Blue economy investment and sustainability of Ghana’s territorial waters: an application of structural equation modelling

ABSTRACT

Despite the fact that Blue Economy Investment (BEI) contributes immensely towards sustainable use of marine resources for economic growth, improved livelihoods, employment and healthy marine ecosystem, little efforts have been made to encourage sustainable investment in this area. In Ghana, the Marine Pollution Act 2016 (Act, 932) was promulgated to prevent, regulate and control pollution within Ghana’s territorial waters. Meanwhile, the health of Ghana’s oceans keeps deteriorating. This paper aims to develop an integrative model to enhance BEI through Sustainable Supply Chain Performance (SSCP) by integrating competing theories to attract sustainable investment to conserve and make sustainable use of marine resources. We anchored our study on quantitative research approach and a cross-sectional survey data. Our hypotheses have been tested using variance-based Structural Equation Modeling and SMART-PLS version 3.3.1. The study has revealed that organisational factors, technological factors, supply chain risk, green environmental awareness, perceived cost and regulatory environment are significant determinants of BEI. Moreover, BEI significantly drives SSCP. Again, BEI significantly mediates the relation between its determinants and SSCP. The implications include the emergence of an integrated model which could be used to improve marine resources investment and the realisation of Sustainable Development Goals 3, 13 and 14.

KEYWORDS:

• Blue economy adoption
• sustainable investment
• marine resources; economic
• Growth
• Climate actions

1. Introduction

Blue Economy (BE) encompasses sustainable use of marine resources for economic growth, improved livelihoods and employment, while maintaining the health of the marine ecosystem. According to the United Nations, more than three billion people depend on marine and coastal biodiversity for their survival. BE is the use of marine resources in a sustainable manner to ensure economic growth, better living conditions and create employment opportunities as well as to ensure the health of marine ecosystem (World Bank 2013; Zhao, Guan, and Sun 2019; Li et al. 2021; Kedong et al. 2022; Xu and Gao 2022; Ozili 2022). This multifaceted concept is derived based on aspects of business, economics, the environment, shipping and maritime issues. BE fundamentally includes investment in the following marine energy, sea food, transportation, eco-tourism, source of medicine and flight recreation transport (Baker and Ricciardi 2014; Nthia 2021; Tettey 2019; Sabela-Rikhotso, van Niekerk, and Nemakonde 2022).

Globally, the (BE is worth more than $24 trillion. It is estimated that fishing and aquaculture, shipping, tourism and other activities generate at least $2.5 trillion in revenue annually. Coastal tourism is one of the fastest-growing marine economic activities in the world, worth £6 billion in countries with coral reefs alone. Aquatic environments, such as oceans, seas, rivers, lakes, marshes and bays, are home to about 2.2 million plant species, a wide variety of wildlife and other life forms that make up more than 50% of life on Earth. These natural resources provide food, medicine and livelihoods that contribute to social and economic development around the world. The western Indian Ocean (which includes Comoros, France, Kenya, Madagascar, Mauritius, Mozambique, Seychelles, Somalia, Tanzania and South Africa) has a total ‘marine wealth’ of at least 333.8 billion. Total annual seafood production (equivalent to a country’s annual gross domestic product (GDP) in the western Indian Ocean is at least 20.8 billion (World Bank 2013; Zhao, Guan, and Sun 2019; Li et al. 2021; Kedong et al. 2022; Xu and Gao 2022; Ozili 2022).

According to United Nations Economic Commission for Africa (2016), the concept of BE includes the recognition that the productivity of healthy freshwater and marine ecosystems is the pathway to a water-based economy that enables not only continental countries, but also islands and other coastal states to benefit from their resources. The BE, considered key to sustainable ocean development, has gained such prominence over the past decade that it is almost impossible to address the issue of ocean policy or development without addressing it. Blue Economy Investment (BEI) Behavior is based on pre-investment decision making which includes the qualitative and quantitative aspects such as previous experiences, personal preferences and beliefs of a particular product before making the actual decision. Hence, positive investment behaviour towards BE is prone to the development and growth of sustainability of every country (Mahon, McConney, and Oxenford 2020; Voyer et al. 2021; Amuhaya and Degterev 2022).

In Ghana, efforts have been made to enforce the Marine Pollution Act 2016 (Act, 932) which is aimed to prevent, regulate and control pollution within Ghana’s territorial waters. Meanwhile, the health of Ghana’s oceans is deteriorating. Pollution and ocean acidification are degrading coastal waters, harming small-scale fisheries, biodiversity and ecosystems. There is therefore an urgent need to find ways to better tackle the many issues that threaten the sustainability of the oceans, such as overfishing, climate change and plastic pollution. To this day, the BE issue remains unresolved (Techera 2018; ALshubiri 2018; Jones and Navarro 2018; Alharthi and Hanif 2020; Rasowo et al. 2020; Mathew and Robertson 2021; Rudge 2021; Ozili 2022; Sabela-Rikhotso, van Niekerk, and Nemakonde 2022), and an increasing number of people are using their commitment to a sustainable ocean agenda to bend it to suit their various interests. This recognition of the ambiguity of the BE is the basis to draw on the National Resource Based View and TOE (Technology Organization and Environment) theories to develop a new model to explain factors affecting BEI intentions and sustainability performance through various BE value chains. Despite the fact that BE encompasses broad areas including, sea food, marine energy, medicine, transportation, flight recreation among others. This baseline study has explored the sector as whole as part of the efforts to provide bigger picture of the industry in order to attract potential investors.

This paper aims to develop an integrative model to explain the extent to which BEI drives Sustainable Supply Chain Performance (SSCP) by integrating Technology Organization Environment (TOE) and National Resource-based view theories with slight variation in order to attract sustainable investment to conserve and make sustainable use of marine resources by focusing on Ghanaian Small and Medium Enterprises (SMEs). Amidst other contributions, there is an emergency of an integrated model for improving BEI has been developed based on TOE and RBV theories with slight variations. The integrated model is expected to improve marine resources for economic growth, improved livelihoods and employment, while maintaining the health of the marine ecosystem. The outcomes of the paper are also relevant for the realisation of SDGs as well as the realisation of Agenda 2063 for African Development. Investors and practitioners could use the newly developed model as a checklist for blue investment decisions. Again, Stakeholders in the Ghanaian SMEs sub-sector can take proactive steps to advance investment in BE in order to enhance SSCP. Besides, SMEs could deploy the findings in this study to generate and share sustainability knowledge and subsequent transfer the knowledge to other operational areas. This paper is first of kind to model investment strategy enhance sustainability of Ghana’s territorial waters, and to protect the marine resources. Besides, this paper could serve as the foundation for further investment strategies in respect of investment in marine resources across other Sub-Saharan African Countries. The paper is guided by the following specific objectives:

RO1- To ascertain the factors that affect BEI intention among SMEs;

RO2 - To determine the relationship between BEI intention and SSCP; and

RO3 - To determine the mediating role of BEI as a driver of SSCP.

The remaining sections of the paper include: section 2 covers the review of literature including theoretical, empirical, and hypotheses development; section 3 covers research methodology; section 4 covers results, section 5 covers discussions, and finally, section 6 covers conclusion and implications.

2. Literature review

2.1. Theoretical review and hypotheses development

Natural Resources-Based View (NRBV) and Technology Organization and Environment (TOE) theory has been deployed to explain the assumptions in the current study and to theoretically position the work. Hart (1995) asserted that firms that adopt sustainability practices comparatively out-performance those without such initiatives. Thus, proponents of NRBV argue that investment in natural resources could lead to reduction in pollution, protection of biodiversity, promote product stewardship, and enhance sustainable development. Moreover, the proponents (Tornatzky and Fleisher, 1990) of TOE model argues that investment in a given environment is determined by critical factors such as technological, organisational, and environmental. BEI is treated as an emerging sector with very little-known knowledge on what investment strategy works better for the critical factors that determine a successful investment in this sector. This paper is among the very few to develop BEI model taking into cognisance NRBV and TOE theories. Most previous papers (Mahon, McConney, and Oxenford 2020; Voyer et al. 2021; Amuhaya and Degterev 2022; Ozili 2022; Sabela-Rikhotso, van Niekerk, and Nemakonde 2022) in this area concentrated on conceptual and review reports the latter argues that the macro-level consideration such as those relating to Technology, Organization, and Environment determine investment success. These two theories complement one another when use in combination. As a result, the current study has merged the dimensions of these theories to form a new paradigm which has been used to explain investment behaviour and sustainability performance in BE. The newly developed integrated model has been shown in the Figure 1:

Figure 1. Research framework.

Read the detailed description of this figure

Research Framework Showing Blue Economy Investment Determinants’ and Sustainable Supply Chain Performance.

2.2. Empirical review on blue economy and identification of gaps

BE has been adequately reported in empirical studies (Caferra and Falcone, 2022; Daly et al., 2021; Wenhai et al., 2019; Fusco et al., 2022, Qi, 2022; Thompson, 2022). This part of the review focuses on some of the most pertinent and crucial studies which are consistent with the current study. These broadly include: BE investment intentions, adoptions, supply chain risk, policy implementations, environmental, organisational, and technological considerations, perceived cost of investment, environmental awareness and SSCP. For instance, Alharthi and Hanif (2020) examined the impact of BE factors on economic development in South Asian Association for Regional Cooperation (SAARC) countries and found that BE factors play a statistically significant role in the economic development of SAARC countries and contribute to the achievement of UN Sustainable Development Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development. Secondly, according to Benzake et al. (2022), the ‘blue economy’ has become a globally influential concept. It is generally associated with developing the oceans in a way that also takes into account their health in the context of increasing demand for ocean resources, marine pollution and climate change. Although the BE can provide an integrated policy framework for sustainable ocean development, there are as yet few practical examples that demonstrate the applicability of the concept. A number of governance challenges have emerged during the transition to implementation, such as maintaining high-level political momentum, stakeholder engagement, institutional coordination and capacity. In this context, Silvestri et al. (2023) provide a systematic contribution to the literature on the BE and aquaculture as challenges for sustainable development and circular economy. Similarly, Wuwung et al. (2022) note that the inclusion of the BE in policy debates has gained momentum in recent years, but a standard definition is still lacking. The BE is based on the triple bottom line of economic growth, social justice and environmental protection. This paper provides an overview of current approaches to managing the BE at national level. Thus, this paper presents the first global assessment of approaches to the BE through the creation of a global BE database and shows that BE governance remains limited at the global level despite the popularity of the concept in international and regional policy debates. Contributing factors are likely to be related to the BE and marine governance in general.

In a related study, Yoshioka et al. (2020) argued that the oceans are threatened by negative human impacts such as climate change, marine plastic pollution, extreme weather events, and sea level rise. In the face of these ocean-related risks, it remains difficult for particularly vulnerable regions, coastal areas, and small island states to secure sufficient funding to support their ocean economies. Ocean sustainability has been the focus of global funding led by multilateral development banks and international organisations. Martinez-Vasquez et al. (2021), asserted that the ‘blue economy’ is an emerging field of study that concerns economic activities dependent on the oceans, often associated with tourism, shipping, energy, and is linked to other economic sectors such as fisheries. Blue growth is essential for the sustainable development of the maritime sector and freight transport, as the ocean is the engine of the global economy and has great potential for growth and innovation. Furthermore, Lee et al. (2020) point out that the concept of BE has created internal conflicts between the two discourses of growth and development and marine resource conservation. These conflicts require solutions that recognise and address the risks while exploiting the opportunities associated with the marine economy. The United Nations Sustainable Development Goals provide a possible global solution. According to Tirumala and Tiwari (2022), it is estimated that investments in BE projects will fall far short of the investments needed to achieve the targets set in the UN Sustainable Development Goals. BE projects are typically funded through traditional government and development financing instruments. However, the nature and characteristics of BE projects are such that there is a need to expand funding beyond traditional multilateral/bilateral assistance. The objective of this paper is to assess whether existing BE initiatives are meeting investment needs and to outline a framework that could catalyse investment in the BE. In doing so, they find that existing initiatives, such as blue bonds, are relatively modest and that access to other financial instruments and a reconfiguration of stakeholders are needed to accelerate investment.

In a recent study, Auad and Fath (2022) reiterated some of the key debates on the need to develop a sustainable BE to support effective mitigation and adaptation measures in a time of rapid climate change. However, the dialogue still lacks basic and concrete principles that illuminate the socio-economic processes needed for sustainable development in all sectors and at all levels. In addition to the above, Louey (2022) approach to the role of sustainable blue economic policy identifies the need to anticipate the emergence of different types of feasible innovations in the business environment of shipping and maritime organisations. This paper contributes to this growing effort by drawing on public knowledge and presenting more than 60 predictions of innovations likely to affect shipping, shipbuilding, ports, offshore wind and maritime infrastructure. The results can be used strategically as a basis for cross-regional innovation planning and policy and reflect public expectations for the future. Again, Niner et al. (2022) argued that the BE is based on international sustainable development and aims to unlock economic and social opportunities while protecting and enhancing the marine environment. To date, there has been no analysis of how this overarching SDG has contributed to the rapid development of BE policies at national and regional levels. This paper analyses the synergies and tensions between national and regional BE policies and the UN SDGs, and concludes that developing a BE that meets the needs of all stakeholders is essential to maintain alignment with the SDGs. Graziano et al. (2022) further argue that the BE is still heterogeneous in terms of its conception and implementation, and that there are differences not only between countries but also between regions. This argument is supported by the results of this paper as we identify a number of approaches to BE and reiterated that the concept has been widely and variously been studied across the globe.

As evident from the extant literature reviews, it has been clearly established that several studies have been conducted from varying countries and regions focusing on distinctive aspects of BE adoption and SSCM performance. Our study is among the very few empirical studies that considers BE investment adoptions, supply chain resilience, policy implementations, environmental, organisational, and technological considerations, perceived cost of investment, environmental awareness and SSCM performance from the unique context of a lower middle-income country (Ghana). It is therefore imperative to explore factors determining investment adoption in BE and their subsequent effects on SSCP which has never been investigated in the Ghanaian context.

2.3. Technological factor and BEI

Technological factors refer to the social media tools utilised by the companies (Pramono and Susanty 2015). The technology factor is the set of technologies available to the organisation. The noteworthy technological factor is the ease of use of the social media tools used. Technological factors; advances in ICT have led to the development of organisational practices that are considered more effective to enable organisations to gain competitive advantage over their competitors (Finney and Corbett 2007; Haug 2012; Liu and Seddon 2009; Poon and Wagner 2001). Technology factors refer to the IT infrastructure (both IT staff and existing technologies) and the technologies used, integrated and deployed, including hardware, software and other system components (García-Moreno et al. 2016; Issa 2020). In this study, technological factors have been considered to assess its influence on BEI. Data quality, technology availability, network complexity, immature technology and technology knowledge comes under the variables of technological factors. In the view of the ongoing presentation/argument the study hypothesises as follow:

H1:

Technological factors will have a positive and significant influence on BEI

2.4. Organizational factors and BEI

Organizational factors include managerial influence and organisational culture. Managerial influences include responsibility for providing appropriate training, positive feedback, participation, and guidance to ensure effective and efficient utilisation of organisational resources (Pramono and Susanty 2015; Kedong et al. 2022; Xu and Gao 2022). According to studies organisational factors has a significant influence on BEI. That is organisational factors include entities, conditions, and events within the organisation that may influence decisions and behaviours and reveal organisational strengths and weaknesses (Ghobakhloo et al. 2012). Further, organisational factors reveal the strengths and weaknesses of an organisation and also create the context in which processes occur and include organisational structure, leadership style/culture, and resource capacity to ensure collaborative work. Base on the aforementioned argument the study hypothesises as follow:

H2:

Organizational Factors will have a positive and significant effect on BEI

2.5. Regulatory framework and BEL

Regulatory framework (RF) is a framework that prescribes certain laws and regulations that are requirement to be met in order to be legally acceptable (Njoroge 2013; Zhao, Guan, and Sun 2019; Li et al. 2021). The RF is also the appropriate regulatory process for a given subject, which includes all relevant legal instruments (laws, regulations and annexes) and describes the authority or body responsible for managing the legal framework. Study on RF in terms of policies, standards directives and guidelines showed that companies whose activities and behaviours are shaped by RF is able to achieve higher sustainable performance (Kimaiga 2015). These directives, laws, policies, rules and regulations are enacted by the government to oversee the implementation and activities of companies. Therefore, RF is a significant contributor to BEI (Kimaiga 2015). In view of the ongoing presentation/argument the study hypothesises as follow:

H3:

Regulatory Framework has a positive and significant effect on BEI

2.6. Supply chain risk (SCR)

Supply chain risk is defined as the risk of disrupting daily processes and activities, thus disrupting supply chain planning (Flynn, Koufteros, and Lu 2016). SCR variable measure low consequences, negative impacts, and errors that adversely affect ocean businesses (Wang et al. 2020; Rasowo et al. 2020; Rudge 2021). In this study, SUCR is taken as a variable that have a substantial influence on BEI. Under this, SUCR is categorised into three including company side-risk, customer side risk and environmental side risk which have a substantial influence on BE. Company-side risks include those that may disrupt the flow of information and products (Ellegaard 2008). Company-side risks include delays in product delivery and acceptance checks, as well as inadequate storage and delivery capabilities which affects the ability to deliver on time. Customer-side risks typically arise from errors between consumer-related behaviour and customer requests for product and product delivery and order repair. In this study, customer-side risks include demand fluctuations, poor forecasting and inaccuracies. The third type of supply chain risk, environmental risk, arises from external environments. Environmental risks are unavoidable and play an important role in the supply chain (Wang et al. 2020). Also, road and border closures and fuel price fluctuations are also included in the environmental risk category. Stemming these presentations/arguments the study hypothesises as follow:

H4:

Supply chain risk will have a positive and significant influence on BEI

2.7. Green and environmental awareness (GENA)

Customers’ behaviour regarding green products is referred to as green awareness (Mourad et al. 2012; Techera 2018; Rasowo et al. 2020; Mathew and Robertson 2021; Rudge 2021). This behaviour is related to customers’ opinions about choosing and recommending environmentally friendly products (Suki 2013). The industry is aware of this behaviour and uses green marketing strategies to attract customers to buy the products they produce. It is believed that green awareness is based on the perception and memory of a brand as a green brand and is the result of green behaviours and associations (Mourad et al.) Environmental awareness has a strong influence on people’s behaviour to purchase green products that do not negatively affect the environment. Thus, the understanding of environmental issues and concerns hence influence BEI (Hu, Parsa, and Self 2010; Lin et al. 2017; Wang, Li, and Zhao 2018). Protecting the environment is a personal moral responsibility, (Steg et al. 2014) found that various values play an important role in reinforcing personal norms and consumer behaviours to use these values for green products, while consumer altruism has a significant impact on purchase intentions. Green and environmental awareness (GENA) complement each other and contribute to the sustainable goals of marine business: by integrating GENA with marine resources management, companies have the opportunity to be environmentally conscious and avert any habit that will destruct the health of the ocean ecosystem. GENA improves environmental and operational performance which in turns promote BEI. Therefore, this study considers GENA as variables to understand the adoption of BEI to improve sustainable performance of marine organisations. Resource efficiency, emission reduction, process and design energy efficiency, implementation of lean production tools and practices, and lean and green technologies are included in the variable ‘green and environmental awareness’. With respect to the ongoing argument/presentation the study hypothesises as follows:

H5:

Green and environmental awareness will have a positive and significant influence on BEI

2.8. Perceived cost (CO)

Cost is defined as the amount of money that must be paid to implement BEI technology in a country. The cost of the technology required for implementation determines the usefulness of implementing BEI and top management decisions (Dwivedi et al. 2016; Jones and Navarro 2018; Ozili 2022; Sabela-Rikhotso, van Niekerk, and Nemakonde 2022). Therefore, this study considered perceived cost as a variable to measure the influence on BEI. The introduction of new technologies usually incurs high cost as result of training requirements to familiarise users with complex technologies (Gallardo, Hernantes, and Serrano 2018; Museli and Jafari Navimipour 2018). The costs associated with BEI implementation are not simple and require complex calculations. Moreover, not only the transaction costs of BEI need to be determined but also the operation and maintenance costs (Wong et al. 2020a). Based on the ongoing presentation/argument, the study hypothesises as follows:

H6:

Perceived cost will have a positive and significant influence on BEI

2.9. Sustainable supply chain performance (SSCP)

In this study, SSCP is a measuring variable based on the triple bottom line (TBL) approach that incorporates environmental, economic, and social indicators to measure sustainability performance. It is imperative to reduce food waste and prevent emission and spillage into the oceans. This will save as costs and furtherance keep the healthy condition of the marine ecosystem. In this study, SSCP is considered as a variable that influence BEI (Voyer et al. 2018; Mukhopadhyay et al. 2020; Upadhyay and Mishra 2020). The economic indicators of SSCP include SC overhead costs (e.g. production costs, transaction costs, transportation and distribution costs, and equipment conversion costs), environmental costs, such as energy costs, and profitability (sales). Environmental indicators refer to the reduction of environmental impact and are related to the reduction of negative impacts and externalities caused by emissions, e-waste, inefficient use of resources, etc. Environmental indicators also include the reduction of food waste and loss through efficient green technologies and supply chain practices (El Bilali and Allahyari 2018; Allaoui et al. 2018; Tsang et al. 2018). Also, social indicators include the number of jobs created, farmers and small farmers. In addition, a section on food security was added to the social indicators of the SSCP. In a view of the ongoing argument/presentation, the study hypothesises as follows:

H7:

Sustainable supply chain performance will have a positive and significant influence on BEI

3. Methodology

The research focus of this paper is private business investors in two major cities in Ghana, namely, Accra and Kumasi. According to the Ghana Enterprise Agency (GEA), there are 2,825 formalised business registered under the agency with 190 operational district offices. The study focused on only businesses in the Kumasi and Accra metropolitan areas. The justification for this choice is based on GEA reports that suggest Kumasi and Accra metropolitan areas continue to dominate the SMEs industry in terms of numbers and business diversity. Besides, Accra being the administrative capital of Ghana, most businesses would like to have a presence in the city in order to tap into the ready market. SMEs Owners and Managers have been randomly selected as the participants for the study. Out of the target 500 sample size, 267 questionnaires were returned. Upon further checks 15 questionnaires were dropped due to inconsistency and multiple answers, seven questionnaires were subsequently dropped due to evidence that they were responded by employees who were neither owners nor managers, bringing the total useable questionnaires to 245 (representing 49% response rate). Recent studies (Appiah, Akolaa, and Ayisi-Addo 2022; Appiah et al. 2022, 2022), within the same location achieved similar useable feedbacks. Moreover, a confirmatory research design and quantitative research approach have been used in this study, Quantitative research approach has been used in this study because it uses statistical models and conforms to the objectivity conception of social reality. Survey strategy was used in this study with numerically rated items. Target Population of the study: The study focusses on all SMEs operating in the areas of food, medicine, transportations, recreation and tourism, fishery amongst others. The inclusion criteria were: i) Ghanaian owned SMEs; ii) legal registered SMEs; iii) SMEs that has existed for over 5 years. The measurement instruments were adapted from previous related studies and the underlying theoretical assumptions. Table 1 presents further details on the measurement of constructs, sources, of the measurement, the number of measurement items and the underlying theories. Data collection have been conducted using structured questionnaires and 5-Point Likert’s Typed Scale of measurement. Where 5-implies Strongly Agree, 4- Agree, 3-Neutral, 2-Disagree and 1- Strongly Disagree.

Table 1. Measurement instruments; sources; number of items and underlying theory.

Construct (Type of construct)	No. of Items	Item Source	Underlying Theory
Dependent Variable
Sustainable Supply Chain Performance	6	Nayal et al. (2021)	Resource-Based View
Independent Variables
Technology Factors	5	Nayal et al. (2021);	Technology, organization, Environment
Organizational Factors	5	Wong et al. (2020b), Nayal et al. (2021)	Technology, organization, Environment
Regulatory Environment	5	Nayal et al. (2021) Appiah et al. (2022)	Technology, organization, Environment
Supply Chain Risk	5	Nayal et al. (2021)	Resource-Based View
Green & Environmental Awareness	5	Nayal et al. (2021)	Resource-Based View
Perceived Cost	5	Nayal et al. (2021)	Authors’ Contribution
Mediating Variables
Blue Economy Investment Behaviour	6	Dodds, Monroe, and Grewal (1991) and Appiah et al. (2022)	Resource-Based View

Note: Authors Compilations.

In this study, PLS-SEM analysis is used to analyse the patterns among all latent variables. This study is based on a reflection model with an explicit variable (indicator) and the structure shown in Figure 1. In PLS-SEM, there are two types of model fit criteria: an external model and an internal model. The external model measures the relationship between variables in terms of validity and reliability, i.e. the fit of the external model evaluates the measurement model, whereas the internal model refers more to regression to evaluate the effect of a variable on other variables (constructs), or referred to as structural model evaluation (Hair et al. 2014, 2019). To effectively assess the structural model the path -coefficients and the T-values of the model were taken to account in order to accept or reject a hypothesis. The measurement model was evaluated using construct validity (Convergent and discriminant validity. For a test to achieve high construct validity, it must have both high convergent and discriminant validity: The results of the test must have a strong positive correlation with the results of other tests that measure the same thing (high convergent validity). The test results should also not correlate with the results of tests designed to measure different structures (high discriminant validity) (Hair et al. 2019). Specifically, Composite Reliability (CR), Cronbach Alpha and Factor Loading were used in this paper to evaluate the acceptability of the convergent validity while Average Variance Extracted and Cross loadings were used to evaluate the acceptability of the discriminant validity.

4. Results

4.1. data distribution, multicollinearity and normality test

Table 2 presents results on data distribution, multicollinearity and normality test. Measure the distribution of the data means and standard deviations have been reported. The results showed that fairly majority of the participants have agreed to some extents that they do consider perceived cost, organisational factors, technological factors, regulatory environment, and supply chain risk when taking investment decision to invest in the BE with mean scores ranging between 3.619 to 3.939. The standard deviation scores ranged between 0.881 to 1.155 implying that there was significant degree of variations among the participants (e.g. S.D > 1). The multicollinearity Problems have been assessed using VIF scores. The results have showed that VIF scores ranged between 0.544 and 4.468 which was below the acceptable minimum score of 5 (Hair et al. 2014). This implies that multicollinearity not a major problem (VIF<5). To assess normality of the distribution skewness and Kurtosis scores were assessed. The results have showed that the skewness scores ranged between−0.044 and 0.403 which is below−2 to 2, likewise kurtosis scores were below−7 to 7. These imply that the distribution does not violate the normality assumption. Therefore, the distribution is normal.

Table 2. Descriptive statistics and variance inflation factor (VIF).

Constructs	Items	Mean	Std.D.	VIF	Skewness	Kurtosis
Blue Economy Investment Behaviour	BEI1	3.816	.981	4.468 3.648 3.153 4.593 4.194 3.379	−.724	.330
	BEI2	3.750	1.055		−.707	−.100
	BEI3	3.873	1.054		−.969	.586
	BEI4	3.914	.978		−.727	.150
	BEI5	3.791	1.037		−.726	−.027
	BEI6	3.857	1.024		−1.044	.981
Perceived Cost	COST1	3.902	.970	1.804 3.429 1.018 2.083 1.345	−.723	.192
	COST2	3.787	1.034		−.727	−.014
	COST3	3.787	1.038		−.868	.382
	COST4	3.836	1.011		−1.054	1.071
	COST5	3.816	1.014		−.835	.450
Green & Environmental Awareness	GEA1	3.828	.934	1.647 2.407 2.122 3.617 3.717	−.470	−.473
	GEA2	3.816	.981		−.724	.330
	GEA3	3.721	1.161		−.673	−.322
	GEA4	3.684	1.128		−.663	−.315
	GEA5	3.693	1.134		−.764	−.024
Organizational Factors	ORGF1	3.754	1.066	1.079 4.984 1.335 3.798 2.752	−.680	−.257
	ORGF2	3.758	1.069		−.680	−.269
	ORGF3	3.861	1.035		−.812	.224
	ORGF4	3.746	1.064		−.813	.105
	ORGF5	3.869	1.036		−.825	.241
Regulatory Environment	RE1	3.791	1.053	0.544 3.427 3.682 2.927 1.737	−.761	.008
	RE2	3.787	1.038		−.868	.382
	RE3	3.770	1.046		−.738	.001
	RE4	3.820	1.012		−.848	.480
	RE5	3.811	.931		−.444	−.488
Supply Chain Risk	SCR1	3.939	.928	2.701 4.387 3.259 3.295 2.696	−.497	−.517
	SCR2	3.865	1.033		−.825	.254
	SCR3	3.775	1.045		−.751	.024
	SCR4	3.836	1.019		−.858	.462
	SCR5	3.885	.875		−.402	−.202
Sustainable Supply Chain Performance	SSCP1	3.619	1.155	3.488 3.990 2.962 2.501 3.081 4.459	−.565	−.579
	SSCP2	3.783	1.112		−.697	−.238
	SSCP3	3.664	1.128		−.654	−.281
	SSCP4	3.754	1.140		−.808	−.002
	SSCP5	3.680	1.140		−.668	−.376
	SSCP6	3.738	1.158		−.749	−.172
Technological Factors	TF1	3.861	1.027	2.601 2.725 2.311 1.781 2.175	−1.043	.964
	TF2	3.914	.881		−.447	−.191
	TF3	3.717	1.015		−.402	−.827
	TF4	3.852	1.030		−.743	.067
	TF5	3.779	.992		−.608	−.109

4.2. Measurement Model (Construct Validity)

As showed in the Table 3 the measurement model was evaluated using construct validity. For a test to achieve high construct validity, it must have both high convergent and discriminant validity: Specifically, CR, CA, and Factor Loading were used in this paper to evaluate the acceptability of the convergent validity. The results have showed that CR scores ranged between 0.886 to 0.953 which is more than the 0.7 minimum accepted value. To validate the CR scores CA and factor loading scores were assessed. CA scores ranged between 0.843 and 0.940 which is more than the 0.7 minimum accepted value. Moreover, the factor loadings as showed in the Table 5 far exceeded the 0.7 minimum accepted value. These results suggest that the model has high convergent validity. The AVEs and cross loadings were used to evaluate the acceptability of the discriminant validity. The AVE scores ranged between 0.610 and 0.791 which is higher than the recommended 0.50 value. The AVE scores were square rooted as showed in diagonal in Table 3. The results were compared with the correlational coefficients of the constructs and revealed that the squared values were more than the coefficients of the inter-constructs correlation suggesting a high acceptable discriminant validity (Hair et al. 2019). For robustness check, the Heterotrait-Monotrait Ratios (HTMT) as indicated in the Table 4 were examined. The results ranged between 0.037 and 0.798 which is less than the minimum recommended (0.85) (Henseler et al., 2015)

Table 3. Discriminant and convergent validity with Fornell and Larcker (1981) Approach.

	CA	CR	AVE	BEIB	GEA	OF	PC	RE	SCR	SSCP	TF
BEIB	0.924	0.940	0.724	.851
GEA	0.873	0.909	0.670	.772	.818
OF	0.934	0.950	0.791	.713	.784	.889
PC	0.918	0.939	0.755	.783	.757	.796	.869
RE	0.924	0.944	0.773	.705	.728	.720	.749	.879
SCR	0.866	0.903	0.654	.718	.745	.782	.736	.713	.809
SSCP	0.940	0.953	0.771	.732	.713	.722	.720	.771	.710	.878
TF	0.843	0.886	0.610	.752	.705	.707	.746	.701	.726	.724	.781

Table 4. Heterotrait-Monotrait Ratio (HTMT) Using Henseler et al. (2015) Criteria.

	BEIB	GEA	OF	PC	RE	SCR	SSCP	TF
BEIB
GEA	.659
OF	.782	.761
PC	.067	.748	.766
RE	.669	.740	.770	.017
SCR	.798	.777	.759	.018	.792
SSCP	.590	.793	.775	.780	.724	.783
TF	.037	.036	.002	.039	.004	.088	.706

Note: BEID=Blue Economy Investment Behaviour; GEA=Green & Environmental Awareness; OF=Organizational Factors; PC=Perceived Cost; RE=Regulatory Environment; SCR=Supply Chain Risk; SSCP=Sustainable Supply Chain Performance; TF=Technological Factors.

Table 5. Cross Loadings.

	BEI	GEA	ORGF	COST	RE	SCR	SSCP	TF
BEI1	.858	.836	.708	.840	.735	.767	.718	.831
BEI2	.821	.772	.743	.793	.832	.696	.658	.813
BEI3	.858	.655	.839	.807	.668	.782	.738	.770
BEI4	.839	.711	.839	.778	.641	.767	.744	.779
BEI5	.856	.751	.772	.896	.657	.854	.699	.811
BEI6	.874	.728	.760	.901	.795	.815	.688	.858
COST1	.850	.726	.843	.783	.645	.772	.757	.784
COST2	.860	.758	.776	.901	.762	.858	.706	.811
COST3	.818	.727	.741	.858	.786	.721	.675	.817
COST4	.874	.729	.755	.898	.785	.807	.690	.852
COST5	.862	.780	.774	.700	.844	.901	.730	.841
GEA1	.504	.624	.463	.529	.686	.609	.508	.652
GEA2	.858	.836	.708	.840	.735	.767	.718	.831
GEA3	.716	.816	.738	.658	.570	.705	.735	.704
GEA4	.722	.894	.633	.720	.769	.671	.667	.725
GEA5	.701	.891	.623	.706	.642	.692	.666	.735
ORGF1	.792	.716	.710	.766	.716	.716	.696	.838
ORGF2	.790	.712	.911	.767	.717	.718	.691	.833
ORGF3	.841	.686	.870	.816	.730	.877	.770	.768
ORGF4	.793	.691	.881	.809	.745	.715	.728	.826
ORGF5	.836	.679	.873	.818	.733	.880	.763	.769
RE1	.852	.736	.776	.899	.967	.865	.709	.811
RE2	.818	.727	.741	.858	.886	.721	.675	.817
RE3	.860	.755	.781	.605	.976	.873	.724	.819
RE4	.863	.778	.776	.602	.846	.903	.727	.842
RE5	.518	.648	.466	.533	.693	.613	.532	.660
SCR1	.509	.544	.584	.480	.462	.673	.499	.608
SCR2	.841	.684	.872	.818	.731	.880	.766	.769
SCR3	.863	.756	.778	.604	.970	.873	.720	.813
SCR4	.854	.763	.775	.699	.841	.903	.711	.833
SCR5	.536	.664	.480	.564	.577	.683	.524	.717
SSCP1	.708	.725	.772	.703	.669	.672	.898	.745
SSCP2	.709	.699	.710	.659	.583	.717	.869	.667
SSCP3	.753	.751	.670	.791	.854	.735	.867	.751
SSCP4	.712	.730	.635	.711	.642	.686	.832	.721
SSCP5	.699	.681	.757	.695	.662	.690	.914	.718
SSCP6	.791	.697	.782	.749	.645	.759	.888	.734
TF1	.871	.724	.757	.897	.790	.810	.685	.856
TF2	.525	.637	.484	.563	.575	.680	.503	.716
TF3	.519	.583	.654	.541	.585	.641	.548	.711
TF4	.805	.704	.853	.719	.605	.720	.702	.778
TF5	.867	.816	.734	.864	.897	.754	.725	.834

Note: BEI=Blue Economy Investment Behaviour; GEA=Green & Environmental Awareness; ORGF=Organizational Factors; PC=Perceived Cost; RE=Regulatory Environment; SCR=Supply Chain Risk; SSCP=Sustainable Supply Chain Performance; TF=Technological Factors.

4.3. Structural model

After the providing adequate results to justify the acceptability of the construct validity (measurement model). The structural model has two main functions: Firstly to assess the model predictive power and secondly to examine the path coefficients and hypotheses testing using the T-values. As showed in the Figure 2, the predictive power of the model ranged between 0.692 and 0.981 suggesting that environmental, organisational, regulation, supply chain risk, cost and green awareness have explained 98.1% of the variance in BEI behaviour, while BE behaviour has explained 69.2 percent variances in SSCP. Construct Cross-validated Redundancy analysis has been performed to assess the predictive relevance of the model as showed in Table 7. The Q² scores ranged between 0.525 and 0.701 which are greater than zero suggesting that the model has high predictive relevance. Table 6 and Figure 3 present the path coefficients and hypotheses testing.

Figure 2. R-Square and Path-Co-efficients Values.

Read the detailed description of this figure

Showing R-Square and Path-Co-efficients Values for Blue Economy Investment Determinants’ and SSC Performance.

Figure 3. Factor loadings and path-coefficients.

Read the detailed description of this figure

Showing Factor Loadings and Path-Co-efficients for Blue Economy Investment Determinants’ and SSC Performance.

Table 6. Path coefficients and hypotheses testing.

Hypothesis	Path-Coefficients	Original Sample	Mean	Std D	T Statistics	Decisions
Direct Effect
H1	TF -> BEIB	.120	.115	.054	2.215*	Supported
H2	OF -> BEIB	.099	.100	.031	3.193***	Supported
H3	RE -> BEIB	−.249	−.249	.046	5.430***	Supported
H4	SCR-> BEIB	−.073	−.068	.036	2.042*	Supported
H5	GEA -> BEIB	.092	.094	.039	2.387**	Supported
H6	PC -> BEIB	1.007	1.003	.060	16.920***	Supported
H7	BEIB -> SSCP	.832	.835	.027	30.388***	Supported
Indirect Effect
H^8	OF -> BEIB -> SSCP	.082	.084	.026	3.134**	Supported
H^9	RE -> BEIB -> SSCP	−.207	−.208	.037	5.532***	Supported
H^10	PC -> BEIB -> SSCP	.837	.838	.054	15.492***	Supported
H^11	SCR -> BEIB -> SSCP	−.061	−.056	.030	2.022*	Supported
H^12	TF -> BEIB -> SSCP	.100	.096	.045	2.220*	Supported
H^13	GEA -> BEIB -> SSCP	.077	.078	.032	2.363**	Supported

Note: BEID=Blue Economy Investment Behaviour; GEA=Green & Environmental Awareness; OF=Organizational Factors; PC=Perceived Cost; RE=Regulatory Environment; SCR=Supply Chain Risk; SSCP=Sustainable Supply Chain Performance; TF=Technological Factors; *p<0.05; **p<0.01; ***p< 0.001.

Table 7. Construct cross-validated redundancy.

	SSO	SSE	Q^2 (=1-SSE/SSO)
Blue Economy Investment Behaviour	1464.000	437.899	.701
Green & Environmental Awareness	1220.000	1220.000
Organizational Factors	1220.000	1220.000
Perceived Cost	1220.000	1220.000
Regulatory Environment	1220.000	1220.000
Supply Chain Risk	1220.000	1220.000
Sustainable Supply Chain Performance	1464.000	695.213	.525
Technological Factors	1220.000	1220.000

As showed in the Table 6, the results have shown that TF (ß = 0.120, T-value = 2.215) significantly influence, on (ß = 0.099, T-value = 3.193) significantly influence BEIB, RE (ß= −0.246, T-value = 5.430) significantly influence BEIB, SCR (ß= −0.073, T-value = 2.042), GEA (ß = 0.092, T-value = 2.387) significantly influence BEIB, and PC (ß = 1.007, T-value = 216.920) significantly influence BEIB. The results further showed that BEIB has significant (ß = 0.832, T-value = 30.388) effect on SSCP. The results further showed that BEIB significantly mediate the relationship between its determinants and SSCP.

5. Discussion of results

This paper has been conducted to develop an integrative model to explain the extent to which BEI drives SSCP by integrating TOE and National Resource based View theories with slight variation in order to conserve and make sustainable use of marine and prevent the worse of climate change. One of the three objectives was to ascertain the factors that affect BEI intention among SMEs. The study has revealed that that organisational factors, technological factors and the regulatory environment are significant determinants of BEI which is consistent with TOE theoretical framework. This suggests that process regulation effectiveness is crucial in BEI (Appiah et al., 2021). Again, the study has revealed that the supply chain risk, green environmental awareness and perceived cost are significant determinants of BEI which is consistent with existing theoretical knowledge. This suggests that resources ownership and capabilities are important considerations in BEI. This paper is among the very few to develop BEI model taking into cognisance NRBV and TOE theories. Most previous papers (Mahon, McConney, and Oxenford 2020; Voyer et al. 2021; Amuhaya and Degterev 2022; Ozili 2022; Sabela-Rikhotso, van Niekerk, and Nemakonde 2022) in this area concentrated on conceptual and review reports. While the former argues that investment in natural resources could lead to reduction in pollution, protection of biodiversity, and enhance sustainable development the latter argues that the macro-level consideration such as those relating to Technology, Organization, and Environment determine investment success. These two theories complement one another when use in combination. As a result, the current study has merged the dimensions of these theories to form a new paradigm which has been used to explain investment behaviour and sustainability performance in BE. Its commitment to an environmentally sustainable and socially inclusive maritime sector has attracted significant interest from governments, civil society, business, intergovernmental organisations, and development actors a key rallying point around which these diverse stakeholders can form alliances to address the interconnected challenges of declining ocean health, climate change, and seemingly ever-increasing demands (Techera 2018; Alharthi and Hanif 2020; Rasowo et al. 2020; Mathew and Robertson 2021; Rudge 2021).

Another objective of the current paper is to determine the relationship between BEI intention and SSCP. The study has revealed that a positive BEI intention has positive and significant relationship with SSCP. This result implies that investors with positive intentions towards BEI could readily enhance sustainable performance through their respective value chain. This is consistent with previous studies (Kedong et al. 2022; Xu and Gao 2022; Ozili 2022). According to the United Nations, more than three billion people depend on marine and coastal biodiversity for their survival. BE is the use of marine resources in a sustainable manner to ensure economic growth, better living conditions and create employment opportunities as well as to ensure the health of marine ecosystem (World Bank 2013; Zhao, Guan, and Sun 2019; Li et al. 2021; Kedong et al. 2022; Xu and Gao 2022; Ozili 2022). It is imperative to reduce food waste and prevent emission and spillage into the oceans. This will save as costs and furtherance keep the healthy condition of the marine ecosystem. In this study, SSCP is considered as a variable that influence BEI (Voyer et al. 2018; Mukhopadhyay et al. 2020; Upadhyay and Mishra 2020).

Again, the final objective of the study was to determine the mediating role of BEI in between the factors affecting its intention and SSCP. The study has found that BEI significantly mediates the association between factors affecting its (organisational, environmental, technological, cost, SC-risk, environmental awareness) intention and SSCP. BE includes the recognition that the productivity of healthy freshwater and marine ecosystems is the pathway to a water-based economy that enables not only continental countries, but also islands and other coastal states to benefit from their resources. The BE, considered key to sustainable ocean development, has gained such prominence over the past decade that it is almost impossible to address the issue of ocean policy or development without addressing it. BEIlue Economy Investment Behavior is based on pre-investment decision making which includes the qualitative and quantitative aspects such as previous experiences, personal preferences and beliefs of a particular product before making the actual decision. Hence, positive investment behaviour towards BE is prone to the development and growth of sustainability of every country (Mahon, McConney, and Oxenford 2020; Voyer et al. 2021; Amuhaya and Degterev 2022).

6. Conclusion, implications, and limitations

In Ghana, efforts have been made to enforce the Marine Pollution Act 2016 (Act, 932) which is aimed to prevent, regulate and control pollution within Ghana’s territorial waters, however the health of Ghana’s oceans is deteriorating. To address this concern, our paper was aimed to develop an integrative model to explain the extent to which BEI drives sustainable supply chain performance by integrating NRBV and TOE theories with inclusion of supply chain risk, and green environmental awareness in order to conserve and make sustainable use of marine and prevent the worse of climate change. Besides, it was critical to undertake studies that could impact on the realisation of SDGs 3, (wellbeing), 13 (climate action) and, 14 (marine resources). The study has revealed that organisational factors, technological factors, supply chain risk, green environmental awareness, perceived cost and regulatory environment are significant determinants of BEI. BEI significantly drives SSCP.

6.1. Theoretical, and practical implications

Theoretically, the emergency of an integrated model for improving BEI has been developed based on TOE and NRBV theories with slight variations. The integrated model offers robust predictability as compare to the strength of the individual theories. As a practical guide, the study has discovered that conceptual factors such as organisational factors, technological factors, supply chain risk, green environmental awareness, perceived cost, and regulatory environment determine investment success in the BE sector. This will guide investment decisions in the sector. The results shall reignite policy reforms and enforcement towards the realisation of Sustainability Development Goals as well as Agenda 2063 for Africa Development. Investors and practitioner could use the newly developed model as a checklist for blue investment decisions. Stakeholders in the Ghanaian SMEs sub-sector can take proactive steps to advance investment in BE in order to enhance sustainability performance. SMEs could deploy the findings in this study to generate and share sustainability knowledge and subsequent transfer the knowledge to other operational areas.

7. Limitations of the study

The main limitation of the study of the study is the scope which was very broad. BE encompasses field likes shipping, sea food, marine energy, transportation, flight recreation among others. It is suggested that future studies should limit their focus on specific filed of the BE. Since this study was a baseline survey it was necessary to consider broad areas of the sector. Moreover, future studies could consider using different research approach including qualitative methods. Finally, inter country comparative should be considered in the future.

Highlights

• This paper aims to develop an integrative model to enhance Blue Economy Investment through sustainable supply chain performance by integrating competing theories to attract sustainable investment to conserve and make sustainable use of marine resources.

• There is very little evidence in literature of similar studies within the context of marine resources investment in Ghana or elsewhere being to determine blue economy adoption and sustainable supply chain performance.

• The implications include the emergence of an integrated model which could be used to improve marine resources investment and the realisation of Sustainable Development Goals 3, 13 and 14 in Ghana.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Michael Karikari Appiah

Michael Karikari Appiah (Ph.D.) is a Research Consultant and a Lecturer at the School of Sustainable Development, University of Environment and Sustainable Development in Ghana. Michael does research in sustainable development, energy economics, environmental economics and policy, renewable energy resources and technologies, development economics, entrepreneurship, management and sociology. His current project is “Modelling SMEs Investment Strategies to Enhance Indigenous Participation in Renewable Energy Transition Industry”. Michael conceptualized the idea, developed the theoretical framework, contributions of the study, designed the data collection instruments, analyzed the data. All the contributors proofread and approved the final draft.

Elikplim Ameko

Elikplim Ameko is a research assistant at the School of Sustainable Development, University of Environment and Sustainable Development in Ghana. Elikplim has interest in Supply chain, sustainable development, renewable energy resources, Green energy technologies among others. Elikplim provided assistance throughout the research process including - theoretical development, data collection, data analysis, instrument development and was sole responsible for the piloting of the instrument. He proofread and approved the final version of the manuscript.

Theodora Akweley Asiamah

Theodora Akweley Asiamah (Ph.D.) is a Senior lecturer at the Department of Water Resources and Sustainable Development, University of Environment and Sustainable Development in Ghana. Theodora teaches and researches in indigenous development, sustainability, community development, gender studies, financial inclusion, and environmental development. Theodora was responsible for the discussion of the result, implications of the study and also assisted in the data collection, proofreading and approval of the final manuscript.

Rahmat Quaigrane Duker

Rahmat Quaigrane Duker (Ph.D.) is a lecturer at the School Natural and Environmental, University of Environment and Sustainable Development in Ghana. Rahmat does teach and research in sustainable development, sustainable communities, aquatic ecotoxicology, aquatic, pollution (sediment pollution and remediation), Water quality monitoring and analysis, Environmental monitoring and emerging pollutants.

Appendix : Survey Instrument

The participants were asked to rate the following items using 5-point Likert’s scale where 5-implies strongly agree and 1-implies strongly disagree.

Table

Constructs	Sources
Organizational Factors	Wong et al. (2020b), Nayal et al. (2021)
OF1: Our company has the needed financial resources to support growth
OF2: Our company has the strong internal organisational practices
OF3: Our company has a supporting management team
OF4: Our company frequently engages in collaborative work
OF5: Our company robust IT infrastructure system
Supply Chain Risk	Nayal et al. (2021)
SCR1: Our company experience delays in products delivery and pickup
SCR2: Our company has inadequate storage and delivery capacity
SCR3: Our company frequently experience demand volatility
SCR4: Our company often experience poor forecasting
SCR5: Our Company sometimes encounter shortage of labor
Regulatory Environment	Nayal et al. (2021)
RE1: Our company always maintain the industry standards
RE2: Our company always complies with regulations and policies
RE3: Our company often adjust in p with policies with the market conditions
RE4: Our company has Regulatory environment for data privacy and security
RE5: Financial support from the associated authorities or regulators
Technology Factors	Wong et al. (2020b), Nayal et al. (2021)
TF1: Our company can boost of high data quality
TF2: Our company has reliable technology availability
TF3: Our company has network complexity
TF4: Our company has standards for technology application
TF6: Our team has technology knowledge
Perceived Cost	Nayal et al. (2021)
COST1: The infrastructure cost of BE investment is high
COST2: The maintenance of BE investment is high
COST3: The operational cost of BE investment is high
COST4: There is high transaction cost associated with BE investment
Green & Environmental Awareness	Nayal et al. (2021)
GEA1: Our company always ensure efficient resource utilisation
GEA2: Our company is focused on reducing emission
GEA3: Our company uses energy efficiency of process and design
GEA4: Our company uses lean tools and practices
GEA5: Our company uses green technologies
Blue Economy Investment Behaviour	Dodds, Monroe, and Grewal (1991), Appiah et al. (2021)
BEI1: Our company will invest in BE due to high probability
BEI2: Our company plan to invest in the BE sector
BEI3: Our company want to invest in the blue economy sector
BEI4: Our company intend to invest in the BE sector
BEI5: Our company would invest in the BE to sustain marine resources
BEI6: Our company would invest in the BE sector to sustain the environment
Sustainable Supply Chain Performance	Nayal et al. (2021)
SSCP1: Our company has reduced the overall SC overall cost
SSCP2: Our company has increased its environmental commitment
SSCP3: Our company has increased its profitability through the sales revenue
SSCP4: Our company has reduced the environmental impact
SSCP5: Our company has reduced food waste and losses
SSCP7: Our company has increased the number of jobs created