a social cognitive theory for shoppin behavior

8/2/2019 A Social Cognitive Theory for Shoppin Behavior

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Does e-Trust Matter? A Social Cognitive Theory of Online Shopping Behavior

INTRODUCTION

E-commerce is an economically significant (U.S. Census Bureau, 2004) and

rapidly growing (Forrester Research, 2003) online activity that challenges researchers in

the fields of consumer behavior and new media to investigate online shopping behavior

(Peterson et al 1997; Cowles and Kiecker 2000).

Questions revolving around the intentions and motivations behind e-commerce

participation were the primary factors driving this study. For example, e-commerce

website managers have an intrinsic need to understand the reasons consumers buy online.

This knowledge allows them to develop strategies that are more effective in driving

consumers to their websites to engage in transactions (Aldridge et al., 1997; Wysocki

2000). In addition, new media researchers have a need to understand how consumer

behavior works in relation to the Internet. This helps them to better understand how to

modify and apply existing media theory to instances of online buying, as well as in what

areas new theory must be developed (Cowles & Kiecker 2000; Phau & Poon 2000).

Considering these driving forces, four major concepts were derived from prior studies.

Each concept relates to some variation in the amount of online buying that consumers

engage in.

Trust has been identified as a key component in e-commerce literature (Ba et al.,.,

1999; Czepiel, 1990; Jarvenpaa et al., 2000), where it is commonly styled as e-trust.

Trust is a psychological state but its conceptual definition is unclear. It has been variously

defined in terms of willingness to be vulnerable (Scanzoni, 1979; Mayeret al., 1995),

perceived probabilities of favorable outcomes (Bhattacharya et al., 1998), confidence


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in finding what is desired (Barney & Hansen, 1994; Deutsch, 1973; Garbarino &

Johnson, 1999) or generalized expectancy on future events (Rempel et al.,., 1985),

where trust is usually associated with the risk and the occurrence of some positive

outcome from the trusting party, independent of the potential of control over that other

party (Mayer et al., 1995).

Regarding the variability of the conceptual definition in trust, Koehn (2003)

pointed out that trust has been described as cognitive (i.e., a matter of opinion or

prediction), affective (i.e., a matter of feeling) or conative (i.e., a matter of choice or

will). McAllister (1995) and Jones and George (1998) viewed trust to be completely

rational assessment of available facts while Lewis while Weigert (1985)viewed it as a

cognitive leap referring to one's instincts, intuitions or feelings concerning other partys

trustworthiness. Dasgupta (1988) stated that trust has been assumed as an action, an

attitude or orientation, a state of character, a relationship while it is taken as a natural

feeling or faith, a belief on which one is willing to act. Several forms of trust such as

goal-based, calculative, knowledge-based, and respect-based were also suggested

(Koehn, 2003).

Mayer et al., (1995) also found that in a dyadic relationship between buyer and

seller, there are three critical attributes that the trusted party must possess to engender

trust: ability, integrity, and benevolence. Furthermore, the trusted party must conduct

itself skillfully and competently in a manner visible to potential trusters, along with the

intention to do good to the consumer and to do so in a manner consistent with the desires

of the trustor (Ridings, Gefen & Arinze, 2002).


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Operationally, numerous methods of measuring e-trust have been utilized in prior

research. For example, Hoffmanet al., (1999) looked at the privacy of consumer online

information with a survey that explored issues of consumer control over personal

information in an online exchange relationship. Jarvenpaaet al., (2000) measured

consumer trust in an Internet store field experiment that specifically examined

intermediary strategies to develop trust in websites. From this, relevant factors in

determining consumer trust were found to include customer risk perception (negative

impact) and attitude toward company (positive impact). Bhattacherjeeet al., (2002)

proposed a scale to measure individual trust in online firms which focused on the

strategies of e-merchants drawing upon previous experiences as well as those of their

peers, self-training opportunities, and relationships with hardware and software vendors

(Turbanet al., 2003).

Other than behavioral, psychological approaches (Dirks and Ferrin, 2001), the

issue of consumer trust has been addressed from different perspectives, including

technological, multi-agent approaches (Brainov and Sandholm, 1999); social,

institutional approaches (Canzaroli et al., 1999); economic, game-theoretic approaches

(Snijders, 1996); and managerial, organizational approaches (Olson and Olson, 2000).

Berry (1995) described trust as the single most powerful marketing tool. According to

Urban et al., (2000), consumers make Internet purchasing decisions on the basis of trust.

The antecedents and consequences of trust were also investigated empirically in the

context of e-commerce (Mayer et al., 1995; Brainov and Sandholm, 1999; Urban et al., ,

2000).


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Drawing from these studies, we can conclude that trust plays a vital role in e-

commerce. Indeed, some practitioners regard it as the key to success (Grabosky, 2001).

But for scholars the question remains, what is trust? How can we theoretically

construct trust in a consistent manner that places it in the context of widely accepted

theories of human behavior and distinguishes it from other known determinants of online

shopping behavior?

E-Trust in Social Cognitive Terms

One such paradigm is Social Cognitive Theory (Bandura, 1986). It holds that

people are not completely at the mercy of external stimuli, nor is their behavior simply

the product of inner forces. Instead, human behavior is explained through triadic

reciprocity, where individuals' behaviors, personal characteristics, and the environment

are determined reciprocally. The many facets of e-trust that have been revealed in prior

research can be understood in Social Cognitive terms.

For example, if we define trust in terms of the expectation of positive outcomes

(cf. Rempel et al., 1985) the concept is conceptually redundant with the construct of

outcome expectations. Positive outcome expectations have been the basis for theories of

electronic markets (Steinfield and Whitten, 2000) and have been used to separate online

shoppers from non-shoppers (Li, Kuo, and Russell, 2000). In Social Cognitive Theory,

outcome expectations are beliefs about the consequences or results of behavior (Bandura,

1986). Another, related theoretical perspective that uses this concept is Expectancy

Value theory (Fishbein and Ajzen, 1975) now known at the theory of planned behavior

(Ajzen, 1991), which maintains that individuals would engage in an action (in this case,


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online shopping) if they could expect the positive benefits associated with the action.

Expected outcomes have been previously discovered to predict both overall use of the

Internet (LaRose, Mastro, and Eastin 2001) and online shopping activity (LaRose &

Eastin, 2002).

As such, outcome expectations have been viewed as embodying the actively

sought merits of e-commerce, including such factors as low price and convenience.

However, in describing e-trust as the process of building an exchange relationship (cf.

Hoffman et al., 1999; Luo, 2002), there is the implication that at a certain point in the

relationship active thinking about the trustworthiness of the vendor is no longer a vexing

issue. At that point, trust may be equated with automatic thinking on the part of

consumers. Then, decisions about whether or not to trust an e-commerce site are no

longer actively processed on a continuing basis, but are automatically triggered by

conditioned stimuli, such as the sight of the Web sites home page or thoughts about

desired products. This phenomenon has been previously described as unregulated buying.

Working within a social cognitive framework (after Bandura, 1986), LaRose (2001)

argued that impulsive (Rook and Fisher, 1995), compulsive (Faber and O'Guinn, 1992),

and addictive (Krych, 1989) buying represent different points along a continuum of

unregulated buying behavior representing varying deficiencies in the socio-cognitive

mechanism of self-regulation. Ample evidence of deficient self-regulation exists. Online

shoppers tended toward more impulsive behavior than offline shoppers (Donthu &

Garcia, 1999). According to Cyber Dialogue (2001), 28% of e-shoppers report that the

internet makes them shop more often and 33% of them tend to exceed their shopping

budget online. LaRose and Eastin (2002) found that deficient self-regulation (DSR) was


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a more important predictor of online shopping activity among college students than were

low price and convenience or the personal or financial characteristics of the shopper..

Confidence is another facet of consumer trust (Garbarino & Johnson, 1999), but

what is the controlling nexus of that confidence? Confidence in an online vendor is itself

meaningless if we lack confidence in our own ability to discriminate honest ones from

dishonest ones and to successfully complete a secure online transaction. In other words,

we may have false confidence, or misplaced trust. On the other hand, we may lack

confidence in a Web site but have great confidence in our ability to overcome any

difficulties that may result from patronizing it -such as by using a credit card that limits

our personal financial loss-- and proceed with an online purchase. So, the key

determinant is confidence in our own perceived ability to make a successful online

transaction.

In social cognitive terms, this is the concept of self-efficacy. Particularly, it

involves ones judgment of ones capabilities regarding task performance. According to

Bandura (1986), self-efficacy influences behavior in four ways. First, self-efficacy helps

individuals to choose the situations and activities they choose to engage in. Next, self-

efficacy determines effort level and persistence when individuals strive to overcome

barriers and persist against adverse results. Third, self-efficacy helps predict

performance and coping behavior. Finally, self-efficacy reduces anxiety.

Self-efficacy has been found to be a significant predictor of computer usage

generally (Compeau and Higgins, 1995) and Internet usage specifically (LaRose, Mastro

& Eastin, 2001). Self-efficacy becomes a more powerful predictor when it is application-

specific (Marakas et al., 1988). Following this logic, the pertinent predictor of online


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buying behavior would be encapsulated in online shopping self-efficacyor the belief in

ones ability to successfully complete and online purchase. However, self-efficacy

specific to e-commerce has not been examined in prior research.

Finally, e-commerce participation can be defined in terms of a consumers

intention to participate in an online purchase or transaction in the future. Behavioral

intentions have been found to be reliable and valid predictors of behavior in a wide

variety of behavioral domains (Ajzen, 1985)

HYPOTHESES

Expected outcomes, self-efficacy, and deficient regulation have recently been

combined in a new model of media attendance (LaRose and Eastin, 2004) grounded in

social cognitive theory. However, the model has only been applied to overall Internet

usage, very broadly defined, so the present research tests the robustness of the model in

explaining a specific type of online activity, online buying.

Thus, Social Cognitive Theory presents an alternative to e-trust for explaining

participation in electronic commerce. Indeed, we may well ask whether e-trust may be

explained away entirely by social cognitive constructs and whether it is a necessary

concept. At the very least, it appears that important aspects of the e-trust concept are

subsumed by well-known socio-cognitive mechanisms. While revisiting the previously

tested variables, the present research incorporates online shopping self-efficacy to predict

intentions of e-commerce participation. Along with examining the respective

relationships between trust, deficient self-regulation, positive outcome expectations and


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online shopping self-efficacy on online buying intention, the comparative power of each

relationship will be tested. This suggests the following hypotheses:

H1: E-trust will be positively related to online shopping intentions.

H2: Deficient self-regulation will be positively related to online shopping intentions

H3: Positive outcome expectations will be positively related to online shopping intentions

H4: Online shopping self-efficacy will be positively related to online shopping intentions

H5: E-trust will not be a significant predictor of online shopping intentions aftercontrolling for DSR, outcome expectations, and online shopping self-efficacy.

RESEARCH METHODS

Sample

Participants were a convenience sample of 273 undergraduate students from an

introductory telecommunication class at a major Midwestern university. Participants

were 67% males and 33% females. The mean age of students in the class was 20.13

years old, with a median age of 19. Most respondents came from families with

household incomes of $75,000 or more (42%) while about 28% reported coming from

families with household incomes of $50,000 to $74,999. The remainder came from

families with household incomes under $50,000. Respondents were offered extra credit

for participating in the study and an alternative form of extra credit was provided for

those who chose not to participate.

A student sample is deemed appropriate for the purposes of exploring the lawful

relationships among such variables as shopping self-efficacy, e-trust, positive outcome


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expectations, and online buying intentions. This is a population of interest because they

are forming consumption habits that may guide a lifetime of purchases.

Operational Measures

A two-item measure of trust was developed by adapting web-site trust measures

(Jarvenpaa et al., 2000; Doney and Cannon, 1997). A Likert-type agree-disagree scale

was used, where 7 corresponded to "strongly agree" and 1 to "strongly disagree." Two

items constituted the online trust index.1

A shortened version of the DSR scale (LaRose and Eastin, 2004) was adapted to

the present study by asking respondents to frame their answers in light of if they go

shopping online to cheer themselves up and if they have tried to unsuccessfully to cut

down on the amount of money they spend online. Two items constituted the deficient

self-regiulation index.2

Here, LaRose and Eastins (2002) online shopping outcome expectation scale

items was used. These included questions about the convenience, timely shipping and

good customer service, wider selection, and easiness, as measured on a similar 7-point

Likert-type scale as described above. Five items constituted the outcome expectations

index.3

1Overall, I believe that purchasing online is a secure activity and Most news and information that I find

online is reliable.2

I go shopping online to cheer myself up and I have tried unsuccessfully to cut down on the amount of

money I spend online.3

Online shopping is convenient, Online purchases are usually shipped correctly and in a timely

manner, Online stores have good customer service, On-line stores offer a wider selection than real

life stores, and Its easy to buy things online


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Online shopping self-efficacy measurement scales have been developed in various

contexts. Internet self-efficacy, for example, was conceptualized and operationalized by

Eastin and LaRose (2000). Their scale items measure an Internet users judgment of his

or her ability to apply Internet skills on a broad basis, rather than focusing in on specific

Internet skills, such as writing the code for a web page. The shopping self-efficacy

measurement scale was developed by adapting the Internet (Eastin and LaRose, 2000)

and computer (Compeau and Higgins, 1995) self-efficacy measurement scale. A Likert-

type agree-disagree scale was used to assess the participants' confidence that they could

use the Internet in each of the ways specified, where 7 corresponded to "strongly agree"

and 1 to "strongly disagree." These include statements on comfort level in providing

personal information, confidence evaluating online privacy policies, and identifying

safe websites. Four items constituted the shopping self-efficacy index.4

A two-item measure of online buying intentions was used. A Likert-type scale

was developed to assess the participants' intentions that they are willing to participate in

e-commerce, where 7 corresponded to "very likely" and 1 to "very unlikely." Consumer

intentions to behave are an important concept as they represent the best estimate of

future behavior available to market researchers (KalWani and Silk 1982). Thus,

"likelihood of future online buying" appears as the ultimate dependent variable in the

model. Two items constituted the shopping self-efficacy index.5

4I can tell when it is safe to shop at an online store and when it is not safe, I feel comfortable providing

personal information when shopping on-line, I know how to evaluate online privacy policies, and I

know how to identify sites with secure servers5

I will probably buy something online in the next month and In the next month how likely is it that you

will buy products online with [your] credit card


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Analysis

The results were examined using the SPSS version 11.5 (SPSS, Inc., 2002)

statistical package. In order to distill the questionnaire items into key groupings of

variables, a factor analysis using varimax rotation was conducted. Variables were

included in factors where the factor loading was .60 or higher. This analysis is the

foundation for the construction of the multi-item indexes that are used for the additional

analyses in the following stages of this research. Factors of interest that emerged, along

with their respective reliability (Cronbach alpha) coefficients, are listed below in Table 1.

Hyotheses 1-4 were tested by examining Pearson product-moment correlations between

online shopping intentions and the four respective independent variables.

Hypothesis 5 was tested through a stepwise multiple regression analysis in which

e-trust was entered in the second step and the other three independent variables in the

first step. An inspection of the zero-order correlations among the independent variables

suggested the possibility of multicollinearity between the outcome expectations and e-

trust variables. However, an inspection of the SPSS multicollinearity diagnostics

revealed that the VIF (maximum observed = 1.84) and condition index (mazimum

observed = 18.90) were with in acceptable limits (VIF < 2.50, condition index < 30; ) and

therefore multicollinearity was deemed not to be a problem.


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Table 1: Means, Standard Deviations, Ranges and Cronbach Alpha Values

Factor Alpha Range Mean Standard

Deviation

Online Buying Intentions .77 2-14 9.49 3.54

Deficient Self-Regulation .60 2-14 7.16 3.06

Positive Outcome Expectations .81 6-35 26.79 5.04E-Trust .73 2-14 10.15 2.38

Shopping Self-Efficacy .80 4-28 19.27 5.03

RESULTS

Pearson product-moment correlation coefficients are shown in Table 2. The first

four hypotheses were all confirmed. Online buying intentions were positively correlated

to e-trust (r =.557, p < .01), outcome expectations (r =.622, p < .01), DSR (r =.298, p

a social cognitive theory for shoppin behavior

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