Analyzing the determinants factors for the implementation of eco-innovation in the developing economies, a case of Tanzania agri-food sector

Abstract

The aim of this article is to address the global imperative of environmental sustainability by enhancing the implementation of eco-innovation practices within industries operating in developing economies. The study focuses on investigating the pivotal factors that influence the implementation of eco-innovation within the food and beverage sector. The research employs multiple regression analysis within a structural equation model (SEM) to examine the relationships between various factors and the implementation of eco-innovation involving 102 industries. The results of the analysis reveal that the successful implementation of eco-innovation is significantly influenced by specific internal and external factors. These include the availability of adequate financial resources, active engagement of internal and external stakeholders in environmental innovation, investments in research and development activities related to environmental issues, and responsiveness to customer concerns regarding industrial eco-innovations. Additionally, the combination of effective environmental regulations and targeted policy instruments, along with access to environmentally friendly technologies, also emerged as significant determinants of eco-innovation success. Whereas, internal knowledge, motivation, and competition, found to be insignificant in their influence on eco-innovation implementation. This study recommends that policymakers and eco-innovation stakeholders prioritize strategies that emphasize the identified significant factors to enhance eco-innovation implementation towards achieving both environmental sustainability and economic growth.

Keywords:

• Eco-innovation determinants: eco-innovation implementation
• food and beverage industries

Reviewing Editor:

• Huifen (Helen) Cai, Middlesex University Business School, United Kingdom

Subjects:

• Environmental Economics; Business, Management and Accounting
• Industry & Industrial Studies

1. Introduction

Eco-innovation has garnered significant attention within the contemporary global sustainable development agenda due to its potential to ameliorate environmental impact and enhance the overall quality of life within communities. This multifaceted concept encompasses two pivotal dimensions: firstly, the influence of innovation on the environment, and secondly, the creation of products and services that yield positive environmental impact (Hasan & Rahman, 2023; Barbieri & Santos, 2020).

Expanding upon this foundational understanding, Ślęzak (2020) provided a more comprehensive definition of eco-innovation, which encompasses a wide range of activities including the creation, integration, or utilization of innovative products, novel production processes, services, unique management approaches, and distinctive business methods tailored to the specific needs and objectives of organizations.

In accordance with this comprehensive definition, eco-innovation activities throughout their lifecycle are expressly focused on reducing environmental risks, mitigating pollution, and ameliorating adverse impacts stemming from resource utilization. Recognizing the advantages of eco-innovation in bolstering sustainability, current research has increasingly focused on elucidating the factors that drive its implementation.

However, despite the growing recognition of the advantages of eco-innovation, its limited application poses a significant challenge to achieving sustainability goals. The failure to implement eco-innovative practices among firms can exacerbate environmental degradation, leading to heightened pollution levels, resource depletion, and ecological imbalance (Ndubisi et al., 2021).

Therefore, understanding determinants factors that drive eco-innovation implementation is crucial for policymakers, businesses, and researchers as it informs strategies aimed at fostering a greener economy. Scholars from diverse disciplines such as innovation, management, environmental economics, stakeholder theory, and institutional theory have extensively explored the core determinants of eco-innovation. However, the factors influencing eco-innovation and its implications for sustainability remain a paramount and contentious subject due to variations in findings across different firms, countries, and cultural contexts (Figures 1–2).

Figure 1. SEM output for the 1st model specification on the influence of Internal Factors on Eco-innovation.

Figure 2. SEM output for the 2^nd model specification on the influence of External Factors on Eco-innovation.

For instance, Li et al. (2020) argued that continuous investment in research and development is among the key determinants of eco-innovation in renewable energy. Additionally, recent studies by Achmad et al. (2023) underscore the pivotal role of government support, policies and regulatory frameworks, while Chen and Liu (2020) emphasize the significance of customer participation through market dynamics factors and consumer preferences in driving eco-innovation.

A comprehensive analysis of previous research, including the work of Scarpellini et al. (2018), underscores the critical role of technological capabilities, financial resources, and research activities in effectively implementing eco-innovation initiatives. Additionally, scholars like Del Río et al. (2015) emphasize the significance of knowledge and regulations in shaping eco-innovation strategies. Conversely, Ceptureanu et al. (2017) highlight the importance of market orientation for eco-products and technology, which contrasts with the findings of Del Río et al. (2015) regarding market factors.

Despite the extensive research on eco-innovation, there remains a notable gap in understanding its dynamics in developing nations such as Tanzania. This gap is particularly critical given the unique socio-economic and environmental contexts of developing countries. The multitude of viewpoints surrounding eco-innovation underscores its complex nature and emphasizes the need for a nuanced understanding of the factors influencing it across various environments. It’s important to acknowledge that the impact of specific determinants may differ among firms in different national contexts, particularly between developed and developing economies, due to variations in key characteristics among nations (Dokas et al., 2022; Bruin’s research, 2016). Moreover, the discrepancies in findings may be linked to the unique contextual aspects of each nation’s innovation system, as well as the stringency of environmental regulations and the environmental awareness of consumers, as argued by Del Río et al. (2015).

In developing countries, the socio-economic and environmental contexts present a diverse array of challenges and opportunities for sustainable development. Economically, limitations in capital, infrastructure, and reliance on primary industries are common (Azolibe & Okonkwo, 2020). Socially, issues like poverty, inequality, and informal employment hinder access to essential services and sustainability efforts (Leal Filho et al., 2021). Environmental challenges, including deforestation and pollution, are exacerbated by rapid industrialization and population growth (Ukaogo et al., 2020). Moreover, the absence of stringent regulatory enforcement in many developing nations, as noted by Kilincarslan et al. (2020) and Gerged et al. (2018), could influence their environmental behaviors, including eco-innovation initiatives.

Therefore, understanding and addressing these complex contexts are essential for promoting eco-innovation and sustainability in developing nations. The scarcity of research on factors impacting eco-innovation in such contexts underscores the necessity of studying this phenomenon in a diverse range of settings. This study seeks to address this gap by investigating the key factors influencing the implementation of eco-innovation, with a particular emphasis on Tanzania’s food and beverage industries. Selecting Tanzania as the focal point of our study holds considerable importance due to its status as an emerging economy within the broader East African region.

The region under consideration, marked by continuous industrial expansion, grapples with substantial hurdles in achieving environmental sustainability. Tanzania, in particular, faces escalating environmental degradation stemming from its industrial operations, exacerbated by a dearth of robust eco-innovation initiatives. The rapid industrialization in Tanzania has led to heightened pollution levels, depletion of natural resources, and ecosystem degradation, posing significant threats to public health and ecological balance. The lack of effective eco-innovation practices exacerbates these challenges, hindering the transition towards cleaner and more sustainable industrial processes. As a result, there is an urgent need to address the environmental impacts of industrialization in Tanzania through the promotion and adoption of eco-friendly innovations that mitigate pollution, conserve resources, and promote sustainable development. The World Bank (2019) has highlighted the pressing need for Tanzania to prioritize environmental conservation efforts and implement policies that foster eco-innovation to address these mounting environmental concerns.

More importantly, the main objective of this study is to investigate the key factors that influence the adoption of eco-innovation in Tanzania’s food and beverage sectors. By elucidating these determinants and their potential impacts, this research seeks to deepen our comprehension of eco-innovation dynamics in developing economies. Moreover, it aims to offer actionable insights that can stimulate the implementation of environmentally responsible eco-innovation practices. Ultimately, the research endeavor aims to foster the development of a sustainable and ecologically-conscious industrial sector in Tanzania.

2. Literature review

2.1. Theoretical background

This study explores the relationship between the implementation of eco-innovation and various determinant factors, encompassing both external and internal capabilities of firms. Drawing on established eco-innovation theories, the study hypothesized certain relationships to gauge how a firm’s internal and external factors influence the adoption of eco-innovation. The foundational approach combines ‘eco’ from ecology and ‘innovation’ from entrepreneurship, with key reference theories including ecological modernization theory and the Schumpeterian entrepreneurial theory of innovation. This foundational approach incorporated two fundamental reference theories: the ecological modernization theory, as proposed by Joseph Huber in the 1980s, and the Schumpeterian entrepreneurial theory of innovation from 1934.

The ecological modernization theory proved valuable in elucidating the implementation of eco-innovation within the food and beverage industries. This theory offers a framework for harmonizing ecological concerns with business activities through innovative approaches. According to this theory, environmental problems are primarily a result of industrial operations that neglect environmental considerations. Furthermore, the theory presents solutions for addressing these environmental problems by advocating for increased industrialization and modernization, emphasizing a path of ‘super industrialization’, as opposed to de-modernization or de-industrialization (Pushpakumara et al., 2019).

The increased focus on industrialization and modernization pertains to environmental innovations, namely, eco-innovation. Huber (1982) underscored the significance of technological innovations in effecting environmental changes, particularly within the domain of industrial production. These changes encompass the development of clean technologies, eco-friendly goods and services, and the complex interplay of multiple factors, including scientific, economic, institutional, legal, political, and cultural elements, which either facilitate or influence such innovations. Consequently, the introduction of environmentally superior technologies can be viewed as a form of fundamental innovation, aligning with Schumpeter’s (1934) conceptualization.

Besides, the genesis of the concept of eco-innovation drew inspiration from the Schumpeterian theory of innovation. As per Schumpeter (1934), innovation was recognized as a crucial entrepreneurial function with substantial implications for economic advancement. Schumpeter identified various forms of innovation within industries, encompassing the introduction of new raw material sources, novel products and services, innovative production processes, the exploration of fresh markets, and the reconfiguration of industry organization and management. This perspective implied that entrepreneurs in various industries were inherently engaged in multiple innovation activities, which, when combined with environmental considerations derived from ecological theory, culminated in the formulation of eco-innovation.

The theoretical assumptions underlying ecological modernization and innovation theories yield two distinct relationships. The first suggests that eco-innovations can lead to environmental sustainability and encourages industries to incorporate them into operational strategies. The second posits that various factors shape the implementation of eco-innovation. Based on these theories, the study hypothesizes and investigate the relationships between several factors on the implementation of eco-innovation within Tanzania’s food and beverage industries. These relationships are explicitly outlined in the hypotheses formulated for this study, where each hypothesis represents a specific factor believed to influence the adoption of eco-innovation within the context of the Tanzanian food and beverage sector.

2.2. Empirical literature review and hypothesis development

2.2.1. The determinants factors for the implementation of eco-innovation

The increasing environmental challenges resulting from industrial activities have prompted efforts to promote sustainable industrial practices, with a particular focus on eco-innovation processes. Scholars have extensively studied the relationship between various factors and the implementation of eco-innovation within industries. However, there is considerable debate among scholars regarding the significance of these factors in influencing environmental practices, including eco-innovations, especially when considering different national contexts.

The influence of various factors on the implementation of eco-innovation has been a subject of debate in the literature. For instance, Silverman et al. (2005) conducted a study examining the factors affecting eco-practices in the wine industries of New Zealand and the United States. Surprisingly, they found that motivation and external stakeholder pressures were not significant determinants of eco-practices in New Zealand, contrasting with their significance in the United States wine industry. Similarly, Ceptureanu et al. (2017) conducted research in Romania, where they discovered that motivational factors had insignificantly influenced the implementation of sustainable eco-innovation. However, their findings contradicted those of Shepherd and Patzelt (2015) in Germany, who found that knowledge and motivation were significant factors influencing the adoption of eco-innovation. These inconsistencies underscore the complex and context-dependent nature of factors influencing eco-innovation implementation, highlighting the need for further research to understand these dynamics comprehensively.

In addition, Edeh and Vinces (2023) delve into the utilization of external knowledge by SMEs in developing economies, drawing from suppliers, customers, competitors, and scientific organizations, to implement eco-innovation. Their findings underscore the significance of external knowledge flows from diverse value chain agents in identifying environmental challenges and crafting innovative solutions to address them. However, it is essential to note that this study was confined to Nigeria, focusing solely on one aspect of knowledge acquisition, which presents a limited scope. In contrast, our research seeks to expand this inquiry by investigating multiple eco-innovation determinants across Tanzania contexts.

Similarly, Popoola and Popoola (2024) conducted a recent study in Nigeria’s manufacturing sector. Their research aimed to identify key drivers influencing eco-innovation adoption among firms. The study revealed that demand-pull factors and regulations significantly influenced firms’ decisions to adopt both product and process eco-innovations. Additionally, organizational innovation emerged as a critical factor affecting firms’ adoption of process eco-innovation. However, the study’s narrow focus on specific variables of determinants factors may limit its comprehensive understanding of the broader eco-innovation landscape.

Furthermore, Hasan and Rahman (2023) conducted a research endeavor exploring the intricate interplay of various factors impacting eco-innovation, with a specific focus on the context of Bangladesh. Their study unveiled a positive and statistically significant relationship among several key factors, including technological capabilities, environmental regulations, the demand for environmentally-friendly products, competitive pressures within the market, and the escalation of energy prices.

While the insights provided by Hasan and Rahman (2023) are valuable for understanding the determinants of eco-innovation within the Bangladeshi landscape, it is crucial to acknowledge the limitations inherent in its generalizability. The geographical scope of their research is deliberately confined to the boundaries of Bangladesh, necessitating caution when extrapolating its findings to other economies. Consequently, the applicability of their findings to diverse economic contexts may be limited, as different economies exhibit distinct socio-economic, regulatory, cultural, and market dynamics that can significantly influence the factors driving eco-innovation.

As Bruin (2016) argues, there is a significant contrast between ecopreneurs in developed regions like the USA and Canada compared to those in the least developed Sub-Saharan countries, highlighting the crucial role of contextual factors in shaping eco-innovation. Additionally, the majority of empirical studies examining the factors influencing eco-innovation implementation have predominantly focused on developed countries and have provided inconclusive results. Consequently, there is a notable lack of information regarding the determinants affecting the adoption of eco-innovation, especially in most developing countries like Tanzania.

Building upon these perspectives, the present study aims to investigate the influence of various factors on the implementation of eco-innovations within the food and beverage industries in Tanzania.

In light of this objective, we have formulated the following hypotheses for empirical testing. These hypotheses are grounded in empirical studies that have investigated various determinants of eco-innovation. Financial robustness has been examined by Hasan and Rahman (2023) and Barbieri and Santos (2020). Environmental awareness has been explored by Li et al. (2020) and Ceptureanu et al. (2017). Stakeholder concerns have been analyzed by Achmad et al. (2023) and Del Río et al. (2015). Motivation levels have been studied by Hasan and Rahman (2023) and Ceptureanu et al. (2017).

Furthermore, the research commitments have been investigated by Scarpellini et al. (2018) and Ceptureanu et al. (2017). Customer demand has been examined by Chen and Liu (2020) and Ceptureanu et al. (2017). Market competition has been explored by Chen and Liu (2020) and Hasan and Rahman (2023). Regulatory frameworks have been analyzed by Li et al. (2020) and Del Río et al. (2015). External stakeholders including regulatory unit and government support has been studied by Achmad et al. (2023). Technological accessibility has been investigated by Hasan and Rahman (2023) and Ceptureanu et al. (2017). Research and development have been investigated by Li et al. (2020). And Li et al. (2020) studies the influence of environmental regulations on eco-innovation.

Nevertheless, despite the abundance of research and investigation on eco-innovation determinants, their applicability within the context of developing countries, particularly within the Tanzanian setting, remains largely unexplored. Therefore, our research seeks to address this gap by specifically examining the significance of these factors within the Tanzanian food and beverage industries, providing insights tailored to the unique socio-economic and environmental landscape of developing countries like Tanzania. The following hypotheses were subjected to testing;

Hypothesis 1 (H₁): The financial robustness of the industry significantly and positively drives the implementation of eco-innovation.

Hypothesis 2 (H₂): The environmental awareness and knowledge of industry workers exert a positive and significant impact on eco-innovation implementation.

Hypothesis 3 (H₃): The concerns expressed by internal stakeholders within the industry regarding environmental issues play a pivotal role, contributing positively and significantly to eco-innovation implementation.

Hypothesis 4 (H₄): Motivation levels among industry workers with regard to environmental considerations positively and significantly influence the eco-innovation implementation.

Hypothesis 5 (H₅): A strong commitment to research and development activities focused on environmental issues has a positive and significant effect on eco-innovation implementation.

Hypothesis 6 (H₆): Customers’ heightened concern for environmental matters significantly and positively fosters the eco-innovation implementation.

Hypothesis 7 (H₇): Intense competition among industries in the market significantly and positively propels the eco-innovation implementation.

Hypothesis 8 (H₈): Stringent policies and regulations pertaining to the environment exert a positive and significant influence on the industry’s eco-innovation implementation.

Hypothesis 9 (H₉): The concerns voiced by external stakeholders in the industry regarding environmental issues significantly and positively impact eco-innovation implementation.

Hypothesis 10 (H₁₀): Enhanced accessibility to eco-technologies significantly and positively contributes to the implementation of eco-innovation.

3. Material and methods

3.1. The selection of Tanzania Agri-food sector

This study focused in on Tanzania’s food and beverage industries, a sector pivotal for the nation’s industrial growth owing to its economic importance and diverse contributions (Wangwe et al., 2014). The significance of this sector was underscored by the National Bureau of Statistics (NBS) in its 2016 Census of Industrial Production (NBS & MITI, 2016). Given the paramount importance of eco-innovation in ensuring sustainability within the agri-food sector and the wider Tanzanian economy, a meticulous approach to the innovation process becomes indispensable.

The study focused on six prominent regions in mainland Tanzania: Dar es Salaam, Arusha, Dodoma, Singida, Mwanza, and Kagera. These regions were chosen for their established status as industrial centers within Tanzania. For instance, Dar es Salaam is renowned for its concentration of beverage industries, while Dodoma and Singida regions are notable for their oilseed industries. Arusha is recognized for its flour mills, and Kagera and Mwanza regions are known for their dairy, tea, and coffee industries. Moreover, these regions were selected due to their significant contribution to environmental sustainability challenges, particularly concerning industrial pollution. Therefore, investigating these regions is crucial for addressing sustainability concerns through the implementation of eco-innovation.

3.2. Sampling design

The research employed a strategic combination of purposive and census sampling designs to carefully select the participants. Purposive sampling was especially instrumental in identifying and choosing food and beverage industries for this study. This selection process was guided by a global emphasis on sustainability, as articulated in the Sustainable Development Goals (SDGs) for the period from 2015 to 2030, which prominently advocate for aspects such as food security, nutrition, sustainable agriculture, and overall well-being (United Nations, 2015).

Additionally, the census technique was involved during data collection from a total of 123 eco-certified industries in the database. The engagement of census is recommended by scholars particularly when the sampling list is containing few numbers units (Kothari, 2004). Nevertheless, in the data collection, researchers achieved to collect data from 104 accessible industries. These accessible industries showed a response rate of 84.5% from the complete list of 123 eco-certified industries in the frame.

3.3. Measurements of variables

This study encompassed two categories of variables: independent and dependent. The independent variables comprised ten latent factors influencing the implementation of eco-innovation, categorized into internal factors (Finance, Knowledge, Internal Stakeholders, Motivation, and Research) and external factors (Customers, Competition, Policies, External Stakeholders, and Technology). Respondents rated the importance of these factors using a 5-point Likert scale. The dependent variable, eco-innovation, was represented by five indicators assessed on a Likert scale ranging from ‘Strongly Disagree’ to ‘Strongly Agree’. Subsequently, Likert scale responses were processed using the Statistical Package for Social Sciences (SPSS) to create dummy variables, facilitating the interpretation of results (Table 1).

Table 1. Summary of variables, measurement scales, and references in the study.

Variable/Code	References	Scale Type	Number of Indicators
Independent Variables	References	Likert Type	Number	Indicators
Finance (IDF)	Scarpellini et al. (2018); Ceptureanu et al. (2017)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Financial resources to fund research and development for eco-innovations. Financial resources for acquisition of eco-technology Financial resources to facilitate production of co-products Financial resources to train employees on eco-related concern
Knowledge (IDK)	Scarpellini et al. (2018); Ceptureanu et al. (2017); Edeh and Vinces (2023).	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Application of employee expertise in eco-innovation Application of employee expertise in entrepreneurship Application of employee expertise in eco-product specifications Application of employee expertise in eco-industrial operations
Internal Stakeholders (IDS)	Scarpellini et al. (2018); Ceptureanu et al. (2017); Achmad et al. (2023)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Employee input Management input Ownership input
Motivation (IDM)	Scarpellini et al. (2018); Ceptureanu et al. (2017); Shepherd and Patzelt (2015)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Management’s altruistic motivation driving eco-innovation Employees’ altruistic inclination towards eco-innovation Industry’s commitment to societal contribution through eco-innovation initiatives Industry’s motivation for incentives in eco-innovation operation
Research (IDR)	Del Río et al. (2015); Li et al. (2020)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Research on the availability of eco-materials Research on the availability of eco-technology Research on the availability of eco-operations Research on the availability of eco-products
Customers (EDC)	Chen and Liu (2020); Ceptureanu et al. (2017)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Three Observed Items	Customer preferences for eco-products Customer preferences for eco-industrial operations Customer preferences for eco-marketing activities
Competition (ECP)	Chen and Liu (2020); Hasan and Rahman (2023)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Competitor dominance in eco-product market Competitor dominance in eco-technology Competitor dominance in eco-marketing Competitor dominance in eco-materials
Policies (EDP)	Li et al. (2020); Del Río et al. (2015); Achmad et al. (2023)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Adherence to environmental policies and regulations Concern about pollution tax and penalties Compliance with environmental standards for eco-products Adherence to environmental standards for eco-technology
External Stakeholders (EDS)	Achmad et al. (2023); Chen and Liu (2019).	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Engagement with other eco-industries Collaboration with environmental organizations Partnerships with external eco-research institutions Influence from external environmental organizations
Technology (EDT)	Hasan and Rahman (2023); Ceptureanu et al. (2017)	Five-point Likert-scale (From 0 = Not Important at all to 4= Absolutely Essential)	Four Observed Items	Access to eco-technology resources Availability of infrastructure to support eco-technology implementation Efficient mechanisms for eco-technology adoption Access to knowledge for utilizing eco-technology
Dependent Variables	References	Likert Type	Number	Indicators
Eco-Innovation	Schumpeter entrepreneurship theory (1934)	Five-point Likert-scale of (1= strongly disagree, 2= disagree, 3= neutral, 4= agree, and 5= strongly agree)	Five Observed Items	Innovation on Eco-Products Innovation on Eco-Process Innovation in Eco-Materials Innovation in Eco-Management Structure Innovation in Eco-Marketing Activities

3.4. Study design and data collection

This study employed a cross-sectional research design to gather primary data from managers of eco-certified industries, aiming to explore factors influencing the implementation of eco-innovation. To achieve this, a survey technique was utilized, involving the distribution of self-administered questionnaires to (102) participants. These survey tools were meticulously designed to capture valuable insights into the dynamics of eco-innovation adoption within the sampled industries, ensuring a structured and systematic approach to data collection.

The selection of (102) managers for participation was conducted based on their pivotal roles and responsibilities within the eco-certified industries. Key informants were targeted, particularly those responsible for decision-making processes related to eco-innovation. Selection criteria encompassed positions such as production managers, sustainability officers, or senior executives directly involved in implementing eco-innovation initiatives.

The questionnaire application process involved several strategic steps to facilitate effective data collection. Initially, contact was established with selected managers through email or phone communication. The purpose and objectives of the study were clearly explained, and their willingness to participate was confirmed. Subsequently, self-administered questionnaires were distributed either electronically or in hard copy format, accommodating participants’ preferences. Clear instructions were provided on how to complete the questionnaires, and participants were afforded adequate time to respond. Follow-up reminders were sent to encourage participation and ensure a high response rate, contributing to the robustness and reliability of the gathered data.

3.5. Data analysis and statistical procedures

In this study, Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized to analyze the factors influencing the implementation of eco-innovation in Tanzanian agri-food industries. PLS-SEM is a robust statistical technique suitable for modeling complex relationships between latent constructs and observed variables, making it particularly well-suited for examining multifaceted phenomena like eco-innovation. By employing PLS-SEM, this study aims to provide a comprehensive understanding of the intricate interplay between various factors shaping eco-innovation within the context of Tanzanian agri-food industries. The analysis process, facilitated through AMOS within SPSS version 21, involved several steps to ensure the reliability and validity of the research instruments, thus enhancing the rigor and credibility of the study’s findings. As per Sarstedt et al. (2021), various management disciplines recognize Partial Least Squares Structural Equation Modeling (PLS-SEM) as a viable methodology. In contrast to Covariance-Based SEM (CB-SEM), PLS-SEM provides flexibility by avoiding stringent criteria or restrictive assumptions regarding measurement scales, distributional characteristics, or sample sizes (Sarstedt et al. ,2021).

3.5.1. Reliability assessment

The reliability of measurement instruments was rigorously evaluated through two pivotal components: internal reliability and composite reliability. Internal reliability, gauging the consistency of results across items within a test, was appraised using Cronbach’s alpha (α) with a threshold set at 0.70 and above, following Cronbach’s seminal work (1951). The results have demonstrated that all constructs attained acceptable scores of α > 0.7, confirming their reliability for use in the study. Furthermore, composite reliability, which evaluates the consistency of measures across different instances, was assessed using confirmatory factor analysis (CFA) due to constrained time intervals, aligning with the guidance of Byrne (2010).

The CFA shown in the Figures 3 and 4 revealed all loaded constructs surpassing the recommended threshold of 0.3, reinforcing their consistency. Furthermore, a pre-test with industries akin to the main study region was conducted to evaluate readability, clarity, and identify potential issues. Positive outcomes from the pre-test, coupled with response consistency during definitive data collection, affirmed the reliability of the instrument.

Figure 3. SEM output for the Confirmatory Factor Analysis on the 1st measurement model.

Figure 4. SEM output for the Confirmatory Factor Analysis on the 2nd measurement model.

3.5.2. Validity assessment

In this research, validity assessment, as recommended by scholars like Kothari (2004), encompassed three key methods: content validity, construct validity, and criterion validity. Content validity was ensured through an extensive literature review and consultation with experts to guarantee that all observed variables adequately represented the study constructs, particularly regarding ecopreneurship, its drivers, and sustainability. Construct validity, which includes convergent and discriminant validity, was addressed through Confirmatory Factor Analysis (CFA). The results indicated satisfactory factor loadings and average variance extracted (AVE) exceeding the threshold value of 0.50, confirming convergent validity. Discriminant validity was established by comparing the AVE with the squared correlation among other constructs, following the criteria by Fornell and Larcker (1981). Additionally, criterion validity was achieved through triangulation of data sources, combining survey responses with examination of ecopreneurs’ financial and environmental audit reports, ensuring coherence between survey responses and actual records. The results of the tests are presented in Tables 2–4.

Table 2. Measurement model assessment for independent factors - internal factors.

Constructs	Factor Loadings	Average Variance Extracted (AVE)	Cronbach’s Alfa (α) (Reliability Statistics)
Independent Variable	Value Obtained	(AVE) Value	(α)Value
Internal Factors
Finance
IDF1	0.683	0.63	0.87
IDF2	0.811
IDF3	0.834
IDF4	0.833
Knowledge
IDK1	0.747	0.50	0.78
IDK2	0.630
IDK3	0.630
IDK4	0.731
Internal Stakeholders
IDS1	0.336	0.51	0.84
IDS2	0.829
IDS3	0.809
Motivation
IDM1	0.809	0.65	0.88
IDM2	0.805
IDM3	0.843
IDM4	0.767
Research and Development
IDR1	0.828	0.53	0.80
IDR2	0.366
IDR3	0.720
IDR4	0.886

Table 3. Measurement model assessment for independent factors - external drivers.

Constructs	Factor Loadings	Average Variance Extracted (AVE)	Cronbach’s Alfa (α) (Reliability Statistics)
Independent Variable	Obtained Value	(AVE) Value	(α)Value
Internal Drivers
Policies and Regulation
EDPR1	0.300	0.50	0.74
EDPR2	0.738
EDPR3	0.912
EDPR4	0.719
Competition
EDCP1	0.422	0.52	0.76
EDCP2	0.855
EDCP3	0.907
EDCP4	0.589
Customers
EDCU1	0.566	0.66	0.83
EDCU2	0.916
EDCU3	0.909
External Stakeholders
EDS1	0.620	0.60	0.84
EDS2	0.862
EDS3	0.899
EDS4	0.690
Technology
EDTI1	0.721	0.53	0.82
EDTI2	0.800
EDTI3	0.735
EDTI4	0.656

Table 4. Measurement model assessment for dependent factor - eco-innovation.

Constructs	Factor Loadings	Average Variance Extracted (AVE)	Cronbach’s Alfa(α) (Reliability Statistics)
Dependent Variable	Value Obtained	(AVE) Value	(α)Value
Eco-innovation
EI1: Product	0.499	0.50	0.71
EI2: Process	0.684
EI3: Materials	0.684
EI4: Management	0.438
EI5: Marketing	0.668

3.5.3. Multicollinearity assessment

Multicollinearity among the independent variables was assessed using the coefficients of Tolerance and Variance Inflation Factor (VIF). Multicollinearity occurs when independent variables in a regression model are highly correlated with each other, leading to unstable coefficient estimates. In accordance with established literature, a Tolerance value greater than 0.10 and a VIF value less than 10 are commonly cited thresholds indicating the absence of multicollinearity among variables.

The results of the tests for multicollinearity revealed values within the recommended thresholds, indicating the non-existence of multicollinearity in the dataset.

• Internal Drivers: The Tolerance values ranged between 0.1 to 0.7, and VIF values ranged between 1.2 to 3.1.

• External Drivers: Similarly, the Tolerance values ranged from 0.2 to 0.7, with VIF values ranging between 1.2 to 4.1.

Given that all Tolerance values exceeded the threshold of 0.10, and all VIF values were below 10, it can be concluded that multicollinearity was not a concern within the dataset. These findings provide confidence in the stability of coefficient estimates and justify further analysis using regression models. This absence of multicollinearity confirms the reliability ensuing analyses and enhances the robustness of the study’s conclusions

3.5.4. Structural Equation model (SEM) analysis and model fit indices

The SEM analysis followed a series of essential steps, beginning with an evaluation of key dataset assumptions, including multivariate normality, absence of missing data, sample size sufficiency, correct model specification, absence of outliers, absence of multicollinearity, and absence of correlation between error terms.

The measurement model illustrated the relationships between latent variables and their observed measures. The Confirmatory Factor Analysis (CFA) confirmed the validity and reliability of the model, with all constructs demonstrating significant factor loadings above 0.3, in accordance with recommended thresholds.

Model fitness assessments were conducted using commonly used tests, including Relative Chi-Square (χ2/df), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA). The results demonstrated satisfactory fit statistics, with Internal factors model and External factors model meeting the established thresholds. Summary of the fit indices for both internal and external factors models, enabling the evaluation of model fit are presented in Tables 5 and 6 below.

Table 5. Fit Indices for internal factors model.

Fit Index	Value
Relative Chi-Square	1.426
Goodness of Fit Index	0.933
Adjusted GFI	0.976
Incremental Fit Index	0.928
Comparative Fit Index	0.926
RMSEA	0.06

Table 6. Fit Indices for external factors model.

Fit Index	Value
Relative Chi-Square	1.807
Goodness of Fit Index	0.905
Adjusted GFI	0.939
Incremental Fit Index	0.982
Comparative Fit Index	0.978
RMSEA	0.07

4. Findings

4.1. Exploring preliminary insights into surveyed industries

The findings on the surveyed industries in this study, as outlined in Table 7, provide a comprehensive insight into the landscape of eco-certified businesses, totaling 102 establishments specializing in food and beverage production. Among these, 46 (45.1%) are dedicated to food production, spanning oilseeds, dairy, tea, coffee, and flour milling, while 56 (54.9%) focus on beverage production, including drinking water and fruit drinks. Furthermore, analysis of respondents reveals that 8 (7.8%) are owner-managers, while 94 (92.2%) are employed managers, indicating a notable presence of operational stakeholders within the surveyed sample. This pattern aligns with previous research, which has also observed employed managers as the primary participants in similar industrial inquiries (Scarpellini et al., 2018).

Table 7. Preliminary information of the sample in this study.

Particulars		Frequency	Percent
Type of Industry	Food	46	45.1
	Beverage	56	54.9
	Total	102	100
Position of Respondents	Owner Manager	8	7.8
	Employed Manager	94	92.2
	Total	102	100
Size of the firm	Small	16	15.7
	Medium	74	72.5
	Large	12	11.8
	Total	102	100
Type of Product	Drinking water	39	38.2
	Fruit Drinks	17	16.7
	Oilseeds	11	10.8
	Dairy, Tea and Coffee	16	15.7
	Flour Mills	19	18.6
	Total	102	100
Number of Industry in Region	Dar es Salaam	56	54.9
	Dodoma	7	6.9
	Singida	4	3.9
	Arusha	19	18.6
	Mwanza	7	6.9
	Kagera	9	8.8
	Total	102	100
Years of experience in eco-innovation	Above three years	102	100

Source: Survey.

4.2. The relationship between internal and external factors on the implementation of eco-innovation

The findings of the multiple regression on investigating the relationship between internal and external factors influencing the implementation of eco-innovation are presented in Table 8 along with Figures 5 and 6. The predetermined significance threshold for the hypothesized relationships was (p) < 0.05, with a minimum Critical Ratio (C.R) of 1.96.

Figure 5. SEM output for the Structural model on the relationship between Internal Factors on Eco-innovation.

Figure 6. SEM output for the Structural model on the relationship between External Factors on Eco-innovation.

Table 8. Structural model results on the relationship between internal and external factors on the implementation of eco-innovations.

Dependent	Independent/Drivers	Estimate	S.E	C.R	P	Decision
H1Eco_Innovation	Finance	0.217	0.103	2.102	0.036	Accept
H2Eco_Innovation	Knowledge	−0.057	0.128	−0.443	0.658	Reject
H3Eco_Innovation	Internal Stakeholders	1.057	0.37	2.855	0.004	Accept
H4Eco_Innovation	Motivation	0.005	0.094	0.05	0.96	Reject
H5Eco_Innovation	Research	0.516	0.113	4.561	<0.001	Accept
H6Eco_Innovation	Competition	−0.069	0.127	−0.548	0.584	Reject
H7Eco_Innovation	External Stakeholders	0.258	0.104	2.49	0.013	Accept
H8Eco_Innovation	Customers	0.359	0.14	2.567	0.01	Accept
H9Eco_Innovation	Policies	0.358	0.126	2.842	0.004	Accept
H10Eco_Innovation	Technologies	0.372	0.152	2.452	0.014	Accept

Source: Survey.

The Partial Least Squares Structural Equation Modeling (PLS-SEM) findings reveal that the implementation of eco-innovation is significantly influenced by various factors. Firstly, finance (β = 0.217, t = 2.102, p = 0.036), internal stakeholders (β = 1.057, t = 2.855, p = 0.004), research (β = 0.516, t = 4.561, p < 0.001), external stakeholders (β = 0.258, t = 2.49, p = 0.013), customers (β = 0.359, t = 2.567, p = 0.01), policies (β = 0.358, t = 2.842, p = 0.004), and technologies (β = 0.372, t = 2.452, p = 0.014) all have significant positive effects on eco-innovation.

However, knowledge (β = -0.057, t = -0.443, p = 0.658), motivation (β = 0.005, t = 0.05, p = 0.96), and competition (β = -0.069, t = -0.548, p = 0.584) do not show significant effects on eco-innovation. Therefore, the study accepts the hypotheses related to finance, internal stakeholders, research, external stakeholders, customers, policies, and technologies, while rejecting the hypotheses related to knowledge, motivation, and competition.

5. Discussion and conclusions

The present study examined the relationship between factors on the implementation of eco-innovation. Ten hypotheses were tested including H₁ (Finance), H₂ (Knowledge), H₃ (Internal Stakeholders), H₄ (Motivation), H₅ (Research), H₆ (Competition), H₇ (External Stakeholders), H₈ (Customers), H₉ (Policies), and H₁₀ (Technology). The statistical results provided support for several hypotheses (H₁, H₃, H₅, H₆, H₈, H₉, and H₁₀) demonstrating a positive and significant impact on the implementation of eco-innovation. Conversely, the findings indicated that three hypotheses (H₂, H₄, and H₇) did not exhibit a significant influence on the implementation of eco-innovation practices.

The significant positive influence of financing on eco-innovation within the Tanzanian food and beverage industries underscores its crucial role in driving implementation of eco-innovation. This outcome aligns with prior research in the field by Hasan and Rahman (2023) and Barbieri and Santos (2020), emphasizing the pivotal role of financial support in driving the adoption of eco-innovation across industries. Financing enables essential research and development (R&D) activities, supporting exploration of sustainable materials, processes, and product designs to meet consumer demand. It also facilitates acquisition and implementation of eco-friendly technologies, enhancing operational efficiency and environmental performance. Additionally, financing supports production of co-products from eco-innovation, minimizing waste and resource depletion while unlocking revenue streams. Moreover, investments in training programs cultivate environmental awareness among employees, empowering them to contribute actively to sustainability efforts. In essence, financing serves as a cornerstone for advancing eco-innovation and sustainability objectives in this sector

Regarding hypothesis H₂, which investigated the influence of knowledge on eco-innovation implementation, the results revealed that the knowledge factor did not have a significant impact in this context. These findings suggest that, within the scope of this study, knowledge may not be a significant driver of eco-innovation. However, it’s important to note that our study focused on internal knowledge possessed by employees and managers, whereas previous research by Ceptureanu et al. (2017) and Edeh and Vinces (2023) emphasized the importance of external knowledge acquisition to enhance eco-innovation implementation.

The discrepancy in findings between our study and the cited research suggests that the source of knowledge may play a critical role in driving eco-innovation. While our study explored internal knowledge within industries, Ceptureanu et al. (2017) and Edeh and Vinces (2023) highlighted the significance of external knowledge, sourced from research activities and external stakeholders. This disparity underscores the need for further investigation into the specific types and sources of knowledge that have the most substantial influence on eco-innovation implementation. Study findings indicate that industries in the Tanzanian food and beverage sector may not heavily rely on internal innovative knowledge from employees and managers to drive eco-innovation initiatives. Instead, external knowledge, particularly from research activities, appears to be more influential. This suggests that industries may benefit from actively seeking external sources of knowledge, such as collaborations with research institutions and engagement with industry experts, to enhance their eco-innovation capabilities.

In relation to hypothesis H₃, which examined the influence of internal stakeholders, the analysis revealed a statistically significant and positive relationship. This underscores the importance of actively engaging and involving internal stakeholders, including employees, management, and owners, in the eco-innovation process, as it appears to be a significant determinant for success in this context. The significant and positive relationship found between internal stakeholders and eco-innovation implementation aligns with prior research emphasizing the importance of engaging internal stakeholders in sustainability initiatives (Del Río et al., 2015; Achmad et al., 2023).

Regarding hypothesis H₄, which investigated the impact of environmental motivation, the results indicate that environmental motivation did not exhibit a significant influence on the implementation of eco-innovation. This lack of significant influence aligns with findings from Ceptureanu et al. (2017) and Hasan and Shepherd and Patzelt (2015), underscoring the complexity of motivational factors in driving sustainability initiatives. The results suggest that altruism towards the environment may not be a critical determinant of implementing eco-innovation. Instead, eco-innovation appears to be more strongly influenced by other external factors. Therefore, these findings emphasize the need to explore alternative motivational factors or variables that may have a more pronounced impact on eco-innovation implementation.

In hypothesis H₅, which examined the impact of research activities, the results clearly indicate a statistically significant and positive relationship. These findings strongly affirm the hypothesis that investing in research for eco-innovations is a vital determinant in promoting the implementation of eco-innovation within various industries. This investment in research encompasses exploring information on the availability of eco-materials, eco-technology, eco-operations, and eco-products. The significant and positive relationship between research investment and eco-innovation implementation is consistent with previous literature emphasizing the importance of dedicating resources to research endeavors related to eco-innovation (Ceptureanu et al., 2017; Hasan & Rahman, 2023).

In hypothesis H₆, which focused on the influence of customer concerns on the environment, the results reveal a statistically significant and positive relationship. These findings robustly support the notion that customer concerns regarding eco-innovation play a pivotal role in driving the implementation of eco-innovation within various industries. This emphasizes the critical importance of recognizing and addressing customer environmental concerns, encompassing aspects such as eco-products and eco-marketing activities, as fundamental determinants for the success of eco-innovation initiatives across diverse sectors. The significant and positive relationship between customer concerns and eco-innovation implementation is corroborated by previous studies that underscore the imperative of addressing and integrating customer environmental considerations (Chen & Liu, 2020; Ceptureanu et al., 2017).

Regarding hypothesis H₇, which investigated the influence of competition, the results suggest that the competition factor did not exhibit a statistically significant positive impact on the implementation of eco-innovation. Surprisingly, the findings revealed a negative influence, indicating that heightened competition may actually deter industries from investing in eco-innovation. This negative relationship can be rationalized by the notion that intensified competition prompts industries to prioritize cost-cutting measures, potentially leading to reduced investment in eco-innovation initiatives as a means to optimize operational efficiency within a competitive landscape. This aligns with the previous findings of Tsai and Liao (2017), whose study delved into the impact of competition on eco-innovation, unveiling a negative correlation between firm competitiveness and engagement in eco-innovation activities, primarily attributable to escalating costs associated with eco-practices. In light of these insights, the provision of subsidies has been suggested as a strategic intervention to bolster firms’ willingness to embrace eco-innovations (Tsai & Liao, 2017).

In hypothesis H₈, which examined the impact of environmental policies, the results unequivocally demonstrate a statistically significant and positive relationship. These compelling findings robustly affirm the hypothesis, asserting that the reinforcement of policies and regulations pertaining to environmental innovation is indeed a critical determinant for fostering the implementation of eco-innovation within industries. This underscores the indispensable role of robust and supportive policies in driving the implementation of eco-innovation across industrial sectors. Key aspects of environmental policies encompass compliance with regulatory frameworks, concerns regarding pollution taxes and penalties, adherence to environmental standards for products, and adherence to environmental standards for technology. The observed significant and positive correlation between environmental policies and eco-innovation implementation resonates with prior scholarly investigations, which underscore the pivotal contribution of supportive policies and regulations in catalyzing eco-innovation endeavors (Li et al., 2020; Del Río et al., 2015).

In hypothesis H₉, which examined the influence of external stakeholders, the results unveil a statistically significant and positive relationship. These findings robustly support the hypothesis, affirming that the engagement and concern of external stakeholders regarding eco-innovation are pivotal determinants influencing the implementation of eco-innovation across various industries. This underscores the paramount importance of external stakeholder involvement and their proactive role in driving the adoption of eco-innovation initiatives within industrial settings. Notably, external stakeholders, including collaboration with other eco-industries, engagement with environmental organizations, collaboration with external eco-research institutions, and pressure from external environmental organizations, emerged as crucial determinants of eco-innovation implementation. These results align with previous scholarship emphasizing the critical role of stakeholders’ involvement and concern in eco-innovation as emphised by Achmad et al. (2023).

In hypothesis H₁₀, which investigated the influence of eco-associated technologies, the results unequivocally demonstrate a statistically significant and positive relationship. These findings strongly suggest that the accessibility of eco-related technologies plays a pivotal role as a key determinant for promoting the implementation of eco-innovation across various industries. This underscores the significance of having access to and utilizing eco-related technologies, including availability and access to eco-technology, infrastructures facilitating the implementation of eco-technology, and streamlined implementation mechanisms for eco-technologies, in driving the adoption of eco-innovation initiatives in industrial settings. The significant and positive relationship between eco-associated technologies and eco-innovation implementation is consistent with previous research in various contexts, emphasizing the importance of technological accessibility (Hasan & Rahman, 2023; Ceptureanu et al., 2017).

5.1. Conclusion

This study sheds light on the multifaceted nature of eco-innovation and its implementation within Tanzania’s food and beverage industries. The research objective was to identify key determinant factors essential for successful eco-innovation implementation, and the empirical findings have provided valuable insights in this regard.

The implementation of eco-innovation serves as a mechanism to reconcile economic growth with environmental sustainability. Against this backdrop, the research aimed to empirically identify and confirm the significant determinants influencing eco-innovation in Tanzania’s food and beverage industries.

The study uncovered several pivotal factors driving eco-innovation initiatives, including financial resources, stakeholder involvement, research and development investment, responsiveness to customer concerns, effective policies, and technological accessibility. Empirical evidence underscored the importance of these determinants in fostering eco-innovation within the selected industries.

Therefore, the findings underscore the critical role of financial support, stakeholder engagement, research and development efforts, customer responsiveness, policy frameworks, and technological readiness in advancing eco-innovation activities. These determinants collectively contribute to enhancing sustainability and competitiveness within the food and beverage sectors.

5.2. Practical recommendations and policy implications

Based on the empirical findings, practical recommendations have been offered to strengthen eco-innovation implementation, emphasizing the need for financial support, stakeholder engagement, research and development efforts, responsiveness to customer concerns, effective policy frameworks, and technological readiness. Policymakers and industry regulators can leverage these insights to design and implement policies that support eco-innovation, fostering collaboration between industry and academia to facilitate knowledge exchange and expertise.

5.3. Theoretical implications

This study contributes to theoretical integration by synthesizing concepts from Schumpeter’s Innovation Theory within entrepreneurship and Ecological Modernization Theory within ecological studies. By merging insights from these two theoretical frameworks, the research advances our comprehension of sustainable industrialization through eco-innovation. It offers a distinctive perspective on how both internal organizational dynamics and external environmental pressures shape the implementation of eco-innovation within Tanzania’s food and beverage industries.

5.4. Limitations and future research directions

While this study provides valuable insights, it is not without limitations. The research focused specifically on eco-innovation within the food and beverage industries across six regions of Tanzania. As such, caution must be exercised when generalizing the findings to other manufacturing sectors and regions. Future research endeavors should explore determinant factors for eco-innovation implementation across diverse sectors and industries to offer a more comprehensive understanding of its effectiveness in different contexts.

Acknowledgements

I would like to express my sincere gratitude to all those who contributed to the completion of this research. Special thanks to the University of Dodoma for providing the necessary resources and support. I am deeply appreciative of the participants who generously shared their time and insights, enabling the collection of valuable data. I am also thankful to my colleagues and peers for their constructive feedback and encouragement throughout this endeavor. Additionally, I extend my appreciation to the reviewers and editors for their insightful comments and suggestions, which significantly improved the quality of this manuscript. Finally, I am grateful to my family for their unwavering love and support, which has been a constant source of inspiration.

Disclosure statement

I declare that I have no conflicts of interest regarding the publication of this research paper. This study was conducted with integrity and impartiality, and the findings presented herein are based solely on the analysis of the collected data and relevant literature. There are no financial or personal relationships that could influence the objectivity or interpretation of the research outcomes. I affirm that all sources of funding and support for this study, if any, have been disclosed transparently.

Data availability statement

The data supporting the findings of this study are available upon request to the author, Buzohera Issa.

Additional information

Notes on contributors

Mohamed Issa Buzohera

Mohamed Issa Buzohera, as an academician at the University of Dodoma, I specialize in entrepreneurship, environmental studies, and the green economy, with a focus on mechanisms for achieving a sustainable world. My research activities center on exploring innovative solutions to pressing environmental challenges while fostering economic growth. This paper represents my independent investigation into eco-innovation practices within Tanzania’s food and beverage industries. It aligns with my broader research agenda aimed at understanding the dynamics of sustainable development and identifying practical strategies for promoting environmentally conscious business practices. By delving into the interplay between economic imperatives and environmental concerns, my work contributes to advancing knowledge in the fields of entrepreneurship and sustainable development, with implications for policy-making and industry practices.