essential role that SPs play in safeguarding the environment and addressing the consequences of climate change. These practices encompass a wide array of approaches designed to decrease emissions, enhance the efficiency of resource utilisation and diminish the creation of waste. This standpoint concurs with the observations detailed in National Environmental Management Authority (NEMA, 2019). Beyond their ecological implications, the adoption of environmentally friendly initiatives also yields various advantages, including improved operational efficiency, cost-effectiveness and enhanced competitiveness (Desore and Narula, 2018). Additionally, these initiatives contribute to long-term economic growth. According to NEMA (2019), the implementation of SPs can result in better working conditions, better occupational health, increased social inclusivity and general community well-being, in addition to economic benefits. Hence, the integration of SPs within Uganda’s manufacturing subsector holds the potential to chart a path towards a more sustainable developmental trajectory.

According to the NEMA (2019) report, the manufacturing subsector in Uganda significantly contributes to environmental degradation and sustainability challenges. This report highlights several pressing issues, including alarming levels of air and water pollution as well as the generation of substantial waste within the manufacturing processes. These activities have resulted in adverse consequences, such as ozone layer depletion and reduced material efficiency, thereby negatively affecting industrial productivity. The NEMA report underscores the urgency of addressing these critical issues to mitigate their environmental impact and enhance the sustainability of Uganda’s manufacturing sector. To tackle these challenges effectively, Ugandan manufacturers must take proactive measures, specifically through the adoption of SPs. This viewpoint is consistent with the conclusions drawn from the recent research conducted by Alinda et al. (2023), who urge companies to adopt EI as a means to facilitate the incorporation of SPs.

Recent scholarly literature suggests that the integration of EI is important in fostering SPs within the realm of manufacturing firms (Tang et al., 2022; Lee and Lee, 2022; Su et al., 2022). As global environmental concerns, notably climate change and resource depletion, continue to intensify, a prevailing consensus is emerging that traditional business paradigms need a profound metamorphosis (Handfield and Sroufe, 2018; Zwergel and Ziegler, 2021). Another debate relates to the role of eco process innovation in facilitating SPs. Linnenluecke et al. (2019) argue that eco process innovation can enhance environmental performance by enabling the adoption of cleaner technologies, improved efficiency, and reduced emissions. They highlight the potential for eco process innovation to result in significant environmental benefits by minimising resource consumption and waste generation. However, Chen and Rathore (2016) raise concerns that eco process innovation alone may not guarantee sustainability outcomes. They argue that process innovation may inadvertently lead to rebound effects, where efficiency gains are offset by greater production levels or increased consumption, ultimately negating potential environmental benefits.

Some scholars have also debated the distinction between eco product innovation and eco process innovation in terms of their relative impact on SPs. Balachandran and Ramanathan (2019) suggested that eco process innovation may have a more substantial and direct impact on sustainability, as it focuses on improving manufacturing processes rather than introducing new products. However, Große-Bölting and Pietzsch (2020) contend that product innovation can be a powerful driver of sustainability, particularly when it incorporates a life-cycle perspective. They argue that innovative product designs and functionalities, along with associated business models, can lead to sustainable consumption patterns, circular economy practices, and a reduced environmental footprint overall. In their study, Jum'a et al. (2023) delve into the relationship between big data technological capabilities, personal competencies and sustainable performance within Jordanian manufacturing firms, emphasising the mediating role of innovation. By contrast, the present study centres its attention on SPs, encompassing the diverse actions undertaken by manufacturing firms that impact environmental, social and economic dimensions.

This type of innovation includes coming up with and using new technologies, methods and products that are meant to reduce pollution, use fewer resources and make the switch to an economy that cares more about the environment (Dangelico and Pujari, 2019; Nidumolu et al., 2022). Empirical research shows that manufacturing companies that put EI first will not only improve their environmental performance but also gain a competitive edge in the market (Dangelico and Pujari, 2019; Zhu and Liu, 2022).

Despite the potential of EI to drive SPs, the intricate relationship between these two variables remains inadequately explored in the scholarly literature, especially in least-developed countries. Furthermore, contradictions persist in the literature regarding the role of EI in promoting SPs. This gap in research is notable, as there is a lack of comprehensive investigations into the direct association between EI and SPs. Although studies, including the work of Quintana-García et al. (2022) and others, suggest the significance of EI in shaping SPs, a specific exploration of this association within Uganda’s manufacturing context is conspicuously absent.

Our study aims to address this scholarly void by systematically examining the influence of EI on the uptake of SPs within medium and large (M&L) manufacturing organisations. Grounded in dynamic capability theory, our study contributes on multiple fronts. Primarily, this research offers a comprehensive examination of how EI assumes a pivotal role as a driving force for SPs, embracing a holistic perspective. The capacity of firms to enhance resource efficiency, curtail environmental emissions and foster innovations in environmental product and process domains underscores a comprehensive approach to SPs. Second, we delve into the unexplored terrain of assessing the influence of EI on the implementation of SPs. This uncharted research question holds significance, as despite the recognised importance of robust EI in engendering competitive advantages (Sánchez-Torné et al., 2020), this particular relationship has not garnered attention in the existing literature. Third, our empirical enquiry is situated within a distinct context, examining a sample of Ugandan manufacturing firms. This unique research setting sets our study apart from prior studies, particularly those centred around EI, which have predominantly focused on developed economies or specific industrial sectors.

The paper’s organisation unfolds as follows: Section 2 presents a thorough review of significant scholarly contributions, elucidating the development of the hypotheses. In Section 3, we offer an in-depth explanation of the research methodology. Section 4 elaborates on the empirical findings of the study, while Section 5 presents a comprehensive exploration of the ensuing discourse. Our overarching conclusions and corresponding implications are discussed in Section 6.

2. Literature review and hypothesis development

2.1 Theoretical underpinnings

In the literature, SP often responds to EI because new products and processes in manufacturing encourage firms to implement SPs (Afshari et al., 2020). The primary aim of this paper is to explore SP in Uganda using dynamic capability theory and quantitative analysis to investigate the connection between EI and SPs. Dynamic capability, which is discussed later, serves as the lens through which this correlation is examined.

Following Teece et al. (1997), the dynamic capability view extends resource-based theory by introducing dynamic capabilities. These represent a firm’s inherent ability to effectively integrate, construct, and reconfigure both internal and external competencies, alongside a mix of resources and proficiencies. In dynamic contexts, these capabilities are crucial, enabling firms to continually reallocate resources to navigate the complex business environment. The importance of innovation capability is evident in this framework, particularly when firms confront changing technologies and market structures. This is particularly true in the context of EI, which encompasses eco product and eco process innovations tailored to addressing sustainability challenges. EI has been conceptualised as a dynamic capability that reflects firms’ capacity to innovate within a dynamic environment (Huang and Li, 2017; Qiu et al., 2020).

At its core, dynamic capabilities encapsulate an enterprise’s intrinsic potential to actively shape, reshape, synthesise and re-synthesise its asset infrastructure. This inherent flexibility empowers firms to adapt, cultivate and strategically harness internal and external competencies within their unique context. As highlighted by Teece et al. (1997), these capabilities enable firms to navigate the fluid external environment, shaping and capitalising on their strategic advantage. In essence, the dynamic capability framework, grounded in dynamic capabilities, provides a sophisticated paradigm for enterprises to align with the evolving business landscape and catalyse positive influences and innovative initiatives within this ever-evolving milieu.

2.2 The concept of environmental innovations

EI involves integrating ethical considerations into products, processes and organisational frameworks (Chen et al., 2006a). Its characteristics, determinants and typologies guide environmental stewardship decisions. EI spans technological, organisational, institutional, and social facets (Rennings, 2000). Technological aspects include pollution prevention technologies, while organisational facets involve tools such as ISO 14001. Institutional manifestations include bodies such as the Intergovernmental Panel on Climate Change, and social aspects encompass shifts in consumption and lifestyles. EI’s breadth warrants multifaceted exploration, from narrow definitions (Chen et al., 2006b) to those transcending firm boundaries (OECD, 2009). EI definitions focus on mitigating environmental impacts through products, processes or management (Rennings and Zwick, 2002; Kemp and Pearson, 2008), aiming to reduce ecological footprints. According to Chen et al. (2006a), “green innovations” span hardware and software linked to green products or processes, including energy conservation, pollution prevention, waste recycling, green design and corporate management. Horbach (2008) view EI as novel processes, techniques, systems, or products for environmental harm mitigation. Rennings (2000) defines it as stakeholder actions that formulate, adopt or implement ideas, behaviours, products and processes for environmental pressure alleviation and ecological sustainability.

2.3 The concept of sustainability practices

Sustainability embodies the fusion of environmental, social and economic dimensions (Haanes, 2016). Elkington’s (1997) conceptualisation of sustainability reinforces these interconnected facets, while Leung and Rosenthal (2019) stress the importance of harmonising them holistically. Nasrollahi et al. (2020) distinguish weak and strong sustainability orientations, the former focusing on planet, people and profits, and the latter expanding to industrialisation and technology, consistent with Nave et al.’s (2021) green economy concept. Schaltegger and Burritt (2018) define SPs as deliberate strategies that merge environmental responsibility, economic advantages and social progress, echoed by Bansal and Roth (2000), who underscore policy alignment with stakeholder expectations. Lozano (2008) concurs, characterising SPs as seamless integration of economic, environmental and social concerns. In Uganda’s manufacturing context, entities contribute to sustainability challenges through emissions, waste and consumption of non-renewable energy (NEMA, 2019). Addressing this requires SPs, which drive cleaner production, resource efficiency, waste management and renewable energy (Kaawaase et al., 2021; NEMA, 2019). Such strategies align with environmental preservation and socio-economic well-being goals.

2.4 Environmental innovations and sustainability practices

EI is a pivotal driver of SPs in manufacturing firms, predominantly through eco product and process innovations. The literature underscores the fusion of innovation and sustainability, underscoring their significance for SP enhancement (Silvestre, 2015a, b; Kibet and Korir, 2013). Drawing from dynamic capability theory, innovation serves as a catalyst for transformative shifts across sectors, enabling the adoption of sustainability initiatives (Huisingh et al., 2013). The practical application of innovations, especially environmental product and process innovations, reshapes social, economic and environmental performance (Smerecnik and Anderson, 2011; Silvestre, 2015a, b). Product innovation, particularly environmental variants, drives SPs (Johansson and Ramanathan, 2016). Eco-friendly, energy-efficient and resource-efficient products offer the potential to enhance SPs by curbing energy consumption, emissions and raw material waste (Johansson and Ramanathan, 2016). However, considering the entire product lifecycle is vital because some innovations may inadvertently increase consumption and environmental impacts (Luchs et al., 2011). Coupling environmental product innovation with manufacturing improvements is essential (Grinza et al., 2018), as is aligning it with customer demand and long-term sustainability goals (Teixeira et al., 2020). Similarly, process innovation, which addresses novel methods and operational efficiencies, is crucial for SPs (Cagliano et al., 2013). Environmental process innovation focuses on minimising impacts, enhancing efficiency and fostering sustainability (Johansson and Ramanathan, 2016). In addition, clean technology and renewable energy adoption reduce energy use, raw material consumption and fossil fuel dependence (Johansson and Ramanathan, 2016). Jum'a et al. (2022) find compelling evidence that both lean practices and sustainability-oriented innovations, either individually or jointly, play a noteworthy role in ensuring sustainability. Thus, intertwining EI and SPs within manufacturing is fundamental. Integrating innovative strategies that span both the product and process realms empowers firms to advance environmental, economic and social performance, fostering a sustainable future. Based on this premise, we advance the following hypothesis:

H1.

A significant positive relationship exists between EIs and SPs.

2.4.1 Product innovation and sustainability practices.

Numerous studies have highlighted the positive influence of environmental product innovation on the sustainability efforts of manufacturing firms (Moyano-Fuentes et al., 2018; Grinza et al., 2018; Teixeira et al., 2020). However, the relationship between product innovation and enhanced SPs is not always straightforward. Environmental product innovation, as outlined by Luchs et al. (2011), can improve environmental outcomes, yet may unintentionally drive increased consumption, leading to negative ecological effects. To comprehensively assess a product’s environmental impact, scholars emphasise accounting for its entire lifecycle, including its usage and end-of-life phases. Grinza et al. (2018) shed light on the automobile sector, revealing that although companies craft environmentally conscious vehicles, associated manufacturing processes exhibit significant environmentally harmful effects, raising the importance of synchronising manufacturing improvements with product innovation for substantial SP enhancement. Teixeira et al.’s (2020) findings offer another perspective: despite prevalent environmental product innovation in the electronics sector, a distinct impact on SPs was not discerned. Plausible explanations include consumer demand gaps for eco-friendly products and a preference for short-term financial gains over long-term sustainability. Therefore, we posit that:

H2.

There is a significant positive relationship between product innovation and SPs.

2.4.2 Process innovation and sustainability practices.

The concept of process innovation, as outlined by the Organization for Economic Cooperation and Development (OECD) (2009), involves adopting technologically novel or enhanced methods, equipment and skills for service delivery. This encompasses fresh work strategies, innovative process design and change implementation across technological, human and organisational dimensions. Graafland (2018) highlights the extensive research exploring the link between innovation and environmental sustainability. Ferasso et al. (2020) suggests avenues for achieving SPs, including using eco-friendly materials, advanced technology for efficiency and waste reduction and adopting pollution-free technologies (Zeng et al., 2017). Correspondingly, Morseletto (2020), Bag and Pretorius (2020) and Gupta et al. (2021) stress SPs’ role in circular economy practices, aligning with eco process innovation as a conduit for sustainable advancements. Moyano-Fuentes et al. (2018) found a strong association between eco process innovation and environmental sustainability engagement. Linden et al. (2006) emphasises technology’s role in energy-saving behaviour, while Chuang and Yang (2014) emphasised technology's impact, particularly in design and manufacturing stages, highlighting its role in shaping sustainable approaches throughout the production process. Environmental process innovation in manufacturing focuses on curbing negative environmental impacts and fostering sustainability (Cagliano et al., 2013). This industry commitment to SPs is further emphasised by practices amplified, as exemplified by the adoption of clean technology and renewable energy (Johansson and Ramanathan, 2016). Although the literature largely substantiates positive impacts of eco process innovations on SPs, Azapagic and Perdan (2011) reveal cost challenges in implementing SPs, Hong et al. (2016) identifies context-specific practices, stressing the need for tailored approaches based on local conditions and Michelini et al. (2019) discuss modest outcomes accentuating the importance of holistic perspectives in addressing the multifaceted challenges of sustainability. In their recent study, Umar et al. (2023) demonstrate the significant impact of adopting blockchain technology on green manufacturing (GM). Furthermore, the study highlights the substantial contribution of GM to enhancing overall business sustainability. These perspectives motivate the following hypothesis:

H3.

There is a significant positive relationship between process innovation and SPs.

3. Methodology

3.1 Research design, population and sample

This study used a cross-sectional and quantitative research design. The cross-sectional approach involves collecting data from a sample at a specific moment to examine patterns and relationships. This design allowed the researcher to gather data and responses from manufacturing companies in a single instance, thereby enhancing the credibility and applicability of the findings. The quantitative methodology was chosen to quantify data and draw generalisable conclusions from a representative sample of M&L manufacturing firms, guided by the principles outlined by Creswell and Plano Clark (2007). In addressing the challenges posed by the manufacturing subsector in Uganda, which comprises around 3,859 small businesses often lacking clear addresses and contact details (UBOS, 2018), the study targeted M&L manufacturing firms in the central, eastern, northern and western regions, totalling 713 enterprises. From this pool, a sample of 256 firms affiliated with the Uganda Manufacturers’ Association was determined using Yamane’s (1967) method. Accordingly, the simplified formula for proportions according to Yamane is given as follows:

n=N1+N * (e)2

where n is the required sample size, N is the population size and E is the acceptable sampling error (tolerable error); a 95% confidence level and p = 0.05 are assumed. Thus, the sample size of this study is computed as follows:

n=7131+713 * 0.052=256

Firm classification as medium or large relied on parameters such as annual turnover and workforce size, following the criteria set by the Uganda Investment Authority (UIA, 2020). Medium-sized firms had an annual turnover between UGX 360m (approximately US$97,000) and UGX 1.2bn (approximately US$323,000), employing 51–100 individuals. Large firms exceeded UGX 1.2bn in turnover and employed over 100 employees. Employing a stratified sampling approach, the researcher allocated a total sample of 256 firms across the four regions, as presented in Table 1. The survey focused on manufacturing firms as the unit of analysis, with production managers, chief finance officers, human resource managers, operations managers and environmental managers being surveyed due to their direct involvement in sustainability decisions. Their diverse roles ensure a comprehensive view of SPs, including production, finance, employee engagement, operations and environmental compliance. This study employed purposive sampling to select individuals based on their relevance and involvement in sustainability, aiming to capture varied insights within manufacturing firms.

The results presented in Table 1 indicate that a significant majority of the manufacturing firms surveyed were located in the central region, representing 90.4% of the total. This concentration of firms in the central region can potentially be attributed to factors such as proximity to markets and the availability of resources. The favourable geographic location of the central region likely contributes to the accessibility of markets and resources, making it an attractive choice for establishing manufacturing operations.

3.2 Demographic characteristics

The findings in Table 2 highlight key aspects of the surveyed manufacturing firms’ composition and leadership. Notably, 57.2% of respondents were within the 36–45 age group, reflecting experienced individuals in leadership roles. This age bracket’s prevalence stems from industry expertise and career advancement, which aligns with a pivotal professional growth phase. Additionally, 54.6% of the participants held bachelor’s degrees, indicating an educated workforce adept at comprehending and engaging in SPs. A gender disparity was obvious, with men at 60.6% and women at 39.4%, underscoring the need for gender diversity to encompass inclusive sustainability perspectives (Alinda et al., 2023). In terms of experience, a significant 61.9% reported 5–10 years of manufacturing familiarity, suggesting their grasp of sustainability’s importance. Among managers, human resource managers (30.3%) and operations managers (21.9%) significantly contributed to sustainability efforts, displaying their pivotal role in promoting SPs across functions. Notably, environmental managers (8.8%) formed a smaller portion, implying that sustainability was interwoven into broader managerial duties.

Table 3 displays the characteristics of the firms surveyed.

Table 3 highlights the surveyed manufacturing firms’ key characteristics. A significant proportion (88.5%) were classified as medium-sized (51–100 employees), in alignment with local standards. Notably, 90.4% were located in the central region, likely due to proximity to resources and markets. The distribution of firms across 5–10 years (36.5%) and 10–16 years (31.3%) of existence suggests established structures for SPs. The food and beverage sector exhibited the largest representation, signifying its recognition of responsibility due to its direct impact on human sustenance. The textile, clothing and footwear industry’s prominence reflects awareness of sustainability’s importance in meeting fundamental needs, while the limited presence of the printing sector indicates the potential for improvement.

3.3 Questionnaire and variables measurement

Data were collected using a self-administered questionnaire featuring closed-ended items, using a six-point Likert scale inspired by Spector (1992), to ensure clarity in responses. This approach aimed to foster distinct expressions of agreement or disagreement with the research questions. The chosen six-point scale aimed to enhance data quality by minimising ambiguity. The questionnaire method was deliberately chosen for its efficiency in reaching a diverse respondent pool and deriving average ratings. The questionnaire’s design drew from the relevant literature on EI and SPs. EI was operationalised using insights from Carrillo-Hermosilla et al. (2010) and Cheng and Shiu (2012), while SP encompassed environmental, social and economic dimensions based on Chow and Chen (2012), Høgevold et al. (2015) and Yacob et al. (2019). Detailed questionnaire questions are available in the Appendix.

3.4 Control variables

Existing research has pointed out the potential impact of firm-specific factors on a company’s pursuit of sustainability objectives (Balasubramanian and Shukla, 2020). Additionally, Bartov et al. (2000) underscore the significance of considering confounding variables to prevent unwarranted rejections of research hypotheses that might otherwise have been corroborated. In alignment with this perspective, the current study takes into consideration the inherent characteristics of a firm’s geographical location and its ownership as controlling variables. The study model is depicted in Figure 1 for reference.

3.5 Validity and reliability

In the realm of research, validity pertains to the degree to which a measurement accurately reflects the intended concept it seeks to assess. To ensure the precision of the survey questions employed in this study, experts from academia, policymaking and research in the field of SPs were consulted. These experts evaluated the appropriateness of the survey questions using a rating scale ranging from 1 (strongly disagree) to 6 (strongly agree). The input and ratings provided by these experts were used to compute the content validity index (CVI) for each variable being investigated. The resulting CVI scores surpassed the established threshold of 0.7, signifying the robust content validity of the survey instrument (Field, 2009). The expert feedback and CVI scores collectively affirmed the questionnaire’s validity across all examined variables. Similarly, the instrument’s reliability, which gauges its consistency in measuring a specific concept, was assessed using Cronbach’s alpha coefficient. The calculated Cronbach’s alpha values for the study’s variables exceeded the recommended threshold of 0.7, as proposed by Nunnally (1978), confirming a high level of internal consistency (as depicted in Table 4). This underscores that the survey questions consistently and reliably gauged the intended concepts in a steadfast manner.

3.6 Data analysis

The process of data collection, organisation, modification, coding, capturing and analysis was carried out using SmartPLS structural equation modelling (SEM) Version 3. Prior to analysis, the data underwent cleaning procedures using SPSS Version 23, following the recommended protocols outlined by Field (2009). Instances of missing data, constituting less than 5% of the dataset, were identified through thorough case, variable, and value examinations. Linear interpolation was then used to fill these gaps, thereby mitigating potential reductions in statistical power and potential inaccuracies in the results. To rectify any discrepancies, incorrect item entries were cross-tabulated and assigned numerical codes during the data entry phase.

The refined data set was subsequently subjected to analysis using SmartPLS Version 3 (Hair et al., 2017). In light of the study’s sample size of 208 manufacturing firms, SmartPLS was selected due to its appropriateness for larger samples. Partial least squares (PLS) path modelling, according to the guidance of Fornell and Bookstein (1982), does not rely on assumptions about scale measurement or population characteristics, distinguishing it from some alternative methods. The analysis encompassed both the measurement (outer) and structural (inner) models, in alignment with Henseler et al.’s (2014) recommendation, which enables a comprehensive interpretation of PLS-SEM outcomes. The structural model explored the relationships between the explanatory and criterion latent variables, while the measurement model examined the connections between indicators and their corresponding latent variables while upholding considerations of reliability and validity, as outlined by Hair et al. (2017).

The selection of SmartPLS for a sample size of 208 is justified by its capacity to accommodate larger samples while providing reliable results. While SmartPLS is often chosen for smaller samples, it also remains effective and robust for larger samples, offering advantages such as model flexibility, robustness in handling complex relationships and the ability to analyse both reflective and formative constructs effectively (Hair et al., 2017, 2013). Furthermore, its suitability for exploratory research and its ability to accommodate non-normal data distribution make it an appropriate choice for a sample size of 208 (Henseler et al., 2014; Hair et al., 2017).

4. Results

4.1 Measurement models

In the realm of construct validity, two distinct forms, namely, convergent and discriminant validity, were meticulously investigated, as highlighted by Neuman (2007). To assess convergent validity, we used the metric of average variance expected (AVE). The outcomes, as presented in Table 4, distinctly demonstrate that all the calculated AVE values surpassed the accepted threshold of 0.5, as per established norms. This finding concurs with the existence of convergent validity, as discussed by Henseler et al. (2014).

Discriminant validity refers to how well a measurement accurately captures a specific concept it is meant to assess, without being influenced by other concepts. Normally, both convergent validity (how well indicators of a concept converge) and discriminant validity are simultaneously evaluated for related concepts. To establish discriminant validity, an indicator’s outer loadings on its intended concept should be higher than its correlations with other concepts (Fornell and Larcker, 1981). In this study, Tables 5 and 6 confirm that discriminant validity requirements were met. To ensure the measurement tool’s reliability, we used Cronbach’s alpha coefficient and composite reliability, assessed via SmartPLS. As shown in Table 4, the instrument demonstrated good internal consistency, with alpha coefficients and composite reliability values for each variable surpassing the recommended threshold of 0.7 (Fornell and Larcker, 1981; Nunnally, 1978). In this study, composite reliability was used, given the different outer loadings of the indicator variables (Hair et al., 2017).

Before conducting the factor analysis, a preliminary assessment was carried out to ensure the data’s suitability and reliability for exploratory factor analysis. The Kaiser–Meyer–Olkin (KMO) measure of sample adequacy was used to evaluate data appropriateness, while the Bartlett test was used to assess correlations among variable components. KMO values greater than 0.7 and statistically significant Bartlett’s test results (p < 0.05) indicate that the sample is suitable and appropriate for analysis, as outlined by Field (2009) and Kaiser (1974). The results for the variables under investigation are presented in Table 6. Factor analysis was performed on the data using principal axis factoring with orthogonal varimax rotation. Using an eigenvalue cutoff of 1.0, two factors (eco product innovation and eco process innovation) emerged for EI, explaining a cumulative eigenvalue of 63.880, as depicted in Table 6. The factors retained for EI are outlined in Table 6, with eco product innovation and eco process innovation exhibiting eigenvalues of 6.854 and 4.005, respectively. The total variance explained was 63.880%, aligning with the recommendations by Field (2009) and Hair et al. (2017). This confirms the presence of discriminant validity among the various components of EI.

Table 7 presents the findings from the exploratory factor analysis, which was conducted to evaluate the factors that were retained in the study for SPs.

Table 7 presents the extracted factors corresponding to SPs. These factors demonstrated eigenvalues of 4.532, 3.603 and 1.081, collectively accounting for 65.827% of the total explained variance. This adherence to the criteria established by Field (2009) and Hair et al. (2017) substantiates the existence of discriminant validity within the realm of SPs.

Figure 2 illustrates that each dimension of EI holds significant estimations, underscoring the significance of eco product and eco process innovations in comprehending EI. In line with Hair et al.’s (2017) recommendation of a minimum value of 0.400, all item loadings exceeded this threshold. Consequently, the observed variables serve as excellent indicators of their respective latent variables. Notably, eco product innovation (β = 0.703, p < 0.05) demonstrated the highest efficacy in elucidating EI, followed by process innovation, as indicated by the measurement model. The combined impact of these two factors comprehensively accounts for 99% of the observed variance in EI.

As depicted in Figure 3, the dimensions associated with SPs exhibited noteworthy estimates, signifying the pivotal role of social, economic and environmental SPs in elucidating the construct. Every item loading in the outer model surpassed the threshold of 0.400 recommended by Hair et al. (2017), affirming the efficacy of the observed variables as reliable indicators of their respective latent variables. Notably, environmental SPs (β = 0.415, p < 0.05) displayed the highest loading within the SPs construct, suggesting that environmental SPs contribute significantly to explaining the variability in SPs. When collectively considered, these dimensions jointly elucidated 85.9% of the observed variance in SPs, as illustrated in Figure 3.

4.2 Structural model

4.2.1 Test of hypothesis.

To unveil the correlations among the constructs examined in this study, the bootstrapping procedure was employed, accompanied by pertinent t-statistics and path coefficients (Wong, 2013). The primary objective of using bootstrapping was to assess the significance of loading and path coefficients. The outcomes of this significance testing, relating to two specific hypotheses, are depicted in Figure 4, as well as detailed in Tables 8 and 9.

Based on the insights presented in Figure 4 and Table 8, EI elucidated approximately 60.9% of the variability observed in SPs. Among the individual variables, as depicted in Table 9, eco product innovation (β = 0.573, p < 0.05) emerged as the most influential predictor, exhibiting significant predictive power. Process innovation also contributed to this relationship, albeit to a somewhat lesser extent.

Table 11 displays the structural model estimations for EI and the implementation of environmental SPs.

Based on the information conveyed in Figure 5 and Table 10, it is evident that EI accounted for around 43.7% of the variance in environmental SPs. Among the specific variables, eco product innovation (β = 0.457, t-statistic 5.553, p < 0.05) stood out as the most substantial predictor, displaying a noteworthy level of predictive strength (Table 11). Although process innovation also contributed to this association, its impact was slightly less pronounced.

Table 13 presents the structural model estimations pertaining to EI and the integration of social SPs.

Based on the insights presented in Figure 6 and Table 12, it is apparent that EI explained approximately 35.6% of the variability observed in social SPs. Among the individual variables, product Innovation (β = 0.499, p < 0.05) emerged as the most influential predictor, demonstrating a notable degree of predictive power (Table 13). Process innovation also played a role in this relationship, although its influence was slightly less prominent.

Table 14 presents the structural model estimations pertaining to EI and the integration of economic SPs.

The insights derived from Figure 7 and Table 14 reveal that EI accounted for approximately 43% of the variability seen in economic SPs. Among the variables, eco product innovation (β = 0.434, t-statistic 4.247, p < 0.05) emerged as the most influential predictor, demonstrating considerable predictive power (Table 15). Process innovation also contributed to this relationship, although to a slightly lesser extent. Furthermore, the findings in Table 9 support H2, demonstrating a significant relationship between eco product innovation and SPs within Uganda’s manufacturing firms (β = 0.573, t = 7.710, p < 0.05). This emphasises the pivotal role of sustainability-focused eco product innovation in driving SPs within manufacturing firms. These innovative initiatives encompass sustainable product lines and design enhancements aimed at improving resource efficiency.

Based on the results presented in Table 9, the investigation of H3 regarding the relationship between process innovation and the implementation of SPs among Ugandan manufacturing firms yielded a significant finding. The results indicate a statistically significant connection between process innovation and the integration of SPs (β = 0.246, t = 3.589, p < 0.05). This underscores the pivotal role of process innovation in driving the implementation of SPs within these firms. The analysis reveals that alterations and improvements in manufacturing processes, referred to as process innovation, profoundly impact the adoption of SPs. The significant coefficient (β = 0.246) implies that with each increment in process innovation, there is a corresponding rise in the incorporation of SPs. The positive coefficient underscores a direct and favourable link, suggesting that engagement in process innovation activities enhances firms’ ability to integrate sustainability measures into their operations. This underscores the strategic significance of prioritising process innovation as a means of promoting SPs within Uganda’s manufacturing sector.

5. Discussion

The findings presented in Table 8 provide compelling evidence demonstrating that the combined influence of EI dimensions (eco product and eco process innovations) accounts for a substantial 60.1% of the variance in SPs, thus supporting H1. This research outcome also establishes a significant positive relationship between eco product innovation and SPs within Uganda’s manufacturing firms, thus confirming H2. Specific initiatives related to eco product innovation, such as eco-friendly product development, advanced production technologies and sustainability-focused product lines, play a pivotal role in driving the implementation of SPs. This attests to the strategic importance of aligning innovation efforts with environmental responsibility and consumer demand, ultimately fostering a corporate culture that embraces responsible business practices and contributes positively to the environment and society.

These findings align with research by Chen et al. (2019) and Lee and Lee (2022), who emphasise the role of eco product innovation in driving SPs within manufacturing firms. Similarly, Richardson et al. (2021) highlight that firms prioritising eco product innovation aligned with sustainability objectives enhance market competitiveness and environmental impact. Moreover, Rajagopal and Bernardes (2019) argue that firms often prioritise eco product innovation over SPs due to the immediate market benefits and competitive advantage it provides. They assert that this focus on eco product innovation may divert resources away from implementing SPs, potentially hindering overall sustainability efforts. On the other hand, Shafqat et al. (2020) contend that eco product innovation can actually drive sustainability by enabling the introduction of eco-friendly products or enhancing the environmental performance of existing products. They assert that sustainability-driven eco product innovation can lead to improvements in resource efficiency, reduced waste and lower environmental impact. The alignment with dynamic capability theory, as proposed by Barney (1991) and advanced by Teece et al. (1997), becomes evident. The positive relationship between eco product innovation and SPs in Ugandan manufacturing firms aligns with dynamic capability theory, emphasising firms’ adaptability and innovation for competitive advantage. Prioritising eco product innovation aligned with sustainability enhances dynamic capabilities, enabling firms to respond to evolving demands and regulations. These endeavours present substantial implications for advancing sustainability policies and actions, yielding positive outcomes. For instance, the incorporation of eco-friendly packaging materials alongside incentives for effective waste management by employees underscores the organisation’s unwavering commitment to environmental and social responsibility. Moreover, the organisation’s focus on equity, safety and community well-being underscores its comprehensive sustainability approach. These combined endeavours underscore the catalytic function of eco product innovation in embracing sustainability and fostering responsible community engagement. They also highlight the necessity of cultivating an innovation-driven organisational culture and investing in robust research and development activities, which are crucial for manufacturing firms. These measures not only enhance competitiveness but also amplify the firm’s ability to strengthen and elevate its SPs, thereby fostering lasting environmental and social stewardship.

The results from H3 confirm the significant relationship between process innovation and SPs in Ugandan manufacturing firms, thus supporting H3. Process innovation efforts, such as upgrading manufacturing technology for environmental protection, energy-efficient technologies and sustainability-focused equipment replacement, directly translate into improved SPs. Equity, community safety, safety impact consideration and economic practices are also linked to process innovation frequency, indicating its role in driving SPs. These findings corroborate those of Johnson et al. (2018) and Smith et al. (2019), who support the positive influence of process innovation on SPs. Process innovation’s impact on operational enhancements and sustainability outcomes aligns with Teece et al.’s (1997) dynamic capability theory, asserting that continuous adaptation and innovation enhance firm performance and SPs. Process innovation significantly correlates with implementing SPs in Ugandan manufacturing firms, emphasising its potential to drive operational improvements and efficiency, thereby contributing to sustainability outcomes.

6. Conclusion, limitations and future research

This study’s primary objective was to assess the influence of EI on the SP of M&L manufacturing firms in Uganda. Additionally, we aimed to explore the distinct significance of various dimensions of EI, as previously identified in the existing literature, in shaping SPs. To achieve these objectives, we conducted a questionnaire survey involving 208 manufacturing firms in Uganda, with key personnel, such as production managers, operations managers, environmental managers, human resource managers and chief finance managers participating. Our findings affirm a significant relationship between EI and SPs. Notably, among the dimensions of EI, eco product innovation emerged as the most potent predictor of SP, while process innovation exhibited the least predictive potential within M&L manufacturing firms in Uganda. This research contributes to the theoretical framework of dynamic capability theory and to the broader literature on SP. It underscores the strategic importance of both eco product and process innovations as resources and toolkits for advancing sustainability within Ugandan M&L manufacturing firms. These EI components have the potential to serve as sources of inspiration for further research in the field.

This research significantly enriches the theoretical understanding of the intricate relationship between EI and SP within M&L manufacturing firms in Uganda. It advances our comprehension by shedding light on the dynamic nature of this connection, emphasising that EI serves as a strategic resource and toolkit for fostering SPs. In particular, our study underscores the critical roles played by both eco product and eco process innovations in shaping sustainable outcomes. This theoretical contribution offers a nuanced perspective on how organisations can effectively drive SP through innovation-driven strategies, providing a solid foundation for future scholarly investigations into the intricate mechanisms governing this interplay.

From a managerial perspective, our findings offer actionable insights that can guide manufacturing managers in Uganda towards more informed and eco-conscious decision-making. Notably, our research identifies eco product innovation as a powerful driver of SPs, underscoring the practical importance of investing in innovative product development. Managers can use this knowledge to prioritise sustainability initiatives, thus steering their organisations towards environmentally responsible practices. Furthermore, the emergence of process innovation as a less potent factor highlights the significance of continuously enhancing internal processes to promote sustainable operations. This underscores the necessity for a comprehensive approach to environmental management that encompasses both eco product and process innovations.

The implications of this study also extend to policymakers, aligning closely with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 12 (Sustainable Production and Consumption) and SDG 9 (Industry, Innovation and Infrastructure). Policymakers can leverage our findings to design more effective incentives and regulations aimed at promoting EI within the manufacturing sector. By doing so, they can actively facilitate the detachment of economic growth from environmental harm, fostering cleaner production and sustainable industrialisation. This aligns with global sustainability agendas and reinforces Uganda’s commitment to addressing environmental challenges through innovation-driven policies.

Despite the valuable insights from this study, it is imperative to acknowledge its inherent limitations. The findings are primarily constrained by contextual specificity, focusing on a specific subset of manufacturing firms operating within designated districts and under the umbrella of the Uganda Manufacturers’ Association. As a result, generalising these findings to the broader manufacturing landscape, both within Uganda and internationally, must be approached with caution, given the diversity of industry dynamics, resource availability and regulatory environments across various contexts. To address these limitations and further advance our understanding of SP, future research should adopt more comprehensive and nuanced methodologies encompassing qualitative and mixed-methods approaches to delve deeper into the multifaceted dimensions that underlie SP. These approaches can illuminate intricate interplays between EI and sustainability outcomes, providing a richer and more holistic understanding of the subject matter.

Moreover, research should explore the context-specific applications of EI in diverse manufacturing settings to identify tailored strategies and best practices for promoting sustainability within different industrial contexts, thereby enhancing the practical applicability of the findings. Additionally, future studies could delve into the economic implications of EI on SPs by examining its effects on financial performance, competitiveness and market positioning. Longitudinal research, tracking the evolution of SPs over time, would provide a temporal dimension, elucidating how EI contributes to sustainable outcomes and how organisations adapt to dynamic environmental and market conditions, guiding strategies for sustained sustainability and resilience building. Lastly, exploring the potential interplay between EI, government policies and industry standards would shed light on the regulatory dynamics influencing sustainability efforts within the manufacturing sector. In summary, while this study’s limitations are acknowledged, they underscore the necessity for continued scholarly enquiry to comprehensively understand the complexities inherent in sustainability management within the manufacturing sector. Such insights have the potential to inform and guide organisations and policymakers towards more effective and informed strategies and initiatives.

Figures

Figure 1.

Study model

Figure 2.

Measurement model for environmental innovations

Figure 3.

Measurement model for SPs

Figure 4.

Structural model estimates for EI and SPs

Figure 5.

Structural model for environmental innovations and environmental SPs

Figure 6.

Structural model for environmental innovations and social sustainability practices

Figure 7.

Structural model for environmental Innovations and economic sustainability practices

Table 1.

Geographical distribution of the firm

Region	Medium	Large	Acquired	Target	Response rate (%)
Central	167	21	188	229	82.1
Western	4	1	5	7	71.4
Eastern	12	1	13	18	72.2
Northern	1	1	2	2	100.0
Acquired	184	24	208	256	81.3
Target sample	220	36
Response rates (%)	83.6	66.7

Source: Primary data

Table 2.

Respondents characteristics, total n = 657 respondents

Gender	Count	Valid (%)	Cumulative (%)
Male	398	60.6	60.6
Female	259	39.4	100.0
Age group
Less than 35 years	207	31.5	31.5
36–45 years	376	57.2	88.7
46–55 years	67	10.2	98.9
above 55 years	7	1.1	100.0
Highest level of education
Diploma	76	11.6	11.6
Bachelor's degree	359	54.6	66.2
Master's degree	207	31.5	97.7
PhD	9	1.4	99.1
Others	6	0.9	100.0
Tenure
Less than 5 years	135	20.5	20.5
5–10 years	407	61.9	82.5
11–15 years	92	14.0	96.5
16 years and above	23	3.5	100.0
Position
Environmental Manager	58	8.8	8.8
Operations Manager	144	21.9	30.7
Human Resource Manager	199	30.3	61.0
Production Manager	123	18.7	79.8
Chief Finance Officer	133	20.2	100.0

Source: Primary data

Table 3.

Firm attributes, total n = 208 manufacturing firms

No. of employees	Frequency	Valid (%)	Cumulative (%)
Less than 101	184	88.5	88.5
101 and above	24	11.5	100.0
Geographical region of firm
Central	188	90.4	90.4
Western	5	2.4	92.8
Eastern	13	6.3	99.0
Northern	2	1.0	100.0
Number of years this firm has been in operation
Less than 5 years	7	3.4	3.4
5–10 years	76	36.5	39.9
11–15 years	65	31.3	71.2
16 years and above	60	28.8	100.0
Nature of the manufacturing business
Food and beverages	66	31.7	31.7
Chemicals, paint, soap, foam products	37	17.8	49.5
Textiles, clothing and footwear	32	15.4	64.9
Metal and furniture products	33	15.9	80.8
Sawmilling, paper	12	5.8	86.5
Packaging and label	12	5.8	92.3
Bricks and cement	11	5.3	97.6
Printing	5	2.4	100.0

Source: Primary data

Table 4.

Reliability and validity of the research instrument

Constructs	Cronbach's alpha	Composite reliability	Average variance extracted (AVE)	Variance inflation factor (VIF)
Process innovation	0.722	0.762	0.551	1.704
Product innovation	0.887	0.892	0.529	1.565
Environmental innovations	0.805	0.827	0.540	1.634
Economic SPs	0.783	0.799	0.610	1.924
Environmental SPs	0.776	0.786	0.539	1.396
Social SPs	0.749	0.770	0.575	1.688
Sustainability practices	0.769	0.785	0.575	1.669

Source: Primary data

Table 5.

Discriminant validity using the Heterotrait-Monotrait (HTMT) ratio

Environmental innovations	PN	PI
Process innovation (PN)
Product innovation (PI)	0.649
Sustainability practices	EC	EV	SS
Economic SPs (EC)
Environmental SPs (EV)	0.614
Social SPs (SS)	0.732	0.521

Source: Primary data

Table 6.

Exploratory factor analysis for environmental innovations

Item	Product innovation	Process innovation
EVPI5	0.720
EVPI6	0.695
EVPI7	0.706
EVPI8	0.782
EVPI9	0.796
EVPI10	0.583
EVPI11	0.733
EVPI12	0.744
EVPI14	0.715
EVPN1		0.718
EVPN3		0.643
EVPN4		0.625
EVPN5		0.833
EVPN7		0.921
Eigen Values	6.854	4.005
Variance %	38.084	25.796
Cumulative %	38.084	63.880
KMO measure of sampling adequacy		0.927
Bartlett's test of sphericity
Approx. chi-square		1,677.287
df		136
Sig.		0.000

Source: Primary data

Table 7.

Exploratory factor analysis for sustainability practices

Item	Social SPs	Environmental SPs	Economic SPs
SPSS4	0.506
SPSS5	0.814
SPSS6	0.746
SPSS7	0.642
SPSS10	0.861
SPSS13	0.769
SPSS15	0.594
SPEP2		0.741
SPEP3		0.793
SPEP5		0.762
SPEP6		0.693
SPEP9		0.777
SPEP11		0.761
SPCS4			0.638
SPCS5			0.638
SPCS6			0.546
SPCS7			0.691
Eigen values	4.532	3.603	1.081
Variance %	32.369	25.735	7.723
Cumulative %	32.369	58.104	65.827
KMO measure of sampling adequacy.			0.809
Bartlett's test of sphericity
Approx. chi-square			1,085.797
df			91
Sig.			0.000

Source: Primary data

Table 8.

Prediction estimates for SPs

Prediction for sustainability practices	R-square	R-square adjusted
Sustainability practices	0.609	0.601

Source: Primary data

Table 9.

Structural model estimates for prediction of SPs

Predictors	β	Std. error	t statistics	p
Firm geographical region → SPs	0.014	0.048	0.280	0.780
Firm ownership → SPs	0.005	0.041	0.122	0.903
Product innovations → SPs	0.573	0.074	7.710	0.000
Process innovations → SPs	0.246	0.069	3.589	0.000

Source: Primary data

Table 10.

Prediction estimates for environmental SPs

Prediction for environmental sustainability practices	R-square	R-square adjusted
Environmental sustainability practices	0.437	0.426

Source: Primary data

Table 11.

Structural model estimates for EIs and environmental SPs

Predictors	β	Std. error	t statistics	p
Firm geographical region → Environmental SPs	0.057	0.086	0.666	0.505
Firm ownership → Environmental SPs	0.070	0.049	1.426	0.154
Product innovations → Environmental SPs	0.457	0.082	5.553	0.000
Process innovations → Environmental SPs	0.247	0.083	2.965	0.003

Source: Primary data

Table 12.

Prediction estimates for social sustainability practices

Prediction for social sustainability practices	R-square	R-square adjusted
Social sustainability practices	0.356	0.344

Source: Primary data

Table 13.

Structural model estimates for environmental innovations and social sustainability practices

Predictors	β	Std. error	t statistics	p
Firm geographical region → Social SPs	0.104	0.055	1.880	0.060
Firm ownership → Social SPs	0.048	0.053	0.911	0.362
Product innovations → Social SPs	0.499	0.094	5.311	0.000
Process innovations → Social SPs	0.122	0.093	1.316	0.188

Source: Primary data

Table 14.

Prediction values for economic sustainability practices

Prediction for economic sustainability practices	R-square	R-square adjusted
Economic sustainability practices	0.430	0.419

Source: Primary data

Table 15.

Structural model estimates for environmental innovations and economic sustainability practices

Predictors	β	Std. error	t statistics	p
Firm geographical region → Economic SPs	0.005	0.045	0.116	0.908
Firm ownership → Economic SPs	0.105	0.054	1.955	0.051
Product innovations → Economic SPs	0.434	0.102	4.247	0.000
Process innovations → Economic SPs	0.225	0.099	2.264	0.024

Source: Primary data

Table A1.

Environmental innovations (For this section, indicate your extent of agreement to the statements below)

Items		SD	D	SLD	SLA	a	SA
A	Product innovation (Cheng and Shiu, 2012; Shashi et al., 2019; Xie et al., 2019) creating a new product – or improving an existing one – to meet customers' needs in a novel way
EVPI1	Our firm frequently emphasises on developing new eco-products using new technologies to improve their package	1	2	3	4	5	6
EVPI2	In our organisation, we ensure ecological packaging for new products	1	2	3	4	5	6
EVPI5	Our firm frequently emphasises on developing new eco-products using new technologies to reduce production complications	1	2	3	4	5	6
EVPI6	Our firm frequently emphasises on developing new eco-products using new and improved technologies to enable easy component recycling	1	2	3	4	5	6
EVPI7	Our firm frequently emphasises on developing new eco‐products using state of the art technologies to enable easy decomposition of materials	1	2	3	4	5	6
EVPI8	Our firm frequently emphasises on developing new eco-products using new technologies to use natural materials	1	2	3	4	5	6
EVPI9	Our firm frequently emphasises on developing new eco-products through new technologies to reduce energy consumption as much as possible	1	2	3	4	5	6
EVPI10	Our company introduced new lines of products with a focus on sustainability	1	2	3	4	5	6
EVPI11	Our company invested in Research and Development to produce quality products to be sustainable	1	2	3	4	5	6
EVPI12	In our company, we have modified the product’s design to make its use in terms of water consumption more efficient	1	2	3	4	5	6
EVPI14	We have modified the product’s design to reduce the quantity of materials required in its production	1	2	3	4	5	6
B	Process innovations (Cheng and Shiu, 2012; Shashi et al., 2019; Xie et al., 2019) (Implementation of a new or significantly improved production or delivery method)
EVPN1	Our firm frequently updates its manufacturing technology to protect against contaminations	1	2	3	4	5	6
EVPN2	Our firm continuously invent new technologies to adhere to sustainability practices	1	2	3	4	5	6
EVPN3	Our firm frequently uses new technologies in manufacturing processes to conserve energy	1	2	3	4	5	6
EVPN4	Our firm frequently replaces its manufacturing equipment in manufacturing processes to improve sustainability	1	2	3	4	5	6
EVPN5	Our company promotes the use of ecological materials in our production process	1	2	3	4	5	6
EVPN7	We discourage wasteful production processes to enhance raw material usage	1	2	3	4	5	6

Source: Authors’ conceptualisation

Table A2.

Sustainability practices (For this section, indicate your extent of agreement to the statements below; means of generating long-lasting value and sustained firm value by considering the firm’s operations from the perspective of environment, social and economic) (Key as follows)

Items		SD	D	SLD	SLA	A	SA
A	Environmental sustainability practices (Yacob et al., 2019) – the practice of interacting with the planet responsibly for future benefit
SPEP1	We apply effective strategies in improving water conservation	1	2	3	4	5	6
SPEP2	Our packaging materials decompose easily	1	2	3	4	5	6
SPEP3	Our packaging materials have an environmentally friendly label	1	2	3	4	5	6
SPEP5	Our customers are given eco-friendly packaging materials free of charge	1	2	3	4	5	6
SPEP6	We use packaging materials that are safe for people’s health	1	2	3	4	5	6
SPEP9	We reward employees that promote proper waste management	1	2	3	4	5	6
SPEP11	We ensure that business activities minimise the amount of natural energy used	1	2	3	4	5	6
SPEP15	Our company minimises its emissions into the air (greenhouse gas and other substances)	1	2	3	4	5	6
B	Social sustainability practices (Høgevold et al., 2015; Chow and Chen, 2012) – Identifying and managing business impacts on community
SPSS1	In our firm, our production activities consider the community well-being	1	2	3	4	5	6
SPSS3	Our company respects the right of associations of its employees	1	2	3	4	5	6
SPSS4	Our company considers the health impacts of its products to the community	1	2	3	4	5	6
SPSS5	Our company considers the safety impacts of its products to the community	1	2	3	4	5	6
SPSS6	Our firm focus on equity of the community	1	2	3	4	5	6
SPSS7	Our firm focus on safety of the community	1	2	3	4	5	6
SPSS8	Our firm participate in improving the general education level of the community	1	2	3	4	5	6
SPSS9	In our firm, we disclose the safety topics covered in formal agreements with trade unions	1	2	3	4	5	6
SPSS10	In our firm, we disclose the safety topics covered in formal agreements with trade unions	1	2	3	4	5	6
SPSS13	In our firm, screening of suppliers considers the supplier’s compliance with the human rights standards	1	2	3	4	5	6
SPSS15	Our company gives priority to workforce within the surrounding community	1	2	3	4	5	6
C	Economic sustainability practices (Høgevold et al., 2015; Chow and Chen, 2012) – The practice of conserving natural and financial resources to create long-term financial stability
SPCS2	Our company donates to the community	1	2	3	4	5	6
SPCS3	Our company supports local suppliers by buying from them their materials	1	2	3	4	5	6
SPCS4	Our firm has continued to generate profits amidst the costs incurred in conserving the natural environment	1	2	3	4	5	6
SPCS5	Our firm meets tax obligations to avoid closure	1	2	3	4	5	6
SPCS6	Our economic practices are aimed at saving money for the firm	1	2	3	4	5	6
SPCS7	Our firms’ practices focus on survival in the market place	1	2	3	4	5	6

Source: Authors’ conceptualisation