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  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>which she has
published in several conference and journal publications. She has participated in national and international
research projects on e</journal-title>
      </journal-title-group>
    </journal-meta>
    <article-meta>
      <title-group>
        <article-title>The Relationship Between Outbound and Inbound Communication in Government-to-Citizen Interaction</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Christian Ø. Madsen</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Willem Pieterson</string-name>
          <email>willem@pieterson.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Sara Hofmann</string-name>
          <email>sara.hofmann@uia.no</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Research Centre for Government IT, IT University of Copenhagen</institution>
          ,
          <country country="DK">Denmark</country>
        </aff>
      </contrib-group>
      <fpage>107</fpage>
      <lpage>115</lpage>
      <abstract>
        <p>While many citizens have adopted digital channels for public service interaction, the use of traditional channels remains high, preventing economic benefits from digitalization. The channel choice (CC) and multi-channel management (MCM) fields of echoice of channels and the management of service interactions across channels. Research has mostly focused on either the citizen or organizational side and single channel communication. visits) is understudied. Therefore, we present a longitudinal quantitative study, which analyzes channel traffic data from two service areas, pensions, and parental leave, across three channels (letters, telephone calls, website visits). We apply interactivity theory and the concept of multiplexity to guide our analysis. We seek to offer contributions to the CC and MCM literature by demonstrating a relationship between outbound and inbound communication and the multiplex nature of government-citizen interaction. Government IT, which is a collaboration between the IT University of Copenhagen, the Danish Agency for Digitisation, and the selffunding partners were not involved in the development of the research questions presented herein, nor the analysis, discussion, or presentation of the study's results.</p>
      </abstract>
      <kwd-group>
        <kwd>Channel choice</kwd>
        <kwd>mixed methods</kwd>
        <kwd>multi-channel management</kwd>
        <kwd>multiplexity</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1. Introduction</title>
      <p>
        Government organizations have spent considerable efforts on migrating citizens from traditional
interaction channels such as counter visits, physical letters, and telephone calls towards digital
selfservice channels, especially websites
        <xref ref-type="bibr" rid="ref10 ref15 ref5 ref9">(Madsen &amp; Hofmann, 2019; Pieterson &amp; Ebbers, 2020)</xref>
        . Several
Northern European countries, such as Denmark and The Netherlands, have high adoption rates for
digital channels
        <xref ref-type="bibr" rid="ref6">(Eurostat, 2019)</xref>
        . However, the use of traditional channels remains high among
adopters and non-adopters of digital channels alike
        <xref ref-type="bibr" rid="ref15 ref16 ref5">(Pieterson &amp; Ebbers, 2020; Rey-Moreno &amp;
Medina-Molina, 2016)</xref>
        . This traditional interaction form is expensive because it requires the
-of-view, the continued use of
traditional channels in public service encounters is problematic because it prevents organizations
from reaping the full economic benefits from digital self-services
        <xref ref-type="bibr" rid="ref4">(Ebbers, Pieterson, &amp; Noordman,
2008)</xref>
        -of-view, this is problematic because it indicates that the current
selfservice applications are incapable of solving all of their needs and problems
        <xref ref-type="bibr" rid="ref10 ref9">(Madsen, Hofmann, &amp;
Pieterson, 2019)</xref>
        .
      </p>
      <p>
        Within the e-government field, two related research streams focus on this phenomenon. Channel
ls in public service encounters
        <xref ref-type="bibr" rid="ref11 ref12 ref18">(Madsen &amp;
Kraemmergaard, 2015a; Pieterson, 2009)</xref>
        . Multi-channel management (MCM) research studies how
public organizations can manage interactions with citizens across multiple channels
        <xref ref-type="bibr" rid="ref10 ref4 ref9">(Ebbers et al.,
2008; Madsen &amp; Hofmann, 2019)</xref>
        . Most CC and MCM research regard this interaction as discrete
event occurring on a single channel, rather than studying the entire service encounter holistically as
it occurs across multiple channels
        <xref ref-type="bibr" rid="ref10 ref9">(Madsen et al., 2019)</xref>
        . Moreover, existing work tends to focus on
inbound contacts only. Few studies have analyzed the relationship between outbound and inbound
channel traffic, i.e., to citizens influences communication
from citizens
        <xref ref-type="bibr" rid="ref10 ref14 ref18 ref9">(Madsen &amp; Hofmann, 2019; Teerling &amp; Pieterson, 2010)</xref>
        .
      </p>
      <p>
        Therefore, this paper presents an ongoing longitudinal quantitative study which seeks to
establish and explain the relationship between public authorities' outbound traffic and the
subsequent inbound communication from citizens. We present and analyze channel traffic data from
a six-year period covering two public service areas (pensions and parental leave) and across three
communication channels (letters, telephone calls, website visits). Following previous CC and MCM
studies
        <xref ref-type="bibr" rid="ref10 ref4 ref9">(Ebbers et al., 2008; Madsen et al., 2019)</xref>
        , we will apply the related concepts of multiplexity
and intermediality to guide our analysis
        <xref ref-type="bibr" rid="ref1 ref7">(Bordewijk &amp; van Kaam, 2002; Haythornthwaite, 2005)</xref>
        .
Next, we briefly describe the background of our study, followed by related CC and MCM research
and the gaps herein we seek to address. Then, we present our study's research aim and method.
Finally, we present preliminary and expected results from the study.
      </p>
    </sec>
    <sec id="sec-2">
      <title>2. Background: Mandatory Self-Service Channels and Udbetaling</title>
    </sec>
    <sec id="sec-3">
      <title>Danmark</title>
      <p>
        In 2015, digital self-service became mandatory for a number of public services in Denmark
        <xref ref-type="bibr" rid="ref19">(The
Danish Government, 2011)</xref>
        . For these service areas, citizens are required to use digital channels and
self-service applications to find information and apply for the services. Citizens who are incapable
of using the digital channels can request to be made exempt from them. All citizens can contact
public authorities in person, by telephone or in writing for help.
      </p>
      <p>
        The study presented here revolves around two public services in Denmark, a lifelong pension
scheme (Pension) and a parental leave scheme (Parental). These services are administered by the
pension's fund ATP, and the public authority Udbetaling Danmark (UDK), respectively. ATP also
administers UDK and the two organizations were partly co-located at the time of the study.
Information and self-service applications for both services are located at the national web-portal
borger.dk. The Pension scheme was established in 1965 as a supplement to the Danish state pension.
Most Danish citizens contribute to this scheme by paying a percentage of their income, such as wages
or unemployment benefits. The pension is paid out at retirement age, either as a lump sum or as
monthly payments. Once yearly, ATP sends out an annual letter to inform pensioners about the
nsioners in Denmark grew from
approximately 950,000 to 1.1 million
        <xref ref-type="bibr" rid="ref17">(Statistics Denmark, 2019)</xref>
        . The Parental leave scheme consists
of four underlying leave schemes (pregnancy leave, maternity leave, paternity leave, and parental
leave). The availability and duration of t
agreements. The economic benefits provided by these schemes often
source of income while on leave. There are approximately 100,000 parental leave cases annually,
including parents and their employers.
      </p>
    </sec>
    <sec id="sec-4">
      <title>3. Channel Choice and Multi-channel Management Research</title>
      <sec id="sec-4-1">
        <title>Channel choice</title>
        <p>
          <xref ref-type="bibr" rid="ref20">(Trevino, Webster, &amp; Stein, 2000, p. 163)</xref>
          . CC studies in e-government
          <xref ref-type="bibr" rid="ref11 ref12 ref18">(Madsen &amp;
Kraemmergaard, 2015a; Pieterson, 2009)</xref>
          . These studies have identified factors that affect this choice
and measured their influence. Recent studies from Northern Europe show that online channels are
now the most used channels there, while people still turn to traditional channels when problems
arise
          <xref ref-type="bibr" rid="ref13 ref15 ref5">(Madsen &amp; Kraemmergaard, 2018; Pieterson &amp; Ebbers, 2020)</xref>
          . Most research has simplified CC
as a single binary choice between several channels for a service interaction. Few studies
acknowledge that most CC is of multiplex nature (cf. e.g.
          <xref ref-type="bibr" rid="ref10 ref7 ref9">(Haythornthwaite, 2005; Madsen et al.,
2019)</xref>
          ). Channel multiplexity describes the sequential or parallel use of several channels in one
service interaction
          <xref ref-type="bibr" rid="ref10 ref9">(Madsen et al., 2019)</xref>
          . Channel multiplexity occurs when citizens encounter
problems that they cannot solve with one channel. For instance, a citizen may search for information
online and then call a government organization (sequential interaction) for help, or they may be
logged into a self-service application while calling (parallel interaction).
        </p>
        <p>
          Multi-channel management (MCM) concerns how government organizations can improve the
public service encounter, integrate channels, and migrate citizens across channels
          <xref ref-type="bibr" rid="ref14 ref18">(Pieterson, 2010)</xref>
          .
Multi
        </p>
        <p>
          <xref ref-type="bibr" rid="ref8">(Kernaghan, 2013, p. 124)</xref>
          . Ebbers, Pieterson and Noordman (2008)
developed a multi-channel strategy for public organizations in response to the discrepancy between
the channels that the public sector wants citizens to use and the actual channels citizens prefer.
According to this strategy, government organizations should guide citizens to the most efficient
channels for a given problem or task based on task complexity (the number of steps involved) and
ambiguity (possible and conflicting interpretations). Simple tasks low in ambiguity should be
handled online, while complicated tasks high in ambiguity should be dealt with on the phone or
inperson
          <xref ref-type="bibr" rid="ref4">(Ebbers et al., 2008)</xref>
          . The strategy combines elements from media richness theory
          <xref ref-type="bibr" rid="ref3">(Daft &amp;
Lengel, 1986)</xref>
          and interactivity theory
          <xref ref-type="bibr" rid="ref1">(Bordewijk &amp; van Kaam, 2002)</xref>
          with empirical knowledge on
channel traffic and service modes. It distinguishes between channel types, which describe what
channels citizens and public organizations interact through, and channel modes, which refer to how
and for what purpose the channels are used. The interaction between citizens and the public sector
is divided according to who initiates the interaction (citizens or public sector) and whether the
interaction is single-sided or two-sided and allows for feedback. Based on these dimensions, four
different channel modes are distinguished. (1) Allocution, the push of information towards citizens
typically via mass media and (2) registration where citizens send information to public organizations
on their request, belong to the government-initiated channel modes. Citizen initiated channel modes
are (3) consultation where citizens consult information sources provided by the public sector to
retrieve information without, however, enabling a real interaction, and (4) conversation where
citizens request information which is then provided by the public sector tailored to the citi
needs, for example via phone. A fifth channel mode (5) transaction, refers to financial transactions.
        </p>
        <p>
          In sum, most CC and MCM studies assume that a government-citizen encounter occurs as a single
interaction via one channel and initiated by the citizen. A few qualitative studies have, however,
shown that citizens may use several channels in one encounter
          <xref ref-type="bibr" rid="ref10 ref11 ref12 ref9">(Madsen et al., 2019; Madsen &amp;
Kraemmergaard, 2015b)</xref>
          , as well as a relationship between channel modes. However, to the best of
our knowledge, no e-government CC or MCM studies have statistically analyzed the relationship
between out- and inbound channel traffic between government and citizens or demonstrated how
to connect channel modes in larger service encounters
          <xref ref-type="bibr" rid="ref10 ref11 ref12 ref9">(Madsen &amp; Hofmann, 2019; Madsen &amp;
Kraemmergaard, 2015a)</xref>
          .
3.1.
        </p>
        <sec id="sec-4-1-1">
          <title>Research Design</title>
          <p>The purpose of our research project is to study the relationship between a public organization's
outbound channel traffic and the incoming channel traffic from citizens. First, we conduct a
quantitative study, which seeks to establish this relationship by analyzing channel traffic. In the
future, we seek to understand why this happens, by gaining insight into the reasons citizens provide
for their behavior, and discuss the implications for CC and MCM models in e-government research.</p>
          <p>
            We have collected and analysed outbound (letters) and inbound (website visits and telephone
calls) channel traffic data. We collected data from two services; the Parental Leave (Paternal) scheme
(four years of data) and Pensions (six years). Data were aggregated on a weekly level. This allowed
us to assess a) how outbound communication impacts inbound communication and b) whether
inbound communication via one channel (e.g. web) affects the other (e.g. phone). In addition to
measuring the direct effect (e.g. an outbound letter leading to inbound traffic that same week), we
calculated the effects with a 1-3 week delay as well (e.g. does an outbound letter translate to inbound
traffic after two or three weeks?). Previous studies have indicated such a lag
            <xref ref-type="bibr" rid="ref13 ref15 ref5">(Ebbers &amp; van de
Wijngaert, 2020; Madsen &amp; Kraemmergaard, 2018)</xref>
            . For this w
the base (outgoing letters) of our time series. Thus, we looked at a number of different models for
the individual services (Pension &amp; Paternal) as well as the total.:
          </p>
          <p>The effect of outbound letters on inbound website traffic, directly or lagged (1-3 weeks).
Effect of outbound communication on inbound phone calls, directly or lagged</p>
          <p>Effect of inbound web traffic on inbound phone calls, directly or lagged.</p>
          <p>There were no missing data fields in the data set: for the years and variables mentioned above,
all inbound and outbound communication was recorded. We used R to calculate the sample
characteristics (Error! Reference source not found.) and the linear regression models discussed
below.</p>
        </sec>
        <sec id="sec-4-1-2">
          <title>3.1.1. Quantitative Sample</title>
          <p>During the sample period, around 12 million letters were sent out, and almost 6 million inbound
contacts were recorded. Of these inbound contacts, 67.20% came via the website and 32.80% via the
telephone. Error! Reference source not found., below, presents the key statistics for both services
and the total.</p>
        </sec>
      </sec>
      <sec id="sec-4-2">
        <title>Parental Pension Total</title>
        <p>Outbound Inbound Inbound Outbound Inbound Inbound Outbound Inbound Inbound</p>
        <p>Web Phone Web Phone Web Phone
4,333,226 2,640,350 1,665,773 7,841,484 1,334,414 274,514 12,174,710 3,974,764 1,940,287
20,733.14 10,116.28 6,382.27 25,025.66 4,263.30 877.04
17,503 9,864 6,343 1,873 4,456 835
11,466.95 3,754.19 1,491.84 118,126.02 2,346.56 272.64
38,896.84 12,698.93 6,199.00
16,510 12,037 6,949
119,054.03 5,992.42 2,844.68</p>
        <p>The statistics vary strongly for both services. Whereas roughly 4 million outgoing letters translate
in an equal number of inbound customer contacts for the Parental leave scheme, twice the outgoing
letters for Pension yield roughly 1.6 million incoming contacts. Similarly, the standard deviation of
outbound letters for Pension is much higher than that of Parental, suggesting a much higher seasonal
fluctuation of this service. The figure below (Error! Reference source not found.1) shows the weekly
fluctuation in the number of contacts for the Pension service (on a logarithmic scale with base 10).
This highlights the annual peak in outgoing letters. The figure also shows smaller peaks in inbound
contacts but does not immediately make clear whether that is caused by the outbound contacts.</p>
        <sec id="sec-4-2-1">
          <title>3.2. Preliminary Results</title>
          <p>Next, we briefly present preliminary results. We first discuss the model parameters (Table 2),
followed by the model estimates (Table 3).</p>
          <p>The model parameters show each of the models to be significant at p&lt;.000. However, they vary
in the (adjusted) variance (R2) they explain. Furthermore, the models tend to explain Inbound Web
traffic better than Inbound Phone traffic and the impact of Inbound Web on Inbound Phone traffic.
Nevertheless, all models produced significant results, and were used to calculate model estimates.
*sign at p=0.05, ** sign at p=0.01, *** sign at p=0.001</p>
          <p>The first of these estimates is the predictive value of outbound communication on any of the
dependent variables, and in none of the models, we find significant estimates. This means that
overall, the total outbound communication does not appear to impact inbound communication
directly. However, when looking at the year (and week) level, we find that the coefficients increase
in size and significance for most periods. This suggests that the impact of outbound on inbound
communication is short term. We also find an influence of one inbound channel on the other channel.
This suggests that cross-channel traffic always happens, regardless of service or period of outgoing
communication. Second, we look at the effects of the delays, and here we do find significant, yet
inconsistent, results. The results in the table suggest, on a global level, that outgoing communication
causes incoming web traffic (after a delay) and that an increase in (inbound) web traffic translates in
a decrease in (inbound) phone (also after a delay). However, this varies per service and calls for a
deeper analysis to help understand what triggers this.
3.3.</p>
        </sec>
        <sec id="sec-4-2-2">
          <title>Future Studies</title>
          <p>
            We are currently planning to expand the study presented here with a qualitative in-depth
component to explain why citizens contact public authorities. This will be an explanatory sequential
mixed method study
            <xref ref-type="bibr" rid="ref2">(Creswell, 2014)</xref>
            . The quantitative analysis presented here constitutes the first
phase of the study. In the second phase, we will analyze the results from 100 observations and
insitu interviews (50 from pensions and 50 from parental leave) conducted in call centers in 2017. The
analysis of channel traffic was used to guide the timing of and inform the data collection for the
second phase. We collected data during peak call periods: Shortly after the submission of the annual
pension letter and during a week where UDK paid out parental leave benefits. Hereby we seek to
gain insight into the reasons citizens provide for calling, and the relationship between channel
modes. Finally, we plan to use the results from this study to develop an new omni-channel strategy,
which covers both outbound and inbound channel traffic and update the previous MCM strategy
by Ebbers, Pieterson and Noordman (2008).
          </p>
        </sec>
        <sec id="sec-4-2-3">
          <title>About the Authors</title>
          <p>Christian Østergaard Madsen
Christian Østergaard Madsen is an assistant professor at the Research Centre for Government IT at the IT
University of Copenhagen. His research concerns public digitalisation, IT projects, and multichannel
management. He has published in Government Information Quarterly, International Journal of Public
Administration in the Digital Age, the Electronic Journal of e-Government and the IFIP Electronic
Government conference.</p>
          <p>Willem Pieterson
Willem Pieterson is the adjunct director of the Center for eGovernment studies (CFES) in the Netherlands.
He also runs his own research and consulting company supporting governments with digital transformation
and innovation in service delivery. He holds a PhD. Cum Laude in communication from the University of
Twente in the Netherlands. His research focuses on multi-channel management and channel behaviors,
digital strategies, and eGovernment. He has published multiple conference papers, journal articles, book
chapters, and reports on these topics.</p>
          <p>Sara Hofmann</p>
        </sec>
      </sec>
    </sec>
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