Human–bot co-working: job outcomes and employee responses
| DOI | https://doi.org/10.1108/IMDS-02-2022-0114 |
| Published date | 10 November 2022 |
| Date | 10 November 2022 |
| Pages | 515-533 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Yu-Qian Zhu,Kritsapas Kanjanamekanant |
Human–bot co-working:
job outcomes and
employee responses
Yu-Qian Zhu
Department of Information Management,
National Taiwan University of Science and Technology, Taipei, Taiwan, and
Kritsapas Kanjanamekanant
National Taiwan University of Science and Technology, Taipei, Taiwan
Abstract
Purpose –Robotic process automation (RPA) has been widely implemented to automate digital tasks. The
resulting new type of human–bot co-working environment, however, has been understudied. This paper
investigated how the depth and breadth of RPA deployment impact employees’job autonomy and work
intensification, as well as perceived RPA performance. It further examined how job autonomy, work
intensification, and perceived RPA performance predict burnout and continuance intention to use RPA.
Design/methodology/approach –Using data collected from online survey of 128 RPA users, whose
organizations have already gone live on RPA, partial least squares is used in the validation of the conceptual
model and analysis.
Findings –The analytical results indicate that RPA deploymentbreadth and depth affect work intensification
differently, and RPA deployment breadth and depth significantly predict perceived RPA performance. While
work intensification increases burnout, job autonomy alleviates the burnout of employees. Finally, job
autonomy and perceived RPA performance are both positive predictors of continuance intention to use RPA.
Originality/value –This study contributes to the literature by investigating how co-working affects
employees’autonomyand quality of work. It also advances the research on technology deployment by showing
how deployment breadth and depth differently affect employees’evaluations of work-related aspects. Third, it
extends the applicability of job demand-resource model into technology deployment and continuance
technology use literature, by illustrating the importance of a job resource such as job autonomy. Finally, it
provides firms with RPA implementation strategies.
Keywords Robotic process automation, Human–bot co-working, Deployment breadth, Deployment depth,
Job autonomy
Paper type Research paper
1. Introduction
In recent years, robotic process automation (RPA) has seen increasing interest from industries. A
global survey by Deloitte found that 94% of enterprises have already or plan to implement RPA
in the next three years (Watson et al.,2020). RPA comprises of software agents, or ‘bots’that
mimic the manual path taken by a human through a range of computer applications (Davenport
and Ronanki, 2018;Lacity and Willcocks, 2016). It has been deployed in various industries and
has been estimated to cut the cost of human resources by 20–50% (Syed et al., 2020).
Responding to the rising adoption of artificial intelligence (AI) and automation, scholars have
explored the implications of the deployment of robots and AI. Research found that customers’
attributions about the firm motivations to implement service robots affect intentions to use them
(Belanche et al.,2021). Belanche et al. (2020) discovered that customers have different attribution of
responsibility toward robots versus humans. Similarly, consumers reported different expectations
in food quality, safety and performance for robot delivery versus human delivery and carry-out
Human–bot
co-working
515
Funding: This work was supported by the Ministry of Science and Technology, Taiwan (Republic of
China) [grant numbers MOST 109-2410-H-011-MY3].
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 3 March 2022
Revised 2 July 2022
27 September 2022
11 October 2022
Accepted 16 October 2022
Industrial Management & Data
Systems
Vol. 123 No. 2, 2023
pp. 515-533
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-02-2022-0114
(Byrd et al.,2021). While these researches investigate how customers adopt and respondto robots
in the service industry, or the customer-facing robots, research on another important facet of robot
application, i.e. enterprise-facing robots, has only recently begun to rise (Syed et al.,2020).Extant
literature has mostly focused on the firm perspective; for example, implications for job losses
(Willcocks, 2020), willingness to adopt RPA (Plattfaut and Koch, 2021), deployment guidelines
(Lacity et al.,2021), and efficiency gains and quality improvements (Marinova et al.,2016). In many
cases of RPA implementation, employees have to co-work with RPA to achieve goals. Yet our
understanding of the implications of human-robot co-working has been very limited (van der
Aalst et al.,2018). Having machines as teammates may have both potential benefits and
drawbacks for employees, suggesting the need for further investigation (Seeber et al.,2020).
Addressing these questions has become more urgent as the adoption of RPA keeps rising (van der
Aalst et al.,2018). A few pioneering conceptual works have debated and explored the possible
impact of automation on the quality of work, such as job intensification, skills needed, learning
and job security (Gallie, 2017;Riemer and Peter, 2020;Willcocks, 2020). Despite these efforts,
empirically validated research is still few and far between, and more research is warranted to
understand the impact of AI and automation on employees (Huysman, 2020).
We draw upon the job demand-resource model (JD-R) (Demerouti et al., 2001) to propose
and empirically validate the implications for employees to co-work with RPA. We address
four important yet unresolved questions. First, what are the impacts of RPA on employees’
job characteristics, namely work intensification and job autonomy, two important factors of
quality of work (Gallie, 2017;Riemer and Peter, 2020). Second, do employees feel that RPA is
helpful for their jobs? Third, what are employees’responses to the work characteristic
changes (autonomy and work intensification) and perceived performance of RPA? Finally,
how does deployment strategy in terms of depth and breadth of RPA affect employees? Prior
research has shown that there needs to be a delicate balance and alignment between these two
(Zhang et al., 2016). In RPA deployment, what are the different impacts of depth and breadth?
2. Theoretical framework and hypothesis
2.1 The job demand-resource model
JD-R model (Demerouti et al., 2001) provides a relevant theoretical framework to evaluate how
a workplace change affects an individual’s well-being. Job demands refer to any aspect of the
job that requires physical or mental efforts from the employees; for example, work overload
or new skills. Job resources are the facets of the job that provide functionality that helps the
employees achieve their goals, or stimulate personal growth and development. The model
postulates that job demands and resources have opposing effects on burnout. Job resources
include the availability of tools to enhance performance, job autonomy and job control. High
levels of job demands lead to negative outcomes such as burnout and exhaustion (Schaufeli
et al., 2009;Schaufeli and Bakker, 2004;LePine et al., 2004). With increased job resources, the
level of burnout decreases (Toker et al., 2015;Crawford et al., 2010;Nahrgang et al., 2011).
The demand to learn and apply information technology in jobs is a primary stressor for
employees (Galluch et al., 2015;Ragu-Nathan et al., 2008). End-users reactions to mandatory
technology adoption have been reported as anxiety, tension (Heinssen et al., 1987), work
pressures and job dissatisfaction (Smith et al., 1981). However, the same technology that
creates stress may also increase access to information and improve control over one’s job and
life, thereby decreasing job stress and increasing employee well-being (Day et al., 2010).
2.2 Depth and breadth of deployment
The dimensions of technology deployment: breadth and depth utilization (Zhang et al., 2016)
may have different impacts on job characteristics because of different types of demands and
IMDS
123,2
516
To continue reading
Request your trial