Factors Influencing the Purchase of Energy-Efficient Appliances by Young Consumers in South Africa
Published Online: Oct 24, 2020
Page range: 151 - 166
DOI: https://doi.org/10.2478/fman-2020-0012
Keywords
© 2020 Olawale Fatoki, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Consumption is a major economic and social activity, and household consumption as a percentage of the global gross domestic product was 63.64% in 2018 (World Bank, 2019). This high level of consumption by households has led to the massive depletion of resources, pollution, and loss of biodiversity (Paddock, 2017). Human needs for natural resources have doubled in the past 50 years, with negative environmental impact and environmental risks accounting for three of the five major risks by likelihood and four by impact according to the 2018 Global Risks Perception Survey (World Economic Forum, 2018). According to the World Wildlife Federation (2016), humans presently need the biocapacity of 1.6 Earths to accommodate the goods and services that are demanded each year and the present consumption pattern is unsustainable.
In addition, the world population is projected to reach 9 billion people by 2050, and this will further increase consumption and deplete resources. Also, global electricity demand increased by 4% in 2018 and in South Africa, electricity generation increased by 2.2% between July 2017 and July 2018, while electricity consumption increased by 1.2% within the same period (Statistics South Africa, 2018; International Energy Agency, 2019). South Africa is one of the least energy-efficient nations and the 11th highest emitter of greenhouse gasses in the world. The reliability of electricity supply is under threat in South Africa, and the main energy supply company (Eskom) is unable to meet demand and rolling blackouts are common. Inadequate supply of electricity is hugely disruptive and negatively impacts on the economy (Department of Energy, 2019).
Household appliances are the biggest contributor to household energy consumption, and around 70% of household carbon dioxide emissions come from household appliances (International Energy Agency [IEA], 2016). The high rate of household consumption of energy yields negative externalities such as climate change, nuclear disasters, and over-reliance on costly fuel importation, especially by countries that do not produce petroleum (Stadelmann, 2017). One of the ways to reduce emissions and conserve energy is to increase the purchase and use of energy-efficient appliances (EEAs) (Hua and Wang 2019; Wang, et al., 2019). Goals 7 and 12 of the sustainable development goals call for a secure access to affordable, reliable, sustainable, and modern energy and sustainable production and consumption (United Nations, 2019). The South African government is encouraging energy conservation in all the sectors of the society through the National Energy Efficiency Campaign and energy efficiency (EE) ratings, and by law, all household appliances are mandated to display labels that accurately show their EE. However, the level of awareness of EE campaign is low in South Africa and this is one of the major implementation barriers of energy conservation (Tholen, et al., 2015). Therefore, it is important to understand the factors that can promote the use of EEAs in South Africa.
The theory of planned behavior (TPB) is the most frequently used theory to predict pro-environmental intentions and behaviors (Ru, et al., 2018). The TPB by Ajzen (1991) contends that the performance of a specific behavior by an individual is determined by the intention to perform the behavior. The behavioral intention of an individual is determined by three factors, which are attitude, subjective norms and perceived behavioral control. According to Chen and Tung (2014), the explanatory power of the TPB can be improved by the inclusion of additional constructs. This study extended the TPB by adding two individual constructs (environmental concern and moral norms) and two situational constructs (informational publicity and perceived benefits) to develop a model of purchase intention for EEAs. Empirical research (Ng, et al., 2018; Huang and Ge, 2019) argues that many factors influence the intention to purchase environmentally friendly products. This study aims to develop an EEA purchase intention model that extends the TPB from the perspective of young customers in South Africa.
The study will be significant in the following ways. First, EEA purchase intention and behavior is an emerging area of research especially in developed countries; however, to the best of the author's knowledge, this is the first study from the South African perspective. Second, the study intends to develop a theoretical model of EEA purchase intention that extends the TPB by incorporating both individual and situational factors. Third, the study examines if purchase intention actually affects purchase behavior, and fourth, the study focuses on young consumers (university students) who are expected to drive sustainable consumption in the future.
In addition, as pointed out by Maistry and McKay (2016), universities in South Africa face the challenge of escalating energy costs and are expected to provide leadership by being exemplars of energy efficiency. Therefore, the reduction of energy consumption by university students is expected to cascade down to parents, households, and businesses. The study is organized as follows. Section 2 provides a review of the literature and the development of the conceptual framework and hypotheses. Section 3 focuses on the research methodology and measures. Sections 4 and 5 present the results and discussion, respectively. The conclusion, implications, limitations, and areas for further study are presented in Section 6.
Shove (2018) pointed out that energy efficiency (EE) can be described as the use of a lower amount of energy to produce the same amount of a product or service. According to the World Energy Council (2013), EE embraces all changes that lead to the reduction of energy used for a given service or level of activity. EE includes behavioral, economic, and technological changes, and is associated with economic efficiency. The benefits of EE include the reduction of waste through the use of a lower amount of energy to execute the same task, reduction of the demand for energy and greenhouse gas emissions, and lowering of households’ and country-level costs associated with energy consumption (Environmental and Energy Study Institute, 2019). A global shift to EEAs will reduce worldwide electricity consumption by more than 10% and $350 billion annually in electricity costs and reduce global carbon dioxide emissions by 1.25 billion tonnes yearly (United Nations Environment Programme, 2014). Energy efficient appliances (EEAs) include air-conditioners, light bulbs, washer dryers, fridges and freezers, washing machines, water heaters, tumble dryers, electric ovens, and dishwashers (Department of Energy, 2019).
• TPB
The TPB extends the Theory of Reasoned Action by Fishbein and Ajzen (1975) and Ajzen and Fishbein (1980) and argues that the performance of a specific behavior by an individual is determined by the intention, which depends on attitude, subjective norms and perceived behavioral control (Ajzen, 1991). The TPB has been used by many empirical studies in predicting behavioral intention in in the context of pro-environmental behavior (Chen and Tung, 2014; Wang, et al., 2016) and EEA research (Hua and Wang 2019; Wang, et al., 2019; Ali, et al., 2019).
• Attitude and EEA purchase intention
Attitude toward a behavior measures the degree to which an individual has a favorable or an unfavorable evaluation of the behavior being measured. A more favorable attitude toward a behavior should produce stronger individual intentions to perform the behavior (Ajzen, 1991; Dickinger and Kleijnen, 2008). Ha and Janda (2014) reported that the attitude toward an energy-efficient product has a strong effect on intention to purchase the product. If consumers have a positive attitude about EEAs, they will probably purchase them; otherwise, they will have no interest (Hua and Wang, 2019). Studies such as Wang, et al. (2019) and Akroush, et al. (2019) found that attitude toward green products positively affects purchase intention. A more favorable attitude by an individual should lead to a stronger intention to purchase EEAs.
Consequently, it is hypothesized that: (
• Subjective norms (SNs) and EEA purchase intention
Subjective norms (SNs) describe an individual's feelings of social pressure from other people or group and measure the likelihood that important individuals or groups will like or dislike the performance of a certain behavior (Ajzen, 1991; Tan, et al., 2017). Empirical findings are inconclusive about the effect of SNs on the purchase of environmentally friendly products. Ha and Janda (2012) and López-Mosquera, et al. (2014) reported that SNs have a positive impact on EEA purchase intention. Arvola, et al. (2008) and Tan, et al. (2017) found that SNs do not have a significantly positive relationship with the purchase intention for EEAs and the lack of a relationship suggests that customers may not be easily influenced by the opinions of other people. The opinions of an important person or group may influence the intention of an individual to purchase EEA.
It is hypothesized that: (
• Perceived behavioral control (PBC) and EEA purchase intention
Perceived behavioral control (PBC) can be described as the perceived difficulty or ease of conducting a behavior. Factors such as opportunity, knowledge, and skill may affect the behavior of an individual (Ajzen, 1991; Chen and Tung, 2014). Ali, et al. (2019) reported that PBC positively influences consumers’ intentions to purchase green products. Hua and Wang (2019) found that the availability of knowledge and skill in respect of EEA should positively influence the intention to purchase EEAs. If consumers think they have the knowledge and skill to use EEAs, they will be more willing to purchase them.
It is hypothesized that: (
Ajzen (1991) and Chen and Tung (2014) pointed out that the power of the TPB can be improved by the inclusion of additional constructs as long as the new variables can be shown to improve the explanatory power of the model. Frederiks, et al. (2015) pointed out that the predictors of household energy consumption include: (1) individual factors which can be divided into two, that is, sociodemographic factors (age, gender, education) and psychological factors (knowledge/awareness, values, beliefs and attitudes, personal and moral norms) and (2) situational predictors (laws, regulations and policies, perceived benefits, technology, pricing, information, mass media, and advertising).
Individual factors are a product of an individual's experiences and can affect his/her decision-making process, and situational factors symbolize situational forces that can encourage or discourage the purchase of green products by individuals (Joshi and Rahman, 2015). Chaudhary and Bisai (2018), in a study on the factors influencing green purchase behavior of millennials in India, extended the TPB by examining environmental concern and willingness to pay as a moderating factor. Ali, et al. (2019), in a study on the factors affecting EEA purchase intention, extended the TBP by adding knowledge (individual) and price (situational), while Wang, et al. (2014) used energy knowledge, demographic variables, living habit, and information publicity to extend the TPB. This study will extend the TPB by adding individual factors (environmental concern and moral norms) and situational factors (information publicity and perceived benefits).
• Moral norms and EEA purchase intention
Moral norms can be described as the perceived moral obligation or responsibility to perform or not to perform certain task and represent an individual's belief that acting in a certain way is essentially right or wrong (Kaiser, 2006; Cheng and Tung, 2014). Moral norms have been used to extend the TPB in studies on energy-saving behavior, and the inclusion of the construct has improved the theory's explanation power (Chan and Bishop, 2013; López-Mosquera, et al., 2014). Petschnig, et al. (2014) and Shalender and Sharma (2019) used moral norms to extend the TPB in studies on the adoption intention of energy-efficient products and found a significant positive relationship.
The findings are consistent with those of Tan, et al. (2017) in that moral norms have a significant positive effect on consumers’ purchase intention of EEAs. Moral obligation by an individual to conserve natural resources and improve the environment can stimulate the intention to purchase EEAs.
Consequently, it is hypothesized that: (
• Environmental concern and EEA purchase intention
Environmental concern can be described as the extent to which an individual is aware of environmental problems, supports efforts to solve them, or indicates a willingness to contribute individually to provide solutions (Dunlap and Jones, 2002; Dagher and Itani, 2012). Li, et al. (2019) reported that environmental concern is positively correlated with the willingness to purchase EEAs.
Consumers with environmental concern will have favorable attitudes toward eco-friendly products or services (Kim and Han, 2010: Aman, et al., 2012; Yadak and Pathak, 2016). Harris (2006), however, found that environmental concern does not directly affect pro-environmental behavior. Individuals who are more concerned about the environment should have a higher tendency to support energy efficiency activities.
It is hypothesized that: (
• Perceived benefits and EEA purchase intention
Hidrue, et al. (2011) and Nosi, et al. (2018) pointed out that perceived benefits can positively affect the willingness to purchase environmentally friendly products. When consumers perceive that there are benefits to be obtained by purchasing a product, they will have a higher purchase intention.
Akroush, et al. (2019) found that perceived benefits positively affect consumer purchasing intentions of EEAs. O’Driscoll, et al. (2013), however, reported that the benefits of renewable energy systems do not affect their adoption intention and further investigations are needed to examine the relationship between perceived benefits and purchase intentions.
Choi and Han (2019) found that perceived benefits are not a sufficient motivator for the adoption of green practices by fashion manufacturers. However, when consumers perceive that there are benefits to be obtained from EEAs, they will have a higher purchase intention.
It is hypothesized that: (
• Information publicity and EEA purchase intention
du Plessis (2015) remarked that on a daily basis, South Africans are bombarded with information in the print and electronic media about the importance of energy conservation and the use of energy efficiency measures in households and workplaces. Wang, et al. (2014) pointed out that one of the obstacles to the adoption of energy-saving behavior by consumers is the lack of sufficient knowledge. Wang, et al. (2014) found that information publicity positively affects behavioral intention toward green products. According to Qader and Zainuddin (2011), the media have made a significant contribution to the widespread dissemination of environmental concern and publicity can transform a specific environmental problem into a major public issue. The study by Qader and Zainuddin finds a significant positive relationship between media coverage and the purchase intention of green products. Chen, et al. (2018) reported that the media can facilitate the sharing of detailed information and promote the recognition of green products. Consumers are more likely to consider energy-saving products if they have access to information.
It is hypothesized that: (
Green purchasing can be described as the purchase of environmentally friendly products and services and the avoidance of products and services that harm the environment. Green purchase intention denotes the willingness of consumers to purchase green products, while green purchase behavior epitomizes a complex form of ethical decision-making behavior, which can be considered a type of socially responsible behavior (Ramayah, et al., 2010; Joshi and Rahman, 2015; Jaiswal and Kant, 2018). Kumar, et al. (2017), Jaiswal and Kant (2018), and Bhutto, et al. (2019) found a significant positive relationship between purchase intention and purchase behavior of environmentally friendly products. A strong intention towards the purchase of a green product can lead to actual purchase and consumption.
It is hypothesized that: (
Fig. 1 depicts the hypothesized research model.
Figure 1
Conceptual framework (
This study followed the quantitative research design, and the cross-sectional survey approach was used to collect data from the respondents. The participants in the survey were final year undergraduate students of the Departments of Business Management of two universities located in the Limpopo and Gauteng provinces of South Africa. University students can be considered as young customers because of their age (Choudhury and Dey, 2014). The participants were conveniently sampled, and the self-administered questionnaire method was used to collect data. Questionnaires were distributed in the class with the assistance of lecturers. The questionnaire was reviewed by two academic experts in the areas of marketing and sustainability and also pretested on 30 students. Minor adjustments were made to the questionnaire based on the feedback from the reviewers and the pilot study. The questionnaire was divided into three sections: (1) biographical details; (2) determinants; and (3) purchase intention and behavior. Descriptive analysis and the Partial Least Square Structural Equation Modeling (PLS SEM) were used for analysis.
Measures: Scales with acceptable psychometric properties were adapted for all the constructs from previous studies on environmentally friendly products. Attitude was measured on semantic differential scale, and the five-point Likert scale was used to measure the other constructs of the study. The questionnaire items are presented in Appendix A.
Three hundred and twenty-five questionnaires were distributed and 298 questionnaires were returned and found usable. The gender composition of the respondents was 53% female and 47% male. All the respondents were between 20 and 30 years. Independent samples T-test did not indicate any significant gender difference in the results.
Table 1 depicts the results of the descriptive statistics. Kolmogorov–Smirnov test (p > 0.05) for the constructs assured the normality of the data. The results indicated that eight variables (attitude, perceived behavioral control, environmental concern, moral norms, perceived benefits, informational publicity, intention, and behavior) have means above. On a five-point Likert scale, a mean value below 3 is considered as low, 3–4 as moderate, and above 4 as high.
Descriptive statistics (
| Construct | Mean | Standard deviation | Kolmogorov–Smirnov |
|---|---|---|---|
| Attitude | 4.10 | 1.06 | 0.175 |
| Subjective norms | 2.75 | 1.18 | 0.102 |
| Perceived behavioral control | 3.80 | 1.03 | 0.127 |
| Environmental concern | 4.10 | 1.05 | 0.133 |
| Moral norms | 3.98 | 1.15 | 0.127 |
| Perceived benefits | 4.15 | 1.09 | 0.121 |
| Informational publicity | 4.02 | 1.02 | 0.125 |
| Intention | 4.05 | 1.01 | 0.127 |
| Behavior | 3.10 | 1.09 | 0.116 |
The Partial Least Square Structural Equation Modelling (The PLS SEM) was used to examine the research model by using the software package Smart PLS 3.0. The PLS SEM is a strong and extensively used method to examine latent variables and can process complicated models without distributional assumptions for the sample (Chin, 1998). The PLS SEM comprises two sub-models and these are the measurement and structural models (Hair, et al., 2019).
The measurement model was used to examine the relationship between the latent variables and their measures, and the structural model was used to test the relationship between the latent variables. Firstly, evaluation of the measurement model was done by the examination of item loadings of the constructs. Hair, et al. (2019) recommended that loadings above 0.708 should be retained and values below this figure should be deleted. The next stage is the use of composite reliability to assess the internal consistency of measures, and values between 0.79 and 0.90 are acceptable. An additional measure of internal consistency is the Cronbach's alpha, and values of 0.70 and above are acceptable (Nunally and Bernstein, 1994). This stage is followed by the examination of the convergent validity of each construct by using the average variance extracted (AVE). The recommended minimum AVE is 0.50. Also, the square roots of the AVEs should be greater than the correlations amongst variables.
The results as presented in Tables 2 and 3 which show that all items except for one under subjective norms have loadings above 0.708 and were retained. The composite reliability values for the constructs ranged between 0.792 and 0.884. In addition, the Cronbach's alphas for all the constructs ranged between 0.726 and 0.808, indicating a satisfactory internal consistency of measures. This implies an acceptable level of construct validity. The AVEs ranged between 0.635 and 0.737, suggesting a good convergent validity of the scales. Furthermore, the square roots of AVEs are depicted on the diagonals and are all greater than the corresponding correlation coefficients within the constructs (Table 3). It can be concluded that the measurement model is satisfactory.
The measurement model (
| Construct | Measurement items | Item loading | Cronbach's alpha | Composite reliability | AVE |
|---|---|---|---|---|---|
| Attitude (A) | A1 | 0.782 | 0.739 | 0.875 | 0.636 |
| A2 | 0.826 | ||||
| A3 | 0.805 | ||||
| A4 | 0.776 | ||||
| Subjective norm (SN) | SN 1 | 0.827 | 0.721 | 0.792 | 0.655 |
| SN2 | 0.792 | ||||
| SN3 deleted | 0.479 | ||||
| Perceived behavioral control (PBC) | PBC1 | 0.848 | 0.814 | 0.856 | 0.664 |
| PBC2 | 0.827 | ||||
| PBC3 | 0.769 | ||||
| Moral norms (MN) | MN 1 | 0.899 | 0.727 | 0.881 | 0.737 |
| MN 2 | 0.836 | ||||
| MN 3 | 0.795 | ||||
| Environmental concern | EC1 | 0.848 | 0.792 | 0.884 | 0.655 |
| EC2 | 0.812 | ||||
| EC 3 | 0.769 | ||||
| EC4 | 0.808 | ||||
| Perceived benefits | PB 1 | 0.826 | 0.816 | 0.882 | 0.651 |
| PB 2 | 0.783 | ||||
| PB3 | 0.811 | ||||
| PB4 | 0.806 | ||||
| Informational publicity | IB1 | 0.836 | 0.729 | 0.857 | 0.667 |
| IB2 | 0.799 | ||||
| IB3 | 0.814 | ||||
| Purchase intention (PI) | PI1 | 0.829 | 0.801 | 0.876 | 0.635 |
| PI2 | 0.786 | ||||
| P13 | 0.817 | ||||
| PI4 | 0.753 | ||||
| Purchase behavior (PBe) | PBe1 | 0.899 | 0.725 | 0.873 | 0.711 |
| PBe2 | 0.852 | ||||
| PBe3 | 0.746 | ||||
Discriminant validity (
| Construct | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| A | |||||||||
| SN | 0.315 | ||||||||
| PBC | 0.446 | 0.520 | |||||||
| MN | 0.401 | 0.464 | 0.499 | ||||||
| EC | 0.416 | 0.508 | 0.514 | 0.459 | |||||
| IP | 0.602 | 0.643 | 0.508 | 0.511 | 0.606 | ||||
| PB | 0.416 | 0.524 | 0.506 | 0.603 | 0.646 | 0.705 | |||
| P1 | 0.524 | 0.595 | 0.621 | 0.575 | 0.429 | 0.558 | 0.602 | ||
| PBe | 0.493 | 0.508 | 0.482 | 0.445 | 0.518 | 0.526 | 0.599 | 0.530 |
Diagonals in bold signify the square root of the AVE while the other figures depict the correlations
The assessment of the structural model includes common method bias (CMB), R2, Q2, and evaluation of the path coefficients (Hair, et al., 2019). The likelihood of CMB was examined as the data was self-reported. CMD can be identified through the variance inflation factors (VIFs) that are obtained through a full collinearity test. VIFs that are greater than 3.3 indicate pathological collinearity, and it is a signal that a model may be contaminated by CMB. However, if the VIFs are equal to or lower than 3.3, the model can be assumed to be free of CMB (Henseler, et al., 2015). The VIFs for the constructs of the study ranged from 1.205 to 2.482, suggesting that the model is free of CMD. R2 shows the proportion of variance in the dependent variable that can be explained by the independent variable. R2 values are 0.25 (weak), 0.50 (moderate), and 0.75 (substantial) (Kock, 2015).
This study obtained an R2 of 0.707, implying that the total variance of purchase intention that is explained by the model is 70.7%. To determine if the model adequately explains the empirical data, the goodness-of-fit (GOF) test was used. The values of GOF range from 0 to 1, with 0.10 considered small, 0.25 medium, and 0.36 large. The GOF is 0.687, suggesting that the empirical data satisfactorily fits the model and has a good predictive power in comparison to baseline values. In addition to the size of R2, a recommended test is the predictive relevance of the model (Q2). The two prediction techniques for Q2 are cross-validated communality and cross-validated redundancy, with Chin (2010) suggesting the use of the former.
A Q2 > 0.5 is considered a predictive model and a Q2 of 0.69 obtained in this study is indicative of a highly predictive model. The effect size (f2) shows the effect of one construct on another construct and the values are 0.02 (small), 0.15 (medium), and 0.35 (large). The effect size, f2, ranged from 0.026 to 0.131, indicating that the effect sizes of different endogenous constructs on the exogenous constructs ranged from small to medium. The results path coefficients and T-statistics of the T-statistics using the bootstrapping technique are depicted in Table 4.
Path coefficient and T-statistics (
| Hypothesized path | Standardized Beta | T-statistics | Decision |
|---|---|---|---|
| H1: A to P1 | 0.136 | 3.042** | Accepted |
| H2: SN to PI | 0.029 | 1.129 | Rejected |
| H3: PBC to PI | 0.188 | 3.886* | Accepted |
| H4: MN to P1 | 0.135 | 3.648** | Accepted |
| H5: EC to PI | 0.117 | 2.912** | Accepted |
| H6: IP to PI | 0.128 | 3.321* | Accepted |
| H7: PB to P1 | 0.148 | 2.991** | Accepted |
| H8: PI to PBe | 0.253 | 3.385** | Accepted |
p < 0.01;
p < 0.05
Hypothesis H1 proposes that attitude (A) is positively related to purchase intention (PI). The results (β = 0.136, T = 3.042, p < 0.05) show a significant positive relationship between A and PI. H1 is accepted.
Hypothesis H2 proposes that subjective norms (SNs) are positively related to PI. The results (β = 0.029, T = 1.129, p > 0.05) depict an insignificant relationship between SNs and PI. H2 is rejected.
Hypothesis H3 proposes that perceived behavioral control (PBC) is positively related to PI. The results (β = 0.188, T = 3.886, p < 0.01) show a significant relationship between PBC and PI. H3 is accepted.
Hypothesis H4 proposes that there is a significant positive relationship between moral norms (MNs) and PI. The results (β = 0.135, T = 3.648, p < 0.05) support a significant positive relationship between attitude and MN and PI. H4 is accepted.
Hypothesis H5 proposes that environmental concern (EC) is positively related to PI. The results (β = 0.117, T = 2.912, p < 0.05) show a significant relationship between EC and PI. H5 is accepted.
Hypothesis H6 proposes that informational publicity (IP) is positively related to PI. The results (β = 0.128, T = 3.321, p < 0.01) support a significant positive relationship between IP and PI. H6 is accepted.
Hypothesis H7 proposes that there is a significant positive relationship between perceived benefits (PB) and PI. The results (β = 0.148, T = 2.991, p < 0.05) support a significant positive relationship between PB and PI. H7 is accepted.
Hypothesis H8 proposes that there is a significant positive relationship between purchase intention (PI) and purchase behavior (PBe). The results (β = 0.253, T = 3.385, p < 0.05) support a significant positive relationship between PI and PBe. H8 is accepted.
The study examined the predictors of EEA purchase intention in South Africa by extending the TPB. In addition, in line with the TPB, the study investigated the effect of purchase intention on purchase behavior. The findings indicated that attitude has a significant positive relationship with EEA purchase intention. Young consumers with a positive attitude toward the use of EEAs are more likely to buy EEAs. The findings support the TPB model that attitude is a predictor of behavioral intention.
The findings are consistent with the results of previous empirical studies (Dickinger and Kleijnen, 2008; Wang, et al., 2019; Akroush, et al., 2019) which showed that the attitude toward an energy-efficient product has a strong effect on the intention to purchase. The findings showed that subjective norms do not have a significant impact on the intention to purchase EEAs. This is contrary to the original TPB model. Armitage and Conner (2001), in a meta-analysis of studies on the TPB and sustainability, found that attitudes and perceived behavioral control are better predictors of behavior than subjective norm, which tends to vary considerably across behaviors and situations. Empirical studies (Arvola, et al., 2008; Tan, et al., 2017) also find that SNs do not have a significant positive relationship with the purchase intention of EEAs.
The lack of a relationship suggests that young customers may not be easily influenced by the opinions of people close to them about their decisions to purchase EEAs. Perceived behavioral control (PBC) has a significant positive relationship with EEA purchase intention. Sung and Wang (2019) and Ali, et al. (2019) found that PBC positively affects consumers’ intentions to purchase green products. Hua and Wang (2019) reported that the availability of knowledge and skill in respect of EEA should positively influence the intention to purchase. The results indicated that moral norms (MNs) positively affect EEA purchase intention.
Bhutto, et al. (2019) remarked that young customers are mostly educated and have perceived responsibilities to act morally in pro-environmental situations. Young people are environmentally and socially conscious customers and have grown up in a world where climate change is part of the daily discourse. Petschnig, et al. (2014) and Shalender and Sharma (2019) found that moral norms significantly influence the intention to purchase environmentally friendly products. In addition, Tan, et al. (2017) found that moral norms have a significant positive effect on consumers’ purchase intention to purchase EEAs. The findings revealed a significant positive relationship between environmental concern (EC) and EEA purchase intention.
Young consumers are more concerned about the environment and climate change than ever and are imbibing sustainable lifestyles and consumption. Studies such as Kim and Han (2010), Aman, et al. (2012), Yadak and Pathak (2016), and Li, et al. (2019) reported that environmental concern is positively correlated with the willingness to purchase eco-friendly products including energy-efficient appliances. The findings revealed a significant positive relationship between informational publicity (EC) and EEA purchase intention. Environmental awareness through information publicity by the media influences individuals, businesses and governments about the negative effect of climate change. Wang, et al. (2014) reported that information publicity positively affects behavioral intention toward green products. According to Chen, et al. (2018), the media can facilitate the sharing of detailed information and promote the recognition of green products.
The results indicated that the perceived benefits are a significant predictor of the intention to purchase EEA. Hidrue, et al. (2011), Nosi, et al. (2018) and Akroush, et al. (2019) found that perceived benefits can positively affect the willingness to purchase environmentally friendly products. The findings revealed that consumers’ purchase intention positively impacts on the purchase behaviour of EEAs. According to the TPB, the purchase intention of consumers is an essential indicator for predicting actual purchase. The findings of this study are consistent with similar empirical studies (Kumar, et al., 2017; Jaiswal and Kant, 2018; Bhutto, et al., 2019) in that there is a significant positive relationship between purchase intention and purchase behavior of environmentally friendly products.
South Africa is one of the least energy-efficient nations in the world, and its reliability on electricity supply is under threat. Household appliances are the biggest contributor to household energy consumption, and around 70% of household carbon dioxide emissions come from household appliances. One of the ways to reduce emissions and conserve energy is to increase the purchase and use of EEAs. The study investigated the factors that influence the purchase of energy efficient appliances by young consumers in South Africa. Young consumers (university students) are expected to drive sustainable consumption in the future.
Theoretically, the study extended the TPB by adding both individual and situational factors to develop a model of EEA purchase intention from the South African perspective. Empirically, the study deepens the knowledge on the factors that influence EEA purchase intention. The study has some practical implications, and the findings of the study can assist both policy-makers and the business to better comprehend how to promote EEAs. The findings revealed that perceived behavioral control, perceived benefits and informational publicity are significant predictors of purchase intention.
Therefore, the government and manufacturers of appliances should create awareness and enhance the knowledge on the benefits of EEAs through the media. Print, electronic, and social media and road shows can help to create awareness about the benefits of EEAs. The findings of the study also showed that environmental concern and moral norms significantly influence purchase intention. Government, non-governmental bodies and media locally and internationally should intensify awareness campaigns about the negative effects of climate change and the need to protect the environment.
The study has some limitations. The use of the non-probability sampling method may lead to respondent bias. Data was collected from only two universities and this limits the generalizability of the findings of this study. Other studies should examine the moderating effects of demographic variables on purchase intention. In addition, the effect of energy efficiency labels on the purchase intention of EEAs can be investigated.