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Understanding Employee Responses to Software Robots: A Systematic Literature Review

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Organizations increasingly use software robots, such as robotic process automation (RPA) and chatbots, to automate tasks previously performed by human employees. While previous research has predominantly focused on technical challenges and organizational outcomes of software robot implementation, less attention has been paid to how employees respond to software robots. Therefore, we conducted a systematic literature review to analyze employee responses to software robots and identify related outcomes and contingency factors. Our results show that there is a wide range of affective, cognitive, and behavioral responses (both positive and negative). While some responses and contingency factors are similar to those found for traditional IS, others seem to be unique to software robots. Our study contributes to IS research by providing a comprehensive overview of employee responses to software robots and sheds light on contingency factors that may influence those responses.
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Seiffer, A., Gnewuch, U., Maedche, A. (2021). Understanding Employee Responses to
Software Robots: A Systematic Literature Review. To appear in: Proceedings of the 42nd
International Conference on Information Systems (ICIS) 2021, December 12-15, 2021,
Austin, Texas, USA.
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Understanding Employee Responses to Software Robots
Forty-Second International Conference on Information Systems, Austin 2021
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Understanding Employee Responses to
Software Robots: A Systematic Literature
Review
Completed Research Paper
Anja Seiffer
Karlsruhe Institute of Technology
Karlsruhe, Germany
anja.seiffer@kit.edu
Ulrich Gnewuch
Karlsruhe Institute of Technology
Karlsruhe, Germany
ulrich.gnewuch@kit.edu
Alexander Maedche
Karlsruhe Institute of Technology
Karlsruhe, Germany
alexander.maedche@kit.edu
Abstract
Organizations increasingly use software robots, such as robotic process automation (RPA) and
chatbots, to automate tasks previously performed by human employees. While previous research
has predominantly focused on technical challenges and organizational outcomes of software robot
implementation, less attention has been paid to how employees respond to software robots.
Therefore, we conducted a systematic literature review to analyze employee responses to software
robots and identify related outcomes and contingency factors. Our results show that there is a wide
range of affective, cognitive, and behavioral responses (both positive and negative). While some
responses and contingency factors are similar to those found for traditional IS, others seem to be
unique to software robots. Our study contributes to IS research by providing a comprehensive
overview of employee responses to software robots and sheds light on contingency factors that may
influence those responses.
Keywords: employee responses, software robots, RPA, chatbot, literature review
Introduction
We are currently witnessing the rise of a new generation of technologies at the workplace; technologies that
are more autonomous in nature and capable of fulfilling tasks without human involvement (Baird and
Maruping 2021; Mendling et al. 2018). These technologies challenge the traditional view of how humans
engage with information systems (IS) and may dramatically impact the future of work, as they take over
formerly human roles and responsibilities (Baird and Maruping 2021; Benbya et al. 2021). One important
class of such information technologies (IT) are software robots: Analogous to manufacturing robots, but
purely software-based and situated in an office environment, they are increasingly used in organizations to
take over tasks from office workers. Software robots perform tasks previously carried out by humans (e.g.,
copying data from one system to another, answering routine questions from customers) and they do so
autonomously, without humans being a part of the task they perform (Lacity and Willcocks 2021; Rutschi and
Dibbern 2020a). While some software robots rely on artificial intelligence (AI) technologies, such as machine
learning (ML), others use traditional rule-based approaches (Abdel-Karim et al. 2021; Rutschi and Dibbern
2020b). Software robots, most prominently chatbots and robotic process automation (RPA) bots, are widely
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applied in practice and have gained increasing interest in research. For example, Gartner estimates that RPA
alone will reach a global revenue of $1.9 billion in 2021 (Gartner 2020).
The implementation of software robots at the workplace may have both positive and negative consequences
for employees. On the one hand, software robots may free employees from repetitive, mundane work, leaving
them to pursue more demanding and interesting tasks. On the other hand, they may cause formerly human
tasks or entire jobs to become redundant, implying that employees need to learn new skills and rethink their
job roles (Rutschi and Dibbern 2020b; Willcocks and Lacity 2016). Either way, software robots have the
potential of causing significant changes to how employees conduct their work. This is mirrored in employees’
responses to software robots: While some employees embrace the support, others fear job loss and
replacement (Asatiani et al. 2020). How employees respond to work environment changes caused by the
implementation of IS such as software robots is therefore important from both an economic and social point
of view. Arguably, the observation that employees respond differently to IS implementation is neither new nor
exclusive to software robots (Willcocks et al. 2015). Indeed, there is a profound body of research on user
responses to traditional IS in organizations (for an overview see Bala and Venkatesh 2016). Research on
enterprise resource planning (ERP) systems, for example, found that employees fear losing control over work
outcomes and even their jobs (Lim et al. 2005). While recent research suggests that the new generation of
technologies may lead to new forms of relationships between humans and information technologies due to
the increasing system intelligence and autonomy (Baird and Maruping 2021; Benbya et al. 2021), it is unclear
whether they also lead to different responses from employees (Venkatesh 2021).
There has been a plethora of studies about specific types of software robots (e.g., chatbots, RPA) and software
robots in general along the lifecycle of development, implementation, use, and impact (see for example
Bavaresco et al. 2020; Hallikainen et al. 2018; Rutschi and Dibbern 2020a, 2020b). In addition, much
research has covered the underlying technology (e.g., ML) (Abdel-Karim et al. 2021). However, while some of
those studies also report on employees’ responses to software robots, the “employee perspective appear[s] to
be under-appreciated and under-researched” (Staaby et al. 2021, p. 164). Furthermore, it is unclear whether
and how employee responses to software robots differ from responses to traditional IS in organizations (e.g.,
ERP systems). To address this gap, we aim to answer the following research questions:
RQ1: What is the state of the art in research on employee responses to software robots?
RQ2: How do these responses differ from employee responses to traditional information systems in
organizations?
To address these research questions, we conducted a systematic literature review following the guidelines of
Kitchenham (2007) to systematically analyze existing research on software robots. We identified 33 relevant
papers, which we analyzed using a grounded theory approach. We examined which employee responses to
software robots are observed in current research and how they are affected by software robot characteristics,
employee characteristics, task, and context. In response to RQ1, we identified a wide range of affective,
cognitive, and behavioral employee responses to software robots. While some are similar to employee
responses observed in traditional IS systems, there are also some noteworthy differences (RQ2).
This paper offers three main theoretical contributions. First, we contribute to IS research by providing a
comprehensive overview of employee responses to software robots. Second, we shed light on important
characteristics of software robots, individuals, tasks, and contexts that may affect employee responses and
outcomes. Third, we add to the understanding of the similarities and differences in employee responses to
software robots versus traditional IS in organizations. More broadly, our research complements existing
research by focusing on the employee perspective and suggests directions for future research in this field. Our
findings can also inform practitioners about how employees may respond to software robot implementation,
helping them to understand and prepare for positive and negative responses of employees.
Conceptual Foundations
Software Robots
Software robots are software systems that mimic human behavior to perform tasks previously carried out by
human employees (Rutschi and Dibbern 2020a, 2020b; Willcocks and Lacity 2016). Unlike traditional IS in
organizations, which are primarily designed to support employees in executing a task to achieve a certain
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outcome, software robots independently work with the input they get from employees, customers, or other
systems, in the way they are programmed to do, leaving the human out of task execution (Dibbern and
Hirschheim 2020; Rutschi and Dibbern 2020a). As such, they may belong to the class of agentic IT artifacts,
a newly coined term that describes software-based agents which are designed to make rational and
autonomous decisions without the influence of a human, therefore taking over formerly human roles and
responsibilities (Baird and Maruping 2021; Dibbern and Hirschheim 2020).
While not the focus of this paper, it is important to note that the technologies underlying software robots can
be very different. Some software robots, like chatbots, use machine learning (ML) methods to adapt and learn
from human input (Rutschi and Dibbern 2020b). However, other software robots, such as most RPA bots, use
purely rule-based approaches (Hofmann et al. 2020). Hence, software robots differ from the broader class of
AI-based systems and do not necessarily require the availability of advanced AI capabilities. Moreover, while
ML methods can be an important asset in process automation, they are not limited to following pre-defined
processes (Abdel-Karim et al. 2021). In addition, ML methods do not necessarily mimic human behavior, but
range from making statistical inferences to automated problem-solving (Abdel-Karim et al. 2021).
Prior research has identified several different types of software robots. Well-known examples are RPA bots
and chatbots (Rutschi and Dibbern 2020a). RPA bots usually interact with system interfaces, log in and out,
and perform tasks such as copy-pasting information from one system to another (Aguirre and Rodriguez 2017;
Lacity et al. 2015). Chatbots, on the other hand, automate conversations with customers or clients, for
example, in customer service and e-commerce (Gnewuch et al. 2017). These task-oriented chatbots are
capable of providing answers to frequently asked questions and handling simple customer requests (Grudin
and Jacques 2019; Rutschi and Dibbern 2020a). In addition, software robots are found for example on
GitHub, supporting development teams in automating tedious tasks (Lebeuf et al. 2018). All software robots
share that they mimic human behavior, automate repetitive tasks, and perform them independently (Rutschi
and Dibbern 2020a). While recently, some literature reviews focusing on specific types of software robots have
been published (e.g., Bavaresco et al. 2020; Ivančić et al. 2019), to the best of our knowledge, there is currently
no consolidated literature review on software robots in general and with a focus on employee responses in
particular. In addition, while software robots have been studied regarding economic outcomes, process
characteristics, and implementation (Fung 2014; Rutschi and Dibbern 2020a, 2020b; Syed et al. 2020),
research on individual-level impacts in the workplace and specifically employee responses is scarce and has
been identified as a gap in previous literature (Syed et al. 2020; van der Aalst et al. 2018; Van Looy 2020).
Employee Responses to Information Systems Implementation
How employees use, respond to, and interact with newly implemented IS in the workplace is one of the core
research areas in the IS discipline (Bala and Venkatesh 2016). This phenomenon has been studied from
various perspectives and research paradigms (Lauterbach and Mueller 2014). In our research, we focus on the
concept of employee responses to IS, which is defined as the set of emotional and behavioral reactions
manifested among users that coemerge as IT is introduced into their work environment” (Bhattacherjee et al.
2018, p. 396). Prior research has shown that how users react to such changes can be classified into three broad
categories: affective, cognitive, and behavioral responses (Wu et al. 2017).
One of the most commonly investigated behavioral employee responses to IS is whether or not employees
adopt and use an IT system (Beaudry and Pinsonneault 2005; Bhattacherjee et al. 2018; Lapointe and Beaudry
2014). Adoption and use of technology have been widely researched with cognitive models of user adoption
and its antecedents, for example, the Technology Acceptance Model (TAM), the Unified Theory of Adoption
and Use of Technology (UTAUT), and their variations (Venkatesh et al. 2003). Moreover, it was investigated
why employees show resistance towards new technology (for an overview, see Ali et al. 2016; Haddara and
Moen 2017). Another research stream focuses on individual-level adaptation, which refers to the process users
undergo when faced with new IS (Bala and Venkatesh 2016; Beaudry and Pinsonneault 2005). Based on the
premise that the implementation of new IS causes a disruption in employees’ work environment, user
adaptation refers to the process of how employees respond by changing or adjusting behaviors, their tasks, or
the involved technology based on their appraisal of the situation (Bala and Venkatesh 2016; Beaudry and
Pinsonneault 2005). One of the most important adaptation concepts is coping, which refers to the process a
user engages in after or when becoming aware of a discrepant IT event, like the introduction of a new system
(Beaudry and Pinsonneault 2005; Elie-Dit-Cosaque and Straub 2011; Tsai and Compeau 2017). Based on how
users evaluate the IT event and their influence on the situation, they engage in problem- or emotion-focused
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adaptation strategies (Beaudry and Pinsonneault 2005). In the process of being exposed to new IT in the
workplace, employees show various affective, cognitive, and behavioral responses. Those include for example
fear, detaching, venting, or seeking social support, the development of workarounds, avoidance, sabotaging
the implementation, active opposition, and influencing peers against the system (Bala and Venkatesh 2016;
Beaudry and Pinsonneault 2005, 2010; Bhattacherjee et al. 2018; Lapointe and Beaudry 2014; Stein et al.
2015), but also enthusiasm towards the systems features, appropriating them to their way of work, for
example by customizing screens, using system features different than intended or find new uses (Barki et al.
2007; Bhattacherjee et al. 2018; Jasperson et al. 2005; Schmitz et al. 2016). Furthermore, employee responses
like resistance, adoption, or adaptation behaviors influence how and to what extent employees use the system
(Barki et al. 2007; Guinea and Webster 2013; Sun 2012). Employee responses also impact job outcomes, like
job satisfaction, job performance, and work commitment, as well as job demands and job control (Bala and
Venkatesh 2013, 2016; Majchrzak and Cotton 1988; Venkatesh et al. 2010).
Furthermore, contingency factors such as technology characteristics and individual differences can also
influence employee responses (Xiao and Benbasat 2007). Employee characteristics, like demographic factors
or experience as well as technology characteristics such as complexity impact employee responses (Bala and
Venkatesh 2013; Barki et al. 2007; Boudreau and Robey 2005; Lapointe and Beaudry 2014; Venkatesh et al.
2003). In addition, task characteristics, like task complexity, and context characteristics, for example,
available support or social networks have also been found to influence employee responses to IS (Bruque et
al. 2008; Fadel and Brown 2010; Wu et al. 2017).
In the context of software robots, there are only a few studies that explicitly investigate employee responses
to one specific type of software robot (Staaby et al. 2021). Asatiani et al. (2020) examine knowledge workers’
reactions to RPA bots in a pre-implementation phase, finding both positive and negative responses.
Waizenegger and Techatassanasoontorn (2020) find four configurations of employees, showing that
depending on the perception of the software robots, different behavioral responses and outcomes evolve based
on employees’ RPA implementation experiences, including attitude towards the robot or collaboration with
the automation team. Contingency factors and outcomes are also studied, for example job and skill outcomes
(Eikebrokk and Olsen 2020; Johansson et al. 2020; Staaby et al. 2021) or system characteristics (Erlenhov et
al. 2020). However, most studies on software robots focus on an implementation or economic outcome
perspective, and only limited attention is paid to the individual perspective of employees. For example, Cooper
et al. (2019) examine the impact of RPA bots in accounting, and while they touch upon the consequences for
employees, they focus on application areas and impacts on clients. Consequently, to better understand
employee responses to software robots, there is a need to integrate research on different types of software
robots and to also analyze studies that do not put employee responses at the center of their research.
Methodology
To address our research questions and assess the state of the art of research on employee responses to software
robots, we conducted a systematic literature review following the guidelines by Kitchenham (2007). The goal
of our search strategy was to identify and select articles that discuss the impact and response of employees to
the introduction of software robots, as stated in our research questions. Table 1 gives a detailed overview of
our search protocol. To identify different types of software robots (e.g., RPA, chatbots) and ensure possible
employee responses are covered, even if they are not the main topic of a study, we formulated a broad search
string, consisting of two parts. The first part includes keywords to cover the topic of software robots based on
the definition, including RPA and chatbots as known examples. The second part then covers employees and
workplace scenarios. We applied the search string in a title-abstract-keyword search across three databases to
include peer-reviewed research from different domains. Our initial search in January 2021 yielded 2863
results. A first scan revealed many articles from medicine and biology, in which “RPA” was used to abbreviate
nonrelated concepts. We refined our search to exclude articles from those fields. The remaining articles went
through a two-step screening process based on the inclusion and exclusion criteria. As a result, a final set of
33 papers remained for analysis. Comparing this number to the number of initial results, we reach the same
conclusion as Staaby et al. (2021), who state that the employee perspective has gathered little attention so far.
Those 33 papers were further analyzed using MAXQDA. To develop a coding scheme, we drew on the
framework proposed by Rzepka and Berger (2018). This qualifies as a directed approach to coding (Hsieh and
Shannon 2005). Two authors discussed the framework and its adaptation to the given context. Rzepka and
Berger (2018) classify user interaction with AI-enabled systems along the lines of user-system interaction.
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They also included user, system, task, and context characteristics as contingency factors. They define user
interaction as “[…] use of the system by the user, as well as the cognitive evaluations that precede the user’s
behavior” (Rzepka and Berger 2018, p. 4). As mentioned, software robots fulfill their tasks autonomously,
therefore we focus on employees’ affective-cognitive processing and behavior, which we subsume under
employee responses. Specifically, we differ between affective, cognitive, and behavioral responses as known
from social psychology: Affective responses broadly refer to emotional responses, cognitive responses are
perceptions and thoughts, and behavioral responses are actions or behaviors (Jhangiani and Tarry 2014),
which employees show towards software robots. As we specifically focus on a workplace setting, “users” are
employees, as opposed to for example customers using a service chatbot. We also differentiate between
behavioral outcomes as part of employee responses, and related instrumental outcomes and humanistic
outcomes, like efficiency or job satisfaction (Sarker et al. 2019). We tested the coding scheme on a random
subsample of 20% of the papers. Two authors coded these papers independently and compared their results.
Since no significant differences were identified at that point, one author then performed the full three-step
coding process as proposed by Wolfswinkel et al. (2011) on the literature sample. First, open coding was
applied to identify excerpts and possible categories that are relevant for answering our research questions,
creating 572 excerpts. An exemplary excerpt would be an employees’ statement about the fear of being
replaced by a software robot (e.g., “[…] these robots will replace a lot of human jobs”, Asatiani et al. 2020, p.
424). Those excerpts and their relations were classified and further refined into 29 codes and subcodes by
using axial coding, e.g., “fear” as a main code with “fear of job loss” as a subcode. We went through the papers
multiple times and added categories if necessary (Saldaña 2013). Finally, selective coding was used to
integrate and refine the identified concepts. This included matching codings to employee responses and
contingency factors as proposed in the framework (e.g., categorizing “fear” as affective response).
EBSCOhost
ACM Digital Library
AIS electronic Library
(“bots” OR “software robot*” OR “intelligent robots” OR “robotic process automation”
OR “rpa” OR chatbot*) AND (“workplace” OR human* OR employee* OR “individual”
OR job)
Title-Abstract-Keywords
Peer-reviewed
Title-Abstract-Keywords
Title-Abstract-Keywords
2863 articles
Removal of papers from fields that use the term “RPA” for non-related concepts
After refinement: 1824 articles
After duplicate removal: 1728 articles
(1) Paper mentions one or more IS that fall under the definition of a software robot;
(2) Paper deals with a direct or indirect impact of software robots on employees.
Posters, call for papers, workshop invitations, editor’s comments, editorials, teaching
cases; Paper not written in English or German; High level, conceptual discussions of
AI- or technology implementations; Missing details on employees or their responses or
focus on external (customer, external user) perspective; Paper dealing with physical
robots, social bots, and spam bots; Paper with purely technical perspective or focus on
software robot development and algorithms.
After Title-Abstract-Scan: 71 articles
After Full Text-Retrieval: 21 articles
After Forward-Backward-Search: 33 articles
Table 1: Search and selection process of the systematic literature review
Results
In the 33 papers analyzed, we found three types of software robots: RPA bots, chatbots, and software
development bots. Our sample included 22 papers on RPA bots, one paper on both RPA and chatbots, six
papers on chatbots, and four papers on software development bots. All papers were published in the last five
years, 18 of them in 2020. More than half of the sample (19 papers) were published at conferences. There are
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only six quantitative or mixed-method studies, while the majority of papers apply qualitative methods. As
most studies are qualitative in nature, with interviews and case studies being the dominant research approach,
they often describe a multitude of employee responses. For example, Waizenegger and Techatassanasoontorn
(2020) develop four configurations of employees’ RPA implementation experiences, each providing different
views and outcomes in terms of employee responses. To provide a comprehensive overview, we therefore
extracted all relevant employee responses, assigning papers to more than one category if necessary. In the
following, we present our results along the framework used in the coding process (see Figure 1).
Employee Responses to Software Robots
For affective responses, we identified two major categories from our literature review: fear and curiosity.
Fear is the employee response most often observed in the examined papers, specifically the fear of
replacement and job loss, as employees fear software robots will take over their tasks and ultimately
replace them (Asatiani et al. 2020; Eikebrokk and Olsen 2020; Fernandez and Aman 2018; Hallikainen et al.
2018; Lacity and Willcocks 2016; Meyer von Wolff et al. 2021; Staaby et al. 2021; Syed et al. 2020; Syed and
Wynn 2020; Waizenegger et al. 2020; Waizenegger and Techatassanasoontorn 2020). At the same time, the
fear of a change in work habits (Viale and Zouari 2020) through the introduction of software robots is
reported. Software robots also create a feeling of uncertainty towards the future roles of employees (Kokina
and Blanchette 2019; Ranerup and Henriksen 2019; Waizenegger and Techatassanasoontorn 2020). As
opposed to fearful responses, curiosity and interest towards software robots are observed (Asatiani et al.
2020; Dias et al. 2019; Eißer et al. 2020), while Waizenegger and Techatassanasoontorn (2020) report both
curious and happy reactions to the opportunities provided by the RPA bot.
Software robots also trigger various cognitive responses. They are perceived as an opportunity to
generally support employees, as they could reduce the workload and even out workload peaks (Cooper
et al. 2019; Meyer von Wolff et al. 2019a; Meyer von Wolff et al. 2019b; Šimek and Šperka 2019; Viale and
Zouari 2020; Wessel et al. 2020). They are seen to reduce time-consuming, repetitive work, freeing up time
for more complex tasks (Asatiani et al. 2020; Denagama Vitharanage et al. 2020; Erlenhov et al. 2020; Meyer
von Wolff et al. 2020; Šimek and Šperka 2019; Waizenegger and Techatassanasoontorn 2020), and an
opportunity to improve work performance and productivity (Asatiani et al. 2020; Erlenhov et al. 2020;
Waizenegger and Techatassanasoontorn 2020; Wessel et al. 2020). Asatiani et al. (2020) report the notion
that automation may lead to a decrease in offshoring activities, as the remaining more complex tasks are
Figure 1: Descriptive framework of employee responses to software robots
Employee
Characteristics
Attitude towards
change
-Experience with
software robots
Job role & hierarchy
Software Robot
Characteristics
Type
Intelligence
Task & Context
Characteristics
Task structure
Industry
Employee involvement
Communication and Support
Humanistic
Outcomes
Job satisfaction
Job meaningfulness
Higher value work
Skill outcomes
Employee Responses to Software Robots
Instrumental
Outcomes
Productivity
Task & routine change
Affective
Fear (replacement,
routine change) (12)
Uncertainty (future
roles) (3)
Curiosity (3)
Cognitive
Employee support (11)
Economic potential (11)
Concerns (control,
security, transparency)
(7)
Skill change (7)
Exaggerated expectations
(6)
Trust issues (3)
Behavioral
Anthropomorphism (6)
User adaptation (4)
Collaboration in robot
implementation (holding
back, sharing information,
active support) (4)
Acceptance problems (2)
Resistance (2)
Note: Numbers in parentheses indicate
the number of papers the particular
employee response was reported in.
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harder to outsource. The economic potential of software robots is also often mentioned, not surprisingly
mostly by management employees. They recognize software robots as strategic tools helping in process
optimization and systemization as well as to generally drive digital transformation (Asatiani et al. 2020;
Figueiredo and Pinto 2021; Meyer von Wolff et al. 2019a; Radke et al. 2020; Rutschi and Dibbern 2020a;
Viale and Zouari 2020). At the same time, software robots are seen as an opportunity for rationalization,
resource optimization, and efficiency gains (Cooper et al. 2019; Denagama Vitharanage et al. 2020; Eikebrokk
and Olsen 2020; Figueiredo and Pinto 2021; Lacity and Willcocks 2016; Meyer von Wolff et al. 2020).
However, employees often lack an understanding of the software robots working and capabilities, leading to
exaggerated expectations or a wrong understanding of the bot’s capabilities (Asatiani et al. 2020;
Lacity and Willcocks 2016; Meyer von Wolff et al. 2021). This leads to skepticism (Oshri and Plugge 2020),
misuse (Wessel and Steinmacher 2020), or trust loss when robots do not live up to those expectations (Syed
and Wynn 2020). Regarding the general capabilities, a failure of the robot to achieve the expected outcome
leads to it being perceived as unhelpful and of low quality (Wessel et al. 2018; Wessel and Steinmacher 2020).
On the other hand, software robots are perceived as useful (Eißer et al. 2020; Wessel et al. 2018; Wessel and
Steinmacher 2020; Wewerka et al. 2020). Eißer et al. (2020) find that perceived usefulness is influenced by
automation anxiety, suggesting that if employees are anxious about the technology being able to perform
tasks, it has to be perceived as useful to be actually threatening. Both positive and negative cognitive responses
are found when it comes to the change in skills that employees perceive would be a consequence of software
robots. Some employees consider the need for additional skills as an opportunity to learn something new both
task- and skill-wise (Asatiani et al. 2020; Johansson et al. 2020; Waizenegger and Techatassanasoontorn
2020) and that they can shape their works by learning new skills (Staaby et al. 2021), but others are less
enthusiastic about the perspective of extensive training and having to learn something new (Fernandez and
Aman 2018; Meyer von Wolff et al. 2021; Waizenegger et al. 2020). A related concern is de-skilling, which
employees state could happen due to the fragmentation of the process and simplification or replacement of
tasks (Asatiani et al. 2020; Güner et al. 2020; Meyer von Wolff et al. 2021). Employees also voice their
concerns about transparency and control with software robots. As those robots work independently,
some employees are skeptical about whether they can see what the robot did and whether it worked correctly
(Erlenhov et al. 2020; Syed and Wynn 2020; Waizenegger and Techatassanasoontorn 2020). Additionally,
employees state it could be more difficult to find errors if they occurred (Asatiani et al. 2020). There are also
security concerns: As robots need to access various systems, some employees voice concerns about data
security (Syed and Wynn 2020) or the bot doing harm in other systems (Kokina and Blanchette 2019). Human
control of outcomes is therefore wanted (Asatiani et al. 2020; Staaby et al. 2021). Trust is also reported to be
a challenge, as low trust in the robot leads to skepticism towards its results, with employees monitoring and
rechecking them (Erlenhov et al. 2020; Waizenegger and Techatassanasoontorn 2020; Wewerka et al. 2020).
Finally, software robots lead to various behavioral responses. Negative responses such as acceptance
problems and resistance to complete tasks were observed due to the fear of being replaced and unclear
responsibilities (Fernandez and Aman 2018; Meyer von Wolff et al. 2021; Oshri and Plugge 2020; Syed and
Wynn 2020). When it comes to user adaptation, employees’ behavioral responses range from pointing out
the robot’s shortcomings via accepting it as assisting technology to actively requesting more robots (Cooper et
al. 2019; Waizenegger and Techatassanasoontorn 2020). Some employees struggle with the change imposed
by software robots and do not adjust their routines, leading to bot malfunctions (Syed and Wynn 2020).
Others embrace change as well as their new roles and responsibilities (Lacity and Willcocks 2016;
Waizenegger and Techatassanasoontorn 2020). Anthropomorphism, which describes the attribution of
human-like characteristics like behaviors, attitudes, or emotions towards nonhuman agents (Epley et al.
2007), is widely observed, especially in the context of RPA bots. The robots are perceived as team members
and treated accordingly. This includes assigning names, evaluating their performance similar to humans, or
referring to bots as “sick” if there are technical problems (Hallikainen et al. 2018; Lacity and Willcocks 2016;
Oshri and Plugge 2020; Staaby et al. 2021; Syed and Wynn 2020; Waizenegger and Techatassanasoontorn
2020). While Waizenegger and Techatassanasoontorn (2020) found that anthropomorphism results in an
overall positive perception of the software robot and especially occurs when employees interact with the robot
and implementation team, Syed and Wynn (2020) argue that anthropomorphism could result in negative
consequences, if personification results in the false assumptions that the robots’ capabilities are equal to
humans. Varying behavioral responses can also be seen when it comes to employees’ collaboration
behavior in robot implementation. Responses range from employees actively holding back to only
reluctantly share information about tasks and processes (Eikebrokk and Olsen 2020; Waizenegger and
Techatassanasoontorn 2020) towards those who support the implementation team, for example in robot
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training, up to those proactively supporting the implementation by providing knowledge or becoming part of
the implementation team (Rutschi and Dibbern 2020a; Waizenegger and Techatassanasoontorn 2020). If
employees feel like the robot benefits them, they also proactively provide process knowledge and suggest the
expansion of software robot tasks (Cooper et al. 2019; Waizenegger and Techatassanasoontorn 2020).
Connections between the three types of employee responses can also be found: Waizenegger and
Techatassanasoontorn (2020) develop four configurations of employees’ RPA implementation experiences,
where they show that the perceived consequences, and therefore affective and cognitive responses, impact
employees’ cooperation with the automation team, interaction and assessment of the robot (behavioral
responses). Syed and Wynn (2020) show that the level of trust in the software robot also depends on affective
and cognitive responses, with for example fear of job loss and security concerns negatively affecting trust. It
can also be noted that there is a change in employee responses and software robot perception during the
implementation process, as employees’ initial skepticism changed when the software robots provided them
with benefits after implementation (Waizenegger and Techatassanasoontorn 2020).
Software Robots Characteristics
We identified three types of software robots in the selected set of papers: RPA, chatbots, and bots used in
software development. These bots are “[…] automated tools that attempt to free developers from particularly
tedious tasks, or support their work in a more general sense” (Erlenhov et al. 2020, p. 445). They are deployed
for example on GitHub, can have different forms, and connect developers and tools, for example in executing
repetitive tasks such as code review processes (Erlenhov et al. 2020; Wessel et al. 2018; Wessel et al. 2020;
Wessel and Steinmacher 2020). They are mostly studied in an open-source context, however, both open
source components and GitHub as a tool are already widespread in professional software development
(Kalliamvakou et al. 2015), which makes those robots also relevant in the workplace context. In our sample,
the impact of software robot type on employee responses was not specifically examined, however, the fear of
replacement is predominantly mentioned with RPA bots and chatbots, while software development bots are
perceived differently. This could be since those robots mostly take over administrative work, rather than the
cognitive task of developing software. We further identified the degree of intelligence as an important
characteristic of software robots. The majority of software robots in our sample are strictly rule-based and
operate in a pre-defined context, following well-defined tasks. This specifically applies to RPA bots in our
sample. Chatbots use AI technology, such as natural language processing, but often follow pre-defined
decision rules to map conversations, which can also be classified as rule-based, like the chatbots considered
in Rutschi and Dibbern (2020a). Erlenhov et al. (2020) raise the point that developers vary in what they define
as a bot, starting at simple autonomous script execution to self-learning applications. As the analyzed sample
provides little information about the system characteristics, an implication for user responses to the
intelligence level of bots cannot be drawn. Rzepka and Berger (2018) found that for AI-enabled systems, a
system’s autonomy and its decision transparency influence cognitive and behavioral outcomes. While this
seems reasonable to assume for software robots too, further research is necessary.
Employee Characteristics
Interestingly, demographic criteria, such as age and gender, are not specifically studied in the context of
employee responses to software robots. While this information is reported in some interview studies, no clear
tendencies are found. Only Dias et al. (2019) indicate that older generations may have more problems with
work changes. Meyer von Wolff et al. (2021) mention that especially long-time employees do not see the
benefit of changing to chatbots. Waizenegger and Techatassanasoontorn (2020) state that employees’
experience with software robot implementation also depends on their attitude towards change in their
work processes. Different attitudes can be found in the different employee configurations they developed, a
more positive attitude towards change is found in configurations that are more positive towards software
robots (Waizenegger and Techatassanasoontorn 2020). The TAM study of Wewerka et al. (2020) shows that
innovation joy positively influences perceived ease of use. Some studies indicate that certain character traits,
like interest in new technologies and cognitive capabilities, like the ability to think analytically and adapt, are
important success factors for employees working in automation projects (Dias et al. 2019; Radke et al. 2020;
Syed and Wynn 2020). Experience with software robots is considered by Wewerka et al. (2020), who
find that the influence of perceived ease of use on usefulness increases with experience. A variety of job roles,
ranging from employees working alongside software robots, process owners, and project managers to mid-
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and high-level management to software robot implementers like developers and providers are represented in
the different studies. This also means different hierarchy levels, hence the responses are not only reported by
employees who work alongside the robot, but also by managers and implementers in charge of delivery and
development. This is especially true for RPA and chatbot implementations (Denagama Vitharanage et al.
2020; Figueiredo and Pinto 2021; Kokina and Blanchette 2019; Meyer von Wolff et al. 2019b; Viale and Zouari
2020), while the studies regarding software robots are predominantly based on software developers (Erlenhov
et al. 2020; Wessel et al. 2018; Wessel and Steinmacher 2020).
Task and Context Characteristics
While many papers confirm that software robots are mainly used for repetitive, standardized, and time-
consuming tasks, the impact of task structure on employee responses is less well studied. Erlenhov et al.
(2020) find that one of the main reasons for using development bots is that they are able to automate such
tedious tasks. Wewerka et al. (2020) find support for their hypothesis that the perceived usefulness of RPA
bots increases, if they take over recurring, time-consuming tasks.
Regarding the context of software robot implementation, software robots are implemented in various
industries. We identified the following sectors: Manufacturing, including raw materials, pharmaceuticals,
automotive, plant engineering, and other products (e.g., Kokina and Blanchette 2019), energy, including oil
and gas (e.g., Eikebrokk and Olsen 2020), ICT and consulting companies (e.g., Erlenhov et al. 2020). The
public sector is also frequently using software robots (e.g., Ranerup and Henriksen 2019) as well as business
process service providers, the finance industry, and the insurance industry (e.g., Asatiani et al. 2020;
Figueiredo and Pinto 2021). Eikebrokk and Olsen (2020) report that downsizing due to RPA bots is slightly
more common with financial companies than in other sectors. However, employee responses in general are
rather similar across the analyzed literature, indicating there is little difference between industries. Another
important context factor is employee involvement in the pre-implementation phase. Employees who share
their professional and process knowledge to support software robot development, may feel responsible and
important, which facilitates work meaningfulness (Staaby et al. 2021). Though not explicitly studied, Dias et
al. (2019) find a similar phenomenon, where employees who feel they do not use their cognitive capacity in
daily work become part of the RPA implementation team. The importance of management support and
communication strategy is specifically highlighted (Figueiredo and Pinto 2021). Syed and Wynn (2020)
show that while a clear software robot strategy results in positive employee responses, employees react with
resistance and loose trust if the strategy is unfocused and incohesive. Management support is also found to be
a key factor in driving trust and increase adoption and acceptance (Fernandez and Aman 2018; Syed and
Wynn 2020). Lacity and Willcocks (2016) report that after management addressed replacement concerns by
stating that only outsourced jobs would be reduced, internal employees embraced the change. Oshri and
Plugge (2020) show that if managers provide employees with information on how and what the bot does,
employees better understand both the robots’ abilities and impacts, also building their trust in the bot. Syed
and Wynn (2020) report a similar effect for the availability of technical support. Again, the analyzed sample
provides only superficial information about the management and communication strategies. A mapping to
specific employee responses is not possible with our results and could be a starting point for future research.
Outcomes
In the following, we present humanistic and instrumental outcomes of software robot implementation on
employees (Sarker et al. 2019). One major humanistic outcome is that software robot implementation changes
employees’ job outcomes. While several authors report enhanced employee and job satisfaction due to
software robots (Denagama Vitharanage et al. 2020; Fernandez and Aman 2018; Radke et al. 2020;
Waizenegger and Techatassanasoontorn 2020), a decrease in job meaningfulness due to having to deal with
discouraging tasks or having problems to justify the value of the employees work is also seen (Oshri and Plugge
2020; Staaby et al. 2021). Waizenegger et al. (2020) also mention role ambiguity of employees as a negative
consequence of chatbots. Employees struggling with the changing nature of their job may eventually be
looking for alternative employments (Oshri and Plugge 2020). Software robots on the other hand enable
employees to engage in higher-value work, i.e. more complex, relevant and interesting tasks (Denagama
Vitharanage et al. 2020; Johansson et al. 2020; Meyer von Wolff et al. 2019a; Meyer von Wolff et al. 2020;
Staaby et al. 2021). Changes in skills are also frequently reported, as different skills are needed, and
the skills required becoming more complex. Most papers mention that on the one hand, remaining tasks
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require higher-level, analytic capabilities and process understanding (Cooper et al. 2019; Dias et al. 2019;
Fernandez and Aman 2018; Johansson et al. 2020), on the other hand, technical and problem-solving skills
are needed if employees work directly with the robot (Figueiredo and Pinto 2021; Johansson et al. 2020;
Kokina and Blanchette 2019). However, this is not a general pattern, as in Johansson et al. (2020), several
employees state that they feel like an increase in skill is not needed, but existing skills can be brought to more
use as repetitive tasks are omitted. Eikebrokk and Olsen (2020) report that employees need to learn to create
value in new ways, as they take over non-automatized tasks. Software robots enable different ways of
upskilling, as users gain technical skills and process understanding needed to work with software robots or
use the freed-up time to develop new skills (Denagama Vitharanage et al. 2020; Johansson et al. 2020; Kokina
and Blanchette 2019; Staaby et al. 2021). Upskilling also occurs as employees take over new roles concerning
the software robot, for example as part of the automation team (Figueiredo and Pinto 2021; Hallikainen et al.
2018). On a more general level, software robot implementation can result in extra responsibilities, which are
perceived as both positive and negative (Staaby et al. 2021; Waizenegger and Techatassanasoontorn 2020).
Regarding instrumental outcomes, employees report a decreased workload due to less manual labor, both
generally and during peak times, which may increase productivity (Denagama Vitharanage et al. 2020;
Radke et al. 2020; Syed et al. 2020; Waizenegger et al. 2020; Wessel et al. 2020). Software robots also lead to
process simplification (Šimek and Šperka 2019) and enable employees to better focus due to decreased
disturbances (Meyer von Wolff et al. 2019b; Meyer von Wolff et al. 2020). However, this is not universal, e.g.
Staaby et al. (2021) report some employees’ workload increased after automation, as they were assigned
additional work from other offices. Especially for development bots, papers also mention that other tasks take
more time, as developers need to fix bot mistakes or are interrupted by the bot (Erlenhov et al. 2020; Wessel
et al. 2020). Another factor is the change in employees’ tasks, including the shift towards executing new
tasks concerning the software robots, for example as part of the automation team or robot operation
and maintenance, also enabling interdisciplinary, cross-team collaboration (Dias et al. 2019; Johansson et al.
2020; Staaby et al. 2021). Specifically with RPA and chatbot implementations, employees contribute to the
robot implementation by participating in the design, sharing process knowledge, and providing
improvements (Hallikainen et al. 2018; Kokina and Blanchette 2019; Oshri and Plugge 2020; Ranerup and
Henriksen 2019; Rutschi and Dibbern 2020a; Syed and Wynn 2020). More generally, the software robots
change employees’ task- and work routines, as tasks are reassigned to the robot or processes changed
according to robot needs (Güner et al. 2020; Meyer von Wolff et al. 2019b; Meyer von Wolff et al. 2020; Viale
and Zouari 2020; Waizenegger and Techatassanasoontorn 2020).
Discussion
When software robots are implemented in the workplace to automate tasks previously performed by humans,
employees’ work environments change dramatically. To better understand how employees react to such
changes, we systematically reviewed existing literature to analyze employee responses to software robots.
Responding to RQ1, we identified a wide range of affective, cognitive, and behavioral responses - both negative
and positive - that can occur when software robots such as RPA bots, chatbots, or software development bots,
are implemented. In addition, we found several contingency factors that can affect these responses. In the
following, we first address RQ2 by comparing employee responses to software robots with responses to
traditional IS and pointing out noteworthy differences. Subsequently, we discuss our theoretical and practical
contributions, identify limitations, and outline an agenda for future research.
Comparing Employee Responses to Software Robots vs. Traditional IS
When we compared our findings to previous research on employee responses to traditional IS (e.g., ERP
systems), we discovered both similarities and differences. In general, some of the employee responses we
identified in our review have also been reported for other classes of IS that lead to changes in employees’ work
practices. For example, the fear of job loss and change often occurs when new organizational IS are
implemented (e.g., Lim et al. 2005). Furthermore, several behavioral responses, such as engagement,
reluctance, and resistance have not only be observed for software robots but also for enterprise systems
(Wanchai et al. 2019), indicating that employees adaptation behavior towards software robots is not too
different from how they adapt to traditional IS. An example are Waizenegger and Techatassanasoontorn’s
(2020) four configurations of employee experiences in RPA implementation, in two of which employees show
responses similar to the coping strategies proposed for mandatory IS use: While in the configuration “software
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robots as a burden and threat”, employee responses seems to be deviant (i.e., voicing concern, refusal of
cooperation in implementation), they may match an engaged response in “software robots as innovative
enablers” (i.e., sharing expertise, engaging with the system) (Bhattacherjee et al. 2018; Waizenegger and
Techatassanasoontorn 2020). Regarding contingency factors, employees’ understanding of software robots is
widely impacted by what employees know and how the robots’ tasks and capabilities are communicated. We
know from research on traditional IS that social factors, like management support or social networks, play an
important role in user adaptation processes (Bala and Venkatesh 2016; Bruque et al. 2008; Wu et al. 2017)
and user involvement facilitates perceived usefulness and use (e.g., Franz and Robey 1986; Swanson 1974).
Taken together, there is reason to believe that certain aspects of employees’ responses to software robots are
similar to responses to traditional IS in the past.
However, our findings also point to interesting differences in employee responses towards traditional IS and
towards software robots. First, employees seem to be particularly concerned about the software robots’
autonomy. They are skeptical if the robot performs correctly, as they are unable to follow and control its work.
A very specific manifestation is the concern of not knowing what the software robot does. Here, “not knowing”
represents a transfer of responsibilities from a human employee to the robot, which can be considered a task
delegation as defined by Baird and Maruping (2021). However, the opposite case (i.e. task transfer from robot
to employee) is possible too, for example, a chatbot escalating a complex customer conversation to a human
service agent (Rutschi and Dibbern 2020a). Second, technology-focused adaptation strategies, such as
combining, extending, or adapting technology features to employee needs are not observed in the context of
software robots. This may be due to the fact that software robots work independently on their tasks, and the
employee has no possibility to customize, change, or develop workarounds to routines (Rutschi and Dibbern
2020a). This suggests that employees’ influence on and involvement in task execution is low or even absent
once a software robot has been implemented. However, employees’ involvement in the development of
software robots before they are ultimately implemented could be higher: Employees contribute not only by
cooperating with an IT unit, but actively take part in the software robot development. They share knowledge
about their work processes (see for example Hallikainen et al. 2018; Rutschi and Dibbern 2020a). Employees
are not intended to use the system, but to enable the robot to take over. Resistance therefore might no longer
show as system non-use, but in providing wrong or incomplete information on tasks to sabotage the
implementation or lower the robot’s performance (Eikebrokk and Olsen 2020; Waizenegger and
Techatassanasoontorn 2020). Finally, anthropomorphism seems to be much more common for software
robots than for traditional IS. Prior research, e.g., with chatbot users, has shown that anthropomorphism
helps to build trust, helps to overcome the fear of the unknown, and positively influences system adoption by
creating a sense of familiarity (Li and Suh 2021; Pfeuffer et al. 2019). Adding human features to a software
robot could hence be a strategy to help employees overcome the initial fear and cope with the situation.
Theoretical Contributions and Practical Implications
This paper makes three theoretical contributions. First, we contribute to IS research by providing a
comprehensive and systematic overview of employee responses to software robots. While previous studies
have focused on specific software robot implementation projects and associated employee responses, our
review integrates research on different types of software robots (e.g., RPA bots, chatbots, software
development bots), allowing a more holistic understanding of how employees respond to the implementation
of software robots in their work environment. We identified a wide range of affective, cognitive, and behavioral
responses in our review, indicating that employees vary greatly in their responses. Second, we shed light on
important contingency factors that may play a role in how employees respond to software robots. In our
review, we find that characteristics of the software robot itself, the individual employee, the task, and the
context affect employee responses. As most studies in our sample adopt a qualitative approach, we were
unable to quantify the relationships between the contingency factors, employee responses to software robots,
and outcomes. However, our findings provide first insights into what factors should be taken into account
when studying individual-level effects of software robots in organizations. Third, we advance our
understanding of the differences in employee responses to software robots compared to traditional
organizational IS. In general, we find that many of the employee responses reported in software robot
implementation are similar to those observed for traditional IS. This suggests that many of our field’s
established theories, such as the Coping Model of User Adaptation (Beaudry and Pinsonneault 2005), may
also apply to the study of software robots. However, we also identify interesting differences that provide
opportunities to further test and extend those theories. For example, some coping strategies are hypothesized
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to occur when employees feel they have high control over the situation (Beaudry and Pinsonneault 2005). As
software robots work largely outside the employee’s control, it would be interesting if the assumption about
perceived control holds true and how employees faced with software robots define control in this situation.
The paper offers two major implications for practitioners. First, we provide managers with a comprehensive
overview of employee responses they can expect when implementing software robots in their organization.
Our findings could help them understand that employee responses and outcomes can take very different
forms. Our overview hence shows managers what to look out for in terms of affective, cognitive, and behavioral
responses from employees. In addition, our list of contingency factors may also provide some insights on what
other factors they should take into consideration. Second, our comparison of employee responses to software
robots vs. traditional IS reveals that managers can draw on established approaches (e.g., management
support, employee involvement) to mitigate negative responses, similar to how they have approached the
implementation of other IS in the past. In addition, we also highlight some differences that help managers to
prepare for responses that they probably have not seen before.
Limitations
Our research is not without limitations. First, as with any literature review, the selection process and inclusion
and exclusion criteria may have impacted our findings. We rigorously applied established guidelines
(Kitchenham 2007), searched three high-quality databases, and included a thorough forward and backward
search. Nevertheless, it is possible that our search did not detect all papers that cover employee responses to
software robots. Future studies could specifically include further databases to cover additional fields of
research and possibly extend the search string based on our results. Second, based on the conceptualization
of software robots by Rutschi and Dibbern (2020a), we first focused on RPA bots and chatbots as two major
types of software robots. Moreover, the majority of papers in our sample focused on RPA, indicating that
employee responses to RPA may be overrepresented in our analysis. Although we identified additional types
of software robots (e.g., software development bots), there may be other types of software robots that were
not covered in our analysis, such as specific software agents or other agentic IS artifacts (Baird and Maruping
2021). Third, we specifically focused on software robots that mimic human behavior to perform tasks
previously carried out by human employees and therefore have the potential to replace employees. However,
software robots and other types of AI-enabled systems may also be used to support employees (e.g., data
analysis tools to improve decision making). In such cases, employee responses may differ, and, for example,
include issues such as trust and transparency (Rzepka and Berger 2018). Future research could compare those
different system classes to determine similarities and differences.
Future Research Agenda
While our research provides a first step towards a better understanding of employee responses to software
robots, many open and promising research questions remain. Based on our literature review, we have
identified three major directions for future research.
Research Direction 1: Strengthen the Theoretical Foundations of Software Robot Research
While we believe that our conceptualization of employee responses and contingency factors based on the
framework of Rzepka and Berger (2018) provides a good starting point for researchers from different
domains, it is by no means exhaustive. Future research could therefore extend our descriptive framework by
building one or more taxonomies (e.g., for employee characteristics, responses, and outcomes) and by
identifying and classifying different types of software robots (e.g., as morphological boxes). Another important
task for future research is to further connect our initial conceptualization with existing theories from existing
research streams in IS and beyond. For example, we found that some observed employee responses match
reactions and behavioral responses found in the IS adaptation literature. It would be interesting to examine
whether there are specific adaptation strategies employees engage in with regard to software robots. If so,
future research could develop an extension of the Coping Model of User Adaptation (Beaudry and
Pinsonneault 2005), specifically considering software robots and employeescoping strategies.
Research Direction 2: Advance the Understanding of the Interplay between Employees,
Software Robots, and Contingency Factors
Since the majority of analyzed literature followed a qualitative approach, another promising avenue for future
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research is to provide further empirical evidence on how employee responses to software robots are shaped
by characteristics of the robot (e.g., degree of intelligence), the task, the context, and the employees
themselves. While our review identified interesting observations, the qualitative insights about these factors
did not allow us to quantify the relationships between them. Future studies should therefore perform more
quantitative research (e.g., surveys, experiments) investigating the outcomes of software robots on employees
on a more fine-grained level. For example, future research could revolve around empirically validating links
between contingency factors like employee involvement and their responses, or the impact of software robots
on employees’ workplaces and routines (cf. Johansson et al. 2020; Staaby et al. 2021). Moreover, statements
about employee responses and outcomes were often reported by management employees, not by affected
employees themselves. Therefore, more research on individual-level effects is necessary to fully understand
how employees perceive and react to software robot introduction in the workplace.
Research Direction 3: Design and Implement Software Robots following a Human-Centric
Approach
Finally, future research could aim to deliver prescriptive knowledge for both designing software robots as well
as delivering methodological guidance and tools for human-centric software robot implementation at the
workplace. The current software robot industry is dominated by vendors that are primarily focused on
business outcomes. Often, a key selling point is to automate high-volume processes to reduce personnel costs
in the short or long term. Therefore, future research is needed to better understand how to design software
robots in a way that takes the employee perspective into account and considers those that would be affected
by their implementation. For example, design science research studies could look at how to design software
robots themselves or how to design methods and tools that facilitate their introduction, specifically with the
employees’ needs and mental states in mind.
Conclusion
Software robots are becoming increasingly common at the workplace. They can significantly and directly
impact employees’ routines and tasks, and as they take over tasks formerly performed by humans, employees
react in different ways, ranging from fear to excitement about the opportunity to be relieved from time-
consuming, repetitive tasks. How employees react to IS implementation is a key topic in IS, but little research
has examined this specific aspect in the context of software robot implementation. This paper presents key
insights from a systematic literature review, which include an overview of employee responses to software
robots, the contingency factors that influence them, and outcomes from an employee perspective. Moreover,
our findings suggest that while employees exhibit many affective, cognitive, and behavioral responses similar
to those reported for IS in prior research, there are also some interesting differences. Therefore, our study is
a starting point for future research on individual-level impacts and employee responses to software robots. A
deeper insight could not only help understanding employees’ responses and their consequences in software
robot implementation. It could also provide a foundation for supporting employees during and after the
implementation process, ensuring a positive experience as well as assisting them in adapting to tasks,
technology, and changing requirements in the future workplace.
Acknowledgements
This research was carried out as part of the MeKIDI project (project no. EXP.01.00019.20), funded by the
German Federal Ministry of Labour and Social Affairs (BMAS).
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... Nevertheless, findings derived from samples of students are considered to hold and can usually be generalized (Compeau et al., 2012). As suggested by several authors, human-AI interaction is also dependent on the individual setting and task (Rzepka and Berger, 2018;Seiffer et al., 2021). Our study peeks at how information designs affect human-AI interaction during an AI supervision setting. ...
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