THE EFFECT OF CONSUMER MINDFULNESS ON GREEN TECHNOLOGY

ACCEPTANCE

by

EMINE ERDOGAN

A Dissertation submitted to the

Graduate School-Newark

Rutgers, The State University Of New Jersey

In partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Graduate Program in Management

Written under the direction of

Professor Sengun Yeniyurt

and approved by

Newark, New Jersey

October 2018

© [2018]

Emine Erdogan

ALL RIGHTS RESERVED

ii

ABSTRACT OF THE DISSERTATION

THE EFFECT OF CONSUMER MINDFULNESS ON GREEN TECHNOLOGY

ACCEPTANCE

By EMINE ERDOGAN

Dissertation Advisor: Dr. SENGUN YENIYURT

This dissertation investigated how consumer mindfulness influences the decision-

making process of accepting green technological products. Based on the theory of

technology acceptance and bounded rationality, this study examined the concept of

mindfulness as an individual difference variable and tested it under the constraining

effects of information ambiguity, cognitive overload, and time pressure in the process of

consumer’s acceptance of green technological products, specifically high-tech vehicles

(Electric Vehicles).

Companies in the automotive industry are categorized as reliability seeking

organizations. These organizations cannot tolerate even minimum reliability gaps since

potential lapses can ultimately threat human life and devastate the firm’s image. Previous

research suggested that mindfulness, mostly associated with enhanced attention, active

awareness, openness to novelty/new information, and sensitivity to context/multiple

perspectives is an essential characteristic of reliability seeking customers. Recent trends

in the industry for electrification and automation of cars promise to ensure consumers’

safety and environmental concerns yet create new ambiguities.

iii

Using a structural equation modeling methodology, findings of this study

indicated that customers’ dispositional mindfulness, normative approach about eco-

friendly vehicles, rational decision-making styles, and counter-intuitively cognitive load

have positive effects on consumers’ situational mindfulness while information

uncertainties, financial risks, and perceived time pressure have adverse impacts on it.

Additionally, the study added to the technology acceptance literature by revealing the

positive and significant impact of situational mindfulness on both perceived ease of use

(effort expectancy) and perceived usefulness of green technology which ultimately drive

customers’ intention to use. Furthermore, the study revealed that situational mindfulness

mediates the relationships between all of its determinants and consumers’ perceptions

(perceived ease of use and perceived usefulness) and consumers’ perceptions mediate the

relationships between situational mindfulness and intention to use green technology.

Finally, this study tested the moderating effect of consumers’ perceived cognitive load,

uncertainty, financial risk, and time pressure on their green technology acceptance

process and found partial support for the proposed effects.

The ultimate purpose of the study is to improve our understanding of mindfulness

and marketing green technological products through expanding the perspective on

mindful technology adoption. By considering the impacts of mindfulness on the green

technology acceptance process, the study also adds to the innovation and management

literature by enabling us to understand better users’ perceptions and intentions of using

green technology.

iv

ACKNOWLEDGMENTS

I thank Dr. Sengun Yeniyurt and Dr. Can Uslay for their constant support and

their valuable suggestions during this research. I also thank the dissertation committee

members Oscar Moreno, Goksel Yalcinkaya and Omer Topaloglu for their valuable

contributions. Further, I thank the faculty and the graduate students in the Marketing

program at Rutgers the State University of New Jersey. Finally, I thank my parents, my

whole family, and friends for their endless support, encouragement, prayers, and love

during my research.

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TABLE OF CONTENTS

Abstract .............................................................................................................................. ii

Acknowledgments.............................................................................................................. iv

Table of contents ..................................................................................................................v

List of tables ....................................................................................................................... ix

List of figures……………………………………………………………………………ix

Chapter 1. Introduction.................................................................................................. - 1 -

Chapter 2. Literature Review ........................................................................................ - 6 -

2.1 Technology Acceptance Model (TAM) .................................................................... - 6 -

2.2 Theory of Bounded Rationality ................................................................................ - 8 -

2.3 Decision Making Styles ............................................................................................ - 9 -

2.4 Personal Norms (PNs)............................................................................................. - 11 -

2.5 Cognitive Load Theory ........................................................................................... - 12 -

2.6 Perceived Uncertainty ............................................................................................. - 13 -

2.7 Perceived Financial Risk ......................................................................................... - 14 -

2.8 Perceived Time Pressure ......................................................................................... - 15 -

2.9 What is Mindfulness? ............................................................................................. - 17 -

2.9.1 Different Definitions of Mindfulness............................................................ - 18 -

2.9.2 Mindfulness in Marketing ............................................................................. - 22 -

2.9.3 Trait Mindfulness and Situational Mindfulness ............................................ - 28 -

2.10 The Proposed Model ............................................................................................. - 31 -

vi

2.10.1 The Effect of Trait Mindfulness on Situational Mindfulness .................. - 31 -

2.10.2 Decision-Making Styles and Situational Mindfulness ............................. - 32 -

2.10.3 Personal Norms and Situational Mindfulness .......................................... - 33 -

2.10.4 Perceived Cognitive Overload and Situational Mindfulness ................... - 34 -

2.10.5 Perceived Financial Risk, Uncertainty, and Situational Mindfulness...... - 36 -

2.10.6 Situational Mindfulness and Perceived Time Pressure ............................ - 37 -

2.10.7 Situational Mindfulness and Technology Acceptance Model ................. - 38 -

2.11 Moderating Effects of Situational Inhibitors on Green Technology Adoption .... - 40 -

Chapter 3. Methodology .............................................................................................. - 42 -

3.1 Data Collection and Sampling Procedures……………………………………....- 44 -

3.2 Measurement of the Constructs .............................................................................. - 46 -

3.2.1 Trait Mindfulness ....................................................................................... - 46 -

3.2.2 Personal Norms .......................................................................................... - 47 -

3.2.3 Decision-Making Style (DMS) .................................................................. - 47 -

3.2.4 Perceived Cognitive load ........................................................................... - 49 -

3.2.5 Perceived Uncertainty ................................................................................ - 50 -

3.2.6 Perceived Financial Risk............................................................................ - 50 -

3.2.7 Perceived Time Pressure ............................................................................ - 51 -

3.2.8 Situational Mindfulness ............................................................................. - 52 -

3.2.9 Perceived Usefulness ................................................................................. - 54 -

3.2.10 Perceived Ease of Use .............................................................................. - 54 -

3.2.11 Intention to Use ........................................................................................ - 55 -

vii

3.3 Data Screening, Measurement Reliability, and Validity ........................................ - 56 -

3.4 Data Analysis .......................................................................................................... - 63 -

Chapter 4. Results........................................................................................................ - 65 -

4.1 Influence of Situational Mindfulness on Green Technology Acceptance Process . - 67 -

4.2 Determinants of Situational Mindfulness in Green Technology Acceptance ......... - 68 -

4.3 The Effects of Bounding Factors on Situational Mindfulness ................................ - 69 -

4.4 Mediation Effects .................................................................................................... - 71 -

4.5 Moderation Effects.................................................................................................. - 73 -

4.6 Post-Hoc Analysis ................................................................................................... - 73 -

Chapter 5. Conclusion ................................................................................................. - 78 -

5.1 Major Findings and Discussion .............................................................................. - 78 -

5.2 Limitations and Future Research ............................................................................ - 80 -

References .................................................................................................................... - 82 -

Appendix A: Table 23: Multicollinearity Diagnosis .................................................. - 100 -

Appendix B: Consent Form for Anonymous Data Collection (IRB Protocol # E17-531)- 101 -

Appendix C: Survey Questionnaire ............................................................................ - 102 -

viii

LIST OF TABLES

Table 1: Mindfulness Concept ...................................................................................... - 20 -

Table 2: Select Literature on Mindfulness in Marketing .............................................. - 26 -

Table 3: Sample Demographics ................................................................................... - 45 -

Table 4: Scale for Dispositional Mindfulness ............................................................... - 46 -

Table 5: Scale for Consumer Norm Orientation ........................................................... - 47 -

Table 6: Scale for Decision-Making Styles .................................................................. - 48 -

Table 7: Scale for the Perceived Cognitive Load ......................................................... - 49 -

Table 8: Scale for the Perceived Uncertainty ............................................................... - 50 -

Table 9: Scale for the Perceived Financial Risk ........................................................... - 51 -

Table 10: Scale for the Perceived Time Pressure ......................................................... - 52 -

Table 11: Scale for Situational Mindfulness ................................................................. - 53 -

Table 12: Scale for the Perceived Usefulness ............................................................... - 54 -

Table 13: Scale for the Perceived Ease of Use ............................................................. - 55 -

Table 14: Scale for Intention to Use ............................................................................. - 55 -

Table 15: Descriptive Statistics .................................................................................... - 57 -

Table 16: Item Loadings of Constructs ......................................................................... - 60 -

Table 17:CR, Square Roots of AVE and Correlations of Latent Variables…………..- 62 -

Table 18: Model Assessment ........................................................................................ - 64 -

Table 19: Goodness of Fit Indices ................................................................................ - 67 -

Table 20: Main Effects of the Structural Model .......................................................... - 70 -

Table 21: Mediation Effects.......................................................................................... - 72 -

Table 22: Moderation Effects ....................................................................................... - 74 -

ix

LIST OF FIGURES

Figure 1: Proposed Model of Green Technology Acceptance ...................................... - 43 -

Figure 2: Car Preferences………….............................................................................. - 45 -

Figure 3: Model of Green Technology Acceptance ...................................................... - 66 -

Figure 4: The Moderating Role of Perceived Cognitive Load ..................................... - 75 -

Figure 5: The Moderating Role of Perceived Uncertainty ............................................ - 76 -

Figure 6: The Moderating Role of Perceived Time Pressure ....................................... - 77 -

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CHAPTER 1

INTRODUCTION

In recent few decades, the transport sector is heavily accused of being the primary

cause of greenhouse gas emissions by generating 23% of CO2 emissions globally

(Creutziget al., 2015; Edenhofer et al., 2014). Individual use of conventional vehicles and

fuel consumption are blamed for leading environmental degradation (Nordlund &

Garvill, 2003). The recommended solution is generally to encourage consumers to reduce

personal car use or to lower emissions using environmentally friendly alternative options.

As an alternative, Electric vehicles (EVs) become a significant topic that governments

and pro-environment platforms (e.g., European Union, Environmental Protection

Agency) discuss and generate policies and regulations.

Companies in the automotive industry are categorized as reliability seeking

organizations. These organizations cannot tolerate even minimum reliability gaps since

potential lapses can threat human life and ultimately, devastate the firm’s image. Recent

trends in the industry for electrification and automation of personal vehicles promise to

ensure consumers’ safety and environmental concerns (Jiang, Petrovic, Ayyer, Tolani, &

Husain, 2015; OECD/IEA, 2016). Industry experts forecast 200% increase in electric

vehicles’ (EVs) sales (Brown, 2013; Larson Viafara, Parsons, & Elias, 2014) and

estimate 15% of market share for fully autonomous vehicles (AVs) until 2030 (McKinsey

& Company, 2016). Another estimate suggests that 54% of new car sales will be EVs by

2040 and EVs will account for 34% of total light vehicles on the road by 2040 and this

will save 8 million barrels fuel of transportation every day (Bloomberg New Energy

Finance, 2017).

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By 2050 the majority of light cars are estimated to be alternative fuel vehicles on

the road (Whitmore, 2016). However, the informational environment, consumer

awareness and confidence for EVs and AVs still do not seem ready for this new market

(Larson et al., 2014). Sales growth is still quite below the expectations (around 1% -

OECD/IEA, 2016). Inadequacies in charging infrastructure and battery range for EVs,

charger speed, ambiguities in regulatory legislation, high costs of the cars, and

availability bound consumers’ adoption intention of this technology (OECD/IEA 2016).

The adoption process of complex technological products requires consumers to

put a significant amount of time and effort into information search. However, the

situation with the overload of information or lack of useful information constrains

individuals’ cognitive and processing abilities for better decision making. The literature

on bounded rationality emphasizes the challenges associated with decision environments

of economic actors. Since humans are limited information processors and attention is the

scarce resource in the process, they cannot constantly be rational and have well-organized

skills to compute all probabilities of events, potential costs, and benefits in decisions, and

ultimately, they cannot consistently choose the self-determined best choice of action

(Mallard, 2012; Simon, 1957). Instead, they basically simplify the complexities by

routinizing them as simple solutions, and in this way, the process becomes “decision

adapting” routine (Laureiro-Martinez, 2014). For example, making an accepting/rejecting

decision about a radical or disruptive new product for a mainstream customer is a risky

move, surrounded by a high level of uncertainties. Not every individual has processing

capabilities that are appropriate to overcome the degree of information imperfection

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originating from the complexity of products. Thus, consumers eventually make their

decisions following either the crowds or their inner aspirations about the technology.

Mindfulness, which is mostly, associated with enhanced attention, active

awareness, openness to novelty/new information, and sensitivity to context/different

perspectives may be one of the driving characteristics of a consumer to adopt this new

technology in this newly growing market. In other words, since mindful consumers

continually trying to discover new ways of doing things in life, they would be the

potential customer base of high-tech car companies. Previous research has revealed the

positive impacts of mindfulness on individuals’ learning process (Langer 1989),

psychological and emotional well-being (Brown & Ryan, 2003), cognitive functioning,

(Carson & Langer, 2006; Sedlmeier et al., 2012) ethical decision making (Ruedy &

Schweitzer, 2010), innovativeness and creativity (Lebuda, Zabelina, & Karwowski, 2015;

Swanson & Ramiller, 2004) interpersonal conflict handling (Fiol, Pratt, & O’Connor,

2009), and problem solving capability (Ostafin & Kassman, 2012).

Taking into account abovementioned discussion, this dissertation explored the

question of how informational and cognitive constraints, and time pressure influence

consumers’ perception and attitudes towards adoption process of green technology and

what role consumer mindfulness plays in the process.

More specifically, this dissertation sought to answer the following research

questions utilizing survey methodology and structural equation modeling technique:

-What role does consumer mindfulness play in green technology adoption?

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-How do consumer decision styles and normative orientation influence consumer

situational mindfulness in green technology adoption?

-How do informational uncertainties, cognitive constraints, and time pressure influence

consumer’s situational mindfulness and perception in green technology adoption?

While previous researchers have documented different predictors of technology

acceptance process, this research focused on consumer mindfulness construct as one of

the indicators of cognitive quality and investigated the adoption of green technological

innovations from consumer’s perspective. This study followed the work of Sun and Fang

(2010), who has also documented the effect of mindfulness on the information

technology (IT) adoption process. The study differs insofar as it mapped the decision-

making sequence as a whole by linking decision makers’ dispositional characteristics,

situational characteristics, and decision task. In line with this, this study combined the

theory of bounded rationality, mindfulness literature, and the Technology Acceptance

Model (TAM). This study also differentiated the trait and situational mindfulness from

each other using a new combination of the existing scales and proposed the hindering

impacts of three significant inhibitors on mindful green innovation adoption.

Additionally, this dissertation is the first research that investigates the connection

between personal norm orientation and consumers’ situational mindfulness in a decision

making process. The study also examined the effect of inhibiting factors of situational

mindfulness. The first inhibitor concerned the information uncertainty and financial risk

of the green technology. The second one concerned the cognitive load of the agent. The

third was about time pressure during the decision process. In this study, the effect of the

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consumer mindfulness on EV adoption process examined along with the influences of

information limitations of the product, and the stress which is originating from time

pressure. This study is unique since it connects the bounded rationality, consumer

mindfulness, and technology acceptance literature from three different fields: economics,

marketing, and information technology.

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CHAPTER 2

LITERATURE REVIEW

This research aims to integrate the decision maker’s dispositional characteristics,

the effects of situational constraints on decision maker’s situational characteristics, and

decision mindfulness by sequencing them in the model of green technology acceptance.

Mainly, how consumer’s personality drives her/his situational mindfulness under the

situational constraints and how consumer’s situational mindfulness shapes the

perceptions, attitudes and consumer preferences. By doing this, the study provided a

holistic picture of green technology adoption process.

2.1 Technology Acceptance Model (TAM)

The mechanism explaining how people make decisions is among the most widely

explored research area in Marketing. Adapted from Theory of Reasoned Action (TRA)

(Ajzen & Fishbein, 1980), Technology Acceptance Model (TAM) (Davis, Bagozzi, &

Warsaw, 1989) is among one of the prominent theories that explain the decision process

of new technology adoption. The primary goal of TAM is to reveal the effect of external

factors on internal beliefs and intentions (Legris, Ingham, & Collerette, 2003). Two

factors were found to be significant predictors of personal technology usage: perceived

usefulness (PU) and perceived ease of use (or effort expectancy, PEOU) (Davis, 1989).

The user’s behavioral intention directly determines the actual use of the technology. The

model has been tested in various technology adoption contexts and proved its predictive

power.

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According to Davis (1989), perceived usefulness is “the degree to which a person

believes that using a particular system will enhance his/her performance” (p.320). Almost

all studies that employed TAM tested the effect of PU on BI and found PU’s positive

significant effect on intention to use (i.e., Davis, 1989; Vantakesh & Davis, 2000;

Vankatesh & Bala, 2008; Vantakesh, Morris, Davis, & Davis, 2003). In their extensive

literature study, Vantakesh & Bala (2008) have revealed that PEOU is a significant

predictor of PU.

According to Davis (1989) perceived ease of use is another main determinant that

predicts user’s acceptance intention, and it is defined as “the degree to which a person

believes that using a particular system will be free of effort” (p. 320). Compared with PU,

the impact of PEOU on BI is not consistent (Sun & Zhang 2006). Prior research showed

that gender, age, experience, and culture moderate the impact of PEOU on BI (Schepers

& Wetzels, 2007; Vantakesh et al., 2003).

TAM is a model that bases its assumptions on rational cost-benefit calculation

(Toft, Schuitema, & Thogersen, 2014). When the decision is about an EV adoption,

information searching and processing might increase the cost because of the additional

details about the sustainability of the product. Limitations in either related information or

attentional and processing capacity can bound the user’s perception and influence the

acceptance process consequently. In the following sections, these limitations will be

discussed utilizing the bounded rationality framework.

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2.2 Theory of Bounded Rationality

The view of rationality in Economics theory assumes that economic actors are

inherently rational and make optimal decisions using the information available to them.

Each actor is assumed to have well-organized skills to compute all probabilities of events,

potential costs, and benefits, and consistently to choose the self-determined best choice of

actions (Mallard, 2012). In this scenario, being rational and maximizing outcomes entail

constant information acquisition through a conscious evaluation and deliberate

processing. However, in the real world, humans are barely so. People are limited

information processors, and attention is a valuable but scarce resource (March, 1994).

Moving from the limits of human nature, Herbert Simon challenged this idea of “rational

man” in his Nobel Prize-winning theory, “Bounded Rationality.” He asserted that

“the capacity of the human mind for formulating and solving complex problems is

small compared with the size of the problems whose solution is required for

objectively rational behavior in the real world – or even for a reasonable

approximation to such objective rationality” (Simon, 1957, p.198).

Even though people intend to be rational and goal-oriented, with such cognitive

capacity – not equally sophisticated to the decision complexity and mostly coupled with

the limits in the necessary information, - time and choice sets, their rationality will be

automatically “bounded” (Mallard, 2012; March & Simon, 1958). Therefore, actors

cannot maximize the utility or optimize objective functions in many decisions, and

eventually end up simplifying situations and choosing the “good enough” option

(satisficing) by using heuristics and biases (Conlisk, 1996; Gavetti, Levinthal, & Ocasio,

2007; Simon, 1947, 1955).

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In bounded rationality framework, economists emphasized the limitations in

attention and short-term memory, which restrict the quality and the quantity of

information collected in decision processes (i.e., Feather, 1999; Foss & Weber, 2016;

Shiffrin & Schneider, 1977). In addition to this view, the idea of cognitive economizing

is stressed as it adds to the boundedness in the process of rationally choosing (Foss &

Weber, 2016). As such, people tend to rely more on heuristics and mindless categorizing

instead of systemically processing overwhelming information masses (Fiske & Taylor,

1991; Gigerenzer, 2003). Moreover, when decision environment involves uncertainties,

complexities or tight deadlines, people inevitably employ or may have to employ

automatic or intuitive decision-making strategies outside of the realm of consciousness

(Smith & Shefy, 2014). As a result, these short-cuts and intuitions, in many cases, end up

being the critical reasons of judgment errors and cognitive biases instead of easing the

processing (Dutton & Jackson, 1987; Foss & Weber, 2016; Tversky & Kahneman, 1974).

2.3 Decision Making Styles

In theory, decision-making styles are defined as stable personality traits that shape

individuals’ approaches to decision tasks (Driver 1979; Harren, 1979; Leong, Leong, &

Hoffman, 1987; Leykin & DeRubeis, 2010). It is defined as “patterned, mental, cognitive

orientations towards shopping and purchasing, which consistently dominates the

consumer’s choice” in the consumption behavior literature (Sproles, 1985, p. 79). Just as

Big-Five personality traits (McCrae & Costa 2003), decision-making styles explain

individual differences in the way of sense-making (Scott & Bruce, 1995) and represent

“likelihoods of behavior across situations” not predicting individual’s behavior (Leykin

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& DeRubeis, 2010, p. 506). Decision-making styles denote that people make product

choices under the influence of a particular decision-making style and this style, over time,

becomes a consistent and major force in their decision-making (Sproles, 1985).

In line with this reasoning, a number of different decision styles were identified,

and at least a dozen distinct measures were published in the decision literature (i.e.,

Harren, 1979; Schwartz et al., 2002; Sproles, 1985; Turner, Rim, Betz, & Nygren, 2012).

Harren (1979) developed a typology of rationalizers, and he suggested that the style of

rational decision-making delineates the style of objective deliberation and rational

decision makers are who make decisions deliberately. The individual who adopted

rational decision-making style seeks information until he/she is identifying the best

choice that will maximize his/her benefit and he/she has “the ability to recognize the

consequences of earlier decisions for later decisions” (Harren, 1979, p.125).

On the other hand, drawing from Simon’s critique of “economic man,” Schwartz

et al. (2002) stated the idea of decision-making with complete information as unrealistic.

They argued that people generally aim at satisficing instead of rationalizing. They

defined satisficers as individuals who search until identifying the “good enough” option

and are unwilling to scour different alternatives for the best choice (Schwartz et al.,

2002). Satisficing is a default style of decision-making for consumers who adopt fewer

standards in the search process (Dalal, Diab, Zhu, & Hwang, 2015). High scorers on

satisficing are willing to accept less perfect option instead of spending too much time

searching (Mitchell & Walsh, 2004).

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2.4 Personal Norms (PNs)

One of the key predictors of pro-environmental behaviors is personal norms

(Norm Activation Theory) (Schwartz, 1968, 1977, Stern, Dietz, & Kalof, 1993). The

concept of personal norms (PNs) is defined as a person’s inner feelings of moral

obligation to act in a pro-environmental and prosocial way, and refrain from actions that

have undesirable consequences to others (Schwartz, 1968, 1977; Schwartz & Howard,

1981). Schwartz and Howard (1981) consider PNs as an antecedent to altruistic and pro-

environmental behavior, and they argue that when people become more aware of adverse

consequences of their actions, they feel more responsible for it and morally obliged to

refrain from specific actions. Similarly, Ajzen (1991) posits that as a psychological

variable, positive attitude towards acting in a certain way strengthens the intention to

perform that particular behavior more than demographic variables do. Studies on

environmental activism found that individuals who are part of or just a supporter of a

social/pro-environmental movement are more willing to take further actions and to make

sacrifices for supporting the pro-environmental movements such as purchasing from

environmental-friendly companies, voting, signing petitions, and paying higher prices

(Leary, Vann, Mittelstaedt, Murphy, & Sherry, 2014; Stern, Dietz, Abel, Guagnano, &

Kalof, 1999; Stern, 2000; Wiidegren, 1998).

Consistent with abovementioned views, Guagnano, Stern, & Dietz (1995), Steg,

Dreijerink, & Abrahamse (2005) and Tanner (1999) have found positive effects of

awareness of undesirable consequences and aspiration of responsibilities on green

consumers’ acceptance of energy policies, recycling, and decreased car usage behaviors.

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Furthermore, PNs have been found to be an influential predictor of willingness to pay

higher prices for organic foods (Wiidegren, 1998), low involvement, and non-durable

consumer products (Minton & Rose, 1997), alternative fuel vehicles adoption (Jannson,

Marell, & Nordlund, 2011), and acceptance of hydrogen fuel stations (Hujits, Molin, &

Van Wee, 2014).

2.5 Cognitive Load Theory

As suggested by rational choice theory, calculating all the costs and benefits in

high technology adoption process requires individuals to gather extensive and up-to-date

information and vivid cognitive resources in working memory. Information seekers need

to spend a significant amount of time, cognitive effort, and resources to find and

comprehend relevant information which will automatically increase the feeling of mental

busyness. This mental busyness is named as cognitive load and defined as the perceived

intensity of mental effort being used in working memory (Paas, 1992; Sweller, 1988).

Like bounded rationality, cognitive load theory addresses the limits of the short-term

memory and attention in the knowledge acquisition (Allen, Edwards, Snyder, Makinson,

& Hamby, 2014; Deck & Jahedi, 2015; Paas, 1992; Paas, Tuoveinen, Tabbers, & Van

Gerven, 2003; Sweller, 1988; Sweller & Chandler, 1991).

While few studies revealed its enhancing effects on trust building under

uncertainty (Zhou, Arshad, Luo, & Chen, 2015), reduced risk evaluation (Kruis, 2017),

and better normative decision behaviors (Drolet & Luce, 2004), the concept of cognitive

load is in general, notoriously famous for its negative impacts on cognitive processing. A

number of studies found that with increased intensity of mental load, individuals tend to

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rely more on automatic, non-conscious processes to make their decisions (e.g., Gilbert

Pelham, &Krull, 1988; Sivaramakrishnan & Manchanda 2003; Allen et al., 2014). Kunda

(1999) suggested that mindless or non-conscious processing arises when cognitive

overload exists; because cognitive overload not only inhibits the effortful process of

information refinement but also lowers the strength of resistance to mindless processing.

In a recent study, Deck & Jahedi (2015) revealed that individuals with a higher level of

cognitive load make poorer decisions, prefer to avoid risky choices and are more prone to

impatience and making mistakes. Similarly, Mukherjee (2010) found that people with

heavy cognitive load reduce risk-seeking behaviors.

Moreover, higher cognitive load, under the moderate effect of high uncertainty,

inhibits the trust-building process (Zhou et al., 2015). Finally, the cognitive load has been

suggested to be an inhibiting factor of consumer satisfaction (Hu, Hu, & Fang, 2017; Yan

Chang, Chou, & Tang, 2015). Thus, it is possible to suggest that the mental capacity to

collect and process relevant information is crucial factors that provide the basis for

making mindful choices.

2.6 Perceived Uncertainty

One of the critical constraints of conscious information processing is the

uncertainty originating from a lack of adequate knowledge or information overload or a

feeling of unease because of environmental ‘noise’ (e.g., unrelated messages) (Case,

2010; Wilson et al., 2000). Uncertainty is defined as the “lack of clarity or consistency in

reality” (March, 1994, p. 553). Studies often employed the constructs of “uncertainty”

and “ambiguity” interchangeable. However, uncertainty can be distinguished from

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ambiguity because people can get rid of informational uncertainty through further

analyses and with gathering more information (Case, 2010).

Schwartz (2000) views uncertainty as an aspect of information processing since

the assumption of complete information is impossible. Uncertainties in the decision-

making process lead consumers to search more information to ease the discomfort caused

by obscurity; therefore, it generally increases the procedural rationality but is negatively

related to the intention to use of products (e.g., Littler & Melanthiou, 2006; Sun & Fang,

2010).

In environmentally friendly technologies, information search becomes more

important as the technology involves both technically complex features and details about

environmental footprint and production cycle (Cerri, Testa, & Rizzi, 2018; McDonald &

Oates, 2006). In a decision situation about the adoption of EVs, acquiring relevant

information requires better cognitive engagement with the search process (Pickett-Baker

& Ozaki, 2008) and lack of information will easily entail user avoidance or lead them to

conventional ways for a solution.

2.7 Perceived Financial Risk

Every new technology, if they require a new consumption pattern and behavior,

involves a certain level of risk and even sometimes speculations because of the lack of

information (Littler & Malenthiou, 2006). Perceived financial risk as one of the sub-

dimensions of perceived risk (Biswas, Biswas, & Das, 2006; Grewal, Gotlieb, &

Marmortein, 1994; Jacoby & Kaplan, 1972) is defined as a subjective belief about the

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potential financial loss because of purchase (Kim, Ferrin, & Rao, 2008). Kahneman &

Tversky (1979) suggested that consumers calculate the potential losses and gains before

making a decision, and they are more cautious about losses if the decision involves risk

than they are about potential gains. Perceived risk in general, was found to be related to

increased anxiety which negatively impacted information processing (Taylor, 1974).

Supporting this argument, Lu, Hsu, & Hsu (2005) found the adverse effect of perceived

risk on consumer’s intention to use online applications. These findings imply the

proximity of financial risk to information processing. Therefore, the effect of financial

risk on mindful processing is included in the framework of this research.

2.8 Perceived Time Pressure

Perceived time pressure is defined as “the lack of time a person perceives there to

be available for doing what needs to be done in his/her life” (Bruner II, James, Hensel,

2001, p. 632; Mittal, 1994). Judgments and decisions are found to be significantly

affected by time restrictions (Weiger & Spaniol, 2015). Time limitation limits the

opportunity of learning about various choices and deteriorates information processing

performance in general. Literature suggests that perceived time constraints entail less

information processing and learning (Maule & Edland, 1997; Wright & Kriewall 1980),

worsened emotional well-being (Garling, Gamble, & Fors, 2016), and more biased

judgments (Wright, 1974). In an emergency (e.g., fire) situation, time pressure was found

to reduce spatial awareness since it limited utilization of environmental cues (Ozel,

2001).

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However, whether time pressure is detrimental or beneficial to task outcomes

depends on the amount of time pressure (i.e., Ackerman & Gross, 2003). While a

moderate amount of time pressure increases individuals’ motivation to the task and more

productive time usage, and thereby fosters learning, too much pressure lead them to more

judgment errors and deteriorate cognitive control (Ackerman & Gross, 2003; Gross 1994;

Isenberg 1981; Rice & Trafimow, 2012). Thus, the literature has not a determined

explanation, and there is more room for additional explanations in the model of time

pressure.

While defining problems related to the rational man, Simon mentions cognitive,

informational, and time-related restrictions in decision environment and he puts a special

emphasis on the attentional capacity that helps to focus, generate alternatives and explore

the environmental facts (Jones, 1999). He addresses the issues in individuals’ attentional

limits as side effects of the abundance of information created in a modern world that

induces suffering from a poverty of attention (Simon, 1962). Because the investigations

of information consume its recipients’ attention first (Simon, 1962), decision-makers

often find themselves overwhelmed in the middle of uncertainties. In an optimization

process under constraints such as ambiguous and complex processes, more information

may entail more uncertainty and complexity by causing more controversy, disagreement,

and conflicts in human minds (Simon, 1947). Consequently, decision-makers that have

the same information end up with different conclusions: the satisficing or rationalizing.

This discussion connects us to Langer’s mindfulness concept which emphasizes the

attentional quality.

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2.9 What is Mindfulness?

Mindfulness was initially studied as an aspect of human functioning and

behaviors in social psychology (Langer, 1989a; Ryle, 1949). It has been defined as “a

state of conscious awareness, characterized by active information processing and drawing

distinctions that leave individual open to novelty and sensitivity to both context and

perspective” (Langer, 1992, p.291). Langer’s mindfulness approach involves five main

characterizations (1) openness to novelty, (2) active information processing, (3)

awareness of various perspectives, (4) sensitivity to different contexts, and (5) being in

the present (Langer, 1997). Contrary to mindless typology which is described as seeking

stability, trying to control things still, and not engaging with changing perspectives and

environmental contexts (Beard, 2014), mindful individuals are aware that everything

around them is always changing and there is more than one way to find a solution. This

understanding enables individuals to accept and engage with the changing contexts, to try

different ways of doing things, ultimately to perform better in life and workplace and to

make quality decisions. In line with this reasoning, prior research associated individual

mindfulness with attention, awareness, clarity, vividness, being in the present, openness,

acceptance, nonjudgement, observation, decentering and curiosity while mindlessness has

been linked to automaticity, routine, path dependence, and inertia (i.e., Bishop et al.,

2004; Brown & Ryan, 2003, Brown, Ryan, & Creswell, 2007; Langer, 1989a; Weick &

Sutcliffe, 2006).

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2.9.1 Different Definitions of Mindfulness

A review of mindfulness literature reveals various definitions of the concept

(Table 1). Mindfulness in Buddhist philosophy has been defined as “paying attention in a

particular way, on purpose, in the present moment, and non-judgmentally” (Kabat-Zinn,

1994, p.4). It is a process of stepping back from experience in mind, and monitoring

thoughts, feelings, or sensations, but not elaborating them since any judgments (i.e.,

regrets about past and worries about future) may distract one’s attention and present

moment orientation. This decentering, non-judgmental observation process will

ultimately help individuals to realize and identify the experiences correctly and not over-

react to them automatically as Giluk (2010) describes it as “a thought is a thought, but

you are not your thought” (p. 12).

Drawing from the Eastern perspective, Langer, in the Western literature,

described the term as “actively noticing new things” which focuses the conscious on the

present moment experience (Beard, 2014; Tippett, 2015). Krieger (2005) underlined the

aspect of information processing by defining mindfulness as “a psychological state in

which individuals engage in vivid information processing while performing their current

tasks such that they are actively analyzing, categorizing, and making distinctions in data”

(p. 127). Taking one step further, Luttrell, Brinol, & Petty, (2014) interpreted

mindfulness from cognitive processing perspective and defined it as

“bringing one’s full resources to a cognitive task by using multiple perspectives

and attending to context, which creates novel ways to consider the relevant

information” and mindlessness as “a way of approaching the same tasks with

reduced attention and reliance on previously developed means of interpreting

information” (p. 258).

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Another Western view of mindfulness is clustered around Brown and Ryan’s

research stream. They defined mindfulness as “the quality of human consciousness” that

encompasses “enhanced attention to and awareness of current experience or present

reality” (Brown & Ryan, 2003, p. 822). In this approach, it is accepted that the conscious

is the place that human brain processes internal and external stimuli (Ortinski & Meador,

2004) and mindfulness as a cognitive ability or a personal trait or a cognitive style

(Sternberg, 2000) encompasses these two crucial activities consciously and focus them on

the current moment experiences. Conscious awareness occurs when internal and external

stimuli are perceived and consciously acted on (Orstinski, and Meador, 2004). Conscious

attention reflects the vivid, cognitive focalization and concentration of consciousness that

withdraw a person from other things (Wu, 2011). According to this view, everybody has

the capacity of attending to and being aware of current experiences, but the degree of

mindfulness differs for each person.

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Table 1: Mindfulness Concept

Langer (1992, pp.

291)

“A state of conscious awareness characterized by active

distinction drawing that leaves the individual open to novelty

and sensitive to both context and perspective.”

Brown & Ryan

(2003, pp.822)

“An enhanced attention to and awareness of current experience

or present reality.”

Jon Kabat-Zinn

(1994, pp. 4)

“Paying attention in a particular way, on purpose, in the present

moment, and non-judgmentally.”

Luttrell et al., 2014

pp. 258

“Bringing one’s full resources to a cognitive task by using

multiple perspectives and attending to the context which creates

novel ways to consider the relevant information.”

Krieger (2005, pp.

127)

“A psychological state in which individuals engage in active

information processing while performing their current tasks

such that they are actively analyzing, categorizing, and making

distinctions in data.”

Rosenberg (2004,

pp. 108)

“Awareness and the ability to see the happenings of one’s inner

and outer worlds.”

Lau et al., 2006 pp.

1447

“A mode, or state-like quality, that is maintained only when

attention to experience is intentionally cultivated with an open,

nonjudgmental orientation to experience.”

Dane (2011, pp.

997)

“A state of consciousness in which attention is focused on

present-moment phenomena occurring both externally and

internally.”

Bishop et al. (2004,

pp.232)

“A kind of nonelaborative, nonjudgmental, present-centered

awareness in which each thought, feeling, or sensation that

arises in the attentional field is acknowledged and accepted as it

is.”

Giluk (2010, pp. 1)

“A quality of consciousness that consists of purposeful attention

to and awareness of the present moment approached with an

attitude of openness, acceptance, and nonjudgment.”

Gunaratana (2002,

pp. 142) “An alert participation in the ongoing process of living.”

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Weick & Sutcliffe

(2001, pp. 42)

Organizational Mindfulness: “the combination of ongoing

scrutiny of existing expectations, continuous refinement and

differentiation of expectations based on newer experiences,

willingness and capability to invent new expectations that make

sense of unprecedented events, a more nuanced appreciation of

context and ways to deal with it, and identification of new

dimensions of context that improve foresight and current

functioning”

Levinthal & Rerup

(2006, pp.505)

“High sensitivity of perception and high flexibility of behavior

to respond to diverse, changing stimuli.”

Holas & Jankowski

(2013, pp. 234)

“A unique state of meta-awareness that is evoked and

maintained by cooperation between some of the executive

functions and attentional processes, a state that is marked by an

open and accepting stance toward the present moment

experience.”

Wallace (2005, pp.

226)

“The nonforgetfulness of the mind with respect to a familiar

object, having the function of nondistraction”

Ndubisi (2014, pp.

238)

“A mode of consciousness that commonly signifies the

presence of mind.”

Sun & Fang (2010,

pp. 4)

“The vigilant state of mind of a person that allows him/her to

examine the technology being considered more

comprehensively and context specifically.”

Roberts, et al. (2007,

pp.1)

“Continuous refinement of expectations based on new

experiences, appreciation of the subtleties of context, and

identification of novel aspects of context.”

Bahl et al. (2016, pp.

1)

Mindful consumption: "An ongoing practice of bringing

attention, with acceptance, to inner and outer stimuli, and the

effects of this practice on the consumption process."

Shapiro, Jazaieri, &

Goldin (2012, p.

505)

“Awareness that arises through intentionally paying attention in

an open, kind, and discerning way.”

Van Doesum, Van

Lange, & Van

Lange, (2013, p. 87)

“Social mindfulness is minding the needs and interests of

others in a way that honors the idea that most people like to

choose for themselves.”

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2.9.2 Mindfulness in Marketing

Almost every study on mindfulness in the field of Marketing agrees that mindless

consumption lowers individual, societal, and environmental well-being (e.g., Bahl et al.,

2016; Kidwell, Hasford, & Hardesty, 2015; Sheth, Sethia & Srinivas, 2011; Rosenberg,

2004, Uslay & Erdogan, 2014). Drawing insight from Langer’s perspective, Rosenberg

(2004) introduced mindfulness concept into marketing for the first time as a possible

antidote of consumerism. She identified corporations as significant instigators of

excessive consumption and suggested that consumers who are mindful assess items more

deliberately, be more aware of what (and how much) they need and be attentive to

alternatives; therefore, become less automatic or impulsive and less prone to exploitations

of corporations and advertisers in the purchase process (Rosenberg, 2004). Thus,

consumer mindfulness may remedy the nonconscious psychological processes and the

endemic of the need for fulfillment by increasing awareness, enhancing people’s

interrelatedness, and ultimately increasing individual well-being (Rosenberg, 2004).

Similar to Rosenberg, Sheth, Sethia & Srinivas (2011) extended the idea of

mindfulness in marketing suggesting the model of “mindful consumption” which

highlights sustainable consumption and care for the triple bottom line

(people/planet/profit). They advocated a customer-centric approach to sustainability and a

focus on marketing’s potential to promote mindful consumption as a way to embrace the

“mindset of caring for self, for the community, and for nature” by tempering “acquisitive,

repetitive and aspirational consumption” (p.21). In another study, Ericson, Kjonstad, and

Barstad (2014) underlined the effect of mindfulness on subjective well-being and

- 23 -

individual well-being’s positive impact on empathy, compassion, and less hedonic

consumption. Based on this reasoning, they proposed that promoting mindfulness

practices can contribute to sustainable consumption and healthier ways of living.

Furthermore, Bahl et al. (2016) drew attention to consumers’ vulnerability to unconscious

behaviors, impulses, compulsions, addictions, and decision biases and evaluated

mindfulness as an empowering tool for policymakers to help consumers make more

mindful choices in the marketplace.

In general, consumer mindfulness is considered to play a critical role in

preventing consumers from engaging in automatic thoughts, habits and unhealthy

behaviors, and fostering behavioral regulation, self-control, societal and environmental

well-being (Bayraktar, Uslay, & Ndubisi, 2015; Brown & Ryan, 2003; Ryan & Deci,

2000; Friese, Messner, & Schaffner, 2012). In this line, Dong and Brunel (2006) were the

first researchers to reveal the impact of mindfulness on persuasion and attitude formation

in an experimental study. They found that mindfulness impacts consumers’ message

evaluation; such that high mindful consumers prefer central route for processing while

less mindful consumers prefer peripheral route. They also differentiated the term from

need-for-cognition (NFC) by characterizing mindfulness as “high level of conscious

awareness, sensitivity to context change, openness to new information, ability to create

new categories in cognition, and awareness of multiple perspectives in problem-solving”

(p.277) which NFC does not inherently contain in its characterization.

Williams and Grisham (2012) revealed the negative relations between

dispositional mindfulness and compulsive buying. Additionally, Van De Veer et al.

- 24 -

(2016) found that when mindful attention is focused on the body, consumers tend to be

more receptive to the satiety cues and avoid mindless eating. They also showed the

positive association between mindful attention and a more constant personal body weight

(Van De Veer et al., 2016). Furthermore, in an empirical study, Schramm and Hu (2014)

examined the moderating role of mindfulness consumers’ product knowledge and

information processing style and found that mindful consumers will put more processing

effort to evaluate every attribute of the new product when they have more knowledge

instead of utilizing less effortful category-based processing. Finally, in their conceptual

work, Bayraktar et al. (2015) proposed that mindfulness affect consumers’ cue

evaluation. According to this study, intrinsic product cues have a stronger effect on

mindful consumers’ quality perceptions and product evaluations, and marketing

communications have less effect on their product evaluation (Bayraktar et al., 2015).

From the managerial perspective, mindfulness has been evaluated as a potential

transformative element to reshape marketing mindset. In strategy creation, mindfulness

can empower marketers in redesigning products to reduce the repetitive buying (e.g.,

more durable products), price to regulate the excessive demand (e.g., decreasing gasoline

consumption), place rearrangement for more convenient and shared use, and promotion

to redesign advertisement and communication channels to reduce the waste (Sheth et al.,

2011). Based on the model of mindful consumption, Malhotra et al. (2012) proposed that

when embraced with a mindful approach, marketing efforts for market and quality

orientations synergistically can create better value co-creation and mindful consumption.

Gordon and King-Schaller (2014) focused on the role of mindfulness in the opportunity

evaluation stage of the entrepreneurial process and proposed that mindfulness can help

- 25 -

entrepreneurs to identify novel markets, new product use, be aware of and process the

relevant information about unique market changes, and seek more information changing

circumstances in the market analysis process. Moving from this perspective, Uslay and

Erdogan (2014) proposed that conventional marketing needs a revision with a mindful

entrepreneurial mindset to overcome the ineffectiveness of its strategies. Taking one step

further, they suggested that with an entrepreneurial spirit, mindful marketing can mediate

the relationship between mindful consumption and production and entail much higher

societal, financial, and environmental outcomes than traditional marketing does.

In sum, prior research mostly tried to establish a conceptual framework of

mindfulness in marketing and only a few empirical research investigated its effect on

food consumption and general evaluation style, but no study to this date examined the

role of consumer mindfulness in mindful consumption and a mindful production

acceptance process. By examining the effect of consumer mindfulness on green

technology (EVs) acceptance process in a decision-making setting, this dissertation fills

this gap and addresses the previously raised research questions (e.g., Uslay & Erdogan,

2014).

- 26 -

Table 2: Select Literature on Mindfulness in Marketing

Study Scope Area Major Findings Source

Rosenberg

(2004) Conceptual

Mindful

consumption

Mindfulness may remedy the problems of

consumer automaticity and need for fulfillment by

increasing awareness and deliberate processing.

Book chapter

Dong & Brunel

(2006) Experiment Consumer Behavior

Mindful consumers prefer central routes in every

stage of message processing even when they

cannot process the information. Low mindful

consumers prefer peripheral routes.

Advances in

Consumer Research

Sheth et al.

(2011) Conceptual

Mindful marketing

and Mindful

consumption

Marketing can transform the consumption patterns

and lower the excess by implementing the

customer-centric approach to improve

sustainability.

J. of Acad. Mark.

Sci.

Scharf &

Cunha (2011) Survey

Mindful purchase

decision,

sustainability

Consumers still do not recognize the concept of

mindful consumption. Book chapter

Roger (2011) Survey Brand managers’

mindfulness

Mindfulness positively moderated the links

between experiences of brand management, sector,

non-marketing, financial management and

occupational self-efficacy.

Journal of Brand

Management

Malhotra, Lee,

& Uslay (2012) Conceptual

Mindful marketing,

market and quality

orientations

When embraced with a mindful marketing

strategy, efforts of market and quality orientations

synergistically can create better value-creation and

mindful consumption.

International Journal

of Quality &

Reliability

Management

Ndubisi (2012) Survey

Mindfulness-based

marketing strategy

in SMEs

Mindfulness-based customer orientation,

competence, and communication are positively

related to customer satisfaction and relationship

quality in small healthcare firms.

International Journal

of Quality &

Reliability

Management

Williams &

Grisham, 2012 Experiment Compulsive buying

Compulsive buying is associated with reduced

dispositional mindfulness and impulsivity. Cogn. Ther. Res.

- 27 -

Liozu et al.

(2012) Interviews Mindful pricing

Mindful learning environment helps to create and

internalize value-based pricing.

Journal of Strategic

Marketing

Uslay &

Erdogan (2014) Conceptual

Mindful

Entrepreneurial

Marketing

Entrepreneurial marketing with a mindful

approach can entail mindful consumption and

production, and better financial performance.

Journal of Research

in Marketing and

Entrepreneurship

Ndubisi (2014) Survey

Consumer Behavior

and Service

marketing

High mindful consumers and low mindful

consumers significantly differ from each other in

relationship quality (trust, commitment,

satisfaction) and outcomes (attitudinal and

behavioral loyalty, switching restraint)

Psychology and

Marketing

Schramm &

Hu, (2014) Survey

New product

evaluation

Mindfulness moderates the relationship between

knowledge about product category and choosing a

processing style.

Atlantic Marketing

Journal

Bayraktar,

Uslay, &

Ndubisi (2015)

Conceptual

Decision-making

process, Cue

evaluation

For mindful consumers, intrinsic product cues

have a stronger effect on their quality perceptions

and product evaluations, and marketing

communications have less effect on their product

evaluation.

Int. J. of Business

Environment

Kidwell,

Hasford, &

Hardesty,

(2015)

Experiment Mindful eating Emotional ability helps to reduce mindless eating

and enhance self-control over personal weight.

Journal of Marketing

Research

Van De Veer,

Van Herpen, &

Van Trijp

(2016)

Experiment Food consumption

If mindful attention is focused on the body, it

increases awareness of hunger cues and avoid

mindless eating.

Journal of Consumer

Research

Bahl et al.

(2016) Conceptual

Mindful

consumption

The study presented a mindful consumption

definition and with three dimensions and presented

its potential transformative impacts on consumer,

societal, and environmental well-being.

Journal of Public

Policy & Marketing

- 28 -

Langer’s perspective on individual mindfulness mainly focused on distinction

drawing and information processing as the essential qualities then it was later classified

as situation-specific mindfulness. On the other hand, Brown and Ryan (2003) highlighted

attentional quality and receptivity of awareness as the core aspects of trait-like

mindfulness. Some of the prior research examined individual mindfulness as a

personality trait or trait-like variable (e.g., Brown & Ryan, 2003; Ndubisi, 2014; Rau &

Williams, 2015; Williams & Grisham, 2012) while some others investigated mindfulness

as a situation-specific or a training-induced characteristic (e.g., Dong & Brunel, 2006;

Kidwell et al., 2015; Lau et al., 2006; Schramm & Hu, 2014). For this dissertation, I will

use Langer’s (1989b) cognitive approach (information processing) as the base of my

situational mindfulness construct and Brown and Ryan’s (2003) approach as the base of

trait (dispositional) mindfulness construct in the conceptual model. The following section

presents more detail about trait and situational mindfulness.

2.9.3 Trait Mindfulness and Situational Mindfulness

Niemiec et al. (2010) defined trait mindfulness as “a disposition characterized by

receptive attention to present experience” (p.344). Trait mindfulness is a relatively

permanent conscious quality that reflects individual differences in the capability of

focusing and attentional quality (Rau & Williams, 2016; Good et al., 2015). According to

Niemiec et al. (2010), trait mindfulness is a mode of conscious processing in which

attention is notified and focused by awareness to the present experience, and it might

drive situation-specific mindfulness through its effects on thoughts, feelings, motivations,

and actions. Trait mindfulness was found to be positively driven by personality traits such

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as conscientiousness, openness to experience, extraversion, and organizational culture

and negatively associated with neuroticism trait (Goswami, Teo, & Chan, 2009; Rau &

Williams, 2016).

Situational mindfulness, on the other hand, is a situation-specific quality of the

mind, which is maintained only when attention to experience is intentionally oriented in

the present which results in active information processing, openness to novelty, curiosity,

and sensitivity to context and multiple perspectives (Bishop et al., 2004; Langer, 1997;

Lau et al., 2006, p. 1447).

Situational mindfulness implies conscious information processing (Teasdale,

1999). It entails a process that messages are vividly processed, and informational cues are

actively scrutinized in the conscious. Hence, mindful processing enhances conscious

processing instead of automaticity, and mindful people own their decision not just follow

the bandwagon (Fiol & O’Connor, 2003).

Situational mindfulness means denying the stability. It requires a certain level of

curiosity and leads individuals to explore and engage ever-changing environmental

contexts around them. Curiosity as an inner essence motivates individuals to be open to

novel ideas, be flexible to different perspectives and create new ways of doing things

(Roberts, Thatcher, Klein, 2006, p.6). Adaptation of different perspectives leaves

individuals open to different alternatives of products, features, and experiences.

However, compared to the trait mindfulness, situational mindfulness is easily

distractible and consists of an individual’s momentary receptive reactions to external and

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internal stimuli. Such a state is not a general tendency or a personal disposition so can be

interrupted by at any time. Personality traits, personal norms, situation-specific

uncertainties, complexities, and pressures can significantly bound or foster the valence of

consumers’ situational mindfulness in the decision-making process. In the following

section, some of the inhibiting and enhancing factors are discussed in more details, and

the research hypotheses were presented.

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2.10 THE PROPOSED MODEL

2.10.1 The Effect of Trait Mindfulness on Situational Mindfulness

Our first contact with reality happens through awareness that works like

background ‘radar’ in consciousness (Brown et al., 2003). Awareness is continuously

monitoring visual objects, events, trains of thoughts, emotions, any inner or outer

changes, and any physical, kinesthetic senses or activities of the mind and if the stimuli

are strong enough, attention spotlights the selected dimension of the reality in our

consciousness (Brown et al., 2007). In this sense, awareness and attention are closely

related, “such that attention continually pulls “figures” out of the “ground” of awareness,

holding them focally for varying lengths of time” (Brown & Ryan, 2003, p.822).

Mindfulness arises when present-centered attention and awareness are both intertwined,

and it enables consumers to respond to an experience effectively not to act reactively

based on automatic habits. When visualized as a consumer disposition, the characteristics

of present-centered awareness and attention constitute a more permanent and stable

mindfulness trait.

Personality literature suggests that traits often predict human behaviors,

motivations, and cognitions (Goswami et al., 2009; Ryckman, 2004). Trait mindfulness

has been found to be significantly associated with working memory capacity (Ruocco &

Direkoglu, 2013) and better problem solving (Ostafin & Kassman, 2012). Kiken,

Garland, Bluth, & Palsson, (2015) found that repeated meditation practices enhance

situational mindfulness and improve dispositional mindfulness over time.

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While Kiken et al.’s (2015) study identified the impact of situational mindfulness

on trait mindfulness; the predictive power of trait mindfulness on situational mindfulness

is not a question that has been explored yet. Based on this discussion the following

hypothesis is developed.

H1: Trait mindfulness has a positive effect on consumer’s situational mindfulness

in green technology adoption process.

2.10.2 Decision-Making Styles and Situational Mindfulness

Rational choice theory failed to explain what the origin of preferences are

(Schwartz 2000). This theory posits that consumers maximize their utility by considering

their preferences but what explains these preferences is not clearly discovered. For

example, there are too many alternatives one can do with $50 such as buying some food

or going to a movie. Knowing all the possible alternatives and rationally choosing is

utterly unrealistic because of the incomplete information (Schwartz 2000). Taking one

step further, Sproles (1985) suggested that people employ particular decision styles to

make preferences, and these satisficer or rational approaches are stable, trait-like decision

patterns. They are assumed to be shaping and predicting consumers’ decisions. Decision

styles often classified into two groups: rationalizers who search only for the best solution,

and satisficers who accept a “good enough,” option are following a relatively consistent

style while making preferences.

Harren (1979) delineated a rational decision maker as:

“The person has a moderate to high level of self-esteem which is based upon

accurate incorporation of the interpersonal evaluations from others. The self-

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concept system is flexible and open to new experiences. The person’s self-concept

is highly differentiated (i.e., the individual has a clear awareness of her or his

interests, values, skills, and other self-attributed traits and is confident in this self-

knowledge). The person takes responsibility for decision making and relies

primarily on a rational style of decision making. Finally, this person has mature

interpersonal relationships and has developed a sense of purpose” (p.128).

Satisficing as decision style has been found to be associated with lessened

problem solving efficiency in career-related tasks (Blustein & Phillips, 1990; Mau, 2000),

while rational style tends to be positively related to information gathering (Jepsen, 1974;

Mau, 2000) and problem-solving appraisal (Phillips, Pazienza, & Ferrin, 1984). Because

rationalizers are open to new experiences, well-aware of their situational contexts

(Harren, 1979) and deliberate processors, one can expect that rational decision style will

have a close connection with situational mindfulness while satisficing is not or negatively

associated. Thus, the following hypotheses are developed to test these relations:

H2: Rational decision-making style has a positive effect on situational

mindfulness in green technology adoption process.

H3: Satisficing in decision-making has an adverse effect on situational

mindfulness in green technology adoption process.

2.10.3 Personal Norms and Situational Mindfulness

Many studies considered PNs as an attitude changer that fortifies the consumption

intention in the context of pro-environmental products. Using the Theory of Planned

Behavior (TPB), Harland, Staats, & Wilke (1999) found that PNs improves the

exploratory power of the behavioral intention when it is added into the model. Onel

(2017) extended this model by presenting the positive contribution of PNs in the pro-

- 34 -

environmental products adoption process. Toft et al. (2014) introduced the concept into

the TAM and revealed the significant relationship between PNs and acceptance of Smart

Grid technology. Finally, Yoon (2018) framed the Green IT adoption model including

consumers’ normative perspective which was represented by PNs.

According to Ruedy and Schweitzer (2010), lack of awareness is the major reason

for many unethical behaviors. Similarly, norm activation theory (Schwartz, 1968; 1977)

argue that when people become more aware of adverse consequences of their actions,

they feel more responsible for the damage and morally obliged to refrain from specific

actions. Pro-environmentalism encompasses awareness of context and consequences and

involves sensitivity and attentiveness to different perspectives in green technology

framework (Stern et al., 1993; Wiidegren, 1998). Thus, one could expect that pro-

environment consumers are more alert when it is about green behaviors and green

product adoption. To this date, no study has explored the relationship between consumer

mindfulness and PNs. Thus, the following hypothesis is developed to test the

relationship.

H4: Favorable norm orientation regarding green technology has a positive effect

on situational mindfulness in green technology adoption process.

2.10.4 Perceived Cognitive Load and Situational Mindfulness

Even though mindfulness is all about the quality of consciousness, people’s

evaluations and views tend to be distracted by situational factors. Cognitive load can be

considered as one of these factors (March, 1994; Sweller, 1988). Quality of attention and

- 35 -

cognition would suffer when there are too many signals to be perceived and when

consumers continiously try to have well-saturated associations, judgments, beliefs, and

feelings in their minds. Cognitive overload or heavy cognitive load refers to the feeling of

overwhelming when a consumer exposed to the larger amount of information than the

amount that his/her working memory can process comfortably (Sweller, 1988). It has

been argued that a heavy cognitive load can cause errors and induce mindless

stereotyping (Biernat, Kobrynowicz, & Weber, 2003; Paas, 1992). If individuals have a

high number of stimuli to scrutinize and if some of them have a higher order of

importance or ambiguities, mindful attention tends to suffer (Weick & Sutcliffe, 2006).

For example, in a case of a car shopping, consumers may perform worse, because all

attention would possibly be weighted to that one particular aspect or feature (e.g., price,

brand) at that time.

Many research addressed cognitive load’s hindering impact on usage of rational

or deliberative processing (i.e., Allen et al., 2014; Deck & Jahedi, 2015; Drolet & Luce,

2004; Roch, Allison, & Dent, 2000; Shulze & Newell, 2016). Because it naturally

depletes mental resources, it negatively impacts motivation to process more information

and makes people less alert (Drolet & Luce, 2004; Kahneman, 2003; Kruis, 2017) and

risk-averse (Gerhardt, Biele, Heekeren, & Uhlig, 2016). Thus, when consumers feel

cognitively overload, one can expect that their situational mindfulness will be adversely

affected and they become less attentive to learning about the product and less aware of

product-related details during an adoption decision. Based on this discussion, the

following hypotheses are developed.

- 36 -

H5: Perceived cognitive load has an adverse effect on situational mindfulness in

green technology adoption process.

2.10.5 Perceived Financial Risk, Uncertainty, and Situational Mindfulness

Processing information can vary from impossibly hard to being trivially simple

(Sweller, 1994). In this process, information ambiguities and perceived risks can affect

consumer’s decision-making significantly (i.e., Frich & Baron 1988; Camerer & Weber

1992). When perceived uncertainty is high, people search for more information to ease

perceived uncertainty and to eliminate perceived risk (March, 1994). Mindful individuals

are willing to process new information and search for more, out of curiosity, by using

deliberate processing (Dong & Brunel, 2006). However, if the information is uncertain

which implies unclarity or inconsistency (March, 1994; p.178) and associated with

possible loss (risks), cognitive energy could suffer, and consumers would have to switch

to more intuitive processing mode (Kahneman, 2003). As a result, consumers would end

up using biases or simply avoiding the technology.

Cognitive energy resources are limited in a human brain and information

processing which occurs under uncertainties, and perceived risk depletes this resource at

a faster pace. The human brain, on the other hand, tries to economize the energy in the

decision-making process. If cognitive energy depletes, the mindful processing system

might suffer and switch to a more automatic way of processing. According to Heat and

Tversky (1991), people take a longer time and pay more attention to negative information

in product evaluation when they exposed high information ambiguity. In a situation with

risky choices like paying $100K for adopting an electric vehicle, a mindful customer

- 37 -

might search better, evaluate more alternative perspectives and put more effort into it;

nevertheless, she/he still may end up following the crowd or avoiding the technology

because of the information inconsistencies. Hence, it is proposed that information

uncertainty and financial risk can affect the consumer’s decision-making process

significantly (Frich & Baron, 1988; Camerer & Weber, 1992, p.330).

H6: Perceived uncertainty has an adverse effect on situational mindfulness in

green technology adoption process.

H7: Perceived financial risk has an adverse effect on situational mindfulness in

green technology adoption process.

2.10.6 Situational Mindfulness and Perceived Time Pressure

Situational mindfulness will be limited if a time pressure exists during a decision

task. It disrupts the attention quality and deteriorates cognitive control by increasing

cognitive tension (Ackerman & Gross, 2003; Denton, 1994; Rothstein, 1986). Time

pressure reduces the number of alternatives evaluated during the information processing

and leads people more biased, less accurate and affective-cognitive evaluation (Wright &

Kriewall 1980).

Prior research found that perceived cost is higher than the perceived benefit when

the pressure exists (Finucane, Alhakami, Slovic, & Johnson, 2000). In line with this,

people tend to focus more on negative information (Wright, 1974; Zur & Brenznitz,

1981), they are more self-focused and acting less ethical when the decision time is shorter

(Shalvi, Eldar, & Bereby-Meyer, 2012). Therefore, it can be expected that perceived time

- 38 -

pressure could lead people to mindless processing. To my knowledge, no study examined

this relationship before. Therefore, the following hypothesis is suggested.

H8: Perceived time pressure has an adverse effect on situational mindfulness in

green technology adoption process.

2.10.7 Situational Mindfulness and Technology Acceptance Model

Previous research suggests that situational mindfulness creates rich awareness to

details, induces vivid information processing with focused attention, improves capacity

for purposeful action (Weick & Sutcliffe, 2006) and eventually influences decision

quality (Weber & Johnson, 2009; Karelaila & Reb, 2014). When consumers are

mindful, they view the situation as a novel task; evaluate the information from multiple

perspectives, attend to situational context vividly and create novel categories for the

information at hand (Langer, 1989a).

Following this, studies on mindfulness revealed positive impacts of situational

mindfulness on emotion regulation, self-regulation, and human functioning (Brown &

Ryan, 2003; Brown et al., 2007). Higher mindfulness helps consumers avoid heuristics

usage in persuasion process (Dong & Brunel, 2006; Luttrell et al, 2014; Stefi, 2015),

reduce the perceived uncertainty and post-adoption regret in technology adoption

process (Sun & Fang, 2010; Sun, 2011; Zou, Sun, & Fang, 2015) and make more

rational decision (Kirk, Downar, & Montague, 2011). Mindfulness state has been found

highly correlated with openness to new experience (Baer, Smith, Hopkins, Krietemeyer,

- 39 -

& Toney, 2006), conscientiousness (Giluk, 2009), creativity (Lebuda et al., 2016),

curiosity (Lau et al., 2006) and better problem solving (Ostafin & Kassman, 2012).

Given these characteristics and pro-environmental purpose of promoting EVs,

mindful consumers are expected to concentrate more on understanding the product-

specific components, attributes, and environmental outcomes that using the product will

entail. Therefore, situational mindfulness will enable consumers to evaluate the

usefulness of green technology better and estimate how much effort needed to use green

technology more precisely. A positive relationship between situational mindfulness and

perceived usefulness of technology was established in previous research (Sun & Fang,

2010, Stefi, 2015). However, no study considered its impact on perceived ease of use of

green technology. Since mindful consumers process the information, be aware of

alternative ways of using new products, and less prone to negativity in new product

acceptance they are more likely to perceive green products benefits (Schramm & Hu,

2014). Thus, the following hypotheses are developed.

H9: Situational mindfulness has a positive effect on the perceived usefulness of

green technology.

H10: Situational mindfulness has a positive effect on the perceived ease of use of

green technology.

H11: Perceived ease of use has a positive effect on intention to use green

technology.

- 40 -

H12: Perceived usefulness has a positive effect on intention to use green

technology.

Taken together, hypotheses 9, 10, 11 and 12 imply the mediating effects of

perceived ease of use and perceived usefulness on the association between consumers’

situational mindfulness and intention to use green technology. Hence, the following

hypotheses were developed.

H13: Perceived ease of use positively mediates the relationship between

situational mindfulness and intention to use green technology.

H14: Perceived usefulness positively mediates the relationship between

situational mindfulness and intention to use green technology.

2.11 Moderating Effects of Situational Inhibitors on Green Technology Adoption

It is suggested that individuals who have dispositional mindfulness enter a

situational mindfulness mode more often than others (Langer & Moldoveanu, 2000).

This statement implies the contingency effect of disposition on situational characteristics

and may apply for characterizations of rationalizers and satisficers. However, when

decision environment involves situational boundaries; for example, when consumers feel

cognitively overload or pressured by the time limitation, a lack of clarity, and a possible

financial loss about decision outcome, one can expect that consumers may tend to act

more automatically regardless of what their dispositional characteristics and personal

norm orientation. Aligning with the situational boundaries, consumers might be less

attentive to learning about the product and less aware of product-related details, so their

- 41 -

perception may be distorted in effort and performance expectancy and they may hesitate

to make an adoption decision. Therefore, situational mindfulness, consumers’ perceptions

about technology and subsequently their intention to use can be negatively affected by

these inhibitors. The following hypotheses are developed to test these relations.

H15: Situational constraints dampen the positive effects of trait mindfulness on

situational mindfulness (a- perceived cognitive load, b- perceived uncertainty, c-

perceived financial risk, d- perceived time pressure).

H16: Situational constraints dampen the effects of decision-making styles

(H16.1.Rational & H16.2.Satisficing) on situational (a- perceived cognitive load, b-

perceived uncertainty, c- perceived financial risk, d- perceived time pressure).

H17: Situational constraints dampen the positive effects of personal norms on

situational mindfulness (a- perceived cognitive load, b- perceived uncertainty, c-

perceived financial risk, d- perceived time pressure).

H18: Situational constraints dampen the positive effects of situational

mindfulness on consumers’ perceptions (H18.1.PEOU & H18.2.PU) about green

technology (a- perceived cognitive load, b- perceived uncertainty, c- perceived financial

risk, d- perceived time pressure).

H19: Situational constraints dampen the positive effects of consumers’ product

perceptions (H19.1.PEOU & H19.2.PU) on their intention to use green technology (a-

perceived cognitive load, b- perceived uncertainty, c- perceived financial risk, d-

perceived time pressure).

- 42 -

CHAPTER 3

METHODOLOGY

In the previous section, I presented literature reviews on TAM, mindfulness, and

possible bounding factors in decision processes, and integrated them with hypotheses of

the research which were explaining the impacts of mindfulness on the green technology

acceptance process. In this section, I will explain the data collection, sampling

procedures, the constructs, measures and, methods that I have used to test these

relationships. Figure 2 below depicts the proposed hypotheses of this research.

- 43 -

Figure 1: Proposed Model of Green Technology Acceptance

H1

H15-H16-H17 H18 H19

H2

H3 H9 H11

H4

H10 H12

H5

H6

H7

H8

*Moderators: Perceived cognitive load, perceived uncertainty, perceived

financial risk, and perceived time pressure.

Perception

Decision Making

Style

Perceived

Usefulness

Situational

Mindfulness

Time Pressure

Uncertainty

Cognitive Load

Satisficing

Personal Norm

To Use

Perceived

Ease of Use

Trait

Mindfulnes

s

Intention

To Use

Financial Risk

Rational DMS

Moderators*

- 44 -

3.1 Data Collection and Sampling Procedures

The data for this dissertation was collected utilizing an online survey of

undergraduate students at Rutgers the State University of New Jersey in April 2017.

Because the proposed model is mostly interested in people’s self-report measures, survey

methodology is chosen to collect relevant data from participants in coordination with

Behavioral Lab at Rutgers Business School. Each participant completed the questionnaire

using their laptops or smartphones. I assumed that millennials, more specifically

undergraduate students are potential car shoppers and in general, more aware about

sustainability issues and environmentally friendliness (Landrum, 2017; Nielsen, 2015).

Therefore, the study sample consisted of 317 undergraduate students who participated in

this study to fulfill a requirement of their Introduction to Marketing course.

Based on the relevant literature, survey items are designed. In addition to this, a

pilot study conducted with 5 participants. Respondents rated whether the questions were

meaningful and understandable. Then, the survey questionnaire was finalized. An online

questionnaire consisting of previously developed measures is delivered using

QUALTRICS survey tool to test the relationships of the proposed model. Out of the 317

responses, 12 of them were identified as disengaged responders, and their responses were

deleted in the data cleaning process. In total, 305 useable responses remained. No missing

value was detected. Sample’s demographic profile was briefly summarized in Table 3

and the participant’s EV preferences depicted in Figure 2.

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Table 3: Sample Demographics Figure 2: Car Preferences

At the beginning of the survey, a consent form was given, and after the

agreement, participants were asked to fill out trait-related survey items (e.g., trait

mindfulness, rationality, satisficing, personal norm, innovativeness). Then, they have

seen a screen that includes the links to the most preferred ten electric cars directing the

participants to Kelley Blue Book’s (http://www.kbb.com) related webpage in the survey.

The webpage included BMWi3, Fiat 500e, Ford Focus, Chevrolet Spark EV, Mitsubishi

i-MiEV, Kia Soul EV, Volkswagen e-Golf, Smart Fortwo electric drive, Tesla Model S,

and Nissan LEAF. Kelley Blue Book is a vehicle valuation website that car shoppers

generally visit while searching for new or used automobiles. Recognized by the

automotive industry, this webpage provides reports about manufacturer’s recommended

retail price, regular retail price, pre-owned value, consumer and expert ratings and

reviews for cars. Participants were asked to search for the information about at least three

cars that they may consider purchasing in the future on KBB webpage. After this search

process, participants were asked whether they would plan to use one of the cars assuming

they did not have any budget constraint. After the decision process, participants were

- 46 -

asked to fill out the rest of the survey questionnaire. The study constructs and survey

items are presented in Table 3.

3.2 MEASUREMENT OF THE CONSTRUCTS

3.2.1 Trait Mindfulness

Trait mindfulness was defined as a disposition characterized by “enhanced

attention to and awareness of current experience or present reality.” (Brown & Ryan,

2003, p. 822). Trait mindfulness was assessed using the scale adopted from Brown and

Ryan’s (2003) Mindful Awareness and Attention Scale (MAAS). MAAS consists of 15

items to evaluate dispositional mindlessness and responses need to be reverse-coded. For

the purpose of this research, I used five of the items from MAAS shown below.

Respondents were asked to rate the frequency of their experiences described in the scale

items on a seven-point Likert scale (1= never, 7= almost always). High scorers on this

scale were accepted as high mindful consumers. The validity of this scale was

established, and its internal consistency was adequate (alpha= .849).

Table 4: Scale for Dispositional Mindfulness (Brown & Ryan, 2003, p. 826)

1. I find it difficult to stay focused on what’s happening in the present.

2. It seems I am “running on automatic” without much awareness of what I’m

doing.

3. I rush through activities without being really attentive to them.

4. I do jobs or tasks automatically, without being aware of what I am doing.

5. I find myself doing things without paying attention.

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3.2.2 Personal Norms (PNs)

PNs were defined as “feelings of a moral obligation to perform pro-

environmentally and refrain from environmentally harmful actions” (Schwartz and

Howard, 1981, p. 191). Participants’ general normative perceptions were assessed using a

brief version of Jansson et al.’s (2011) normative perception scale for alternative fuel

vehicles. In total, three items were used to measure personal norms. Participants were

asked to indicate their agreement with the statements below. Responses were obtained on

a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

The validity of this scale was established, and its internal consistency was adequate

(alpha= .827).

Table 5: Scale for Consumer Norm Orientation (Jansson et al., 2011, p. 60)

1.I feel guilty when wasting fossil fuels such as oil/petrol/diesel.

2.I feel a moral obligation to conserve oil/petrol/diesel no matter what other

people do.

3. I feel a moral obligation to use electricity or any other biofuel such as

ethanol/biogas instead of fossil fuels such as oil/petrol/diesel.

3.2.3 Decision-Making Style (DMS)

Decision-making style was defined as “a patterned, mental, cognitive orientation

towards shopping consumer choices” (Sproles, 1985, p. 79). Rational DMS was defined

as “a consumer's search for the highest or best quality in products” (Sproles, 1985, p. 81)

while the style of satisficing was defined as always “choosing good enough option”

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Satisficing measure is adapted from Turner et al. (2012) and Rational decision-making

style from Harren (1978). Participants were asked to indicate their agreement with the

statements below. Responses were obtained on a seven-point Likert-type scale ranging

from 1 (strongly disagree) to 7 (strongly agree). The validity of these scales was

established, and the internal consistency for Rational DMS (alpha= .82) and satisficing

(.794) were adequate.

Table 6: Scale for Decision-Making Styles (Harren, 1978, p.2; Turner et al.,

2012, p.55)

Rational DMS

1. When I need to make a decision I take my time to think through it carefully.

2.Before I do anything, I have a carefully worked out plan.

3.I do not make decisions hastily because I want to be sure I make the right

decisions.

4.I like to learn as much as I can about the possible consequences of a decision

before I make it.

Satisficing DMS

1. I try to gain plenty of information before I make a decision, but then I go ahead

and make it.

2.In life, I try to make the most of whatever path I take.

3.I can not possibly know everything before making a decision.

4.At some point, you need to make a decision about things.

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3.2.4 Perceived Cognitive load

The construct was defined as “perceived mental effort being used in the working

memory” while making a decision (Sweller, 1988). Cognitive overload (heavy cognitive

load) refers the feeling of overwhelming when a person exposed to the larger amount of

information than the amount that his/her working memory can process comfortably

(Sweller, 1988). Cognitive load was assessed using a shorter version of the NASA Task

Load Index (NASA-TLX) (Hart & Staveland, 1988) subjective mental workload

questionnaire. Responses were obtained on a five-point Likert-type scale ranging from 1

(strongly disagree) to 5 (strongly agree). The validity of this scale was established, and its

internal consistency was adequate (alpha= .827).

Table 7: Scale for the Perceived Cognitive Load (Hart & Staveland,1988,p.

56)

1. How much mental and perceptual activity was required (e.g., thinking,

deciding, calculating, remembering, looking, searching, etc.) in your search?

2. How hard did you have to work (mentally) to accomplish your level of

performance while making your decision in car shopping?

3. How much time pressure did you feel during your search?

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3.2.5 Perceived Uncertainty

The construct was defined as “as a person’s perceived inability to predict what the

technology is about, how it can be used to help his/her work, and whether he/she will be

able to respond appropriately to any changes/updates to the technology.” (Sun & Fang,

2010, p.5). It was assessed using Sun and Fang’s (2010) uncertainty of technology

acceptance scale. Responses were obtained on a Likert-type seven-point scale ranging

from 1 (strongly disagree) to 7 (strongly agree) points Likert-scale. Internal consistency

of this measure is adequate (alpha= .876).

Table 8: Scale for the Perceived Uncertainty (Sun & Fang, 2010, p. 9)

1. I am not sure what this electric car is about and what it could do for me.

2. I feel uncertain whether my needs while driving could be met by using this

electric car.

3. I feel uncertain whether I would be able to respond appropriately to any

changes/upgrades of this electric car.

4. I feel that using this electric car involves a high degree of uncertainty.

3.2.6 Perceived Financial Risk

The perceived financial risk was defined as customers’ subjective belief of

suffering a financial loss if they would buy an EV and was measured by using three items

from Grewal et al.’s (1994) perceived financial risk scale. Responses were obtained on a

Likert-type seven-point scale ranging from 1 (not risky at all/very little risk) to 7 (very

- 51 -

risky/substantial risk) points Likert-scale. Internal consistency of this measure is adequate

(alpha= .909).

Table 9: Scale for the Perceived Financial Risk (Grewal et al., 1994, p. 152)

1.Considering the potential investment involved, how risky (financially) do you

feel it would be to purchase this car? (very risky, not risky at all)

2.Given the expense involved with purchasing this car, how much is the risk

involved in purchasing this car? (very risky, not risky at all)

3. Given the potential financial expenses associated with purchasing this car, how

much overall financial risk is involved while purchasing this car? (substantial

risk/very little risk)

3.2.7 Perceived Time Pressure

The construct was defined as “the lack of time a person perceives there to be

available for doing what needs to be done in his/her life” (Mittal, 1994; Bruner II et al.,

2001, p. 632). Participants’ perceptions of time deprivation were assessed using Mittal’s

(1994) perceived time pressure scale. In total, three items were used to measure perceived

time deprivation. Participants were asked to indicate their agreement with the statements

below. Responses were obtained on a five-point Likert-type scale ranging from 1

(strongly disagree) to 5 (strongly agree). The validity of this scale was established, and its

internal consistency was adequate (alpha= .829).

- 52 -

Table 10: Scale for the Perceived Time Pressure (Bruner II et al., 2001, p.

632)

1. I am too busy to relax.

2.I am often juggling my time between too many things.

3."So much to do, so little time;" this saying applies very well to me.

3.2.8 Situational Mindfulness

Situational mindfulness was defined as a situation-specific quality of the

consciousness, which is maintained only when attention to experience is intentionally

oriented in the present with openness, curiosity, and decentering (Sun & Fang, 2010; Lau

et al., 2006). To measure situational mindfulness scale, I employed two different

situational mindfulness scales originated from two different perspectives. One of them is

the Mindfulness of Technology Acceptance (MTA) scale developed by Sun and Fang

(2010). They adopted an approach that follows Langer’s (1989a) definition, and their

scale mostly reflects consumers’ situational mindfulness during the technology adoption.

The second measure was the Toronto Mindfulness Scale (TMS), which includes curiosity

and decentering dimensions of mindfulness and reflects consumers’ internal situational

mindfulness. I adapted items from both scales to capture practical and internal situational

mindfulness. Combining MTA and TMS, a second order factor was generated to assess

situational mindfulness construct. Responses were obtained on a Likert-type seven-point

scale ranging from 1 (strongly disagree) to 7 (strongly agree) points Likert-scale. Internal

consistency of this measure is adequate (alpha= .875).

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Table 11: Scale for Situational Mindfulness (Sun & Fang, 2010, p. 9; Lau et al.,

2006, p. 1452)

1. I tended to figure out how this electric car was unique in relation to the car/s

that I am currently using/planning to use. (Novelty seeking)

2. I gathered factual information about this electric car before making my

decision. (Engagement with the technology)

3. When making the decision to adopt this electric car, I thought about how this

electric car might help me. (Awareness of local context)

4. When making the decision to adopt this electric car, I thought about how this

electric car might change the way I live. (Awareness of local context)

5. I attended to alternative views regarding the electric car before making the

adoption decision. (Cognizance of Alternative technologies)

6. I was more concerned with being open to my experiences during car shopping

than controlling or changing them.

7. I was curious to see what my mind was up to from moment to moment.

8. I approached each experience by trying to accept it, no matter whether it was

pleasant or unpleasant.

9. I remained curious about the nature of car shopping when I was searching.

10. I was aware of my thoughts and feelings without over-identifying with them.

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3.2.9 Perceived Usefulness

PU was defined as “the degree to which a person believed that using an EV will

enhance his or her performance” (Davis, 1989, p. 320). PU of the green technology in my

study was assessed using Davis’s (1989) PU scale for EVs. Participants were asked to

rate their agreement with the five statements below. Responses were obtained on a seven-

point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Internal

consistency of this measure is adequate (alpha= .928).

Table 12: Scale for the Perceived Usefulness (Davis, 1989, p. 324)

1. Using this electric car could improve my driving performance in general.

2. Using this electric car could increase my productivity while driving.

3. Using this electric car could enhance my effectiveness in my life.

4. Using this electric car would make driving easier for me.

5. I would find this electric car to be useful in my life in general.

3.2.10 Perceived Ease of Use

PEOU was defined as “the degree to which a person believes that using an EV

will be free of effort” (Davis, 1989, p.320). PEOU of the green technology was assessed

using Davis’s (1989) PEOU scale for EVs. Participants were asked to rate their

agreement with the six statements below. Responses were obtained on a seven-point

Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Internal

consistency of this measure is adequate (alpha= .925).

- 55 -

Table 13: Scale for the Perceived Ease of Use (Davis, 1989, p. 324)

1.Learning to use this electric car would be easy for me

2.I expect my interaction with this electric car would be clear and understandable.

3.Interacting with this electric car does not seem to require a lot of my mental

effort.

4.It would be easy to become skillful at using this electric car.

5.I would find it easy to get this electric car to do what I want it to do.

6.I would find this electric car to be easy to use.

3.2.11 Intention to Use

The construct was defined as participants’ intention to use EVs. It was assessed

using Davis’s (1989) scale of intention to use. Participants were asked to rate their

agreement with the three statements below. Responses were obtained on a seven-point

Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Internal

consistency of this measure is adequate (alpha= .964).

Table 14: Scale for Intention to Use (Davis, 1989, p. 331)

1. Assuming I had access to this car, I intend to use it.

2. Given that I had access to this car, I predict that I would use it.

3. I plan to use this car in the near future.

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3.3 Data Screening, Measurement Reliability, and Validity

In the first step, data screening was conducted using SPSS 24 statistical software

package. No missing value was observed in the dataset. Through checking total time

participants used for the survey and the standard deviation, I identified 12 unengaged

response cases and removed them (they picked the same option to almost every Likert

scale item). I observed reasonably normal distributions for the indicators of latent factors

regarding of skewness and kurtosis which ranged from benign to 2.031. It is within the

more relaxed rules normality suggested by Kline (2011) who recommends 3.3 as the

upper threshold for normality (Table 15).

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Table 15: Descriptive Statistics

N Mean

Std.

Dev. Skewness Kurtosis

Statistic Statistic Statistic Statistic

Std.

Error Statistic

Std.

Error

TM_1 305 3.85 1.400 .067 .140 -.776 .278

TM_2 305 3.90 1.395 .000 .140 -.486 .278

TM_3 305 3.86 1.399 -.015 .140 -.732 .278

TM_4 305 3.84 1.280 -.005 .140 -.697 .278

TM_5 305 4.00 1.420 -.202 .140 -.526 .278

PNs_1 305 3.29 1.153 -.328 .140 -.648 .278

PNs_2 305 3.01 1.078 .076 .140 -.542 .278

PNs_3 305 3.04 1.122 -.028 .140 -.696 .278

Sat_1 305 5.92 .995 -.884 .140 .382 .278

Sat_2 305 5.77 1.105 -.999 .140 1.366 .278

Sat_3 305 5.62 1.103 -.900 .140 1.104 .278

Sat_4 305 5.46 1.337 -.817 .140 .300 .278

PU_1 305 4.33 1.593 -.146 .140 -.660 .278

PU_2 305 4.41 1.568 -.221 .140 -.533 .278

PU_3 305 4.43 1.584 -.300 .140 -.455 .278

PU_4 305 4.28 1.601 -.137 .140 -.523 .278

PU_5 305 4.63 1.578 -.320 .140 -.454 .278

PEU_1 305 5.32 1.365 -.770 .140 .296 .278

PEU_2 305 5.08 1.380 -.491 .140 -.152 .278

PEU_3 305 5.21 1.311 -.649 .140 .308 .278

PEU_4 305 5.26 1.332 -.600 .140 -.003 .278

PEU_5 305 5.32 1.321 -.591 .140 -.158 .278

IU_1 305 5.93 1.204 -1.387 .140 1.930 .278

IU_2 305 5.92 1.217 -1.381 .140 2.031 .278

IU_3 305 4.48 1.726 -.271 .140 -.776 .278

- 58 -

CL_1 305 2.75 1.154 .124 .140 -.662 .278

CL_2 305 2.93 1.038 .103 .140 -.483 .278

CL_3 305 2.89 1.039 .172 .140 -.447 .278

SM_1 305 4.64 1.406 -.707 .140 .016 .278

SM_2 305 4.53 1.430 -.494 .140 .059 .278

SM_3 305 4.64 1.435 -.544 .140 .047 .278

SM_4 305 4.69 1.434 -.578 .140 .143 .278

SM_5 305 4.56 1.385 -.507 .140 -.064 .278

SM_6 305 4.21 1.527 -.286 .140 -.333 .278

SM_7 305 3.85 1.754 -.174 .140 -.880 .278

SM_8 305 4.12 1.623 -.251 .140 -.587 .278

SM_9 305 4.15 1.626 -.244 .140 -.568 .278

SM_10 305 4.38 1.552 -.360 .140 -.242 .278

UNC_1 305 3.53 1.533 .215 .140 -.723 .278

UNC_2 305 3.81 1.479 .092 .140 -.649 .278

UNC_3 305 3.84 1.516 .047 .140 -.728 .278

UNC_4 305 3.68 1.492 .072 .140 -.447 .278

TP_1 305 3.33 .996 -.019 .140 -.669 .278

TP_2 305 3.69 .927 -.438 .140 -.287 .278

TP_3 305 3.73 .972 -.462 .140 -.458 .278

FR_1 305 4.35 1.453 -.045 .140 -.665 .278

FR_2 305 4.34 1.436 -.090 .140 -.643 .278

FR_3 305 4.45 1.444 -.099 .140 -.586 .278

Rat_1 305 5.23 1.112 -.420 .140 .104 .278

Rat_2 305 4.97 1.242 -.390 .140 -.157 .278

Rat_3 305 4.95 1.280 -.428 .140 -.099 .278

Rat_4 305 5.18 1.111 -.420 .140 .207 .278

Valid N

(listwise)

305

- 59 -

In the second step, measurement reliability and convergent validity were assessed.

Using the SPSS statistical software package, Cronbach alpha coefficients were examined

relative to the minimum level of .70 to evaluate the reliability of every measure in the

model (Nunnally, 1978). As shown in Table 5, all Cronbach’s alphas of latent variables

are greater than the 0.7 thresholds, presenting good reliability or internal consistency as

suggested by Fornell and Larcker (1981). Every measurement item loaded on its

construct and loadings were above the cut off value of 0.5 that implied convergent

validity (Hildebrandt, 1987). Thus, the data presented an adequate convergent validity

and reliability (as evidenced by the CR value above .70).

In the third step, Confirmatory Factor Analysis (CFA) using AMOS graphics was

conducted to test discriminant validity. As shown in Table 6, all Average Variance

Explained (AVE) values were higher than the 0.5 threshold level as recommended by

Barcley, Higgins, and Thompson (1995). There were not any cross-loadings or

problematic correlations that implied good discriminant validity. Thus, the data presented

adequate discriminant validity (also as evidenced by the square root of AVE greater than

correlations).

- 60 -

Table 16: Item Loadings of Constructs

Items

Factor

Loading

Cronbach's

Alpha

Std. t-

values

Trait Mindfulness (MAAS) 0.842

MAAS_1 0.535

MAAS_2 0.777 9.545

MAAS_3 0.811 9.485

MAAS_4 0.719 9.265

MAAS_5 0.755 9.435

Personal Norm 0.827

PNs_1 0.767

PNs_2 0.875 12.504

PNs_3 0.693 12.245

Rational Decision-Making Style 0.82

RAT_1 0.755

RAT_2 0.628 10.715

RAT_3 0.756 11.529

RAT_4 0.754 12.216

Satisficing 0.794

SAT_1 0.871

SAT_2 0.695 12.078

SAT_3 0.629 12.524

SAT_4 0.619 8.521

Time Pressure 0.829

TIM_PRE_1 0.710

TIM_PRE_2 0.875 12.069

TIM_PRE_3 0.776 12.089

Cognitive Load 0.827

CL_1 0.653

CL_2 0.832 11.944

CL_3 0.859 11.866

Uncertainty 0.876

Uncertainty_1 0.761

Uncertainty_2 0.880 14.598

- 61 -

Uncertainty_3 0.832 14.414

Uncertainty_4 0.724 12.754

Perceived Financial risk 0.909

FIN_Risk_1 0.826

FIN_Risk_2 0.924 20.287

FIN_Risk_3 0.859 18.519

Perceived Usefulness 0.928

PU_1 0.835

PU_2 0.961 21.317

PU_3 0.908 19.884

PU_4 0.822 18.309

PU_5 0.662 15.824

Perceived Ease of Use 0.925

PEU_1 0.7773 18.014

PEU_2 0.795 18.01

PEU_3 0.835 20.234

PEU_4 0.890 23.089

PEU_5 0.902

Intention to Use 0.964

IU_1 0.992

IU_2 0.899 27.514

Situational Mindfulness 0.875

SM_1 0.678

SM_2 0.817 13.621

SM_3 0.881 14.839

SM_4 0.792 14.34

SM_5 0.694 12.426

SM_6 0.631 SM_7 0.722 11.141

SM_8 0.754 10.928

SM_9 0.726 11.153

SM_10 0.662 10.454

- 62 -

Tab

le 1

7:

Com

posi

te R

elia

bil

ity, S

qu

are

Roots

of

AV

E a

nd

Corr

elati

on

s of

Late

nt

Vari

ab

les

SM

0.7

4

IU:

Inte

nti

on

to U

se

T

M:

Tra

it M

ind

fuln

ess

F

R:

Fin

an

cial

Ris

k

PE

OU

: P

ercei

ved

ease

of

use

U

NC

: U

nce

rtain

ty C

L:

Cogn

itiv

e L

oad

PU

: P

erce

ived

Use

fuln

ess

T

P:

Tim

e P

ress

ure

R

at:

Rati

on

al

dec

isio

n s

tyle

Sat:

Sati

sfic

ing

P

Ns:

Per

son

al

Norm

s S

M:

Sit

uati

on

al

Min

dfu

lnes

s

IU:

Inte

nti

on

to U

se

PE

OU

: P

ercei

ved

ease

of

use

PU

: P

erce

ived

Use

fuln

ess

Sat:

Sati

sfic

ing

TM

: T

rait

Min

dfu

lnes

s

UN

C:

Un

cer

tain

ty

TP

: T

ime

Pre

ssu

re

Rat

0.7

3

0.3

6

CL

0.8

0.2

5

0.5

3

FR

0.8

78

0.0

04

0.2

16

-0.0

9

PN

s

0.7

86

-0.1

5

0.2

09

0.0

81

0.4

12

TP

0.7

9

0.0

3

0.2

2

0.1

3

0.3

5

0.0

4

UN

C

0.8

04

0.0

96

-0.0

3

0.1

82

0.1

25

0.0

02

-0.0

7

TM

0.7

3

0.1

0.2

8

0.1

1

0.1

1

0.1

0

0.0

7

Sat

0.7

18

0.0

03

-0.0

6

0.2

5

0.0

75

0.1

37

0.0

86

0.4

34

0.1

73

PU

0.8

51

0.0

18

0.1

33

-0.1

0.1

02

0.4

14

-0.0

9

0.1

83

0.2

08

0.4

92

PE

OU

0.8

44

0.2

03

0.3

74

-0.0

2

-0.2

8

0.0

92

0.1

23

-0.2

0.0

02

0.1

94

0.2

86

IU

0.9

65

0.4

6

0.3

19

0.3

97

0.0

98

-0.3

2

0.1

31

0.2

16

0.0

24

0.0

38

0.2

72

0.2

57

MaxR

(H)

0.9

67

0.9

32

0.9

38

0.8

28

0.8

55

0.8

9

0.8

5

0.8

34

0.9

22

0.8

65

0.8

28

0.7

1

MS

V

0.2

1

0.2

1

0.2

4

0.1

9

0.0

8

0.1

0.1

2

0.1

7

0.0

5

0.2

9

0.1

9

0.2

9

AV

E

0.9

3

0.7

1

0.7

2

0.5

2

0.5

3

0.6

5

0.6

3

0.6

2

0.7

7

0.6

4

0.5

4

0.5

5

CR

0.9

6

0.9

3

0.9

3

0.8

1

0.8

5

0.8

8

0.8

3

0.8

3

0.9

1

0.8

4

0.8

2

0.7

1

IU

PE

OU

PU

Sat

TM

UN

C

TP

PN

s

FR

CL

Rat

SM

- 63 -

Configural invariance in the proposed model is good as evidenced by good model

fit measures when estimating two groups freely – i.e., without constraints (Gaskin, 2016).

Additionally, according to the metric invariance test results, the groups for gender are

different that indicated the data has no issue for group comparisons. The p-value is

significant which means there is a significant difference between different gender groups

at the regressions/loadings level (Gaskin, 2016).

Last, I run Cook’s distance analysis to determine if any (multivariate) influential

outliers existed. In no case, I observed a Cook’s distance greater than 1. Most cases were

far less than 0.300. I examined variable inflation factors for all predictors on the

dependent variables and observed no VIFs greater than 1.675, which is far less than the

threshold of 10.0 suggested by Hair, Black, Babin, Anderson, & Tatham (2006). More

details about multicollinearity diagnosis can be found in the Appendix A (Table 23).

3.4 Data Analysis

In data analysis, there were essentially three phases. First, the overall model was

tested. Second, the structural model was reevaluated to examine the potential mediators.

Third, multi-group differences were assessed.

Structural Equation Modeling (SEM) techniques were utilized to examine the

overall structural model and to test the proposed hypotheses using SPSS AMOS 24

statistical software packages. Maximum Likelihood Estimation approach (MLE) was

employed as suggested by Ding, Velicer, and Harlow (1995) to evaluate the measurement

and structural model with 305 being the sample size. SEM allows estimating the

- 64 -

relationships among latent variables, to predict the dependent variable, and to validate the

measurement model simultaneously (Klem, 1995). It also evaluates the measurement

error using a CFA and provides the overall fit of a proposed model. After running a CFA

for the proposed model, I got a good model fit in general (Table 18).

Table 18: Model Assessment

Measure Model Result

Chi-square 1845.366

Degrees of Freedom 1295

Chi2/Df 1.425

CFI 0.944

RMSEA 0.037

SRMR 0.0447

- 65 -

CHAPTER 4

RESULTS

The focus of this chapter is to present the results of data analyses discussed in the

previous chapter. The focus of data analysis was to test the association between

situational mindfulness and green technology acceptance process under the influence of

bounding factors and, if necessary, to modify the structural model by refining the model

to generate a better fitting model which accurately explains both data and theory. For that

purpose, a model was estimated using AMOS to test all the hypotheses and mediators

(Figure 3). The model presented all the direct effects and mediations. The results of the

path analyses suggested that the proposed structural model fit the data well according to

the criteria recommended by Bagozzi and Yi (1998). During the process of data analysis,

no abnormalities, violations of the structural equational modeling assumptions, and

problems were encountered, and relationships appropriately established. Therefore, the

study concluded that the path coefficients in the model explained the relationships

correctly. Figure 3 below, presents the beta coefficients and Table 19 shows the goodness

of fit indices for the conceptual model.

- 66 -

Figure 3: Model of Green Technology Acceptance

.44*** -.17***

.09**

H4 .39*** .8*** .24***

.33*** .48*** .31***

H2-H3

.1*** -.09** -.1***

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Perception

Perceived

Usefulness

Perceived

Ease of Use

Situational

Mindfulness

Decision Making

Style

Trait

Mindfulnes

s

Intention

To Use

Uncertainty

Financial Risk

Rational DMS

Satisficing

Personal Norm

To Use

Time Pressure

Cognitive Load

- 67 -

Table 19: Goodness of Fit Indices

Measure Model Result

Chi-square 22.407

Degrees of Freedom 15

Chi2/Df 1.571

CFI 0.993

RMSEA 0.040

SRMR 0.018

4.1 Influence of Situational Mindfulness on Green Technology Acceptance Process

The results of the model to test the hypotheses showed that consumers’ situational

mindfulness had positive effects on perceived usefulness (β = 0.8, t = 13.912, p < .01)

and perceived ease of use (β = 0.48, t = 7.848, p < .01), in support of H9 and H10 and the

associations are statistically significant. Additionally, consumers’ perceived usefulness (β

= .24, t = 5.227, p < .01) and perceived ease of use (β = .31, t = 5.885, p < .01) as

expected, positively influence consumers’ intention to use and the relationships are

significant in support of H11 and H12. These findings are compatible with the obtained

results of many studies related to TAM in management and IT literature (i.e., Davis,

1989; Venkatesh & Davis, 2000; Venkatesh & Bala, 2008).

- 68 -

4.2 Determinants of Situational Mindfulness in Green Technology Acceptance

The structural path diagram provides a moderate level of support for the

hypothesis regarding the effect of trait mindlessness on the situational mindfulness. Trait

mindlessness measured by MAAS has a significant effect on situational mindfulness (β =

.09, t = 2.425, p < .05) supporting the first hypothesis of the model. As the first study that

compares MAAS and technology consumption, this result adds to the literature by

showing the effect of trait mindfulness measured by MAAS on consumer’s situational

mindfulness for the first time. The second hypothesis stated that consumers’ rational

decision style has a positive impact on situational mindfulness in green technology

adoption process. That means, consumer’ decision style, whether he/she is a rationalizer

or a satisficer, predicts her/his situational mindfulness in the green technology adoption

process. The path analysis provided support for the significant positive impact of rational

DMS on situational mindfulness (β = .33, t = 8.870, p < .01) (in support of H2). This

finding is compatible with Kirk et al.’s (2011) study that reveals the positive effect of

mindfulness on rational decision making. However, the path analysis also showed that

there is a moderate level of influence of satisficing DMS on situational mindfulness (β =

.1, t = 2.609, p <.05) rejecting H3. The fourth hypothesis stated that consumers’

favorable normative orientation towards green technology has a positive effect on their

situational mindfulness. The results provided support for this hypothesis by the

significant positive path coefficient (β = .39, t = 11.244, p < .01), in support of H4.

- 69 -

4.3 The Effects of Bounding Factors on Situational Mindfulness

Contrary to the proposed hypothesis in the second chapter, (H5), the analysis

revealed a positive impact on consumers’ situational mindfulness in green technology

adoption process (β = .44, t = 12.424, p < .01). The reason why the personal cognitive

load strongly predicts situational mindfulness can be because situational mindfulness is

defined as lively engagement in the information search process, context awareness and

curiosity and decentering. These fundamental functions of consciousness may be

activated by a certain level of cognitive workload in green technology adoption process.

Therefore, this finding is not too unrealistic.

Furthermore, the model showed that perceived decision uncertainty (β = -.09, t = -

2.571, p < .05), perceived financial risk (β = -.1, t = -2.901, p < .01), and perceived time

pressure (β = -.17, t = -4.371, p < .01) have negative and significant impacts on

consumers’ situational mindfulness. Table 20 summarized all the tested hypotheses and

the findings related to main effects in the model.

- 70 -

Table 20: Main Effects of the Structural Model

Hypothesized Relationship Path

coefficient Assessment

H1: Trait mindfulness → Situational Mindfulness .09** Supported

H2:Rational DMS → Situational Mindfulness .33*** Supported

H3: Satisficing DMS → Situational Mindfulness .1*** Not Supported

H4: Personal Norm → Situational Mindfulness .39*** Supported

H5: Cognitive Load → Situational Mindfulness .44*** Not Supported

H6: Uncertainty → Situational Mindfulness -.09** Supported

H7:Financial Risk → Situational Mindfulness -.1 *** Supported

H8:Time Pressure → Situational Mindfulness -.17 *** Supported

H9: Situational Mindfulness → PEOU .48*** Supported

H10: Situational Mindfulness → PU .8*** Supported

H11:PEOU → Intention to use .31*** Supported

H12:PU → Intention to use .24*** Supported

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

- 71 -

4.4 Mediation Effects

Following the Hayes and Preacher’s (2010) bias-corrected bootstrapping

procedure in AMOS, I tested the mediation effects suggested in H13 and H14. In support

of H13 and H14, consumers’ situational mindfulness has a positive and significant

indirect effect (β =.279, p < .05) on their intention to use green technology. No

significant direct effect was detected from situational mindfulness to intention to use.

Thus, it is concluded that perceived usefulness and perceived ease of use variables fully

mediates the positive relationship between consumer’s situational mindfulness and

intention to use green technology.

In addition to that, I examined the mediating effects of consumers’ situational

mindfulness on the relationship between all of its determinants and consumers’ intention

to use through perceptions (PEOU & PU). The results revealed that situational

mindfulness fully mediates the relationships between trait mindfulness and perceptions,

trait mindfulness and intention to use, rational decision style and perceptions, rational

decision style and intention to use, personal norms and perceptions, and personal norms

and intention to use. The size of indirect effects and significance level were summarized

in Table 21.

- 72 -

Table 21: Mediation Effects

Standardized indirect effects β (95% BC CI)

Indirect effect of SM on IU via PEOU 0.268 (.135, .416)***

Indirect effect of SM on IU via PU 0.346 (.197, .534)***

Indirect effect of TM on PEOU via SM 0.06 (.013, .121)***

Indirect effect of TM on IU via SM and PEOU 0.02 (.005, .047)***

Indirect effect of TM on PU via SM 0.121 (.023, .235)***

Indirect effect of TM on IU via SM and PU 0.026 (.007, .061)***

Indirect effect of Rat on PEOU via SM 0.224 (.145, .33)***

Indirect effect of Rat on IU via SM and PEOU 0.077 (.036, .133)***

Indirect effect of Rat on PU via SM 0.454 (.341, .608)***

Indirect effect of Rat on IU via SM and PU 0.099 (.053, .16)***

Indirect effect of PNs on PEOU via SM 0.257 (.175, .358)***

Indirect effect of PNs on IU via SM and PEOU 0.088 (.045, .147)***

Indirect effect of PNs on PU via SM 0.52 (.395, .654)***

Indirect effect of PNs on IU via SM and PU 0.113 (.062, .18)***

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

N: 305. β = standardized indirect effects. The bias-corrected confidence intervals were

based on 2000 bootstrap samples

TM: Trait Mindfulness Rat: Rational Decision Style

SM: Situational Mindfulness Sat: Satisficing Decision Style

PNs: Personal Norms PU: Perceived Usefulness

IU: Intention to Use PEOU: Perceived Ease of Use

- 73 -

4.5 Moderation Effects

To test the proposed moderation effects, I used a split-group approach (multi-

group analysis) and divided sample into two groups (high vs. low) based on median splits

for each bounding variables (perceived cognitive load, uncertainty, financial risk, and

time pressure). Then, the proposed moderation effects were tested. For each moderation

effect, critical ratios were produced for the regression weights differences. P-values were

calculated to measure the significance of each difference using these critical ratios. The

results were compatible with chi-square difference tests for each moderation path.

The results of the multi-group analysis showed support to the H15a, H16.1.b, and

H19.2.b, and H19.2.d. High cognitive load dampens the positive effect of trait

mindfulness on situational mindfulness significantly (β = .015, z= 1.68, p < .1). High

perceived uncertainty dampens the positive effect of rational decision style on

consumers’ situational mindfulness (β = .229, z= -1.955, p < .1). The effect of perceived

usefulness of green technology on intention to use weakens when the perceived time

pressure is high (β = .143, z= - 2.733, p < .01). Table 22 presents the results of the multi-

group analysis.

4.6 Post-Hoc Analysis

I did a post-hoc power analysis, and the result revealed that the proposed model

had the power to detect significant effects that may have been existed. Therefore, we are

confident that those non-significant effects that we observed are not truly significant.

- 74 -

Table 22: Moderation Effects

Estimate P Estimate P z-score Hypothesis

Cognitive Load

Low

Cognitive Load

High

TM-> SM 0.120 0.013 0.015 0.705 1.68* H15a supported

PNs->SM 0.341 0.000 0.299 0.000 0.737 H17a rejected

Rat->SM 0.291 0.000 0.262 0.000 0.411 H16.1.a rejected

Sat->SM 0.060 0.332 0.131 0.004 -0.930 H16.2.a rejected

SM->PEOU 0.684 0.000 0.973 0.000 -1.429 H18.1.a rejected

SM->PU 1.400 0.000 1.815 0.000 -1.802* H18.2.a rejected

PEOU->IU 0.413 0.000 0.301 0.000 0.965 H19.1.a rejected

PU->IU 0.278 0.000 0.140 0.015 1.646 H19.2.a rejected

Uncertainty

Low

Uncertainty

High

TM-> SM 0.103 0.037 0.054 0.171 -0.761 H15b rejected

PNs->SM 0.306 0.000 0.343 0.000 0.631 H17b rejected

Rat->SM 0.377 0.000 0.229 0.000 -1.955* H16.1.b supported

Sat->SM 0.052 0.459 0.112 0.009 0.736 H16.2.b rejected

SM->PEOU 0.581 0.000 1.046 0.000 2.345** H18.1.b rejected

SM->PU 1.660 0.000 1.445 0.000 -0.939 H18.2.b rejected

PEOU->IU 0.343 0.000 0.339 0.000 -0.035 H19.1.b rejected

PU->IU 0.134 0.011 0.301 0.000 2.016** H19.2.b rejected

Financial Risk

Low

Financial Risk

High

TM-> SM 0.091 0.042 0.071 0.104 -0.322 H15c rejected

PNs->SM 0.295 0.000 0.331 0.000 0.612 H17c rejected

Rat->SM 0.287 0.000 0.293 0.000 0.087 H16.1.c rejected

Sat->SM 0.125 0.016 0.097 0.074 -0.367 H16.2.c rejected

SM->PEOU 0.812 0.000 0.791 0.000 -0.103 H18.1.c rejected

SM->PU 1.798 0.000 1.446 0.000 -1.508 H18.2.c rejected

PEOU->IU 0.409 0.000 0.288 0.000 -0.998 H19.1.c rejected

PU->IU 0.218 0.000 0.206 0.001 -0.143 H19.2.c rejected

Time Pressure

Low

Time Pressure

High

TM-> SM 0.064 0.209 0.089 0.034 0.376 H15d rejected

PNs->SM 0.286 0.000 0.366 0.000 1.357 H17d rejected

Rat->SM 0.341 0.000 0.244 0.000 -1.366 H16.1.d rejected

Sat->SM 0.084 0.084 0.127 0.023 0.570 H16.2.d rejected

SM->PEOU 0.685 0.000 0.834 0.000 0.737 H18.1.d rejected

SM->PU 1.582 0.000 1.589 0.000 0.028 H18.2.d rejected

PEOU->IU 0.321 0.000 0.339 0.000 0.148 H19.1.d rejected

PU->IU 0.372 0.000 0.143 0.005 -2.733*** H19.2.d supported

- 75 -

Figure 4: The Moderating Role of Perceived Cognitive Load (Unstandardized)

H: .02(ns) .54*** -.21***

L: .12**

H4 1.4*** .28***

.34*** .68*** .41***

H2-H3 .29***

.06(ns) -.05(ns) -.05*

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Perception

Perceived

Usefulness

Perceived

Ease of Use

Situational

Mindfulness

Decision Making

Style

Trait

Mindfulnes

s

Intention

To Use

Uncertainty

Financial Risk

Rational DMS

Satisficing

Personal Norm

To Use

Time Pressure

Cognitive Load

- 76 -

Figure 5: The Moderating Role of Perceived Uncertainty (Unstandardized)

.37*** -.22***

.1**

.31***

1.66*** .13**

.05(ns) .58*** .34***

H2-H3

H: .38*** -.06(ns) -.06*

L: .23***

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Perception

Perceived

Usefulness

Perceived

Ease of Use

Situational

Mindfulness Decision Making

Style

Trait

Mindfulnes

s

Intention

To Use

Uncertainty

Financial Risk

Rational DMS

Satisficing

Personal Norm

To Use

Time Pressure

Cognitive Load

- 77 -

Figure 6: The Moderating Role of Perceived Time Pressure (Unstandardized)

.44*** -.05(ns) (H: .14***)

(L: .37***)

.06(ns)

H4 .29*** 1.58***

.69*** .32***

H2-H3 .34***

.08* -.05(ns) -.1***

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Perception

Perceived

Usefulness

Perceived

Ease of Use

Situational

Mindfulness

Decision Making

Style

Trait

Mindfulnes

s

Intention

To Use

Uncertainty

Financial Risk

Rational DMS

Satisficing

Personal Norm

To Use

Time Pressure

Cognitive Load

- 78 -

CHAPTER 5

CONCLUSION

This chapter aims to summarize the findings, present theoretical and practical

implications and discuss limitations and future research opportunities. In general, green

technology adoption is an updated version of classical TAM (Hujits, et al., 2012; Toft et

al., 2014). According to this new theory, normative motives predict the adoption of green

technology. Extending the Sun and Fang’s (2010) mindful technology acceptance study,

this study examined the cognitive processes of adopting a green high technological

product, mainly, the effects of constraining (perceived time limitation, risks, uncertainty,

and cognitive load) and enhancing factors (trait mindfulness, personal norm, rationality)

on individual situational mindfulness and its supportive impact on green technology

adoption process. The study also addressed the suggestion of Sun et al. (2016) to test the

effects of self-efficacy on situational mindfulness in the technology adoption process and

found no significant impact of self-efficacy on situational mindfulness. I discussed the

findings of this study and discussed limitations and future research suggestions in the

following.

5.1 Major Findings and Discussion

High technology adoption is a risky decision and requires a significant amount of

cognitive processing. The study assumed that mindfulness, as it reflects the quality of

consciousness, could be an enriching lens for consumers to understand the utility of the

technology better and decrease the perceived clutters in the understanding of its ease of

use. The results of this study provided support to the proposed positive impact of

- 79 -

dispositional mindfulness on situational mindfulness. This means that if a consumer has

mindful as his/her disposition, she/he is more likely to act mindfully in every situation-

specific experience. This relationship was not discovered before, and it is one of the main

contributions to the literature of this dissertation.

Additionally, this study revealed that PNs is strongly associated with situational

mindfulness. This result implies that having a strong normative orientation predicts

situational mindful in technology acceptance process. To my knowledge, this relationship

was also not established in the literature before this study. Another significant finding

was the existence of a strong association between rational decision-making style and

situational mindfulness. Kirk et al. (2011) showed the impact of mindfulness in

generating more rational decision outcomes, but this study revealed the predictive effect

of the rational decision style of the decision as a personality trait on situational

mindfulness for the first time. The discovered link between cognitive load and

mindfulness was counterintuitive and may need further examination. Finally, the study

found significant and positive effects of situational mindfulness on both consumers’

perception of ease of use and perceived usefulness of green technology.

As managerial implications for marketing, this study suggested that consumer

mindfulness is an essential antecedent in green technology adoption. It directly correlates

with both consumers’ perceived utility and perceived effort expectations of green

technology which ultimately impacts consumers’ purchase intention. Additionally, even

if this study showed that situational mindfulness of individuals is affected by

informational uncertainty, financial risk, and time limitations, the effects are at a low or

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moderate level. Furthermore, cognitive load enhances situational mindfulness. Because

situational mindfulness can be improved by changing the way of marketing messages or

marketing activities (Langer, 1989b) marketing managers in the green technology sector

can cultivate situational mindfulness and lessens the negative impacts of bounding effects

of informational uncertainty, financial risk, and time limitations on consumers’ decision-

making process.

5.2 Limitations and Future Research

In this dissertation, I tested the proposed model with data collected only from

undergraduate students as participants. Student samples limit age variability. In this

research, age has a skewed distribution between 18 and 24. Limited age distribution and

high education level make this study’s findings more prone to desirability effect (Kaiser

et al., 2008). However, considering the green consumers’ demographic characteristics,

student sample can present a good fit for a green technology acceptance study.

Considering their familiarity with green products, students were counted as

representatives of potential green technology buyers even though they are mostly not the

actual users of high technology electric vehicles.

Additionally, even though the sample is from one of the most diverse universities

in the world, it does not guarantee a geographical diversity. One can expect that green

technology consumers in the USA could differ in many ways from consumers in different

countries such as Argentina, Egypt or China concerning their normative motives,

perceived cognitive busyness and situational uncertainty. Thus, this study concluded that

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the generalizability of the research is limited because of the sample characteristics. Future

research with a diverse demographic population can eliminate these concerns.

Another significant limitation is related to a potential self-reporting bias. All of

the variables are self-reported and prone to social desirability bias. Some measures such

as perceived cognitive load, perceived time pressure, and intention to use need to be re-

evaluated and adapted after careful revision. For measuring perceived cognitive load, for

example, number memorization in an experimental setting could provide more accurate

results. Similarly, time pressure can be better understood by using actual time

manipulation. The study resolved the measurement problem for intention to use scale by

comparing the model with the alternative model that measures IU using only the third

item of the scale (IU_3: I plan to use this electric car in the near future). A Chi-square

difference test revealed that models were not significantly different from each other.

Finally, this research examined only the intention to adopt green technology.

Even though intention to use a technology strongly predicts actual adoption behavior,

there is still not a consensus on this statement. Future research that tests the model with

actual adopters can provide a more accurate picture of green technology adoption process

and better exploratory power in the model.

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APPENDIX A: Multicollinearity Diagnosis

Table 23: Multicollinearity Diagnosis

Model

Unstandardized

Coefficients

Std.

Coef.

t Sig.

Collinearity

Statistics

B

Std.

Error Beta Tolerance VIF

1 (Constant) .000 .050 .000 1.000

SM -.074 .073 -.054 -1.007 .315 .730 1.371

Rat .086 .091 .055 .947 .345 .627 1.596

CL -.062 .083 -.039 -.751 .453 .792 1.263

PNs .151 .074 .107 2.048 .041 .766 1.305

TP -.043 .094 -.025 -.459 .647 .698 1.432

FR .080 .050 .087 1.616 .107 .728 1.373

UNC -.239 .053 -.227 -4.511 .000 .828 1.208

TM .106 .056 .095 1.896 .059 .839 1.191

Sat .466 .099 .279 4.709 .000 .597 1.675

PU .163 .046 .184 3.522 .000 .770 1.299

PEU .253 .054 .261 4.657 .000 .669 1.495

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APPENDIX B: Consent Form for Anonymous Data Collection (IRB Protocol # E17-

531)

You are invited to participate in a research study that is being conducted by Emine Erdogan, who is a Ph.D. student in the Marketing Department at Rutgers University. The purpose of this research is to study your decision making experiences in the process of car shopping. This research is anonymous. Anonymous means that I will record no information about you that could identify you. There will be no linkage between your identity and your response to the research. This means that I will not record your name, address, phone number, date of birth, etc. The research team and the Institutional Review Board at Rutgers University are the only parties that will be allowed to see the data, except as may be required by law. If a report of this study is published, or the results are presented at a professional conference, only group results will be stated. All study data will be kept for 3 years. There are no foreseeable risks to participation in this study. You will receive course credits for participating in this research. Rutgers Business School behavioral lab will manage the credit allocation. Other than that, you may receive no direct benefit from taking part in this study. Participation in this study is voluntary. You may choose not to participate, and you may withdraw at any time during the study procedures without any penalty to you. In addition, you may choose not to answer any questions with which you are not comfortable. If you have any questions about the study or study procedures, you may contact me at emine.erdogan@rutgers.edu Phone: 862-703-9158 1Washington Place 1029/A, Newark/NJ. You can also contact my faculty advisor: Sengun Yeniyurt at yeniyurt@business.rutgers.edu Phone: 973-353-3442 Address: 100 Rockafeller Road, Piscataway, NJ 08854 If you have any questions about your rights as a research subject, please contact an IRB Administrator at the Rutgers University, Arts and Sciences IRB: Institutional Review Board Rutgers University, the State University of New Jersey Liberty Plaza / Suite 3200 335 George Street, 3rd Floor New Brunswick, NJ 08901 Phone: 732-235-9806 Email: humansubjects@orsp.rutgers.edu Phone: 732-235-9806 Email: humansubjects@orsp.rutgers.edu If you are 18 years of age or older, understand the statements above, and will consent to participate in the study, click on the "I Agree" button to begin the survey/experiment. If not, please click on the “I Do Not Agree” button which you will exit this program.

I Agree

I Do Not Agree

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APPENDIX C: Survey Questionnaire

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