Understanding Ecommerce Consumer Privacy From the Behavioral Marketers' Viewpoint

Understanding Ecommerce Consumer Privacy From the

Behavioral Marketers' Viewpoint

Section 1: Foundation of the Study

The study of consumer privacy is not a new topic for behavioral marketing

managers, although the subject is complex due to the various definitions of privacy.

Using the theory of planned behavior (TPB), I examined the issue of privacy for

ecommerce consumers from the behavioral marketer’s perspectives. Understanding the

methods that behavioral marketing managers use to collect and store customer data and

the importance placed on protecting the data were relevant to this study. Mishandling data

may result in a security breach. Security breaches are costly to the organization the

ecommerce consumer.

The topic of ecommerce privacy is relevant as an increasing number of consumers

use the Internet to purchase products and services. In 2017, ecommerce sales increased

$2.3 trillion USD, with a project increase of $4.8 trillion by 2021 (Statista, 2019).

Behavioral marketers not focused on ecommerce consumer privacy may lose sales and

loyal customers as the result of a security breach (Kude, Hoehle, & Sykes, 2017).

Security breaches negatively affect the revenue and profits of the organization.

Background of the Problem

Behavioral marketing is the advertising process of targeting the consumer

population based on an interest or topic (Milne Pettinico, Hajjat, & Markos, 2017).

Targeting ecommerce consumers requires the collection, use, and sale of personal

information (Milne et al., 2017). This relatively new phenomenon brings value and a

competitive edge to the marketer’s organization (Martin, 2016). Behavioral marketers

increase sales and revenue by targeting potential customers based on the use of the

ecommerce consumer’s personal information (Todor, 2016). The problem with the

process of targeting consumers was the collection of personal information and the threat

of a security breach. A security breach may decrease sales and revenue for the

organization. Behavioral marketers must balance the benefits of targeted marketing with

the risks to the consumer (Libaque-Saenz, Chang, Kim, Park, & Rho, 2016).

Behavioral marketing offers many benefits to the marketer, as an advertising

strategy to increase revenue (Wu, Ke, & Nguyen, 2018). The online sales growth

potential renders the phenomenon of behavioral marketing as profitable for the behavioral

marketer (Todor, 2016). Behavioral marketers design advertising strategies to increase

sales. Increased sales were the goal of targeted advertising (Wu et al., 2018). The results

of this research project may add to the literature on ecommerce consumer privacy through

the marketing lens of the behavioral marketer. Unlike previous ecommerce privacy

concerns, which offer the ecommerce consumer’s point of view, in this study, I focused

on the viewpoint of the behavioral marketer.

Problem Statement

Although behavior marketing is a profitable low-cost advertising method, when

compared with traditional marketing methods, there may be a threat of security breach,

for organizations (Milne et al., 2017). In 2014, data breaches cost the organization

approximately $5.9 million per incident (Sen & Borle, 2015). The general business

problem that I addressed in this study is that behavioral marketers may have placed more

importance on organizational profits than the risk of a security breach (Choong, Hutton,

Richardson, & Rinaldo, 2017). The specific business problem that I addressed is that

some behavioral marketers may not understand the correlation social norms, and PBC

concerning the collection of ecommerce consumer’s personal information, resulting in a

possible security breach.

Purpose Statement

The purpose of this quantitative correlational study was to examine the correlation

between the behavioral marketer’s intention to protect ecommerce privacy, as it relates to

their attitude, SNs, and PBC, as measured by the TPB tool regarding the collection of

ecommerce consumer’s personal information. The dependent variable was the behavioral

marketer’s intention to protect ecommerce consumer privacy. The independent variables

include the behavioral marketer’s attitude, SNs, and PBC. The targeted population for

this study was behavioral marketing managers from the American Marketing Association

(AMA) located in the USA. A study of privacy from the behavioral marketer’s viewpoint

within ecommerce organizations was meaningful based on the expected growth of

ecommerce sales and the risk of a security breach (Milne et al., 2017; Sen & Borle,

2015). In this study, I have contributed to social change by helping marketing leaders

understand the correlation between their attitudes, SNs, and PBC concerning an

organization’s ecommerce information collection processes. This awareness may lead to

strategies, which reduce the amount of information collected or policies to safeguard the

consumer’s personal information.

Nature of the Study

In this research study, I used input from the behavioral marketing manager’s

perspective to determine the importance of ecommerce privacy and the degree of

relationship between the dependent and independent variables. The selected research

method must be appropriate to answer the research question (Sukamolson, 2016).

Conducting quantitative and qualitative research studies provides answers to phenomena

related questions, but researchers can convert the data into numerical values, which allow

statistical analysis in quantitative designs (Sukamolson, 2016). The quantitative approach

allows the representative population to provide responses not readily available from other

sources (Sukamolson, 2016). Qualitative designs give meaning to the data from a

personal perspective with face-to-face interviews and participant observations, which

leads to the identification of trends (Echambadi, Campbell, & Agarwal, 2006). The

identification of trends in the data assists the researcher with conclusions. Qualitative

research was beneficial for a study when the researcher desires to add flexibility and have

direct contact with the study population (Farah, 2017). The direct contact of qualitative

research was useful in understanding phenomena. Echambadi et al. (2006) wrote that

researchers using a quantitative design alone might miss essential elements of the

phenomena due to bias and incomplete conclusions. A mixed methods approach may

bring out the best in both methods. Mixed methods have the capacity to answer more than

one type of research question (Frels & Onwuegbuzie, 2013). Echambadi et al. (2006)

suggested that researchers look for opportunities to combine qualitative and quantitative

methods. A mixed methods approach will allow researchers to identify new variables, not

studied in previous research (Echambadi et al., 2006). The three methodologies,

quantitative, qualitative, and mixed methods have merit but the best research design

allows the researcher to answer the research question successfully. I eliminated

qualitative and mixed methods because the two methodologies did not meet the need of

the research question, which establishes the relationship between variables. The research

question for this study renders quantitative methods the best methodology for this

research project.

The research design was the method of collecting data (Sukamolson, 2016).

Several designs exist for collecting data. The designs include survey research,

correlational, experimental, and causal-comparative research (Vogt, Gardner, & Haeffele,

2014). A correlational nonexperimental design is most common in social and behavioral

studies, when the researcher examines the participant’s attitudes, beliefs, or values (Vogt

et al.). The correlational design allows researchers to make comparisons between the

constructs and was useful for predicting an outcome from one or more variables

(Sukamolson, 2016). Ajzen and Sheikh (2013) were successful in predicting behavioral

intentions with a correlational design. My purpose in this study was to predict the

behavioral marketer’s intention toward ecommerce consumer privacy based on their

attitude, SNs, and PBC. A correlational design was appropriate for this study based on

predicting the behavioral marketer’s intention to protect ecommerce consumer privacy.

Research Question

The data from the research question for this doctoral study may provide

knowledge of the behavioral marketer’s role in maintaining the ecommerce consumer

privacy by understanding the manager’s behavioral intentions using the TPB. The

primary research question was:

What was the correlation between behavioral marketer’s attitudes, SNs, and PBC,

regarding the collection of ecommerce consumer’s personal information?

Hypotheses

H01: There was not a significant correlation between the behavioral marketer’s

attitudes concerning the collection of ecommerce consumer’s personal information.

Ha1: There was a significant correlation between the behavioral marketer’s

attitudes concerning the collection of ecommerce consumer’s personal information.

H02: There was not a significant correlation between the behavioral marketer’s

SNs concerning the collection of ecommerce consumer’s personal information.

Ha2: There was a significant correlation between the behavioral marketer’s SNs

concerning the collection of ecommerce consumer’s personal information.

H03: There was not a significant correlation between the behavioral marketer’s

PBCs concerning the collection of ecommerce consumer’s personal information.

Ha3: There was a significant correlation between the behavioral marketer’s PBCs

concerning the collection of ecommerce consumer’s personal information.

Theoretical or Conceptual Framework

The theoretical framework for this study was the TPB first introduced by Ajzen

(2011), in the 1980s. The TPB is an expanded construct of Ajzen’s theory of reasoned

action (TRA) (Ajzen & Sheikh, 2013). Ajzen identified an individual’s propensity to

perform a behavior, determined by the individual’s SNs and their behavioral intent (BI),

or attitude toward the behavior with TRA (Ajzen & Sheikh, 2013). Building on the TRA,

Ajzen (1991) added PBC. PBC is the best predictor of future behavior (Ajzen, 1991). The behavioral

manger’s attitudes, SNs, PBCs, and their intention to protect ecommerce consumer privacy was the

focus of this study. The TPB variables, attitude, SNs, and PBC form the foundation of this study.

Although previous researchers used the TPB to study psychological and health care issues, use of the

TPB helped understand the intentions of behavioral marketers.

The TPB has more applications in the psychological and health care industry

(Ajzen & Sheikh, 2013). Ajzen and Sheikh (2013) used TPB to understand individual

behavior in the social science industries with excellent results. In the business world, TPB

was a reliable predictor of online managerial behavior when behavior was volitional and

the manager has the required information to form a stable online decision (Shin &

Hancer, 2016). The TPB was useful for understanding online consumer behavior. Choong

et al. (2017) used the TPB to identify ecommerce, privacy protection measures. Choong

et al. revealed that 47% of ecommerce consumers expressed concern about their privacy

in ecommerce applications. Applying the TPB to the current study contributed to the

literature of ecommerce consumer’s privacy with the collection, use, and sale of personal

information by providing behavioral marketers additional insight into consumer’s future

ecommerce behavior. The principles of TPB provided behavioral marketer’s introspection

into their future behavioral intention and increased their understanding of safeguarding

consumer privacy by reducing the collection, use, and sale of personal information.

Operational Definitions

Several terms were specific to the study of ecommerce privacy concerns.

Definition of the key terms will aid in the understanding of this study. The terms used

throughout this study listed in this section.

Behavioral intention: Behavioral intention is a predictor of behavior when the

behavior is volitional, and the individual has the information to decide. (Kammer,

Niessen, Schmid, & Schwendener, 2016).

Behavioral marketing: Behavioral marketing is the systematic process of

collecting, storing, and selling personal information for use in targeting consumers. The

practice of collecting and storing online consumer information for targeting

advertisements for products or services, based on browsing history (Milne et al., 2017).

PBC: PBC represents a manager’s perception of entering into strategic agreements

as a representative of their firm or organization and the availability of resources (Kassim,

Arokiasamy, Isa, & Ping, 2017).

Personal Information: Information, which directly or indirectly identify an

individual. Personal information includes IP address, digital footprint, location, and

online profile (Goddard, 2017).

Privacy concern: Ecommerce privacy concern is the unknown outcome of the

collection and use of personal information (Milne et al., 2017).

Privacy paradox: Privacy paradox is the complex situation where consumers

desire privacy in online settings but will provide personal information in exchange for a

reward (Dienlin & Trepte, 2015; Lwin, Wirtz, & Stanaland, 2016).

Privacy violation: Privacy violation is the act of collecting, using, and selling an

individual’s personal information without the consent or knowledge, resulting in harm to

the individual (Milne et al., 2017).

Right to privacy: The ability to control privacy by requesting marketers not

collect, use, or sell personal information, to third parties (Walsh, Parisi, & Passerini,

2017).

SNs: Subjective or social norms are the effect reference groups have on the

decision-making process (Kammer et al., 2016).

Assumptions, Limitations, and Delimitations

Assumptions, limitations, and delimitations were the parameters of the research

study. They include the unverified truths or assumptions, the weak areas or limitations of

the study and the boundaries or delimitations of the study. Together, assumptions,

limitations, and delimitations set the expectations of the research study.

Assumptions

The assumptions of this study were concepts accepted as truths not but not

verified (Simon, 2011). Leedy and Ormrod (2010) stated that assumptions are the basis of

a doctoral study. The first assumption that I made was the population of behavioral

marketing managers responding to the survey will possess the information required to

answer the questionnaires truthfully. A second assumption that I made was the number of

valid survey responses will result in a sample size representative of the population of

behavioral marketing managers. Research validity depends on the response rate of the

questionnaires (Vogt et al., 2014). A high response rate yields a sample size

representative of the population. The third assumption that I made was consumer privacy

concerns were important to behavioral marketers. The importance of consumer privacy

research in the literature focused on the consumer. The topic of this research study on

consumer privacy concentrated on the managerial perspective.

Limitations

The limitations of a study are the things that the researcher cannot control, which

may affect the validly of the research (Simon, 2011). Essentially, limitations are the weak

points of the research study. The first limitation based on the TPB was, that knowledge is

not a consistent predictor of behavior (Ajzen, 2011). A manager may be aware of a

knowledge point but choose to ignore it if his or her actions negatively affect

organizational profits. The second limitation was the anonymity of the survey responses.

The respondents or participants represented several industries. Focusing on one industry

may produce different results. For example, behavioral marketers within the elevator

industry may have a different perspective on privacy concern compared to the behavioral

marketer representing an online retailer. Related to industry was the geographic region.

Concentrating on one geographic region outside of the USA will yield different results.

Behavioral marketers in European countries have different viewpoints of privacy (Fortes

& Rita, 2016). Third, the TPB specific variable, attitude, SNs, or PBC, which will

motivate behavioral marketing managers to protect personal information online was

unknown. Fourth, there was not a concrete definition of privacy. Previous researchers

used differing terminology for the same privacy concepts (Milne et al., 2017). For the

purpose of this study, I defined consumer privacy as the privacy the behavioral marketers

can affect, by their information collection, use, and sale strategies. Last, there was a

2week time constraint for collecting data. The populations of marketing managers are

busy and many may delete the survey link without opening. This action may influence the

number of qualified survey results returned.

Delimitations

Delimitations shape the structure of a research study. The delimitations function

as the boundaries of the study (Leedy & Ormrod, 2010). The first delimitation was the

defined specific problem (Simon, 2011). Some behavioral marketers may not realize the

relationship between their attitudes, SNs, and perceived behavior toward the protection of

ecommerce consumer privacy, resulting in possible security breach (Shin & Hancer,

2016). The target population of this study includes marketing managers with knowledge

of targeted or behavioral advertising and will not include ecommerce consumers who are

not behavioral marketers. The behavioral marketers with ecommerce behavioral

marketing experience are the subject of this study regardless of their years of experience

or size of their organization. Behavioral marketers from a large behavioral marketing

organization, such as Amazon or Target may yield different results (Craciun, 2018).

My focus in this study was the behavioral intention of ecommerce or behavioral

marketing managers on protecting ecommerce consumer’s online privacy. The

participants, behavioral marketers form the boundaries of this research. I included

behavioral marketing managers who have experience with targeted advertising and

collecting, storing, and selling consumer’s personal information in the study. Behavioral

marketing managers who have experience with targeted advertising and collecting,

storing, and selling consumer’s personal information were included in the study.

Behavioral marketing managers who were members of the AMA, within the USA

received a survey. Surveys distributed directly to behavioral marketing managers using

their company email address may yield different results. The results may vary in the

number of surveys returned, due to changes in job classifications, promotions resulting in

a different marketing role, and email message recognized as spam.

Significance of the Study

The growth of ecommerce provides the backdrop for a study of ecommerce

privacy from the behavior marketer’s perspective. Increased sales from the use of

ecommerce consumer’s personal information were the driving force for behavior

marketers (Wu et al., 2018). At the same time, concern for consumer privacy should also

be a factor to prevent a possible security breach.

A limited amount of literature was available concerning the behavioral marketer’s

role in protecting consumer privacy. As a relatively new phenomenon, most of the

information readily available concerning ecommerce consumer privacy was from the

ecommerce consumer’s perspective (Kude et al., 2017). The significance of this study

was the importance of ecommerce privacy from the behavioral marketer’s point of view.

The amount of ecommerce privacy information from the behavioral marketer’s

perspective was limited. As the number of ecommerce platforms continues to grow, the

rise in ecommerce sites prompted a need to understand the behavioral marketer’s

perspective on ecommerce privacy (Kude et al., 2017). The results of this doctoral study

may add to the limited research on ecommerce consumer privacy from the behavioral

marketer’s perspective. The results of this research may lead to an increased knowledge

of the behavioral marketer’s attitude, SNs, and PBC concerning the collection of

ecommerce consumer’s personal information as this relates to ecommerce privacy.

Contribution to Business Practice

The growth of online shopping necessitates behavioral marketers understand and

operate within the consumer privacy concern. Statista (2019) listed ecommerce sales in

North America at 8.1% of global sales. The acceptance of social media and the amount of

personal information on social media led scholars to suggest privacy was dead, and

privacy concern was not relevant to ecommerce consumers (Weinberg, Milne, Andonova,

& Hajjat, 2015). An effective behavioral marketing strategy will continue to increase

revenue and profits for the organization without putting the ecommerce consumer at

considerable risk of a security breach (Choong et al., 2017). The results of this study

provided insight into the correlation between the behavioral marketer’s attitude, SNs, and

PBC concerning the collection of ecommerce consumer’s personal information and

ecommerce privacy. In short, this study provided insight into the importance behavioral

marketers place on ecommerce privacy.

Implications for Social Change

The social implication of this study provided behavioral marketers an increased

awareness of the ecommerce consumer privacy based on the collection of personal

information. Behavioral marketer’s attitude, SNs, and PBC as measured by the degree of

importance placed on ecommerce privacy determines if the ecommerce organization was

at high risk for a security breach. Behavioral marketers may align this awareness to the

profits and revenue of ecommerce transactions and take the appropriate action to protect

the collected personal information. This awareness and subsequent action may prevent

future security breaches. Social change aligned with shareholder interests has a greater

chance for implementation (Sonenshein, 2016). An awareness of consumer privacy

concerns with the collection of personal information assisted with differentiating an

organization’s ecommerce site from the ecommerce sites, which choose not to address

consumer privacy concerns about the collection of personal data (Weinberg et al., 2015).

Consumers will be more willing to provide personal information, and shop, if behavioral

marketers were actively pursuing safeguards to protect ecommerce consumer’s personal

information (Acquisti, Taylor, & Wagman, 2016). Increased knowledge of behavioral

marketer’s attitude, SNs, and PBC toward the collection, and use of personal information

drove social change through behavioral marketer’s intentions.

A Review of the Professional and Academic Literature

My purpose in this quantitative correlational study was to understand the strategic

intention of behavioral marketing managers when developing ecommerce advertising

strategies, which collect, use, and sell ecommerce consumer’s personal information. The

process of collecting, using, and selling ecommerce consumer’s personal information

may put the information at risk for a security breach. Using TPB to frame the study, I

examined the behavioral marketer’s strategic intentions to understand the attitudes, social

norms, and perceived behavioral control of the marketing manager. Studying the

attitudes, social norms, and perceived behavioral control of the marketing manager who

develop targeted ads with ecommerce consumer’s personal information will add to the

existing literature. The results of this study contributed to the existing literature by adding

the element of consumer privacy, from the behavioral marketing manager’s perspective.

This literature review was composed of references from three subject areas,

behavioral marketing, ecommerce consumer privacy, and the TPB. Together the articles

allowed an in-depth study of the concepts of ecommerce behaviors, attitudes, intention,

and ecommerce consumer’s privacy concerns. The literature retrieved from electronic

database searches including Business Source Complete, ABI/INFORMS Complete,

Emerald Management, Sage Premier, and PsycINFO comprised most of the references.

The keywords included consumer privacy, the theory of planned behavior, behavior

marketing, target marketing, digital privacy, database marketing, Internet marketing,

personalized advertising, identity marketing, market research, online advertising, the

theory of reasoned action, and ecommerce transactions for years 2006 to 2019. The

collection of peer-reviewed journals collectively addresses the role of behavioral

marketers and their attitude, social norms, and PBC regarding ecommerce consumer

privacy concerns, ecommerce consumer privacy, and TPB, which frames this study. This

study includes 89% peer reviewed studies published within 5 years of CAO approval.

This literature review consists of 76 peer-reviewed articles.

TPB has limited applications in the ecommerce industry. The TPB theory evolved

from the psychology and health care construct thus identifying a gap in the literature and

a need for further study in the ecommerce environment. The limited information for TPB,

applications in ecommerce, and business decisions necessitated this literature review to

consist of three major sections. The three sections include the TPB framework, the

importance of consumer privacy, and behavioral marketing as a strategy. Each of the

major areas includes a history and current state of the topic.

The Theory of Planned Behavior

The TPB was the theoretical framework used to study the behavioral marketer’s

future intentions toward consumer privacy on ecommerce sites. According to the TPB an

individual’s intention toward an action consists of three constructs; attitude, SNs, and

PBC (Ajzen & Sheikh, 2013). Central to the theory was an individual’s capability to

perform the action (Shin & Hancer, 2016). Shin and Hancer (2016) stated that perceived

behavioral control was an individual’s confidence that he or she can perform the action.

The focus of this study was twofold; the importance placed on behavioral marketer’s role

in protecting consumer privacy and the behavioral marketer’s capacity to make the

necessary changes to protect ecommerce consumer privacy. Behavioral marketers may

understand the importance of protecting ecommerce consumer privacy and have a

positive attitude toward TPB but lack the capacity to make changes.

The TPB was a reliable fit for this study. It has predictive validity for determining

how an individual will perform a future action based on attitude, social norms, and PBC

(Shin & Hancer, 2016). The behavioral marketer’s intentions toward ecommerce privacy

were necessary to answer the research question. TPB helped to understand how the

behavioral marketer’s attitude, social norms, and perceived behavior influence the

marketer’s intention. Behavioral marketers were instrumental in designing protective

privacy controls in the initial phases of the ad design process. What was not clear is the

importance behavioral marketer’s place on ecommerce privacy and the need to protect the

privacy from security breaches.

Ajzen (1991) developed the TPB, from the TRA, which had more applications in

social and behavior research. Ajzen stated that an individual’s intention depends on the

individual’s attitude toward the behavior and the SNs within TRA (Ajzen, 2011). Users

of the TRA tool measured an individual’s intention to perform a behavior with the

person’s attitude as the barometer of the behavior’s importance (Woolley, 2015). TPB

had an additional component, PCB. Ajzen (2011) defined PCB as the perceived capability

of performing the behavior or action and assumed the effect of prior experiences. Kassim

et al. (2017) stated PCB was a significant factor in predicting intention. Based on the TPB

an individual’s behavioral intention was dependent on attitude, SNs, and perceived

behavioral control (Ajzen, 2014;).

Understanding the behavioral marketer’s attitude toward ecommerce privacy was

not enough if the capability to change was not present. Ajzen and Sheikh (2013) stated

that the capacity to make changes reflects the behavioral marketer’s intentions. The

application of TPB to the study of ecommerce privacy was relatively new. There were

many theories linked to the study of consumer privacy. The theories focused on the

customer’s behavior rather than the marketer’s behavior.

Kassim et al. (2017) used TPB to understand car purchase decision intentions

among potential car purchasers. The research results for Kassim et al. indicated social

norms, reference groups, or individuals who influence decisions have a positive effect on

the decision to purchase a safe car. Whereas Shin and Hancer (2016) found that PBC was

a predictor for intentions. The focus on the behavioral intention of the student population

provides insight into the decision-making process before the adoption of the behavior

(Shin & Hancer, 2016).

Fortes and Rita (2016) realized a significant correlation between ecommerce

purchase intention and a positive attitude. They concluded a positive attitude toward a

behavior leads a greater intention to participate in ecommerce. Last, Fortes and Rita

found differences in the ecommerce consumer’s intention to purchase products compared

to services. Participants were less likely to purchase services online if a brick and mortar

retail store was available (Tabari & Abroud, 2017). This phenomenon was explained by

Tabari and Abroud (2017) as an adverse reaction to risk among ecommerce consumers.

Choong et al. (2017) and Fortes and Rita used a modified version of TPB survey tool to

examine online behaviors. The results indicated a positive correlation between social

norms and intention. The relationship between social norms and intention suggests

external influence affects an individual’s intention to engage in a protective online

behavior. Choong et al. indicated peer influence had a positive effect on the participant’s

intention to implement ecommerce protective actions. Like Choong et al., the research

studies of Fortes and Rita shared the similar results between the correlation of SNs and

the intention to perform the behavior. In each study, the SNs of the individual had a

positive impact on the behavior of the individual. Consistent with TPB, peers, and

significant relationships influence a managerial decision.

Brahmana, Brahmana, and Memarista (2018) used TPB to research psychological

drivers toward attitudes, SNs, and PBC for purchasing life insurance. The results of

Brahmana et al. revealed a positive relationship between the consumer’s attitude toward

purchasing health insurance and the intention to purchase. Also, Brahmana et al. found a

positive correlation between the managers’ PBC and their intention to purchase health

insurance. In contrast, Libaque-Saenz et al. (2016) found that education and professional

network influenced their social norms, for refusing to provide personal information for

secondary use. Brahmana et al. suggested the social norm component of the TPB was a

positive influence toward the purchase of health insurance. In each of the studies, the

individual had the capability to carry out the action. Their results were consistent with the

principles of the TPB; the individual must have the capacity to perform the behavior

(Ajzen, 2011). Like Brahmana, the results of Hegner, Fenko, and Teravest (2017)

suggested an individual’s attitude significantly affected intention toward brand loyalty

and their social circle provided the stimulus toward brand loyalty.

Fortes and Rita (2016) found a negative relationship between SNs and the

propensity to disclose personal information in exchange for incentives or rewards, among

Internet users. The results reflect the influence peers had on Internet users and the

tendency to disclose personal information online (Fortes & Rita, 2016). If the peer group

suggested online disclosure of personal information is safe, then the Internet users in the

study would be more willing to disclose their personal information. Fortes and Rita

suggested ecommerce consumers might be comfortable with disclosing personal

information, but a security breach may result in negative reviews and negatively impact

revenue. Seungsin, Younghee, Joing-in, and Jungkun (2015) confirmed Fortes and Rita’s

statement; a security breach may result in negative reviews and lost revenue, sales, and

profits. Rita and Fortes revealed the discomfort level of Internet users and publishing

personal information.

Choong et al. (2017) and Fortes and Rita (2016) found a significant a relationship

between the TPB variables, and the capacity to perform the behavior. Although, Chong et

al. and Fortes and Rita used the TPB to frame their research they cited a positive

experience, with the ability to perform the action. Along with the positive aspects of the

TPB, there were limitations. Scholars used TPB research with varying results, often citing

the limitations of the theory.

The shortcomings of the theory included differing viewpoints of the PCB

construct, individual attitudes, and the critics of the theory. Ajzen (2011) and Han and

Stoel (2017) used TPB with positive results but identified the deficiencies of the theory

related to the SN construct. The subjective norm was found to be the weakest construct in

the model for understanding socially responsible behavior. The inability to link behavior

to the intention was fundamental with the TPB (Ajzen, 1991). The three constructs,

attitude, SNs, and PBC must align with the intention, and the individual must have the

capacity to perform the behavior (Ajzen & Sheikh, 2013). The essence of the perceived

control component was a concern for a person’s ability to follow through with the

identified behavior (Ajzen & Sheikh, 2013).

Han and Stoel (2017) suggested that the TPB did not determine the beliefs

associated with a defined behavior. Ajzen (2011) countered this by stating that TBP does

not indicate where the beliefs originate, but the theory identified some constructs, which

may influence beliefs, such as education, age, gender, income, exposure to media, and

personality. Han and Stoel identified the intention-behavior gap as a limitation of TPB.

Shin & Hancer (2016) revealed the gap between intention and behavior concerning PBC,

the type of behavior, and the individual’s environment. This gap between intention and

behavior corresponds to the participant’s perception of the behavior.

An individual’s perception of the behavior, whether positive or negative, was a

factor between intention and behavior accomplishment, as well as the level of difficulty

in performing the behavior (Ajzen & Sheikh, 2013). The intention to behavior gap may

be more difficult when the sample populations were diabetics who require a diet

modification (Caro-Bautista et al., 2015). The diabetic has knowledge a diet modification

was required for sustained good health but may not be able to accomplish the behavior

(Caro-Bautista et al., 2015). Similar to Caro-Bautista et al. (2015) Moghavvemi, Sellah,

Sulaiman, and Abessi (2015) found intention alone was not a strong predictor for

determining behavior. Moghavvemi et al. suggested a set of related events or changes

were required for behavior changes. In addition to events or changes, Hassan, Shiu, and

Shaw (2016) suggested adequate planning assists with the intention to perform a specific

behavior, thus closing the behavior to intention gap. The individual’s attitude was also a

concern for the intention to behavior gap.

Shin and Hancer (2016) found social norms to be a shortcoming of TPB. The

attitude component determines the individual’s viewpoint of the behavior. The research

of Shin and Hancer revealed that social norms were not significant toward the purchase of

local food products and PBC to be significant influence on attitude to purchase. The

attitude toward the behavior can be either positive or negative. If the attitude toward the

behavior was favorable and the intention to perform the behavior was present, then the

capacity to carry out the behavior will be present (Shin & Hancer, 2016). In accordance

with the TPB, an individual’s attitude toward a behavior was a predictor of behavior

(Ajzen & Sheikh, 2013). The attitude component relates to how an individual feel about

the behavior or a tendency based on prior experience (Kammer et al., 2016). Kammer et

al. (2016) found that the manager’s attitude toward the behavior was a stronger indicator

of behaviors compared to PBC. Shin and Hancer (2016) determined attitude were an

influence on PBC.

Sniehotta, Presseau, and Araújo-Soares (2015) commented on additional variables

to explain the intention to behavior gap. Sniehotta et al. indicated demographics, age,

socio-economic status, and income affected the outcome of behavior. Choong et al.

(2017) found demographic characteristics were insignificant in understanding the gap

between intention and behavior. Age was an influencer of behavior intentions based on

the behavior. Studies concerning personal information disclosure on social network sites

will result in a positive correlation between age and intention to disclose with student

populations than with baby boomer participants (Wu et al., 2018). Dismissing an

individual’s age as a determiner for the intention to behavior gap, Ajzen (2011) wrote

knowledge or information was the link between intention and behavior and that

knowledge does not have to be accurate as a predictor of behavior. Along with the

shortcomings related to PBC and attitude, there were critics of the TPB. The critics state

the theory was too old for contemporary studies, and the theory was not an acceptable

predictor of future behavior.

Armitage (2015), Hall (2015), Ogden (2015), and Sniehotta et al. (2015) criticized

the TPB. The most profound criticism was the theory was outdated and past usefulness

for behavioral research. Hall advocated the retirement of TPB based on the age of the

theory and three shortcomings. First, Hall stated, the TPB was not useful for developing

interventions to change behavior although the intention of TPB was to predict behavior.

Second, participant self-reporting as the default methodology for assessing predictive

construct was a weakness for the theory. Last, the lack of understanding for boundary

conditions leading to research results, which the researcher accepts or rejects based on the

situation. Hall was correct in his assessment of the TPB. The TPB does not provide

interventions to the behavior. Researchers using the TPB can identify the attitudes, SNs,

and PBCs, which contribute to the intentions of performing the behavior and the

intentions to accomplish the behavior (Ajzen & Sheikh, 2013). Armitage, Hagger (2015),

Hall, and Trafimow (2015) understood the importance of TPB but acknowledged the

theory may have reached the end of usefulness and time to retire the theory. A researcher

should base the theory’s usefulness on the research results and not the age of the theory.

The TPB continues to have favorable results as it transitions from the health care and

psychological studies to business decisions (Hagger, 2015; Hall, 2015; Trafimow, 2015).

Sniehotta et al. (2015) scrutinized the research designs and selection of sample

populations used for examining the behavioral intentions in TPB studies. Sniehotta et al.

indicated that the TPB was not an acceptable predictor of behavior when research studies

used different designs and when participants were not college students. Contrary to

Sniehotta et al. (2015) the study design must be able to answer the research question

(Ajzen & Sheikh, 2013). Gurung and Raja (2016), Han and Stoel (2017), Hegner et al.

(2017), and Shin and Hancer (2016) found favorable success with both qualitative and

quantitative research methods using a sample population of participants with knowledge

of the subject and willingness to participate, for a reward. The sample of knowledgeable

participants was important

Shin and Hancer (2016) and Yeh et al. (2018) indicated the participant’s

knowledge of the behavior was important when conducting TPB studies. The sample

population must have knowledge of and the capability to perform the behavior (Choong

et al., 2017). Once the sample participants meet this condition, the TPB was a useful tool

to examine intentions (Shin and Hancer 2016; Yeh et al., 2018). For approximately 30

years, researchers have used the TPB to frame their research and used the TBP survey

tool to extract data on behavior intentions (Ajzen, 2014). Although the criticisms were

valid, future researchers should know the TPB was a useful theory for understanding

behavioral intentions in both the business and health care realms.

While Armitage (2015) and Hall (2015) stated the TPB should be retired, they

recognized the theory for its 30 years of contributions. They agreed the theory framed

many research projects over the past 30 years with success, in the psychology, health, and

social science disciplines. Trafimow (2015) admitted the problems with the theory but

recognized the theory for the contributions the theory provided in the study of health

related issues. If the TPB has outlived its psychology, health and social science

disciplines, the theory can be useful for business decisions. The limited number of

research projects for business decisions using the TPB as a theoretical framework

indicates an opportunity to understand manager’s intention toward specific business

decisions (Liu, Wang, Wang, Xia, & Xu, 2019). Although managers make business

decisions based on the availability of data or financial reasons, understanding the

attitudes, SNs, and the PBC added a new element to the decision-making process.

I measured the relationship between the independent variables, attitude, SNs, and

PBC, with the dependent variable, the behavioral marketer’s intention to protect

ecommerce consumer’s personal information. The assumption was the behavioral

marketer’s rational behavior influence their actions and behaviors (Ajzen, 2011). The

independent variables or predictors for this study include attitude toward ecommerce

privacy, SNs, and PBC. The predictors will determine the intentions toward the behavior,

of protecting ecommerce consumer’s behavior. While behavioral marketers may have

good intentions to reduce the amount of personal information collected, stored, and sold,

the results of this study may confirm the behavioral marketer’s attitude, SNs, and PBC as

well as their capacity to change behaviors, with PBC.

The TPB formed the framework for this behavioral research study regarding the

behavioral marketer’s intention to protect ecommerce consumer privacy. The behavioral

marketer’s intention to increase or reduce privacy protection may affect the sales,

revenue, and profits of the organization (Acquisti et al., 2016). The collection of

ecommerce consumer’s personal information contributes to the success of behavioral

marketing (Martin, 2016). The collection and storage of ecommerce consumer’s personal

information presented the double edge problem. Jai and King (2016) stated that a

disconnection of expectations was visible between the ecommerce consumer and the

behavioral marketer concerning privacy. While the collection and storage of personal

information drive ecommerce sales and revenue, the collection of ecommerce consumer’s

personal information also subjects the information to the risk of a security breach

(Choong et al., 2017). Based on the amount of personal information on social media

websites, for data mining, understanding the behavioral marketer’s intention toward

ecommerce consumer privacy was important (Acquisti et al., 2016). The literature

contains mixed opinions on the importance of privacy from the ecommerce consumer’s

perspective. Ecommerce consumer’s desire privacy but will readily provide personal

information voluntarily at the point of purchase (Cracium, 2018).

Behavioral marketers can answer the questions regarding the importance of

ecommerce consumer privacy and whether ecommerce privacy requires protection.

Although the financial aspects of protecting ecommerce consumer privacy cost will not

be a question on the TPB research tool, behavioral marketers may need to consider the

economic aspects of privacy changes. Choong et al. (2017), Liu et al. (2019), and Zhang

(2014) examined the cost of security breaches for the consumer and the organization. The

organization loses about $191 per transaction and the ecommerce consumer’s loyalty

(Zhang, 2014). Choong et al. and Zhang noted the high cost of resolution to the

organization for security breaches. Zhang noted consumers are more likely to remain with

the retailer while the organization remedies the security breach because of the high cost

associated with switching ecommerce retailers. Liu et al. suggested behavioral marketers

compare the added cost of ecommerce privacy protection with the estimated cost of

repairing the damage from a security breach. The worst-case scenario was a security

violation, which reaches far beyond the financial implications of the issues of consumer

trust and negative word of mouth advertising (Choong et al., 2017).

Consumer Privacy

Since 1890, consumer privacy studies have been of interest to consumers and

marketers (Walsh et al., 2017). While the topic of consumer privacy was not new, it was a

complex subject based on the various definitions of consumer privacy. The different

definitions of privacy render the subject difficult to study and compare results, across

studies (Milne et al., 2017). New to the collection of consumer privacy studies was

ecommerce privacy from the behavioral marketer’s point of view. Literature devoted to

ecommerce consumer privacy within the behavioral marketing strategy was limited

because this phenomenon was relatively new (Kude et al., 2017). Privacy from the

consumer’s perspective forms the majority of consumer privacy literature (Barbu, 2015).

Milne et al. (2017 and Cracium (2018) suggested personal information on the Internet

assists marketing managers in targeting consumers. Targeting consumers was profitable

for corporations (Milne et al., 2017). An ecommerce privacy viewpoint from the

behavioral marketer’s perspective would provide a different perspective on the topic of

ecommerce privacy.

A study of ecommerce privacy from the behavioral marketer’s perspective

includes an examination of the behavioral marketer’s attitude, SNs, and PBC toward

ecommerce privacy (Choong et al., 2017). Comparing the behavioral marketer’s

importance of ecommerce privacy from three different constructs, attitude, SNs, and PBC

may determine the behavioral marketer’s intent to protect ecommerce consumer privacy

(Choong et al., 2017). Privacy from the behavioral marketer’s perspective was difficult to

define based on the differing definitions of privacy and the ecommerce consumer’s

expressed concern for privacy (Barth & de Jong, 2017). Fortes and Rita (2016) stated that

privacy and security appear as one variable. Another difficulty to the study of consumer

privacy was the various degrees of ecommerce privacy concerns (Milne et al., 2017). The

privacy paradox adds to the difficulty of studying ecommerce privacy (Barth & de Jong,

2017; Dienlin & Trepte, 2015). The use of social media platforms implies privacy was

not a concern for ecommerce consumers (Weinberg et al., 2015). Whereas, security

breach victims may view ecommerce privacy as important (Choong et al., 2017). This

section includes a history of consumer privacy, an overview of the various definitions of

privacy, the definition of privacy used for this study, and a review of previous research on

consumer privacy.

Consumer privacy concerns due to technological innovations were an ongoing

trend. The issue of consumer privacy dates to Warren and Brandeis (1890), as cited by

Walsh et al. (2017), were the first authors to address privacy related to technical devices.

In 1890, Warren and Brandeis approached privacy as a need for protection from

surveillance technology or cameras (Walsh et al., 2017). Warren and Brandeis sought

privacy protection from photographers who photographed individuals without prior

permission. Warren and Brandeis viewed this action as a breach of privacy for the

individual. According to Warren and Brandeis, privacy was an inherited right of every

citizen. In their viewpoint, the photographers were responsible for requesting permission

before taking photos.

The present Internet technology makes ecommerce consumer’s personal

information available. The existing literature included a mixture of viewpoints, which

indicate two schools of thought. One viewpoint was consumers were not comfortable

with the collection and use of their personal data for marketing purposes (Fortes & Rita,

2016). The second position relates consumer’s wiliness to provide personal information

for compensation in the form of a reward, discount, or another monetary reward (Milne et

al., 2017).

Milne et al. (2017) revealed that ecommerce consumers have mixed opinions

regarding consumer privacy protection. Some researchers identified the importance of

protecting the privacy whereas other researchers advocated privacy was not an issue

based on the amount of personal information on the Internet and the amount of personal

information provided for frequent purchase and loyalty cards applications (Milne et al.,

2017). An aspect of the research was the behavioral marketer’s intention toward

protecting ecommerce consumer privacy and the capability to make a decision regarding

the privacy of ecommerce consumer privacy. Although behavioral marketers may have

the capability to reduce the amount of information collected on ecommerce consumers,

they may not have the incentive. The profits realized because of behavioral marketing

may be the driving force to continue with collecting vast amounts of personal information

(Campbell, Goldfarb, & Tucker, 2015). Kude et al. (2017) suggested the tradeoff of

reducing the amount of information collected and the incremental increase in profits

realized from behavioral targeting compared to traditional methods may not be incentive

enough to reduce the amount of personal information collected. Cracium, (2018)

suggested that behavioral marketers consider the competitive environment and weigh the

benefits of collecting personal information with the detrimental effects of a security

breach.

The common theme among privacy literature was the control over personal

information (Markos, Milne, & Peltier, 2017). Ecommerce consumers want control over

who collects, stores, and sells their personal information (Barth & de Jong, 2017). Barth

and de Jong (2017) reviewed 35 theories to better understand the privacy paradox, in an

online environment. They concluded ecommerce consumers were concerned about their

privacy when providing personal information but the ecommerce consumer desired

control over who sees the information (Barth & de Jong, 2017). Milne et al. (2017)

offered a differing opinion from Barth and de Jong; ecommerce consumers do not have

rights to their personal information. There was an implied contract that marketers may

use the information ecommerce consumers provided to expand sales revenue (Milne et

al., 2017). Milne et al. 2017 referenced a study where 91% of consumers believe control

over personal information was lost to marketers.

The definition of privacy changes with the introduction of new data collection

technology based on the Internet of things (IoT) (Weinberg et al., 2015). The definitions

evolve as the technological devices evolve. Personal information can be collected from

Fit Bit devices, Polo tech shirts, self driving cars, and climate control devices (Markos et

al., 2017). There were several definitions of privacy based on the different consumer

situations and various technological devices. Ecommerce consumers may be more

concerned about the collection, use, and sale of their personal information and have a

higher expectation for behavior marketers to avoid or reduce the amount of collected

information (Markos et al., 2017). Three ecommerce privacy definitions include the

unauthorized use of personal information, as a consumer right, a tradeoff for obtaining

discounts, and a right to be left alone.

Existing literature provides several definitions of privacy. Hallam and Zanella

(2017) suggested privacy can be defined as a commodity, state, or right. Milne et al.

(2017) defined consumer privacy as consumers concern with the unauthorized collection,

use of personal information and a desire for control. Milne et al. separated privacy into

two components, privacy and regulation of access to self. Their definition of privacy

included the unauthorized use of personal information and security as the protection of

personal information. Based on Milne’s et al. privacy definition, privacy was not an issue

if the retailer requests authorization for the personal information before collection. The

request for personal information provides the ecommerce consumer with a degree of

control. Milne et al. suggested the ecommerce consumer had an expectation the requester

would protect their personal information. Walsh et al. (2017) agreed with Milne et al. that

marketers obtain permission prior to the collection of personal information but they

expanded the privacy definition by adding privacy was a consumer right, not to be

violated.

Lowry, Dinev, and Willison (2017) viewed privacy with traditional marketing as a

consumer right. As a consumer right, marketers obtained authorization prior to collecting

personal information (Lowry et al., 2017). If behavioral marketers apply the definition of

privacy used by Lowry et al., then ecommerce privacy was a consumer right, and the

behavioral marketer must view ecommerce privacy as important. Identification of the

importance of ecommerce privacy results in measures to protect consumer privacy by

requesting permission before collecting, using, and selling the ecommerce consumer’s

personal information (Milne et al., 2017). The use of mobile devices and the popularity of

social network sites made consumer information readily available (Milne et al., 2017).

Unlike traditional market research methods, asking for permission to use personal

information was not required when personal information was available on public Internet

sites (Borgesius, 2015).

Traditional marketing researchers asked the consumer’s permission before

requesting the information (Milne et al., 2017). In this case, the consumer provided only

the information they were comfortable with sharing (Milne et al., 2017). Milne et al.

(2017) suggested that consumers will provide information they were comfortable with

sharing; usually demographic information. In other instances, the consumer would

fabricate the responses and provide the information they thought the researcher wanted to

know (Milne et al., 2017). The difference between traditional market research and current

market research methods was the behavioral marketer obtains reasonably accurate

information on consumer preferences based on browser histories and other information

readily available (Borgesius, 2015). The present problem was whether extracting

ecommerce consumer’s personal information from browsers and other databases for use

in behavioral marketing strategies leads to privacy concerns of accountability and

security for the behavioral marketer (Weinberg, et al, 2015).

Cracium (2018) defined consumer privacy as a trade-off between the amount and

type of information provided and security. The premise was that consumers increase their

privacy by limiting the amount of information they disclose (Cracium, 2017). In some

situations, ecommerce consumers provide incorrect personal information to protect their

identity (Milne et al., 2017). Milne et al. (2017) referenced Goodwin (1991) in stating the

desire to provide incorrect information may be based on judgements from peers.

Increased ecommerce consumer privacy, in the form of nondisclosure of personal

information, ultimately results in increased security and a reduction in the probability of a

security breach (Cracium, 2018). Applying the privacy definition of Cracium suggests

behavioral marketers view ecommerce consumer’s personal information as important,

and the need to protect the ecommerce consumer’s personal information was necessary.

Although the ecommerce consumers were willing to share personal information in

exchange for something tangible, they remain cautious with the handling of their personal

information (Cracium, 2018). When ecommerce consumers limit the amount of

information they share the need for privacy concern and tighter security decrease.

Restricting the amount of personal information ecommerce consumers disclose also limits

the available useable information for the behavioral marketer (Milne et al., 2017). Like

Milne et al., Mikhed and Vogan (2018) stated the collection of personal information was

useful but security breaches have a devastating impact for the ecommerce consumer and

the retailer. Milne et al. described the personal information tradeoff in terms of monetary

value. Milne et al. stated ecommerce consumers were not concerned about information

privacy per se, when the consumer’s trade personal information for a

discount or monetary reward.

The dominant theme of privacy for Hallam and Zanella (2017) and Thompson,

Tuzovic, and Braun (2019) was accountability. Accountability was a concern for the

ecommerce consumer when they desire privacy but disclose personal information. Their

attitudes were not in line with their intentions to disclose (Hallam & Zanella, 2017).

Accountability may be a concern for the behavioral marketer, when stereotypical targeted

ads may result in tarnished reputations and reduced sales based on word of mouth (WOM)

from ecommerce consumers (Haryani & Motwani, 2015). The security of the ecommerce

consumer’s personal information was a primary concern for ecommerce consumers

(Choong et al., 2017). Understanding the importance behavioral marketers place on the

security and the privacy of the information collected forms the basis of this study.

Thompson et al. (2019) stated accountability was important because the collected

information was required for ecommerce growth. The behavior marketer’s intention to

protect ecommerce privacy was evident in the behavioral marketer’s attitude toward

ecommerce privacy. Their SNs, or how peers view privacy, and subjective behavior

control will also be evident (Tamimi & Sebastianelli, 2015).

The ecommerce consumer’s information was Internet-based in data files based on

browser preferences, email address, and social media (Milne et al., 2017). The

widespread use of social media sites suggests users were not concerned about the security

of privacy. Milne et al. (2017) identified this phenomenon, the widespread use of social

media as the privacy paradox. The privacy paradox may be controversial for the

behavioral marketer based on the assumption; information readily available on social

websites was public information (Barbu, 2015). Fortes and Rita (2016) suggested

ecommerce consumers did not know how their personal information found on social

media sites was used. Ecommerce consumers provide personal information with the

understanding this information was secure and only selected individuals will have access

to the information (Lowry et al., 2017). Technological innovations such as the Internet

made it possible for third parties to access ecommerce consumer’s personal information

(Choong et al., 2017). To this end personal information found on social media sites was

public and accessible by behavioral marketers.

Milne et al. (2017) supported situations where consumers will provide personal

information in exchange for a reward. The position of Milne et al. was contrary to the

research findings of Fortes and Rita (2016), which suggested ecommerce consumers do

not want to share their personal information. Milne et al. suggested the presence of a

discount or reward blurs the lines between personal use and privacy concerns. When

ecommerce consumers express concern for privacy but provide personal information for a

specified reward, this results in a privacy paradox (Walsh et al., 2017). When Milne et al.

suggested consumers were willing to trade their personal information for a reward; the

reward included all the common incentives, such as discounts, travel points, merchandise,

and monetary rewards. Milne et al. suggested ecommerce consumers do not have privacy

concerns regarding providing personal information when rewards were available, in

exchange for the information. In this situation, behavior managers do not need to view

ecommerce consumer’s personal information protection, as important. Contrary to the

findings of Milne et al., Barbu (2015) advocated for consumers to protect their personal

information by controlling the amount of information shared and made available to

marketers, regardless of offered incentives.

This research study on privacy focused on the behavioral marketer’s intentions to

protect ecommerce consumer’s personal information. The behavioral marketer can access

the ecommerce consumer’s information from email accounts, browsing histories, and

social media (Milne et al., 2017). This activity was valuable for the behavioral marketer

as it results in an increase in sales, revenue, and profits (Kude et al., 2017). The opposite

of increased sales revenue is a security breach, whereby ecommerce consumers may face

identify theft (Kude et al., 2017). Security breaches result in large expenses for the

corporation, the behavioral marketer represents (Benson, Saridakis, & Tennakoon, 2015;

Choong et al., 2017). The impact of security breaches includes the costs for consumer

support, data security improvements, and incident investigation costs (Choong et al.,

2017). If the behavioral marketer has a positive attitude toward ecommerce privacy and

their intentions to protect or safeguard the collected information were high, then

ecommerce consumers should have a minimal concern or worry about the misuse of the

personal information (Lwin et al., 2016).

Researchers defined consumer privacy in several ways and placed varying degrees

of importance to consumer’s privacy (Milne et al., 2017). The privacy definition for this

study was the protection of personal information gathered from email accounts, browser

histories, purchase histories, and social network sites, for targeted advertising purposes

(Weinberg et al., 2015). Understanding the importance behavioral marketers place on the

collection and use of ecommerce consumer’s personal information was important to

understanding the behavior marketer’s perspective on ecommerce privacy. The

behavioral marketer increases sale and revenue by using personal information to target

advertising to the ecommerce consumer most likely to purchase the goods and services

(Milne et al., 2017; Weinberg et al., 2015). The collected personal information adds value

to the targeted marketing strategy (Walsh et al., 2017; Milne et al., 2017). Targeted

advertising provides a financial benefit to the organization or corporation from the use of

personal information (Walsh et al., 2017). Although the benefits of behavioral marketing

were profitable, the value added benefit may have negative consequences if the

information was hacked or stolen resulting in a security breach (Benson et al., 2015). The

behavioral marketer must balance the risks of obtaining the personal information with the

increased revenue benefits along with the possibility of a security breach (Choong et al.,

2017). In order to balance the risks of collecting ecommerce personal information for

targeted advertising, the behavioral marketer must understand the two privacy themes,

promotion focus and prevention focus (Cracium, 2018).

Two privacy themes of ecommerce privacy appear in the literature; the

consumer’s promotion focus and the consumer’s prevention focus (Lwin et al., 2016).

Acquisti et al. (2016), Jai and King (2016), and Milne et al. (2017) provided support for

the promotion focus viewpoint. Ecommerce consumers will provide personal information

for a reward. Acquisti et al. suggested consumers have a threshold for monetary

incentives, whereas Milne et al. suggested ecommerce consumers would provide personal

information for any incentive or discount. The prevention focus includes ecommerce

consumers who prefer not to disclose personal information. Barbu (2015), Fortes and Rita

(2016) and Weingerg et al. (2015) stated ecommerce consumers were inclined to protect

their privacy by not disclosing personal information. Barbu and Fortes and Rita agreed

ecommerce consumers should protect their personal information by not participating on

social Internet sites. Cracium (2018) suggested ecommerce consumers protect themselves

through education regarding the disclosure of personal information on the Internet as a

method of protection. While the two privacy themes tend to contradict each other, the

promotion focus includes ecommerce consumers who trade personal information for a

reward. The prevention focus consumers would prefer not to disclose personal

information without a clear picture of who was collecting the information and the

intentions for the collected information (Lwin et al., 2016; Milne et al., 2017).

Promotion focus. The promotion focus aspect of privacy illustrates the

consumer’s willingness to provide personal information for a reward (Acquisti et al.,

2016; Fortes and Rita, 2016). Behavioral marketers describe the situation as the privacy

calculus (Aguirre, Roggeveen, Grewal, & Wetzels, 2016; Fortes & Rita 2016; Lwin et al.,

2016). In this situation, ecommerce consumers regard their personal information as a

commodity, which they trade or sell for promotional rewards, such as a percentage

discount (Cracium, 2018). Milne et al. (2017) suggested consumers who provide personal

information for a reward or discount were not concerned about their privacy. Barbu added

to Milne et al. and described the ecommerce consumer as providing their personal

information in exchange for a reward as selling their personal information. Cracium

(2018) further stated privacy concerns were part of the ecommerce consumer’s value

system. The privacy calculus suggests ecommerce consumers may have privacy concerns

but will quickly sell their personal information for a reward (Lwin et al., 2016); Milne et

al., 2017). Once the ecommerce consumer releases their personal information, the

consumer does not have a claim on the information after accepting a reward or discount

in exchange for the information (Milne et al., 2017). Using consumer information to

target advertising appears to be a win-win situation for both the behavioral marketer and

the ecommerce consumer (Walsh et al., 2017). The additional discounts for the

ecommerce consumer translate into an increase in sales for the retailer and increased

information for the behavioral marketer (Barbu, 2015). In some cases, the consumer was

willing to provide the information but wants to maintain control over who uses the

information (Walsh et al., 2017).

Veltri and Ivchenko (2017) suggested privacy concerns related to promotion focus

or privacy calculus relates to the ecommerce consumer’s control of their personal

information. If the ecommerce consumer believes they have control over their

information, they may provide the requested information for a reward or discount

(Benson et al., 2015). Ecommerce consumers lose control over their information when

they post information on social media sites (Weinberg et al., 2015). This loss of control

led Markos et al. (2017) to state privacy was dead in the digital age. While accessing

personal information from social network site is not the only means for obtaining

ecommerce consumer’s personal data, behavioral marketers may obtain personal

information from frequent flyer cards, membership cards, and other loyalty cards or other

tangible incentives (Lwin et al., 2016; Milne et al., 2017)

The use of loyalty cards was widespread (Milne et al., 2017). Ecommerce

consumers using loyalty cards for discounts on products and services provide a certain

amount of information for the retailer to customize advertising ads (Milne et al., 2017).

American Airlines established the first documented loyalty program in 1981

(MeyerWaarden, 2015). Loyalty cards were a convenient method for collecting personal

information from ecommerce consumers (Milne et al., 2017). The ecommerce consumer

provides personal information when they sign up for the loyalty or frequent users card

(Meyer-Waarden, 2015). The behavioral marketer uses this information, to target

advertising ads to the consumer (Meyer-Waarden, 2015). In exchange for the

information, retailers provide discounts, which translated into monetary benefits

(Cracium, 2018). Lwin et al. (2016) suggested consumers were aware of the risks but

were willing to provide personal information in exchange for rewards. Cracium (2018)

concluded ecommerce users were knowledgeable concerning personal privacy,

understand the privacy risks, and will assume a certain degree of risk for a reward,

compensation, or promotion. Contrary to Cracium, Milne et al. (2017), and Weinberg et

al. (2015) implied ecommerce consumers were not aware of the dangers of providing

personal information for a discount and ecommerce retailers should self-regulate

themselves when collecting personal information. Meyer-Waarden suggested ecommerce

consumers engaged in ecommerce purchasing and use loyalty cards were not overly

concerned with ecommerce privacy. The fine print on the loyalty card indicates marketers

may use the information provided for marketing purposes (Cracium, 2018). Milne et al.

2017 took a different view; they stated ecommerce consumers were aware of the personal information

they provide on loyalty card applications and provide the information voluntarily, in exchange for a

monetary benefit. If ecommerce consumers were conscious of the risk and trade their personal

information as a commodity, for compensation or discount, there is no need for behavioral marketers to

make the effort to protect the ecommerce consumer’s personal information (Walsh et al., 2017). The

ecommerce consumer provided the information as a part of doing business with the ecommerce retailer.

The widespread use of loyalty cards would confirm the privacy calculus; ecommerce consumers were

willing to disclose personal information for compensation (Walsh et al., 2017). The use of loyalty cards

indicated ecommerce consumers will assume the risk of a security breach in exchange for a benefit

(Barbu, 2015). Although privacy statements were present when the consumer applies for a loyalty card,

few consumers read the fine print, which explains how behavioral marketers may use the information

(Barbu, 2015).

Walsh et al. (2017) used privacy calculus to understand why consumers disclose

personal information. The privacy calculus states a consumer will disclose personal

information for a perceived benefit, regardless of the risk (Aguirre et al., 2016). Contrary

to the privacy calculus, Barbu (2015) found a weak link between privacy concerns and

willingness to disclose personal information for loyalty programs. This weak link may

suggest ecommerce consumers may try to protect their privacy once they understand the

process of collecting, using, and selling their personal information. There were

disadvantages concerning the use of loyalty cards for commerce consumers, which may

cause concern for the ecommerce consumer (Acquisti et al., 2016). Situations may arise

with inaccurate targeting and the sale of personal information to third parties.

A known problem with loyalty cards was inaccurate consumer targeting (Walters,

Wiese, & Bruce, 2018). Inaccurate targeting occurs when the wrong ad reaches the

ecommerce consumer. Behavioral marketers identify incorrect targeting as a message

backfire because the recipient did not identify with the message (Walters et al., 2018).

Walters et al. (2018) attributed the identification problem to behavioral marketers

obtaining information from data miners selling outdated information. Understanding the

role of data brokers for behavioral marketers will determine if behavioral marketers were

purchasing outdated or fraudulent consumer data. Data brokers turn random pieces of

personal information into a sought after commodity (Weinberg et al., 2015). Data mining

was easier with a large amount of data on the Internet (Weinberg et al., 2015). The use of

consumer data from third-party sources including social media sites may result in

erroneous ecommerce consumer targeting (Corrigan, Cracium, & Powell, 2014).

Erroneously targeting the ecommerce consumer was a reason for concern.

The Target Corporation found a solution for message backfire, when an ad for

baby products went to a family loyalty card prior to the young woman disclosing her

pregnancy (Corrigan, et al, 2014). The behavioral marketers at Target Corporation use a

3-prong approach to deliver targeted advertising (Corrigan et al., 2014). In addition to

gathering data from data brokers, Target provides a 5% discount on each purchase with

the loyalty card, and they mix the ads to avoid message backfires (Campbell et al., 2015;

Corrigan et al., 2014). Because ecommerce consumers do not want to filter through ads

for products, they have little interest in, Campbell et al. (2015) suggested the need to

target the correct ad to the correct customer.

Inaccurate consumer targeting provides a reason for the ecommerce consumer to

discontinue use of the card, thus leaving the behavioral marketer without a convenient

source for obtaining consumer information (Campbell et al., 2015). Behavioral marketers

on behalf of corporations do not notify ecommerce consumers when they collect and use

personal information for marketing purposes (Cracium, 2018). For example, the Target

Corporation marketers do not notify ecommerce consumers, the information consumers

provide was for marketing purposes (Cracium, 2018). The site designers buried the

privacy information in the fine print without a statement of how the information was

utilized (Cracium, 2018). The security breach in 2013 impacted 41 million customers

(Lowry et al., 2017). The debate continues with the pros and cons of ecommerce privacy

concerns with the use of loyalty cards. Ecommerce consumers enjoy the benefits of the

loyalty cards and do not perceive a problem with providing personal information to

obtain a discount or monetary reward (Milne et al., 2017). The amount of information

accessible from the Internet renders ecommerce privacy difficult to protect (Weinberg et

al., 2015). The last problem ecommerce consumers may incur with the uses of a loyalty

card was the sale of personal information to a third party broker or data miner (Jai &

King, 2016; Spiekermann, Acquisti, Bohme, & Hui, 2015). Spiekermann et al. (2015)

stated selling personal information to third party broker or data miner presents the

problem of identifying where the security breaches occurred.

Retailers sell personal consumer information to third parties on a regular basis (Jai

& King, 2016). The information was readily available from social network sites, browser

histories, and previous experience with ecommerce retailers (Jai & King, 2016). Jai and

King (2016) discovered consumers were willing to provide personal information for

loyalty programs but do not want the information sold to third parties. The problem with

loyalty card retailers purchasing and selling ecommerce consumer’s personal information

was the lack of knowledge to explain the third parties’ intended use of the personal

information (Acquisti et al., 2016). This lack of knowledge results in inaccurately

targeting the ecommerce consumer (Acquisti et al., 2016). The behavioral marketer can

review the data for the relevancy of the information to ensure correctness (Cracium,

2018). Ecommerce consumers with strong privacy concerns can avoid providing personal

information (Barbu, 2015). The prevention method relies on opting out of all loyalty card

promotions, no interaction with social network sites and limited ecommerce sales

(Choong et al., 2017). The action of protecting personal information from the collection,

use, and sale incorporate the prevention focus aspect of ecommerce consumer privacy.

Prevention focus. The second theme for ecommerce privacy was prevention

focus. Campbell et al. (2015) defined privacy prevention focus as the process of reducing

privacy concerns by reducing the amount of personal information provided to ecommerce

retailers, through regulation. Ecommerce consumers fail to understand the process of

using personal information to increase ecommerce profitability (Libaque-Saenz et al.,

2016). Ecommerce consumers protect their privacy by engaging in protective behaviors

and not providing personal information (Taylor, Ferguson, & Ellen, 2015). There were

conflicting schools of thought regarding the need for consumers to protect their privacy

and minimize the collection, use, and sale of personal information.

Campbell et al. (2015) indicated ecommerce consumers require protection through

prevention and ecommerce education regarding the collection, use, and sale of personal

information. Education was the ecommerce consumer’s best defense for reducing the

amount of personal information provided to ecommerce retailers (Campbell et al., 2015).

Declining invitations for loyalty cards was only one source for preventing the behavioral

marketer from obtaining ecommerce consumer’s personal information (Choong et al.,

2017). Contrary to Taylor et al. (2015), Milne et al. (2017) indicated consumers cannot

stop the widespread use of personal information based on the numerous data collection

devices in use by e commerce consumers. The personal information was available from

several sources. Choong et al. and Cracium (2018) iterated opting out of requests for

personal information might not solve the privacy prevention situation, due to lack of

transparency and the availability of personal information via the Internet. Also, the

behavioral marketer has several sources for obtaining personal information for ad

targeting. The sources include data mining, previous purchase histories, cookies, and

social network sites (Kayhan & Davis, 2016).

The availability of personal information may be available from several sources (Kayhan

& Davis, 2016). The ecommerce consumer cannot avoid leaving an information footprint

(Milne et al., 2017). Therefore, declining requests for personal information may not be

enough to prevent security breaches.

The ecommerce privacy researchers Campbell et al. (2015) voiced a different

opinion concerning privacy through prevention compared to Taylor et al. (2015).

Campbell et al. argued privacy concerns were part of a consumer’s value system and do

not require protection by prevention, but through regulation. Contrary to Campbell et al.,

Cracium (2018) suggested ecommerce consumers do not need privacy prevention, and

ecommerce consumers should maintain control of their personal data, through the options

provided by the ecommerce websites, such as opt in and opt out opportunities as well as

privacy notices.

Based on the TPB model behavioral marketers who have a strong correlation

between ecommerce privacy and attitude will view ecommerce privacy as important

requiring protection or through a reduction of information requested (Seungsin et al.,

2015). If the intention of behavioral marketers was the protection of ecommerce

consumer’s personal information, then ecommerce consumers do not have to be overly

protective of the information provided to ecommerce retailers (Campbell et al., 2015).

The behavioral marketer will focus their attitudes on protecting consumer’s personal

information. Behavioral marketing peers who share similar personal information

protection strategies will influence their SNs (Kammer et al., 2016). Last, the behavioral

marketers will have the capability to modify the personal information security strategies,

where required as part of their PBC (Tiwari, Bhat, & Tikoria, 2017).

The best overall solution to protecting ecommerce consumer privacy was through

education (Barbu, 2015; Kammer et al., 2016). Cracium (2018) suggested ecommerce

consumers do not require protection through prevention. There was an implied contract

between the ecommerce consumer and the behavioral marketer on behalf of the

organization and the request for personal information. Seungsin et al. (2015) noted

ecommerce consumers know how to protect their privacy. Ecommerce consumers were

aware of the risks and how to minimize the risks of a security breach based on public

information regarding previous security breaches (Seungsin et al., 2015). Acquisti et al.

(2016) found that consumers were more likely to reject requests for personal information

in exchange for reward if they believed their personal information was at risk.

If Cracium (2018) was correct and an implied contract exists then, the behavioral

marketer on behalf of the retailer has an obligation to store ecommerce consumer’s

personal information in a method, which will protect the personal information. There

were several options behavioral marketers can utilize to manage the ecommerce

consumer’s information in a sensitive manner (Cracium, 2018). The literature contains a

few examples of methods for handling the ecommerce consumer’s information in a

sensitive way. One method was by setting the ecommerce consumer’s expectation. Peer

and Acquisti (2016) implied the prevention of personal information misuse was best if

addressed by the manager to set the consumer’s level of expectation. Setting the

consumer’s expectation through open disclosure was a good method for reducing the

ecommerce consumer privacy concerns (Choong et al., 2017). Another approach was a

time stamp on the personal information. Campbell et al. (2015) and Craciun (2018)

suggested ecommerce retailers provide consumers with an element of control for their

privacy with an opt-out clause in their privacy statement to reduce the amount of

information a consumer was required to provide. Another retailer self-regulating process

was the development of an easy to read and understand privacy clause (Cracium, 2018).

The privacy clause should contain an informed consent section and an expiration date for

the collected information (Campbell et al., 2015). Behavioral marketers have an

opportunity to take the lead by informing the ecommerce consumer with information

regarding what information they collect, how they use the information, and at what point

the information the behavioral marketer sells the information (Choong et al., 2017). The

flow of information from behavioral marketers will aid the ecommerce consumer in

understanding how their personal information fit into the behavioral marketers

advertising strategy (Walsh et al., 2017). Last, a well-constructed privacy statement

located at the point where ecommerce consumers provide their personal information will

aid in the ecommerce consumer’s education of how the behavioral marketer will use the

provided information (Cracium, 2018). An expiration date will ensure the ecommerce

consumer when the information will no longer be available (Cracium, 2018). If the

request for personal information was a social contract then having as much information

regarding how the behavioral marketer uses the personal information was helpful in

easing the ecommerce consumer’s prevention privacy concerns (Campbell et al., 2015).

The behavioral marketer’s intentions toward ecommerce consumer privacy will

determine the need to protect ecommerce privacy through prevention or education

(Tiwari et al., 2017).

TPB Variables and the Marketer. Behavioral marketers know the advantages of

behavioral marketing. Behavioral marketers increase organization sales and revenue by

obtaining information supplied by data mining (Tamimi & Sebastianelli, 2015). The

capability to access and use this data was advantageous to the behavioral marketer (Lwin

et al., 2016). What were not clear were the behavioral manger’s attitudes, SNs, and PBC

toward ecommerce privacy concerns. Lwin et al. (2016) and Milne et al. (2017) argued

against the need to protect the consumer’s personal information based on the widespread

use of promotional rewards. Research was required to understand the behavioral

marketer’s intention toward the collection, use, and sell of ecommerce consumer’s

personal information based on the behavioral marketer’s attitude, SNs, and the

ecommerce manager’s PBC. Internet usage provides a challenging argument for the

protection of ecommerce consumer privacy.

The behavioral manger’s attitude toward the ecommerce consumer privacy

includes the behavioral marketer’s capability to form a positive or negative evaluation of

consumer privacy. Ajzen and Sheikh (2013) stated beliefs about a behavior’s

consequences determine attitudes concerning the behavior. In this case, the use,

collection, and storage of ecommerce consumer’s personal information in a database may

result in a security breach (Cracium, 2018). The behavioral marketer’s previous

experience with consumer privacy will shape their attitudes regarding ecommerce

consumer privacy (Vasalou, Joinson, & Houghton, 2015). Previous experience includes

the behavioral marketer’s personal experience with Internet privacy and the behavioral

marketer’s participation in a group or organization, which was the target of a security

breach (Clemons & Wilson, 2015).

SNs of the behavioral marketer measure the effect peer groups have in shaping the

importance of protecting ecommerce consumer privacy. Ajzen and Sheikh (2013) related

SNs to the perceptions of significant outside individuals. Featherman and Hajli (2016)

extended the thought of Ajzen and Sheikh to include the experience level of manager,

which affects the manager’s subjective norm. If the manager was inexperienced, then the

manager may depend on their peer group because they lack confidence in their

knowledge of consumer privacy (Fortes & Rita, 2016). Experienced managers tend to

trust their knowledge of ecommerce privacy, while collecting information from several

sources (Moore, Moore, Shanahan, Horky, & Mack, 2015). Moore et al. (2015) referred

to the process of collecting information personal from several sources, as creepy

marketing.

The PBC component to the TPB determines the behavioral marketer’s capacity to

perform the task of protecting ecommerce consumer’s behavior (Ajzen, 1991). PBC

determines the capability to perform the behavior (Ajzen & Sheikh, 2013). The PBC

component allows the manager to perform or not perform the intended behavior (Ajzen,

1991). The behavioral marketer will either perform the behavior of protecting ecommerce

consumer privacy with ease or difficulty. The two perspectives on consumer privacy

promotion and prevention suggest differing approaches to consumer privacy. Increased

Internet usage and the popularity of social networks supports the argument consumer

privacy on ecommerce websites was not important and security breaches were not

important to ecommerce consumers (Weinberg et al., 2015). Based on the limited

information regarding behavioral marketer’s intent to protect ecommerce consumer

privacy, the outcome of this study may add to the literature concerning ecommerce

privacy on websites using a behavioral theoretical framework, TPB.

Behavioral Marketing

The study of behavioral marketing was beneficial based on the amount of personal

information that was required to make this strategy successful and the profitability

(Cracium, 2018). Behavioral marketing was successful as a marketing strategy because it

increases revenue from online sales, lowers overall marketing costs, and positively

influences the profitability of the organization (Cracium, 2018). As an effective strategy,

behavioral marketing increase sales and overall profits for the organization (Cracium,

2018). Summers, Smith, and Walker-Reczek (2016) defined behavioral marketing as the

practice of collecting and storing online consumer information, to target advertisements

for products or services to consumers who showed interest in the product or service from

the browsing history and other information sources. Moore et al. (2015) expanded on the

definition of behavioral marketing by defining the behavioral portion of behavioral

marketing to mean the type of ecommerce consumer’s personal information behavioral

marketers collected. Similarly, Campbell et al. (2015) narrowed the information used by

behavioral marketing managers to consumer’s personal information derived from past

online activity. The use of personal information leads to a successful behavioral

marketing strategy (Lwin et al., 2016). Behavioral marketers were successful when

ecommerce consumer information was available for designing targeted marketing

messages (Cracium, 2018). In addition, the collection of personal ecommerce consumer’s

information from social sites allows the behavioral marketer to obtain current consumer

information (Weinberg et al., 2015). The availability of consumer information for use in

marketing strategies evolved from a product based to consumer-based strategies (Barbu,

2015).

In the 1920s through 1940s, manufacturers made products first then advertised the

product based on a perceived need of the consumer (Barbu, 2015). The consumer did not

provide input into the product design, nor the advertising message (Barbu, 2015). As time

evolved, advertising collateral included several mediums, including print ads, radio spots,

television spots, and billboard advertising (Barbu, 2015). The challenge for marketers

was matching the product to the customer. To know and understand the consumer’s likes

and dislikes, marketing researchers gathered consumer information related to consumer

behavior through research projects (Barbu, 2015).

Prior to the Internet marketers collected consumer information through surveys

mailed to random homes, in prepaid envelopes for returning the survey (Barbu, 2015). In

some situations, researchers conducted in-person interviews or focus groups. In each of

these information-gathering cases, the researcher never knew how the participant’s

external environment affected the responses. Behavioral marketing evolved from

traditional advertising methods as a means for marketing organizations to recover

ecommerce sales lost to increased retail competition and online comparison-shopping

(Cracium, 2018). Increased technological innovations contributed to the availability of

ecommerce consumer’s personal information (Milne et al., 2017). Access to the Internet

opened the door to ecommerce retail sales and shortened the distance between the

ecommerce consumer and the retailer (Weinberg et al., 2015). Consumers and marketers

benefited from the technological advancements (Milne et al., 2017).

Technological innovations were the core of a successful behavioral marketing

strategy. The expanded technologies of the Internet increased the knowledge of

consumers as they interacted with ecommerce retailers (Milne et al., 2017). Access to the

Internet allows for communication beyond geographical boundaries and browsing activity

(Milne et al., 2017). Ecommerce consumers were part of the technological changes

related to how ecommerce consumers interact with and access the Internet (Milne et al.,

2017). The desire to have the convenience of information available prompts the use of the

Internet (Milne et al., 2017).

The average consumer evolved through the technological phase; from a desktop

computer to laptops and tablets to handheld devices, such as phones (Milne et al., 2017).

The widespread adoption of digital devices provides fertile ground for behavioral

marketing (Vasalou et al., 2015). The modern consumer has Internet access readily

available, day and night. The easy access to the Internet prompted an interest from

researchers to understand the ecommerce consumer (Campbell et al., 2015). Marketing

through the Internet gives marketers the ability to level the playing field between large

and small organizations (Milne et al., 2017).

Acquisti et al (2016) called behavioral marketing a tool of choice for online

advertisers. The information found on the Internet allows behavioral marketers an

opportunity to target consumers directly based on previous purchase history (Milne et al.,

2017). The difference between online and offline advertising was the ability to target

consumers effectively (Campbell et al., 2015). Although, behavior marketing increases

the efficiency of advertising by targeting ads toward consumer preference, privacy

concerns were present (Campbell, et al, 2015; Walters et al., 2018). Behavioral marketing

requires data mining to collect consumer information, to be effective (Cracium, 2018).

Internet technology has allowed marketers an opportunity to refine the process of

collecting personal customer information regarding buying patterns (Milne et al., 2017).

Data mining allows marketers to gain access to the personal data based on technological

advances, of the Internet (Milne et al., 2017). Data mining presents the significant risk to

behavioral marketing. Obtaining personal information from ecommerce consumer’s

personal information may present a risk to behavioral marketing managers.

The profitability of behavioral marketing for organizations was encouraging for

organizations (Wu et al., 2018). The benefits of behavioral marketing include increased

revenue by targeting products and services based on the consumer’s preferences and

allow for a reduction in marketing costs to reach the target consumer segment (Wu et al.,

2018). Behavioral marketing as a strategy increases the competitive advantage, of an

organization, which lead to increased revenue and improved consumer-targeting strategy

(Walsh et al., 2017). While Walsh et al. (2017) discovered targeted marketing creates a

positive relationship between the advertiser and the consumer there were disadvantages to

collecting, using, and selling ecommerce consumer’s personal information. Conversely,

behavioral marketing may have a negative effect when identity marketing backfires and

behavioral marketers target ads to incorrect consumers (Clemons & Wilson, 2015).

Behavioral marketing may also backfire when the marketer uses the ecommerce

consumer’s information for the purpose of price discrimination (Clemons & Wilson,

2015). The largest concern for behavioral marketers was the issue of consumer privacy

and the threat of privacy breaches (Choong et al., 2017). The question regarding

ecommerce privacy was the importance behavioral marketers place on ecommerce

consumer’s personal information. An increase in ecommerce transactions year over year

lends creditability to the theme of consumer privacy concern with the behavioral

marketer’s process for collecting, using, and storing consumer’s personal data (Choong et

al., 2017). Although behavioral marketing was profitable and contributes to increased

revenue for the corporation, there were disadvantages and risk associated with behavioral

marketing (Steijn & Vedder, 2015).

While behavioral marketing was profitable and contributed to increased revenue

for the corporation, there were disadvantages to the implementation of a behavioral

marketing strategy. The disadvantages included incorrectly targeting the ecommerce

consumer and security breaches (Walters et al., 2018). Liu et al. (2019) suggested

ecommerce retailers could lose a portion of their existing market if behavioral marketers

mishandle consumer information. Lwin et al. (2016) added to this by stating discovered

privacy concerns would prompt the consumer to change retailers. From the consumer’s

standpoint, Campbell et al. (2015) added to Lwin et al. (2016) by stating that improved

technology may result in lower switching costs and advertising avoidance tools for

consumers. Conversely, Walters et al. (2018) stated ecommerce consumers might view

targeted advertising as annoying and have increased concerns regarding targeted

advertising. The personal information component of behavioral marketing, personal

information, which leads to a successful strategy for increasing organizational revenue

can alienate consumers using outdated information or targeting by body type and shape

(Walters et al., 2018). Outdated information incorrectly targets the consumer and presents

the greatest challenge to consumer privacy (Steijn & Vedder, 2015). Incorrectly targeting

the ecommerce consumer based on outdated information results in a frustrated

ecommerce consumer (Clemons & Wilson, 2015). The behavioral marketer requires

current ecommerce consumer data for a successful behavioral marketing strategy.

Incorrectly targeting the consumer presents a problem, resulting in a mismatch between

the targeted consumer and the brand, and the possible risk of privacy breaches (Walters et

al., 2018)

Walters et al. (2018) revealed a weak link between behavioral marketing and the

collection of personal data based on a lack of correlation due to the collection of personal

information without a significant relationship to the brand. Walters et al. further stated the

successful behavioral marketing strategy would associate targeted brands to the lifestyle

of the consumer, like the Harley Davidson and Apple brands. To achieve a better

understanding of consumer targeting, Campbell et al. (2015) suggested companies

develop a positive relationship with consumers to target the best products to the

consumer. A relationship between the retailer and the ecommerce consumer is required

for correctly targeting brands, products, and services (Campbell et al., 2015). The

behavioral marketer’s intention to safeguard the ecommerce consumer’s personal

information will lower the risk of a security breach (Steijn & Vedder, 2015).

The second risk to behavioral marketing profitability was security breaches. The

potential of behavioral marketing is the misuse of personal information or a security

breach (Sen & Borle, 2015). Moore et al. (2015) discussed the fine line between

advertising and privacy invasion. As additional retailers incorporate behavioral marketing

strategies into their advertising programs, the risk of security breaches will continue to

grow (Steijn & Vedder, 2015). Security breaches have a cost associated with each breach

for the retailers and the ecommerce consumer. Choong et al. (2017) explored security

breaches from the marketer’s viewpoint. In 2013, 43% of American companies reported

data breaches (Choong et al., 2017). Target, Home Depot, and JP Morgan Chase were a

few of the high-profile companies affected by breaches (Choong et al., 2017). Consumers

who have privacy concerns tend to limit on line purchase activities, resulting in lost sales

(Choong et al., 2017). Additionally, lost sales due to customer loyalty were

approximately 3 million dollars (Campbell et al., 2015). Security breaches were not only

costly to the organization, but consumers display less than desirable reactions to targeted

advertising (Campbell et al., 2015). Behavioral marketers understand the risks of

behavioral marketing. The benefits of behavioral marketing tend to outweigh the risks

and disadvantages.

Walsh et al. (2017) examined the benefits of behavioral marketing while

addressing the issue of consumer privacy. The inherent benefits of behavioral marketing

were lower advertising costs and increased marginal profits (Walsh et al., 2017). Tabari

and Abroud (2017) found the success of behavioral marketing depended on the

assurance the ecommerce retailer or behavioral marketer provided to protect personal

information used for marketing purposes. Cracium (2018) expanded on this by stating

behavior marketers should balance the benefits of behavioral marketing with the risks.

Choong et al. (2017) stated a specific personalized behavioral marketing strategy

worked best to minimize the risk of security breaches. The personalized strategy used

the ecommerce consumers name in the ad. Personalized advertising brings a level of

comfort to the consumer that the ecommerce retailer will use their personal information

for the intended purposes (Walsh et al., 2017). Ecommerce retailers must understand the

frequency of personalizing ads to the consumer (Fortes & Rita, 2016). A high frequency

may result in ad avoidance, which negatively affects organizational revenue (Fortes &

Rita, 2016). Campbell et al. (2015) agreed with Walsh, behavioral marketers should

monitor the frequency of the targeted ads.

The ecommerce consumer information was available for both domestic and

global ecommerce retailers (Milne et al., 2017). The challenge for global marketers was

the development of business to consumer relationship. The development of a relationship

was beneficial for global ecommerce retailers (Weinberg et al., 2015). Milne et al.

(2017) indicated the ecommerce consumer was willing to share personal information

with global retailers provided there was familiarity. Global consumers develop

familiarity through repeat purchases and relationship management (Weinberg

et al., 2015).

Craciun (2018) and Weinberg et al. (2015) agreed based on their research, that

behavioral marketing may increase revenue and reduce marketing costs but at a

significant cost to the ecommerce consumer privacy. As researchers, Cracium and

Weinberg et al. advocated for ecommerce consumers to exercise control over their

personal information. Ecommerce consumers were more willing to provide personal

information when they felt in control of their personal information (Walsh et al., 2017).

Walsh et al. suggested a disconnection between the ecommerce retailer’s use of personal

information and the expectation of privacy protection. Although control over personal

information allows ecommerce consumers to take risks with their personal information,

control does not lead to privacy protection (Walsh et al., 2017). This disconnection may

damage the relationship between the retailer and the consumer resulting in lost sales and

reduced profitability (Cracium, 2018).

Behavioral marketing has benefits for the organization and to the consumer

(Todor, 2016). The marketing managers can target a consumer with an interest in the

product or service and the consumer’s exposure to targeted advertising directed to a

product or service may increase the likelihood of a sale. Although behavioral marketing

increased revenue, behavioral marketing was not beneficial for all organizations. Todor

(2016) revealed behavioral marketing increases revenue if the organizations were similar

in size and the competition was sufficient. For smaller organizations with small demand

for the product or service, traditional advertising methods may be more profitable (Todor,

2016).

There was a cost associated with behavioral marketing (Choong et al., 2017).

Choong et al. stated behavioral marketing was profitable for all organizations. While

behavioral marketing was profitable there was a down side in the event of a security

breach (Hallam & Zanella, 2017). A security breach can have a negative impact on stock

prices and result in large cash expenditure to repair the damage to the affected consumers

(Hallam & Zanella, 2017). For example, the data breach at Target in 2013 resulted in a

40% loss of profits (Kude et al., 2017).

The information found in the literature demonstrated the importance of collecting

and using ecommerce consumer’s personal information as it related to increasing sales

and revenue for the business or corporation (Choong et al., 2017). What was not clear

was the behavioral marketer’s attitude toward protecting ecommerce consumer privacy.

Additionally, it was not clear if behavioral marketers intended to protect ecommerce

consumer privacy. What was widely known was how ecommerce consumers view online

privacy.