customer retention online: the influence of switching barriers betsy b. holloway assistant professor...
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![Page 1: Customer Retention Online: The Influence of Switching Barriers Betsy B. Holloway Assistant Professor of Marketing Samford University Sharon E. Beatty Professor](https://reader031.vdocuments.site/reader031/viewer/2022013004/56649e115503460f94afdfff/html5/thumbnails/1.jpg)
Customer Retention Online: The
Influence of Switching BarriersBetsy B. Holloway
Assistant Professor of Marketing
Samford University
Sharon E. BeattyProfessor of Marketing
University of Alabama
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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
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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
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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?
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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).
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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)
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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.
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Service Failure & Recovery (Cont.)
Holloway and Beatty (2003) found significant online service failure problems.
First, Qualitative study with 30 informants.
Then, Quantitative study.
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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
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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%)
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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
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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
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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%)
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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
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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).
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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.
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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
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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).
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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.
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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
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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
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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.
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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
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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”.
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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…
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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.
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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
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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%)
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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
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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
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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
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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
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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
(-)
(-)
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Interpretation:
Relationship quality matters more when post-recovery trust is low.
AOA matters more when recovery satisfaction is low.
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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
(+)
(-)
(+)
(-)
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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:
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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)
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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
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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
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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.
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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
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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
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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)