Chapter 4 Data collection and Analysis

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1 Exploring the intention of the Chinese consumers to purchase solar panels based on TPB model

by Jing Ouyang

Supervisor Prof. Dr. W. Jager

Dr. P. van Eck

University of Groningen Faculty of Economics and Business

MSc Marketing Management

January 2014

Professor Rankestraat 29a 9717 NT Groningen

+31 614900637 j.ouyang@student.rug.nl Student number 2160234

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2 Abstract

This research focuses on the Chinese individual consumers and aims to investigate what factors will influence them to purchase solar panels. In this study, purchase intention is tested as the dependent variable on one side and the attitude, subjective norm and perceived behavioral control as the independent variables on the other side. Furthermore the effect of the environmental concern as the moderator on the relationship between attitude and purchase intention is tested. With the help of TPB model (Ajzen, 1991), the author constructed a conceptual model and formulated six hypotheses. All the data was collected with the convenience sampling method and the returned data was analyzed by using SPSS statistic software.

The main findings have identified that positive attitude towards solar panels; high degree of perceived behavioral control; and perceived consumer effectiveness all have a significant positive influence on the purchase intention of solar panels. Meanwhile, subjective norm and environmental concern have no influence on the purchase intention regarding solar panels. Moreover, the research also found out that the Chinese consumers are quite conservative regarding new innovations and environmental concern has a significant negative relationship with innovativeness. At the end, this research also discussed how the present findings may assist Chinese government and solar panel manufactures to achieve an increase in solar panels uptake in the Chinese market.

Key words: sustainability, solar panel, purchase intention, attitude, subjective norm, perceived behavioral control, environmental concern, innovation diffusion theory

Research theme: The social dimension of sustainability, Thesis Paper of MSc Marketing Thesis supervisor: Professor Dr. Wander Jager

Dr. Peter van Eck

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3

Chapter 1 Introduction

1.1 Background and Problem

Since the discovery of fire, humankind and energy share an intensive and probably an everlasting relationship. We use energy for lighten up our houses, to cook our food, to warm up our houses, for transportation and for many other practices. Life without energy seems impossible. Unfortunately, the availability of our traditional energy resources (non-renewable energies) is diminishing sharply, because unrestrained use by humankind. The widespread use of our non-renewable energy resources, for example oil and coal, has negative consequences for the well-being of our planet. According to the report of the Intergovernmental Panel on Climate Change (IPCC, 2013), human activities are the main cause of global warming. The use of the non-renewable resources results in pouring large quantities of CO2 in our air which is blamed by many scientists for heating up the planet (e.g. Moore, 2008; Seinfeld, 2001). In order to conserve valuable resources for future generations as well as to protect the already fragile environment, scientists keep looking for new energy resources from other possible objectives to replace the traditional energy resources. In this research the concept of sustainability plays a significant role, which is gratefully picked up by various professional disciplines, like marketing. One of the more promising renewable ways to generate energy is by the use of solar panels which can capture the energy from the sun. Since solar panels are a cleans and sustainable way of generating energy, the solar panel business was first booming in the western country for nearly ten years. However, things have changed. According to Forbes.Com (2012), a large number of solar panel manufactures have gone bankrupted worldwide over the last two years, due to unfair pricing strategies from manufactures in developing countries. In this context, China rises as the world’s epicenter for solar panel manufacturing. In order to protect the domestic producers of solar panels, many developed countries are setting up barriers to protect the national market against Chinese manufactures. For instance, the EU imposed provisional tariffs of 11.8 percent on imports of solar panels from Chinese companies (Taipei Times, 2013). To overcome these difficulties, the Chinese government uses state owned banks and utilities to finance solar factory expansion and stimulates domestic demand for solar panels (Taipei Times, 2013).

In 2007, the Chinese State Council announced the medium and long-term development planning for renewable energy, clarified that solar power is an important part of renewable energy development, it need to speed up the development and utilization. According to the Chinese Electronic Power Research Institution estimates that in 2050, the renewable energy will have a share of 25 percent in the whole energy supply, and the solar PV (photovoltaic) generation will have a share of 5 percent (The capacity of PV generation is 100 GW ¹(gigawatts) in 2050). Hundreds of manufactures have produced millions of PV equipment’s in the last 5 years since 2010. (Liu, Wang, Zhang & Xue, 2010) Thereby, China became the biggest manufacturer (greenpeace.org, 2013) and expected to become one of the biggest consumption countries for solar panels (EIA, 2012). This is a great opportunity for Chinese companies to become active in the domestic market and therefore it is very important to know and understand the possible intentions among the individual Chinese consumers for purchasing this product. Hence, the above discussion can be summed up as a main research question as the following:

What are the factors influencing intention of the Chinese consumers to purchase solar panels?

1 1 GWh = 1000 Megawatt hour [MWh]. Source from

http://www.convert-measurement-units.com/conversion-calculator.php?type=energy.

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6 Every day people have to deal with many buying decisions, the circumstances which are related to the buying decision process, such as what people buy, where, how much, when, and why they buy makes this process a very interesting research topic. Such a research deals with understanding consumer behaviors.

The term of consumer behavior is defined as the behavior that consumers show in searching for, purchasing, using, evaluating, and disposing of products and services that they expect will satisfy their needs (Schiffman & Kanuk, 2007). Ajzen (1991) point out that consumers’ behavior can be predicted by his or her intention because a certain level of planning and instruction on behalf of the individual is necessary to adopt a behavior. He states further that the greater an individual’s motivation to perform behavior, the greater chance that behavior will be executed (Ajzen, 1991). In addition, Ajzen and Fishbein (1980) also found out that there is a strong empirical relation between intention and behavior in their study. They argued that an individual’s behavior is defined by his intention to perform the behavior, and this intention is a function of his attitude toward the behavior and his subjective norm. Therefore, the study of attitudes and subjective norms can be seen as the first step to understand why people behave the way they do regarding the decision making process to purchase.

Attitude toward a specific behavior is described by Eagly and Chaiken (1993) as the degree to which a person has a favorable or unfavorable evaluation of the relevant behavior, based on the likelihood that a behavior has particular outcomes, and the evaluation of the importance of these outcomes. Ajzen and Fishbein (1980) also conclude that attitude correlates positively with behavioral intention.

Furthermore, the subjective norm is another major driver of purchase intention and crucial in consumer decision making. Consumers often take expectations and behavior of others into consideration when they decide what is appropriate and social norms thus profoundly influence their preferences and behavior (Cialdini, Reno & Kallgren, 1990). According to Ajzen and Fishbein (1975), subjective norms are individual’s perception of social normative pressures, or are relevant to the beliefs of others regarding whether they should perform the behavior in question. Therefore, if people think their significant others agree with their purchasing of solar panels, or make such purchases themselves (Albers-Miller, 1999), purchasing intention and likelihood are increased.

However, an individual’s intention to perform a behavior not only can be predicted by his or her attitude and subjective norm (Ajzen, 1991). The Theory of Reasoned Action (TRA) was related to voluntary behavior, later on behavior appears not to be 100% voluntary and under control, which results in the addition of perceived behavioral control (Chennamaneni, 2006). These three variables together have a stronger predictive power of behavior than only other two variables do, additionally this additional element of perceived behavioral control can attribute to understand the limitation of individuals to show certain behavior (Ajzen, 2006). Thus, the theory of planned behavior was developed by Ajzen.

Based on the above theoretical basis, the Theory of Planned Behavior (TPB) is suitable to be applied for this research as the theoretical framework. In many studies, this theory has been used to exploring the factors which influence the decision to engage in behavior for environmental friendly products (Chan, 1998; Umberson, 2008; Mahesh & Ganapathi, 2012). Hence, it seems highly likely this theory can be applied in understanding different factors which are affecting the purchase behavior for solar panels.

Moreover, to the author’s knowledge, only one study has examined Australia consumer’s purchase intention of solar panels within a TPB framework (Murray, 2012) and no study has investigated and virtually published about solar panel purchase intention on an individual level in china; whereas in fact, China is the largest market in terms of production and consumption of solar panels. Thus, this research attempts to fill up this gap by exploring individual Chinese consumers’ purchase intention regarding solar panels.

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7 1.2 Research objectives

The main objective of this research is to investigate what factors will influence the intention of purchasing solar panels among individual Chinese consumers. More specifically, the author is interested to identify which of the factors has the greatest impact on the purchase intention of solar panels in the Chinese market. Furthermore, this research also aims to identify who is the most likely to purchase solar panels first, namely by investigating the innovativeness of the Chinese consumers. Finally, this research is expected to help market players to understand consumer behavior and develop more effective marketing strategies.

1.3 Research outline

This research paper will be divided into five chapters. Chapter 2 focuses on the review of the relevant literature regarding dependent variable, independent variables and a moderator. Additionally, the hypotheses are proposed here as well. In Chapter 3 the research methodology is explained. Chapter 4 tests the hypotheses and also presents the major findings of the empirical study. Chapter 5 consists of conclusions of the empirical findings, some research contributions and limitations.

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Chapter 2 Literature review and Hypotheses

First, this chapter provides a review of the relevant literatures including an introduction of solar panel and Ajzen’s Theory of Planned Behavior. Subsequently, this chapter will focus on the theoretical framework with the consumer’s purchase intention as a dependent variable linked with three independent variables which are attitude, subjective norm and perceived behavioral control and together with a moderating variable, the environmental concern. Meanwhile, the hypotheses are formulated accordingly and will be empirically tested in the next chapter.

2.1 Solar panel

Depletion of traditional energy resources has led to an international energy crisis and the negative effects of generating energy with traditional energy resources, like coal, on the environment has increasingly become more and more severe. The development of new ways of generating energy has become an inevitable choice. The alternatives to the use of non-renewable and fossil fuels have to be investigated.

One such alternative is solar energy. Scientists found out that the sun creates its energy through a thermonuclear process that converts about 650,000,000 tons of hydrogen to helium every second (Aschbacher, Degreenia, Peterson & Sancetta, 2012). The process creates heat and electromagnetic radiation (Brown, 1988). Stand on these scientific findings the photovoltaic panel or called solar panel was invented to convert the radiation into electricity.

A solar panel is a set of solar photovoltaic modules (A number of photovoltaic cells will be connected together in a "Module") electrically connected and mounted on a supporting structure. A photovoltaic module is a packaged, connected assembly of solar cells. The solar module can be used as a component of a larger photovoltaic system to generate and supply electricity in commercial and residential applications.

(Libra Energy, 2010)

Solar panel attracts close attention in many countries and regions all over the world due to its unparalleled advantages. Of all the energy sources available, solar power is perhaps the most promising. It is capable of producing the power that satisfy residential energy use or at least for the major part. According to the U.S. Northwestern Energy report (2009), a typical supplemental solar electric system for a conventional home would be 1 to 2 kW (one kilowatt (kW) =1,000 watts) and 2 to 50 kW for a business. Since an average residential customer uses 9000 kWh/yr., an average 2 kW system would offset approximately 30 percent (2600 kWh/yr) of electricity use. Furthermore, it can be used to power nearly everything and is characterized the least destructive of energy resources. Clearly, solar energy is a resource of the future.

According to McKinsey (2009), China’s power demand will increase by 5.5 percent per year from 2005 to 2030. The total demand would therefore reach 9250 TWh (1TWh=1,000,000,000KWh) in 2030 as industrialization and urbanization continue and living standards evolve. In order to meet this growing demand, China is pursuing the development of renewable energy usage. Accordingly, the Chinese government passed the Renewable Energy Law in 2006 and published its mid-long-term renewable energy development plan in 2007. The effort to drive the solar energy use in China was further assured after the Chinese government announced to expand the installed PV capacity to 20 GW by 2020 (Ariel, 2009). In order to reach this goal, the Chinese government has taken several steps to support the solar power industry, for instance, recently, the government has offered a 50% tax rebates to solar panel manufacturers (BBC News, 2013) and national subsidies for solar panel installations. Thereby many solar projects are being developed, as solar companies are eager to take advantage of government subsidies and grab a larger market share. For example, with the help of 47.45 million yuan (US $6.95 million) from the

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9 nation’s solar energy plan issued in March 2009, a 3.65 MW PV project broke ground in Ningxia region (Northwestern of China). (Ariel, 2009)

In addition to the support of the government, the Chinese consumers’ consciousness of the energy consumption is also improving. According to the GfK Roper Green Gauge Global survey, in China 33%

of the consumers choose a more efficient source of energy for their homes or cars, up from 26% in 2010.

In fact, the adoption rate of alternative energy is higher in China than in green conscious Germany, where 29% choose a more efficient source of energy all or most of the time, up from 27% in 2010. (Kenyon, 2012) However, the improving energy consciousness cannot directly certify that the consumers will purchase relevant energy saving products. This is reflected by the consumption capacity of solar panels in the Chinese market. According to the data from the Ministry of Commerce which indicates that more than 90 percent of Chinese solar products are being exported to the euro zone and the US market. Only a small part of the solar panel production is digested by the domestic market. However, many analysts are increasingly positive about the expansion of the Chinese domestic market for solar panels and they believe that China’s solar panel industry can grow exponentially due to the huge available potential population in the domestic market (People Daily Online, 2012). In order to investigate what motivates individual consumers to purchase solar panels, this research will apply the Theory of Planned Behavior (TPB) as its theoretical framework. This theory will be reviewed in the following section.

2.2 Theory of Planned Behavior

Before discussing the theory of planned behavior more specifically, it is worth to know that this theory is not the only one to be used to predict intention. Fishbein and Ajzen introduce TRA in 1975 which states that an individual’s level of behavior is determined by intentions to carry out the behavior and intentions are jointly determined by an individuals’ attitude and subjective norm concerning the behavior. However, as elaborated before, the TRA was criticized that the model is neglecting the social factors which influence an individual‘s behavior. Thus, TPB was invented by adding the additional element of perceived behavioral control in the model. Many scholars find that both models can be used in the research in investigating purchase intention, however the TPB model is superior to TRA (Seewon, Hee &

Ingoo. 2003; Chennamaneni, 2006). Besides, there is another theory which is often discussed as a predictor of an individual’s intention as well. It is called Technology Acceptance Model (TAM). This model was proposed by Fred Davis (1985) in his doctor thesis. He proposes that the TAM is an information systems theory that models how users come to accept and use a technology, which, in turn, it can explain or predict motivation. However, according to Chuttur (2009), the TPB provides more details that explain the intention of the consumers than the TAM. This is because TPB is a more complex model which has several independent variables that can

capture various aspects of an individual’s belief.

For instance, the perceived behavioral control construct can help identify specific barriers to a new technology usage such as limitations in user skills. Furthermore, the model also can identify groups whose opinions might be important to future users through the subjective norms construct. (Chuttur, 2009) Moreover, the TPB has proven to be able to predict, with a high degree of accuracy, behavioral intentions (Beck & Ajzen, 1991; Chang, 1998; Kalafatis et al., 1999;

Amitage & Conner, 2001; Tonglet et al., 2004; Chen & Yang, 2007; Harding et al. 2007). TAM instead, is a simpler model, which provides only broad information about perceived ease of use and perceived FIGURE 1: Azjen’s Theory of Planned Behavior

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10 usefulness (Chuttur, 2009). Thus, TPB provides more specific information and it has an advantage of predicting intention over TAM. And together with many other advantages that are mentioned in chapter one, it is more suitable to be applied for this research.

The TPB model considers behavior to be a function of behavioral intention, and the stronger the intention the more likely it is that the actual behavior will be carried out (Ajzen, 1991). TPB consists and measures three different factors, attitude towards the behavior, subjective norm and perceived behavioral control (Ajzen, 2005). The first two factors were introduced from the Theory of Reasoned Action, and the last factor was introduced with the TPB. At the same time the TPB also states that the attitude toward the behavior, subjective norm, and perception of behavioral control combined together forms behavioral intention. A general rule was approved by this theory that the more favorable the attitude and subjective norm, and the greater the perceived control, the stronger should be the person’s intention to perform the behavior in question. More detailed explanations of each factor will be present in the coming content.

2.2.1 Purchase intention

Purchase intention is defined in different forms. Ajzen and Fishbein (1980) define purchase intention as an individual’s readiness and willingness to purchase a certain product or service. Additionally, Dodds, Monroe and Grewal (1991) give purchase intention a similar definition that it refers to the possibility that consumers will purchase a certain product. These definitions imply that while consumers select one particular product, the final decision on accepting a product to buy or rejecting it depends on consumers’

intention. According to TPB, intention describes how hard people are willing to try to perform a behavior and it is based on attitude toward the behavior, subjective norm, and perceived behavioral control (Ajzen, 1991). In this research purchase intention can be proposed as the likelihood to purchase solar panels in the future.

2.2.2 Attitude towards behavior

There are many definitions regarding attitude. One of the early definitions of attitude suggested by Zanna and Rempel (1988) is the categorization of a stimulus object along an evaluative dimension. More specific definition of attitude was developed by Verdurme and Viaene (2003), they described the attitude as the psychological tendency of a person to respond or behave, in a consistently positive or negative manner with respect to a stimulus as a result of their attitude toward that stimulus. The attitude can also be seen as an overall evaluation that expresses how much we like or dislike an object, issue, person or action (Petty, Unnava & Strathman, 1991; Hoyer & MacInnis, 2001; Solomon, 2004). This means attitude is an evaluation with two sides, it can occurs positively and negatively.

Consistent with the TRA, attitude towards the behavior is determined by the consumer’s beliefs about the consequences of engaging in the behavior and the consumer’s evaluation. Therefore, attitude can be changed by influencing the thoughts or beliefs that consumers have about the offering (Hoyer, Maclnnis

& Pieters, 2013). Additionally, Heberlein (1981) also illustrates that a series of beliefs with the combination of evaluation create an attitude. Several researchers argue that not all beliefs are correlated to attitude formation, and the beliefs that are the most critical in relation to attitude formation toward behavior are the salient ones, which imply that salient beliefs are directly connected to attitude formation (Fishbien & Ajzen, 1975; Ivan & Penev, 2011). However, only a small number of the beliefs can contribute as attitude (Zakersalehi & Zakersalehi, 2012), because the cognitive capacity of people is limited (Blythe, 1997; Peter & Olson, 1999). Referring to Fishbien and Ajzen (1975), a person learns or

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11 forms beliefs about an object by associating attributes to the given object. Based on this point of view, the author also find nine common attributes of solar panels after reviewing various relevant articles from the internet (SECO, 2013; Affordablesolarsolutions, 2013; Soluxsavings, 2013): economic, save money by reducing or eliminate electricity bill; environmental friendly, energy is transmitted directly from sun light without any pollution; valuable, installing solar panels will add value to the property ; reliable, since solar panels are installed independently to every building, it will result in less susceptibility to central systems failures; life expectancy, life time long enough to earn investment back. Esthetic, visually acceptable;

modern, compatible with modern living; safe and bright future, expect the user will expand more in the future. These attributes mentioned above are the main considerations that consumers deciding to purchase solar panels. Be aware that people may attach different importance to these attributes, and that they may experience more or less uncertainty concerning the attributes. These attributes will be applied in chapter three to measure the attitude towards solar panels.

Fishbein and Ajzen (1975) state further that an attitude can be viewed as favorable when the positive beliefs outnumber the negative beliefs. According to TPB, the attitude is formed by behavioral beliefs, it is about the likely outcomes of the behavior and the evaluations of these outcomes and the behavioral beliefs produce a favorable or unfavorable attitude toward the behavior. In another words, behavioral beliefs reflect an individual’s belief that an action will lead to perceived outcome (Ajzen, 2005). For example, “solar panels will reduce or eliminate my electricity bill”. This statement implies that we learn to favor behavior we believe to have largely desirable consequences and we form unfavorable attitudes towards behaviors we associate with mostly undesirable consequences (Ajzen, 1991). As mentioned before, belief is the foundation of attitude, which in turn leads to intention to perform a behavior.

Behavioral beliefs represent beliefs about the expected consequences of a specified behavior and the favorable or unfavorable evaluation of these consequences. Sustainability is one of the most important ideologies nowadays. It is a well-established fact that generating energy via solar panels offers a much cleaner alternative than the current ways of generating energy, such as coal fueled power plants. When purchasing solar panels, people can lower the contribution of coal factories in the total energy supply and therefore attribute to a cleaner and healthier environment.

This well accepted belief is an important influential factor of the consequences of purchasing solar panels has, because the individual buyer of solar panels can contribute to a more sustainable world which in the eyes of many is a positive desirable consequence. The TPB theory also indicates that a positive attitude toward a specific behavior will strengthen the behavioral intention and thus strengthen the likelihood that the behavior will be performed (Ajzen, 1991). Based on this standpoint, the author expects that the more positive attitude towards solar panels, the higher chances that consumer will purchase this product. Hence, the first hypothesis can be proposed as follows:

H1: A positive attitude towards solar panels has a positive relationship with the purchase intention of solar panels.

2.2.3 Subjective norm

According to Ajzen and Fishbein (1975), subjective norm refers to an individual’s perception of the extent to which important social referents would desire the performance of a behavior. In general, subjective norm can be defined as the perceived social pressure to carry out a particular behavior and together with the motivation to comply with the social referents (Ajzen, 1991). Ajzen and Fishbein (1975) defined that the normative beliefs are beliefs about how the people who are important to us expect us to behave and motivation to comply. This means when the opinion regarding a certain object comes from an important person and people would like to comply with that important person, then the behave will very

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12 likely to be implemented accordingly. For example, a businessman who would like to buy a big luxury SUV may also have the belief that many of his customers are extremely environmentally conscious and would disapprove this purchase. The TPB further argues that subjective norm can predict intent (Ajzen, 1991). This perspective was proved by many other researchers. For instance, Robinson and Smith (2002) in their study concluded that subjective norm is perceived to affect purchase intention independently.

Vermeir & Verbeke (2006), Chen (2007) and Gotschi et al. (2007) have indicated that subjective norm and a consumer’s intention appear significant and positive relationship.

Furthermore, it is worth taking a moment to flag another fact which is that a positive subjective norm was more predictive to an intention than a negative subjective norm (Sutton, McVey & Glanz, 1999).

Similarly, Chen (2007) also expresses his point of view that positive subjective norm significantly enhances the consumers’ purchase intention. The positive subjective norm is reflected in when people believe certain referents think they should act in certain behaviour and are motivated to meet the expectations of referents. And, vice versa when people who believe the referents think they should not perform the behavior will have a negative subjective norm (DuBay, 2009). For example, if consumers believe the suggestion of their family members regarding purchasing solar panels is respectable and encourages consumers to buy it, then consumers are likely to believe that they should buy it, and in turn develop an intention to purchase solar panels. Therefore, this research assumes that a positive subjective norm will have a positive impact on the purchase intention for solar panel products.

By following the definition above presented by Ajzen and Fishbein (1975), we know that subjective norm refers to how other people in the social environment influence consumer behavior (Hoyer, Maclnnis &

Pieters, 2013). This influence consists of two forms: normative influence and informational influence.

Deutsch and Gerard (1955) offer the following definition for these two forms of influences. A normative influence defined as an influence to conform to the positive expectations of another. For instance, hotels use normative influences to encourage eco-friendly behavior when they place signs in guest rooms saying

“the majority of guests reused their towels,” in the hope that guests will not request freshly laundered towels every day (Hoyer, Maclnnis & Pieters, 2013). An informational influence defined as an influence to accept information obtained from another as evidence of reality. This implies that the influence is based on the facts rather than the other person’s opinion. Moreover, informational influence is an internalization process, which occurs when a user perceives information as enhancing his or her knowledge above that of reference groups (Kelman, 1961). Normative influence is a form of identification and compliance. Identification occurs when a user adopts an opinion held by others because he or she is concerned with defining himself or herself as related to the group. Compliance occurs when a user conforms tothe expectations of another to receive a reward or avoid rejection and hostility. (Hsu &

Lu, 2003) Deutch and Gerald (1955) also point out that informational influence is applicable when people are uncertain because of social disagreement or stimuli are ambiguous. Additionally, informational influence does not involve approval or disapproval as a component while normative influence does (O'Reilly & Caldwell, 1985).

Hoyer, Maclnnis and Pieters (2013) also mentioned that a social influence is how other people influence our behavior through social pressure. Social pressure can be perceived from many different reference groups. There are two groups regarded as the most important reference groups and these groups will be chosen as the focus in this research: government and peers (e.g. family; friends). According to Hofstede’s dimensions of culture in 2001, China has a collectivist culture. Family and friends often represent the most influential in-groups in a collective culture (Ramesh & Gelfand, 2010; Wasti, 2003). Another group of researchers point out that in China, individuals’ behavior is guided by family and authoritarian values (Lee-Ross, 2005; Thanacoody & Bartram, 2006). These findings imply that the opinion from peers can have a significant influence on the purchase intention of consumers, especially for Chinese consumers.

Therefore, in China, the opinion of the peers (e.g. family; friends) can be a very powerful influential

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13 factor that will likely have a significant impact on the purchase intention of solar panels by Chinese consumers.

Apart from this, many studies indicate that social pressure can also come from government or third-party stakeholders (Ayres & Braithwaite, 1991; McCaffrey & Hart, 1998; Parker, 2002). The governmental initiatives have a significant influence on the purchase intention (Mei, Ling & Hooi, 2012). Therefore, the initiatives which are carried out by the Chinese government can be a very powerful influential factor that will likely have a significant impact on the Chinese consumers’ purchase intention for solar panel products. Hence, the conjecture of a positive effect is formulated as follows:

H2: A positive subjective norm has a positive relationship with the purchase intention of solar panels.

2.2.4 Perceived behavioral control

According to Vermeir and Verbeke (2006), the perceived behavioral control refers to the extent to which the consumer believes that his personal efforts can contribute to the solution of a problem. Vermeir and Verbeke (2004) also state that the perceived behavioral control can be explained by perceived availability of a product and perceived consumer effectiveness. Very often we can see that although the consumer is highly motivated to purchase a product, it may become a constraint because of unavailability of that product. Therefore, the availability of a product has a direct impact on consumers’ purchase intention of that product (Vermeir & Verbeke, 2008). Another important element of perceived behavioral control is perceived consumer effectiveness. Straughan and Roberts (1999) define perceived consumer effectiveness as the conviction that the individuals have the ability to manipulate the outcome in a positive manner as a result of their action in this regard. It can be measured as how effective consumers perceive their actions to be (Sparks & Shepherd, 1992; Vermeir & Verbeke, 2008). Furthermore, Roberts (1996) emphasizes that consumers must believe that the actions carried on by them have an impact on the outcome and therefore the consumer will perform the behavior. Similarly several researchers have also claimed that an individual’s confidence in his or her ability to control and thereby display the behavior has positive relationship with the purchase intention or the purchase behavior (e.g., Baker et al., 2002; Taylor & Todd, 1995). In summary, the perceived behavioral control can be explained in this research as follows, if consumers feel the solar panels are readily available through the existing sales channels, the motivation for purchasing this product will be high. At the same time, if consumers believe or are very confident that the action of purchasing solar panels will have many positive consequences for the environment, then they will be also more motivated to purchase this product. Thereby, in view of these discussions, the hypotheses can be formulated as below.

H3: High degree of Perceived behavioral control has positive relationship with the purchase intention of solar panels.

H3a: Perceived availability of solar panels has positive relationship with purchase intention of solar panels.

H3b: Perceived consumer effectiveness has positive relationship with purchase intention of solar panels.

However, there may be some physical constraints present which can cause the consumer’s purchase intention to become lower. A very important factor for the success of solar panels is the possibility to integrate the solar panels physically in the residential area. The most obvious place to install the solar panels is the roof. When people live in an apartment it might be difficult or even impossible for an individual to purchase and install solar panels on the roof, because the roof is shared with the other

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14 residents. Another factor which might influence the purchase intention is the possible high capital costs which are involved with installing solar panels. If these costs are too high it may cause an issue of cost recovering, because it will then take too long before the initial investment is earned back, in other words how attractive is the return-on-investment. The third reason why solar panels might not be possible to purchase is the state of the energy infrastructure. It must be possible for the solar panels to be connected safely onto the grid in order to generate income when the surplus energy is sold back to the state electric grid. These additional revenues are a very important reason for consumers to decide to purchase solar panels. It is highly likely that if it is not possible to sell back the energy surplus, the opinion of solar panels will be influenced negatively for the potential solar panel consumer, which in turn might influence the other referents within the social group, in other words make the purchase intention lower.

2.2.5 Actual behavior

According to TPB, the factors that are discussed above, results in an actual behavior. Currently the number of Chinese consumers who already purchased solar panels is extremely limited as the share of the total Chinese population, therefore the actual behavior which is purchasing solar panels will only be briefly introduced in this paragraph. This factor will be presented in the conceptual model as well in a dotted line box. As Ajzen (1975) proposed, intention is the likelihood an individual will perform a certain behavior. This implies a logical that the higher an individual’s intention to perform a behavior, the higher chance that behavior will be executed (Ajzen, 1991).

2.3 The moderating effect of environmental concern

With the worsening of environment, the Chinese government has begun to realize the seriousness of the problem. Accordingly many environmentally-friendly regulations and initiatives are created in China for the past decades. However, the actual effectiveness of these regulations and initiatives still need a long time to become clear. Since many influential parties are too content with the rapid economic development, most of them do not feel an anxious need to deal with the urgent threat from the environmental deterioration which is happening around the country. Mcgougall (1993) claims that consumer plays a vital role to boost a country’s green revolution. This claim was supported by the study of Grurnert (1993). In the study he points out that approximately 40 percent of the environmental degradation has been caused the consumption activities of private households. So we can assume that if a large number of the consumers show more environmental concerns, the motivation for companies to promote and create products that can fulfill this need will increase significantly. This assumption was supported by Ottman in 1992, which stating that the buyer–seller interaction will consequently lead to further advancement of the green revolution across the whole country. Therefore, carrying on a research of environmental concern at individual level is very important.

Dunlap and Jones (2002) defined environmental concern as the degree to which people are aware of environmental problems and assist struggles to solve them or signify the readiness to contribute personally to their solution. Dunlap and Van Liere (1978) propose that environmental concern represents a new way of thinking called the New Environmental Paradigm (NEP). This method has conducted empirical studies to measure the degree of an individual’s concern on environment (Dunlap, Van Liere, Mertig, & Jones, 2000; Kostova, Vladimirova, & Radoynovska, 2011; Alcock, 2012). The details of how to measure environmental concern via NEP will be presented in the next chapter. Furthermore, Sadorsky (2011) argues that environmentally conscious consumers are actively seeking products that have a limited impact on the environment. According to the study of Laskova (2007), people with high environmental

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15 concerns show a more positive attitude towards the environment. Furthermore, Kim and Choi (2005) also argue that environmental concerns have a direct and positive influence on the customer purchasing intention. A similar conclusion was formed by Ali and Aham in 2012, the customers who have strong environmental concerns are more interested in consumption of products which reflect that concern. Based on these research findings, environmental concern is included in the conceptual model as a moderator factor, and the relationship between environmental concern, attitude and purchase intention can be understood as follows: since solar panels is classified as an environmental friendly product, consumers with a strong environmental concern will show a positive attitude toward solar panels and will therefore have a higher intention of purchasing this product. Accordingly, the hypothesis is presented as below:

H4: Environment concern strengthens the positive relationship between attitude towards solar panels and the purchase intention of this product.

2.4 Conceptual model

The following conceptual model (Figure 2) graphically displays the relationships found in the literature.

In this model, the dependent variable is consumers’ purchase intention for solar panels. In addition, three variables, attitude, subjective norm and perceived behavioral control are independent variables that are expected to influencing purchase intention in this model. Besides, the environmental concern plays a role of moderator influencing the relationship between consumers’ attitudes towards solar panels and purchase intention.

FIGURE 2: Conceptual model

Perceived behavioral control

Attitude towards solar panels

Subjective norm

Environmental concern

Purchase

intention of solar panels

Actual behavior

Perceived availability of a product

H2 +

H1 + H4 +

H3 +

Perceived consumer effectiveness

H3a + H3b +

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16 2.5 Innovation Diffusion Theory (IDT)

In order to increase the success rate when introducing a new technology, Cater (1998) suggests us to

‘single out’ the most interested person in the new product, learn what drives them and design the strategies specifically to reach and convince them. However who are the innovators, early adopters, later adopters, late majority or laggards for a specific product is a common question faced to many market players in practice (Cater, 1998). The IDT that is designed by Rogers (1995), would be helpful for understanding public responses to solar panels and provides an approach to find out which segment that is likely to purchase solar panels first. Since people have different characteristics and needs, it is crucial for market players to recognize these facts in order to help them making effective strategies to satisfied consumers’ needs and increase revenue.

As Rogers (1995) described an innovation is “an idea, practice, or object that is perceived as new by an individual or another unit of adoption”. Diffusion, on the other hand, is “the process by which an innovation is communicated through certain channels over time among the members of a social system”.

Therefore, the IDT argues that “potential users make decisions to adopt or reject an innovation based on beliefs that they form about the innovation” (Agarwal, 2000).

The core of the theory is when a new product or a new technology is introduced, the target market can be divided into five adopter categories (Rogers, 1995). From figure 3 we can see that these five categories are: (1) innovators,who want to be the first to try the innovation; (2) early adopters, who love to getting an advantage over their peers and they have time and money to invest. Once the benefit start to become apparent, early adopters leap in. (3) early majority, who are rarely leaders, but they do adopt new ideas before the average person; (4) late majority, who are skeptical of change, and will only adopt an innovation after it has been tried by the majority, because they are

afraid of not ﬁtting in their social environment, hence they will follow mainstream fashions and established standards; and (5) laggards, who are bound by tradition and very conservative. They resist trying new things (new product or new technology). Figure 3 shows that these five categories follow a standard deviation-curve, only small amount of innovators adopt the innovation in the beginning (2,5%), after a short period of time, early adopters increase to 13,5%, gradually the early majority and the late majority both reach up

to 34% and after some time finally the laggards make up for 16% (Rogers, 1995). Although the majority of the population belongs to the early majority and late majority categories, it is still necessary to understand the characteristics of the population in other categories. An innovation is expected to be diffused more rapidly and widely if the innovation can meet the needs of all five categories. Thus, similarly, when promoting solar panels, marketers can design different strategies to appeal to the different adopter categories in order to optimize the benefits.

FIGURE 3: Relationship between types of adopters classified by innovativeness and their location on the adoption curve (Rogers, 1995) 1995).

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17

Chapter 3 Research Methodology

In the previous chapters the research question and hypotheses were formulated. This chapter will discuss the construction of the questionnaire in order to acquire the information necessary to provide an answer to the research question. Moreover, the specific methods that will be applied in this research will be discussed as well in order to test these hypotheses. At the end, the measurements of each variable will be presented as well.

3.1 Research method

There are two main data collection methods: quantitative and qualitative (Schiffman et al. 2008). A quantitative data collection method is chosen to be applied in this research. The reason for this is that quantitative research is generally used to statistically test hypotheses and theories (Amaratunga, 2002).

Furthermore, this research method refers to the practice for obtaining large amounts of statistical information and used to predicting the intention of consumers. Additionally, this approach is chosen because TPB was predominantly associated with quantitative paradigm (Ajzen, 1991). On the other hand, qualitative research is well known as interpretivism, aiming to understanding consumer experiences (Schiffman et al. 2008).

3.2 Data collection

A self-administered questionnaire would be appropriate for this research. The reasons are that firstly, the questions can be formulated in different dimensions and the respond of the questionnaire are more objective than interviews. Secondly, it is a fast way to collect information. And last, the questionnaire can be handed out in multiple ways with limited costs, such as online questionnaire or via email. An online questionnaire will be used as primary collection method. The questionnaire will be translated into the Chinese language for the local respondents. At the end, the collected data will be transformed into analyzable data by applying the SPSS statistic program.

3.3 Sampling

The target population can be depicted as man and woman with a Chinese nationality, including consumers who already have had solar panels application experience before and consumers who have never applied solar panels before. Respondents have to be 22 years or older, under the assumption that these people have sufficient knowledge in order to understand the subject matter of sustainability or solar energy. Due to a limitation of the author’s research network, the author will conduct this research via different channels and tools in order to obtain a sufficient number of respondents within a short period of time. For instance, step one: a convenience sample will be applied, the questionnaire will be sent to the friends or relations from the author. These respondents in turn, will be asked to identify other candidates who are eligible for this research, and in this way a snowball sampling method will be used. Step two: spread the questionnaire on microblogging² website and spread the questionnaire by at (@) famous artists who are

2 Microblogging is a broadcast medium that exists in form of blogging, it allows users to exchange small elements of content such as short sentences, individual images, or video links. (source from Wikipedia: http://en.wikipedia.org/wiki/Microblogging)

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18 in the author’s contact list or important representatives who are engage in solar panel business. After explaining the research objectives clearly, the author expects these representatives can react on the request and transmit the request within their network and in this way encourage more people to participate in this research. Step three: contacting the existing solar panel manufacturers via email directly and expect them to provide an indication in order to reach the respondents who already purchased solar panels. In determining samples size, Kline (1998) suggests that an ideal sample size should be 20 times larger than the number of parameters. Referring to this research, there are five parameters, thus, the minimal sample size of 100 respondents in total would be sufficient in this case. It should be noted that the number of people in China who have never purchased solar panels far outnumber the people who did purchase solar panels in the past.

3.4 Questionnaire

The questionnaire of this research has two versions, one version is designed for non-solar panel user and another version is designed for solar panel user. The majority of the questions regarding to the five factors of the conceptual model are similar. An additional seven questions are added in order to create a broader understanding of solar panel users. The three common sections of the two questionnaires are described as following. The first section of the questionnaire will present a brief introduction, explaining the objectives of the survey and general instructions. The second section contains questions in where the participants are asked to provide some demographic information, like residents’ age, gender, education, and income. The third section includes questions that relate to the variables of the research. The questionnaire is translated into the Chinese language to serve the target respondents and the two versions make it easier for the respondents to distinguish themselves.

3.5 Variables and measurements

3.5.1 Dependent variable Purchase intention (PI)

According to Schiffman and Kanuk (2007), the behavioral intention measurement deal with the likelihood that consumers will act in a certain way in the future. Baker and Churchill (1977) developed a four items scale for measuring purchase intention in order to obtain reliable research results. Accordingly, the author adapts two intention statements for this research to measure the purchase intention for people who have never applied solar panel before (See Appendix A, page 54). The measurement of this variable will use a five point likert scale ranging from (1) totally disagree to (5) totally agree. The intention statements are:

3.5.2 Independent variables

Attitude towards solar panels (Attitude)

From the previous chapter we understand that beliefs are the fundamental base of attitude. It can be viewed that a person’s attitudes can be assessed by considering beliefs about the attitude object and evaluations of the attributes associated with the object (Fishbein & Ajzen, 1975). According to Fishbein and Ajzen (1975), the key to measuring a belief is to identify the attribute that is linked to the object. Thus any judgment linking an object to an attribute category constitutes a measure of belief content. The attributes related to solar panels are: economic, environmental friendly, valuable and reliable are expressed in chapter two. Ten statements are formulated in Appendix A on page 51. First nine statements are measured on a five point likert type totally agree-totally disagree scale (1 = Totally disagree, 5 = Totally agree). At last, the general evaluation of solar panels was measure by five point likert scales reflecting overall unfavorable or favorable ((1 = very unfavorable, 5 = very favorable).

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19 Subjective norm (SN)

As discussed, subjective norm in this research consists of two elements: social influence that refers to how references influence consumer behaviour, and the motivation to comply with what these referents think. Thus this section is divided into two parts accordingly. The first part is mainly to measure social influence based on scales developed by Bearden, Netemeyer and Teel (1989). The author formulated a similar scale in order to provide a more detailed perspective on the social influence position of a consumer by measuring both the normative and informative influences (see Appendix A, page 52). The first five questions are normative oriented questions consist questions like: 15, 16, 17, 18, 19 and the informational oriented questions consist question like: 20, 21, 22, 23, 24, 25. These questions were measured using a five point likert scale (1 = Totally disagree, 5 = Totally agree). In the second part, the measurement of motivation to comply is based on scales developed by Taylor and Todd (1995). Only three questions were selected to apply in this construct (resp. 26, 27, 28). Each question was measured using a five point likert type extremely unlikely – extremely likely scale (1 = Extremely unlikely, 5 = Extremely likely).

Perceived behavioral control (PBC)

Perceived behavioral control is the extent to which a person feels able to enact the behavior. It has two aspects: perceived availability of a product (PAP) and perceived consumer effectiveness (PCE). The perceived availability of a product can be measured as how easily accessible consumers believe the products to be (Sparks & Shepherd, 1992; Vermeir & Verbeke, 2008). Furthermore, the statements that are formulated regarding perceived availability of a product (respectively 29, 30, 31, 32) are modified from the measurement that designed by Vermeir and Verbeke (2008). The perceived consumer effectiveness can be measured as how effective consumers perceive their actions to be (Sparks &

Shepherd, 1992; Vermeir & Verbeke, 2008). In this research the statements regarding perceived consumer effectiveness (respectively 33, 34) is formulated according to the measurement that is proposed by Robert (1996). Each statement was measured using a five point likert type totally agree-totally disagree scale (1 = Totally disagree, 5 = Totally agree). All these questions can be find in Appendix A, on page53.

3.5.3 Moderator

Environmental concern (EC)

NEP is the most frequently used measurement of environmental concern (Kostova, Vladimirova, &

Radoynovska, 2011). It is aimed at measuring people’s views on the human-environment relationship.

Thus, the new environmental paradigm scale is a useful tool to measure environmental concern. It can be considered as a worldview on the vulnerability of the environment to human interference (Poortinga, Steg,

& Vlek, 2002). To examine the level of environmental concern, 12 statements that were selected which is in line with Dunlap and Jones (2002) definition of environmental concern (see Appendix A, page53). This variable will be measured on a five point likert type totally agree-totally disagree scale (1 = Totally disagree, 5 = Totally agree).

3.6 Innovativeness

As previously illustrated, one of the goals of this research is to identify the innovativeness of the Chinese consumers. In order to reach this goal the degree of innovativeness is measured among the respondents.

Domain specific innovativeness (DSI) is one of the most frequently used scales to measure consumer innovativeness. It is defined as “the tendency to learn about and adopt innovations (new products) within a specific domain of interest” (Goldsmith & Hofacker, 1991). This scale proved to be the most useful scale to measure consumer innovativeness in a specific product category (Citrin, Sprott, Silverman, &

Stem, 2000; Hynes & Lo, 2006). Therefore, this scale will be used in this research which is drawn from

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20 Goldsmith and Hofacker (1991) consumer innovativeness measurement scales (see Appendix A, page 54).

All these items will be measured on a five point likert type totally agree-totally disagree scale (1 = Totally disagree, 5 = Totally agree).

The innovativeness score can be calculated via “Compute Variable” function in SPSS. The calculation steps are described as following: firstly, create a new variable by formulate a new name (innovativeness_score). Secondly, the average value of all these 6 items was calculated by summing up the total score of these 6 items and divides the total score with 6. The last step was applying this new variable (average value) in order to run a frequency analysis. The results from this frequency analysis showed the size of each category (see Appendix F, page 94). Meanwhile, the evaluation score range was adjusted by the author in order to match more closely to the distribution proposed by Rogers in 1995. The distribution of Rogers is as follows: Innovator: 2.5%; Early adopter: 13.5%; Early majority: 34%; Late majority: 34%; Laggards: 16%.

3.7 Analysis plan

Descriptive

Descriptive analysis will be used, to understand the structure and distribution of the sample being investigated through statistics data like frequencies, mean, percentages, and the cumulative percentage.

Reliability

After gathering the entire questionnaire, this research will employ the use of Cronbach’s Alpha, to analyze the internal consistency of each variable and constructs which is one of the most important data analyses step.

Factor analysis

Factor analysis can be considered as a data reduction technique because it reduces a large number of variables that often overlap to a smaller number of factors (Malhotra, 2009). In this research two variables were applied for a factor analysis, namely attitude and environmental concern. The purpose of conducting a factor analysis for the variable “Attitude” is to reduce the amount of data and to group the variables into fewer factors. By doing this, it would be easier to conduct regression. The questions regarding the second variable, “Environmental Concern”, are found in existing literatures and its Cronbach’s Alpha score was very high which normally means that regression as a calculated mean value would be sufficient. However, when the mean value of the environmental concern was used in the regression test, the result caused a multicollinearity on Attitude and EC_moderator (resp. VIF=75.564;

77.937), which made the author decide to still conduct a factor analysis on this variable (see Appendix E, page 85 ).

Regression

Regression will be used to analyze the result between the independent variables and dependent variable.

This study will focus on consumer purchase intention as a dependent variable, and the positive attitude;

positive subjective norm; and perceived behavioral control as independent variables together with environmental concern as a moderator variable to carry out multiple regression analysis in order to investigate whether the hypothesis are supported.

Correlation

The correlation analysis is a statistical method that is used to study the relation between variables.

Pearson Correlation Coefficient will be employed in this research to analyze the relationship between the variables, such as positive relation or negative relation.

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21 Multicollinearity

Sometimes, the results of regression analysis may seem paradoxical when the correlations among the independent variables are strong. This undesirable situation called multicollinerarity. To diagnose the potential for multicollinearity among the independent variables, collinearity statistics such as tolerance and variance inflation factor (VIF) were used. Tolerance is the proportion of variability of that variable that is not explained by its linear relationships with the other independent variables in the model (Norusis, 2005). The VIF measures how much the variance of the estimated coefficients is increased over the case of no correlation among the variables (Allison, 1999). According to Allison (1999), if any of the tolerances are less than 0.4 and any of VIFs are larger than 2.5, multicollinearity may be a problem.

Mean value

Due to the fact that in this research all variables are tested on the same likert scale (5 point likert scale) and some variables are originally from trustworthy research studies, accordingly the reliability score of those variables is very high. Based on these facts, the average score is calculated of these variables and this average is used as a new variable for the continuing research (e.g. SN, PBC, PI and Innovativeness).

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22

Chapter 4 Data collection and Analysis

This chapter presents the results of the data analysis and hypothesis. It will include demographic data, variables’ reliability, factor analysis results and hypothesis results via the regression analysis. Due to the fact that the significant differences in sample size between non-solar panel users and solar panel users, this paper will mainly focus on analyzing the data that was collected from the non-solar panel users. The small amount of data which was collected from solar panel users will be interpreted as additional information that is presented at the end of this chapter.

After the pre-tested questionnaire was firstly conducted among three Chinese residents and was revised afterwards, the finalized questionnaire was distributed to the author’s network and multiple Chinese public social forums. The questionnaire was filled in electronically by 107 non-solar panel users, which is compliant with the design of the research, but was only filled in by 6 users of solar panel. After deducting some unusable questionnaires (under age limitation and incomplete), a total valid number of questionnaires came out on 104 for non-users of solar panels and 6 respondents for users of solar panels.

4.1 Demographic characteristics

1. Gender: The respondents were asked if they were male (M) or female (F). Of the 104 individuals who provided gender information, 46 (44.2%) were male, whereas 58 (55.8%) were female.

2. Age: The respondents were asked to fill in their age in an open-ended question. The youngest respondent was 22 years of age while the oldest was 58 years of age. The average age of the respondents is 28.66 years.

3. Education status: according to the final test results, 51.9% respondents have a bachelor degree, which accounts for the overwhelming majority of the total sample. The educational level of the second largest response group was the master’s degree, 25%. The third largest response group was people who have junior college degree, which consist of 16.3% of the total sample. The other educational degrees are less than 3% of the total sample. The minimal educational level is middle school and higher because none of the respondents have a primary school education degree as their highest degree.

4. Income: the result shows that the majority of the respondents (42 people) have an income between 2001RMB to 4000RMB which is equal to a percentage of 40.4%. A total of 21 respondents told they have an income between 4001RMB to 6000RMB (20.2%) and 18 respondents indicated their monthly income is below 2000RMB (17.3%). Furthermore, 11 respondents said their monthly income is above 10001RMB, which is 10.6% of the total sample. The both remaining respondent groups (6001RMB-8000RMB and 8001RMB-10000RMB) present each a percentage of 5.8% of the total sample size.

5. Where consumers prefer to obtain solar panels: from the table below, we can see that the majority of 48.1% of respondents prefer to buy solar panels by themselves and 33.7% of respondents want to receive free solar panels from government. Another 16.3% of respondents would like to lease solar panels in the future.

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23 TABLE 1

Demographic characteristics

Gender Male

Female

44.2%

55.8%

Age Minimum

Maximum Average

22 58 28.66

Education level Primary school

Middle school High school Junior college Bachelor Master Doctor Post doctor

0.0%

2.9%

1.0%

16.3%

51.9%

25%

1.9%

1.0%

Income (¥) 2000-

2001-4000 4001-6000 6001-8000 8001-10000 10001+

17.3%

40.4%

20.2%

5.8%

10.6%

Where consumer prefer to obtain solar panels in the future

Buy by myself

Receive from government Receive from charity Financial lease

48.1%

33.7%

1.9%

16.3%

4.2 Reliability analysis

Reliability is a method which measures the degree of consistency of the results. The higher the reliability of the questionnaire, the more the results are credible. In this research, Cronbach’s alpha coefficient is applied to measure the questionnaire’s reliability. Tavakol and Dennick (2011) refer the alpha was developed by Cronbach in 1951 and this technique is to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1 in their research paper. The closer Cronbach’s alpha coefficient is to 1 the greater the internal consistency of the items in the scale. George and Mallery (2003) provide the following rules of thumb: “above 0.9--Excellent, above 0.8--Very Good, above 0.7—Good, between 0.6-0.5-- Acceptable, below 0.5—Unacceptable”.

The reliability of the ten questions measuring attitude towards solar panels was determined to be 0.852 by using Cronbach’s alpha reliability test, which is a very high reliability score.

The reliability of the fourteen questions measuring subjective norm was determined to be 0.782, which is also a high reliability score. Additionally, the reliability score of the two elements of subjective norm in this research are shown in Appendix C (Page 64). The Cronbach’s alpha value of five variables used to measure normative social influence is 0.724. The Cronbach’s alpha value of six variables used to measure informative social influence is 0.788. The Cronbach’s alpha value of three variables used to measure motivation comply is 0.635, which can all be indicated as sufficient results.

The reliability of the six questions measuring perceived behavioral control was determined to be 0.596 by using Cronbach’s alpha reliability test. Moreover, the Cronbach’s alpha value of the four variables

Chapter 4 Data collection and Analysis

Table of Contents

Chapter 1 Introduction

Chapter 2 Literature review and Hypotheses

Chapter 3 Research Methodology

Chapter 4 Data collection and Analysis