Social psychological predictors of adoption intention for solar water heaters in rural China.
However, even with this progress, a wide gap still exists in solar water heater (SWH) technology between China and other countries. For example, the total capacity of SWHs in China in kilowatt thermals per 1,000 inhabitants was only 194.3 by the end of 2013, which is much less than the 385.2 kilowatt thermals reported in Austria, 373.8 in Israel, and 271.5 in Greece (Mauthner, Weiss, & Spork-Dur, 2015). The available supply of hot water is not always adequate in China, especially in rural areas. In a survey conducted in rural China, more than 90% of the respondents said they needed more hot water than they currently had available in daily life (Han, Hsu, & Sheu, 2010). With rich potential for solar energy and great demand for hot water, there is a long-term capacity for greater SWH adoption in China, which would slow environmental degradation and shrink energy deficits.
Rural residents' willingness to install SWHs is essential to increase the share of renewable energy in their daily energy consumption. Thus, it is important to understand the factors that affect their adoption. Although researchers have studied the behavioral factors that influence renewable energy adoption (Guo et al., 2014; Liu, Wang, & Mol, 2013), we believe greater importance should be attached to the effect of social psychological factors associated with the adoption of new technology. If the aim of policy makers is to raise the rate of SWH adoption, such research is necessary to make policies more effective. Indeed, as Sovacool (2014) pointed out, in most energy studies scholars have attempted to deal with technological fixes rather than human-centered research methods. Technological, social, regulatory, and cultural aspects are all included in the term social technical, and it is this approach that is vital to understand public adoption of renewable energy technology (Sovacool, 2009).
Literature Review and Development of Hypotheses
Numerous researchers have examined rural residents' adoption of new technologies and practices (e.g., Hou et al., 2017; Noll, Dawes, & Rai, 2014). Some have identified factors that affect adoption decisions (Borchers, Xiarchos, & Beckman, 2014; Michelsen & Madlener, 2016), and others have addressed willingness to pay or willingness to accept new technologies and practices (X. Wang et al., 2016). Studies in which rural residents' adoption of SWHs is specifically addressed are more limited. Chang, Lee, Lin, and Chung (2008) found that the adoption of SWHs in Taiwan is associated with climatic conditions, population structure, urbanization, housing type, and status of new construction, in addition to SWH cost and the energy price index. Benli (2016) studied the determinants of SWH adoption using data from Turkey and found the probability of adoption differs across economic conditions, degree of popularity of SWHs in different regions, climatic conditions, and price. However, even though the use of SWHs in China has drawn growing research attention, little is known about rural residents' willingness to adopt SWHs or what social psychological factors influence their adoption.
Popular theoretical frameworks used in individual-level innovation adoption research include the theory of planned behavior (TPB; Ajzen, 1991) and the technology acceptance model (TAM; Davis, 1989). The majority of environmental behavioral intentions or behaviors can be explained by the TPB (Abrahamse & Steg, 2011; Chen & Knight, 2014). For example, Kaiser and Gutscher (2003) showed that the TPB variables explained 81% of the variance in the conservation behaviors assessed in their research. However, in the field of technology acceptance, the TAM is a popular adaptation of the TPB and, over the last 35 years, it has been a preferred model in comprehending predictors of human behavior or behavioral intentions toward acceptance of technology (Marangunic & Granic, 2015). In the TPB, the individual's perceived behavioral control and subjective norm are used to measure affective responses. Although the original TAM and the TPB can be used to reliably predict the adoption of renewable energy technology, researchers have suggested that extending these theories will provide better explanatory power in predicting the adoption of new technology. For example, Borchers et al. (2014) found that the government passing renewable energy policies increased the likelihood of solar energy adoption on farms in the United States, and Ma, Song, Smardon, and Chen (2014) found the same result in China. Other researchers have found that economic cost plays an important role in SWH adoption (Urban, Geall, & Wang, 2016). Therefore, in this study we extended the TPB and the TAM by adding the factors of policy support and economic cost to test the influence of social psychological factors on rural residents' intention to adopt SWHs.
Technology Acceptance Model: Perceived Ease of Use and Perceived Usefulness
Davis (1989) suggested that individuals' acceptance of new technology depends on two factors: perceived ease of use, which is the degree to which the technology is perceived to be easy to understand, learn, or operate, and perceived usefulness, which is the degree to which use of the technology will enhance the achievement of valued goals. When these factors are applied to the acceptance and adoption of renewable energy technologies, researchers have consistently reported finding that there is a positive relationship between perceived ease of use and perceived usefulness. Viardot (2013) pointed out that many potential consumers perceive renewable energies, such as wind and solar, as having low usefulness, and think that implementing energy-saving practices or installing such equipment takes too long or is too complicated. As SWH technology becomes more economical, highly developed, and popular in China, we expected that rural residents would have high perceptions of usefulness and ease of use if they believe that it can be easily used at home, supplies hot water for the whole family, and/or reduces consumption of energy from nonrenewable sources. Further, we expected that willingness to adopt the technology would depend on whether it is perceived as helpful and effortless to use. Therefore, we formed the following hypotheses:
Hypothesis 1: Perceived ease of use will be positively related to Chinese rural residents' intention to adopt solar water heaters.
Hypothesis 2: Perceived usefulness will be positively related to Chinese rural residents' intention to adopt solar water heaters.
Theory of Planned Behavior: Subjective Norm and Perceived Behavioral Control
Subjective norm is defined as perceived social pressure from significant others, or beliefs about how significant others expect one to behave in a given situation (Ajzen & Fishbein, 1977). The influence of subjective norm on environmentally relevant behaviors is well established. Scholars have shown that subjective norm has a positive influence on individuals' decision to use public transportation (Bamberg, Hunecke, & Blobaum, 2007) and engage in other environmentally responsible behaviors (Thogersen, 2006). In a study conducted in China it was found that subjective norm is essential in encouraging electricity-saving behavior in urban households (Z. Wang, Zhang, Yin, & Zhang, 2011). In a study of renewable energy conducted in Australia subjective norm was found to have a positive impact on the intention to support wind farms, partly because of perceived pressures from neighbors, friends, and family members (Read, Brown, Thorsteinsson, Morgan, & Price, 2013). In another study in China subjective norm was found to positively influence public university students' intention to adopt SWHs (Chen et al., 2016).
However, conclusions regarding the influence of subjective norm on environmental behaviors have been inconsistent. Abrahamse and Steg (2011) reported that, when controlling for attitude and perceived behavioral control, subjective norm inadequately explains individual intention to save energy. Some psychologists have argued that the prediction of subjective norm is inconsistent partly because of the way it is measured (Louis, Davies, Smith, & Terry, 2007). To better measure subjective norm, Louis, Taylor, and Douglas (2005) found that choosing reference groups that are closer to the individuals can produce results more predictive of intention or behavior. As neighbors and friends are strongly identified reference groups for rural residents, we assumed that the subjective norm of pressure from these groups may affect rural residents' intention to adopt SWHs (X. Wang et al., 2016). Therefore, we proposed the following hypothesis:
Hypothesis 3: Subjective norm will be positively associated with rural residents' intention to adopt solar water heaters.
Perceived behavioral control (PBC) is the perceived ability to regulate performing a behavior and is a function of control belief and perceived power of the control factors (Armitage & Conner, 1999). Ajzen (2002) pointed out that lacking the necessary know-how might directly hinder individuals' intention to act, regardless of their levels of cognition, affect, and subjective norm. In studies of environmentally relevant behaviors researchers have indicated that PBC has a positive impact on recycling intention and behavior (Cheung, Chan, & Wong, 1999), energy conservation (Kaiser, Hubner, & Bogner, 2005), and household electricity-saving behavior (Z. Wang et al., 2011). P. Wang, Wu, Zhu, and Wei (2013) found that PBC is positively related to Chinese residents' purchasing of green products, and Alam and Rashid (2012) found that PBC is positively associated with renewable energy adoption intention. Further, Chen et al. (2016) showed that PBC positively influences Chinese public university students' intention to adopt SWHs. Thus, we formed the following hypothesis:
Hypothesis 4: Perceived behavioral control will be positively related to rural residents' intention to adopt solar water heaters.
Policy Support and Economic Cost
Kollmuss and Agyeman (2002) defined environmentally relevant behaviors as an individual consciously seeking to minimize his or her negative impact on the natural world. Rauwald and Moore (2002) claimed that policy support for environmentally relevant behaviors is very important. Policy support has been found to have a positive impact on electricity-saving technology adoption (Z. Wang et al., 2011), sorting household waste for recycling (Dietz, Stern, & Guagnano, 1998), and energy conservation (Kablan, 2004). In regard to the adoption of renewable energy, some scholars have found that subsidy policies and offering no-interest loans leads to increased dissemination of renewable energy technologies (Han et al., 2010; Urban et al., 2016). On the basis of the foregoing discussion, we proposed the following hypothesis:
Hypothesis 5: Policy support will be positively associated with rural residents' intention to adopt solar water heaters.
Scholars have found that economic cost is one of the main barriers to maintaining environmental conscientiousness or adopting renewable energy technologies (Carrete, Castano, Felix, Centeno, & Gonzalez, 2012; Karakaya, Hidalgo, & Nuur, 2014). Economic cost has been reported as the most common motivation for reducing energy use in a household (Dalvi-Esfahani & Rahman, 2016) and has been found to predict energy-saving behaviors (Verbeeck & Hens, 2005). In China, the average SWH price is CN[yen]2,160 (US$306) plus CN[yen]90 (US$12) for connecting pipes (Han et al., 2010), and household income has become the main barrier to SWH adoption in China, especially in rural areas (Ma et al., 2014). Thus, we formed the following hypothesis:
Hypothesis 6: Economic cost will be negatively associated with rural residents' intention to adopt solar water heaters.
Our conceptual model to explain the SWH adoption intention of Chinese rural residents is shown in Figure 1. We used this model to test the association between the factors of the TAM (perceived ease of use and perceived usefulness) and the rural residents' adoption intention. In addition, we postulated two factors of the TPB (subjective norm and PBC) as predictors of adoption intention. Last, we examined the influence of policy support and economic cost on adoption intention.
Participants and Procedure
In-person interviews were carried out in Jiangxi Province with 1,000 randomly selected rural residents recruited using a three-stage sampling strategy. Of the 100 counties in this province, we selected 10 in the first stage. In the second stage, we randomly chose five villages from a sampled county. In the final stage, we used systematic random sampling to select 20 households per village. Only residents who had not already installed SWHs were asked to answer the second part of a survey regarding the social psychological factors that would influence their doing so. This sampling method resulted in 972 usable survey forms after the removal of 28 that were invalid.
Of the participants, 56% (n = 545) had SWHs in their homes and the rest of the participants (n = 427, 44%) did not. Among the not-adopted sample, which was our focus in this study, 59.7% (n = 255) of participants were men and 40.3% (n = 172) were women. Of the participants, 28.3% (n = 121) were aged under 35 years, 50.1% (n = 214) were aged from 35 to 55 years, and 21.6% (n = 92) were aged over 55 years. In terms of education level, 9.1% (n = 39) of participants had not completed primary school, 83.1% (n = 355) had completed junior high school or finished their schooling before completing junior high, and 7.8% (n = 33) held an undergraduate degree or higher academic qualification. Regarding family size (i.e., number of permanent residents in the household), 54.1% (n = 231) of participants had a family of more than five persons and 45.9% (n = 196) had a family of five or fewer persons. The annual household income of most participants (78.0%, n = 333) was CN[yen]20,000-50,000 (US$3,000-$8,000), 12.2% (n = 52) had an income of under CN[yen]20,000 (US$3,000), and 9.8% (n = 42) had an income of more than CN[yen]50,000 (US$8,000).
Except for demographic questions and the item on adoption of SWHs, all responses were made on a 7-point Likert scale ranging from 1 (do not agree at all) to 7 (strongly agree). A higher score indicates a higher level of the assessed variable. All measures were adopted from scales developed in the English literature, then translated into Chinese by one teacher of English and two graduate students majoring in English. Divergence between the Chinese and English versions was addressed through careful checking with a back-translation procedure.
Perceived ease of use. Three items were used to measure the construct of perceived ease of use (Toft, Schuitema, & Thogersen, 2014): "It will be easy to use a solar water heater in my home," "I expect that having a solar water heater on my roof will not require any effort from me," and "It will be easy to learn how to operate a solar water heater in my home."
Perceived usefulness. Three items were used to measure perceived usefulness (Toft et al., 2014): "A solar water heater will contribute to a reliable hot water supply for my family," "A solar water heater will reduce my electricity consumption," and "A solar water heater will supply hot water for my family whenever we want it."
Subjective norm. The items measuring subjective norm were sourced from Chan (1998) and Harland, Staats, and Wilke (1999): "If I want to install a solar water heater I will seek information about them from friends (neighbors)," "The evaluations and preferences of my friends (neighbors) influence my choice of a solar water heater," and "My decision to purchase a solar water heater is influenced by the preferences and expectations of my friends (neighbors)."
Perceived behavioral control. Three statements sourced from Oreg and Katz-Gerro (2006) were used to measure PBC: "I have control over whether to buy a solar water heater," "I will buy a solar water heater whether or not my neighbors/friends buy one," and "It is mostly up to me whether to buy a solar water heater."
Policy support. We designed two statements to measure policy support: "With tax incentives, I will choose to buy a solar water heater," and "With government subsidies, I am willing to buy a solar water heater."
Economic cost. Economic cost was measured with two items (Steg, De Groot, Dreijerink, Abrahamse, & Siero, 2011): "Price is the main factor that influences my decision to buy a solar water heater," and "The purchase of a solar water heater will greatly increase my household expenses."
Adoption intention. One item was used to assess the intention to adopt an SWH soon: "I will have a solar water heater on my roof in the near future."
The Influence of Demographic Variables on Adoption Intention
An analysis of variance of adoption intention revealed that family size, F(2, 424) = 2.84, p = .060, had a statistically significant positive effect on rural residents' intention to adopt SWHs. None of the other demographic variables we assessed had a statistically significant influence on adoption: age, F(4, 422) = 0.85, p = .493, education level, F(3, 423) = 0.33, p = .722, annual household income, F(4, 422) = 1.35, p = .794, and gender, F(1, 425) = 0.02, p = .899.
Summary of Statistics
Internal consistency reliability analysis showed that all variables had high Cronbach's alphas ([alpha] > .70). Therefore, we created six variables by averaging the relevant items. The means, standard deviations, Cronbach's alpha, and bivariate correlations between the studied variables are shown in Table 1.
A multiple regression analysis was conducted to test the hypotheses. Adoption intention was analyzed based on six predictors: perceived ease of use, perceived usefulness, subjective norm, PBC, policy support, and economic cost. These predictors were entered simultaneously into the regression equation. As shown in Table 2 and Figure 2, perceived ease of use was not a significant predictor of adoption intention, but perceived usefulness had a significant positive influence on adoption intention. Therefore, Hypothesis 1 was not supported but Hypothesis 2 was. Subjective norm and PBC had a significant positive influence on adoption intention, supporting Hypotheses 3 and 4, and policy support had a significant positive influence on adoption intention, whereas economic cost had a significant negative influence on adoption intention, supporting Hypotheses 5 and 6. Multicollinearity was also checked between the independent variables, and the variance inflation factors for all independent variables were well below the maximum suggested level of 10 (Mason & Perreault, 1991). Therefore, multicollinearity was not a serious concern in the regression analysis. These results support the sound explanatory power and validity of the integral estimate.
We assessed the social psychological predictors of intention to adopt SWHs in rural China. Aside from testing the influence of two TAM dimensions (perceived ease of use and perceived usefulness) and two main TPB constructs (subjective norm and PBC), we further examined if policy support and economic cost affected rural residents' SWH adoption intention. In line with previous researchers, we confirmed the importance of SWH technology being perceived as easy to use and useful (Henderson & Divett, 2003; Toft et al., 2014). Our results show that perceived usefulness was an important antecedent of SWH adoption, and perceived ease of use influenced adoption intention negatively but not significantly. As Davis, Bagozzi, and Warshaw (1992) pointed out, perceived usefulness is a type of extrinsic motivation that can have a strong impact on technology adoption. However, perceived ease of use involves user-intrinsic aspects of technology, such as how it works and the process involved in using it. The fact that extrinsic, rather than intrinsic, aspects of technology are important in many cases may be the reason that perceived usefulness affects technology adoption more significantly than does perceived ease of use (Gefen & Straub, 2000). Descriptive statistics from this study indicate that rural residents had a strong perception of the usefulness of SWHs but did not have a strong perception of ease of use, which is a barrier to their adoption of this type of renewable energy (Viardot, 2013).
Our results have important practical implications. To promote SWH adoption in Chinese rural areas, not only must residents know that SWH technology is useful, they also have to understand how to use it, or it must be made easier for them to use.
The two main TPB constructs, subjective norm and PBC, were found to be important predictors of Chinese rural residents' intention to adopt SWHs, and among all the predictors subjective norm had the strongest positive effect on this intention. This result is consistent with those reported in previous studies, suggesting that significant others are important determinants of environmentally relevant behaviors (Bamberg et al., 2007; Thogersen, 2006). The implication of this finding is that policy makers and SWH marketers in China should focus on the important influence of neighbors and friends to facilitate and increase rural residents' intention to adopt SWHs. We found that PBC also had a strong positive effective on rural residents' SWH adoption intention. This result highlights that the perception of self-control to perform a behavior has a direct impact on that behavior (Ajzen & Fishbein, 1977), and it is consistent with the findings in prior studies on renewable energy adoption that PBC positively affects adoption (see, e.g., Alam & Rashid, 2012). The more that individual factors (e.g., perceived ability as measured with the first item of the PBC scale, and cost and availability as measured with the first two items of the perceived usefulness scale) are involved in the adoption decision, the stronger the adoption intention will be. Policy makers and SWH marketers can cultivate Chinese rural residents' adoption of SWHs through decreasing the cost of purchase, making them easy to install, and improving rural residents' ability to use them.
Our results also show that policy support was of great importance in motivating Chinese rural residents to adopt SWHs, as respondents identified economic cost as a constraint on adoption. Despite the economic benefits and energy-saving potential of SWHs, low-income families in rural China still cannot afford the current market price. Government support policies could be effective in further promoting adoption in these regions. As Urban et al. (2016) pointed out, the Chinese government's policy of reducing by 13% the wholesale price of household appliances going to rural areas, which began in 2008 and was suspended in 2013, had led to a quick expansion of SWH installation in these areas. Evidence from Taiwan (Chang et al., 2008) also indicates that the 6-year subsidy policy (1986-1991) expanded the installation of SWHs very quickly, but it slowed down during a period when the subsidy policy was suspended (1995-1999). Offering no-interest loans may be another way to increase adoption of SWHs in rural China, especially for low-income households whose members cannot afford to pay for an SWH all at once.
This study has several limitations that provide areas for future research. First, the main implications of the research pertain to SWH adoption in rural China, but future researchers in other developing countries could explore the importance of assessing social psychological predictors of SWH adoption and policy incentives in a wide variety of organizational and cultural contexts. Second, some geographic factors were left out in our study. For example, Mills and Schleich (2009) claimed that a higher average of hours of daily sunshine on the roof leads to a greater propensity to adopt SWHs. Future researchers could, thus, investigate the influence of geographic factors on the adoption of SWHs. Third, our results are based on research with a cross-sectional design; however, as this design is likely to inflate the correspondence between intention and behavior, future researchers could use a longitudinal design to investigate the translation of intention to behavior.
This work was supported by the National Natural Science Foundation of China (71663028 and 71963021), Jiangxi Social Science Planning Fund Program (19GL10).
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Xingdong Wang (1), Yanling Xiong (2), Rong Yang (3), Peijuan Yu (2)
(1) School of Economics and Management, Jiangxi Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, People's Republic of China
(2) School of Economics and Management, Jiangxi Agricultural University, People's Republic of China
(3) Jiangxi Vocational College of Mechanical and Electrical Technology, People's Republic of China
CORRESPONDENCE Xingdong Wang, School of Economics and Management, Jiangxi Rural Revitalization Strategy Research Institute, Jiangxi Agriculture University, Nanchang 330045, People's Republic of China. Email: email@example.com
Table 1. Means, Standard Deviations, Reliabilities, and Intercorrelations Between Study Variables Variable M SD PEU PU SN PBC PS PEU 4.64 1.82 .73 PU 5.96 0.99 .01 .94 SN 6.44 1.05 .29 (**) .06 .91 PBC 3.24 1.30 .03 -.04 -.09 .75 PS 5.90 1.30 .15 (**) .19 (**) .37 (**) .03 .75 EC 5.71 1.32 .27 (**) -.07 .29 (**) .05 .21 (**) AI 4.42 1.79 .04 .15 (**) .26 (**) .10 (*) .26 (**) Variable EC PEU PU SN PBC PS EC .85 AI -.03 Note. Cronbach's alpha coefficients are shown parentheses along the diagonal. PEU = perceived ease of use, PU = perceived usefulness, SN = subjective norm, PBC = perceived behavioral control, PS = policy support, EC = economic cost, AI = adoption intention. (*) p < .10, (**) p < .05. Table 2. Results of Regression Analysis for all Hypotheses Independent variables P SE VIF PEU -.02 0.05 1.14 PU .10 (**) 0.08 1.05 SN .24 (***) 0.09 1.31 PBC .13 (**) 0.06 1.02 PS .17 (***) 0.07 1.23 EC -.12 (**) 0.07 1.17 [R.sup.2] 0.14 Adj. [R.sup.2] 0.13 F(6, 420) 11.11 (***) Note. VIF = variance inflation factor, PEU = perceived ease of use, PU = perceived usefulness, SN = subjective norm, PBC = perceived behavioral control, PS = policy support, EC = economic cost. (*) p < .05, (***) p < .01.
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|Author:||Wang, Xingdong; Xiong, Yanling; Yang, Rong; Yu, Peijuan|
|Publication:||Social Behavior and Personality: An International Journal|
|Date:||Dec 1, 2019|
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