Consumer Perceptions of Eco-Friendly Products

Section 1: Foundation of the Study

In 2009 the Environmental Protection Agency (EPA) reported that

U.S. consumers generated over 3.19 million tons of e-waste including

televisions, telephones, video cameras, and computer equipment. In the

United States, only 430,000 tons, or 13.6%, of these electronic items had

been disposed of and recycled (Environmental Protection Agency, 2009).

The creation of global electronic waste was 40 million tons per year, and the

United Nations Environment Programme (UNEP) estimated that, by 2020,

e-waste levels could rise by as much as 500%. As the global e-waste has

grown by about 40 million tons a year concerns about e-waste ramifications

have increased (Sanitation Updates, 2010). Walsh (2009) suggested that the

massive amount of improperly disposed e-waste has raised toxicity in the air

to dangerous levels. Consequently, researchers have begun to investigate

strategies to mitigate the negative ramifications of e-waste (Robinson,

2009).

One strategy to reduce e-waste is to encourage consumers to purchase

electronic products that are environmentally friendly (Ngo, 2008). Research

by Ngo (2008) found that consumers were more likely to make purchases

based on product labeling design combining specific environmental details

and a numerical rating system. Consumers who would pay more for eco-

products believed that eco-friendly products would reduce ewaste variables

(Datta, 2011). The purpose of the present study was to assess the level of

consumer willingness to pay more for eco-friendly products, and consumers

willing to recycle e-waste at drop-off recycling centers. The relationships

among quality (Ladhari, Souiden, & Ladhari, 2011), price (Bennett, 2011),

and brand loyalty (Muk, 2012) have been the subject of research for several

decades; however, the relationship between these variables and consumer

outcomes related to eco-friendly products has not been extensively explored

in the current literature. This paper will add to the current research on

product factors and consumer behavior, thus attempting to close a gap in the

professional literature regarding eco-friendly products and consumer

conservation behavior.

Background of the Problem

Consumers play a large role in the management of e-waste. Due to

increased global interest, 90% of American consumers were concerned

about the way their purchases affected the environment, and they would be

willing to change their purchasing behavior in an effort to improve the

environment (Choi, 2012). Consumer interest in the environment had an

effect on the success of manufacturing, and manufactures that have

associated themselves with environmental causes have rebounded from the

recession significantly faster than traditional manufacturers who had not

done so. Companies that had profited from developing and selling green

and sustainable products have increased over the years (Berger, 2010).

Green sustainable products met the following criteria: sustainability, cradle-

to-cradle design, source reduction, innovation, and viability (Green

Technology, 2010). Cradle-to-cradle design is a holistic economic,

industrial, and social framework, which seeks to create systems that are not

just efficient but essentially wastefree (Watson, Boudreau, & Chen, 2010).

For example, General Electric (GE) introduced compact fluorescent light

bulbs in 2005. At first, GE captured less than 5% of the market; however,

only 2 years later, corresponding to an increase in public awareness of

threats to climate change, GE captured 20% of the market (Banon Gomis,

Guillén Parra, Hoffman, & Mcnulty, 2011; Dhiman, Marques, & Holt,

2010).

Companies’ leaders are able to increase their competitive position by

using ecofriendly products. Bonini and Oppenheim (2008) suggested that

GE increased its revenues, enhanced its brands, and strengthened its

competitive position because of its increased focus on eco-friendly products

and the consumers’ positive response to them. Other companies have also

seen the green evolution as a way to save and cut the overhead cost. If

consumers decided to purchase only eco-friendly products, then

manufacturers would have to comply and make more profit (Orange, 2010).

Although the findings indicated that not all consumers believed that they

would actually have an impact on the environment, researchers have not

established whether enough consumers believe that purchasing eco-friendly

products is good for the environment and that this could amount to a viable

strategy for reducing e-waste (Peattie, 2010).

Voinea and Filip (2011) analyzed the main changes in consumer

buying behaviors during the 2008 North American economic crisis which

threatened the collapse of large financial institutions and found that price

played a critical role in purchase decisions. Similarly, Braimah and

Tweneboah-Koduah (2011) demonstrated that price ranks ahead of green

concerns as a major influence in a purchasing decision. Whereas some

researchers suggested using a cost-based technique to establish the price of a

product (Alvarez & Lippi, 2012; Ferson & Lin, 2011), others suggested that

the cost of manufacturing was the most important determinant in product

pricing (Gordon, 2012).

Guth, Levati, and Ploner (2012) argued that full and marginal cost pricing

was consistent with the satisficing model. Ryan (2011) explained that the

satisficing model showed how a consumer made a purchase decision when

faced with an array of similar choices that were all for sale at the same

physical location. In this study, I assessed consumer decisions based on

their preference for eco-friendly products versus non-eco-friendly products.

In this model, a company objective was not only to maximize profit, but also

to earn a satisfactory return on investment. Gordon (2012) and Atkinson

(2013) suggested that price would not be the only determinant in the

marketing mix. It was currently unknown how the price points of eco-

friendly products would affect consumer behavior and whether consumers

who believed in the efficacy of eco-friendly products were willing to pay

more for those products (Lee, 2011). It was also unclear how willing

consumers would be to recycle e-waste at drop-off recycling centers

(Saphores,

Ogunseitan, & Shapiro, 2012).

Some researchers demonstrated that quality had an impact on

consumer behavior as consumer behavior models revealed that quality was a

positive antecedent to purchase intentions (Gallarza, Gil-Saura, & Holbrook,

2012; Melnik, Richardson, & Tompkins, 2011; Monroe, 2012). According

to the Zeithaml model (as cited in Gallarza et al., 2012) perceived quality

and purchase intention are measurable. In the Zeithaml model, the

consumer perception of perceived quality shows consumers’ judgments

about a product’s overall superiority or excellence. Although other

researchers have studied the effect of consumers’ green purchasing behavior

using quality attributes as a contributors to the formation of purchase

intention (Chen & Chai, 2010; Lindqvist, 2010), researchers do not currently

know how quality affects consumers’ willingness to pay more for eco-

friendly products or the consumers’ willingness to recycle e-waste at drop-

off recycling centers.

This study will add to the existing knowledge base surrounding these topics.

In addition to the important role that product quality plays, Han and

Ryu (2009) concluded that brand loyalty also influences consumer behavior.

Research also suggested that customer satisfaction was influenced by

physical surroundings and price perception (Ariffin, Bibon, & Saadiah,

2011; Han & Ryu, 2009). Other researchers maintained that these factors

had an impact on customer satisfaction and that customer satisfaction

depended on customer loyalty (Ladhari et al., 2011). Loyal customers were

more likely to recommend products and services and engage in positive

word-of-mouth behaviors as a result they spend extra money in service

operation than nonloyal customers were more likely to do so (Ladhari et al.,

2011). In addition, loyal customers were less costly to serve because they

already knew the product or service well and required less information

(McKercher & Guillet, 2011). Thus, in recent years, service providers have

focused on achieving customer loyalty by delivering superior value and by

identifying and enhancing the key factors that determine loyalty (Chen,

2010). The key factors that make up customer brand loyalty are captive

customers or convenience seekers and contented and committed customers

(Mao, 2010).

Mao (2010) defined captive customers as repeatedly purchasing the

same product, service, or brand because of a lack of opportunities to

substitute alternatives, whereas convenience-seekers might not respect the

brand, but act out of convenience. Mao contended that consumers, who had

a positive attitude toward a brand, did not consume extra products or

services. Lastly, committed consumer loyalty was active in both attitude

and behavior.

The concept of green branding had slowly started to emerge. Green

branding consists of a set of attributes and benefits that are associated with

reduced adverse environmental impact and the ability to make a positive

impression on consumers and raise their concerns for the environment

(Wong, 2010). It was unknown how brand loyalty would affect consumers’

willingness to pay more for eco-friendly products and the consumers’

willingness to recycle e-waste at drop-off recycling centers. In this study, I

attempted to clarify the relationships among service quality, price, brand

loyalty, and eco-friendly products.

Problem Statement

In 2009 the Environmental Protection Agency (EPA) reported that

U.S.

consumers generated over 3.19 million tons of e-waste including televisions,

telephones, video cameras, and computer equipment. In the United States,

only 430,000 tons, or 13.6%, of these electronic items had been disposed of

and recycled (Environmental Protection Agency, 2009). The power

generated from recycling a million laptops can power 3,500 U.S. homes for

a year (EPA, 2012). As consumers continue to purchase and replace

electronic items, these figures will continue to rise (Rani, Singh, &

Maheshwari, 2012). Despite the high rate of e-waste, Sharma and Bagoria

(2012) contended that green marketing for eco-friendly products would

reach $3.5 trillion by the year 2017, due to catering to environmentally

conscious consumers. The general business problem is the need to manage

the high rate of failure of e-waste and to produce more eco-friendly

products, thus not missing profits and a growing eco-friendly customer

market. The specific business problem was that business managers did not

have sufficient evidence to develop marketing and pricing strategies

reflecting addressing the relationship between the high level of e-waste and

the consumer’s preference for eco-friendly products.

Purpose Statement

The purpose of this quantitative study was to examine the relationship

between the high level of e-waste and the consumer’s preference for eco-

friendly products and provide business managers with the information they

need to develop advertising and pricing strategies. The method used was

convenience sampling. The geographic location used for this study was

central Florida. The population sampled was comprised of students from

University of South Florida (USF) registered on the SurveyMonkey

database. I used correlation analysis to determine the relationships between

the independent variable consumers’ views on eco-friendly products on

reducing waste, and consumers’ willingness to pay more money for eco-

friendly items. Product price perceptions, quality perceptions, and brand

loyalty perceptions were the three dependent variables used in this study.

The findings of this study might contribute to social change by

encouraging product manufacturers to produce more environmentally

friendly products than nonenvironmentally friendly products. This increase

could lead to a reduction in e-waste by providing more justification for the

proliferation of products with a lower environmental liability rating rather

than having products with high environmental

liability.

Nature of the Study

To explore and investigate consumer views on eco-friendly products I

used a quantitative correlational design to address the purpose of this study.

A qualitative methodology would explore attitudes, behavior, and

experiences through such methods as interviews or focus groups. A smaller

pool of participants is required to participate since this type of research

yields in-depth opinions from participants. Smaller groups allow consumers

to express clear ideas and share feelings that do not typically come out in a

quantified survey or paper test. In qualitative research, the contact with

participants tends to last quite a bit longer than in a quantitative study (Chen

& Macredie, 2010). In contrast, the quantitative methodology is an

exploration that aims to measure variables and their relationships (Jandaghi

& Matin, 2011). Unlike qualitative research, quantitative research uses

measurable data to determine facts and patterns. A quantitative method

offered the best approach for this study because data gathering from a large

sample via survey and collecting quantitative data allowed me to determine

consumer perceptions and intentions though statistical means. I

administered an online survey through SurveyMonkey (see Appendix A) to

University of South Florida members of the SurveyMonkey database, and

the data gathered helped to assess consumer perspectives on eco-friendly

products.

The design of this study was nonexperimental and correlational. In an

experimental design, the researcher would measure the impact of an

intervention on an outcome (Chen & Macredie, 2010; Smith, Wright, &

Breakwell, 2011). Without a random assignment, manipulation, or

treatment, nonexperimental investigations are possible (Holbrook, 2011).

The correlational design was appropriate for this study to find answers to the

research questions, which required estimating the degree of association

between variables (Chen & Macredie, 2010).

Although correlational methods cannot imply causation, correlation

does allow for the determination of the strength and nature of the

relationship between two variables. Only a small number of empirical

investigations explore what motivates a consumer to purchase eco-friendly

products, the present study provides a description of the consumers’

understanding of whether eco-friendly products are suitable for the

environment, whether they are beneficial in reducing e-waste, and whether

consumers would be willing to pay more for eco-friendly products.

Research Questions

The research question this study will answer is how does the high

level of e-waste correlate with consumer preference for eco-friendly

products? The following research questions examined consumers’ views on

eco-friendly product quality, eco-friendly products price, and eco-friendly

product brand loyalty and how these views would relate to consumers’

willingness to recycle e-waste at drop-off recycling facilities and

consumers’ willingness to pay more for green products.

RQ1: To what extent does eco-friendly product quality relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ2: To what extent does eco-friendly product price relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ3: To what extent does eco-friendly product brand loyalty relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ4: To what extent does eco-friendly product quality relate to

customer willingness to pay more for green products?

RQ5: To what extent does eco-friendly product price relate to

customer willingness to pay more for green products?

RQ6: To what extent does eco-friendly product brand loyalty relate to

customer willingness to pay more for green products?

RQ7: To what extent do gender and age differences relate to customer

willingness to pay more for green products?

RQ8: To what extent is a relationship extant between e-waste and

eco-friendly product purchasing?

Hypotheses

The null hypotheses and alternative hypotheses set forth this study

were as follows:

Ho1: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha1: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to recycle e-waste at drop-off

recycling facilities.

Ho2: There is no significant statistical relationship between eco-

friendly product price and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha2: A significant statistical relationship exists between eco-friendly

product price and customer willingness to recycle e-waste at drop-off

recycling facilities.

Ho3: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to recycle e-waste

at drop-off recycling facilities.

Ha3: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ho4: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to pay more for green

products.

Ha4: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to pay more for green products.

Ho5: There is no significant statistical relationship between eco-

friendly product price and customer willingness to pay more for green

products.

Ha5: A significant statistical relationship exists between eco-friendly

product price and customer willingness to pay more for green products.

Ho6: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to pay more for

green products.

Ha6: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to pay more for green

products.

Ho7: There is no significant statistical relationship between gender,

age, and customer willingness to pay more for green products.

Ha7: A significant statistical relationship exists between gender, age,

and customer willingness to pay more for green products.

Ho8: There is no significant statistical relationship between e-waste

recycling, income, and age.

Ha8: A significant statistical relationship exists between e-waste

recycling, income, and age.

Survey Questions

All survey information is completely confidential. Your responses are

very important. Thank you for participating in the survey.

Please circle the option that applies to you

Section 1 1 2 3 4 5

Demographics

1. Your gender male Female

2. Your age range 18-24 25-31 32-38 39-45

46-52

3. Education level high some AA BA/ BS Master’s

school college – degree degree Degree or

graduate no degree higher

3b. Income 0-24,999 25,000-49,000 50,000-100,000-150,000-+

99,999 149,000

Please circle the option that applies to you

Section 2 - Willingness to

pay more for green

products Never Rarely Sometimes Often Always

4. I have used green 1 2 3 4 5 product before.

5. I believe that green products are more expensive than nongreen

products.

6. I am willing to pay

more for green

products.

1 2 3 4 5

7. Indicate the

percentage you are

willing to pay for green

products

between

1% -

10%

between

11% -

20% more

between

21% -

30% more

betwee

n 31% -

40%

between

41% -

50%

8. I believe the price of

green products effect

my decision to purchase

them.

1 2 3 4 5

Strongly

Disagree

Neutral

Agree

Strongly

Agree

9. I believe the quality of 1 2 3 4 5 green

products effect my decision to purchase.

10. I believe that green 1 2 3 4 5

products are of better quality than nongreen products.

11. I would recommended green products based on quality to

my friends.

12. I would switch to 1 2 3 4 5 green products if

they were more available at my local store.

13. I would switch to 1 2 3 4 5 green products if

they were promotional deals such as TVs ads and local printed coupons

available at my local store.

14. I am more likely to buy a certain product because it has a brand

name I have used in the past.

Select the option that best describes you best

Section 3

Willingnes

s to

Recycle e-

Waste

Never

Rarely

Sometimes

Often

Always

15. I recycle electronic

devices or e-waste

(products such as

computers, televisions,

VCRs, stereos, copies,

fax machines, cellular

phones as opposed to

discarding them as trash).

1 2 3 4 5

Select the option that best describes

you best

Strongly

Disagree Disagree

Neutral

Agree

Strongly

Agree

16. I would start recycling 1 2 3 4 5 electronic

devices if I receive a financial incentive for doing so.

17. If I had the choice of 1 2 3 4 5 discarding an old electronic

device I would use a drop-off recycling facilities.

18. I would buy and 1 2 3 4 5

recycle electronic devices if more drop-off recycling facilities were

available in my area.

19. I would you buy and recycle electronic devices if there was an

awareness campaign in my area about the dangers of not recycling.

Theoretical or Conceptual Framework

Consumer behavior theories and buying behavior in advertising were

the theoretical frameworks used in this investigation. Through the results

of this research, I will explain an aspect of buyer behavior.

Consumer Behavior Theories

Consumer behavior theories cover two areas: consumer perception

and collective consciousness (Cohen, n.d.). The consumer perception

theory suggests that consumers understand how perception of a product or

1 2 3 4 5

service influences their behavior. Researchers studying consumer

perception explored branding, buyer’s remorse, positioning, repositioning or

depositioning, sensory perception and value, and quality (Kher et al., 2010;

Monday, 2011; Rosenzweig & Gilovich, 2011).

Perception relates to the consumer’s ability to make some sense of reality

from external sensory stimuli (Rosenzweig & Gilovich, 2011). Branding

involves imposing an identifying feature on products or services so that they

would be easy to identify by the public (Kher et al., 2010). Positioning

occurs when marketers try to build up their brand. Positioning involves

actively creating images that are both appealing to and recognizable by

certain target groups. Repositioning relates to altering the image to appeal

to a larger market of consumers to help influence a larger target market,

whereas depositioning relates to the practice of trying to devalue a substitute

(Timofte, 2013). Value relates to the customer’s perception that a product’s

benefits outweigh its cost. These benefits can be either qualitative or

quantitative. Quality relates to value, while taking into account measuring

goods and services against the competition (Timofte, 2013). Buyer’s

remorse relates to a feeling of regret that occurs after one has made a

1 2 3 4 5

purchase and, then, realizes that one has missed a better opportunity to buy a

product or service (McKnight, Paugh, McKnight, & Parker, 2010).

In the cognitive dissonance theory, cognition (e.g., attitudes, desire,

intention) is dissonant, or conflicted, when consumers are unable to keep

away from a situation, as well as from information, that might add

dissonance (Sahgal & Elfering, 2011). This is apparent when a consumer

chooses one brand over another. Similarly, cognitive dissonances that occur

after a purchase is post purchase dissonances (Bose & Sarker, 2012). Saleh

(2012) was able to show that post purchase regret comes from low consumer

satisfaction, and low satisfaction leads to no-repurchase intention, the

tendency to shift to alternative brands, and negative word-of-mouth reports

about the brand in question.

Theories of collective consciousness reflect the shared beliefs and

attitudes held within a society. Researchers such as Dekker, Hummerdal,

and Smith (2010); Filippakou and Tapper (2010); and Jung (2012) suggested

that an autonomous individual would come to identify with a larger group.

While this was true for some groups (as for example in Japan), other groups

(for instance in the United States), had a more selfaggrandizing need over

others (Cohen, n.d.). Self-aggrandizing nations had a high opinion of them

and viewed themselves as very different from others.

Collectiveconsciousness information helped marketers target their market by

appealing to consumers’ individualism in the United States but not in other

parts of the world.

Buying Behavior Theories

Some theories related to buying behavior include the generic theory of

buying behavior, cultural theory of buying behavior, and the environmental

theory of buying behavior. These three theories are explaining how

consumers tend to buy products and services. Consumers would go through

a series of steps before making a purchase and customer decisions depend

on a number of different factors such as cultural influences, personality, and

environmental elements (Lehtinen, 2012).

The generic theory of buying behavior highlights the basic procedures

followed by consumers when making a purchase. The customer would

recognize a need to make a purchase and start researching potential products

and pricing. An example would be a customer about to buy a television set:

He or she would evaluate features, benefits, and pricing, and finally make a

decision to purchase. Additionally, the way the customer feels about the

brand would also tell how likely the customer is to purchase from the same

company again. In a 2000 study, 89% of teenagers said that they “would

likely switch brands to one associated with a good cause” (Hyllegard, Yan,

Olga, & Attmann,

2010).

Proponents of the cultural theory of buying behavior highlight the

cultural influences shown to affect the buyers’ behavior (Penn, n.d.). An

individual’s cultural beliefs and values develop over time and within the

context of a community. These values and beliefs lead to certain purchases

(Yuan, Song, & Kim, 2011). Researchers have explored cultural variables

and their effects on online shopping (Ha & Stoel, 2012) and brand loyalty

(Carman, 2011).

Supporters of the environmental theory of buying behavior suggested

that purchasers would buy different items based on different situations and

variations in customer knowledge. For example, a buyer in the United

States would buy winter clothes in November or December and not during

the summer (Bloch, 2011). Mazar and Zhong (2010) used environmental

theory to explore the occurrence of green purchase decisions using socio

demographic variables and personality indicators that measured

environmental consciousness.

Operational Definitions

This section clarifies terms in this study. Some are topic specific,

whereas others might convey a variety of different meanings in relation to

other subject matter. e-Waste: A popular, informal name for electronic

products nearing the end of their useful life. Computers, televisions, VCRs,

stereos, copiers, and fax machines are common electronic products

(California Department of Resources Recycling and

Recovery, 2013).

Green: The term green encompasses a variety of environmental

concerns. Some of the current concerns relate to the depletion of natural and

scarce resources. Examples include bad and excessive production and

consumption activities, waste accumulation, and emissions because of

production processes, the use of hazardous materials, fast replacement,

consumption patterns and usage, and usage and disposal habits. There are

also unhealthy products and side effects arising from unhealthy

environments, the use of improper materials, improper choices, and uses due

to uninformed consumer decisions, unsafe or unpleasing work environments

due to inadequate safety management, and lack of appropriate aesthetics

(Chen, 2010).

Green sustainability products: Such products meet the following

criteria: (a) sustainability by meeting the needs of society in ways that can

continue indefinitely into the future without damaging or depleting natural

resources, and (b) sustainability meeting present needs without

compromising the ability of future generations to meet future needs (Green

Technology, 2010).

Greenwashing: Greenwashing occurs when a company or

organization spends more time and money claiming to be green through

advertising and marketing than through implementing business practices that

minimize environmental impact. Some consider it an example of

whitewashing, but with a green brush

(Greenwashingindex.com, 2011).

Innovation: Innovation involves developing alternatives to existing

technologies, whether fossil fuel or chemical-intensive agriculture, which

have demonstrated to damage health and the environment (Green

Technology, 2010).

Source reduction: The attempt to reduce waste and pollution by

changing patterns of production and consumption (Green Technology,

2010).

Sustainable products: Such products reduce the impact on the

environment by virtue of being responsibly sourced products (e.g., those that

are either renewable or sustainably harvested). A sustainably harvested

source material does not harm the surrounding area, pollute the air, or

permanently reduce the supply (Sebhatu, Enquist,

Johnson, & Gebauer, 2011).

Viability: Viability involves creating a center of economic activity

around technologies and products that benefit the environment, speeding

their implementation, and creating new careers that truly protect the planet

(Green Technology, 2010).

Assumptions, Limitations, and Delimitations

Assumptions

This study contains two foundational assumptions. The primary

assumption was that participants would be honest in their responses to the

survey. Honest responses were essential to the integrity of the study, and I

made every effort to elicit honest answers. For example, I would assure

participants that their responses were confidential and would remain

anonymous. Additionally, the survey questions were short to keep

participants interested and focused on providing the most pertinent

responses. The survey was also pilot tested to ensure that questions were

straightforward and easy to understand and that respondents were likely to

answer honestly and appropriately.

A second assumption was that consumers were aware of recycling

efforts and able to answer questions about the likelihood of their practicing

recycling. There was an assumption that participants would know the

location of their nearby recycling centers. Daoud (2011) stated that

American households account for most of the electronic market, but they

recycle only 26% of the time, thereby producing an enormous amount of

ewaste. The assumption that consumers were becoming more aware of the

effect of their spending habits on the environment and the trend that they

were making changes to protect natural resources for future generations

appears to be accurate (Spiegel, 2011).

Limitations

There were several potential limitations in this study. One limitation

of the survey was administration within an online database so that only

participants who had access to the Internet and had a survey account would

able to participate. I analyzed a large number of responses by age and

gender representing a diverse pool of online USF student registered with

Survey Monkey. This provided a level of validity to the data analysis.

Another limitation was the availability of persons to participate. Although

participants would receive no incentives for participation, the survey was

brief in order to encourage responses. Participants received a number of

reminders to encourage them to take part in the survey. Another limitation

was the potential for a social desirability bias. Respondents might indicate

that they were more likely to recycle or pay more for a green item because

they considered it an environmentally conscious activity. This was

consistent with information found through the literature review (Lee, 2011).

With this study, I also explored whether consumer’s attitude and behavior,

environmental consciousness and willingness to pay more for green

products still prevailed. Lee was able to prove those college students who

were more concerned about the environment tended to be willing to pay

more for green apparel. The quantitative methodology also limits exploring

the conclusions from an investigation. In nonexperimental research,

causality cannot be determined. The correlational method allows for the

examination of significant statistical relationships to be reported (Leedy &

Ormrod, 2010). Information on these relationships helps to close a gap in

the professional literature.

Delimitations

A delimitation of the study was the selection of products within the

consumerelectronics industry; thus, the results might not apply to products

from other industries. Another delimitation was that the sample consisted of

persons who currently reside in the state of Florida; the results might not

generalize to individuals who are not Florida residents. Last, University of

South Florida students between 18 and 24 years of age, who have registered

as members of SurveyMonkey, made up the sample. Accordingly, the

results might not generalize to individuals outside this university and age

range or to persons who are not members of SurveyMonkey. Based on E-

Marketer (2008) research suggesting that this demographic shows the

greatest tendency to integrate green behavior into their daily lives, I chose

this age range for my research.

Significance of the Study

Reduction of Gaps

Recent studies indicated that eco-friendly product choices share a

relationship with product pricing. Researchers Draper, Dawson, and Casey

(2011) and Lee (2011) were able to identify target consumers who were

willing to pay more for environmentally friendly products. Other

researchers such as Millson (2012) focused on determining green customers’

purchase intentions and the usefulness of ecological product labels and

pricing. No research was extant on the relationship between belief in

products being good for the environment and willingness to purchase eco-

friendly products. Currently, the EPA (2011) defined green products as

products made in a way to reduce their environmental impact. There was

also a paucity of research on consumers’ self-reported understanding of the

role played by eco-products in reducing e-waste and creating appropriate

outcomes for the environment. This information could be useful for

business, and it might influence business practices. This study adds to the

existing knowledge on the topic and is a step in the direction of closing a

gap in the literature.

Implications for Social Change

As dissemination of information related to the advantages of green

technology increased, manufacturing companies were making decisions

about their products. Some companies were already becoming more

socially and environmentally responsible and found that their profits

increased as they changed along with their consumers’ preferences. Other

companies were lagging behind in these considerations. I began this

investigation with a firm belief that, if the results of my study would

demonstrate a significant statistical relationship between consumers’ belief

that purchasing eco-friendly products is good for the environment and

inspired their willingness to pay more for such products, then I needed to

promulgate this information. This information might be valuable for

businesses, especially ones subscribing to traditional business models, and it

might contribute to social impact. The results of this study might benefit

society by encouraging product manufacturers to make investments and

explore development opportunities in green products. The findings might

also encourage manufacturers to pursue higher environmental ratings per

product rather than lower ones. If I could demonstrate to product

manufacturers that investing in environment-friendly factors will directly

affect their ability to increase their profits, then they might consider

implementing more green technology in their consumer electronics, which,

in turn, will create social benefits for consumers and society by reducing e-

waste. The results of the current investigation might also be informative for

consumers who had decided for themselves which factors were most

important when they made a purchase decision. If consumers were aware of

the relationship between a product’s price and its impact on the

environment, perhaps it would motivate them to modify their purchasing

decisions.

A Review of the Professional and Academic Literature

The purpose of the literature review was to provide a background of

the issues and factors surrounding consumer behavior related to eco-friendly

products and to determine if a relationship existed between consumer

understanding and their willingness to pay more for eco-friendly products.

Previous research suggested that customer satisfaction shares a relationship

with the physical surroundings, price perception, brand loyalty, and the

quality of goods and services (Ariffin et al., 2011; Han & Ryu, 2009). The

cost to the environment could be overwhelming because most of these

products produce e-waste. In 2010, the United Nations Environment

Programme (UNEP) reported that the generation of global electronic

garbage was 40 million tons per year, and estimates suggested that by the

year 2020, e-waste levels could rise by as much as 500%. Electronic waste

and the role businesses play in managing electronic waste are critical issues

under these circumstances. The enormous amount of environmental

pollution related to industrial manufacturing worldwide and evidenced in

recent years has caused society in general to become more concerned about

environmental conditions (Chen,

2010).

Researchers and experts agree that e-waste is an enormous emerging

environmental problem, and some companies have become invested in

reducing e-waste by creating products that reduce the amount of e-waste

generated (Bereketli, Genevois, Albayrak, & Ozyol, 2011). This has created

an entire industry promoting a green environment, eco-friendly products,

green branding, and green jobs (Ahn, 2010). Green jobs would grow from

610,000 in 2008 to 810,000 in 2013, while green investment would grow

from $2.02 billion to $115.2 billion US (Ahn, 2010). These emerging

changes would also increase the overall demand for eco-friendly products

and with it the cost to the business sector, which, inevitably, would cause

higher prices for consumers. The purpose of this research was to investigate

how willing were consumers to pay more for eco-friendly products if they

believed that such products would reduce e-waste and, further, whether a

statistically significant relationship existed between these variables.

Consumer Perspectives

In this study, I sought to clarify, through a review of the literature,

whether customer perspectives were related to purchase decisions and

perceived risks and also the extent to which they might be related to a

number of conditions such as brand loyalty, advertising effectiveness,

innovation, and pricing (Becker, 2009; Cheung & Thadani, 2010). To

reduce customer doubt related to purchase decisions, consumers process

available information regarding each product and form a first impression.

To that end, consumers viewed products in an arrayed queue where they

could evaluate each product to make a basic judgment about the product

(Muhamad, Melewar, & Alwi, 2011).

Product price perceptions. Of all the elements in the queue, price

turned out to be the most salient influence for consumers (Bennett, 2011).

Price was a powerful piece of information for the consumer, reported

Farrell and Shapiro (2010, p. 12).

Balakrishnan (2011) called price “the sacrifice to obtain a product” (p. 253).

Consumers can attach a value to price; therefore, price plays an important

part in their decisionmaking process about a product. Customers used price

as a cue in evaluating their experiences with a product or service and in

shaping their attitude toward a provider (Han & Ryu, 2009). What was

unknown, however, was whether consumers were willing to pay more for an

item if it offered environmental advantages, and it was to that question that

the present study addressed itself.

Brand loyalty perceptions. Han and Ryu (2009) and Ariffin et al.

(2011) suggested that physical surroundings and price perception influence

customer satisfaction. Other researchers also maintained that these factors

had an impact on customer satisfaction and that customer satisfaction led to

customer loyalty (Ladhari et al., 2011). Loyal customers were more likely

to engage in positive word-of-mouth behaviors and spending extra money in

a service operation than nonloyal customers were likely to do so (Ladhari et

al., 2011). In addition, loyal customers were less costly to service because

they knew the product or service well and required less information

(McKercher & Guillet, 2011). Thus, in recent years, service providers

focused on achieving customer loyalty by delivering superior value and

identifying and enhancing the main factors they had determined to inspire

loyalty (Chen & Chen, 2010).

Product quality perceptions. Numerous researchers have conducted

investigations into the relationship between price and quality (Bennett,

2011; Zheng, Chiu, & Choi, 2012). Hui (2010) explored how brand names

can affect the consumers’ reliance on technology-adoption decisions and

protection from security technologies. Hui used an experimental research

method to study the effects of brand name and knowledge on the adoption

decision of antivirus software. In the 2 x 2-research method, two groups of

students used two different brands. Hui randomly selected subjects to

participate in different groups, presented them with information about

different brands of antivirus software, and the respondent indicated their

product choices. Hui used z tests and logistic regression to analyze the data

received from each group. The findings demonstrated that, with other cues

held constant, price was the only factor to predict the consumers’ perceived

quality. Hui also reported that the brand name did affect product choice. A

strong brand tended to inspire a false sense of security and lead to poor

product choices, whereas knowledge could reduce the consumers' reliance

on brand name in a security-technology adoption decision.

Rao (2007) measured the two forms of market information, price, and

store, in his study. The results indicated that, although price was the

dominant variable, the inclusion of store image had a significant impact on

consumers’ product-quality perception. Rao conducted a meta-analysis that

investigated the influence of price and brand name or store name on buyers'

evaluations of product quality. Results of the analysis revealed that, for

consumer products, the relationships between price and perceived quality

and between brand name and perceived quality were positive and

statistically significant. Overall, these early investigations demonstrated that

price strongly affected the consumer’s quality perception.

Ho (2010) examined customer satisfaction and the role-played by total

quality management (TQM). The authors were able to demonstrate that

improved quality could actually save money. With the use of a meta-

analysis, existing research studies on TQM revealed that TQM significantly

increased customer satisfaction across various industrial sectors and cultural

settings. The researchers noted that this result challenged a fundamental

assumption of the day, namely that producing higher quality goods and

services meant incurring greater costs. Most people assumed that

development of higher quality products would require raw materials that

were more expensive, extra care in processing, more inspections, and the

hiring of more skill workers. Hassen, Rahmanb, and Haruna (2012)

demonstrated that quality could be improved by reducing and reworking

mistakes to ensure that things would be corrected the first time (better

process control), which would result simultaneously in financial savings and

a better quality product.

Consequences of Electronic Waste

Many electronic items contain dioxin, and an inappropriate disposal

strategy can release dioxin into the environment. Numerous health

problems have resulted from high levels of dioxin, including stillbirths, low

birth weight, and premature deliveries. E-waste is one of the causes of

dangerous gases and other chemicals into the environment as well,

specifically lead, beryllium, arsenic, mercury, antimony, and cadmium, all

of which affect people’s health and the environment in a negative way.

Based on the health and environmental ramifications associated with e-

waste, researchers were beginning to investigate strategies to reduce, or at

least stop increasing, the amount of e-waste.

However, additional research is required in this area.

Role of Business in Managing Electronic Waste

Researchers and experts agreed that e-waste was an emerging

environmental problem and some companies were starting to invest in

reducing e-waste. For them, managing e-waste provided augmented

business opportunities, especially given the volumes of e-waste currently

generated and the content containing both toxic and valuable materials

(Bereketli et al., 2011).

Not all businesses agreed on e-waste management strategies

(Lepawsky, 2012; Wu, 2011). Although most researchers and consumers

agreed on the necessity of preserving a livable planet, some maintained that

environmental regulation hampers business competitiveness. In addition,

despite presumed social benefits of environmental standards, leaders in

private industry maintained that prevention cost and clean-up cost would

lead to higher prices for electronics and reduced competitiveness (Redclift,

2009). The differences that have come to characterize the discussion of the

environment and nature in the social sciences descriptions are the

distinctions between critical realism and social constructivism, and Redclift

reviewed the main intellectual challenges of both positions. Redclift blamed

a lack of theoretical development in carbon dependency on an apparent

stalemate.

Green environment. Because of the enormous amount of

environmental pollution evidenced in recent years, which relates to

industrial manufacturing worldwide, society has become increasingly

concerned about environmental conditions (Chen, 2008b). Because of the

increased societal attention and consumer demand for environmentally

friendly products, more and more companies were willing to accept the

environmental responsibility (Zeng, Meng, Yin, Tam, & Sun, 2010).

Currently, environmental concerns were rapidly emerging as a mainstream

issue for consumers, especially because of global warming, and many

companies were seeking to profit from the opportunity. Environmental

pollution could result from the inefficient use of resources, but businesses

could increase their productivity with the use of green innovation (Zeng et

al., 2010). Green innovation relates to innovation in environmentally

responsible products and services that were both sustainable and

contributing to reducing the impact of greenhouse gases (GHG) on the

environment (Cooke, 2012).

Chioua, Chana, Letticea, and Chung (2011) promoted the concept of

core competence, and many previous studies explored the relevant issues of

core competence; however, no research to date has explored core

competencies of firms with green innovation or environmental management.

In order to achieve core competencies, some researchers maintained that a

company should meet three requirements by (a) gaining potential access to a

wide variety of markets, (b) contributing to the customer benefits of the

product, and (c) developing products that were difficult for competitors to

imitate (Gimzauskiene & Staliuniene, 2010). The creation of core

competencies is beneficial for company performance and corporate success

(Paik, 2011). If companies want to adopt green marketing successfully,

their environmental concepts, and ideas should be in all aspects of marketing

(Sandhu, Ozanne, Smallman, & Cullen, 2010). When companies are able to

provide products or services that satisfy their customers’ environmental

needs, the customers might be more favorably disposed toward their

products or services.

Eco-Friendly Products

Eco-friendly products, or green products, are products that do not

harm the environment whether in their production, use, or disposal.

Businesses and consumers alike were attempting to reduce their impact on

the environment by practicing energy conservation and reducing pollution to

the environment; thus, many environmental factors were currently under

review. In addition, GreenPeace (2010) corroborated the importance of

environmental factors, in their ranking of the top 18 manufacturers of

consumer electronics such as personal computers, mobile phones, TVs, and

game consoles, according to their policies on toxic chemicals, recycling, and

climate change. GreenPeace aimed at eliminating hazardous substances,

recycling obsolete products, and reducing the impact of the manufacturers’

operations on the climate. The eco-rating system helps to prevent

greenwashing, a term used to describe false or misleading advertising by

leading companies, designed to convince consumers that their products were

environmentally friendly, when in actuality they were not.

According to one research, more than 95% of consumer products

claiming to be green commit at least one of the greenwashing offenses such

as hidden trade-off, no proof of being green, and vagueness (Mitchell &

Ramey, 2011). At this writing, there were only a few consumer-product

rating companies in existence. The Electronic Product Environmental

Assessment Tool (EPEAT) was a standard tool used for evaluating,

certifying, and registering green computers and other electronic consumer

products according to three tiers of environmental performance: Bronze,

Silver, and Gold (Obrien, 2010). No current industry standards were

available for rating green products.

Environmental positioning. Environmental positioning was an

effective way to lure consumers to try new brands and product variants.

Firms with established brands were increasingly leveraging the brand equity

associated with their core products and launching green brand extensions.

Some companies were taking independent action to improve environmental

performance by self-advertising their environmental activities or by

participating in voluntary environmental programs (VEP) that required

participants to self-monitor and publicly report their environmental

performance (Darnall, Potoski, & Prakash, 2010; Harrington, Khanna, &

Deltas, 2011). In other instances, companies received a third-party

certification for environmental activities (Darnall et al., 2010). Keller and

Lehman (2009) suggested that marketers of leading brands usually advertise

heavily to reinforce some of the brand attributes as a way of positioning the

brand schema effectively in the consumers' minds. Although this might be

effective, consumers were likely to have already attributed their own

opinions and existing perceptions as part of their brand schemas for well-

established and highly familiar brands (Laceya, Close, & Finney, 2010;

Völckner, Sattler, Ringle, & Thurau, 2010).

The nature of the product category itself would produce some

expectations of product attributes (Kocyigit & Ringle, 2011). Consumers

were likely to have strong notions of typical product attributes for highly

familiar brands, as there was relatively little room for ambiguity in the

perceptions of these brands in comparison to other brands. A number of

researchers have suggested that product attributes dominate consumer

decision making, which also link pioneering advantage to attribute typicality

(Perera & Chaminda, 2013). Perera and Chaminda (2013) explored

corporate social responsibility (CSR) and its relationship with identifying

stakeholders along with categorizing types of CSR initiatives and linking

corporate social performance to firm performance. The researchers

suggested that CSR should enhance its sustainable competitive advantage in

social performance. Using literature reviews, the researchers were able to

demonstrate that, for CRS to gain competitive advantage, it should be part of

the company’s mission and visible to external audiences.

Absolute levels or values of product attributes alone cannot be the

basis for new product variants or line extensions. Rather, evaluations based

on the congruency between an extension product’s attributes and

consumers’ existing expectations about the parent brand schemas as well as

product categories (Völckner et al., 2010). Völckner et al. (2010)

investigated the importance of brand extension in consumer expectations.

The researchers used two large data sets to identify four areas, namely

generalizability of relevance of brand extension factors, the research results

beyond the lab into conditions with real extensions, generalizability of

findings across consumers, and product categories and parent brand and

their generalizability across success measures.

The results indicated that there were major differences across

customer segments. The researchers concluded that green product-line

extensions were product variants in the product category that satisfied the

functional needs of the customers, but eco-friendly positioning could help

customers reduce their carbon footprint. The researchers also noted that

consumers had to reconcile the perceptions of benefits associated with

environmental green claims and how such perceptions correlated with

dominant attributes in a product-category schema for familiar and parent-

brand schemas (Völckner et al.,

2010).

Green branding. Green branding and imaging were important when

distinguishing products and services based on quality features (Hur, Yoo, &

Hur, 2010). Brand images included symbolic meanings with the attributes

of a brand that could help customers develop a mental picture of the brand

and link it to offers (Chen, 2010). According to Myrden, Kelloway, and

Scotia (2012), brand image covered functional benefits, symbolic benefits,

and experiential benefits. Based on the understanding that green brands are

those that consumers associate with environmental conservation and

sustainable business practices, the green-brand image was becoming more

important for companies, especially due to the widespread environmental

consciousness of consumers and strict international regulations of

environmental protection. A well-implemented green brand identity could

provide benefits to companies that were environmentally conscious, and

consumers could select products that were greener than other products.

Commercial success of green branding could become successful only if the

communication of branding messages was effective (Paço, Alves, & Shiel,

2013).

Green positioning. Sharma and Singh (2013), along with Schaper

(2010), suggested that green positioning was an essential factor in the

success of green branding strategies. By utilizing a green positioning

strategy, a company could build functional brand attributes that built brand

associations by delivering information on environmentally sound product

attributes. In order to be effective, this positioning strategy should be based

on relevant environmental advantages of the product compared to competing

conventional products and might refer to production processes, product use,

or product elimination, or all of these in combination (Sabchez, Martínez-

Ruiz, JiménezZarco, & Megicks, 2012). For example, a car brand is

environmentally sound if the models in question produced significantly

lower emissions than their competitors did. Several studies addressed the

value perception of selected environmental product attributes (Park, Choi, &

Kim, 2011). Park et al. (2011) explored a number of variables to

understand consumer behavior and the choices consumers made with regard

to environmentally friendly products. The researchers reviewed current

research on the topic to try to find the relationships among

sociodemographic variables and preferences for environmentally sustainable

products. Findings were mix especially in the area of income, where

previous research showed that income could be negatively, positively, or

insignificantly related to green consumer choices.

Researchers also suggested that there was a negative correlation

between proenvironmental attributes and attributes in product categories for

nonhuman consumption. Kayande, Roberts, Lilien, and Fong (2007)

examined the incoherence of fuel-efficient and powerful cars on consumer

uncertainty perceptions, preference, and likelihood of purchase. The

subjects in this study were (N = 77) 2nd-year MBA students. The

researchers were able to prove, through a mathematical model, that products

that positively combined valued attributes might increase some elements of

preference for the product. However, if those attributes occurred in

unexpected combinations, incoherence would also increase uncertainty,

which, in turn, might lower other elements of preference.

The results of the investigation corroborated earlier research on this topic.

Prior research on schema incongruity suggested that, when an

additional attribute in a product variant is congruent with dominant

attributes in the product category schema, it improves product evaluations.

The findings also indicated product improvement drove its salability even

when the improvement was irrelevant to the main operation of the product

(Ahearne, Rapp, Hughes, & Jinal, 2010).

Because neither regulations nor independent verification of product

sustainability existed, consumers had to make their purchase choices based

on unsustainable environmental claims. Although there was no regulation

of claims, consumers preferred some claims to others. Kangun, Carlson,

and Grove (1991) indicated that consumers were able to distinguish between

specific (tangible and concrete environmental benefits) and vague claims.

Kangun et al. investigated how organizations increased their target in

advertising as consumers became more environmentally conscious than they

had been before. The researchers developed two typologies; the first one

sorted advertised environmental claims into five distinctive types, and the

second one delineated categories of misleading or deceptive environmental

claims. The researchers found that certain types of claims placed among

environmental advertisements were more susceptible to causing consumer

confusion and perceptions of possible deception. Further, the findings of

Simula, Lehtimäki, and Salo (2009) suggested that environmental claims

perceived as clear and straightforward would result in positive perceptions

of the product as well as the advertiser, whereas vague claims tended to

result in negative perceptions and suspicions of greenwashing. Simula et al.

noted that the growth of sustainable development required a high level of

directed innovation. The authors reported that the relationship between

scale effects and administration, purchasing, pricing, technology, marketing,

and profitability had an effect on the environment, and they suggested

alternatives to quality management standards as well as codes of practice to

influence the sustainable development on business practices. Chang and

Fong (2010) maintained that green marketing and improving brand image,

which was an important determinant of customer satisfaction could achieve

differentiation between products.

Literature Related to Research Design

The methods used most often in the reviewed literature were

descriptive in nature. Some researchers used experimental models such as

the brand loyalty model, the wordof-mouth, or WOM, model (Becker,

2009), and the TQM model (Ho, 2010) to guide their research. In this study,

I chose the descriptive method as the most appropriate approach.

I utilized a cross-sectional, descriptive survey method to explore the

relationships among demographic variables (age, gender), consumer

perspectives on eco-friendly products (product quality, product price, brand

loyalty), and consumer behaviors (willingness to pay more for an item,

willingness to drop off e-waste). Descriptive research would explore

relationships between nonmanipulated variables and phenomena, or existing

problems, with the intent of providing a potential solution (Adu-Agyem,

Sabutey, & Emmanuel, 2013). Descriptive research methods explored the

phenomenon under present conditions, without modifying the variables

under study (Redmond, 2010).

In the current study, consumer perception factors of product quality;

perceived value; and brand loyalty, as defined by Chen (2008a) in his 2008

model, were under consideration. Demographic data analyzed in this study

included general consumer information such as age and gender. Consumer

perception factors and demographics were part of the collection process of

the online sample survey of adult consumers.

Transition

Researchers indicated that consumers were becoming more

environmentally conscious than ever before as information about the

scarcity of natural resources increasingly entered the public discourse.

Consequently, companies are starting to price and manufacture products for

emerging market with environmentally conscience consumers in mind.

With their empirical research, researchers had clearly demonstrated the

importance of pricing in consumers’ decision-making behavior; however,

there is a paucity of literature on the relationship between environmental

factors and pricing, quality, brand loyalty, and the relationship between

understanding how eco-friendly products affect the environment and

consumers’ willingness to pay more for them.

The purpose of this quantitative correlational study was to describe

consumer behavior related to eco-friendly products and determine if a

relationship existed between consumer perceptions and behaviors regarding

eco-friendly products. Results from the current investigation might

contribute to the field of business practice by increasing the understanding

of product manufacturers and by providing information on the strength of

the relationship between price and a product’s environmental impact and its

effect on consumer behavior. The results from this study might also

contribute to social change by encouraging product manufacturers to better

price their environmentally friendly products in order to sell more, which in

return could create more social benefits for the community by reducing e-

waste. The results of the current investigation might also provide relevant

information to consumers who are willing to pay more for a product that has

fewer negative environmental consequences.

Section 2 of the study describes the research method chosen for this

study. The section provides information on the sampling technique used;

the role of the researcher; a discussion of the data collection, the instrument

used for data collection, and its reliability and validity; and, finally, the data

analysis.

Section 3 of the study presents the findings of the (data analysis; a

discussion of the applications to professional practice, the implications for

social change, recommendations for actions and further study, and

reflections). Section 3 ends with a summary of the findings and

conclusions.

Section 2: The Project

Purpose Statement

The purpose of this quantitative study was to examine the relationship

between the high level of e-waste and the consumer’s preference for eco-

friendly products and provide business managers with the information they

need to develop advertising and pricing strategies. The geographical

location for this study was central Florida. A convenience sample of

randomly selected registered members of SurveyMonkey who were

currently attending the University of South Florida participated in the

research by completing an online survey hosted by SurveyMonkey. The

researcher-designed questionnaire assessed consumer demographics (gender

and age), the consumers’ product perception (i.e., consumers’ views on eco-

friendly products, using the dependent variables price, quality, and brand

loyalty), and consumer behaviors (using the independent variables

willingness to pay more for eco-friendly products and willingness to recycle

e-waste at drop-off recycling centers).

With inferential data analysis, I used correlation and regression

analysis to determine the extent of the relationships among consumer

perceptions, consumer behaviors, and demographic variables. Information

gained through this research should provide business managers with greater

insights into the consumers’ views regarding ecofriendly products, their

willingness to reduce e-waste, and their willingness to pay more for eco-

friendly products. The findings of this study could bring about positive

social change by encouraging product manufacturers to produce more

environmentally friendly products than environmentally harmful ones. This

will lead to a reduction in e-waste by providing incentives for the

proliferation of products with a low environmental liability rating in

preference to products with high environmental liability.

Role of the Researcher

Researchers (as citied in Smith, Wright, and Breakwell, 2011 and

Komesaroff, 2012) actively anticipate and address each ethical dilemma that

might occur at every stage of their research. A researcher must ensure that

the sources of data used in the study are reliable and that the data analysis

and interpretations are ethical. To that end, the researchers must make every

effort to maintain the integrity of the data and the protection of study

participants and their rights.

Prior to inviting subjects to participate, I obtained approval to conduct

the study from the Internal Review Board (IRB) of Walden University. This

approval was contingent upon my appropriate and adequate description of

the research process, including participant identification, invitation to

participate, informed consent, data collection, data analysis, and data

management procedures. After extending the invitation to participate in the

study (Appendix B), I had no plans for interacting with the subjects, unless

they contacted me for additional information about the study. As the

researcher, I assured the participants that their anonymity and online data

would be password protected and accessed only by me. Further, as the

researcher, I would not be a member of the staff of their university or have

any affiliation with this university or with potential research participants. I

chose the University of South Florida for conducting this study based on the

size of the student body and its geographical location. As indicated in the

invitation, after completion of the study and upon the request of the

participants a final report and summary of descriptive and inferential

statistics and the study’s findings will be available for their review.

Participants

The population consisted of young adults attending the University of South

Florida (USF) in the United States. The average age of students at USF was

23 years (USF college portrait, 2011). According to research by E-Marketer

(2008), a leading marketing group, this age group shows the greatest

tendency to integrate green behavior into their daily lives when compared to

other age groups. The demographic data collected (i.e., age and gender)

helped to explore the salient perceptions of young adults regarding eco-

friendly products. Currently, SurveyMonkey has 600 USF students

registered.

Approximately 381 students received an e-mail invitation to participate (see

Appendix B) out of the 600 USF college students who registered with the

SurveyMonkey Contributor Member database subgroup. The Tabachnick

and Fidell (2007) formula was used as the sample calculator to test for the

minimum number of participants to complete this study. The Tabachnick

and Fidell (2007) formula for sample size is 50 + 8(m), where m=# of

predictor variables. There are three independent variables attached to this

study i.e. price, quality and brand loyalty. Therefore, the sample size

calculation 50 + 8

(3) = 74 a minimum of participants. I was fortunate to have a sample

population of 381. This represents the population of 47,214 potential

participants residing on the USF campus.

In the event that not enough of the SurveyMonkey Contributor

Members responded positively to the invitation to participate in the study, a

plan was in place for contacting the USF student body to gather more

participants. Potential participants received an informed-consent form

together with information about the study (see Appendix B); they had to

give their consent by completing the online consent form before they could

participate in the survey. The online survey enabled gathering data from a

large group inexpensively. The survey was a workable way to assemble a

sufficiently large pool of subjects for addressing the hypotheses (Amponsah-

Tawiah, Dartey-Baah, & Ametorwo, 2012); the survey is an

environmentally friendly, paperless method (McPeake, Bateson, & O'Neill,

2014) and provided a faster response rate than other methods such as

telephone interviews or in-person interviews (Kaplowitz, Lupi, Couper, &

Thorp, 2012; Novick et al., 2011).

The method of participant selection was nonprobability convenience

sampling (Kakinami & Conner, 2010) of a target population at USF.

Researchers often use convenience sampling when it is the only way to gain

access to certain groups such as such as marijuana users (Hathaway et al.,

2010) or incarcerated youth (Abrams, 2010). Such samples might have to

satisfy additional IRB requirements because they are in protected groups.

Convenience sampling in the current study facilitated recruitment of a

sample large enough to perform data analysis.

Research Method and Design

Research Method

The three methodological approaches to conducting research are

quantitative, qualitative methods and mixed method. (Marczyk, DeMatteo,

& Festinger, 2010). In qualitative research, one could explore attitudes,

behaviors, and experiences with the use of such methods as interviews or

focus groups (Church & Ekberg, 2013). The yield of qualitative research

consists of in-depth opinions from the participants who usually number far

fewer than in quantitative studies, but the contact with the former tends to

last much longer (Chen & Macredie, 2010). By contrast, in quantitative

research, one can quantify attitudes and behaviors or measure variables

(Jandaghi & Matin, 2011). Unlike qualitative research, quantitative research

uses measurable data that rely, facts, and patterns. The quantitative

approach was best suited for this study because I intended to obtain data

from a large sample via questionnaires assessing consumer behaviors and

perceptions using numerical data. I also planned to use statistical means to

quantify, measure, and analyze the data and express the results numerically.

Quantitative methodology also allowed me to test multiple variables of

costumer behavior reported by the sample to determine which variables have

a significant effect on e-waste reduction.

Chen and Chai (2010) distributed 200 questionnaires to undergraduate

students at a major private university in Malaysia to assess their attitudes

toward the environment and green products and to measure the relationship

between attitude toward the environment and the use of green products.

Results indicated that there was no difference according to gender in the

students’ attitude toward the environment and their use of green products.

One important finding through multiple linear regression analysis was that

how consumers view both the government’s role and their personal norms

toward the environment contributed significantly to their attitude on

purchasing green products and recycling e-waste.

Lee (2011) described how researchers such as Laroche used a

conceptual framework that considered many factors such as demographics,

knowledge, values, attitudes, and behavior that influence consumers’

willingness to pay more for environmentally friendly products. Laroche (as

cited in Lee, 2011) disseminated 2,387 questionnaires to selected household

in a North American city. The questionnaires included Likert scales and

measured participant responses to several questions. The first part of the

survey collected demographic information (i.e., gender and age), the second

part measured consumer attitudes toward a variety of topics related to the

environment, and the last part measured behaviors of the respondents toward

the environment. One significant finding was that values played an

important role in the consumers’ willingness to spend more for green

products.

A mixed method was not appropriate for this study since there was

insufficient time to explore the qualitative rationale for the respondents’

responses.

Research Design

In the current study, I used a quantitative design. Smith et al. (2011)

and Komesaroff (2012) explained that quantitative research designs fit two

basic types: experimental and nonexperimental designs. Nonexperimental

designs consist of descriptive research and correlational studies, whereas

experimental designs include experiments and causal-comparative or quasi-

experimental research.

The first design, descriptive research, is to determine and describe the

status of an identified variable. Descriptive research involved the gathering

of data that describe events, and then the data collection organization,

tabulated, depicted, and described (Graney, Martínez, Missall, & Aricak,

2010). Tom and Eves (1999) provided an example of this type of

descriptive research, where 120 pairs of advertisements were collected to

test whether they used rhetorical figures. The researchers found that 45% of

the advertisement had used some form of rhetorical figures. The conclusion

was that advertisements that used rhetorical figures performed better in

terms of recall and persuasion than advertisements that did not.

The second design, and the method used in this study is correlational

research. A study qualifies as nonexperimental and correlational if the data

lend themselves only to interpretations about the degree to which certain

things tend to co-occur or relate to each other. Chang and Zauszniewski

(2011) used a nonexperimental, cross-sectional, correlational design to

examine the interrelationships among a situational factor (maternal

depression), learned resourcefulness (LR), and target behaviors (depression

and adaptive functioning in school-aged children). The major advantage of

a correlational design in this study was that the collected data were easy to

interpret. The major disadvantage of the correlational designs was that the

reason for the associations discovered was unclear. As the purpose of the

current study was to gather information on the relationships among

consumer perceptions, consumer behaviors, and demographic variables, the

correlational design was appropriate.

The third design, experimental research, is an attempt to maintain

control over all factors that might affect the results of an experiment. In

doing so, the researcher attempts to determine or predict what might occur

(Li, Hung, & Tangpong, 2012). Some of the steps involved in experimental

research are identifying and defining the problem, formulating hypotheses

and deducing the consequences, constructing an experimental design that

represents all the elements, conducting the experiment, compiling raw data

and reducing it to usable forms, and applying an appropriate test of

significance. Some of the advantages of this method are researcher control

over the variables by determining the ideal population for achieving clear

results (Weathington, Cunningham, & Pittenger, 2012). Some of the

disadvantages of this method are potential personal bias of the researcher,

the sample might not be representative, and the results might apply only to

one situation and might be difficult to replicate (Weathington et al., 2012).

Gruppen (2008) who examined the dispersion of airborne infectious viruses

and the development of brain pathology because of exposure conducted an

example of this type of research.

The researcher used lab rats to perform this study and controlled all the

variables.

The fourth design, causal-comparative or quasi-experimental

methodology, identifies cause-and-effect relationships between independent

and dependent variables

(Smith et al., 2011). D’Onofri, Lahey, Lichtenstein, and Turkheimer (2013)

conducted an example of this type of research. The researchers explored

how genetic and biological influences, environmental risks, and behavior act

and interact across development to result in psychological and physical

health problems. The researchers were able to show, by examining past

studies, that a need existed for more quasi-experimental studies to further

the understanding of the true causes of human health and development.

Population and Sampling

The focus of this study was to describe self-reported consumer

behaviors related to eco-friendly products to determine if a relationship

exists between consumers’ perceptions related to eco-friendly products and

their willingness to pay more for such products. The survey target audience

was USF Students between the ages of 18 and 24 years from the

SurveyMonkey database of respondents. According to E-Marketer (2008)

research, this demographic had the greatest tendency to integrate green

behavior into their daily lives when compared to other age groups. As

reported in the University of

South Florida Fact Book, the total student population for the 2011 academic

year was

47,214 (USF System, 2011), and of those students, 600 were registered in

the

SurveyMonkey database.

Eligibility criteria for participating in this study required that the

respondent be a student at USF, between 18 and 24 years of age, a registered

user of SurveyMonkey, and live in the United States. Of the entire 600 USF

student population registered on the SurveyMonkey database, I invited 381

potential participants. The only exclusion criterion used specified age, in

that participants had to belong to the 18 to 24 year age range.

Ethical Research

To protect the participants’ rights I addressed a number of ethical

considerations throughout the research process. All potential respondents

received an invitation, Informing them about the purpose of the study,

requirements for participation, and the rules for completing the survey. By

signing the consent document, the participant acknowledged his or her

voluntary participation in this study (see Appendix B). In the consent

document, I informed the respondents that they could withdraw from the

study at any time by exiting from the survey or by not submitting their

responses at the end of the survey.

I assured the respondents about complete confidentiality and

anonymity and that I would not use any identifying information anywhere

on the completed survey. The researcher would be the only person to know

the identities of the participants and the responses to the questionnaires. As

an added measure of security, I converted the names of survey respondents

to Participant 1 (P1), Participant 2 (P2), and so forth. These generalized

categories provided enough information without compromising the

respondents’ privacy. Respondents received a small financial incentive

through

SurveyMonkey upon the successful completion of their questionnaires.

All online information regarding potential participants is stored in a

passwordprotected electronic folder and accessible only to the researcher.

Data deletion will take place 5 years after the completion of the study with

the use of a freeware program called CyberShredder. The SurveyMonkey

research profile removal will take place after the study to guard against any

misuse of the participants’ information.

Data Collection

Data Collection Instruments

I developed a survey for data collection (see Appendix A). Existing

measurement instruments were not appropriate for this study, and

customized instruments by variables were different from study to study.

Therefore, for this study a new instrument was developed.

Before launching the data collection, I performed a pilot survey to

ensure the validity of the questionnaire. Five participants received the

questionnaire via e-mail from SurveyMonkey.com to make sure that the

participants clear and readily answered the questions. The results from the

pilot survey ensured instrument validity. The questions’ purpose was to

examine consumer perspectives on product price, product quality, and brand

loyalty, as well as self-reported consumer behavior of paying more for an

item and willingness to drop off e-waste. Also collected were demographic

variables (i.e., age and gender). With the pilot study, I also wanted to make

sure that the survey was comprehensive and had a high level of content

validity. High content validity was a necessary attribute of the questionnaire

survey in this study. Each survey question corresponds to one of the study

variables and the research questions. The rating scale for each question

indicates a respondent’s level of agreement or disagreement with the

statement. For example, the response to Question 4 on the survey (“I have

used green products before”) would yield a score from 1- 5. This score

became the data for data analysis of the applicable variable. A score of 1

would indicate a low level of agreement, whereas a score of 5 would

indicate a high level of agreement.

Data Collection Techniques

Study variables and questionnaire items. The purpose of the study

was to evaluate relationships among consumer perspectives on product

price, product quality, and brand loyalty; consumer behaviors of paying

more for an item and willingness to drop off e-waste at drop-off centers; and

demographic variables of age and gender. To that end, a researcher-

developed questionnaire assessed respondents’ perceptions, behaviors, and

demographic variables.

Product price perspectives. The two survey questions used in this

research helped to elevate participants’ perspectives on price. Survey

questions pertaining to price perceptions were Questions 5 and 8.

Responses to these items would be in the form of a 5-point Likert-type scale

where 1 = never, and 5 = always. The calculation score will be the total for

the responses of the two questions and the total product price perspective.

Product quality perspectives. The three survey questions used in

this research helped to elevate participant’s perspectives on quality. Survey

questions pertaining to product quality perceptions were Questions 9, 10,

and 11. Responses to these items would be in the form of a 5-point Likert-

type scale where 1 = strongly disagree, and 5 = strongly agree. The

calculation score will be the total responses of the three questions and the

total product price perspective.

Perceptions on brand loyalty. The four survey questions used in

this research helped to elevate participant’s perspectives on brand loyalty.

Survey questions pertaining brand loyalty perceptions were Questions 12,

13, and 14. Responses to these items will be in the form of a 5-point Likert-

type scale where 1 = strongly disagree, and 5 =

strongly agree. The calculation score would be the total responses of the

four questions and the total product price perspective.

Consumer behaviors. I used survey questions to measure

consumers’ selfreported behaviors of willingness to pay more for a green

items and willingness to recycle e-waste. The survey question used to

enquire about willingness to pay more for green products was Question 6.

Responses to this item will be in the form of a 5-point Likert-type scale

where 1 = never, and 5 = always. For Question 6, 1 = strongly disagree, and

5 = strongly agree. The calculation score will be the total responses of the

10 questions and the total product price perspective.

Questions 18 on the survey inquired about willingness to recycle e-

waste. Responses to this item will be in the form of a 5-point Likert-type

scale where 1 = never, and 5 = always. The calculation score will be the

total responses to Question 18 and the total product price perspective.

Following approval by the IRB of Walden University, I sent an e-mail

invitation to the target sample of 381 randomly selected potential USF

participants registered with SurveyMonkey (see Appendix B). The

participants first had to agree to the informedconsent conditions (Faden,

Beauchamp, & Kass, 2014), and then they would move onto the survey link.

Participation was voluntary, and subjects could quit the study at any time.

The participants did not need to provide any identifying information.

Measuring the first three items of the survey established a relationship

among product price, product quality, and brand loyalty and labeled a

measure either as effective or ineffective. A product price was effective if

the price of the product inspired the consumer to pay more for an item and

drop it off at an e-waste drop-off station at the end of its usefulness. Once a

consumer deemed a product effective or ineffective, I conducted a

correlation analysis to determine if the remaining survey items had a

positive correlation with the consumer behaviors of paying more for an item

and willingness to drop off e-waste. The collection and validity test data

from the pilot survey were able to measure the internal consistency for each

question in the survey.

After obtaining IBR approval, I conducted the pilot study began. The

pilot study participants had 2 weeks to submit their comments for analysis

and validation of the research questions. After the completion of the pilot

study, the online survey participants also had 2 weeks to respond to the

survey. When the survey responses did not reach the set target number

within 2 weeks, I sent a reminder e-mail to the invited participants. The

survey closed when 381 respondents had taken the survey; then, the data

collected with SurveyMonkey went to the Statistical Package for the Social

Sciences (SPSS) for analysis. A summary of the analysis of raw data is

available Section 3.

Data Organization Technique

Following receiving approval from IRB, I distributed an e-mail

invitation targeting 381 randomly selected potential participants from

SurveyMonkey. The SurveyMonkey (2013) website reported that more than

30 million unique subjects responded to SurveyMonkey surveys each

month. This online resource collected information from a large group of

participants in a relatively short period about purchasing habits.

The SurveyMonkey Contributor Member database consisted of 30

million members. The selection of participants was from SurveyMonkey

Contributor Member database, for a target sample of 381 participants. The

participants knew that they could stop their participation at any time; they

provided their answers on a voluntary basis. I kept the responses

confidential, and the participants remained anonymous. I analyzed the

collected data using SPSS software (Appendix A).

Data Analysis

I used SurveyMonkey for data collection in this quantitative study.

Access to the survey on the SurveyMonkey website is password protected.

As the researcher, I was the only one able to check on the number of

responders and review their responses. Once the participants had completed

the survey, the responses went from SurveyMonkey to the SPSS software

for analysis. I ensured that the SPSS data file would take each subject’s

scores on each of the 19 survey questions, and I then analyzed the results.

I used SPSS Version 17 to perform data organization, analysis,

calculated, and reported descriptive and inferential results. Descriptive

statistics included the means, standard deviations, and the ranges of

variables (i.e., responses to each question). I used

Spearman correlation coefficients for RQs 1-6 and multiple regressions

models for RQs 7-8 to analyze and evaluate the data and to answer the

research questions.

To evaluate the answers to Research Question 1, I used Spearman

correlation analysis to analyze the relationship between the total product

quality score and willingness to recycle e-waste at drop-off recycling

facilities. To evaluate answers to

Research Question 2, I calculated the Spearman correlation score using the

total product price perception scores and willingness to recycle e-waste at a

drop-off recycling facilities. To evaluate answers to Research Question 3, I

calculated the Spearman correlation coefficient to analyze the correlations

between the total brand loyalty scores and willingness to recycle e-waste at a

drop-off recycling facility scores. To evaluate answers to Research

Question 4, I calculated the Spearman correlation to analyze the relationship

between the product quality scores and willingness to pay more for a green

product. To evaluate answers to Research Question 5, I calculated the

Spearman correlation to analyze the relationship between total product price

perception scores and willingness to pay more for a green product. To

evaluate Research Question 6, I calculated the Spearman correlation to

analyze the relationship between total brand loyalty scores and willingness

to pay more for a green product. To evaluate answers to Research Question

7, I conducted a multiple regression analysis using age and gender as

predictor variables and customer willingness to pay more for green products

and customer willingness to recycle e-waste at drop-off recycling facilities

as criterion variables. Finally, to evaluate answers to Research Question 8, I

conducted a multiple regression using e-waste as the predictor variable and

eco-friendly product purchasing as criterion variable.

Reliability and Validity

Reliability

I used Cronbach’s alpha to test the internal consistency of the survey

instrument for the subject population. There are four general classes of

reliability estimates. Firstly, Inter-

Rater or Inter-Observer Reliability, assesses the degree to which different

raters/observers, gives consistent estimates of the same phenomenon.

Secondly, TestRetest Reliability assesses the consistency of a measure from

one time to another. Thirdly, the Parallel-Forms Reliability assesses the

consistency of the results of two tests constructed in the same way from the

same content domain. Finally, Internal Consistency Reliability assesses the

consistency of results across items within an instrument. Internal

consistency reliability assesses the reliability of the summation scale and

several items from a total score (Kurtz, McCrae, Terracciano and Yamagata,

2010). Some of the tests used to calculate these results are the Average

Inter-item Correlation, Average Item total Correlation, Split-Half

Reliability, and Cronbach's Alpha. Cronbach’s alpha tests the inter-item

reliability of the survey questions to examine their relationship to each other.

The coefficient alpha measures the degree to which the questions examine

the same core constructs. Cronbach’s alpha values measure between 0 and

1, where the acceptable values of alpha ranges from 0.70 to 0.95 (Dennick &

Tavakol, 2011. The

Cronbach’s alpha value for this study was 0.685. Cronbach’s alpha value

means that the survey questions were adequate per the internal consistency

reliability coefficient. One of the means for ensuring the validity and

reliability was to assure each respondent could only take the survey once.

The survey questions were the same for all respondents, and the survey

remained opened for 2 weeks to ensure that respondents’ experience was

consistent.

Validity

There are two types of study-centric validity, internal validity, and

external validity (Thomas, Nelson, Silverman, & Silverman, 2010). Internal

validity refers to both how well a study is being conducted (research design,

operational definitions used, the measurement of variables, what is being

measured, among other considerations) and how confidently one might

conclude that the observed effect(s) are attributable to the independent

variable and not some extraneous ones (Kidd & Morgan, 2010). External

validity represents the extent to which a study's results can apply to other

people or settings (Thomas et al., 2010).

I used the online survey instrument to determine the relationships

among consumer perspectives, consumer behavior, and demographic

variables, and claim no causality between the study’s variables. The use of

an online survey allowed for wide selection of candidates from University of

South Florida.

Applying my knowledge of the green industry and the geographical

region, I ensured that all of the necessary, fundamental elements of the

survey applied. Additionally, the distribution of the pilot survey to five

participants not associated with the study enabled me to examine the clarity

of the measurement instrument (i.e., the survey. The purpose for the pilot

study was to ensure that the instrument was clear, comprehensible, and easy

to understand. If the results of the pilot study had revealed some question

clarity issues, I would have applied corrective measures to all such issues,

before using the questionnaire in the main data collection stage.

I used a non-parametric method for data analysis, which does not

require parametric assumptions because interval data conversion to rank-

ordered data. The Spearman's rank correlation provides a distribution free

test of independence between two variables. This method helps to improve

validity in the study since handling rankordered data is one of the strengths

of non-parametric tests.

Transition and Summary

The purpose of the current investigation was to examine the

relationship among consumer perspectives and consumer behaviors such as

willingness to pay more for ecofriendly products. In order to explore this

information, I used a quantitative, nonexperimental, correlational design.

The results of this research will contribute to the business practice

literature with an increased understanding of the strength and nature of the

relationship between customers’ preferences and their willingness to pay

more for eco-friendly products. The results of the investigation might be

relevant for researchers, product manufacturers, and consumers. The

information garnered from the study might serve as an impetus for social

change by encouraging product manufacturers to address the environmental

concerns of environmentally conscious consumers. The information gained

from this study might also create some social benefits for communities in

which certain companies are operating by leading to an eventual reduction

in e-waste through proper disposal of electronic waste. Included in this

section was information about the (research method and design of the study,

population and sampling technique, instrument, data collection and data

analysis procedures, and the role of the researcher). I discussed the

appropriateness and justification of the research method chosen, along with

the research questions.

Section 3 presents the findings of the (data analysis; a discussion of

the applications to professional practice, the implications for social change,

recommendations for actions and further study, and reflections). Section 3

ends with a summary of the findings and conclusions.

Section 3: Application to Professional Practice and Implications for

Change

Introduction

The purpose of this quantitative correlational study was to examine

the relationships among demographic variables (gender and age), consumer

perspectives, and consumer behavior. The research questions and

hypotheses were put forth to examine consumers’ views on eco-friendly

product quality, eco-friendly product price, and ecofriendly product brand

loyalty as they relate to consumers’ willingness to pay more for

green products and willingness to recycle e-waste at drop-off recycling

facilities.

This section provides a restatement of the research questions and

hypotheses and an explanation of the statistical methods employed, namely,

the Spearman correlation coefficient to measure the strength of the

association between ranked variables and ordinal regression to examine the

customer behavior relationships of interest. Provided in this section is a

detailed description of the results of the study, including the (presentation of

finding, application of the finding to professional practice, implications for

social change, recommendations for action and further research, and a

reflection of the researcher’s experience with this topic). The section ends

with a summary and conclusion.

Research Questions and Hypotheses

Research Questions

The following eight research questions were guiding the study:

RQ1: To what extent does eco-friendly product quality relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ2: To what extent does eco-friendly product price relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ3: To what extent does eco-friendly product brand loyalty relate to

customer willingness to recycle e-waste at drop-off recycling facilities?

RQ4: To what extent does eco-friendly product quality relate to

customer willingness to pay more for green products?

RQ5: To what extent does eco-friendly product price relate to

customer willingness to pay more for green products?

RQ6: To what extent does eco-friendly product brand loyalty relate to

customer willingness to pay more for green products?

RQ7: To what extent do gender and age differences relate to customer

willingness to pay more for green products?

RQ8: To what extent is a relationship extant between e-waste and eco-

friendly product purchasing?

Hypotheses

In this study, I used the significance value of less than 0.05 to reject

any of the following null hypotheses addressing the research questions.

Ho1: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha1: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to recycle e-waste at drop-off

recycling facilities.

Ho2: There is no significant statistical relationship between eco-

friendly product price and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha2: A significant statistical relationship exists between eco-friendly

product price and customer willingness to recycle e-waste at drop-off

recycling facilities.

Ho3: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to recycle e-waste

at drop-off recycling facilities.

Ha3: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ho4: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to pay more for green

products.

Ha4: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to pay more for green products.

Ho5: There is no significant statistical relationship between eco-

friendly product price and customer willingness to pay more for green

products.

Ha5: A significant statistical relationship exists between eco-friendly

product price and customer willingness to pay more for green products.

Ho6: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to pay more for

green products.

Ha6: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to pay more for green

products.

Ho7: There is no significant statistical relationship between gender,

age, and customer willingness to pay more for green products.

Ha7: A significant statistical relationship exists between gender, age,

and customer willingness to pay more for green products.

Ho8: There is no significant statistical relationship between e-waste

recycling, income, and age.

Ha8: A significant statistical relationship exists between e-waste

recycling, income, and age.

The research findings indicated that price was not the primary factor why

people were unwilling to pay more for green products or recycle e-waste at

drop-off recycling facilities. Brand loyalty and brand awareness played a

major role in consumer willingness to recycle e-waste at drop-off recycling

facilities and the consumers’ willingness to pay more for green products.

The next heading contains a detailed presentation of the findings.

Presentation of Findings

Pearson’s Versus Spearman’s Coefficient

The total number of respondents in this study was 313. Pearson’s

correlation coefficient measures the linear relationship between two

normally distributed variables, that is, the line of best fit, whereas

Spearman's correlation measures the relative rank order of the points. The

selection chosen was Spearman's correlation, in preference over Pearson’s

because Spearman’s correlation coefficient does not require any

assumptions about the frequency distribution of the two variables.

Specifically, the variables reflect ordinal data and the calculation of

Spearman’s correlation results do not assume that the relationship between

the variables is linear (Lund, 2013).

Spearman’s correlation coefficient is a statistical measure of the

strength of a monotonic relationship between two variables. If the value of

one variable increases, so does the value of the other variable, or,

conversely, as the value of one variable increases, the value of the other

variable decreases. Spearman's rank correlation coefficient, or Spearman's

rho, denoted by the Greek letter ρ (rho), or as rs, which is a nonparametric

measure of statistical dependence between two variables. One can verbally

describe the strength of the correlation using the following guide for the

absolute value of rs where

0.00-0.19 expresses a very weak relationship, 0.20-0.39 expresses a weak

relationship, 0.40-0.59 expresses a moderate relationship, 0.60-0.79

expresses a strong relationship, and 0.80-1.0 expresses a very strong

relationship (Lund, 2013).

Consideration 1: Customer Willingness to Recycle e-Waste at Drop-Off

Recycling Facilities

Research Question 1. To what extent does eco-friendly product

quality relate to customer willingness to recycle e-waste at drop-off

recycling facilities?

Research Question 1 addressed respondent views on products

reliability and assessed whether respondent would keep or recycle a product

based on the available of having recycling facilities. This question’s aim

was to capture the buying and recycling habit of respondents. The two

survey items related to Research Question 1 were:

Item 15: I recycle electronic devices or e-waste (products such as

computers, televisions, VCRs, stereos, copiers, fax machines, and

cellular phones) as opposed to discarding them as trash.

Item 18: I would buy and recycle electronic devices if more drop-off

recycling facilities were available in my area.

This research question addresses how likely, based on quality, customers

recycle devices and if they consider using a local drop-off recycling facility.

To answer Research

Question 1, I tested the following hypotheses:

Ho1: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha1: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to recycle e-waste at drop-off

recycling facilities.

Table 1 shows the results of the Spearman’s correlation test for

customer willingness to recycle e-waste at drop-off recycling facilities and

eco-friendly product quality. Product quality is defined through two

primary dimensions, product features (e.g., e-friendly) and the products that

are reflecting the intended features. Research Question 1 addresses

respondent views on products quality/reliability and assess whether

respondent would keep, get rid or recycle a product based its value at a

recycling facilities. The aim is to capture the buying and recycling habit of

respondents when it comes to assessing the quality of a product.

Table 1.

Spearman’s Correlation Test for Customer Willingness to Recycle e-Waste

at Drop-Off

Recycling Facilities and Eco-Friendly Product Quality

Questions from the Questionnaire

I recycle

electronic

devices or

e-waste

(products

such as

computers,

televisions,

VCRs,

stereos,

copies, fax

machines,

cellular

phones as

opposed to

discarding

them as

trash).

I would

buy and

recycle

electronic

devices if

more drop-

off

recycling

facilities

were

available

in my area.

Spearman'

s ρ

I recycle electronic

devices or e-waste

(products such as

computers,

Correlation

Coefficient

Sig.

(2-tailed)

-.213*

televisions, VCRs,

stereos, copies, fax

machines, cellular

phones as opposed to

discarding them as

trash).

N 313 313

I would buy and

recycle electronic

devices if more drop-

off recycling facilities

were available in my

area.

Correlation

Coefficient

Sig.

(2tailed)

-.213*

N 313 313

Note. *I tested the correlation at the significance level of 0.05.

Per the values in the table above rs = -.213, n = 313

Because the calculated significance was less than 0.05 or 1.437E-4, I

rejected the null hypothesis (Ho1). The rejected hypothesis stated that there

is no significant statistical relationship between eco-friendly product quality

and customer willingness to recycle e-waste at drop-off recycling facilities.

Quality did not have a positive correlation with customer willingness

to recycle ewaste at drop-off recycling facilities and this could be the

strength expressed by this variable, that is, product quality. Product quality

and reliability have steadily improved over the years; Energy Star-qualified

refrigerators currently last longer than they did 5 years ago. Refrigerators

that were sold in 2010 are 20% - 30% more energy efficient than

nonqualified refrigerators and, at least, 40% more energy efficient than

nonqualified refrigerators sold in 2001 (General Electric, 2014). The change

in quality of the product has allowed consumers to keep products longer and

delay recycling.

Research Question 2. To what extent does eco-friendly product

price relate to customer willingness to recycle e-waste at drop-off recycling

facilities?

The two survey items related to Research Question 2 were:

Item 16: I would start recycling electronic devices if I received a

financial incentive for doing so.

Item 17: If I had the choice of discarding an old electronic device I

would use a drop-off recycle facilities.

This research question asked would consumers use the local drop-off

recycle facilities if product pricing that included a financial incentive is

available. To answer Research Question 2, I tested the following

hypotheses.

Ho2: There is no significant statistical relationship between eco-

friendly product price and customer willingness to recycle e-waste at drop-

off recycling facilities.

Ha2: A significant statistical relationship exists between eco-

friendly product price and customer willingness to recycle e-waste at

drop-off recycling facilities. Table 2 shows the results of the Spearman’s

correlation test for customer willingness to recycle e-waste at drop-off

recycling facilities and eco-friendly product price.

Table 2.

Spearman’s Correlation Test for Customer Willingness to Recycle e-Waste

at Drop-Off

Recycling Facilities and Eco-Friendly Product Price

Questions from the Questionnaire

I would start recycling

electronic devices If I had the

choice of if I receive a

discarding an old

electronic financial incentive

device I would use a dropfor

doing so. off recycling

facilities.

Spearman's I would start recycling 1.000 .166*

Correlation Coefficient ρ electronic devices

if I receive a Sig. (2-tailed)

financial incentive for doing

so.

. .003

313 313

If I had the choice of Correlation

Coefficient discarding an old

electronic

Sig. (2-tailed)

device I would use a drop-off

recycling facilities.

.166* 1.000

.003 .

313 313

Note. * I tested the correlation at the significance level of 0.05.

rs = .166, n = 313

Because the calculated significance was less than 0.05 r (0.0032), I

rejected the null hypothesis (Ho2). The rejected hypothesis stated that there

is no significant statistical relationship between eco-friendly product price

and customer willingness to recycle e-waste at drop-off recycling facilities.

Product price correlating with customer willingness to recycle e-waste

at drop-off recycling facilities might be due to the existence of a secondary

market. Wang, Zhang, Yin, and Zhang (2011) found two factors that could

affect recycling styles: economic benefit and convenience. The authors

showed that reclaiming by peddlers played a major role in e-waste recycling

in Beijing because the price offered for e-waste was much higher and onsite

services were convenient. Similarly, reused cell phones in the United States

are at 65%, and the buy-back price can range from a few dollars to $40 or

$50, depending on the model of the phone (Geyer & Blass, 2010). Most

consumers can easily sell their old phones, rather than recycle them.

Research Question 3. To what extent does eco-friendly product

brand loyalty relate to customer willingness to recycle e-waste at drop-off

recycling facilities?

The survey items related to Research Question 3 were:

Item 18: I would buy and recycle electronic devices if more drop-off

recycling facilities were available in my area.

Item 19: I would buy and recycle electronic devices if there were an

awareness campaign in my area about the dangers of not recycling.

This research question addressed the relationship between consumers'

awareness of the dangers of not recycling and consumers’ likelihood to use

local drop-off recycles facilities. Awareness campaign about the dangers of

not recycling helps to encourage consumer to purchase more products eco-

friendly products. To answer Research

Question 3, I tested the following hypotheses:

Ho3: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to recycle e-waste

at drop-off recycling facilities.

Ha3: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to recycle e-waste at drop-

off recycling facilities.

Table 3 shows the results of the Spearman’s correlation test of customer

willingness to recycle e-waste at drop-off recycling facilities and eco-

Friendly product brand loyalty. With a recycling awareness campaign,

managers could promote responsible habits from respondents to use

recycling facilities when products have reached the end of their useful

life.

Table 3.

Spearman’s Correlation Test of Customer Willingness to Recycle e-Waste at

Drop-Off Recycling Facilities and Eco-Friendly Product Brand Loyalty

Questions from the Questionnaire

I would buy and I

would buy and recycle

electronic recycle

electronic devices if

more devices if there

was drop-off recycling

an awareness facilities

were campaign in my

area available in my

about the dangers

area. not recycling

Spearman'

s ρ

I would buy and

recycle electronic

devices if more

dropoff recycling

facilities were

available in my

area.

Correlation

Coefficient

Sig. (2-tailed)

1.000

313

.537*

.000

313

I would buy and

recycle electronic

devices if there

was an awareness

campaign in my

area about the

dangers of not

recycling

Correlation

Coefficient

Sig. (2-tailed)

.537*

.000

313

1.000

313

Note. * I tested the correlation at the significance level of 0.05.

rs = .537, n = 313

Because the calculated significance was less than 0.05 (9.569E-25), I

rejected the null hypothesis (Ho3). The rejected hypothesis, which stated

that brand loyalty did not relate to customer willingness to recycle e-waste at

drop-off recycling. Research

Questions 3 addressed respondents’ views on brand loyalty as it related to

customer willingness to recycle e-waste at drop-off recycling facilities. The

questions also addressed whether an awareness campaign would prompt

respondents to start using the ewaste at drop-off recycling facilities. The

results could demonstrate the importance of customer awareness of using

recycling facilities, which is alignment with Wang et al. (2011) findings that

stated that consumers education played an important role in recycling, as did

the convenient location of recycling facilities, both these aspects tended to

enhance public participation in recycling (Wang et al., 2011). Management

of companies should begin programs to start an awareness campaign to

shape consumer behavior. Some managers of management companies have

adopted an Extended Producer Responsibility (EPR) policy. These policies

required manufacturers to finance the cost of recycling or of safely

disposing of products that consumers no longer want. Some businesses

management saw programs that encourage consumers to bring back

products for recycling as opportunities for strengthening brand loyalty (Nash

& Bosso, 2013). For example, Nestlé Waters, a major producer of bottled

water products, recently funded the start-up called Recycling Reinvented, a

new organization dedicated to advocating EPR for packaging (MacKerron,

2012). Other companies’ leaders such as those at Waste Management

Incorporated have lent financial support to organizations’ leaders advancing

EPR policies in the hope that these efforts will generate business for them

(Nash & Bossi, 2013).

Consideration 2: Customer Willingness to Pay More for Green Products

Research Question 4. To what extent does eco-friendly product

quality relate to customer willingness to pay more for green products?

The four survey items related to Research Question 4 were:

Item 6: I am willing to pay more for green products.

Item 9: I believe the quality of green products affects my decision to

purchase.

Item 10: I believe that green products are of better quality than

nongreen products.

Item 11: I would recommend green products based on quality to my

friends.

This research question compared the extent to which the quality of a

green product relates to customers’ willingness to pay more for green

products than for a nongreen product. To answer Research Question 4, I

tested the following hypotheses:

Ho4: There is no significant statistical relationship between eco-

friendly product quality and customer willingness to pay more for green

products.

Ha4: A significant statistical relationship exists between eco-friendly

product quality and customer willingness to pay more for green products.

Table 4 shows the results of the Spearman’s correlation test of customer

willingness to pay more for eco-friendly product quality.

Table 4.

Spearman’s Correlation Test of Customer Willingness to Pay More for Eco-

friendly

Product Quality

Questions from the Questionnaire

believe that

green products

are of better I am

willing to quality

than pay more

for nongreen

green products.

products.

I would

I believe the

recommended

quality of green

green

products products

affect my based on

decision to quality

to my purchase.

friends.

Spearman's I am

willing to pay more ρ

for green products.

Correlation

Coefficient

Sig. (2-

tailed)

1.000

-.445*

.000

-.327*

.000

-.517*

.000

N 318 318 318 318

I believe that

green

products are

of better

quality than

nongreen

products.

Correlation

Coefficient

Sig. (2-

tailed)

-.445*

.000

318

1.000

318

.157*

.005

318

.461*

.000

318

I believe the

quality of

green

products

effect my

decision to

purchase.

Correlation

Coefficient

Sig. (2-

tailed)

-.327*

.000

.157*

.005

1.000

.392*

.000

N 318 318 318 318

I would recommended

Correlation Coefficient

green products based on

Sig. (2-tailed)

quality to my friends.

-.517*

.000

.461*

.000

.392*

.000

1.000

N 318 318 318 318

Note. * I tested the correlation at the significance level of 0.05.

rs = -.445, n = 318 (Item

9) rs = - .327, n = 318

(Item 10) rs = - .517, n =

318 (Item 11) Because

the calculated

significance level was

less than 0.05 (4.9373E-

6, 2.3675E9 and

3.8545E-23)

respectively, I rejected

the null hypothesis

(Ho4), which stated that

there is no significant

statistical relationship

between eco-friendly

product quality and

customer willingness to

pay more for green

products.

Quality might not be much of a concern in consumers’ willingness to

pay more for a green product because consumers might have not developed

a high level of trust in eco-friendly products. Datta (2011) showed that a

high percentage of respondents (82%) would consider buying eco-friendly

products, but only a few (36%) actually trust the quality of the eco-friendly

products. This apparent discrepancy might have been due to the perception

of product performance and hesitation to use eco-friendly products.

Research Question 5. To what extent does eco-friendly product

price relate to customer willingness to pay more for green products?

The three survey items related to Research Question 5 were:

Item 6: I am willing to pay more for green products.

Item 5: I believe that green products are more expensive than

nongreen products. Item 8: I believe the price of green products

affects my decision to purchase them.

This research question compared the extent to which the price of a

green product relates to customer willingness to pay more for green products

than nongreen products.

To answer Research Question 5, I tested the following hypotheses.

Ho5: There is no significant statistical relationship between eco-

friendly products price and customer willingness to pay more for green

products.

Ha5: A significant statistical relationship exists between eco-friendly

product price and customer willingness to pay more for green products.

Table 5 shows the results of the Spearman’s correlation test of customer

willingness to pay more and eco-friendly product based on price.

Table 5.

Spearman’s Correlation Test of Customer Willingness to Pay More and

Eco-Friendly

Product Based on Price

Questions from the Questionnaire

believe that green

products are more

I am willing to

expensive than

pay more for

nongreen green

products.

I believe

the price

of green

products

affects

decision

purchase.

Spearman'

s ρ

I am willing to

pay more for

green products.

Correlation

Coefficient

Sig. (2-tailed)

1.000

-.157*

.005

-.271*

.000

N 318 318 318

I believe that

green products

are more

expensive

than nongreen

products.

Correlation

Coefficient

Sig. (2-tailed)

-.157*

.005

318

1.000

318

.409*

.000

318

I believe the

price of green

products

effect my

decision to

purchase.

Correlation

Coefficient

Sig. (2-tailed)

-.271*

.000

.409*

.000

1.000

N 318 318 318

Note. * I tested the correlation at the significance level of 0.05.

rs = -.157, n = -.318

rs=-.271, n =- .381

Because the calculated significance was less than 0.05 (0.005 and

9.1365E-7) respectively, I rejected the null hypothesis (Ho5). The rejected

hypothesis stated that there is no significant statistical relationship between

eco-friendly products price and customer willingness to pay more for green

products.

Customers are willing to pay more for green products because they

are willing to pay a premium for product sustainability as a baseline

condition for consumer products. Doh, Howton, Howton, and Siegel (2010)

showed that management should not ignore sustainability, as it would lead

to negative results. Doh et al. stated that, since social performance is

difficult for investors to track, they rely on expert endorsements from

companies such as the Calvert Group. When the Calvert Group maintained

and endorsed a company, the company’s stock would remain stable.

Research Question 6. To what extent does eco-friendly product

brand loyalty relate to customer willingness to pay more for green products?

The four survey items related to Research Question 6 were:

Item 6: I am willing to pay more for green products.

Item 12: I would switch to green products if they were more available

at my local store.

Item 13: I would switch to green products if they were promotional deals

such as

TV ads and local printed coupons available at my local store.

Item 14: I am more likely to buy a certain product because it has a

brand name I have used in the past.

This research question compared the extent to which brand loyalty to

a green product related to customer willingness to pay more for green

products than for nongreen products. To address Research Question 6, I

tested the following hypotheses.

Ho6: There is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to pay more for

green products.

Ha6: A significant statistical relationship exists between eco-friendly

product brand loyalty and customer willingness to pay more for green

products.

Table 6 shows the results of the Spearman’s correlation test for customer

willingness to pay more and eco-friendly products and brand loyalty.

Table 6.

Spearman’s Correlation Test for Customer Willingness to Pay More and

Eco-Friendly

Products and Brand Loyalty

What are these

statements?

would switch to

green products

if I am willing

to they were

more pay more

for available at

my green

products. local

store.

I would

switch to

green

products if

they were

promotional

deals such as

TV ads and

local printed

coupons

available at

my local

store.

I am

likely to

buy a

certain

product

because

it has a

brand

name I

have

used in

the past.

Spearman's

I am

willing to

pay more

for green

products.

Correlation

Coefficient

Sig. (2-

tailed)

1.000

-.551*

.000

-.285*

.000

.050

.374

N 318 318 318 313

I would

switch to

green

products if

they were

available at

Correlation

Coefficient

Sig. (2-

tailed)

-.551*

.000

1.000

.484*

.000

.038

.501

my local

store.

N 318 318 318 313

I would switch to

Correlation green

products if they

Coefficient were

promotional

Sig. (2-

tailed)

deals such as TVs ads

and local

printed

coupons

available at

my local

store.

-.285*

.000

318

.484*

.000

318

1.000

318

.121*

.032

313

I am more likely to

Correlation buy

a certain product

Coefficient

because it has a brand

Sig. (2-

tailed)

name I have used in

.050

.374

313

.038

.501

313

.121*

.032

313

1.000

313

the past.

Note. * I tested the correlation at the significance level of 0.05.

rs =-.551, n = 318 (Item

12) rs = -.285, n = 318

(Item 13) rs = .05, n =

313 (Item 14)

Because the calculated significant was less than 0.05 (1.2839E-26,

2.3995E-7), I rejected the null hypothesis (H₀6). The rejected hypothesis,

which states that there is no significant statistical relationship between eco-

friendly product brand loyalty and customer willingness to pay more for

green products.

Repeat purchasing of green products might induce consumers to pay a

higher price because the consumers might now consider a store’s green

credentials when choosing where to shop (Tucker, Pearce & Bruce, 2012).

Green credentials help to ensure that the consumer understands why the

company’s products are superior to those of other stores. Leaders of car

companies understand that consumers are becoming increasing concerned

about the effect the automobile has on the environment (Tucker, Pearce &

Bruce, 2012). Marketing professionals of the car companies have developed

an advertising campaign for their hybrid car that lets consumers know that

the hybrid cars are the most efficient gas-and-electricity vehicle on the

market. Hybrid cars are now the brand that most consumers have in mind

when purchasing or shopping for an automobile that will save money on gas

and reduce harmful effects to the environment.

Consideration 3: Customer Willingness to Pay More and to Recycle at

Drop-Off

Recycling Facilities

Research Question 7. To what extent are there gender and age

differences in customers’ willingness to pay more for green products?

For the three survey items related to Research Question 7, I collected

demographic information for gender, age, and income.

To address Research Question 7, I used an ordinal regression analysis

to test the following hypotheses:

Ho7: There is no significant statistical relationship between gender,

age, and customer willingness to pay more for green products.

Ha7: A significant statistical relationship exists between gender, age,

and customer willingness to pay more for green products.

To examine the issue of willingness to pay more, Table 7 shows the results

of the Ordinal regression analysis of customer willingness to pay more for

eco-friendly products based on gender, age, and income information.

Table 7.

Ordinal Regression Analysis of Customer Willingness to Pay more for Eco-

Friendly

Products based on Demographic Information

Parameter Estimates

95% Confidence Interval

Estimat

e SE Wald

f Sig.

Lowe

Boun

Uppe

Boun

Threshol

[Sec2_Price_

2 = 1] -2.262 .43

27.15

4 1.00

3.113

1.411

[Sec2_Price_

2 = 2]

-.350 .35

.99

1 .32

1.041

.34

[Sec2_Price_

2 = 3]

2.410 .38

39.10

1 .00

1.655 3.166

[Sec2_Price_

2 = 4]

4.085 .44

82.91

1 .00

3.206 4.964

Location [Gender=1] .667 .23

8.188

1 .00

.21

1.124

[Gender=2] 0a - - 0 - - -

[Age=1] -.093 .38

.058 1 .81

-.849 .66

[Age=2] .145

.38

5 .142 1

.70

6 -.610 .901

[Age=3] -.533

.40

9 1.694 1

.19

1.334 .269

[Age=4] .304

.37

0 .672 1

.41

2 -.422 1.030

[Age=5] 0a - - 0 - - -

[Income=1] 1.249 .41

8.869 1 .00

.427 2.071

[Income=2] 1.002 .41

5.755 1 .01

.183 1.821

[Income=3] .712 .38

3.439 1 .06

-.040 1.464

[Income=4] .799 .48

2.674 1 .10

-.159 1.756

[Income=5] 0a - - 0 - - -

Note. Link function: Logit.

Note. Threshold: Response categories ‘logit functions’ intercepts for each

pricing category’s logit function.

Note. Location: Independent variables ‘logistic regression models’

coefficients for willingness to pay more.

Note: a: Reference category

Ordinal regression analysis models enable researchers to examine the

relationship between a set of predictors or independent variables and a

polytomous ordinal dependent variable response. The first ordinal

regression model (results in Table 7) measured “I am willing to pay more

for green products” (dependent variable) against gender, age, and income

(independent variables).

Table 8 shows the results of the goodness of fit for the ordinal regression

analysis of customer willingness to pay more and eco-friendly products and

table 9 shows that the assumption of the parallel lines cannot be rejected.

Table 8.

The Model Fitting Information, Which Shows the Statistical Significance of

the Ordinal Regression Analysis for Customer Willingness to Pay More for

Eco-Friendly Products

Table 9.

Test of Parallel Line, Which Shows the Statistical Significance of the

Ordinal Regression Analysis for Customer Willingness to Pay More for Eco-

Friendly Products

Since the calculated significance level was less than 0.05 the model

fitting information above validate my decision to reject the null hypothesis

(H₀7), which stated that there is no significant statistical relationship

between gender, and customer willingness to pay more for green products.

Furthermore, as shown in Table 9, the test for parallel logit lines calculated

the chi-square significance value as 0.658, which is larger than 0.05, which

implies that the assumption of the parallel lines cannot be rejected.

The income range of the participants who expressed a readiness to

pay more for green products was respondents who earned up to $49,999.

The data results showed the need to develop awareness campaigns targeting

respondents with incomes greater than $50,000. We also observe a

significance value of 0.004, which shows that males were more likely to be

willing to pay more for recyclable products than females.

Research Question 8. To what extent is there, a relationship between

customer willingness to recycle e-waste at drop-off recycle facilities based

on gender, age, and income?

The response items in the ordinal regression test were:

Item 6: I am willing to recycle more for green products.

Item 18: I would buy and recycle electronic devices if more drop-off

recycling facilities were available in my area.

The testing, done via the following hypotheses, addressed Research

Question 8 as it related to willingness to recycle e-waste at drop-off

recycling facilities.

H₀8: There is no significant statistical relationship between e-waste

recycling intent and gender, income, and age.

Ha8: A significant statistical relationship exists between e-waste

recycling intent and gender, income, and age.

Table 10 shows the results of the Ordinal regression analysis of

customer willingness to recycle e-waste at drop-off recycling facilities based

on their demographic characteristics. Table 10 shows the results of the

ordinal regression analysis of customer willingness to recycle e-waste at

drop-off recycle facilities based on demographic information.

Table 10.

Ordinal Regression Analysis of Customer Willingness to Recycle e-Waste

at DropOff Recycling Facilities Based on Demographic Information

Parameter Estimates

95% Confidence Interval

Estimate SE Wald df Sig.

Lower

Bound

Upper

Bound

Threshol

[Sec3_Recycle_4

= 1] -6.192 .800 59.880 1 .000 -7.760 -4.624

[Sec3_Recycle_4

= 2] -3.965 .438 81.898 1 .000 -4.824 -3.106

[Sec3_Recycle_4

= 3] -2.199 .373 34.657 1 .000 -2.931 -1.467

[Sec3_Recycle_4

= 4]

-.074 .348 .045 1 .831 -.755 .607

Location [Gender=1] -.805 .225

12.815 1 .000 -1.246 -.364

[Gender=2] 0a - - 0 - - -

[Age=1] -.578 .371 2.432 1 .119 -1.304 .148

[Age=2] -.858 .372 5.323 1 .021 -1.587 -.129

[Age=3] -.180 .393 .210 1 .646 -.949 .589

[Age=4] .138 .359 .148 1 .701 -.565 .840

[Age=5] 0a - - 0 - - -

[Income=1] -.546 .404 1.832 1 .176 -1.337 .245

[Income=2] -.760 .405 3.523 1 .061 -1.553 .034

[Income=3] -.572 .374 2.339 1 .126 -1.305 .161

[Income=4] -.359 .473 .574 1 .449 -1.286 .569

[Income=5] 0a - - 0 - - -

Note. Link function: Logit.

Note. Threshold: Response categories ‘logit functions’ intercepts for each

recycling willingness category’s logit function.

Note. Location: Independent variables ‘logistic regression models’

coefficients for customer willingness to recycle e-Waste at Drop-Off

Recycling Facilities Note: a: Reference category

Tables 11 and 12 below show the results of the model of fit and test of

parallel lines for the customer willingness to recycle e-waste at drop-off

recycle facilities based on demographic information. The model fitting

information in Table 11 validated the decision to reject the null hypothesis

(H₀8) and the test of parallel lines implied that the assumption of the parallel

lines cannot be rejected.

Table 11.

The Model Fitting Information, Which Shows the Significance of the Ordinal

Regression

Analysis for Customer Willingness to Recycle E-Waste at Drop-Off

Recycling Facilities Base

Table 12.

Test of Parallel Line, Which Shows the Significance of the Ordinal

Regression Analysis for Customer Willingness to Recycle E-Waste at Drop-

Off Recycling Facilities Base

Using ordinal regression enabled me to model the polytomous ordinal

dependent variable response’s relationship with the set of independent

demographic variables gender, age, and income. The ordinal regression

analysis estimated the correlation between the response to “I would buy and

recycle electronic devices if more drop-off recycling facilities were

available in my area (Sec3_Recycle_4 - dependent variable)” against

“gender, income, and age” (independent variables).

As reflected in Table 10, the results of testing Hypotheses for

research question 8 were:

•Gender: Gender=1= Male participants were less willing to

recycle e-waste at drop-off recycling facilities than female

respondent.

•Age: Age=2=25-31 years old participants were less willing to

recycle ewaste than the AGE=5=46-52 respondents.

•Income: There was no significant difference (at the .05 level)

in willingness to recycle associated with the income categories.

Research question 8, inquired about customers’ willingness to recycle

e-waste. The results of testing the hypotheses for independent variable i.e.

gender, income, and age along with the dependent variables for willingness

to recycle e-waste at drop-off recycling facilities demonstrated that

respondents between ages 25-31 were not as willing to recycle e-waste

products as respondents between the ages of 46-52 even with the increased

availability of recycling facilities. The calculated significance level was less

than 0.05 and as a result, I rejected the null hypothesis (H₀8), which stated

that there is no significant statistical relationship between e-waste recycling

intent and gender, income, and age. Some respondents might not be willing

to practice in recycling behaviors as previously noted under Research

Question 2, due to an inverse relationship with the existence of a secondary

market. Consumer products such as Apple iPhones resell for as much as

10% to 50% of the cost of a new iPhone in emerging markets such as Africa

and

Latin America (Laseter, Ovchinnikov, & Raz, 2010).

The question I used to answer if customers would buy and recycle

electronic devices in their area was “I would be willing to recycle electronic

devices if more dropoff recycling facilities were available in my area.” Both

gender and income had a calculated significance level of less than 0.05 for

customer willingness to recycle at a drop off facility. The model fitting

information in Table 11, which had significant level of 0.004, validated my

decision to reject the null hypothesis (H₀8), which stated that there is no

significant statistical relationship between e-waste recycling intent and

gender, income, and age. Furthermore, the test of parallel lines calculated

the chi-square significant value as 0.361, which implied that the assumption

of the parallel lines cannot be rejected. The results of this ordinal regression

analysis showed that male participants and customers ages 25-31 were not

as willing to buy and recycle electronic devices as female customers or

customers in the older age categories. This is an indication that by

promoting recycling habits, including awareness campaigns and recycles

drives, is vital to encourage consumers ages 25 and 31 to start developing

recycle habits and use recycling facilities.

Summary

In conclusion, the key findings are that product quality and price are

significant for attaining consumers’ brand loyalty, and in relationship to

customers’ willingness to recycle e-waste at drop-off recycling facilities and

their willingness to pay more for green products. The findings indicate that

male participants and participants between the ages of 25-31 were not as

likely to recycle e-waste as female participants and participants in the older

age group. Additionally, as reflected in Table 7, male participants and the

participants who earned up to $49,999 expressed a readiness to pay more

for green products.

Applications for Professional Practice

In Consideration 1, I explored customers’ willingness to recycle e-

waste at dropoff recycling facilities and found brand loyalty, as shown in

Table 3, plays a significant part in customers’ decision to recycle.

In Consideration 2, I explored customers’ willingness to pay more for

green products and found similar results: As shown in Table 6 when

customers had used a certain brand before, they were more likely to

continue buying that brand, even if the price went up.

Consideration 3, in Table 7 explored the association of Willingness to

Pay More according with gender and income. The income range that

expressed a readiness to pay for more for green products contained

respondents that make up to $49,999.

Consideration 3, Table 8 demonstrated the association of willingness to

recycle electronic devices at a drop-off facility for green products and

demographic variables. The age range and income group that were less

willing to recycle e-waste at drop-off recycling facilities was the

respondents in the 25-31 age group.

The results based on consumer views highlighted the fact that

business managers should focus on brand awareness to inform their

customers of the benefits of using their products as well as the availability of

local recycling centers. Business managers should also use customer

testimonials, or experiences with green products, to encourage new

customers to switch from using non green products to eco-friendly products.

Business managers could create brand awareness by investing in marketing

and advertising to promote eco-friendly products.

Implications for Social Change

In Section 1, I indicated that findings from this study could provide an

opportunity to bring more awareness to the social responsibility of the

business community. Data analysis from Table 6 revealed that a significant

statistical relationship existed between social responsibility and brand

loyalty, the implications for social change became much clearer. Social

responsibility reflects a business manager’s willingness to promote and

address environmental responsibility. Social responsibility within

businesses drives social change and produces an atmosphere conducive to

better business practices (Chaminda and Perera, 2013).

The significant relationship between social responsibility and product

innovation creates a venue for social change (Close, Finney, and Laceya,

2010). The socially responsible activities of business leaders can help to

promote awareness. Therefore, the evaluation of the findings of this study

supported the need for more socially responsible practice from business.

These few actions could help to promote environmentally responsible

behavior by consumers.

There is a need to transform the current markets into green markets by

replacing inefficient processes with green, sustainable processes (Chang and

Fong, 2010). Some of the strategies that several companies’ leaders have

used to separate themselves from the competition are by developing and

rewarding businesses for promoting green and fair product strategies.

Recommendations for Action

The evaluation of the results of this study provided an opportunity to

recommend actions that will continue to promote social responsibility within

the business community. The first action that might further support socially

responsible behavior of businesses is to quantify the variable savings by

using a green-product alternative and include it on a company‘s balance

sheet, or profit-or-loss statement. The steps toward achieving this task

would require a combined effort from major groups (businesses, consumer

advocate groups, government policy groups, marketing groups,

shareholders, and the EPA). The Research and Development department

will be able to develop better products, which will make their promotion

more cost effective.

Business management should adopt a more environmentally and

socially responsible supply-chain management-practice and promote such

practices to consumers and other businesses. Starbucks (2013) and Google

are two companies whose leaders have held themselves accountable for

becoming greener. Starbucks stores’ owners purchase coffee beans only

from companies that are part of the Fair Trade Certified and Certified

Organic Coffee. Starbucks’ storeowners are going green, whereby each

storeowner will achieve LEED® certification. This focus has enabled

Starbucks’ leaders to reduce both operating costs and the environmental

impact of its business practices (Starbuck, 2013).

Business managers need to communicate the environmental and

social impacts associated with product use to their consumers. This means

addressing and making consumers aware of any hidden costs of product

ownership and educating consumers on how to decrease their “carbon”

footprint when they make purchases as, for example, through energy use of

electronic devices or waste avoidance upon product disposal. For example,

every pack of Walkers potato crisps made by PepsiCo has a carbon-

emissions label. PepsiCo found that 44% of carbon emissions, associated

with each bag of crisps, came from the production of the raw materials, most

notably the way in which its potatoes were cultivated, processed, and stored.

Such information increases awareness of both the impact of the products and

the carbon footprint of everyday foods (Ecopromising, 2008).

The second recommendation, based on research done by Kondon,

Kurakwa, Kato, Umeda, and Takata (2006), is to explore a number of ways

to reduce costs while investing in green products such as using best practice

for the management of the product life cycles, expansion of the business

scale, and technological innovation among others.

The final recommendation is business managers should focus on

brand awareness to inform their customers of the benefits of using their

products as well as the local recycling center. Business managers should

also use customer testimonials, or experiences with green products, to

encourage new customers to switch from using nongreen products to eco-

friendly products. Business managers could create brand awareness by

investing in marketing and advertising to promote the benefits of purchasing

eco-friendly products.

Recommendations for Further Study

There are many potential follow-ups to this study. The first and most

obvious follow-up would be to widen the industry and geographical location

to see if similar results exist. In this study, I explored consumer views on

recycling and willingness to pay more for green products. It is unclear

whether the same findings and conclusions would apply in other industries

such as housing and the energy sector.

Second, the data in Section 1 include an EPA report on e-waste

figures for 2009. This EPA report contained the most currently data

available at the time of this study; another researcher could revisit the EPA

figures as more recent data become available. It would be interesting to

track the changes from 2009 to a future point in time to see if any significant

changes occurred.

Third, researchers should focus on government policies regarding e-

waste in light of increases or decreases in a country’s population. Countries

with larger populations may have more comprehensive polices due to their

need to identify, control and improve more environmental variables.

Exploring questions on the policies’ effectiveness and efficiency could

provide the foundation for further studies.

Reflections

With this research, I examined the level of consumer willingness to

pay more for eco-friendly products and consumers willing to recycle e-waste

at drop-off recycling centers. The research process has been both rewarding

and challenging for two reasons. First, the results of this research provided

significant insights into the decision-making process customers go though

and helped focus on which variables (price, quality, or brand name) play an

important part in the final purchase decision. Secondly, the literature and

research findings revealed consumers (Chang & Fong, 2010) and business

(Eco-promising, 2008) views on eco-friendly products. I believe that the

information from this study provides a point in time reference of

participating customers’ spending decisions and propensity to recycle. I was

surprised that business managers have not noticed the eco-friendly product

trend sooner since this information about scarcity of resources has been

around for several years. Based upon completing this study, I believe as

customer demand for ecofriendly products increases, business managers will

respond and more eco-friendly products will be available in stores along

with people using dropoff center to recycle goods.

Summary and Conclusions

In conclusion, to promote green products as the wave of the future,

the focus should be on product stewardship and product marketing. Because

evolving and changing customers’ views drive business product

development, it is the customers’ expressing their newly found interest in

green products that should prompt businesses leaders to refocus their efforts

and dedicate their resources to explore how they can harness this new and

potentially competitive advantage to increase companies’ bottom lines while

satisfying the customer base.

Consumer Perceptions of Eco-Friendly Products

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