Customer Retention Online: The

Influence of Switching BarriersBetsy B. Holloway

Assistant Professor of Marketing

Samford University

Sharon E. BeattyProfessor of Marketing

University of Alabama

US Internet Growth

Overall:

2.4 bil-9748 bil-2002104 bil-2005130 bil-2006*

30% growth rate in 2003 for the holiday season*

People spent 20% of their budgets online during 2003 holiday season (18.5 bil)*

10.6% of all consumer transactions initiated by website visit (WSJ, July 6 04)

*Source: Nielson//Net Ratings

Rationale for Research

Customer service may be the “Achille’s heel” of online retailing.

Limited understanding of service failure and

recovery issues (Tax, Brown, and Chandrashekaran 1998).

Failure &Recovery Satis/Dissat Switching is too simplistic

We askHow important are switching barriers in the

online service recovery process?

How do they keep a customer loyal to the firm after a service failure?

Do all switching barriers work the same?

Background:Online Customer Service

Research emphasizes the benefits of technology for – Improving service encounters (Bitner, Brown, and

Meuter 2000; Zeithaml, Parasuraman and Malhotra 2002)

– Improving customer complaint management (Strauss and Hill 2001) and

– For recovering from service failures (Brown 1997).

Online Customer Service ProblemsHowever…

Only 35% of consumers consider their online shopping experiences satisfactory (Dodson 2001).

Poor online service impacts consumer trust and hurts offline sales.

70% dissatisfied w/online experience plan to spend less at retailers offline store (Jupiter Media Metrix 2002)

Service Failure & Recovery

Service failure is poor service delivery.

Service recovery occurs when the firm tries to correct the problem.

Service recovery management is critical to retention.

It has received extensive attention but little attention online.

Service Failure & Recovery (Cont.)

Holloway and Beatty (2003) found significant online service failure problems.

First, Qualitative study with 30 informants.

Then, Quantitative study.

Quantitative Study

Critical incident technique (CIT)  Self-administered survey—recent

service failure and reactions to failure and recovery

 Pretested with 50 online shoppers Convenience sample

online,recruited by marketing research students

 295 valid responses

Categorization of Failure Types

Delivery problems (46.6%) Website design problems (16.6%) Customer service problems (13.8%)

Payment problems (12.2%) Poor quality merchandise (5.3%)

Security problems (3.8%)

Complaint Behavior

54.2% of our respondents chose to complain to the company

•  54.7% via phone

•  33.7% via email

•  10.5 via letter

•  1.1% in person

 

Reaction to Recoveries

42.5% satisfied with recovery effort while 57.5% were not!

 Of those who were dissatisfied: __36.9 felt they deserved more than they got

 __22% criticized customer service provided in the recovery

 __16% did not receive any sort of response at all

 __13.5% felt interaction was poor (e.g., apology was insincere)

 __5.7% lack of apology

 __5.7 felt experience was so bad there was no way to rectify it

Post-Failure Repurchase Intention of Complaining Customers

Future Intentions

Returning Not

Returning Total

Satisfactory 29/68 39/68 68

(42.6%) (57.4%) (42.5)

Recovery

Not Satisfactory 12/92 80/92 92

Effort (13.0%) (87.0%) (57.5)

Total 41/160 119/160 160

(25.6%) (74.4%) (100%)

Getting back to our current study: (recall we are looking at switching barriers online)

Switching barriers are any factors that make it more difficult or costly for consumers to change providers.

satisfaction retention: contingent upon switching barriers (Jones, Mothersbaugh, and Beatty 2000).

3 online switching barriers:– Quality of the firm/customer relationship online and

offline– Perceived switching costs– Attractiveness of available alternatives– Incorporated within SF/SR paradigm

Conceptual Development

Recovery - Satisfaction– “The degree to which a customer is satisfied with a

service firm’s transaction-specific service recovery effort following a service failure” (Boshoff 1999, p. 237).

Post-Recovery Trust – Occurs when “one party has confidence in an

exchange partner’s reliability and integrity” (Morgan and Hunt 1994, p.23).

– Dissatisfaction with the recovery efforts serves to reduce consumer’s post-recovery trust in firm (Tax, Brown, and Chandrashekaran 2000).

Measurement ScalesPost-Recovery Satisfaction (Goodwin

and Ross 1992; Oliver and Swan 1980; Tax, Brown, and Chandrashekaran 1998; Weun 1997)– I was pleased with the service I experienced.– I was happy with how the company handled

my problem.– Overall, I felt the service response I received

was good.– Overall, I was satisfied with the way my

complaint was resolved. – Overall, I was pleased with how the company

handled my service problem.

Measurement Scales (Cont.)Post-Recovery Trust (Morgan and Hunt

1994; Tax, Brown, and Chandrashekaran 1998; Weun 1997)

– I believe that (the named company):1)Can generally be trusted2)Is honest and truthful3)Is trustworthy4)Can be counted on to do what is right5)Is a company I have great confidence in6)Has high integrity7)is a company I can depend upon to do the right

thing8)Can be relied upon

Conceptual Development (Cont.)

Switching Costs– Perception of the magnitude of the additional

costs (i.e., time, money, and effort) that would be required to terminate an existing relationship and begin a new one (Porter 1980; Ping 1993).

Attractiveness of Available Alternatives– Customer’s estimate of satisfaction available in

an alternative relationship (Rusbult 1980).

Intention to Remain – A consumer’s intention to continue to use a given

service provider in the future (Oliver and Swan 1989).

Measurement Scales (Cont.)

Perceived Switching Costs (Ping 1993; Jones, Mothersbaugh, and Beatty 2000)– In general, it would be a hassle for me to

change to another online retailer.– It would take a lot of time and effort to change

to another online retailer. – For me, the costs in time, money and effort to

switch to another online retailer are high.– It would be inconvenient for me to switch to

another online retailer to purchase the products/services I need.

Measurement Scales (Cont.) Attractiveness of Alternatives (Ping 1993; Rusbult 1980; Jones,

Mothersbaugh, and Beatty 2000)– If I had to change online retailers, I’m aware of at least one other

company that would be at least as good as this one.* – If I needed to find another online company to shop with, there is at

least one with whom I could be satisfied.– I would probably be happy with the products and services of another

online retailer.– Compared to this online retailer, I think there probably is another

company with whom I would be equally or more satisfied.– If I needed to change online retailers, there is at least one other

good company to choose from. Intention to Remain (Blodgett and Tax 1993; Oliver and Swan

1989)• Very Unlikely----Very Likely • Very Unprobable---Very Probable• Impossible---Very Possible• No Chance---Certain

Conceptual Development (Cont.)

Relationship Quality– The magnitude and character of a consumer’s

relationship with a company.– Operationalization (e.g., Garbarino & Johnson 1999)– Cumulative Satisfaction– Trust– Commitment

Measurement Scales (Cont.) Relationship Quality

– Satisfaction (Crosby and Stephens 1987; Oliver and Swan 1989) • Very Displeased…Very Pleased• Very Dissatisfied with…Very Satisfied with• Unhappy With…Happy With• Very Unfavorable…Very Favorable• Has done a good job for me…Has done a poor job for me*

– Trust (Morgan and Hunt 1994; Tax, Brown, and Chandrashekaran 1998; Weun 1997)

• Can generally be trusted• Is honest and truthful• Is trustworthy• Can be counted on to do what is right• Is a company I have great confidence in• Has high integrity• is a company I can depend upon to do the right thing• Can be relied upon

– Commitment (Lassar, Mittal, and Sharma 1995; Garbarino and Johnson 1999)

• I care about the long-term success of this company.• I am a loyal customer of this company.• I am likely to grow fond of this company.• I believe I could develop a good relationship with this company.• I think I might become loyal to this company.

The Role of Switching Barriers in the Online Service Recovery Process

Recovery Satisfaction

Post-Recovery Attitudes

Post-Recovery

Trust

Perceived Switching

Costs

Intention to Remain

Switching Barriers Post-Recovery Behavior

Ongoing Relationship Quality

Attractiveness Of

Available Alternatives

H5BH5AH4B

H1

H3

H2

H4A

H7H6B

H6A

Research Design First, considerable background based on previous

work.

Web-based survey design. Quota sample recruited by 174 business students (516 respondents).

Hypothetical scenarios with lack of recovery.

Role-playing technique (e.g., Bitner 1990; Mohr and Bitner 1995) with actual company named by respondent and cited throughout.

Two types of online companies: online only versus “bricks and clicks”.

Service Failure Scenario

You go to (the company)’s website to order a gift as a present for your best friend’s birthday. You find and select a gift that you believe your friend will like very much. As your friend’s birthday is only 4 days away, you decide to spend an extra $10 for overnight delivery to be certain the gift will arrive in time. Three days later, the gift you purchased still has not arrived…

 

Failure to Recover Scenario

As your friend’s birthday is now only 1 day away, you decide to contact the company in order to confirm the status of your purchase. (The company)’s representative informs you that due to an unusually high volume of sales, all orders are running 4-5 days behind schedule. You are informed that your gift is not due to arrive until 2 days after your friend’s birthday. The explanation does not include an apology or any sort of compensation for your trouble.

Characteristics of the Sample264 (51%) for offline/online companies / 252

(49%) for online only companiesGender

– 49% maleAge

– 37% 18-29; 41% 29-49Household Income

– 36% over $100,000Ethnicity

– 90.7% white/caucasianFull-time student

– 20% YES/ 80% NO

Characteristics of the Sample

Began shopping online– 2 years ago or more (62%)

Frequency of online purchasing– About once every 3 months (47%)

Number of online purchases in past 2 years– Between 1-10 purchases (61%)

Total Amount Spent Online in past 6 months– Less than $100 (37%)– $100 - $500 (36%)

Results of Reliability Assessment (n=508)

Construct # of Items Cronbach’s Alpha

Satisfaction 4 0.97

Trust 8 0.98

Commitment 5 0.96

Switching Costs 4 0.96

Attractiveness of Alternatives 4 0.94

Recovery Satisfaction 5 0.97

Post-Recovery Trust 8 0.98

Intention to Remain 4 0.97

Confirmatory Factor Analysis

Goodness of Fit StatisticsDegrees of Freedom = 84

Minimum Fit Function Chi-Square = 235.149 (P = 0.0)Root Mean Square Error of Approximation (RMSEA) = .0596

Standardized RMR = .0208Goodness of Fit Index (GFI) = 0.944Comparative Fit Index (CFI) = 0.987Incremental Fit Index (IFI) = 0.987

Relative Fit Index (RFI) = 0.974

* Two Trust items deleted and several error terms were allowed to correlate- satisfaction, trust measures

Analysis

Composite were created by averaging total # of items

Moderated multiple regressions

Various regression approaches

In preliminary analyses:– Switching cost variable had no impact and so

was removed from further assessment

Final Regression Results

Variable Standardized Coefficient T-value Online Off/Online Online Off/Online

RSAT 0.170 0.209 2.809*** 3.989****PRT 0.563 0.564 9.218**** 10.838****AOA -0.034 -0.136 -0.707 -3.285****RelQ 0.054 0.103 1.014 2.341**RSAT x AOA -0.152 0.016 -2.285** 0.284PRT x AOA 0.073 0.003 1.186 0.062RSAT x RelQ 0.078 -0.118 1.180 -2.140**PRT x RelQ -0.141 0.166 -2.204** 2.856***

Online Group: Offline/Online Group:F for Full Model=27.957 (p<.001) F for Full Model = 44.820 (p<.001)R2=0.489 R2=0.586

Dependent Variable- Intention to RemainKey: RSAT=Recovery Satisfaction; PRT=Post-Recovery Trust; SWC=Switching Costs;

AOA=Attractiveness of Alternatives; ORQ=Ongoing Relationship QualityOnline Only Companies (n=243) ; Offline/Online Companies (n=262)*p<.10, **p<.05, ***p<.01, ****p<.001

Results of Hypothesis Testing: Online Group

Recovery Satisfaction

Post-Recovery Attitudes

Post-Recovery

Trust

Perceived Switching

Costs

Intention to Remain

Switching Barriers Post-Recovery Behavior

Ongoing Relationship Quality

Attractiveness Of

Available Alternatives

H5BH5AH4B

H1

H3

H2

H4A

H7

H6B

H6A

Note: …. for non-supported hypotheses

(-)

(-)

Interpretation:

Relationship quality matters more when post-recovery trust is low.

AOA matters more when recovery satisfaction is low.

Results of Hypothesis Testing: Offline/Online Group

Recovery Satisfaction

Post-Recovery Attitudes

Post-Recovery

Trust

Perceived Switching

Costs

Intention to Remain

Switching Barriers Post-Recovery Behavior

Ongoing Relationship Quality

Attractiveness Of

Available Alternatives

H5BH5A

H4B

H1

H3

H2

H4A

H7H6B

H6A

Note: … for non-supported hypotheses

(+)

(-)

(+)

(-)

Direct Effects: RQ and AOA

Moderation interpreted:

– Relationship quality matters more when recovery satis. is low.

– Relationship quality matters less when post recovery trust is low (odd finding)

Interpretation:

Why the odd finding?

Different view of the data/relationship quality

Relationship quality was viewed at a buffer here and this was mostly true

Relationship quality may also sometimes be a burden (see Brady, Roehm, and Cronin, working paper - Brand Equity)

New Analyses (online/offline data only)

After failed service recovery…– repurchase intentions and trust

depreciate more in high RQ group– anger was higher in high RQ group

Discussion of Findings

Attractiveness of alternatives and ongoing relationship quality both influence the service recovery process to varying degrees

Proposed model fits data in the offline/online group best – Suggests Rel. Q. buys you more if you are

bricks and clicks– General lack of support for the moderator

model

Discussion of Findings (Cont.)

Dominant roles of 1) post-recovery trust and 2) ongoing relationship quality across both groups

– Recovery trust is clearly most influential predictor of intention to remain in both groups

– Recovery trust is 3-4 times more influential than recovery satisfaction

– Support for the RelQ interactions in both groups– So RelQ is more influential for offline/online

companies than that of online only firms.

Theoretical Implications

Holistic examination of the service failure/recovery process

Illustrates need to consider additional variables and antecedent states in the service failure/recovery

Furthers work on using relationship quality construct

Limitations & Future Research

Measurement of recovery satisfaction was limited to one scenario (high failure/low recovery).

Multicollinearity is a problem; redoing now with SEM.

Need to assess importance consumers place on switching barriers

Limitations & Future Research (Cont.)

Need to develop theory better.

The use of scenarios versus actual failures/recoveries

Need for actual purchase data (and longitudinal design)