An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications for Improving Employee Participation

Abstract

Crowdsourcing has become one of the main resources for working on so-called microtasks that require human intelligence to solve tasks that computers cannot yet solve and to connect to external knowledge and expertise. Instead of using external crowds, several organizations have increasingly been using their employees as a crowd, with the aim of exploiting employee’s potentials, mobilizing unused technical and personal experience and including personal skills for innovation or product enhancement. However, understanding the dynamics of this new way of digital co-working from the technical point of view plays a vital role in the success of internal crowdsourcing, and, to our knowledge, no study has yet empirically investigated the relationship between the technical features and participation in internal crowdsourcing. Therefore, this chapter aims to provide a guideline for organizations and employers from the perspective of the technical design of internal crowdsourcing, specifically regarding issues of data protection privacy and security concerns as well as task type, design, duration and participation time based on the empirical findings of an internal crowdsourcing platform.

Full text

An Empirical Analysis of an Internal
Crowdsourcing Platform: IT Implications
for Improving Employee Participation
Neslihan Iskender and Tim Polzehl
Abstract Crowdsourcing has become one of the main resources for working on
so-called microtasks that require human intelligence to solve tasks that computers
cannot yet solve and to connect to external knowledge and expertise. Instead of
using external crowds, several organizations have increasingly been using their
employees as a crowd, with the aim of exploiting employee’s potentials, mobilizing
unused technical and personal experience and including personal skills for innova-
tion or product enhancement. However, understanding the dynamics of this new way
of digital co-working from the technical point of view plays a vital role in the success
of internal crowdsourcing, and, to our knowledge, no study has yet empirically
investigated the relationship between the technical features and participation in
internal crowdsourcing. Therefore, this chapter aims to provide a guideline for
organizations and employers from the perspective of the technical design of internal
crowdsourcing, specifically regarding issues of data protection privacy and security
concerns as well as task type, design, duration and participation time based on the
empirical findings of an internal crowdsourcing platform.
Keywords Internal crowdsourcing · Enterprise crowdsourcing · Technology
implications · Employee participation
1 Introduction
Thanks to the widespread use of the Internet, a fast and relatively inexpensive
resource, so-called microtask crowdsourcing, has emerged, meaning that the cost
and time barriers of qualitative and quantitative laboratory studies, controlled exper-
iments, challenges in innovation and product enhancement can be easier overcome
(Kittur et al. 2011; Gadiraju 2018). Microtask crowdsourcing has been primarily
N. Iskender (*) · T. Polzehl
Quality and Usability Lab, TU Berlin, Berlin, Germany
e-mail: neslihan.iskender@tu-berlin.de;tim.polzehl@qu.tu-berlin.de
©The Author(s) 2021
H. Ulbrich et al. (eds.), Internal Crowdsourcing in Companies, Contributions to
Management Science, https://doi.org/10.1007/978-3-030-52881-2_6
103

used for simple, independent tasks such as image labelling or digitizing print
documents (Kittur et al. 2011). One of the well-known examples of crowdsourcing
is Wikipedia, where crowdsourcing is used to gather knowledge from people from
all over the world. They bring together a collection of images, links and topics, tag
the content, sort it into categories and link and recommend content to each other.
Inspired by the results of microtask crowdsourcing, some researchers have begun to
investigate crowdsourcing for complex and expert tasks such as writing, translating,
product design or product innovation (Kittur et al. 2013; Valentine et al. 2017;
Zuchowski et al. 2016). Progressive digitization and the global networking associ-
ated with it are also leading to a change in the professional world, meaning that the
use of crowdsourcing in enterprises has increased substantially as a direct result of
widespread use of Internet applications and digitalization.
This new form of digital agile work, both in terms of collaboration and the
knowledge transfer processes, has aroused the interest of many enterprises where
the same pattern of online working is used internally. In that way, crowdsourcing has
evolved and created a new form which is referred to as ‘internal crowdsourcing’.It
involves outsourcing certain work steps and tasks from the daily tasks, production or
innovation processes or any kind of topic that might be interesting for employees, via
company-owned online platforms or intermediaries to a predefined group of com-
pany employees (Zuchowski et al. 2016; Erickson et al. 2012; Vel et al. 2018). In this
case, value-added activities are not outsourced to an indefinite mass of people, the
so-called external crowd, but to a closed group of people such as employees or other
stakeholders (Leimeister et al. 2015).
This also has far-reaching consequences for companies and the way they use the
Internet for their diverse work processes. This new way of digitalization and work
organization is increasingly becoming an alternative to the traditional way of
working in organizations, especially in the innovation and participation domain
(Benbya and Leidner 2016; Benbya and Leidner 2018; Malhotra et al. 2017).
Apart from collecting information on any topic, organizations can apply internal
crowdsourcing for complex tasks such as idea creation, product evaluation or
innovation generation, which are mostly quite complex and require special knowl-
edge and skills. This new concept is implemented in practice at numerous large
companies. For example, the American consumer goods producer Procter & Gam-
ble, the pharmaceutical company Bayer HealthCare and the toy manufacturer LEGO
use the crowdsourcing strategy to achieve higher-quality, cheaper and faster inno-
vation processes (Zuchowski et al. 2016).
The idea that the innovation or idea creation processes are opened to all the
employees (not just the employees of strategy or innovation department) is interest-
ing for many companies because this type of crowdsourcing uses the principle of the
‘wisdom of the crowd’. This is the phenomena whereby a heterogeneous group of
individually decisive people can produce qualitatively better solutions than certain
experts under certain conditions (Lüttgens et al. 2014). In that way, organizations
can benefit from the internal knowledge and personal experience of all employees
and involve them in the innovation process without any additional costs. For
example, Dieter Zetsche—the Chairman of the Board of Management of Daimler
104 N. Iskender and T. Polzehl

AG—recently announced that 20% of the employees will be transformed into an
internal crowd to operationalize a series of innovation tasks (Daimler 2017). How-
ever, designing and building such a large internal crowd involves a significant
organizational transformation process that needs to be managed (Malhotra and
Majchrzak 2014). Allianz UK has shown how tedious this change can be when
they launched an internal crowdsourcing platform for innovation development
(Benbya and Leidner 2016; Benbya and Leidner 2018). More than 8 years have
passed from commissioning to the efficient use of the platform.
Since internal crowdsourcing is not yet a standardized procedure, the internal
crowdsourcing task design, the foundation of the employee’s motivation to partic-
ipate, the measures of quality and success for internal crowdsourcing as well as
ground rules for protecting employee rights and privacy should be established for an
ethical and successful operationalization of internal crowdsourcing (Zuchowski et al.
2016). Chapters ‘Introduction to “Internal Crowdsourcing: Theoretical Foundations
and Practical Applications”’and ‘Systematization Approach for the Development
and Description of an Internal Crowdsourcing System’provide the theoretical
background for internal crowdsourcing; however, to our knowledge, no study has
yet empirically investigated the effects of crowdsourcing campaign topics, the
estimated timeframes involved, the time and day for employee participation in the
campaign or the question types to be used in the campaign concerning the partici-
pation of employees in the internal crowdsourcing. As such, the empirical results of
internal crowdsourcing should be further investigated to find out what kind of
relationship exists between the technical characteristics of an internal crowdsourcing
platform and employee participation.
This chapter aims to provide some initial insights about how to shape and
communicate the rules regarding data protection, privacy and security concerns of
employees as well as the guidelines for technical implementation of such a platform
and its daily operation. Its anticipated contribution towards the practical implemen-
tation is the establishment of ground rules for fair internal crowdsourcing to lower
the barriers to employee participation. The following section of the chapter explains
the review and synthesis of the external and internal crowdsourcing in the literature.
The subsequent section then presents the basic function of an internal crowdsourcing
platform based on an empirical example of it. The penultimate section highlights the
research methods and internal crowdsourcing tasks that have been conducted using
an internal crowdsourcing platform and provides an empirical analysis of these. In
the last section, the discussion and conclusion of the empirical results are presented.
2 Theoretical Background
The term ‘crowdsourcing’was first used by the American journalist Jeff Howe
(2006) in an article entitled ‘The Rise of Crowdsourcing’, which was published in
the technology magazine Wired (Howe 2006). It is a neologism from the words
‘crowd’and ‘outsourcing’, and Howe describes it as follows:
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 105

The technological advances in everything from product design software to digital video
cameras are breaking down the cost barriers that once separated amateurs from profes-
sionals. Hobbyists, part-times, and dabblers suddenly have a market for their efforts, as smart
companies in industries as disparate as pharmaceutical and television discover ways to tap
the latent talent of the crowd. The labor isn’t always free, but it costs a lot less than paying
traditional employees. It’s not outsourcing; it’s Crowdsourcing. (Howe 2006)
So, crowdsourcing is a form of participatory online activity involving an indi-
vidual, an institution, a charity or a company—an undefined group of individuals—
through a flexible open call to perform a task voluntarily or in return for some
monetary benefit (Geiger et al. 2012).
Brabham takes the results of Howe and focuses on the crowdsourcing from the
company’s perspective saying that:
A company posts a problem online, a large number of individual solutions to the problem,
the winning ideas are some form of a bounty, and the company mass products the idea for its
own gain. (Brabahan 2008)
It becomes clear that Brabahan (2008) considers the crowdsourcing process
particularly as a problem-solving method, thus emphasizing the crowd’s swarm
intelligence. However, the authors Lopez, Vukovic and Laredo limit the
crowdsourcing principle only to the company’s own perspective and see
crowdsourcing as an Internet-based production model that enables distributed and
Web-based human collaboration (Lopez et al. 2010).
Taking the above definitions into account, three main components of
crowdsourcing can be identified: (1) requester or initiator (crowdsourcer),
(2) crowd or Internet users (crowdworker) and (3) Internet-based crowdsourcing
platform. This is distinguished from outsourcing in that it has a mediator (Internet-
based crowdsourcing platform) which enables the communication between an
unknown group of people, crowdworkers and the requester (Leimeister et al.
2015). In the case of outsourcing, the requester knows exactly who the task executer
is and instructs him/her by giving a certain task in return for monetary payment. In
contrast to outsourcing, in external crowdsourcing, the crowdsourcers outsource
certain tasks to an Internet-based platform for processing. The undefined mass of
people or the so-called crowdworkers take over the processing of outsourced tasks
voluntarily or in return for a monetary benefit (Leimeister and Zogaj 2013; Hirth
et al. 2012). The entire process, as well as the interaction between crowdsourcers and
crowdworkers, takes place on Internet-based crowdsourcing platforms (Blohm et al.
2014; Hoßfeld et al. 2012). It follows that if the crowdworkers are a defined group of
people such as employees, stakeholders or members of an organization, then we talk
about ‘internal crowdsourcing’(Leimeister and Zogaj 2013).
In the light of the above definitions, crowdsourcing is defined as a mechanism of
task sharing, especially the outsourcing of tasks or orders by crowdsourcers to a wide
crowd (crowdworkers) via an open call to solve a particular problem as quickly and
effectively as possible. Following that, we separate crowdsourcing based on the
crowdworkers into two categories: external and internal crowdsourcing. External
crowdsourcing, as explained in chapter ‘Introduction to “Internal Crowdsourcing:
106 N. Iskender and T. Polzehl

Theoretical Foundations and Practical Applications”’, deals with an undefined open,
heterogonous and usually unskilled crowd. However, the crowd in internal
crowdsourcing describes a closed group of people with certain skills, usually the
employees of the company, defined by the requester (Benbya and Leidner 2018).
2.1 Internal Crowdsourcing
In recent years, the still relatively new strategy of crowdsourcing has become
increasingly interesting for companies in various industries as a way to redesign
and speed up innovation processes and to benefit from the unused internal knowl-
edge of the employee in an organization (Howe 2006,2009; Hammon and Hippner
2012; Blohm et al. 2014; Simula and Ahola 2014). This type of crowdsourcing is
referred to as ‘internal crowdsourcing’(IC), which is defined according to
Zuchowski et al. (2016) as follows:
Internal crowdsourcing is (a) IT-enabled (b) group activity based on (c) open call for
participation (d) in an enterprise.
By definition, certain work steps and tasks of a production or innovation process
are outsourced using internal crowdsourcing via company-owned online platforms
or intermediaries to a predefined group of employees who participate voluntarily in
the completion of these crowd tasks online (Leimeister and Zogaj 2013). These are
then used to produce innovative, marketable products and services, thus contributing
towards increasing the company’sefficiency and profit.
Internal crowdsourcing is still a poorly researched practical phenomenon.
Although corporate crowdsourcing is a lucrative process from a corporate perspec-
tive, there is an imbalance between the burden and the benefits of crowd activity
from the employee’s point of view. For this reason, an incentive system should be
developed so that employees can motivate themselves or see benefits in the process
as a whole. To ensure that crowdsourcing for employees does not lead to an extra
workload, the labour law framework for internal crowdsourcing should also be
defined. Also, there are no standards or standardized procedures for task design,
task decomposition, task typology, technical requirements for an internal
crowdsourcing platform and measurement of the quality of results in internal
crowdsourcing. For this purpose, methods that can be used to measure the quality
of a creative process are also to be explored. Finally, an ideal process flow
(workflow) is needed, which takes into account all aspects of internal crowdsourcing
and in an optimal order with associated roles and resources.
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 107

2.2 Employee Motivation
In the case of internal crowdsourcing, the incentive system is more diversified, as
employees are generally financially secure due to their employment in the company.
Fundamentally, there are intrinsic and extrinsic motivations to contribute to the
crowdsourcing task from the employee perspective. Intrinsic motivations are when
carrying out an action is rewarding for the employee himself, for example, because it
brings satisfaction, while extrinsic motivation comes from the expectation of a
reward from the outside world as a consequence of an action (Meffert et al. 2018).
One of the intrinsic motivations for employees is their enjoyment of the activity,
for example, having fun while testing an internal software, contributing to the
ideation of a new product or being intrigued by the competitive nature of
crowdsourcing projects (Leimeister and Zogaj 2013). The prospect of helping to
shape products in line with their own wishes and ideas and to influence their
development can also be crucial from the employee’s perspective (Sixt 2014). This
motivation is particularly evident in crowdsolving and crowdcreation, where the
motivation here is not in the material reward but in the positive feeling that arises
from being involved in the task. Another intrinsic motivation is social exchange
within the crowd where the desire to exchange and interact with like-minded people
serves as an incentive (Leimeister and Zogaj 2013). Brabham (2013) also mentions
the following as essential motivations for participating in crowdsourcing initiatives:
‘to network with other creative professionals’and ‘to socialize and make friends’.
Another intrinsic motivation for participating in internal crowdsourcing is the
opportunity to learn within the crowdsourcing engagement and thus enhance per-
sonal skills, competencies and experiences through the exercise of relevant tasks so
that creative skills can be improved by tackling complex tasks and gaining
experience.
Extrinsic motivation comes from the outer world, and the desire for appreciation
by other people can also serve as an essential extrinsic motivation in internal
crowdsourcing (Leimeister and Zogaj 2013). To feel appreciated by the outside
world, e.g. managers and colleagues, crowdsourcing contributions should be visible
to other participants, like in competitions where the winner is chosen by the
community itself. Participants hoping for recognition from the crowd (their colleges)
and company (their employers) are usually motivated to generate high-quality
contributions to gain prestige in the company (Franke and Klausberger 2010).
Another extrinsic motive is the desire for self-expression or self-marketing. In
addition to showing their own contributions to other members of the crowd, self-
marketing through crowdsourcing may also improve an employee’s own career
opportunities by opening up the possibility that the employer becomes aware of an
employee’s high-quality contributions (Leimeister and Zogaj 2013). Monetary
rewards can be mentioned as a last extrinsic motive. These can be in the form of
monetary compensation, benefits in kind, discounts or certain premiums (Franke and
Klausberger 2010).
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2.3 Labour Law Framework
As this new way of working sets in motion a fundamental shift in work organization
and in the division of labour, minimum standards for fair work in the crowd and fair
digital work environment should be defined. To avoid additional workload, the
crowd concepts should be designed in a way that takes into account labour policy
/legal requirements such as works constitution law, occupational health and safety,
trade association regulations, collective bargaining agreements and data protection
regulations. Therefore, it is recommended that an official company agreement that
regulates all these aspects be drawn up. One of the latest group-wide company
agreements for internal crowdsourcing in Germany, ‘Lebende
Konzernbetriebsvereinbarung als soziale Innovation’(the living group works coun-
cil agreement as social innovation), establishes the rules for a fair crowdsourcing
environment for employees (Otte and Schröter 2018). According to Otte and
Schröter (2018), the following rules can be applied to any internal crowdsourcing
environment:
•The participation of employees in the internal crowdsourcing is voluntary, and
employees are not affected by participation or non-participation.
•The participation of employees in the internal crowdsourcing takes place during
working hours. The time working on the platform is working time.
•Accessibility to internal crowdsourcing is provided at work via mobile phones or
laptops and guaranteed by the company in employees’home offices as well.
There will be no additional IT accesses or IT jobs provided. Employees without
IT access can place IC initiatives directly through the crowd manager.
•The participation of employees in group-public or employer-public points or
ranking systems is always voluntarily. Participants have the right to use their
real name or a pseudonym.
•The company is committed to handling data security and data protection in a
highly sensitive manner that goes beyond the regulations of the national data
protection law. Any and all platform data will be made available for use only in an
aggregated and anonymized form to the corporate bodies as well as department
heads and supervisors and, upon request, to the research community. A person-
related breakdown of data does not take place. The aim is to strengthen
employees’right to determine themselves what happens to their information
and to reinforce their confidence in the company and its careful corporate culture.
•In order to protect employees’privacy, there will be no online tracking or
controlling of employee performance or behaviour.
•Innovation ideas by employees that are brought into an internal crowdsourcing
platform are legally transferred to the company. The idea providers are entitled to
appropriate remuneration.
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 109

2.4 Tasks in Internal Crowdsourcing
Tasks in internal crowdsourcing are usually like crowdsolving and crowdcreation as
described in chapter ‘Systematization Approach for the Development and Descrip-
tion of an Internal Crowdsourcing System’. In larger and more segmented enter-
prises, it is difficult to match people to the right problem. However, with the help of
internal crowdsourcing, the unutilized or unnoticed knowledge of employees can be
used by the company internally to generate faster innovation processes, improve
existing products or solve current problems in the organization (Lopez et al. 2010;
Benbya and Van Alstyne 2010; Gaspoz 2011). In this way, a social innovation
community can be built, and the quality of social capital in an enterprise can be
improved with the help of internal crowdsourcing (Bharati et al. 2015).
The content of the internal crowdsourcing tasks is determined by the needs of the
company and is therefore highly varied. For example, IBM established a platform
called ‘InnovationJam’to drive innovation and collaboration by providing a plat-
form to discuss innovative ideas (Bjelland and Wood 2008). In the InnovationJam
platform, the idea creators must first consider concrete aspects such as costs, quality
and deadlines, simulate planned projects and then reach the declared goal. After that,
every employee gets a virtual wallet and uses their budget to invest in their
colleagues’ideas and vote for them. If the virtual test run is successful, the company
decides to follow through with a real implementation.
Similarly, Allianz UK uses an internal crowdsourcing platform to generate as
many different ideas as possible while also encouraging the submission of ‘smaller’
ideas, as these are more common in the financial services sector (Benbya and
Leidner 2016). Idea generation takes the form of idea campaigns, and the idea
campaigns are geared to the needs of the respective area to increase employee
participation. Qualitative feedback mechanisms, in particular, ensure that the ideas
submitted are treated with respect and provide for a close exchange between the
employees who are involved in the process and those responsible for the process of
developing the ideas.
Telekom AG has been conducting so-called forecast markets for product inno-
vation since 2012 (Zuchowski et al. 2016). Their platform is accessible to all
employees, and, instead of an employee, a department places various topics and
problems in the platform. Usually, tasks that are placed in Telekom’s platform, such
as sales channels, target groups, benefits analysis, product design or market poten-
tial, would be outsourced to external market research agencies. However, due to the
short trading time of the questions that have been asked, that is, within 5 working
days, results from the forecast markets are available faster and generally exceed the
quality of conventional market analyses. The individual contributions by the
employees are aggregated into an overall contribution whereby the calculation
basis for the forecast is formed from the median of all individual forecasts.
SAP uses internal crowdsourcing to make a social impact and aims to use the
ideas of SAP employees to improve the lives of one billion people on the planet by
2020 (Durward et al. 2019). In doing so, SAP technologies should be used to
110 N. Iskender and T. Polzehl

promote sustainable ideas of social significance that are economically feasible at the
same time. For this purpose, SAP APJ invests 1 million €annually to finance social
start-ups within SAP. The orientation of the foundations focuses mainly on the
business areas of health and disaster management.
2.5 Crowdsourcing Forms
Depending on the task characteristics and skill demands, crowdsourcing can gener-
ally be divided into two categories: microtask and macrotask crowdsourcing.
Microtask Crowdsourcing In the case of microtask crowdsourcing, crowdworkers
collectively work on a large number of tasks so that the traditional human resource
requirements of requesters can be reduced. In doing so, the online crowdsourcing
platform splits the big tasks of the requester into small subtasks, which are as small
as possible so as to ensure quick and easy processing. This process is then referred to
as ‘microtasks’or ‘micro-jobs’(Difallah et al. 2015). In the end, all subtasks are
brought together again and sent back to the requester. Amazon Mechanical Turk is
one of the best-known platforms for this type of activity.
This kind of crowd task is used by requesters to handle less complex, often
repetitive, tasks such as image tagging and video tagging or the transcription,
translation or digitation of documents that are easy for humans to process but cannot
be processed easily by machines (Geiger et al. 2011). However, the task description
should contain all the necessary information about the task execution because
crowdworkers only see a small portion of a bigger task which they do not know
about, and they usually do not have an opportunity to contact the requester for
further information about the task (Deng et al. 2016; Felstiner 2011; Leimeister et al.
2016). Therefore, it is extremely important to formulate the task as concretely and
specifically as possible to obtain high-quality solutions (Deng et al. 2016).
Macrotask Crowdsourcing In macrotask crowdsourcing, the task is divided into
units that are quite large and therefore still relatively complex and require
preprocessing. It is an interactive form of service delivery that is organized collab-
oratively or competitively and involves a large number of extrinsically or intrinsi-
cally motivated actors using modern ICT systems. Among other things, this variant
of crowdsourcing uses the principle of the ‘wisdom of crowds’, which James
Surowiecki described in his book in 2005 (Surowiecki 2005). It states that the
solution achieved and the decision-making are often better when information is
accumulated in a heterogeneous group of individuals than when it is given by a
single expert. Macrotasks are difficult to take apart, and the solutions for macrotasks
require a great deal of sharing of contextual information or dependencies on inter-
mediary results. Therefore, creative tasks such as design contests or coding chal-
lenges are executed in the form of macrotasks (Niu et al. 2019).
In contrast to microtask crowdsourcing, macrotask crowdsourcing promotes
collaborative working among crowdworkers because of its complex subtasks.
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 111

Different abilities and skills meet in the crowd and connect productively to one or
more end solutions. In this way, both innovation ability, generation and preservation
are promoted.
2.6 Process Management
Crowdsourcer Institutions, e.g. public authorities or universities, non-profit orga-
nizations or an individual may act as crowdsourcers (Leimeister and Zogaj 2013).
Typically, an organization passes one task on to more than one external
crowdworker and uses the results from their work to complete the crowdsourcing
process. The ‘crowdsourcer’(requester, client) usually designs the process by
defining the task typology, the required crowdworker profile, the budget and time
constraints and by executing the task decomposition. After the task is completed, the
crowdsourcer analyses the results based on the predefined evaluation criteria
(Difallah et al. 2015) (Fig. 1).
Crowdworker Generally, the crowdworkers consist of an undefined large group of
people who have participated in the completion of a given task voluntarily or in
return for monetary compensation (Difallah et al. 2015). The optimal number of
crowdworkers depends on the type of crowdsourcing task, the task specifications
and the information and skills needed to solve the problem (Leimeister and Zogaj
2013). The crowdworkers’expertise and abilities can determine the quality of the
results; therefore the credentials and experience of crowdworkers might play an
important role when the requester is selecting the right crowd (Allahbakhsh et al.
2013).
Platform Crowdsourcing platforms provide the medium of interaction and thus the
(only) point of contact between the requesters and the crowdworkers. These plat-
forms control all processes starting with the registration of a crowdworker on the
platform, then continuing with the placing of the crowdsourcing task defined by the
requester on the platform, assigning of the task to the given crowdworker profile,
providing support with technical issues and collecting the answers from the
crowdworkers (Difallah et al. 2015). The crowdsourcing platforms can be divided
into the following types:
Fig. 1 A simple
crowdsourcing process (Niu
et al. 2019)
112 N. Iskender and T. Polzehl

•Microwork platforms: These platforms are mostly designed for microtask
crowdsourcing. These tasks are of low complexity and high granularity. The
best-known platforms for microtasks today are Amazon Mechanical Turk,
Clickworker and CrowdFlower.
•Development, test and marketing platforms: Tasks are (very) complex and have
low granularity. The best-known platforms for this are rapidusertests and
Testbirds.
•Innovation platforms: Tasks may have both low and high complexity. The best-
known platform for this is InnoCentive.
•Internal crowdsourcing platforms: Tasks are business-driven and may have low
or high complexity. There is no generally known platform for internal
crowdsourcing; each company uses its own platform or one of the platforms of
the external providers.
As explained in chapter ‘Systematization Approach for the Development and
Description of an Internal Crowdsourcing System’, the ICU Process and Role Model
describes the process of internal crowdsourcing, in particular the individual steps
that are necessary to solve an explicit task. Exploiting the full potential of the
employees, the following steps should be designed by the requester, usually a
department in a company or an employee: (1) impetus; (2) decision; (3) conceptual-
ization; (4) execution; (5) assessment; (6) exploitation and (7) feedback (see chapter
‘Systematization Approach for the Development and Description of an Internal
Crowdsourcing System’).
Stage 3 in particular plays an important role in the success of internal
crowdsourcing and should be defined before the task execution. The definition,
decomposition, integration and allocation of tasks are crucial in this process because
the task should be explained unambiguously, in a focused manner and precisely so
that employees can understand it easily by reading it in an online environment and so
that they can complete the task in a short timeframe (Bailey and Horvitz 2010).
Additionally, the bigger picture that lies behind the task should be communicated
beforehand so that the employees can understand the context better and feel appre-
ciated by contributing to something bigger (Simula and Ahola 2014). After the task
is completed, the results evaluation and the quality control can be performed either
by employees in the form of a crowdrating or by the requester (the department or an
individual employee) or by an expert in the given domain (Thuan et al. 2017).
Another important aspect is that the final results and the next steps with the achieved
results should be communicated clearly to the employees.
2.7 Role of IT in Internal Crowdsourcing
The internal crowdsourcing platforms can be divided into two categories: generic
social IT platforms (i.e. multipurpose tools such as social networking sites or wikis)
and specific crowdsourcing IT platforms (i.e. tools developed specifically for
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 113

crowdsourcing, possibly even for a particular purpose in a particular enterprise)
(Zuchowski et al. 2016).
Generic Social IT Platforms In the case of generic social IT platforms, the
company uses an internal social platform such as wikis, intranet, yammer or slack
as a tool for internal crowdsourcing (Stocker et al. 2012; Rohrbeck et al. 2015).
These platforms are established not only for crowdsourcing; it is also easier to reach
out to all of the employees in the company by integrating internal crowdsourcing
into existing IT infrastructures. Since the employees are already using them and
information about how to use the platform is already established, the entry barriers
will be low.
However, it can also be challenging if specific IT features are needed to perform
the internal crowdsourcing task. Specific IT requirements cannot be implemented in
most cases, and the company needs to work with the given IT structure (Rohrbeck
et al. 2015). Different user interfaces and new question and data entry types that are
not offered by the generic platform cannot be implemented. Also, the other postings
regarding the company may distract the attention of the employees, e.g. the most
recent internal crowdsourcing task might be shown at the bottom of the webpage
because of other postings which lead to lower participation and attention. Further-
more, it is harder to reach out to a specific group of employees if a generic social
platform is used.
In addition, security guidelines and regulations (e.g. privacy, barrier-free access)
might be a problem in most cases (Rohrbeck et al. 2015). Large companies espe-
cially have strict regulations regarding the security and use of companywide IT
platforms that can be accessed by a large number of employees. The data shared on
the platform might include sensitive data regarding both the company strategy as
well as personal information of employees the sharing of which with third-party
organizations is not permitted.
Specific Crowdsourcing IT Platforms Specific IT platforms enable repeatable and
well-defined internal crowdsourcing processes that have the same characteristics
(Geiger et al. 2011). These platforms can typically be adjusted to the enterprise’s
very particular needs, and new IT features can be developed specifically for the
particular problem category crowdsourcing is addressing (intelligence, design, deci-
sion) (Zuchowski et al. 2016). Having an expert platform that is specifically
designed for the company’s own needs might enhance the motivation of employees
to participate, since the user experience on such platforms is usually better than on
generic social IT platforms for internal crowdsourcing. Moreover, the security and
privacy issues can be addressed and designed in line with the company’s require-
ments. However, maintaining such a platform, developing new features if needed
and providing technical support 24/7 are very time-consuming and costly for an
enterprise.
Additionally, the entrance barriers for employees are usually higher than a
generic platform because of the aversion to ‘yet another platform’. Creating another
account and using another tool might be seen as a burden, especially in big corporate
organizations, since the employees are already overloaded with the IT tools
114 N. Iskender and T. Polzehl

(Rohrbeck et al. 2015). Another challenge that organizations might face is that the
organization itself is constantly changing. Together with this change, the IT require-
ments for the internal crowdsourcing platform also change, while an IT tool cannot
be developed as fast as the change requires. Therefore, a flexible and configurable IT
implementation is an important aspect for specific crowdsourcing IT platforms.
For our study, we chose to implement a specific internal crowdsourcing platform
because it enhances the effectiveness of internal crowdsourcing in avoiding security
and privacy issues and offers the opportunity to adjust the platform to the specific
needs of the application partner.
3 An Internal Crowdsourcing Platform: Idealab
Based on the findings in the literature explained earlier, a German energy company,
the industry partner, established a specific internal crowdsourcing platform to
support innovation activities, promote employee participation in the internal deci-
sion processes and identify the undiscovered competencies of its employees. The
platform is a white-label version of an external crowdsourcing platform, specifically
designed for this industry partner in that it has all the functionalities that an external
crowdsourcing platform might have. Before the platform was developed, the strat-
egy, incentive mechanisms and roles in the platform were carefully defined and
discussed so that the IT requirements, the technical feasibility of these requirements
and their prioritization could be planned and addressed precisely.
At first, the possibilities of IT integration into the industry partner’s IT, the
concrete requirements for data protection and intellectual property rights, terms of
use, use of pseudonyms, feedback functions of the platform and registration process
were discussed and established. The use of the platform is completely voluntarily,
and the opportunity to stay anonymous is provided if desired. To protect the internal
information in the company and block people who are not an employee of the
industry partner, the employees were allowed to register on the platform using
only their work email address. When registering, they were asked to fill out the
fields for username (it could be their real name or pseudonym), email, password and
checkbox asking if they were using their real name. In addition, there were optional
fields for age group, gender, the position at the company and the occupation. To
inform the employees about privacy and security regulations, the terms of use and
unbundling regulations were presented during registration, and employees were
asked to accept the conditions of use and unbundling regulations. They were not
allowed to create an account without accepting these regulations and filling out the
required fields (Fig. 2).
After confirming registration per email, the employees see a landing page where
the active tasks (called campaigns) and the results of the past tasks are placed using
card user interface design (see Fig. 2). In the first row of the landing page, the active
campaigns are listed with a title, short description and remaining duration of the task.
A preview of the next active campaigns can be also placed here to inform the
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 115

employees about the next topics. Following that, the results of past campaigns are
displayed in the second row on the landing page (Fig. 3).
In the last row on the landing page, there is relevant information about the
platform, rules about the platform use and the internal crowdsourcing activities of
other enterprises along with two different kinds of feedback button (see Fig. 3). One
of these feedback buttons was designed to collect technical feedback about the
platform such as bug reporting, suggestions for new desired features and also general
feedback about the campaigns that have been or are being executed on the platform.
The other feedback button offers the opportunity to suggest new campaign ideas
anonymously and to influence the use and topic direction of the platform together
with the employees.
Fig. 2 The Idealab landing page
116 N. Iskender and T. Polzehl

3.1 Data Protection: Privacy and Security
The privacy and security concerns of employees are one of the most important
barriers to using the platform. For this reason, special regulations regarding these
issues were established during the commissioning of the platform (Rohrbeck et al.
2015; Zuchowski et al. 2016). These regulations were communicated clearly to the
employees to win their trust in the platform at every stage of platform use, starting
with registration and continuing with active participation.
At first, internal crowdsourcing netiquette was shaped as a code of good behav-
iour on the platform. To ease online communication, it is suggested that people
desist from using abbreviations and capital letters, that the correct sentence structure
and spelling be followed, that quotation sources be named and that the already
existing contributions be read before participation in order to avoid double contri-
butions. Additionally, the employees were informed that inappropriate posts, e.g. if
they deviate significantly from the netiquette or if they make factually untrue
statements that could lead to confusion among the workforce, will be removed.
Following that, a security and privacy policy was created in line with the European
Union General Data Protection Regulation for ‘natural persons’. This includes the
following information: sources and data use on the platform, the legal base for the
data processing, access to user data, data storage, privacy rights and also the use of
cookies.
While using the platform, the IP address of the user, the date and time of the
activity, the number of tabs clicked on in the platform and the name of the
downloaded files as well as the scope of transmitted data are temporarily stored
and used in a log data file. This data is stored for statistical purposes and also to
prevent or detect unauthorized access to the platform components. After leaving the
platform, this temporary data is deleted. There is no link between any other data from
other sources and the personal data of employees. While actively using the platform,
in particular, further personal data may be exchanged and stored if the employee uses
the feedback buttons or contacts the requesters through other communication chan-
nels. However, this happens voluntarily and is based on employee initiative.
Fig. 3 Last row on the landing page
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 117

Nonetheless, if necessary, the processing of personal data might go beyond the
scope that is described above. The reasons for this might be to review and optimize
needs analysis procedures, to ensure the IT security and IT operation of the systems,
to assert legal claims and provide defence in legal disputes, to prevent crime, to carry
out business management measures and to further develop offers and services. As
part of the system optimization, key figures about the platform use are recorded and
analysed and might include such items as the number of registered users, participants
per campaign, cancelled campaigns, help/feedback use and new campaigns. Fully
automated decision-making according to data protection law GDPR (including fully
automated profiling) is not used on the platform.
The personal data and the data pertaining to employee use of the platform are not
disclosed to the industry partner, in order to ensure the full anonymity of employees,
unless they want to provide this information. The platform use data and the platform
activity data are only made available in an aggregated and anonymized way to the
company committees as well as department heads and supervisors. Consequently, no
tracking of personal information takes place, thus ensuring employees’rights of self-
determination rights, reinforcing their confidence in the company and creating a
trusted corporate culture.
However, the external platform provider has access to this personal data to
provide IT support and maintenance, archiving, document processing, call centre
services, compliance services, controlling, data destruction, customer management,
media technology, website management and accounting services. This data storage
is limited to 3 years due to legal limitations. To ensure a high level of usability on the
platform, cookies are used for remembering settings between the various visits to the
platform, preventing the username and password from having to be entered repeat-
edly and analysing the use of the platform for further improvements.
3.2 Technical Task Typology
The task typology in internal crowdsourcing differs from external crowdsourcing
since the complexity, knowledge and skills of employees and the incentives for
participation differ a great deal from those in external crowdsourcing. Moreover,
observing the task typologies from the technical point of view leads to a modified
version of the task typologies explained in chapter ‘Systematization Approach for
the Development and Description of an Internal Crowdsourcing System’. In Idealab,
the following task typologies are implemented: info question, free text input, single
choice, multiple choice, vote and comment.
In the info question, the task description, the expected input from employees, the
campaign initiator, the time schedule and the next steps are explained clearly so that
employees can understand the task completely and no direct input from the
employees is expected. In the open-ended text question, the employees are asked
to write directly their responses to the questions, and their answers are not shown to
other employees participating in the campaign. This type of technical question might
118 N. Iskender and T. Polzehl

be used for crowdsolving or crowdcreation if the answers of the participants are to be
in full disclosure and creative ideas are desired. In single-choice and multiple-choice
questions, the employees answer the questions by choosing one possible offered
(single choice) or more than one of the answers offered (multiple choice) from the
given list. This kind of question might be used in crowdrating campaigns, where all
of the answers are treated equally and combined. Again, the answers given by
employees are not shown to the other participants in single-choice and multiple-
choice questions.
On the contrary, in the last two question types ‘vote’and ‘comment’, the
participants can see the other answers. In the vote question, employees can vote
for an option or options, and every participant sees the aggregated votes for each
option while voting. In the comment question, the employees are asked to leave a
comment or question describing their ideas in detail using free text input, but their
answers are seen by the other employees immediately. This enables a discussion
between the participants which might be very helpful in ideation and creation tasks.
The differences between these technical task typologies are illustrated in Table 1.
During the design process for an internal crowdsourcing campaign, the requester
can select the question types described above and use more than one question type in
a single crowdsourcing campaign. In addition to task typologies, the platform has the
technical features for limiting the number of participations for an individual
employee (max. assignment function), determining the duration of the campaign,
limiting the total number of participants and setting conditions using profile keys to
address the targeted group of employees if necessary. All the question types can be
styled using HTML, and multimedia such as video, picture or documents can be
added to the question descriptions.
3.3 Roles and Tools for Platform Management
Along with the roles defined in chapter ‘Systematization Approach for the Devel-
opment and Description of an Internal Crowdsourcing System’, an internal
Table 1 Technical task typologies
Task type
Feedback from crowd
necessary?
Text
input Selection input Publicity
Info No No No No
Free text Yes Yes No No
Single choice Yes No Yes, limited to 1 No
Multiple
choice
Yes No Yes, more than
1 possible
No
Vote Yes No Yes, more than
1 possible
Yes
Comment Yes Yes No Yes
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 119

crowdsourcing platform needs additional roles such as crowd master, crowd tech-
nology manager and campaign master as well as a second technical platform, a
dashboard, for the micromanagement of internal crowdsourcing tasks. Figure 4
shows the roles and their relationship to each other in an internal crowdsourcing
environment.
The campaign owner might be an employee of the company who is assigned to
manage the communication between the content owners, the crowd and the crowd
technology manager. It is an objective position where the crowd master aims to
create a free and fair internal crowdsourcing environment for employees and to
provide solutions to the problems of campaign masters by designing an optimal
internal crowdsourcing campaign. In addition to these, the campaign owner com-
municates all the technical problems regarding the platform and desired new features
if necessary. During the course of platform management, we found out that a
dashboard would be very beneficial to help the crowd technology manager micro-
manage the internal crowdsourcing tasks. These tasks might include creating new
tasks, prolonging the duration of a task, making some text or style changes on active
tasks, observing the answers and deleting the answers that violate the netiquette rules
or other internal company regulations, activating or deactivating tasks or observing
and reporting platform statistics.
Platform:
Specific IC
Platform
Content Owner:
Department,
an employee or
a manager
Crowd worker:
Employees
Campaign
Owner
defines the task
characteristics
communicates
the task
delivers results
gives feedback
and comments
implements the
task
participates in
campaigns
IC Dashboard
Micromanagemen
t
of IC Tasks
Crowd
Technology
Manager
gets campaign results
& platform usage data
communicates IT issues
Fig. 4 Roles and tools for internal crowdsourcing
120 N. Iskender and T. Polzehl

4 Empirical Results: Case Studies
The platform was released in June 2018, and 11 different campaigns were realized
between its release and October 2019. The communication of new campaigns,
campaign results and news regarding the platform was carried out by the crowd
master using the company’s intranet, yammer and sending email newsletters to the
registered user of the platform when there were new campaign releases, campaign
results releases or news regarding the platform.
According to the latest figures provided by the industry partner, 1820 people are
employed by the company. Out of 1820, 535 employees have registered on the
platform with a registration ratio of 29.4%, which is quite high for a voluntary
platform. After the release of each new campaign, an email newsletter and a post in
internal communication channels were sent. In total, 11 different campaigns were
released, and the number of participants on the platform has increased after each
campaign release, as shown in Fig. 5. Additionally, out of a registered 535 users,
360 employees have voluntarily specified the information about their age group,
gender, position and occupation at the company, and 50.8% of the employees have
used their real names during the registration. This shows that the opportunity to use a
pseudonym is an important feature from the employee’s perspective. The campaign
participants were mostly young employees working in governance or sales, with an
almost equal number of males and females, as shown in Fig. 6. Surprisingly,
employees over 40 years old were relatively active on the platform. However,
employees with a management position were not as interested as employees without
a management role.
Figure 7shows the total work duration on the platform including all the answers
completed for 11 campaigns. More than half of the contributions were done in under
160 s ~ 2.67 min, and the average work duration was 351 s ~ 5.85 min. Although the
campaign complexity and durations vary a lot, this is not reflected in the average
work duration because employees have chosen to participate mostly in short tasks
and are not interested in long complex tasks.
Task 1, 2
Task 4, 5, 7
Tas k 3
Task 6, 8
Tas k 9
Tas k 10
Tas k 11
Fig. 5 Number of users over time
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 121

Fig. 7 Work duration in seconds for all campaigns
Fig. 6 Descriptive statistics about the campaign participants
122 N. Iskender and T. Polzehl

Table 2displays the detailed participation statistics regarding 11 campaigns that
were executed after the release of the platform. The campaigns are listed and
numbered by release time, whereby they are categorized into four different topics:
location, product, QA and survey. The technical task typologies used and the number
of questions in each campaign are listed next to each other. The ‘average work
duration’column shows the average completion time in seconds per campaign. Next
to it, the ‘started tasks’column presents the number of task started without com-
pleting the task, meaning that employees did not proceed through to completion,
although they clicked on the campaign and read the campaign description.
In some campaigns, employees were allowed to participate more than once, so the
total number of submitted answers might be higher than the number of unique
participants. To differentiate between these two figures, the ‘completed tasks’
column presents the overall number of submitted answers without differentiating
the uniqueness of the participants, and the ‘unique users’column shows the number
of unique participants in campaigns. To measure the achievement of a campaign, we
introduced the ‘participation ratio’, which illustrates the task completion based on
the number of registered users on the platform by the time of campaign release. Since
the number of participants has increased over time, the absolute number of partic-
ipants is not sufficient to describe the success of a campaign. The average partici-
pation ratio on the platform was 19.69%, with campaign number 5 having the
highest participation ratio of 51.94%.
Participation Ratio ¼Completed Tasks=Number of Registered Users
Another ratio that we introduced is the ‘task completion ratio’. It helps to
understand how many of the users continue and finish the campaign after starting
it and how many read the campaign description. The 11 campaigns were started a
total of 2,875 times but only completed 564 times, which results in an average task
completion ratio of 19.62%, with campaign number 5 having the highest task
completion ratio of 47.52%.
Task Completion Ratio ¼Completed Tasks=Started Tasks
Analysing the results by topics, the first topic contains all the campaigns related to
the relocation of the industry partner employees to another building, called ‘loca-
tion’.In the context of the location topic, five different campaigns were released, and
this topic was the most interesting in the platform with an average participation ratio
of 27.25% and a task completion ratio of 26.86%. It was realized using only the
comment and vote functions of the platform, meaning all the answers or rather
results were directly shown to the participants. Additionally, the number of questions
was one in four of the campaigns and four in one of the campaigns, leading to lower
expected workload measured in time.
The second topic, called the ‘product’, contains all the campaigns related to the
product, e.g. further development of an existing product, collecting new product
ideas or collecting feedback about the existing product. In the context of the product,
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 123

Table 2 Overview of the participation numbers in internal crowdsourcing campaigns
Task
ID Topic Task typology
#
Questions
Average work
duration
Started
tasks
Completed
tasks
Unique
users
Participation
ratio (%)
Task completion
ratio (%)
1 Survey Multiple choice 2 163 204 41 41 28.87 20.10
2 Location Comment 1 159 349 61 27 45.86 17.48
3 Product Single Choice, Multiple
choice, Freetext
20 790 117 25 25 18.80 21.37
4 QA Comment 1 264 221 14 7 6.36 6.33
5 Location Vote 1 275 282 134 134 51.94 47.52
6 Product Single Choice, Freetext 4 452 141 8 8 2.37 5.67
7 Product Single Choice, Freetext 7 787 109 28 27 8.95 25.69
8 Location Comment 4 1528 223 17 16 5.04 7.62
9 Location Comment 1 207 195 51 30 13.18 26.15
10 Location Vote 1 100 422 150 143 28.68 35.55
11 QA Comment 1 1235 611 35 19 6.51 5.73
124 N. Iskender and T. Polzehl

three campaigns were released and completed with an average participation ratio of
10.04% and an average task completion ratio of 17.58%, both lower than the ratios in
the location topic. The campaigns contained multiple questions in the form of a
single-choice, multiple-choice and open-ended questions. The reason for lower
participation might be the higher expected workload because the average number
of questions in campaigns was 10.3 and average work duration approx. 11 min,
which are above the platform’s overall average.
The third topic, called ‘QA’,is about campaigns that provide employees with the
opportunity to ask questions or leave comments about the current issues in the
company or to ask any question directly to the top management of the company.
In the context of the QA topic, two different campaigns were released, and both
campaigns contained one question in the comments. The average participation ratio
was 6.43%, lower than the location topic, and the average task completion ratio was
6.03%. Although the expected workload measured in time was not so high, the
participation ratio is quite a bit lower than the location topic. The low task comple-
tion ratio also shows that the number of started tasks was high, meaning that the
employees were generally interested in this topic, but they did not want to participate
although every contribution was stored anonymously. This shows the effect of the
selected topic on participation and also shows that sensitive topics such as asking a
question to the manager are quite interesting but perceived as being risky.
The last topic, called ‘survey’, contains one campaign which had two questions in
multiple-choice format. The participation ratio was 28.87% and the task completion
ratio 20.1%, similar to the location topic. This was the first campaign released on the
platform, and the expected workload was low; therefore the participation ratio is
quite high in comparison to the QA and product topics.
For further investigation, the campaigns are analysed based on two categories:
topic and technical task typology. As shown in Table 2, the campaigns with task IDs
2, 5, 8, 9 and 10 belong to the topic location; with task IDs 3, 6 and 7 to the product
topic; with task IDs 4 and 11 to the QA topic and with task ID 1 to the topic survey.
For categorizing the technical task typologies, the campaigns with the technical task
typologies single choice and multiple choice are considered as one category and
called ‘selection’. Campaigns with task IDs 1, 3, 6 and 7 belong to this category. The
campaigns with task IDs 2, 4, 8, 9 and 11 belong to the technical task typology
‘comment’and with task IDs 5 and 10 to ‘vote’.
4.1 Work Duration and Participation
Figure 8illustrates the boxplots of work duration categorized by topic (left) and
technical task typology (right). Because of the non-normal distribution of the data,
the Kruskal–Wallis test was conducted to find out differences between topics and
task typologies in terms of working duration. There was a statistically significant
difference (Chi-square ¼85.511, p¼0.000, df ¼3) among the four different
categories of topics (location, product, QA, survey) regarding the work durations.
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 125

The post hoc test (Dunn criterion) revealed that the mean of product (M¼745.12)
was significantly higher than the mean of survey (M¼163.90, p<0.05) and higher
than the mean of location (M¼238.97, p<0.01). A second Kruskal–Wallis test
showed that there were also significant differences (Chi-square ¼49.095, p¼0.000,
df ¼2) among the categories of technical task typologies (selection, comment, vote).
The post hoc test (Dunn criterion) revealed that the mean of vote (M¼182.53) was
significantly lower than the mean of comment (M¼529.91, p<0.01) and lower
than the mean of selection (M¼511.49, p<0.05).
Furthermore, the Spearman rank-order correlation coefficients are calculated to
investigate the relationship between the average work duration in seconds and the
participation (N¼11). There was a moderate negative significant correlation
between the average work duration and completed tasks (r
s
¼–0.618, p¼0.043).
This result confirms that the higher average work duration gets, the lower the number
of completed tasks will be. The Spearman rank-order correlation coefficient between
average work duration and the participation ratio shows a substantial trend towards
significance with p¼0.060 and is negative with a moderate value of r
s
¼–0.582.
Figure 9illustrates this relationship in a regression plot.
4.2 Participation Day and Time
As shown in Fig. 10, the time of the day when participation takes place is mostly in
the early mornings between 8 and 10 (N
8–10
¼197), and the most popular day is in
the middle of the week, Wednesday and Thursday (N
wednesday
¼141, N
Thursday-
¼130). This is a good indication of what time to publish an internal crowdsourcing
campaign on the platform and when to announce it to the employees. Looking at the
descriptive statistics, late Friday afternoons after 4 pm is not an optimal time to
release a new crowdsourcing campaign on the platform.
To see if this pattern is applicable for all the categories of topic and technical task
typology, Kruskal–Wallis tests were conducted. No significant differences were
found between the four categories of topic (Chi-square ¼5.025, p¼0.170,
Fig. 8 Boxplots of work duration for categories topic (left) and technical task typology (right)
126 N. Iskender and T. Polzehl

df ¼3) and between the three categories of technical task typology
(Chi-square ¼5.470, p¼0.065, df ¼2) regarding the participation time of the
day. Similarly, there was no significant difference between the four categories of
topics (Chi-square ¼0.901, p¼0.825, df ¼3) and the three categories of technical
task typology (Chi-square ¼1.691, p¼0.429, df ¼2) regarding the day of
participation.
Fig. 10 Time of the day and the day of participation
Fig. 9 Regression plot of average work duration and participation
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 127

4.3 Predicting the Participation
Multiple linear regression was carried out to investigate whether average work
duration, number of questions, topic and task typology of each campaign could
significantly predict the number of completed tasks per campaign. The results of
regression showed that the model explained 77.8% of the variance and that the
model was a significant predictor of completed task number, F(4, 6) ¼5.265,
p¼0.036. While task typology contributed significantly to the model ( p¼0.036),
average work duration ( p¼0.112), topic ( p¼0.862) and number of questions
(p¼0.377) did not. The final predictive model was:
Completed Tasks ¼33:037 0:037 Average Work Duration 3:549
Topic þ57:359 Task Typology þ2:028
Number of Questions
The task typology being the only significant predictor of the model was the
strongest contributor to the model. Therefore, for further investigation of task
typology, we carried out curve fitting and compared linear, logarithmic, inverse,
quadratic, cubic, power, compound, S-curve, logistic, growth and exponential
models based on their relative goodness of fit where the number of completed
tasks is predicted by task typology. The results revealed that the quadratic and
cubic models are the best fitting significant models with R
2
¼0.895 and
p¼0.000 for both models. Figure 11 illustrates the different curve fittings for task
typology.
Fig. 11 Curve fitting for task typology
128 N. Iskender and T. Polzehl

Since a better portion of the variance in the number of completed tasks is
explained by the quadratic model of task typology, quadratic regression was carried
out to finalize the model coefficients. The results showed that the model explained
89.5% of the variance and that the model was a significant predictor of completed
task number, F(2, 8) ¼34.253, p¼0.000, and task typology also contributed
significantly to the model ( p<0.05). The final predictive model was:
Completed Tasks ¼111:7134:35 Task Typology þ48:15 Task Typology
Task Typology
5 Discussion
This chapter has analysed the use of internal crowdsourcing from the IT perspective
based on the empirical results of the successful implementation of different kinds of
internal crowdsourcing campaigns.
Our findings from the active use data of the internal crowdsourcing platform
show that there is a general interest in using the platform proven by the relatively
high registration ratio for a voluntary platform, although no specific incentive
mechanisms or programmes were implemented. The only motivation to participate
in the platform comes from enjoyment in the activity or the chance to shape products
according to one’s own wishes. This puts a specific emphasis on the topics to be
selected for internal crowdsourcing campaigns on the platform. Therefore, the needs
and wishes of the employees should be asked and considered while choosing and
shaping crowdsourcing tasks. What is more, the number of registered users increased
after the release of each campaign because of the active marketing of new campaigns
via newsletters, intranet and yammer. This shows how important it is to communi-
cate and actively promote the platform.
Looking at the profile of registered users, we observed that mostly young
employees without a management role preferred to participate on the platform,
which is a common pattern for new technologies introduced to companies, because
technology affinity decreases with age. As a solution to this problem, onsite courses
about how to use the platform could be given by the company. Following on from
this, looking at the absolute number of started tasks and completed tasks in each
campaign, we observed that the interest in starting a campaign was always high,
while the task completion ratio was quite low in some cases. The reason for this
might be the uninteresting topic, complex task design or employees being distracted
by their daily tasks, which in most cases have higher priority.
In terms of participation ratio and task completion ratio, the most successful
campaign was about voting on the name of the company’s new building (ID 5). This
is a perfect example of a successful task design and topic selection, because it was
relevant for every employee in the company, easy to complete in under 3 min and the
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 129

idea of naming the industry partner’s new building motivated the employees intrin-
sically. As such, three important aspects of an internal crowdsourcing task—optimal
duration, low complexity and interesting topic—were met in that way.
Furthermore, the findings about significant different average work durations
between three technical task typologies could help by estimating the expected
work duration and consequently optimizing the task design, as the average work
duration negatively affects the number of completed answers. This shows that the
employees of the industry partner are mostly interested in short tasks. If this is
already known about employees, then complex tasks such as ideation, innovation or
technical challenges might not be the right topic selections for this type of employee
group, as these usually require longer timeframes to be completed.
Another important empirical finding was about the time and day of participation.
The employees mostly preferred to participate in the middle of the week and early in
the morning between 8 and 10, and they tended not to participate on Friday
afternoons. There was also no difference in different kinds of topics or task typol-
ogies regarding the participation day and time. So, the optimal time for releasing all
kinds of new campaigns would be Wednesdays in the early mornings, while Friday
afternoons should be avoided.
Next, we tried to predict the number of participants based on the data that was
available before releasing the campaign, e.g. work duration, topic, task typology and
the number of questions. The most important factor is again task typology,
explaining the variance in participation of up to 90% with a quadratic regression
model. Apparently, task typology is the determining factor for the success of an
internal crowdsourcing campaign and should be selected very carefully. Based on
these findings, the crowd manager could estimate the expected number of completed
tasks and accordingly predict the success of a crowdsourcing campaign in terms of
participation.
6 Conclusion
In this chapter, we tried to find out empirically which kind of internal crowdsourcing
task is desired by the employees. Although the scope of campaigns and variation of
topics and typologies were limited, some preliminary results regarding the optimal
work duration, ideal task typology and interesting topics could be found out.
The most important finding is that how interesting the internal crowdsourcing
campaign is with respect to the intrinsic motivation of the employees determines the
success of a campaign. Therefore, before introducing such a platform into a com-
pany, conducting surveys regarding the desired topics and motivating factors could
help to select optimal topics. In addition to that, the task typology played an
important role while predicting participant numbers, because indirectly the task
typology determines the expected work duration. Therefore, the reasons for the
desired low work duration should be investigated in detail. This might be due to
the company culture: the company might not accept the time spent on internal
130 N. Iskender and T. Polzehl

crowdsourcing as working time, or other daily tasks might have a higher priority, or
it might have to do with the general preference of employees. If the corporate culture
limits the working duration, then special time slots for spending time on the platform
could be defined.
Companies must understand how they can design and use an internal
crowdsourcing system technically and from the employee’s perspective (Fitzgerald
and Stol 2015) so that the potential of internal crowdsourcing can be used effec-
tively. To our knowledge, this study is the first to address this gap by summarizing
and deriving requirements for the implementation of an internal crowdsourcing
system comprehensively considering the privacy and security concerns of
employees, company intern regulations regarding crowdwork and the technical
aspects of an internal crowdsourcing task.
Concerning the limitations of the study, they are mainly due to the fact that the
empirical investigation involved only 11 crowdsourcing campaigns with a narrow
scope of topics and task typologies performed by a single company; thus, results are
not generalizable, since corporate culture plays an important role in the preferences
of employees regarding topics, task typologies and work duration. However, the aim
of the study was not to provide a complete picture about the issue under analysis but
to derive some preliminary insights that can provide some impulses to scholars and
professionals and open the way to further research.
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Neslihan Iskender received her Bachelor and Master of Science degree in Industrial Engineering
and Management at KIT. Since May 2017, she has been employed as a research assistant at the
Quality and Usability Lab of TU Berlin where she is working towards her PhD in the field of
crowdsourcing. Currently, Neslihan is an awardee of a BMBF-funded Education programme for
future IT and development leadership (Software Campus) involving partners such as MediaMarkt
Saturn, Merck, Zeiss, Huawei, Holtzbrinck.
Dr.-Ing. Tim Polzehl studied Science of Communication at Berlin’s Technical University and
joined the Quality and Usability Lab as a researcher in 2008. He has worked in both industrial and
academic projects with a focus on speech technology, machine-learning crowdsourcing solutions.
In 2014, Tim was awarded a PhD for his work on the automatic prediction of personality attributes
from speech and since 2014, he has been working as a Postdoc at QUL. At the same time, he is
driving the start-up activity (Crowdee) and working on the development of crowdsourcing
solutions.
An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications... 133

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