The concept of bioeconomy possesses interpretative flexibility in ways that can be employed to the specific challenges and meet the needs of diverse actors and objectives (Meyer, 2017; Barañano et al., 2021; Mijailoff and Burns, 2023; Trigkas and Karagouni, 2023). Recent theoretical developments acknowledge the technology-based implementation pathway to bioeconomy (Leitão, 2016; Meyer, 2017; Hernández-Pérez et al., 2020; Bröring and Thybussek, 2023).

Previous research has confirmed that TAM is a valid model that represents an important theoretical framework to explain and predict acceptance of low-carbon technological innovations such as bioeconomy (Tran and Cheng, 2017; Liu et al., 2018; Ali et al., 2020; Bagheri et al., 2021; Khoza et al., 2021; Park, 2021; Yang et al., 2021). The TAM is widely employed in explaining and predicting the acceptability of innovative products and technologies (Liu et al., 2018; Al-Tarawneh, 2019; Ali et al., 2020; Dhagarra et al., 2020; Naseri et al., 2023). A revised version of TAM by Venkatesh and Davis (1996), referred to as TAM2, proposes that the influence of external factors on behavioral intention (BI) is mediated by perceived usefulness (PU) and perceived ease of use (PEOU). The external factors, which are antecedents of PEOU and PU, are crucial in explaining technology adoption behavior (Al-Tarawneh, 2019; Khoza et al., 2021; Zhang and Liu, 2022). Several TAM-related studies have revealed the evolving role of users’ psychological characteristics on their acceptance of new technology (Rajaee et al., 2019; Hsu and Lin, 2021; Khoza et al., 2021; Acikgoz et al., 2023).

This study employs an extended TAM comprising Stern’s Value-Belief-Norm (VBN) theory of environmentalism (Stern et al., 1999; Stern, 2000) influenced external psychological constructs, namely subjective knowledge, environmental attitude (measured by New Environmental Paradigm, NEP), and belief about climate change. Pro-environmental behavior intentions in response to climate change have been predicted using the VBN theory, which focuses on finding predictors of environmentally significant behavior (Hartmann et al., 2018; Joffre and King Jr., 2020; Zhang et al., 2020). Prior VBN studies have employed the three external psychological factors in this study to understand pro-environmental behavior intentions: subjective knowledge (Rajaee et al., 2019; Rizkalla and Erhan, 2020; Wang et al., 2020), the New Environmental Paradigm (Chen, 2015; Han et al., 2018; Liu and Wu, 2020), and awareness (belief) of climate change problem (van der Werff and Steg, 2016; Kim and Kim, 2018; Liobikiené and Poškus, 2019). Subjective knowledge is involved in this study because it influences behavior and decision-making more than objective knowledge (Eberhardt et al., 2020; Acikgoz et al., 2023; Viot et al., 2023; Zheng et al., 2023). Attitude is a predictor of behavior, and environmental behavior is predicted by environmental attitudes—the overall relationship between humans and the environment. The NEP is a widely used unidimensional measure of environmental attitudes. An ecocentric orientation that reflects a commitment to the preservation of natural resources and environmental protection is indicated by a higher NEP score (Matsiori, 2020; Sh Ahmad et al., 2022; Gansser and Reich, 2023). The role of society in mitigating climate change is particularly important. Belief about climate change describes a person’s attitude toward climate change and predicts pro-environmental behavior (Shadiqi et al., 2022; Tarinc et al., 2023).

Public acceptance is a major dimension of the diffusion and adoption of bioeconomy in society (Bröring et al., 2020; Nagy et al., 2021; Oguntuase and Adu, 2021). Typically, acceptance studies focus on identifying factors that foster or inhibit the adoption of bioeconomy among the population, including the purchase and consumption of bioeconomy products and the desirability of contemporary scientific and technological developments in a bioeconomy (Rudolph, 2018; Hempel et al., 2019; Eversberg and Holz, 2020). A lack of public acceptance of bioeconomy products has been reported in the literature (Sijtsema et al., 2016, Stern et al., 2018; Bröring and Vanacker, 2022; Ruf et al., 2022; Macht et al., 2023).

Knowledge is an important factor in the acceptance, appreciation, and promotion of bioeconomy (Mukhtarov et al., 2017; Dallendörfer et al., 2022; Harrahill et al., 2022). It is recognized as a positive predictor of public acceptance of bio-based technological innovations (Zografakis et al., 2010; Herbes et al., 2018; Zander et al., 2022), and consumer knowledge is a determining factor in the purchase of bio-products (Lynch et al., 2017; Dilkes-Hoffman et al., 2019; Ende et al., 2023; Skouloudis et al., 2023). Attitude influences the acceptance of bioeconomy products (Russo et al., 2019; Macht et al., 2023). Studies found a link between environmental attitudes and choice-based behavior with regard to bioeconomy products (Rumm et al., 2013; Scherer et al., 2017; Tran and Cheng, 2017; Scherer et al., 2018b; Zander et al., 2022). Product acceptance intentions in the bioeconomy are influenced by perceived usefulness (Soland et al., 2013; Wurster and Schulze, 2020; Bagheri et al., 2021).

^Due to political obscurity just a mere decade ago, governments and businesses all over the world are currently promoting the idea of a bioeconomy as a new paradigm for a sustainable economy, with several countries and jurisdictions formulating dedicated wholesome bioeconomy policies, initiatives, or strategies (Vogelpohl, 2021; Dietz et al., 2023; Gardossi et al., 2023). However, bioeconomy is not popular in Africa yet (Rosa and Martius, 2021) and is not governed by any explicit bioeconomy strategy in most African countries. On the continent, only South Africa has a well-defined bioeconomy strategy. There are some bioeconomy-related policies and initiatives in place in nations such as Democratic Republic of the Congo, Nigeria, Ghana, Namibia, Uganda, Ethiopia, Kenya, Senegal, Mozambique, Mali, Mauritius, Malawi, Rwanda, Congo, Tanzania, and Zimbabwe, but there is no evidence that these have had a particularly positive effect on the continent’s economy or society as a whole (Oguntuase, 2018; Rosa and Martius, 2021). Comparing African countries’ bioeconomy potential to those of nations with dedicated bioeconomy policies or strategies, the former have lower potential. The only nation on the continent with a defined bioeconomy strategy is South Africa, which has the highest potential for a bioeconomy. This has policy implications in that developing a national bioeconomy strategy is the first step toward implementing bioeconomy in Africa (Oguntuase and Adu, 2021).

By substituting renewable biological resources for fossil fuels in the bioeconomy, greenhouse gas emissions are prevented or reduced, and the effects of global climate change are mitigated (Lima, 2022; Perišic et al., 2022; Dees et al., 2023). The main way the bioeconomy mitigates climate change is by lowering the net flow of CO2 into the atmosphere by replacing carbon-intensive fossil fuels and products with less carbon-intensive bioeconomy products. Contrary to carbon-intensive fossil fuels, biomass produces the same amount of CO₂ ingested during its growth, which is addressed as ‘carbon neutral’ in scientific terms (Timmons et al., 2016; Martínez et al., 2020). In addition to the literature on the role of bioeconomy in mitigating climate change, the important role of bioeconomy in supporting countries to reach the goals of the agreement as reflected in their Nationally Determined Contributions (NDCs) has also been discussed in the literature (Machado et al., 2019; von Braun and Mirzabaev, 2019; Boyarov et al., 2021; Fava et al., 2021).

The bioeconomy offers numerous opportunities for carbon removal and management by incorporating biological carbon fixation into a wide range of different end bioeconomy products, with biofuels, bioplastics, biochar, and wood products offering near-term carbon removal potentials (Dees et al., 2023). Almost all intergovernmental panel on climate change (IPCC) mitigation scenarios that are consistent with the 1.5–2°C target and that constrain end-of-century atmospheric CO₂ to 450 parts per million rely on a large-scale contribution from biofuels (Daioglou et al., 2017; Sebos, 2022; Usmani, 2023). According to Bang et al. (2009), industrial biotechnology, biofuels, and bioenergy could cut greenhouse gas emissions worldwide by 1.0–2.5 billion tonnes of CO2 equivalent annually by 2030. Because they have smaller carbon footprints than petro-plastics, bioplastics derived from second-generation biomass considerably lessen the effects of climate change (de Paula et al., 2018; Lamberti et al., 2020; Rosenboom et al., 2022). By 2050, bioplastics could eliminate more than 1 billion tonnes of CO2 annually (Meys et al., 2021). The use of biochar has demonstrated a major impact on the total greenhouse gas emissions’ global warming potential (GWP) (Ashiq et al., 2020; Shakoor et al., 2021). Globally, biochar systems could deliver emissions reductions of 3.4–6.3 Pg CO₂e, half of which constitutes CO₂ removal (Lehmann et al., 2021). Wood products help mitigate climate change in addition to storing carbon in forest ecosystems and harvesting wood products. This is especially true if they are used to replace more fossil-intensive products such as steel and concrete (Leskinen et al., 2018; Himes and Busby, 2020; Hurmekoski et al., 2023). Beyond reducing the effects of climate change, the bioeconomy and climate change adaptation has a lot of potential to work together to improve people’s quality of life and provide energy and food security (Mukhtarov et al., 2017; Yang et al., 2021).

There are two sections to the questionnaire. The demographic data of the respondents are surveyed in Part 1. Subjective knowledge of the bioeconomy (SK), environmental attitude (measured by the New Environmental Paradigm, NEP), belief about climate change (BCC), perceived usefulness from climate change (PU), and intention to accept the bioeconomy (INT) are the five individual-level factors about which data are collected in Part 2. Based on a review of the literature, a total of 19 items were created for the five constructs. A 5-point Likert scale was used to rate the items used to measure the five constructs: strongly agree = 5, agree = 4, not sure = 3, disagree = 2, and strongly disagree = 1. Reverse coding was used for the subjective knowledge (SK) items, SK1, SK2, and SK3.

The draft test instrument was reviewed for readability, clarity, content relevance, and comprehensiveness by eight reviewers comprising academics, teacher scientists, environmental scientists, business scientists, and policy scientists. The eight reviewers are sufficient to validate the questionnaire items (Faris and Ahmad Ramli, 2016; Boateng et al., 2018). The reviewers commented on the format, wording of questions, order/flow of the questions, made corrections, and wrote comments and suggestions on the items. The three major amendments made to the questionnaire based on the reviewers’ opinions were the removal of two variables—household size and household income—from Section A and the rephrasing of five items in Section B for better understanding.

A pilot survey was carried out between 4th October 2021 and 30th October 2021 among 50 residents of Lagos, Nigeria, who would not be part of the main study in line with the submission by Treece and Treece (1982) as well as Connelly (2008) that a pilot study sample should be 10% of the sample projected for the larger parent study. The questionnaires were re-administered, and 46 valid questionnaires were collected for analysis. A time of 6 weeks was the only source of variance in the test–retest reliability. The calculated test–retest reliability coefficient in this study was 0.81, which was reliable for a developing questionnaire (Matheson, 2019). The computed Cronbach’s alpha coefficients for the constructs based on the pilot study findings are shown in Table 1. All the coefficients exceed the conventional lower limit of 0.70 (Taber, 2017).

The pilot study provided the following insights into how the actual process of data collection should proceed: (1) To make certain questionnaire items easier for respondents to understand, simple and easily understood words were used in place of terminologies; (2) The need to reformat the location of tick boxes under the section; (3) The rephrasing and substitution of some items in the questionnaire; and (4) The length of the questionnaire was found appropriate considering that the time taken to answer the questionnaire was an average of 5 min. Table 2 displays the final measures for each construct.

While ethical approval was not required for this study, critical ethical principles of freely given consent, deception, debriefing, withdrawal from the survey, confidentiality, and protection of participants were observed. The nature and purpose of the survey were explained to all the respondents so they could make an informed decision about whether they wanted to participate or not. Respondents were asked for verbal consent before the questionnaires were administered. Anonymity and confidentiality, which are crucial, were also made clear to the respondents, and clearly informed that their participation is voluntary and re-negotiable. Each questionnaire contained a reference code that ensured the anonymity of all the respondents so that their identity would never be linked to their responses and that no personal details would be made public.

The sample size of the survey research was calculated using the simplified formula by Yamane (1967). The Yamane formula is n = N/1 + N (e)², where n is the sample size, N is the population size, and e is the margin of error. This formula assumes a level of precision of 0.05 and a confidence level of 95%.

The sample size was estimated to be 400, based on estimates of the population in Lagos (Famuyiwa et al., 2022). However, to overcome the risks of non-responses or poorly answered questionnaires, the number obtained was divided by the expected response of 80%, which is considered acceptable (see Fincham, 2008; Ewing et al., 2018) to get 500 as the study population. Proportional stratified random sampling was employed to distribute 500 questionnaires among the accessible population—the residents of Ikeja, Ikorodu, and Badagry local government areas based on their populations (Lagos State Government, 2019). Ikeja, Ikorodu, and Badagry local government areas are urban (Afolabi et al., 2017), peri-urban (Adedire, 2017), and rural (Otekhile and Verter, 2017) areas, respectively. The survey was carried out between February 2022 and July 2022. A face-to-face administration of the questionnaires was done by the researcher and three trained field assistants.

Of the 500 questionnaires distributed, 35 contained missing data. To prevent the study variables and constructs from being artificially correlated, the 35 incomplete questionnaires were removed. Therefore, 465 valid questionnaires with a 93% response rate were analyzed for interpretation.

Table 3 displays the calculated Cronbach’s alpha values for the study’s constructs: SK, NEP, BCC, PU, and INT have values of 0.87, 0.77, 0.87, 0.71, and 0.70, respectively. Every one of them exceeds or equals the widely accepted lower bound of 0.70 (Taber, 2017).

To evaluate the measurement model’s validity and reliability, confirmatory factor analysis was performed; the results are displayed in Table 4. The study’s standardized factor loadings are all higher than the 0.50 threshold for acceptable loading, supporting the constructs’ validity as appropriate indicators for measuring the variables (Chen and Tsai, 2007; Truong and McColl, 2011). The construct variables’ average variance extracted (AVE) values are greater than the generally accepted threshold of 0.50, which indicates that the instrument variables are valid and the tested model does not have a convergent validity issue (Bagozzi and Yi, 1988; Gangwal and Bansal, 2016). The squared multiple correlations (R-squared) were well defined by the measure items; most R-squared values were higher than the 0.50 threshold (Bryne, 2001; Al-Hawari et al., 2005). All of the composite construct reliabilities were above the acceptable threshold of 0.70, implying that every item consistently measures the same latent factor (Hair et al., 2010; Raykov and Marcoulides, 2016).

We recruited participants with heterogeneous demographic backgrounds to ensure fair representation. The sample included 237 male (50.97%) respondents and 228 female (49.03%) respondents. There is not much gap between the male respondents and the female respondents. This shows that there is a fairly even gender distribution among the respondents. Nearly a quarter of the respondents (24.73%) were 25 years old and below, followed by those aged between 26 and 41 years (23.65%), between 42 and 57 years (22.37%), between 58 and 76 years (16.56%), and ≥ 77 years old (12.69%). Furthermore, 38.50% of the respondents were single (n = 179), 35.70% were married (n = 166), and approximately one-tenth were separated (n = 48), while the remaining respondents were equally divorced or widowed (n = 36). The respondents had different levels of education, beginning with secondary school (21.94%), followed by Nigeria Certificate in Education (NCE) and equivalent National Diploma (ND) (23.01%), bachelor’s degree and its equivalents (40.00%), and master’s and above (15.05%). The places of residence of the respondents were urban (n = 182), peri-urban (n = 183), and rural (n = 100).

The level of residents’ perception of the studied constructs was reflected in the mean of the construct. In contrast to perceived usefulness (mean = 12.61) and intention (mean = 9.56), subjective knowledge (mean = 8.43), environmental attitude (mean = 11.71), and belief about climate change (mean = 11.23) showed comparatively smaller mean scores.

To gain a comprehensive understanding of the respondents’ perceptions, the responses to each of the measures were further categorized into three groups: positive (agree + strongly agree), neutral (not sure), and negative (strongly disagree + disagree), as illustrated in Figure 1. The respondents who expressed poor subjective knowledge of the bioeconomy were 44.5%, with 216 respondents indicating they feel knowledgeable about it (SK1), 193 respondents claiming they know less about it than most other people (SK2), and 211 respondents stating they do not really know a lot about it (SK3); 53.0% of respondents indicated low belief in climate change, and 65.0% of respondents also had a negative attitude toward the environment. Using bioeconomy products would be useful (PU1) and convenient (PU2) for 44.52% of the respondents. Residents’ opinions toward using biofuels, if they are available, were favorable in 46.5% of cases (INT1). Individuals who will make an effort to find bioeconomy products (INT2) and encourage their friends and family to purchase bioeconomy products (INT3) made up 48.6 and 44.3% of the sample, respectively. The survey participants generally exhibited favorable opinions regarding the perceived usefulness of the bioeconomy and the intention to accept it.

A standard path coefficients analysis was conducted to investigate potential relationships between individual-level psychological factors, perceived usefulness from the bioeconomy, and intention to accept the bioeconomy. The results of the relationships between the variables are shown in Table 5.

All three of the study’s hypotheses were supported at the significance level of 0.05 in each scenario that was investigated. Subjective knowledge (β = 0.29, p = <0.01) and belief about climate change (β = 0.25, p = <0.01) both have a positive influence on perceived usefulness. Perceived usefulness is less strongly predicted by environmental attitude (NEP) (β = 0.13, p = 0.04). Additionally, environmental attitude (β = 0.07, p = 0.04) and subjective knowledge (β = 0.09, p = 0.01) have a positive influence on the intention to adopt the bioeconomy. Belief in climate change is a strong and positive predictor of intention to accept bioeconomy (β = 0.68, p = <0.01). Perceived usefulness and intention to accept the bioeconomy had a strong and statistically significant relationship (β = 0.76, p = <0.01). As seen in Figure 2, the analysis revealed that four of the seven path relationships in the structural model are positively significant and supported.

The Bioeconomy Technology Acceptance Model (BTAM) was developed, and its fit was assessed using five measures. The metrics included were the goodness-of-fit index (GFI), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMSR), comparative fit index (CFI), and Tucker–Lewis index (TLI). Overall, the model has an acceptable fit. Table 6 presents the findings of the model’s fitness used in this study. The model has an acceptable fit with a goodness-of-fit index (GFI) of 0.92 and an adjusted goodness-of-fit index (AGFI) of 0.90, both of which are approximately 1 (Schermelleh-Engel and Moosbrugger, 2003). The root mean square error of approximation (RMSEA) is 0.06, and the root mean square residual (SRMR) of the model is 0.04. A good fit is indicated by the small RMSEA and SRMR values (Hoe, 2008; Cangur and Ercan, 2015). The model has a perfect fit because the Tucker–Lewis index (TLI) value is 0.96 and the comparative fit index (CFI) value is 0.97, both of which are close to 1 (Hu and Bentler, 1999: Cangur and Ercan, 2015).

The majority of African nations lack a dedicated circular bioeconomy policy, which is a prerequisite for the responsible advancement of the circular bioeconomy. It is imperative for national and sub-national governments to formulate cohesive bioeconomy policies to drive initiatives such as public enlightenment about the economy.

The identified factors that influence the acceptance of circular bioeconomy in this study include limited knowledge of bioeconomy and moderate belief in climate change. These underscore the need for initiatives to improve public knowledge of circular bioeconomy products and belief about climate change. Local governments and authorities should support communication campaigns that reinforce climate action attributable to bioeconomy. To improve the bioeconomy awareness and knowledge of students who may become future adopters and influencers of the circular economy, higher education universities and institutes can modify their curricula to include education and training programs in the circular economy.

Understanding pre-conditions for public use of circular bioeconomy products is crucial when embarking on product development and commercialization to prevent investment loss. The findings of this study implied that promotional activities by manufacturers of bioeconomy products should target individual psychological attributes of the target consumers. Collaboration between academia and business is essential to fully understand the psychological dynamics of consumer markets so that producers can introduce circular bioeconomy products to specific market niches.

This study focused on circular bioeconomy, which is one of several low-carbon technologies. It also applied a theoretical lens, the Technology Acceptance Model (TAM), out of an array of such models. Due to the novelty of the study’s objectives, the dearth of literature on public acceptance of circular bioeconomy in Africa, and the absence of literature on public acceptance of bioeconomy as climate action, related studies provide some guidance in this study. Furthermore, the lack of resources to undertake a longitudinal and country-wide study has restricted the collection of participants’ and respondents’ data to a single point and the accessible population to the adult population of three local government areas in Lagos Metropolis.

This study exclusively examined the public acceptance of circular bioeconomy. Future researchers are encouraged to validate the thesis of this study with other low-carbon technologies such as green building, electric vehicle, and solar photovoltaic. It is also desirable to combine the TAM with other theoretical models, such as the Innovation Diffusion Theory (IDT), Technological-Personal-Environment Framework (TPE), Technology Readiness Index (TRI), and Unified Theory of Acceptance and Use of Technology (UTAUT), to study the acceptance of circular economy. Furthermore, the predictors involved in this study are not exhaustive for acceptance of new technology. Additional findings from more extensive studies in other contexts, such as organizational-level or social-level and larger populations, would contribute to efforts to ensure the adoption and diffusion of circular products in society.