10.5061/DRYAD.T4B8GTJ50
Peasley, Michael
0000-0001-9448-1818
Middle Tennessee State University
Bauer, Carlos
University of Alabama, Tuscaloosa
Bachrach, Daniel
University of Alabama
Patil, Ashutosh
Cleveland State University
Hochstein, Bryan
University of Alabama
See no evil in the voice-to-voice customer service context
Dryad
dataset
2022
call duration
adaptiveness theory
resolution problem-solving
relational problem-solving
customer frustration
customer satisfaction.
FOS: Social sciences
N/A*
2022-08-16T00:00:00Z
2022-08-16T00:00:00Z
en
https://doi.org/10.5281/zenodo.6993807
5049 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
A sample of more than 28,000 front-line employee (FLE) - customer
interactions, extrapolating from foundational framing, we pit conventional
service approaches against one another to propose a dual-process model,
situating customer frustration/satisfaction as mediators of the indirect
relationships between resolution/relational tactics and call duration – a
key customer service efficiency outcome.
In order to provide an internally valid empirical foundation to test our
model, the sampling frame we adopt was based on a random sample of inbound
calls received over a one-year time frame, resulting in 28,103 dyadic
interactions, handled by a total of two-hundred and seventeen separate
FLEs, employed at four call centers located across the US. The data on
which our analyses are based was provided by a Fortune 500, nationally –
branded, market-leading insurance company headquartered in the US. To
account for the nested structure of the complex data that underlie our
analyses, we estimated one omnibus moderated-mediation, random-effects
model in Mplus v. 8 (Muthén & Muthén, 2017). This approach allows
us to take into account complex sampling features related to our panel
data, including generating robust standard errors (Thompson, 2011),
teasing out variations potentially caused between FLEs, thereby
controlling for omitted, unobserved effects related to the ways in which
the 217 FLEs handled each call (Germann et al., 2015).
We are including our analysis code because the data is not available due
to its proprietary nature and a non-disclosure agreement. An example of
the data has been provided.