Working apart together: How performance information supports operations when
working from home
Henri C. Dekker*
Vrije Universiteit Amsterdam, School of Business and Economics
De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands
h.c.dekker@vu.nl
Melanie L. Feldhues
Copenhagen Business School, Department of Accounting
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
mls.acc@cbs.dk
Nikolay Georgiev
Nordea, Strategy & Portfolio oversight
Grønjordsvej 10, 2300 København, Denmark
Nikolay.Georgiev@nordea.com
JEL codes: G21; G28; L23; M11; M41; M54
Keywords: Working From Home; Performance Information; COVID-19; Team Operations;
Team Communication; Performance
* Corresponding author.
The research plan of the study reported in this paper was submitted to the Research Ethical
Review Board of the School of Business and Economics, Vrije Universiteit Amsterdam before
survey data collection, and complies with the ethical guidelines (reference number
SBE5/11/2020hdr500). We are grateful to Nordea GFCP for the extensive input to this project.
No funding was received from Nordea. The academic researchers (first and second author) were
not compensated or rewarded by the firm in any other way than being provided the opportunity
to use the data collected for research and publication. The use of the data for research purposes
is subject to a non-disclosure agreement between the academic researchers and Nordea. We are
also grateful for the helpful comments and suggestions on earlier draft versions from seminar
participants at WU Vienna, the 2021 Working From Home conference of the International
Centre for Economic Analysis, and the 2022 EAA annual conference.
Working apart together: How performance information supports operations when
working from home
ABSTRACT
Working from home (WFH) significantly impacts a firm’s operations by introducing altered
control challenges regarding the facilitating and influencing of decisions, while reducing the
control options that managers can rely on. We investigate how performance information
supports team operations and performance when shifting to WFH. Our field setting covers two
large units of Group Financial Crime Prevention Operations of Nordea Group, a large Nordic
bank: (1) KYC Operations Mass Markets, which conducts customer due diligence activities for
private and corporate clients, and (2) KYC Operations Managed Relations, which focuses on
large corporate clients and financial institutions. Multilevel analysis of survey data collected
from leaders and members of customer due diligence teams matched with other firm data,
shows that the use of performance information supports favorable task performance changes
after shifting to WFH. This effect is mediated by changes in team communication effectiveness,
which is supported by performance information and enables to better manage operations
conducted remotely. Collectively, our findings highlight the role of performance information
in supporting WFH by enabling remote team operations and performance.
JEL codes: G21; G28; L23; M11; M41; M54
Keywords: Working From Home; Performance Information; COVID-19; Team Operations;
Team Communication; Performance
1
1. INTRODUCTION
‘I think that the home office system […] thrives very well. There is a strongly increased
productivity and satisfaction of our employees.’ [Nordea Group Financial Crime
Prevention Team Leader, June 2020]
As a result of the COVID-19 pandemic and following government–mandated measures
and lockdowns, in many organizations across the globe employees had to shift to working from
home (WFH) in early 2020. This posed significant unanticipated challenges to keep operations
running, particularly if supporting practices and information systems to enable remote work
were insufficiently in place.
A few studies have proposed outcome control, which is based on performance
information, as a viable solution for managing operations at distance when firms adopt WFH
(Bloom, 2020; Downes, Daellenbach, & Donnelly, 2023; Felstead et al., 2003; Gajendran &
Harrison, 2007). Yet, empirical evidence on the impact of control choices on operations and
performance when employees conduct operations from home is lacking (Sihag & Rijsdijk,
2019). Our study addresses this issue by investigating the role of performance information in
supporting team operations when a firm adopts WFH, a setting in which other control options
based on monitoring behavior or informal control are difficult to establish.
We exploit the impact of the sudden pandemic on a firm’s workplace environment to
study the question how teams’ use of performance information supports team operations and
particularly task performance under collective WFH. Performance information is a core element
of outcome control (e.g., Abernethy, Dekker, & Grafton, 2021; Ouchi, 1979; Simons, 1991),
and is deemed critical for managing remotely working employees (Bloom, 2020; Felstead et
al., 2003). We comprehensively consider how managers’ use of performance information (e.g.,
for work allocation, prioritizing activities, managing resources, and evaluating performance) as
well the content of the performance information (e.g., regarding targets, productivity, quality,
handling time, and work allocation), influence key aspects of task performance when employees
work from home.
2
The setting of COVID-19-induced WFH provides an unprecedented opportunity for
studying complete organizational units that suddenly need to work from home, confronting
firms with altered control challenges to overcome in order to reap WFH benefits and preserve
or improve the status quo (Wenzel, Stanske, & Lieberman, 2021). Although prior studies have
considered the overall impact of (typically voluntary) WFH on task performance (e.g., Bloom,
Liang, Roberts, & Ying, 2015; Sherman, 2020), less is known about the mechanisms that enable
firms to manage the altered challenges when operations take place remotely.
Studies acknowledge the tension between WFH benefits (e.g., motivational effects,
schedule control, better work environment) and concerns about control losses (e.g., monitoring
difficulties, shirking, ineffective information exchange) (Bloom et al., 2015; Emanuel &
Harrington, 2023), as well as coordination challenges that require contact and information
exchange between leaders and their employees (Gajendran & Harrison, 2007; Lautsch, Kossek,
& Eaton, 2009), all of which controls need to address. We hypothesize that a greater use of
performance information enables team leaders and team members to better address the altered
control challenges of WFH, resulting in a more favorable change in task performance over predisruption performance. We also predict that this performance effect is mediated by changes in
team communication effectiveness, as in the remote work setting (with virtual interactions)
outcome control can enable team communication about the remotely executed operations.
Particularly, in a setting with online communication between team leaders and team members,
we expect that performance information supports team communication about key aspects of
remote operations including planning, work allocation, decision making, progress monitoring,
and performance evaluations.
We test our hypotheses in a field setting covering two large units of Group Financial
Crime Prevention (GFCP) Operations at Nordea, a large Nordic bank: (1) KYC Operations
Mass Markets, which conducts customer due diligence activities for private and corporate
3
clients, and (2) KYC Operations Managed Relations, which is responsible for large corporate
clients and financial institutions. Before the disruption, KYC teams maintained extensive
interaction in the office, guided by outcome control in form of key metrics linked to throughput,
quality, and timeliness, and WFH practically being non-existent. After the disruption, Nordea
followed local government COVID-19 guidelines in March 2020 and sent employees home to
continue their work remotely. Because of the critical nature of tasks performed at GFCP,
efficient adaptation to the WFH setting was required. Nordea swiftly set up WFH guidelines
and encouraged leaders to communicate with employees to assess their well-being, their
flexibility to continue their work remotely, and address technical aspects such as equipment.
Except for changing the team (co-)location to WFH, with consequently no more interaction in
the physical office environment, other key aspects of the job and operations, such as type and
hours of work, backlog, and goals, remained largely the same, enabling us to isolate the effect
of outcome control. This setting is similar to the field experiment of Bloom et al. (2015), with
the notable exception that employees in their setting spent one day per week at the office for
training, while in our setting all communication, including integration of new employees and
training, took place online enabling to study the phenomenon of interest, i.e., collective WFH.
The core data for the study consist of matched surveys of KYC team leaders and team
members collected in May/June 2020, providing multilevel team information. We complement
these survey data with extensive pre- and post-disruption interview data, GFCP documentation
such as management reports, and other firm data. We find that after shifting to WFH, teams
with a greater use of performance information, measured as team leaders’ purposes of use as
well as the content of the information, were more likely to enhance task performance (i.e.,
throughput, quality, on-time completion, and contribution to team performance). We also find
that this effect is mediated by post-disruption changes to team communication effectiveness,
4
indicating that, with the shift to remote work and virtual interactions, the performance
information helped maintaining team communication and task performance.
Our findings contribute to the literatures on business operations, organizational control,
and WFH. First, our findings underscore the critical role of performance information in coping
with the effects of a significant disruption on firm operations and particularly for supporting
WFH where altered control challenges regarding decision facilitating and decision influencing
limit control options. Our findings show how in the COVID-19 induced WFH setting, teams’
use of performance information supported remotely operating team members in enhancing task
performance. This speaks to the value of this information to support WFH, even when not
explicitly designed for that purpose. Our findings also show how after the shift to WFH, the use
of performance information enabled team leaders and members to maintain effective
communication about team operations to support task performance. Our analysis thus uncovers
a key mechanism through which outcome control is enacted in a remote work setting, namely
by supporting effective online team communication that enables managing remote operations.
Second, whereas prior WFH research mainly examines (voluntary) selection effects of
WFH at the employee level and its performance implications, our results provide evidence
about the effects of WFH induced by a major disruption. Unique to our field setting is that the
empirical evidence comprises WFH imposed on a whole unit for a continued period of time
instead of the typical partial WFH in time and numbers of (self-selected) employees (e.g., as
manipulated in prior field experiments). We are thus able to show that outcome control and
team communication play a critical role to address altered control challenges when a whole
organization shifted to WFH and other control options were largely absent.
As practical implication, the insights and especially the holistic facets of the
operationalized performance information construct embedded in the field setting may assist
practitioners in evaluating and potentially adjusting control and communication structures in
5
anticipation of continued relevance and more structural WFH (Ameen, Papagiannidis, Hosany,
& Gentina, 2023; Barrero, Bloom, & Davis, 2021a, 2021b, 2023; Bloom, 2020) and ‘working
from anywhere’ (Choudhury, Foroughi, & Larson, 2021).
2. THEORY AND HYPOTHESIS DEVELOPMENT
The next section bridges prior research on WFH and research on control challenges,
including the development of our first hypothesis which zooms in on the role of performance
information in addressing the outlined challenges. Section 2.2 investigates the role of team
communication as primary channel of how the use of performance information is enacted to
influence team operations and ultimately task performance.
2.1. Control challenges related to working from home
Control systems support managers in implementing strategy and effective operations by
enabling monitoring and motivating employees (‘decision influencing’), as well as by providing
information needed to perform desirable actions and make informed decisions (‘decision
facilitating’) (Abernethy et al., 2021; Grafton et al., 2010; Simons, 1991). WFH studies
emphasize that the introduction of WFH is accompanied with altered control challenges since
remote operations can increase monitoring difficulties, motivational problems, ‘shirking from
home’, and adverse selection of employees (Bloom et al., 2015; Emanuel & Harrington, 2023).
Similarly, coordination challenges increase when remotely located employees need to uphold
contact and information exchange about operations with their leader(s) and peers (Barrero et
al., 2023; Gajendran & Harrison, 2007; Gibbs, Mengel, & Siemroth, 2023; Lautsch et al., 2009).
Despite the described challenges, prior studies document positive effects of WFH on
performance outcomes (e.g., Barrero et al., 2021a; Bloom et al., 2015; Sherman, 2020),
suggesting its benefits (e.g., enhanced time management and work environment, motivational
effects) often outweigh the concerns (e.g., information exchange and monitoring losses).
Barrero et al. (2023) observe though that productivity is under pressure most for fully remotely
6
working employees. Key issues in prior studies of the performance effects of WFH relate to
self-selection (e.g., differences between employees who do and do not select WFH), a lack of
insight into how remote operations are managed, and limitations in studying group effects (if
only part of a group selects WFH) (Bailey & Kurland, 2002; Bloom et al., 2015; Sherman,
2020). A unique aspect of the COVID-19 pandemic for organizations worldwide was the
typically involuntary physical separation of whole groups of employees who suddenly needed
to operate remotely and interact from distance. This effectively enforced WFH adoption for
many organizations, avoiding the common self-selection into WFH practices (Bloom et al.,
2015; Emanuel & Harrington, 2023; Schmelz & Ziegelmeyer, 2020; Sherman, 2020), uniquely
enabling to study whole units or teams operating remotely. This includes the opportunity to
assess how control practices support effective operations conducted remotely, which can also
inform the design of future (voluntary and partial) WFH arrangements in less extreme settings.
Recent evidence on WFH induced by COVID-19 finds that productivity increased for
many home workers, and that WFH will remain to be substantially more than before (Barrero
et al., 2021a). Outcome control based on performance information is considered a critical
enabler of effective WFH as it supports managing the operations and performance of remote
workers by planning and monitoring output and coordinating from distance (Bloom, 2020;
Downes et al., 2023; Felstead et al., 2003). This is of particular importance given the reduced
direct observability of operations and reduced interactions with superiors and peers. The
absence of direct interaction in the office and greater cognitive complexity of remote
interactions (often through videoconferencing and/or collaborative virtual environments
(Ferran & Watts, 2008; Montoya, Massey, & Lockwood, 2011)), increases the demand for
management information (Powell, Piccoli, & Ives, 2004) that enables monitoring and guiding
remote work, providing feedback, and facilitating online interactions between dispersed team
members. The ability of existing control practices to enable effective decision facilitating and
7
decision influencing after shifting to WFH will depend on how well these practices match the
greater demand for managing on outcomes (Bloom, 2020; Downes et al., 2023), since the mere
presence of an IT–based control system does not guarantee its control purposes to be fulfilled
(Mehta & Mehta, 2018; Moskowitz, Drnevich, Ersoy, Altinkemer, & Chaturvedi, 2011). While
prior research finds that physical presence is beneficial in the complex task of evaluating team
members’ individual contributions to team performance (Uribe, Carnahan, Meluso, & AustinBreneman, 2022), WFH can exacerbate the complexity of performance evaluations. Similarly,
performance information may fail to adequately capture task performance of dispersed team
members, rendering it less useful (Downes et al., 2023).
Despite such potential limitations, we expect that after a shift to WFH, with reduced
opportunities for direct supervision and informal coordination (Sihag & Rijsdijk, 2019), the use
of performance information gains importance for managing team operations (Bloom, 2020;
Downes et al., 2023; Felstead et al., 2003; Gajendran & Harrison, 2007). For team leaders,
information about performance targets, progress, and outcomes can contribute to guiding and
coordinating team members in conducting operations from distance and to enabling monitoring
progress and task performance. Such information can also enable team members in their
decision-making about task execution and coordination with remote team members, and to keep
track of performance progress and outcomes. We thus predict that after the shift to WFH the
use of performance information within teams will be positively associated with changes in task
performance as compared to pre-disruption performance. We recognize that also before shifting
to WFH, use of performance information could affect performance, which is why we predict an
incremental performance effect after the disruption based on the altered importance of
performance information.
Hypothesis (H1): The use of performance information is positively associated with
performance change after a shift to working from home.
8
2.2. Team communication in a working from home setting
We follow the literature to argue that communication challenges are critical in WFH (Barrero
et al., 2023; Gibbs et al., 2023), relating these to the control challenges of WFH, and to prior
research on ‘virtual teams’ that arise as a result of (extensive) WFH (Montoya et al., 2011;
Schmidt, Montoya-Weiss, & Massey, 2001; Vuchkovski, Zalaznik, Mitręga, & Pfajfar, 2023).
Studies emphasize increased communication challenges in ‘virtual teams’ with non-co-located
team members who predominantly interact online through information and communication
technology (Bisbe & Sivabalan, 2017; Montoya et al., 2011). Communication challenges can
result from fragmentation and absence of totality, slower and reduced information transfer
(Geister, Konradt, & Hertel, 2006), weak non-verbal communication and communication cues,
and interpretation differences (Powell et al., 2004). Challenges also involve reduced informal
contact, motivation, participation and commitment (Jarvenpaa & Leidner, 1999; Vuchkovski et
al., 2023; Warkentin, Sayeed, & Hightower, 1997), and team members lacking a common frame
of reference and agenda (Lee-Kelley & Sankey, 2008). In virtual teams, formal controls such
as regulations, procedures and performance reporting can be more difficult to activate, as team
leaders and team members may face challenges in interpreting information about team member
activities and performance (Bisbe & Sivabalan, 2017; Majchrzak, Rice, Malhorta, & King,
2000). Likewise, coordination–oriented technology (Kendall, 1997) such as videoconferencing
can place higher demands on cognitive capacity compared to face-to-face communication
(Ferran & Watts, 2008). Bisbe and Sivabalan (2017) observe how a virtual team with remotely
located members and sporadic face-to-face interaction faced issues concerning coordination,
knowledge integration, and the management of urgency and uncertainty. Kennedy, Vozdolska,
and McComb (2010) compare team decision-making between face-to-face, computer–mediated
teams, and mixed teams, and call for more research on coordination and communication.
Lautsch et al. (2009) emphasize the importance of team leaders and team members of remotely
9
operating teams to stay in close contact and share information. Collectively, prior studies point
to the role of effective team communication to minimize or prevent communication challenges.
In their meta-analysis on psychological aspects linked to WFH, Gajendran and Harrison
(2007) discuss difficulties of managers leading their remotely working employees, but do not
find statistical support for a detrimental effect of WFH on the relationship between team leaders
and members. Yet, when WFH is suddenly imposed on teams that were not composed for
remote work, the described communication challenges could be exacerbated as compared to
virtual teams formed with an ex-ante intention that communication and information exchange
will be aided by IT. On the other hand, crisis–driven changes to team communication may also
provide opportunities, such as establishing more structured communication in virtual meetings,
tightening the prioritization of topics, and improving meeting efficiency. Accordingly, a shift
to WFH may come along with both negative and positive changes in team communication.
We expect that performance information supports effective team communication in the
WFH setting and enables to mitigate the communication challenges described above.
Particularly, after shifting to WFH, the use of performance information can aid team leaders in
maintaining or enhancing team communication by reducing cognitive demands and
communication challenges. For instance, such information can enable structured and efficient
virtual meetings in which information is exchanged on planning, task allocation, prioritization,
performance progress, and achievements; effectuating the decision facilitating and decision
influencing roles of outcome control through communication. Even if the specific content of
performance information may be more difficult to convey, discuss and comprehend in virtual
meetings (as compared to physical meetings), it can still support reducing fragmentation,
provide oversight, direction, and a common frame of reference, enhance the speed and extent
of information transfer, and limit interpretation differences. As a result, communication during
virtual team meetings can reflect team leaders coping with the WFH setting with the overall
10
aim to keep control over team operations performed remotely. For team members, performance
information can add to the understanding of and contribution to team discussions and insight
into team operations, reducing cognitive load and communication challenges. Accordingly, we
expect that after the shift to WFH, the use of outcome control is positively associated with the
change in team communication effectiveness.1
Hypothesis (H2): The use of performance information is positively associated with
changes in team communication effectiveness after a shift to working from home.
Finally, we expect changes in team communication effectiveness to influence team operations
and ultimately task performance, as team communication enacts the performance information
through supporting decision influencing and decision facilitating. We thus expect a positive
mediation effect on performance change, through changes in team communication effectiveness
(i.e., with enhanced team communication yielding improved task performance).
Hypothesis (H3): The association between the use of performance information and
performance change after a shift to working from home is mediated by changes in team
communication effectiveness.
3. FIELD SETTING
Our study took place at Group Financial Crime Prevention (GFCP) at Nordea Group,
which is responsible to ensure the bank adheres to regulatory requirements in all client
relationships of the bank. GFCP’s aims are to safeguard the integrity of financial markets with
regard to anti-money laundering and counter-terrorist regulations.2 Critical activities performed
by GFCP Operations entail transactions monitoring and investigations, sanctions screening and
investigations, and KYC activities for new and existing clients. The study covered two large
1
We note again that while before the shift to WFH performance information may already have supported team
communication, the hypothesis predicts an incremental effect pertaining to the change in team communication
effectiveness, based on the predicted increased importance of performance information .
2
Annually, approximately 2 billion money transfers go in and out of Nordea accounts. In recent years, the bank
invested heavily in financial crime prevention, including the establishment of GFCP in 2015
(https://www.nordea.com/en/about-us/nordea-in-society/preventing-financial-crime).
11
units of GFCP Operations: (1) KYC Operations Mass Markets, which conducts customer due
diligence activities for private and corporate clients, and (2) KYC Operations Managed
Relations, which focuses on large corporate clients and financial institutions. By May 2020, the
two units had 31 teams operating from six countries: Denmark, Estonia, Finland, Norway,
Poland, and Sweden.
The research project initially aimed at examining both KYCs’ performance management
practices, for which mid-2019 a project team was formed including the academic researchers
and a Nordea Financial Crime Risk Data Development manager. Later in the process, the Global
Production Planner of GFCP joined the project team, adding in-depth knowledge of systems,
reporting and operations across countries. Over the span of a year, the team collaborated
intensively to assemble and interpret GFCP documentation, conduct interviews, hold status
meetings with management, design surveys, and organize data collection, analysis, and
reporting. In adjusting the project to the COVID-19 disruption in March 2020, based on
interviews and discussions at GFCP, the planned surveys were extended to assess the
disruption’s impact on KYC operations when they shifted to WFH.3
3.1. Tasks, information, and communication before the outbreak of COVID-19
In the following, we describe the nature of the tasks conducted in both KYC units, the
information environment, and communication structures before the outbreak of COVID-19.
KYC tasks differ between KYC Operations Mass Markets and KYC Operations Managed
Relations in light of the different due diligence requirements across customer types. They also
differ between and within teams of each unit, which focus on different customer types and
countries where the bank operates. It is important to note that the demand of GFCP activities is
driven by regulatory requirements and/or changes in practices of the sector or bank. GFCP
3
Ethical approval for the study was obtained from the Research Ethical Review Board at the first author’s
university before collecting survey data.
12
operates with a backlog containing cases with different risk profiles and deadlines that are
allocated to the different teams on an ongoing basis.
Team leaders of both units regularly receive standard management information reports
(or: ‘global reports’). These reports include aggregated key performance indicators linked to
throughput, quality, and timing. Throughput measures include for example the handling time
of cases and the number of cases completed per team member, and per team. The quality of
performed tasks is measured based on the number and/or prevention of major mistakes or
internal auditor remarks, and the proportion of cases correctly solved without receiving
additional remarks from quality control. Timing is measured as on-time completion, i.e., closing
a case before its respective deadline.
The differences between KYC tasks and across units limit the comparability of objective
performance information across units and teams. For instance, measures of throughput and ontime completion have different meaning for due diligence activities of private clients versus
(large) corporations, which differ significantly in requirements and lead times. Similarly quality
assessment processes differ across units and customer types. Because identical metrics differ in
informativeness across and within teams, team leaders typically complement the standard
management information reports they receive with their analyses or variations of the key
measures adapted to their specific team (or: ‘local reports’) to enhance insight and decisionmaking. These additional analyses are typically conducted by production planners who
maintain a support role for team leaders. Since local reports are built on data from global
reports, both types of performance information do not operate in isolation.
Before the disruption, KYC teams maintained extensive interaction in the office, guided
by performance information. Most teams held stand-up meetings several days per week (called
‘daily visual meetings’ around whiteboards/screens with performance and planning
information), and (bi-)weekly meetings to coordinate and review work and performance.
13
During the meetings, team leaders and members would discuss key issues such as productivity
and quality performance, performance targets, work backlog, team capacity, task allocation,
and work planning. The few locally dispersed teams typically also sat together as a team in a
meeting room to attend virtual meetings with their team colleagues working at a different
location. WFH was practically non-existent for investigation functions within the KYC units.4
3.2. Situation after the outbreak of COVID-19
Nordea Group followed local government COVID-19 guidelines that came into place in
March 2020 to enable employees to work from home. It responded swiftly by setting up
guidelines for employees WFH and encouraged leaders to communicate with employees to
assess their well-being, their flexibility to continue their work from home and address technical
aspects (e.g., equipment, connectivity). Given the critical nature of the tasks performed at GFCP
that require continuity, the disruption in conjunction with government guidelines asked for
efficient adaptation to the new work setting within a short time frame. Given that the KYC units
operate with a backlog of cases to be investigated within a certain time frame, the work volume
(number of cases to be handled) was not directly affected by the outbreak of COVID-19, nor
did the units’ goals change in the initial phase when employees were sent to work from home.
In the initial phase after the disruption, team leaders were particularly focused on enabling
their teams to work remotely and were expected to take the lead in establishing remote team
communication and coordination. Interviews with team leaders and production planners
confirmed that the performance information that teams relied on remained unchanged while the
communication channel became purely virtual. Consistent with the communication challenges
associated with virtual teams described in the theory section, the sudden new reality of WFH
4
In contrast, basic experience with WFH of GFCP non-investigation functions existed, which facilitated
technology availability at the organizational level when the pandemic disrupted the organization.
14
was found to place different demands on communication and information exchange. A team
leader echoed this sentiment:
‘We are able to turn around quickly. Challenging to be the same leader you are used to
being when in the office every day. Not getting the same flow in meetings and the same
good discussions remotely. Not easy to follow-up on everyone on the same level.’ [Nordea
Group Financial Crime Prevention Team Leader, June 2020]
4. DATA COLLECTION
The primary data for the study were obtained through surveys of team leaders (TLs) and
team members (TMs). Data collection included extensive company documentation,5 site visits,
informal conversations, and 14 interviews and six formal meetings with GFCP management,
country heads, TLs, and production planners (see Table 1). Insights obtained informed survey
design, allowing to contextualize the instrument to enhance construct validity. All interviews
and meetings involved at least one of the GFCP research project members, who also contributed
to survey development, providing deep GFCP knowledge to develop or customize scales.
Table 1: Overview of interviews and formal meetings
No. Date
Interviewee function
1 Nov-19 Head GFCP Operations
2 Jan-20 Production Planner I
3 Jan-20 Production Planner II
4 Jan-20 Head GFCP Operations
5 Jan-20 Country Head
6 Jan-20 Production Planner III
7 Jan-20 Head KYC MM
8 Feb-20 Head KYC MR
9 Mar-20 Production Planner IV
10 Mar-20 Team Leader I
11 Mar-20 Team Leader II
12 Mar-20 Production Planner V
13 Apr-20 Team Leader III
14 Apr-20 Team Leader IV
5
Duration
Recorded
60
66
71
59
39
23
87
89
41
39
58
45
51
45
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Medium
Face-to-face
MS Teams
MS Teams
Face-to-face
Face-to-face
Face-to-face
Face-to-face
MS Teams
MS Teams
MS Teams
MS Teams
MS Teams
MS Teams
MS Teams
Company documentation included strategy and governance reports; organizational charts; performance and
planning reports; policies and procedures; purpose, values and code of conduct statements; and pictures of teams’
office whiteboards used to communicate performance information.
15
No. Date
Formal meeting purpose and participant/s
1 Jun-19 Project Kick-Off Head GFCP Operations, former Head KYC MM
2 Apr-20 Survey Design Presentation 3
4
5
6
Head GFCP Operations, Head KYC MR, Production
Planner II
May-20 Survey Design Presentation MR Country Heads
May-20 Survey Design Presentation Head of KYC MM, Production Planner II
May-20 Survey Design Presentation MM Country Heads
Sep-20 Project Presentation - Survey Results COVID-19 Head GFCP Operations, Head Production Planning &
Analytics, Production Planner II
Duration
Recorded
Medium
60
no
Face-to-face
60
no
MS Teams
25
no
MS Teams
45
no
MS Teams
25
no
MS Teams
60
yes
MS Teams
Different surveys were developed for TLs and TMs, with the intention to match selected
constructs in the planned multilevel analysis.6 Survey design followed recommended practices
to enhance measurement reliability and response (Tourangeau, Rips, & Rasinski, 2000). Final
versions were presented to and approved by GFCP management and the heads of the two KYC
units. The review by management generated support for the relevance and validity of the
measures within GFCP, as well as additional input to adjust and add a few items.
The heads of both GFCP units (KYC Operations Mass Markets and KYC Operations
Managed Relations) pre-announced and endorsed the survey by email, and one also did so in
an online general meeting to all employees. Invitations to complete the survey during work time
were distributed in late May 2020 to all 31 TLs and 400 TMs. Two friendly reminders were
sent before closing the survey mid-June 2020.
In the survey, TLs and TMs were promised strict anonymity and confidentiality to
stimulate open and unbiased answers.7 Nordea employees are well used to taking surveys that
elicit their opinions about their company and organizational unit, such as in the form of
6
Table 3 provides an overview of constructs. “Level” indicates whether they were collected from TLs or TMs.
To preserve anonymity and confidentiality, in later analyses we also do not disclose results on several control
variables that could allow for identification of teams or individuals.
7
16
quarterly People Pulse surveys conducted by an outside firm. While the TL and TM surveys
were presented to employees as a joint project by the academic researchers and Nordea GFCP,
the communication about the project also clarified the involved academics would analyze the
data to provide insights. All TLs (100%) and 245 TMs (61.3%) responded.8 Respondents were
anonymous to the researchers and were matched to their team using unique identifiers.
We further gained access to other firm data to derive control variables. We also use these
additional data in construct validity tests, which we conduct for key model constructs to
ascertain that the operationalization is adequate (see Table A1 in the appendix). These
additional data include selected human resources management data (e.g., team size, TL and TM
gender), ‘process excellence’ team task complexity ratings, and team-level ‘People Pulse’ data
based on a quarterly employee engagement survey conducted by an external survey firm.
5. VARIABLE MEASUREMENT
Construct measurement is attuned to GFCP and guided by the insights obtained from
the qualitative data and the GFCP project team managers. The separate surveys for TLs and
TMs were distributed online, with the purpose of matching the TL, TM, and team constructs in
a multilevel analysis. In both TL and TM surveys, questions were distributed over more than
ten pages to enhance reader-friendliness and to mitigate bias due to consistent answer patterns.
Items were also randomized within blocks to mitigate response bias (Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003; Tourangeau et al., 2000). Table A1 in the appendix summarizes
measurement of all model and construct validity test variables. Table 2 reports measurement
properties of multi-item constructs. Items load well on their construct, and the reliability of
multi-item constructs (Cronbach´s α) is adequate (0.75 and higher). We compute mean scores
across items for multi-item constructs to generate construct scores.9
8
There are no significant differences in the primary (dependent or independent) model variables between early
and late respondents (i.e., responses before and after the first reminder).
9
Missing values in the TM and TL survey data mainly result from unfinished surveys. We drop cases with missing
data on all items of a construct. For seven TMs and ten TLs with partially missing construct item responses (eight
17
Table 2: Item descriptive statistics and factor loadings for multi-item constructs
Dependent variable
Performance change#
(Cronbach´s α=0.92)
Mean
SD
Min
Max
Loading
Throughput
4.67
1.18
2
7
.87
Quality
4.58
1.04
2
7
.84
On-time completion
4.62
1.11
1
7
.90
Contribution to team performance
4.63
1.08
3
7
.83
Case allocation
4.60
1.73
1
7
.94
Resource management
4.61
1.87
1
7
.89
Matching skills with cases
3.54
1.83
1
7
.77
Prioritizing
5.19
1.48
1
7
.80
Making trade-offs
4.79
1.50
1
7
.76
Evaluating throughput
5.31
1.25
1.5
7
.78
Evaluating handling of cases
5.02
1.44
2
7
.87
Evaluating on-time completion
5.44
1.65
1
7
.69
Evaluating quality
4.33
1.92
1
7
.83
Evaluating team performance
5.09
1.48
2.5
7
.81
Evaluating member performance
3.77
1.78
1
7
.72
Targets
4.66
2.50
0
7
.80
Productivity
4.87
2.36
0
7
.79
Backlog
4.36
2.61
0
7
.69
Handling time
2.73
2.75
0
7
.64
Quality
3.95
2.76
0
7
.68
On-time completion
3.00
2.76
0
7
.71
Team capacity
3.93
2.78
0
7
.63
Work allocation
3.86
2.81
0
7
.65
Team matters
4.12
2.60
0
7
.68
Meeting effectiveness
4.65
1.42
1
7
.50
Standard team communication
4.31
1.22
1
7
.98
Informal communication
4.00
1.44
1
7
.70
Throughput
5.50
1.14
3
7
.91
Quality
5.69
1.02
3
7
.65
On-time completion
5.60
1.21
1
7
.75
Independent variables
TL performance
information*
(Cronbach´s α=0.95)
TM performance
information†
(Cronbach´s α=0.90)
Team communication
effectiveness change
(Cronbach´s α=0.75)
Control and validation test variables
Prior performance #
(Cronbach´s α=0.86)
vs. 13 missing data points), we compute construct scores as the mean of the available items. Our treatment of
missing data and the matching of TM and TL variables across estimated models cause sample sizes to vary.
18
TL team absolute
performance*
(Cronbach´s α=0.87)
TL relative team
performance*
(Cronbach´s α=0.89)
TL team member task
performance*
(Cronbach´s α=0.90)
TM shared
performance
information
(Cronbach´s α=0.84)
Contribution to team performance
5.69
1.22
1
7
.82
Throughput
5.48
1.01
3
7
.79
Quality
5.44
1.09
3
7
.66
On-time completion
5.46
1.45
3
7
.88
Contribution to GFCP performance
6.12
0.82
4
7
.83
Throughput
5.46
0.98
4
7
.85
Quality
5.21
1.06
3
7
.74
On-time completion
5.36
1.35
3
7
.83
Contribution to GFCP performance
5.63
1.10
3
7
.79
Complete assigned duties
6.00
0.94
4
7
.91
Fulfill responsibilities
5.92
1.06
3
7
.87
Perform tasks expected
6.12
0.95
4
7
.82
Meet performance requirements
5.77
1.14
3
7
.91
Affect performance evaluation
5.31
1.35
2
7
.49
Fail essential duties (rev.)
6.04
1.11
3
7
.78
Regular information on team
performance
5.65
1.29
1
7
.73
Regular information on individual
performance
4.87
1.68
1
7
.56
Team performance information is
shared during meetings
5.65
1.48
1
7
.90
Team performance is discussed
during meetings
5.36
1.56
1
7
.83
Goal congruence team goals
5.17
1.58
2
7
.91
Goal congruence GFCP goals
5.14
1.27
3
7
.73
Accuracy information
4.66
1.67
1
7
.95
Influence performance
5.46
1.36
2
7
.69
Confidence team capability
6.15
1.01
3
7
.96
Complete difficult tasks remotely
5.90
1.12
2
7
.83
Manage issues remotely
6.07
1.10
3
7
.90
Solve tasks remotely
6.22
0.95
3
7
.93
Formal team communication
2.88
1.70
1
6
.71
Informal communication
3.29
1.71
1
6
.71
Additional analyses variables
TL performance
information quality*
(Cronbach´s α=0.90)
Team remote ability
(Cronbach´s α=0.95)
Communication
challenges*
(Cronbach´s α=0.76)
# A fifth measured performance dimension, customer interactions, is only relevant for employees with customer
contact (57% of TM sample). Given its irrelevance for part of the TM sample, we dropped the item.
† Descriptive statistics and item loadings for TM performance information relate to the rated usefulness of
information as included in the main tool supporting team meetings indicated by the respondent.
* Descriptive statistics and item loadings for team leader constructs are at the team level.
7
19
5.1. Dependent variable: Performance change
Task differences between the KYC units and between teams complicate the direct
comparison of objective information about performance outcomes (also see 3.1.). For instance,
firm archival measures of throughput, quality and on-time completion cannot be easily
compared across teams responsible for clients such as private customers, large corporations or
financial institutions, and whether computed absolutely or as change variable lack
comparability when included in the same model (e.g., due diligence processes, quality control,
and lead times differ between private and corporate clients). Similar to Sherman (2020), we
therefore elicit employees’ self-ratings of their job performance on key outcomes. A key benefit
of this approach is enhanced comparability between and within KYC teams. The self-reported
scales ‘equalize’ the basis of comparison, allowing us to assess performance change given the
nature of tasks conducted. Performance change captures the change since the COVID-19
disruption in four key operational performance dimensions for which TMs are accountable: (1)
throughput, (2) quality, (3) on-time completion, and (4) contribution to team performance
(Cronbach´s α=0.92).
We also control for TMs’ Prior performance on these dimensions (Cronbach´s α=0.86),
since employee capability (i.e., more capable employees better adjusting to the disruption and
WFH) and differences in how well the team is managed (both proxied by prior performance)
may affect performance change. Additionally, if self-ratings of performance change and level
are consistently subject to bias (e.g., inflation; Bailey & Kurland, 2002), controlling for selfrated performance helps removing this bias. To assess construct validity, we compute mean TM
performance for each team and find this to correlate positively with TLs’ assessment of (1)
absolute team performance, (2) team performance relative to other teams, and (3) team
members’ task performance (adapted from Williams & Anderson, 1991) (r1=0.44, p<0.01;
r2=0.43, p<0.05; r3=0.43, p<0.05).
20
5.2. Independent variables: Performance information
Our hypotheses relate the use of performance information in teams to changes in task
performance when shifting to WFH. Performance information is key to outcome control (e.g.,
Ouchi, 1979; Simons, 1991), and we develop construct measures of its use by TLs and by TMs.
This enables us to test the consistency of effects, avoid common method bias (Podsakoff et al.,
2003), and assess the robustness of findings across alternative construct operationalizations.
The construct measures capture performance information use based on its (1) purposes of use
in managing the team (TLs), and (2) informational content (TMs), and are embedded within the
organizational context of GFCP, based on the interview data and expert knowledge acquired.
TL performance information is based on the purposes of use of the information available
to the TL for managing his or her team. TLs assessed how useful they rated available
performance information for 11 management purposes identified during interviews (e.g.,
allocating cases, prioritizing activities, evaluating performance; see Table 2). They did so for
both the global reports they receive (e.g., standard performance reports) and local reports
generated (e.g., own reports/dashboards complementing the global information), with a greater
usefulness reflecting greater use for the respective management purpose. For each of the 11
purposes, we compute the average score of global and local reports (Cronbach´s α=0.95), and
then calculate the average across all items to compute TL performance information. Construct
validity is supported by a positive correlation (r=0.39, p<0.05) with the reported number of
hours per week that the TL and/or a support function spent on generating additional information
(i.e., analyses/reports complementing the standard reports, indicating that investments in
generating additional information are associated with greater use of performance information.
TM performance information is based on the content of the performance information
available to TMs. Our interview data and GFCP collaborators provided in-depth understanding
of the information used within KYC teams and indicated that after the shift to WFH
21
performance information shared with TMs remained the same. Before the disruption, team
communication about operations such as during stand-up meetings and regular team meetings
was supported by up-to-date information presented on either an office whiteboard or a digital
tool (e.g., screen/PowerPoint). In the survey, TMs indicated if their team primarily used a
whiteboard (65% of responses) or a digital tool (35% of responses), and then indicated the
inclusion and usefulness of nine types of commonly used performance information: targets,
productivity, backlog, handling time, quality, on-time completion, team capacity, work
allocation, and team matters. Data on the availability and assessed usefulness of each
information element (cf. Pizzini, 2006) enables us to comprehensively measure TMs’
information use. TM performance information is the mean score across all information elements
(Cronbach´s α=0.90), with the score of items indicated not available set at zero. Greater
construct scores reflect a broader use of performance information relevant to the TM’s work.
Construct validity is supported by positive correlations with TM measures of information
sharing and the management of team operations: (1) the sharing and discussion during team
meetings of information on team and individual performance (four items, Cronbach´s α=0.84),
(2-4) how the TL assigns TMs to tasks, schedules work to be done, and let’s TMs know what
is expected, and (5) how the information they receive guides their work (r=0.39, r=0.29, r=0.35,
r=0.38, and r=0.40, respectively, all p<0.01). We also find that a mean team-level score of TM
performance information converges with TL performance information (r=0.35, p<0.10).
5.3. Mediator variable: Change in team communication effectiveness
Team communication effectiveness change (Cronbach´s α=0.75) is based on three items
that capture TMs’ assessment of changes to team communication effectiveness as compared to
before the disruption. The first item asked TMs to assess changes in the effectiveness of team
meetings (How effective have the team meetings been since the outbreak of COVID-19 as
compared to before? (1: much worse, 4: same, 7: much better)). The second and third items
22
asked TMs to assess the impact on their work on standard team communication (e.g., team
meetings, stand-ups) and informal communication as compared to before COVID-19 (How
have the following aspects impacted your work since the outbreak of COVID-19? (1: negatively
impacted; 4: no impact; 7: positively impacted)). To assess construct validity, we compute mean
team construct scores and find this to correlate positively with (1) TLs’ assessment of the
effectiveness of team meetings as compared to before the disruption (r=0.58, p<0.01), and (2)
a May 2020 People Pulse question on whether TMs felt their TL provided appropriate guidance
and support during the time at home (r=0.43, p<0.05). In further support of construct validity,
it correlates negatively with TLs’ assessed challenges in team communication after the
disruption (r= -0.46, p<0.05). To test discriminant validity, we subject the items of Performance
change and Team communication effectiveness change to a confirmatory factor analysis. A twofactor solution where items load on the theorized construct provides better model fit and
loadings than a single-factor specification.10
5.4. Control variables
We control for a range of characteristics linked to communication patterns, team leaders,
teams, team members, and tasks that may influence post-disruption changes in performance.
Table A1 in the appendix details the measurement of each control variable. In all models, we
also include fixed effects for unit (KYC Operations Mass Markets or KYC Operations Managed
Relations) and country where the team is located (Denmark, Estonia, Finland, Norway, Poland,
or Sweden). Given the importance of communication through meetings in the field setting, we
first control for teams’ meeting characteristics: (1-2) the frequency of one-to-one and team
meetings, and (3) changes to the meeting structure (i.e., removed or added meetings).11
10
Fit statistics for the two-factor model: Χ2=25.98 (p<0.01); RMSEA=0.07; CFI=0.98; TLI=0.97; SRMR=0.05,
and for the one-factor model: Χ2=116.76 (p<0.01); RMSEA=0.20; CFI=0.87; TLI=0.80; SRMR=0.10.
11
TLs adapted the meeting structure in 62% of all teams, mostly by adding meetings. Frequently indicated reasons
were to assess and keep up team spirit, discuss targets and issues, follow up on progress, and share knowledge.
23
Team leader characteristics include: (1) tenure, (2) outside experience, (3) gender, and
(4) dual role (i.e., also being a country head). These variables control for effects of team
leadership experience/expertise, gender, and role differences.
Team characteristics include: (1) team size, (2) mean case risk across team members, (3)
mean TM tenure, (4) dispersion of the team over multiple countries, (5) presence of a cell
coordinator, and (6) share of the team WFH. These variables control for team characteristics
regarding size, task complexity, experience, structuring, and exposure to WFH.
Team member characteristics include: (1) tenure in the team, (2) short tenure (<3 months,
providing limited/no office experience), (3) prior relevant work experience, (4) self-rated
expertise, (5) gender, and (6) number of weeks WFH since the COVID-19 disruption at the
time of responding. These variables control for experience and expertise effects in the job, the
team and with WFH, and potential gender effects.
Task characteristics include: (1) the nature of KYC cases handled by the TM (percentage
high/very high risk), (2) having customer contact, (3–6) the TM’s time allocation over KYC
activities including investigations, quality control, supporting others, and team communication,
and (7) the reliance on a whiteboard as primary tool for information sharing. These variables
control for inherent differences in the nature of TMs’ work, time allocation over operational
activities, and the communication mode of performance information.
6. ANALYSIS AND RESULTS
6.1. Descriptive statistics
Table 3 shows that TMs report on average modest improvements in performance after the
COVID-19 disruption (mean 4.63, where 4 indicates ‘no change’), although variation is
substantial. When presenting these insights to GFCP management, the self-reported change in
performance was assessed consistent with their management information of how performance
24
had changed since the disruption.12 Consistent with their leadership and control role, TLs report
relatively greater use of outcome control than TMs (mean 4.70 vs 3.94), and for both, there is
significant variation in use. TLs indicated that 93% of their team worked from home since the
disruption,13 and TMs indicated they did so for over ten weeks on average. Table A2 in the
appendix reports variable correlations, which do not cause concerns about multicollinearity.
Table 3: Descriptive statistics
Dependent variable
Performance change
Independent variables
TL performance information
TM performance information
Mediator variable
Team communication effectiveness change
Controls
1:1 meetings
Team meetings
Change in meeting structure
Prior performance
TL tenure
TL prior experience
TL female
Team size
Team case risk
Team tenure
Team dispersion
Share team WFH
TM team tenure
TM short tenure
TM prior experience
TM expertise
TM female
TM weeks WFH
TM case risk
TM customer contact
Whiteboard
Level
N
Min
P25
TM
196
2.50
4.00
4.63
TL
TM
27
245
1.82
0.00
3.41
2.56
TM
216
1.67
TL
TL
TL
TM
TL
TL
TL
TL
TL
TL
TL
TL
TM
TM
TM
TM
TM
TM
TM
TM
TM
31
31
29
211
31
31
31
31
31
31
31
31
245
245
245
244
245
245
244
237
234
0.00
0.00
0.00
3.00
0.06
0.00
0.00
3.00
0.00
0.31
1.00
63.64
0.00
0.00
0.00
1.00
0.00
2.00
0.00
0.00
0.00
12
Mean Median
P75
Max
SD
4.25
5.00
7.00
0.99
4.70
3.94
5.14
4.00
5.75
5.44
6.45
7.00
1.37
1.96
3.67
4.32
4.00
4.83
7.00
1.11
0.33
8.00
0.00
5.00
1.50
0.00
0.00
9.00
17.50
0.93
1.00
90.00
0.50
0.00
0.00
3.00
0.00
9.00
5.00
0.00
0.00
1.17
1.00
1.26
4.40
13.77 14.73 20.00 27.24
0.62
1.00
1.00
1.00
5.62
5.75
6.25
7.00
2.38
2.00
3.50
5.00
2.55
0.00
3.00 16.00
0.52
1.00
1.00
1.00
12.84 12.00 15.00 33.00
47.71 46.67 79.17 97.25
1.58
1.50
2.28
3.14
1.23
1.00
1.00
4.00
93.45 100.00 100.00 100.00
1.60
1.00
2.00
7.00
0.14
0.00
0.00
1.00
1.38
0.00
1.00 25.00
3.75
4.00
5.00
5.00
0.66
1.00
1.00
1.00
10.49 10.50 12.00 20.00
46.75 50.00 90.00 100.00
0.57
1.00
1.00
1.00
0.65
1.00
1.00
1.00
1.14
7.61
0.49
0.97
1.35
4.02
0.51
5.58
32.36
0.82
0.62
8.86
1.51
0.35
3.04
1.10
0.48
2.43
38.80
0.50
0.48
These findings correspond well with described self-reported positive productivity and performance effects in
recent research on WFH after the COVID-19 disruption as reviewed by Bloom, Han, and Liang (2023).
13
This percentage includes critical non-investigation functions likely working (partly) from the office, which were
excluded from sample selection. Consistent with interview insights, our TM sample shows almost 100% WFH.
25
Additional analyses
TL PI quality
TL
29
2.50
4.00
5.10
5.75
6.00
7.00
1.30
Team remote ability
TM
205
2.75
5.50
6.08
6.25
7.00
7.00
0.97
This table shows descriptive statistics. ‘Level’ indicates team/team leader (TL) or team member (TM) variables. TL
missing values mainly concern information use and quality. TM missing values mostly concern performance, team
communication and team remote ability, located late in the survey when some respondents had stopped. We omit
statistics regarding time allocation, dual role, cell coordinator, and the unit and country indicators to ensure anonymity
of respondents and data confidentiality. Table A1 summarizes variable measurement.
6.2. Multilevel analyses of the influence of performance information on performance
change (H1)
In our analyses, we estimate the effects of outcome control on performance change,
accounting for the nested data structure of GFCP teams by estimating multilevel regression
models with random team effects (Raudenbush & Bryk, 2002). We include unit and country
fixed effects to control for unmeasured unit and location differences. In all analyses, we weigh
responses by team response rate ([number of responses/team size]), such that teams with higher
response rates receive higher analytic weights.
The direct effect estimates in Table 4 show that both TL performance information and
TM performance information are positively associated with Performance change, supporting
H1 which posits that outcome control supports WFH (Model 1 b=0.11, p<0.05 Model 2 b =0.10,
p<0.05). Both models also show positive influences on performance change when after the
disruption the team meeting structure was adapted, for TMs with a higher pre-disruption
performance level, when TLs had prior outside experience, and for teams with a larger share of
members WFH. TMs with customer contact as part of their job were more likely to experience
a negative influence on performance.14
Customer contact involves verifying information provided by customers. Apart from site visits, the form of
customer contact remained unchanged after the pandemic and consists of phone calls and email conversations. The
negative coefficient may result from complications to call customers from home instead of from the office.
14
26
Table 4: Estimation results of two-level multilevel model with team random effects
Performance change
Model 1
0.10
(0.09)
0.11**
(1.97)
Model 2
0.29
(0.24)
Intercept
TL performance information
0.10**
(2.02)
TM performance information
0.11
(1.48)
0.04
(0.50)
1:1 meetings
-0.01
(-1.01)
-0.01
(-0.64)
Team meetings
0.68***
(5.94)
0.62***
(4.75)
Change in meeting structure
0.17**
(2.08)
0.19**
(2.18)
Prior performance
0.03
(0.55)
-0.02
(-0.34)
TL tenure
0.09***
(3.08)
0.08***
(2.82)
TL prior experience
-0.39**
(-2.55)
-0.21
(-1.08)
TL female
0.01
(1.17)
0.01
(1.41)
Team size
-0.01
(-1.44)
-0.00
(-1.23)
Team case risk
0.04
(0.29)
0.18
(1.48)
Team tenure
-0.12
(-0.99)
-0.11
(-1.03)
Team dispersion
0.04***
(4.12)
0.03**
(2.21)
Share team WFH
0.01
(0.11)
-0.01
(-0.24)
TM team tenure
-0.37
(-1.39)
-0.37
(-1.39)
TM short tenure
0.04**
(2.44)
0.03
(1.48)
TM prior experience
0.10
(1.29)
0.11
(1.47)
TM expertise
0.20
(1.27)
0.13
(0.85)
TM female
-0.06**
(-2.09)
-0.05
(-1.49)
TM weeks WFH
-0.00
(-0.68)
-0.00
(-1.08)
TM case risk
-0.49**
(-2.33)
-0.49**
(-2.39)
TM customer contact
0.09
(0.31)
-0.08
(-0.34)
Whiteboard
yes
yes
Further controls
yes
yes
FE unit
yes
yes
FE country
323
359
AIC
168
184
N
This table presents the results of multilevel estimations of the effects of TL performance information and TM
performance informationon Performance change. Cell statistics are the coefficient estimate and z-value.
***p<0.01, **p<0.05, *p<0.10 (two-tailed). Coefficients of other controls (time allocation, dual role, cell
coordinator presence), and unit and country fixed effects are omitted to ensure anonymity of respondents and
data confidentiality. Table A1 summarizes variable measurement.
6.3. The mediating effect of team communication (H2 and H3)
Hypotheses 2 and 3 jointly predict that after shifting to WFH, performance information
use facilitates team communication, and ultimately task performance. To estimate the expected
indirect effect of changes to team communication effectiveness, we use multilevel structural
equation modeling (i.e., Generalized SEM). For this purpose, we add Team communication
effectiveness change as dependent variable and as a predictor of Performance change. This
allows us to simultaneously estimate the effects on both dependent variables, including the
indirect effect through Team communication effectiveness change.
27
Models 1 and 2 in Table 5 relate Team communication effectiveness change to the two
outcome control measures. Consistent with H2, both TL performance information and TM
performance information relate positively to Team communication effectiveness change (Model
1 b=0.11, p<0.01; Model 2 b=0.12, p<0.05). In both models, Team communication effectiveness
change is positively associated with Performance change. In support of H3, the estimated
indirect effects are positive (Model 1 b=0.05, p<0.01; Model 2 b=0.05, p<0.10). Together, these
results indicate that performance information enabled effective team communication with and
between locally dispersed team members in the WFH setting.15
15
Using a TL measure of Communication challenges (based on two items capturing the TL’s assessed challenges
to formal and informal communication in the team after the disruption; Cronbach´s α=0.76) provides similar results
and inferences. Untabulated results show that Communication challenges is negatively associated with
Performance change, while TL performance information has a negative effect on Communication challenges. The
indirect effect of TL performance information is positive (b=0.07, p<0.05), consistent with the findings in Table
5.
28
Table 5: Generalized Structural Equation Modeling (GSEM) estimation results
Model 1
Direct effects
Performance
change
Intercept
Team communication
effectiveness change
TL performance
information
TM performance
information
1:1 meetings
Team meetings
Change in meeting
structure
Prior performance
TL tenure
TL prior experience
TL female
Team size
Team case risk
Team tenure
-1.36
(-1.46)
0.46***
(6.29)
0.05
(1.10)
-0.02
(-0.27)
-0.01
(-1.25)
0.52***
(4.93)
0.16**
(2.43)
0.07*
(1.71)
0.02
(0.91)
-0.52***
(-4.28)
0.02**
(1.99)
-0.01**
(-2.51)
-0.05
(-0.50)
Model 2
Indirect effects
Performance
change
Direct effects
Performance
change
4.15**
(2.49)
1.93**
(2.18)
-1.44
(-1.26)
0.44***
(7.02)
4.34***
(2.81)
1.90***
(2.62)
0.11***
(2.91)
0.05***
(2.59)
0.05
(1.25)
-0.03
(-0.58)
-0.01
(-1.04)
0.50***
(4.29)
0.19***
(2.58)
0.02
(0.44)
0.02
(0.70)
-0.37**
(-2.46)
0.02**
(2.21)
-0.01*
(-1.79)
-0.03
(-0.24)
0.12**
(2.30)
0.22***
(3.40)
0.01
(0.78)
0.18
(0.80)
0.08
(0.94)
-0.04
(-0.68)
0.14***
(5.58)
0.23
(1.22)
-0.01
(-1.05)
0.00
(0.36)
0.35**
(2.45)
0.05*
(1.95)
0.09***
(3.30)
0.01
(0.76)
0.08
(0.79)
0.04
(0.98)
-0.02
(-0.65)
0.06***
(4.04)
0.10
(1.25)
-0.01
(-1.06)
0.00
(0.36)
0.15**
(2.53)
Team
communication
effectiveness
change
0.30***
(4.97)
0.00
(0.28)
0.25
(1.21)
0.10
(1.15)
-0.03
(-0.45)
0.16***
(4.45)
0.18
(1.09)
-0.01
(-1.08)
0.00
(0.71)
0.15
(0.87)
0.14***
(4.02)
0.00
(0.28)
0.12
(1.17)
0.05
(1.22)
-0.01
(-0.44)
0.07***
(3.44)
0.08
(1.11)
-0.01
(-1.12)
0.00
(0.70)
0.07
(0.88)
Team
communication
effectiveness
change
Indirect effects
Performance
change
Team dispersion
-0.26**
0.30**
0.14*
-0.22**
0.25*
0.11
(-2.41)
(2.23)
(2.13)
(-2.26)
(1.83)
(1.74)
Share team WFH
0.03***
-0.00
-0.00
0.03***
-0.01
-0.00
(4.16)
(-0.15)
(-0.15)
(3.09)
(-0.50)
(-0.50)
TM team tenure
-0.05
0.12
0.06
-0.04
0.07
0.030
(-1.16)
(1.51)
(0.173)
(-0.90)
(0.91)
(0.86)
TM short tenure
-0.21
-0.13
-0.06
-0.24
-0.16
-0.07
(-1.05)
(-0.54)
(-0.52)
(-1.16)
(-0.62)
(-0.61)
TM prior experience
0.02
0.03
0.01
0.01
0.02
0.01
(1.24)
(1.19)
(1.21)
(0.85)
(0.77)
(0.79)
TM expertise
0.17**
-0.17**
-0.08*
0.14**
-0.09
-0.04
(2.19)
(-2.11)
(-1.81)
(2.05)
(-1.30)
(-1.19)
TM female
0.12
0.09
0.04
0.07
0.06
0.03
(0.80)
(0.77)
(0.77)
(0.47)
(0.55)
(0.55)
TM weeks WFH
-0.03
-0.03
-0.01
-0.03
-0.01
-0.01
(-1.37)
(-0.97)
(-0.98)
(-1.18)
(-0.43)
(-0.43)
TM case risk
-0.00
0.00
0.00
-0.00
0.00
0.00
(-0.74)
(0.02)
(0.02)
(-1.40)
(0.44)
(0.45)
TM customer contact
-0.36*
-0.27*
-0.13*
-0.34*
-0.32**
-0.14**
(-1.89)
(-1.88)
(-1.75)
(-1.86)
(-2.32)
(-2.13)
Whiteboard
0.11
-0.08
-0.04
-0.03
-0.13
-0.06
(0.43)
(-0.28)
(-0.27)
(-0.14)
(-0.52)
(-0.51)
yes
yes
Further controls
188
204
N
This table presents the multilevel estimation results of the direct effects of the TL and TM construct measures of performance information on
Performance change and Team communication effectiveness change assessed by TMs, and their estimated indirect effects. Cell statistics are the
coefficient estimate and z-value. ***p<0.01, **p<0.05, *p<0.10 (two-tailed). Coefficients of other controls (time allocation, dual role, cell
coordinator presence), and unit and country fixed effects are omitted to ensure anonymity of respondents and data confidentiality. Table A1
summarizes variable measurement.
30
7. ROBUSTNESS TESTS
Table 6 provides further evidence on the hypothesized effects by examining (1)
performance information quality as alternative measure of TL outcome control, and (2) TMs’
assessment of their team’s ability to work remotely as alternative outcome variable.
Table 6: Alternative independent and dependent variables
Intercept
TL performance information quality
TL performance information
TM performance information
1:1 meetings
Team meetings
Change in meeting structure
Prior performance
TL tenure
TL prior experience
TL female
Team size
Team case risk
Team tenure
Team dispersion
Share team WFH
TM team tenure
TM short tenure
TM prior experience
TM expertise
TM female
TM weeks WFH
TM case risk
TM customer contact
Whiteboard
Performance
change
Model 1
-1.88*
(-1.71)
0.21***
(4.15)
0.14*
-0.00
0.72***
0.21***
0.02
0.09***
-0.26*
0.03***
-0.01
0.08
-0.08
0.04***
-0.00
-0.39
0.04**
0.10
0.19
-0.06**
-0.00
-0.45**
-0.08
(1.81)
(-0.18)
(7.00)
(2.67)
(0.37)
(3.04)
(-1.78)
(3.00)
(-1.40)
(0.76)
(-0.74)
(4.41)
(-0.11)
(-1.43)
(2.49)
(1.31)
(1.31)
(-2.21)
(-0.97)
(-2.30)
(-0.34)
Team remote ability
Model 2
4.88***
(4.33)
0.09**
(2.44)
-0.04
-0.04***
0.24
0.19**
0.14***
0.01
-0.04
-0.01
0.00
0.20
0.12
-0.01
-0.06
0.20
-0.04***
0.08
0.01
-0.03
-0.00
-0.25
0.10
(-0.93)
(-3.75)
(1.56)
(2.53)
(3.22)
(0.50)
(-0.34)
(-0.77)
(0.39)
(1.42)
(0.93)
(-0.86)
(-1.04)
(0.66)
(-2.70)
(0.79)
(0.11)
(-0.76)
(-0.00)
(-1.59)
(0.46)
Model 3
3.64**
(2.57)
0.11*
-0.13**
-0.02**
0.11
0.16**
0.06
-0.01
-0.17
-0.00
0.00
0.09
0.16
0.01
-0.06
0.15
-0.05***
0.08
-0.09
-0.01
-0.00
-0.16
0.08
(1.92)
(-1.99)
(-2.06)
(0.75)
(2.29)
(1.26)
(-0.22)
(-1.02)
(-0.50)
(0.65)
(0.69)
(1.34)
(0.48)
(-1.31)
(0.53)
(-3.14)
(0.96)
(-0.82)
(-0.41)
(-0.47)
(-0.99)
(0.47)
yes
yes
yes
Further controls
yes
yes
yes
FE unit
yes
yes
yes
FE country
359
343
375
AIC
184
178
194
N
Model 1 presents the results of multilevel estimations of the effects of TL performance information quality on
Performance change. Models 2 and 3 present the results of multilevel estimations of the effects of TL
performance information and TM performance information on Team remote ability. Cell statistics are the
coefficient estimate and z-value. ***p<0.01, **p<0.05, *p<0.10 (two-tailed). Coefficients of other controls (time
allocation, dual role, cell coordinator presence), and unit and country fixed effects are omitted to ensure
anonymity of respondents and data confidentiality. Table A1 summarizes variable measurement.
31
7.1. Alternative independent variable: Performance information quality
In our first robustness test, we examine the assessed quality of the underlying
performance information as an alternative independent variable. Specifically, based on
economic contracting theory (e.g., Banker & Datar, 1989) we capture the TL’s evaluation of
the properties of available performance information: congruence (with team and GFCP goals;
two items), precision (information accuracy), and sensitivity (team influence over performance)
(Cronbach´s α=0.90). Positive team-level correlations of TL performance information quality
with TL performance information and TM performance information show the measures
converge (r1=0.75, p<0.01; r2=0.37, p<0.05). Re-estimating Model 1 in Table 4 corroborates
our inferences about the role of performance information in supporting remotely executed
operations (Model 1 b=0.21, p<0.01, Table 6).
7.2. Alternative dependent variable: Team remote ability
Our second test concerns an alternative dependent variable regarding TMs’ assessment
of their team’s ability to operate remotely. To obtain a construct of ‘Team remote ability’, based
on interviews with GFCP management and TLs, we adapted a four-item construct of ‘collective
efficacy’ (Chong & Mahama, 2014; Salanova, Llorens, Cifre, Martínez, & Schaufeli, 2003) to
the remote work setting (see Tables 2 and A1, Cronbach´s α=0.95). In support of construct
validity, the average team construct score correlates well with (1) TLs’ assessment of how well
their team coped with the challenges arising from the COVID-19 outbreak (r=0.46, p<0.05),
and (2) the team score on a People Pulse question about how well employees felt supported
during the COVID-19 crisis (r=0.35, p<0.10). Results reported in Table 6 show positive
coefficients of both the TL and TM measures of performance information on Team remote
ability (Model 2 b=0.09, p<0.05; Model 3 b=0.11, p<0.10). These findings indicate that teams
with greater use of performance information are assessed by their members to have a greater
ability to operate remotely.
32
8. DISCUSSION AND CONCLUSION
8.1 Theoretical implications
This study investigates the role of performance information as key element of outcome
control in supporting WFH. Remote work can offer organizations various benefits including
improved performance and job satisfaction, and fixed costs and overhead reduction (Ameen et
al., 2023; Barrero et al., 2023; Bloom et al., 2015; Sherman, 2020); yet, it also poses significant
coordination, monitoring and communication challenges (Emanuel & Harrington, 2023;
Schmelz & Ziegelmeyer, 2020). These challenges may be exacerbated when WFH is ‘enforced’
following from lockdowns during the COVID-19 pandemic, and of larger scale than the typical
partial WFH in voluntary arrangements. Our analysis facilitated by this setting provides several
contributions to the literature.
First, the study contributes to the literature on organizational control by providing
evidence on the role of performance information in supporting firm operations when shifting to
WFH. Particularly, our field setting offered the opportunity to provide evidence on the role of
performance information in the early phase of the COVID-19 pandemic where after moving to
WFH control alternatives were largely absent. We base our multilevel analysis on two large
units of Group Financial Crime Prevention Operations of Nordea Group. As the units’ activities
are of significant importance to the bank and are subject to regulatory requirements, continuity
of the operations conducted by teams was critical. The abrupt shift to WFH for complete teams
enabled us to examine the question how the use of performance information supports teams in
operating effectively remotely. Our findings speak to the importance of performance
information for guiding and coordinating between remotely located team members; a setting
where control alternatives are limited. We also uncover effective team communication as a key
mechanism for maintaining or even enhancing performance after the shift to WFH.
33
Our findings also complement prior research on performance information during
disruptive periods (Abernethy et al., 2021). While prior studies typically examined uncertainties
and change emerging over a longer time period, instead of a sudden disruption requiring
immediate response, our study examines outcome control in the early phase of the pandemic
and emphasizes managers’ and employees’ use of performance information in an altered
context with a major disruption to the workplace. With the anticipation of more structural WFH
(Ameen et al., 2023; Barrero et al., 2021a, 2021b; Bloom, 2020; Choudhury et al., 2021), our
findings hopefully motivate decision makers and designers of WFH arrangements to be aware
of the critical role of outcome control in addition to classical WFH themes (Gajendran
& Harrison, 2007). This can also include the question how combining outcome control with
other forms (e.g., behavior control and informal control; Ouchi, 1979; Sihag & Rijsdijk, 2019)
supports performance when work arrangements become more flexible.
Second, this study complements earlier field experiments (Bloom et al., 2015; Sherman,
2020) that document positive performance effects of WFH. Several conditions imposed by the
COVID-19 disruption mirror characteristics of a natural experiment, although our setting lacks
an ‘experimental control group’ and pre-disruption data. Yet, the existence of pre-existing
variation between teams and eliciting evaluations of changes in team communication
effectiveness and task performance enables us to assess how the shift to WFH influenced teams
differentially. The enforced nature of WFH for almost the entire organization created a
possibility to study an important contemporary phenomenon and to create novel insights on the
role of performance information in supporting team operations. Despite the absence of choice,
our evidence suggests that WFH can be beneficial for a whole unit, particularly when
facilitating mechanisms are in place that enable keeping up or enhancing effective team
communication and coordination. These findings are in line with better-than-expected
experiences by organizations forced to ‘experiment’ with WFH (Barrero et al., 2021a) and
34
demonstrate how associated control problems can be addressed through performance
information and effective team communication.
8.2 Practical implications
The key practical implication of our findings is that they underscore the value of
performance information in the setting of WFH, in our field setting related to the need to cope
with significant and sudden change. The results also point to the value of establishing good
communication structures that organizational members can rely upon when regular working
and communication patterns are disrupted. Insights from our study and especially the facets of
the outcome control constructs linked to both team leaders´ and team members´ use of
performance information to influence and facilitate decisions may help practitioners to evaluate
their performance information in place. The contextualization of measures might further
provide inspiration on how to alter the design of outcome control and performance information
particularly to enable WFH, given the expectations that WFH will ‘stick’ in the future (Ameen
et al., 2023; Barrero et al., 2021a, 2021b).
8.3 Limitations and outlook
A key limitation of our analysis is that, since the disruption affected all teams around the
same time, we cannot compare changes in outcome variables to ‘untreated’ teams. Thus, while
we control for an extensive set of team meeting, team leader, team member, and task
characteristics to limit the risk of potential confounds, we cannot fully rule out that outcomes
were influenced by unobserved changes coinciding with the disruption, or by unobserved
factors systematically associated with the different measures of outcome control. In addition,
while the timing of our survey data collection allowed GFCP team leaders and team members
to gain experience with WFH for about ten weeks, our results in essence speak to the effects
observed regarding one period in time. Future studies can examine the effects of broad-scale
WFH during a longer period, and the use of controls and other supporting mechanisms.
35
We also cannot unambiguously attribute the identified effects to WFH only. For instance,
GFCP management noted that while the pandemic caused uncertainty for many organizations
and employees, GFCP employees could continue working remotely with no direct threat to job
security. Indeed, responses to the May 2020 People Pulse indicate that employees overall felt
well supported. While this may have influenced performance changes and perceptions about
WFH, it is less clear if and how this could influence the identified impact of outcome control.
Additionally, our key variables rely on survey data, which, while allowing to obtain in-depth
insights, are susceptible to measurement concerns. Importantly, our collection and matching of
data from different respondents and multiple sources avoids risks of common method bias, and
we took care to contextualize and validate construct measurement and included an extensive
set of control variables which should limit concerns regarding omitted variables.
Finally, our findings are based on a firm operating in the financial sector, in which most
firms have invested heavily in digital technology and have a high capacity for WFH. Yet, our
findings have implications for other settings where WFH is a possibility, and particularly where
communication and coordination among employees is important (e.g., (digital) service
provision). While our insights link to the response to a sudden major disruption in the early
phase of a pandemic, future research may explore the (control) implications and effects of WFH
over a longer period and/or adopted and implemented in a more structured way.
36
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Appendix A. Additional tables
TABLE A1 Summary of variable measurement
Dependent variable
Performance change
Independent variables
TL performance
information
TM performance
information
Mediator variable
Team communication
effectiveness change
Control variables
1:1 meetings
Team meetings
Change in meeting
structure
Mean score of four items on which employees rated the change in their
performance since the outbreak of COVID-19 (1: strongly decreased; 4: no change;
7: strongly increased)
1. Throughput (such as handling time or number of cases completed).
2. Quality (such as “first-time-right”, “no major mistakes”, “no auditor
remarks”).
3. On-time completion (such as closing cases before deadline).
4. My contribution to team performance.
Mean score of eleven items relating to the average score of global reports (e.g.,
standard MI reports) and local reports (e.g., dashboards/reports prepared by
production planners).
How useful is the performance information that you have available for managing
your team to... (1: not useful – 7: very useful, n/a)
1. … allocate cases to team members.
2. … perform resource planning, training and hiring.
3. … match team member skills with cases.
4. … prioritize activities.
5. … make trade-offs among activities.
6. … evaluate throughput.
7. … evaluate the overall handling of cases.
8. … evaluate on-time completion.
9. … evaluate the quality of work performed.
10. … evaluate overall team performance.
… evaluate individual team member performance.
Mean score of nine information items potentially included in the team’s main tool
used for status meetings (0: not included – 1: included, not useful – 7: included,
very useful).
1. Targets (such as weekly production).
2. Productivity (such as actual cases completed).
3. Backlog.
4. Handling time (such as two-eyes, four-eyes, or quality board processes).
5. Quality.
6. On-time completion.
7. Team capacity (such as available team members).
8. Allocation of work to team members.
9. Team matters (such as team pulse).
Mean score of three items on which employees rated changes regarding team
communication:
1. How effective have the team meetings been since the outbreak of COVID-19
as compared to before? (1: much worse, 4: same, 7: much better)
How have the following aspects impacted your work since the outbreak of COVID19 (1: negatively impacted; 4: no impact; 7: positively impacted)
2. Standard team communication (such as team meetings, stand-ups).
3. Informal communication.
Number of bilateral meetings between team leader and members per month before
the COVID-19 disruption.
Number of team meetings per month before the COVID-19 disruption.
Change in frequency of daily, weekly and/or bi-weekly team meetings since the
COVID-19 disruption (0: no – 1: yes).
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Prior performance
Mean score of four items on which employees rated their performance before the
COVID-19 disruption (1: very low – 7: very high):
1. Throughput (such as handling time or number of cases completed).
2. Quality (such as “first-time-right”, “no major mistakes”, “no auditor remarks”).
3. On-time completion (such as closing cases before deadline).
4. My contribution to team performance.
TL tenure
Tenure in years as team leader.
TL prior experience
Team leader experience in years outside Nordea relevant to current function.
TL female
Team leader gender (0: male – 1: female).
Dual role
Team leader with country head role (0: no – 1: yes).
Team size
Number of team members per team.
Team case risk16
Team mean proportion (%) of high/very high risk cases investigated.
Team tenure
Mean employee tenure (in years) within the team.
Team dispersion
Number of countries in which the team operates.
Cell coordinator
Indicator of a cell coordinator in the team (0: no – 1: yes). Cell coordinators are
present in several KYC MR teams (e.g., when dispersed over multiple countries).
Team leaders may delegate several coordination tasks to them including leading
team meetings.
Share team WFH
Proportion (%) of the team working from home since the COVID-19 disruption.
TM team tenure
Employee tenure within the team in years.
TM short tenure
Indicator variable for employee team tenure <3 months (0: no – 1: yes).
TM prior experience
Employee relevant experience in similar jobs outside Nordea in years
TM expertise
Employee self-rating of job expertise relative other team members (1: among the
least experienced – 7: among the most experienced).
TM female
Employee gender (0: male – 1: female).
TM weeks WFH
Weeks working from home since COVID-19 disruption.
TM case risk
Proportion (%) high/very high (compared to low/medium) risk cases investigated.
TM customer contact
Indicator of customer contact in job (0: no – 1: yes).
Time allocation
Time allocation (% time) over activity types (categories 5+6 as reference category):
1. Conducting KYC investigations (for example two-eyes review activities).
2. Quality control (for example four-eyes reviews).
3. Supporting team members or other teams.
4. Team communication.
5. Out-of-production projects.
6. Other (please clarify which kinds of activities).
Whiteboard
Whiteboard as main tool before COVID-19 disruption (0: no – 1: yes).
Unit
Unit fixed effect (0: KYC Operations Mass Markets – 1: KYC Operations Managed
Relations).
Country
Country fixed effects based on team leader location (Denmark, Estonia, Finland,
Norway, Sweden, with Poland as reference group).
Variables used for additional analyses
TL performance
Mean score of four items team leaders rated their agreement with. The performance
information quality
information that you have on your team (such as management information reports
and local information)… (1: strongly disagree – 7: strongly agree)
1. … relates to the true goals of your team.
2. … is unambiguously linked to the goals of GFCP.
3. … accurately captures team performance.
My team has significant influence over the reported performance.
Team remote ability
Mean of four items capturing employees’ agreement with statements on the team’s
ability to work remotely (1: strongly disagree – 7: strongly agree).
1. I feel confident about the capability of my team to perform its tasks well
remotely.
2. My team is able to complete difficult tasks remotely without additional effort.
3. I feel confident that my team will be able to effectively manage unexpected
issues remotely.
My team is competent to solve its tasks remotely.
16
High-risk cases are more complex. Mean team case risk correlates strongly with internal expert assessments of
team task complexity (KYC MM: r=0.61, p<0.01; KYC MR: r=0.74, p<0.01).
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Communication
challenges
Mean score of two items on which team leaders rated the following statements
linked to communication after the outbreak of COVID-19 (1: strongly disagree – 7:
strongly agree):
1. Formal team communication (such as team meetings, stand-ups) is a challenge.
2. Constraints to informal communication complicate my team’s work.
Variables used for validation tests of Performance
TL absolute team
Mean score of four items on which team leaders rated their team’s performance
performance
before the outbreak of COVID-19 (1: very low –7: very high, n/a):
1. Throughput (such as handling time or number of cases completed).
2. Quality (such as “first-time-right”, “no major mistakes”, “no auditor
remarks”).
3. On-time completion (such as closing cases before deadline).
4. The team’s contribution to GFCP performance.
TL relative team
Mean score of four items on which team leaders rated their team’s performance
performance
before the outbreak of COVID-19 compared to other (comparable) teams in GFCP
(1: very low – 7: very high, n/a):
1. Throughput (such as handling time or number of cases completed).
2. Quality (such as “first-time-right”, “no major mistakes”, “no auditor
remarks”).
3. On-time completion (such as closing cases before deadline).
4. The team’s contribution to GFCP performance.
TL team member task
Mean score of six items capturing team leaders’ agreement with statements that the
performance
members of their team (1: strongly disagree – 7: strongly agree):
1. …adequately complete assigned duties.
2. …fulfill responsibilities specified in their job description.
3. …perform tasks that are expected of them.
4. …meet formal performance requirements of the job.
5. …engage in activities that will directly affect their performance evaluation.
6. …fail to perform essential duties (reverse coded).
Variables used for validation tests of the use of performance information
TM shared
Mean score of four items capturing team members’ assessment of how well the
performance
following statements apply (1: not at all – 7: to a great extent):
information
1. I regularly receive information about my team's performance.
2. I regularly receive information about my individual performance.
3. During team meetings, information about team performance is shared.
4. During team meetings, team performance is discussed.
TM information
Team member assessment of how information they receive guides them in doing
guidance
their work (1: disagree – 7: agree).
Time local reporting
Sum of team leader responses to two questions on how much time (in hours per
week) a support function (for instance, production planners) and the team leader
spend on preparing additional reports to manage the team´s activity.
TM assessment of TL
Team member agreement with the statement: My team leader assigns team
task assignment
members to particular task (1: strongly disagree – 7: strongly agree).
Team member agreement with the statement: My team leader schedules the work to
TM assessment of TL
be done (1: strongly disagree – 7: strongly agree).
work scheduling
TM assessment of TL
Team member agreement with the statement: My team leader lets team members
expectation
know what is expected (1: strongly disagree – 7: strongly agree).
management
Variables used for validation tests of Team communication effectiveness change
Team meeting
Team leader assessment of effectiveness of team meetings for managing
effectiveness change
their team as compared to before the disruption (1: worse, 4: same, 7: better)
Guidance and support
Team level score on Q2 2020 People Pulse question of the extent to which
employees felt their team leader provided appropriate guidance and support during
the COVID-19 outbreak (1: disagree – 5: agree)
Variables used for validation tests of Post Covid team remote ability
Team leader assessment of how well the team coped with the challenges arising
Coping with COVIDfrom the outbreak of COVID-19 (1: not very well – 7: very well)
19 challenges
Experienced support
Team level score on Q2 2020 People Pulse question of how well employees felt
supported during the COVID-19 outbreak (1: disagree – 7: agree).
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