How can a chatbot support the HR function? Exploring the operational interplay
A qualitative interview study Tina Taule
Master thesis at the Department of Psychology
UNIVERSITY OF OSLO
15.10.2021
Author: Tina Taule
Title: How can a chatbot support the HR function? - Exploring the operational interplay Supervisors: Knut Inge Fostervold (UIO) and Asbjørn Følstad (SINTEF)
Abstract
In recent years there has been an increased interest in chatbots. Chatbots are software systems that interact with humans through natural language. Chatbots are thought to support the HR function in organizations in areas such as recruitment and selection, onboarding and training, automation of routine processes and answering employees’ frequently asked questions. However, as the application of chatbots in HR is relatively new, there is a
knowledge gap concerning the way in which the chatbot currently supports the HR function and how chatbot implementation affects HR work.
This thesis contributes insight in this area by exploring the operational interplay between the chatbot and the HR function, and by empirically investigating the lived experiences of HR personnel managing and working with the chatbot. The study was designed as a qualitative exploratory study, and data gathering was conducted through semi- structured interviews with 13 HR practitioners.
The findings suggest that the chatbot mainly supports the HR function by relieving the volume of repetitive inquiries from the organization, and by enabling HR to provide better service through the use of chatbot statistics to tailor information to organizational needs. The findings further show that the implementation of a chatbot is experienced to affect the HR function and HR work in various ways. The chatbot requires HR personnel to develop new technological competences and skills. Furthermore, the implementation creates several new chatbot related tasks, such as training and testing, continuous updating, new
collaborations, and promoting chatbot use. Organizational characteristics such as planning and resources were found to impact chatbot work, and people factors such as user acceptance and managing employees’ expectations were put forward as central aspects of successful chatbot deployment. Lastly, the findings provide insights regarding how the chatbot is being used in the participating companies and how different chatbot characteristics may contribute to or inhibit wide uptake and effective use. This study provides an initial contribution to building knowledge on how chatbots support the HR function, and suggests several factors that may be of importance when companies seek to implement an internal chatbot.
Keywords: HR, e-HR, chatbots, AI chatbots, support, interview study
Acknowledgments
The topic of this thesis is a result of two big interests: the changing role of human resources in the workplace, and the field of human factors. After the course ‘Human, Technology and Organization’ at University of Oslo, I knew that I wanted to learn more about the intersection where humans and psychology meet technology in the complex and social context of an organization.
In that sense, I feel grateful for my two supervisors, who show genuine enthusiasm for the interactions between humans and technology, and who provided invaluable insights regarding this perspective. Asbjørn, thank you for your incredible guidance and support, and for being available beyond mentioning. Your interest in this study has sparked motivation on several occasions when my own energy was running low, and your inputs and chatbot knowledge have been defining in this project. Knut Inge, thank you for constantly reminding me of why I am drawn towards the field of human factors, and for always providing the much needed pep talks and for placing my thesis into perspective. I would also like to thank you for sharing your own experiences of producing reports and for giving valuable tips in the writing process. Thank you both for being understanding and supportive during a challenging time. I am deeply appreciative of the kindness and the uplifting words you both provided when my own ‘human factors’ created obstacles that collided with the project.
Additionally, I want to thank the contact persons in the two chatbot companies for being so open to this research and for contributing with chatbot courses and contact
information to potential participants. Thanks for your ideas and feedback in the beginning of this project, and for your efforts and ability to recruit the participating companies.
Importantly, I owe a huge thank you to all the participants. Thank you for gifting your time and for sharing your thoughts and experiences, even during the times of a global
pandemic and a sudden shift in the work environment. This thesis would not be possible without your contributions from the kitchens, children’s bedrooms, gardens and living rooms.
Last, but certainly not least, I want to thank my family and friends for believing in me and for supporting me on the grey days. My family in Drøbak, thank you for your love and care. My friends, thank you for listening, for your inputs, and for your help in balancing thesis and life.
Casper, thank you for all the walks and for all the hours you’ve spent calming my nerves. Thank you for making me smile, for teaching me how to enjoy life, and for making exceptional good food.
Table of contents
Introduction 1
Workplace automation through chatbots 3
Automation and work 3
Technology as organizational change 4
Chatbots 5
Chatbots for internal purposes 5
Human Resource Management 7
Brief overview of the developments within the field 7
HR today 8
Electronic Human Resources (e-HR) 9
How e-HR supports the HR function 9
Chatbots in HR 11
Research questions 12
Method 12
The project 12
Research design 13
Study context 13
Participants and recruitment 14
Study material 15
Ethical considerations 16
Analysis 16
Preparing for analysis 16
Thematic analysis 17
Quality and reflexivity in qualitative research 19
Findings 20
How the chatbot supports the HR function 21
How the chatbot affects the HR function 26
The HR chatbot in the wider organizational context 33
Discussion 42
Findings and existing literature 42
Implications 46
Limitations and future research 47
Conclusion 48
References 49
Appendix A - Information document/informed consent form 56
Appendix B - Interview guide 59
Introduction
The Human Resource (HR) function is seen to be under two pressures; to become more strategic and contribute to the organizational performance, and to reduce costs. This has increased the incentive for HR to reduce administrative tasks with the aim of spending more time on strategic work and change management (Wilton, 2016). For decades, technology has been regarded as a central enabler of these objectives, and scholars note a recent shift towards HR analytics and the adoption of artificial intelligence (AI) and other advanced technologies (e.g., Vrontis et al., 2021). These technologies are, among other things, considered to amplify the automation of administrative HR activities – including various information-centred tasks (Vrontis et al., 2021; IBM, 2018).
One AI technology that is suggested to support the achievement of these efficiency gains is the chatbot. Chatbots are often put forward as companies’ most common application of AI (Gartner, 2019), and are considered to be valuable due to the efficient assistance and easy access to information (Brandtzaeg & Følstad, 2017). Chatbots are noted to have several potential use areas in HR work (Majumder & Mondal, 2021; IBM, 2018; Vrontis et al., 2021), and are especially expected to be widely adopted to manage administrative and routine HR inquiries (Vrontis et al., 2021). To illustrate, ‘chatbot first’ is an increasing trend in customer support and it is believed that chatbots may serve a similar purpose for the HR function’s internal users by answering routine questions and guiding employees to the right information (Gartner, 2019; Accenture, 2018; Majumder & Mondal, 2021). Furthermore, authors note an increased focus on employee experience and the use of consumer-oriented technologies, such as chatbots, to raise employee engagement and increase task efficiency in daily work (CIPD, 2020; White, 2012; Deloitte, 2017; IBM, 2018).
Despite the popularity and wide application of chatbots, the technology is not yet widespread in the enterprise context (Meyer von Wolff, Hobert & Schumann, 2019;
Brachten, Kissmer & Stieglitz, 2021). Even though there is a growing body of chatbot research on user experiences and user motivations (e.g., Brandtzaeg & Følstad, 2017), research exploring the adoption of chatbots in organizational contexts is scarce (Meyer von Wolff, Hobert & Schumann, 2019; Brachten, Kissmer & Stieglitz, 2021). As such, there is a need to expand our knowledge regarding what characterizes the implementation of chatbot technology in specific organizational contexts and for different internal purposes (Meyer von Wolff, Hobert & Schumann, 2019). More specifically, considering the increasing
digitalization and uptake of chatbots in HR functions, there is a need for empirical investigation of how a chatbot can support the HR function (see research question 1).
Although decades of research and several reviews on technology in HR exists (e.g., Bondarouk & Brewster, 2016; Bondarouk et al., 2017; Stone et al., 2015), little is known about specific applications of chatbots for HR purposes. Consequently, there is a knowledge gap regarding how the implementation of an HR chatbot affects the HR function, as well as how the operational interplay may look like (see research question 2). According to
Bondarouk, Parry & Furtmueller (2017), factors affecting the adoption of electronic HR (e- HR) can be divided into technology, organization and people, and are referred to as the TOP framework. Furthermore, consequences of e-HR adoption are divided into operational, relational and transformational. The TOP framework serves to illustrate the shift in research perspectives on technology within the field of HR, where scholars have gradually moved away from a deterministic view on implementation of technology in organizations, and increasingly call for a contextual and human-machine understanding (Kim, Wang & Boon, 2020). Therefore, taking these factors into consideration and acknowledging the
implementation of a new technology as an organizational change, makes it necessary to explore how organizational characteristics impact the implementation and use of the HR chatbot (see research question 3).
As there exists a knowledge gap regarding the operational interplay between a chatbot and the HR function, an exploratory approach is appropriate. This thesis presents findings of an in-depth exploratory interview study with 13 HR practitioners in 10 different Norwegian organizations. As this thesis looks into the experiences of HR personnel and contributes to HR literature, the TOP framework along with the factors affecting e-HR outcomes is applied to structure and shed light on the findings.
The thesis contributes knowledge within the fields of HR and chatbot research. In terms of HR research, the current study presents findings indicating how HR personnel experience the implementation of an internal chatbot and how this affects HR work tasks and roles, as well as how this new interplay is perceived by HR practitioners. In terms of chatbot research, this study provides needed knowledge regarding an important future chatbot application area.
The thesis is structured as follows: First, an initial overview of the area of technology and work is presented, along with relevant chatbot literature concerning important chatbot characteristics and use. Second, a short introduction to the theory and field of HR
accompanied by research and empirical findings on technology in HR is given. Third, the methods part describes how the interviews were conducted and analyzed. Lastly, the findings are presented and discussed in light of relevant literature.
Workplace automation through chatbots Automation and work
We are currently witnessing a rapid increase of automation in the workplace, where humans to a larger degree will interact and work with machines (Kirlik, 2012; Cascio &
Montealegre, 2016). Automation involves technology that carries out tasks or functions that would normally be performed by humans (Parasuraman et al., 2000), ranging from simple mundane tasks to comprehensive decision support systems (Lee, 2008). Although automation of tasks is not new, recent developments in organizations are influenced by disruptive and ubiquitous technologies like AI, mobile platforms and transformative digitalization (Cascio &
Montealegre, 2016; CIPD, 2019; Deloitte, 2017). AI usually refers to intelligent machines capable of performing more human-like reasoning and actions, and usually entails reaching some type of goal without human input at every step (De Chodhury et al., 2020; Bjørkeng, 2019, p. 17).
Automation and AI involve a broad specter of significant benefits, including cost savings and efficiency. Specifically, AI is reported to improve quality of goods and services as well as increase revenue (CIPD, 2019). For example, AI can facilitate customized service interactions, work activities, and decision making (Vrontis et al., 2021; Malik et al., 2020), and thus contribute positively to employee engagement and overall performance.
It is however widely acknowledged that the introduction of automation does not guarantee improved performance or increased efficiency, and the allocation of functions between computers and humans is regarded as challenging (Lee, 2008; Cascio &
Montealegre, 2016). For example, decreased situation awareness, distrust or overtrust in automation, misuse, disuse and abuse are some of the well-known problems with automation (Parasuraman & Riley, 1997 in Lee, 2008; Cascio & Montealegre, 2016). The introduction of AI and automation in the workplace have also been reported to lead to negative experiences in terms of an overload of tools and repetition of work, additional tasks related to
maintenance, and expectations to use the systems regardless of their efficiency (CIPD, 2019).
Concern has also been voiced regarding the potential or actual unemployment of humans resulting from the introduction of AI (Kirlik, 2012), and several studies show that about half of today’s paid activities could be, or is at a high risk of being, automated by adapting current technology (McAfee & Brynjolfsson, 2017; Frey & Osborne, 2017).
Nevertheless, the concept of job augmentation is seen to become more relevant as AI will most likely replace specific tasks to begin with, and alter and change rather than replace jobs (CIPD, 2019; Vrontis et al., 2021). This is, among other things, argued to include necessary
upskilling of employees to match the new demands, as well as appropriate support for employees to adapt to these changes (CIPD, 2019).
Drawing on the field of human factors and human-computer interaction, unanticipated negative effects of automation may result from failure to understand how people adapt to the introduction of automation. Mismatch in expectations may lead to insufficient support of the users in applying and managing the automation effectively (Parasuraman & Riley, 1997 in Lee, 2008). As such, authors increasingly note the importance of functional human-machine collaboration (e.g., Cascio & Montealegre, 2016; Vrontis et al., 2021) as well as the fact that the consequences of technology largely depend on context (Bondarouk, Parry & Furtmueller, 2017; Vrontis et al., 2021).
Technology as organizational change
Organizational change poses a continuous challenge to managers, employees and organizations (Armenakis & Harris, 2009). In particular, the implementation of a new technology is seen to represent a change in the organizational context. Hence, it needs to be effectively planned and adopted. Defining technology implementation as organizational change makes it essential to consider theory and practice of change management (Arnold et al., 2016, p. 587).
It is commonly noted that most organizational change initiatives fail (Arnold et al., 2016; Burnes, 2015), and decades of research has sought to establish knowledge and concrete change practices that can increase its success rate and effectiveness (Arnold et al., 2016). A large portion of research and literature has focused on employee resistance to change, which is noted as one of the most influential reasons for failure (see Burnes, 2015 for a review and discussion). However, it is now widely acknowledged that organizations are complex social systems, and that successful change initiatives are highly context-dependent and situation- specific (Arnold et al., 2016, p. 615; Burnes, 2015). For example, Arnold et al. (2016) argue that the size of the change required, its intended outcomes and the organizational context can serve to inform the specific change process and present a change framework to guide a tailored approach to organizational change (p. 616-620).
This view does not exclude the notion of individual attitudes and behaviors during change initiatives (Burnes, 2015), as it is argued that change essentially must be implemented by change recipients (Armenakis & Harris, 2009). This means that it is central to understand the employees’ motivations (Armenakis & Harris, 2009), as well as how various types of change initiatives may impact individuals differently. As such, although there are several widely adopted and researched approaches to change management (planned/organizational
development vs. emergent), there exists no unified approach or recipe for a successful change process. Rather, to achieve an effective implementation of change, research and literature suggest that organizations and change managers must engage in careful and thorough evaluation of several contextual aspects (Burnes, 2015; Arnold et al., 2016).
Chatbots
Chatbots, also referred to as digital assistants, conversational platforms or virtual assistants, are software applications with which users interact through natural language, and where some result or goal is achieved through this conversing (Dale, 2016). Studies on interaction with computers through natural language date back to the 1960s with Weizenbaum’s ELIZA (Weizenbaum, 1966), and later initiatives have led the natural language user interface closer to human-like conversations (Dale, 2016). The last few years have witnessed a resurged interest in chatbots (Dale, 2016), and today consumers are getting accustomed to interacting with text-based chatbots or voice-driven systems like Apple’s Siri, Amazon’s Alexa and Google’s assistant on a daily basis. This is thought to be connected to recent advances in artificial intelligence and machine learning, along with the wide adoption of mobile phones and messaging apps (Dale, 2016; Brandtzaeg & Følstad, 2017).
Chatbots can serve a wide range of purposes, and application areas include customer service, emotional and social support, information activities, entertainment and connection to other people or machines (Brandtzaeg & Følstad, 2017). Among these, productivity is
reported to be the main reason for use, where the chatbot provides users with timely and efficient information or assistance (Brandtzaeg & Følstad, 2017).
Chatbots for internal purposes
According to Gartner research, chatbots have reached peak interest in enterprises as the most common uses of AI (Gartner, 2019). To illustrate this trend, Gartner (2019)
predicted that within a few years, most white-collar workers will interact with conversational platforms on a daily basis, and the benefits and areas of application are only expected to increase as the AI technology becomes more sophisticated (Accenture, 2018).
There are several reasons why chatbots may serve as companies’ first implementation of AI technology. Chatbot characteristics such as convenience and immediacy constitute central aspects (Brandtzæg & Følstad, 2017), along with the fact that chatbots are described as “(...) inexpensive to design, and quick to train” (IBM, 2018). It is expected that chatbots will influence productivity positively and also reduce company costs. For example, a study conducted by Accenture claims that 80% of customer queries can be handled by a chatbot (People Management, 2017). Another potential benefit is directly connected to knowledge
work and the digital workplace that accounts for the daily lives of many employees today.
Due to the vast amount of information, use of several communication channels and many different applications involved in this type of work, AI powered chatbots are believed to reduce both workload and time spent searching for the information needed to finish all sorts of tasks (von Wolff, Hobert & Schumann, 2019). In other words, one essential advantage of employing a chatbot for internal purposes, is the improvement of information management, along with the automation of administrative and routine tasks (von Wolff, Hobert &
Schumann, 2019; White, 2012).
Furthermore, the conversational interface may be intuitive and easy to use, and is therefore often considered to be in line with today’s emphasis on customer experience and usability. Natural language is for many the default mode of interaction online (Følstad &
Brandtzæg, 2017), and continuous conversation is now a part of today’s always-connected world (Dale, 2016). It is therefore argued that the tools and softwares applied in the work context need to stimulate the behavioral trends of consumers, and “(...) supplement faster, better and more efficient collaboration in the workplace” (Gartner, 2019). Put differently, employees expect to be able to use technologies that are familiar from personal use, and execute their work in a frictionless and efficient manner (White, 2012; von Wolff, Hobert &
Schumann, 2019). This self-service access is in turn thought to contribute to higher employee engagement (DiRomualdo, El-Khoury & Girimonte, 2018; Forbes, 2019; Majumder &
Mondal, 2021), and is also seen in relation to organizational reputation and employee
branding where the investment in modern technology is becoming a central part of attracting the right knowledge and keeping employees engaged over time (Accenture, 2018; Majumder
& Mondal, 2021).
Another reason is seen to be connected to the demographic changes taking place in the workplace, where millennials are demanding instant, digital connections that give access to information anytime and anywhere, along with the general consumer demand for quick query resolution and online self-service (Gartner, 2019; Speechtechmag, 2020). Thus, advances in AI and machine learning, making conversational interfaces more human-like, along with consumer technologies and the wide adoption of messaging platforms, are factors contributing to the rise of the chatbots and the increase in adoption of these conversational platforms in the enterprise setting.
Although practitioners point to great potential benefits with the employment of AI driven chatbots, others point to the fact that the technology is still in an early stage, and statistics show that the uptake of chatbots for internal purposes is not yet widespread in
enterprise settings (Meyer von Wolff, Hobert & Schumann, 2019; Accenture, 2018;
Brachten, Kissmer & Stieglitz, 2021). For example, some state the fact that these chatbots need continuous training and human supervision, and the output is limited to the examples and cases that the chatbots have been exposed to (Accenture, 2018). Furthermore,
implementing a new technology is not only dependent on the system or tool itself; people factors such as company culture, habits and attitudes are shown to have significant impact on the adoption of technologies (Bondarouk, Parry & Furtmueller, 2017). Similarly, it is argued that chatbots possess specific and special characteristics and that therefore it is important to gain understanding of their acceptance in the organizational context, as well as an
understanding of the employee needs and expectations (Brachten, Kissmer & Stieglitz, 2021;
Følstad, Nordheim & Bjørkli, 2018).
Human Resource Management
Human Resource Management (HRM or HR) concerns organizational activities related to the management of people at work (Wilton, 2016; Storey, Ulrich & Wright, 2019).
This includes external activities such as recruitment and selection, and internal practices related to training and development, rewards, motivation, health and safety, employee well- being, the design of work, and administration (Wilton, 2016). Depending on the company’s organization of the HR function, the HR department can also be a part of strategic and operational managerial activities such as change management and employee branding
(Wilton, 2016). The size of the organization, the status of the HR function, the credibility the HR function has in a given country or culture, as well as where power resides within the company, are all factors that impact the makeup of an HR department. As such, the HR function may vary a great deal between companies, and it is beyond the scope of this text to describe the different organizations of HR departments. However, some common features in the labour market context are worth noting, as this is connected to what is considered to be the core matters of the HR function today.
Brief overview of the developments within the field
The field of HR has over the past three decades evolved from a mainly administrative and operational function to what is now often considered to be a key contributor to
organizational competitiveness (i.e., a strategic and value-adding part of the company) (Wilton, 2016, p. 4; Ulrich & Dulebohn, 2015). As such, HR and Strategic HR are often used interchangeably (Storey, Ulrich & Wright, 2019), although Strategic HR can be defined as
“the pattern of planned HR developments and activities intended to enable an organization to achieve its goals” (Wright & McMahan, 1992, p. 298 in Boon, Eckardt, Lepak & Boselie,
2017), and differs from operational and administrative activities. This is based on the long history of changing assumptions about how employees can be controlled and motivated, from the early days of control and Taylorism to the Human Relations movement and recent times characterized by an increased focus on worker autonomy and flexible working arrangements.
Developments in the area of HR are also driven by changes in the external business
environment, where increased global competition, the shift towards a knowledge-intensive economy, ubiquitous technology and the changes in workforce demographics are considered to play a role in fundamentally changing the HR function and the role of HR practitioners (Ulrich & Dulebohn, 2015; Storey, Ulrich & Wright, 2019).
With the emergence of the resource-based view of the firm, employees are seen as a valuable source of competitive advantage, often referred to as human capital resources.
Technology, knowledge, culture and networks are considered to be strategic assets if they are rare, valuable, hard to imitate and specific to the organization (Wilton, 2016, p. 66). As such, HR of today is largely focused on activities related to internal resources that can contribute to organizational performance and competitive advantage (Storey, Ulrich & Wright, 2019).
HR today
One of the main focuses of the HR function is to deliver value to key stakeholders, including employees, line managers, external customers and investors (Ulrich, Younger &
Brockbank, 2008; Ulrich & Dulebohn, 2015). Due to the demands regarding both strategic, measurable contributions to the organization's performance and cost-effectiveness, the HR department is now seeking to reduce time spent on administrative tasks and strengthen the role as a strategic business partner and change agent (Wilton, 2016). A part of this is the notable trend of the outsourcing of HR administration and shared service centers, which ultimately is enabled by technology (Wilton, 2016). In addition, higher employer and employee expectations (CIPD, 2020) require HR to consider customer experience when delivering HR services (Ulrich, Younger & Brockbank, 2008).
In line with the abovementioned developments within the field and the positioning of the HR function as a strategic partner, the role of HR practitioners is described as changing in a fundamental manner. For example, the required competencies for HR professionals are increasingly complex, including analytical competencies and technological and
implementation skills (Storey, Ulrich & Wright, 2019; Kryscynski et al., 2018). As the HR function transitions towards a more digital operation, it is argued that many roles involving administrative and routine transactional work will disappear and that the role of HR
professionals will become more specialized (DiRomualdo, El-Khoury & Girimonte, 2018).
Due to the increased responsibility and the expected contribution to business success, HR practitioners must also develop an understanding of business processes and act proactively and based on data (Wilton, 2016; Ulrich & Dulebohn, 2015). A report from CIPD (2020) addressing future developments in the people profession states that a key area of strategic HR will be to lead and influence organizational change and development, and that HR
practitioners have to manage resistance to adopt digitalized ways of working.
Electronic Human Resources (e-HR)
In a broad sense, electronic Human Resources (e-HR) refers to the use of ICT in a wide range of HR activities, and can be understood as a system where managers, applicants and employees can access HR-related information and services (Wilton, 2016, p. 426). In their review article, Bondarouk, Parry and Furtmueller (2017) argue that e-HR needs to be viewed “(...) as the unique scholarly field of inquiry that focuses on all types of HR content that is shared through IT to make HR processes distinctive, consistent, and efficient that create long-term opportunities within and across organizations for targeted users'' (p. 99).
Developments in the adoption of technology in HR can be seen in the shift from traditional HR to Human Resource Information Systems (HRIS) to e-HR and strategic HR. HRIS is considered an older concept and tends to only be used by the HR department itself (Martin &
Reddington, 2010). In contrast, e-HR applies the internet and web-based systems (web 2.0 technologies like social media and mobile communication) to alter the interaction between the HR function and managers and employees to a more technology-mediated one, and is used by all employees at all levels within the organization (Martin & Reddington, 2010;
Parry, 2014).
How e-HR supports the HR function
The field of e-HR is relatively new, and the research field is considered to be complex due to the diverse perspectives and contributions derived from different disciplines (e.g., IT
implementation, Information Systems and HR). There is a general consensus in the literature regarding the potential benefits associated with e-HR adoption, although there are mixed results in terms of the way in which the positive consequences of such systems are derived (Bondarouk, Parry & Furtmueller, 2017).
The intended benefits or organizational goals for e-HR implementations mainly revolve around three objectives; cost reduction through streamlining and automating HR operations, improved effectiveness through better delivery of HR services, and an increased strategic orientation of the HR function (Marler & Fisher, 2013). At a basic level, e-HR can support the HR function in carrying out the administrative tasks and thereby potentially
freeing up HR practitioners for more strategic concerns (Wilton, 2016, p. 21). This is reported as the most common objective for the introduction of e-HR (Parry, 2014; Bondarouk & Ruel, 2009; Bondarouk, Harms & Lepak, 2017; Marler & Fisher, 2013), although some authors note a shift in goals for e-HR towards improved service provision and a strategic
reorientation of HR departments (Bondarouk, Parry & Furtmueller, 2017). In line with the resource-based view, these objectives can be related to the achievement of competitive advantage for a company (Parry, 2014) and are therefore widely accepted as value-adding investments. However, evidence suggests that e-HR does not necessarily lead to a positive change for HR (Marler & Fisher, 2013), and that HR departments experience difficulties with adopting new technologies (Bondarouk, Parry & Furtmueller, 2017).
In their review article, Bondarouk, Parry & Furtmueller (2017) summarize the research on the adoption and consequences of e-HR, and show that the factors affecting the adoption of e-HR can be divided into three areas: technology, organization and people. This is referred to as the ‘TOP’ framework. Technology factors concern aspects such as clarifying required technology characteristics prior to modifying or adopting new systems, as well as identifying the organization’s concrete needs before deciding on in-house- or outsourcing of technology development. Organizational factors consist of four categories influencing e-HR adoption, and these include organizational characteristics; planning and project management traditions; data access, security and privacy; and capabilities and resources. People factors include top management support; user acceptance; communication and collaboration between units; HR skills and expertise; and leadership and culture.
Furthermore, consequences of e-HR may be operational, relational or
transformational. Operational consequences include HR effectiveness, efficiency gains, cost and time savings. Relational consequences include HR service improvements; HR
professional’s status as information brokers; and new communication channels with HR, and have been detected in the form of improved communication, cooperation, relationships and HR service improvements. Lastly, transformational consequences entail strategic support through e-HR, where HR can be transformed to strategic partners. Research indicates that operational (cost savings and efficiencies) and relational outcomes (HR service quality) of e- HR are more steadily obtained (Florkowski, 2020; Bondarouk, Harms & Lepak, 2017; Parry, 2014), while several authors point to a lack of evidence concerning the strategic benefits of e- HR adoption (Florkowski, 2020; Bondarouk, Parry & Furtmueller, 2017). Moreover, articles also discuss the impacts of contextual factors, both internal and external to the organization, and note that the outcomes of combining technology and HR may depend on innovation
climate, external stakeholders and vendors and pre existing HR strategy (Florkowski, 2020;
Marler & Parry, 2016; Marler & Fisher, 2013).
Based on their observations, Bondarouk, Parry & Furtmueller (2017) argue that the most important factors affecting adoption of e-HR, as well as consequences of e-HR, are the different ‘people factors’. For example, the relationship between e-HR and HR service delivery seems to depend on the engagement of managers and employees with the e-HR system as well as the usability of the technology (Parry, 2014). Furthermore, the skill levels of the HR personnel in the areas of technical, consultancy and project management
competences are thought to be potential limiting factors in the realization of relational and transformational gains (Parry, 2014). Bondarouk, Parry & Furtmueller (2017) discuss that since e-HR affects the organization as a whole, employee and management support and commitment are essential, and that employees need to become convinced about the value of the new technology as well as trained for effective usage. In other words, the literature suggests that leaders promoting e-HR, trust, change management, confidence with
technology skills, user acceptance of e-HR applications, and communication about system usefulness, are the most central factors (i.e., people factors) for successful e-HR adoption that lead to organizational effectiveness (Bondarouk, Parry & Furtmueller, 2017; Ruel &
Bondarouk, 2014).
Chatbots in HR
Chatbots have been adopted for several HR-related purposes in enterprises (e.g., Gartner, 2019; Accenture, 2018; IBM, 2018), and a number of articles and reports describe potential areas of adoption and benefits. For example, chatbots are reported to add value by assisting the HR function in recruitment processes, onboarding, training, employer branding, answering frequently asked questions (both internal and external), automate HR routine tasks, and increase employee engagement (Majumder & Mondal, 2021; Sheth, 2018).
Even though the uptake of HR chatbots is not yet widespread (Accenture, 2018;
Meyer von Wolff, Hobert & Schumann, 2019; Brachten, Kissmer & Stieglitz, 2021), scholarly interest is seen to be growing. For example, Malik, Budhwar, Patel & Srikanth (2020) used an in-depth qualitative case study design and analyzed interview, documentary and observational data to gain insights regarding artificial intelligence in human resource management. The authors developed a conceptual framework for understanding how exchange of HR practices supported by AI-based chatbots and virtual agents leads to better individual and HR outcomes, and suggested that such technology-supported exchange leads
to more personalized and individualized employee experiences and improved cost- effectiveness of the HR function.
Research questions
Literature has suggested several use cases for chatbots in HR, but research regarding current chatbot applications for HR purposes and lived experiences of HR practitioners working with an HR chatbot is scarce. In order for chatbots to provide support and create value for the HR function as well as the organization as a whole, it is important to study the interplay between the chatbot and the HR personnel and gain empirical insights into central factors in this organizational change initiative. This study is intended as a first step towards building the needed knowledge regarding how HR personnel experience a chatbot, how such chatbots affect HR work and roles, as well as how HR personnel perceive the organizational aspects that may influence the success of the chatbot implementation. The research questions guiding this study are:
1) How can a chatbot support the HR function?
2) How does the implementation of a chatbot affect the HR function?
3) How does organizational characteristics impact the implementation and use of the chatbot to support the HR function?
As chatbots have not been studied as an HR technology, this study explores the adoption of chatbots in the context of HR. Specifically, this study explores the
implementation of HR chatbots in the view of e-HR research, while acknowledging the potential differences between such chatbots and other technologies for e-HR purposes.
Method
In this section, the project and the research design developed to explore the presented research questions, are described.
The project
This master project was conducted in collaboration with the research institute SINTEF in conjunction with their research and innovation projects “Human-Chatbot
Interaction Design” and “Chatbots for Loyalty”. Furthermore, two companies that deliver HR chatbots in the Norwegian market contributed with chatbot courses and information, along with access to potential participants through their networks. The master student managed all parts of the study, including initial formulation of the research questions and the interview guide, contacting respondents, coordinating interviews, collecting and analyzing data and the
final reporting of the project and research findings. The two supervisors, Knut Inge
Fostervold (UiO) and Asbjørn Følstad (SINTEF), guided the project, and contributed with input throughout the process, from initial planning to reporting. Feedback was given on drafts and writings, and further matters were discussed during frequent supervisor meetings held every three weeks.
Research design
This study applied a qualitative exploratory approach, as this is beneficial to
investigate new areas of interest where there exists little prior knowledge. Data collection was conducted through semi-structured interviews with various types of HR practitioners who held a role connected to the chatbot. Semi-structured interviews are particularly suitable when the research focus concerns experiences connected to the phenomenon under study (Willig, 2013, p. 29). The interviews were conducted through video calls and later transcribed in preparation for thematic analysis. To gather the needed insights, it was deemed beneficial to collect data from companies that have already implemented a chatbot for internal purposes.
The onboarding of such companies is described in the section about respondent recruitment.
Epistemological position
Turning to the clarification of the epistemological standpoint, it is here assumed a realist approach to knowledge creation. This is due to the practical and investigative focus of this master project, where the aim was to access information about the HR practitioners’ reality in relation to the chatbot. A realist approach assumes that the accounts given are direct
representations of what happened, and that the data tells something meaningful and true about the thoughts and behaviors of the participants (Willig, 2013, p. 15).
Study context
The study was conducted in Norway and addressed Norwegian companies with a chatbot for internal purposes as the object of study. Norway is considered a digitally advanced society (Meld. St. 27 (2015-2016), p. 17) with a population that scores relatively high on trust in technology (Ministry of Local Government and Modernisation, 2020, p. 5). In addition, the companies included in this study were all well-established companies with enough resources to participate in this study, even during times of a global pandemic. This is considered a good starting point for an initial exploration of the research questions,
considering that these companies already had some experience with the chatbot and that they exist in a society with a high uptake of new technologies.
The project was conducted in collaboration with two providers of chatbots for HR purposes in the Norwegian market. These two have provided HR chatbots to more than one
hundred customers in Norway. Consequently, all participants were users of chatbots from one of these providers. This was seen as beneficial to the study, as it allowed the master student to familiarize thoroughly with the solution in question, and it allowed for making the analysis more cohesive.
Participants and recruitment
Ten Norwegian companies that had implemented a chatbot for internal purposes were initially onboarded by a contact person at either one of the two chatbot companies or
SINTEF. The onboarding process then continued with the student sending an email,
explaining the purpose of the research study and why the person was being contacted in more detail. Inclusion and onboarding of companies was based on the following criteria: (1) had already implemented or in the process of implementing a chatbot and (2) the chatbot was implemented for internal purposes and mainly belonged to the HR function of the company.
The companies were all contacted based on the abovementioned criteria, and hence, their relevance for this study was established from the beginning.
In total, 13 respondents participated in the study. It was seen as desirable that participants held a role within the company closely linked to the HR function or HR work.
The respondents were sampled through email with an invitation to participate in the project, based on their role in the organization and their role connected to the chatbot. The emails included the informed consent document as an attachment. This to give the respondents time to read through everything in advance and to provide an option to ask questions before the interview. The overall research objective was also orally repeated in the video call before the interview started. Subsequent recruitment of interview participants happened mainly through word-of-mouth and the snowball-effect, where respondents would contact other people in the organization who could be interested in participating.
The specific roles that the participants held in relation to the chatbot varied. This was regarded as beneficial for data saturation, considering the general exploratory approach towards the interplay between HR practitioners and the enterprise chatbot.
HR chatbot experience
The participants represented a broad range of experiences with the HR chatbot. Ten of the participants worked directly with the chatbot at the time of the interview. Seven of the participants reported that they had been involved with the chatbot project from the beginning, and often held the role of project leader or driver of the chatbot initiative. This included the majority of implementation responsibility, such as having meetings with the vendor and coordinating the training and testing of the chatbot. The remaining six participants had not
been involved in the early implementation phase and had only received chatbot
responsibilities at a later point in time. Three of these reported that they started working with the chatbot as a part of their new job in HR (i.e., the chatbot was already implemented).
Three of the participants did not work directly with the chatbot at the time of the interview.
Of these, one participant had worked as the project leader and had brought the chatbot into the organization. One participant was leader of the HR department that is responsible for the chatbot technology. Lastly, one participant reported on being included early on to recruit other departments and work as a driver of the chatbot initiative and also worked directly with the chatbot for some time.
Furthermore, the participants represented organizations with different maturity in their implementation of the chatbot and also in terms of different approaches to how the chatbot was implemented. Nine of the participants told that the chatbot had existed in the company for one year or more. Three of the participants reported that the chatbot had been
implemented and deployed for a couple of months, and that they were still in an early phase in terms of expanding the chatbot content and uptake. In terms of approach to chatbot implementation, the participants worked with one of two different types of chatbots.
Although based on the same conversational platform, one type of chatbot was customized and built within the company, and the other type was purchased from a vendor that provided the chatbot content as a service. These chatbots will be referred to as “inhouse HR chatbot” and
“HR service chatbot” respectively. It is worth noting that these different types of chatbots account for parts of the variations observed in the participants’ reports.
Study material
An interview guide was constructed by the master student with input from the supervisors. It consisted of four main topics with more detailed follow-up questions
belonging to each of these. In the following, the four main topics are briefly described. The full interview guide is provided in Appendix B.
● Topic 1: The participant’s role and experiences connected to the chatbot.
The purpose of this topic was to make the participant confident in the interview setting and share openly and generally about chatbot experiences.
● Topic 2: The interplay between the HR function and the chatbot. These questions sought to explore the interplay between the HR practitioner and the chatbot in greater detail. This included past and current experiences, as well as perceived possibilities and limitations.
● Topic 3: How the implementation of an HR chatbot affects the HR
function. The purpose of this topic was to explore how the chatbot affects the HR function. This included ways in which the chatbot has altered HR work and how the chatbot is perceived by users in the organization, along with expectations regarding future chatbot impact.
● Topic 4: Organizational conditions that can promote or inhibit successful implementation. This theme sought to get an understanding of what the participants perceived to be relevant or impactful organizational aspects for a successful implementation of the chatbot.
Ethical considerations
Data collection included participants’ personal information such as name,
organization, department and contact information (email), as well as audio recordings of the interview. The project was therefore reported to the Norwegian Centre for Research Data (NSD) in advance of the study, and received approval to start the collection of data before the fall of 2020 (960546). The interviews were deidentified during transcription. All data was anonymized after the completion of the project by deleting the audio files. Contact
information was kept in a separate form and saved on a password protected memory stick, with no connection to the audio recordings. Audio recordings were stored at secure university servers and only accessed through university computers.
All participants received an information letter several days before their participation.
Here the purpose of the study and details regarding data processing were clearly stated, along with the notion that the participant could withdraw at any time. The main points of the
informed consent letter and the audio recordings were repeated once the interviewer and the participant met over video call. Due to constrictions following Covid-19, all interviews were conducted through video calls. Video calls offered a flexible and low threshold way for respondents to participate in the study, which was seen as beneficial since the invitation to the current project involved no form of material incentive. In this way, the effort required of the participants could be minimized. Further, the study did not contain any health sensitive information, and it was not assumed any noteworthy negative effect by interview participation.
Analysis Preparing for analysis
In preparation for the analysis, the voice recordings from the interviews were
transcribed. Transcription of verbal data can be done in numerous ways depending on, among
other things, how the data will be analyzed and the knowledge that the study aims to produce.
For the purpose of this study, the emphasis during transcription was put on the content and meaning of what was said, and therefore the voice recordings were transcribed word by word (verbatim). The raw data consisted of the transcripts from 13 qualitative interviews.
Thematic analysis
The qualitative data were made subject to a thematic analysis, which is a method for identifying, analyzing and interpreting themes within qualitative data (Braun & Clarke, 2017). Thematic analysis involves searching through the data set to find repeated patterns of meaning, and this search is guided by the research question along with the researcher’s approach to knowledge creation. More specifically, based on a recent clarifying article by Braun & Clarke (2020), the student conducted a reflexive thematic analysis. In this TA approach, the researcher’s subjectivity is an important analytical resource, as is reflexive engagement with theory, data and interpretation. Coding is open and organic, and themes are developed iteratively throughout the process (Braun & Clarke, 2020).
An inductive analysis was conducted in this thesis, and the codes were data-driven and later discussed in the light of relevant e-HR and chatbot literature.
Following the much-cited framework for thematic analysis developed by Braun &
Clarke (2006), the analysis was structured in six phases. These phases range from getting to know the data to presenting the themes in the final text.
Phase 1: familiarizing with the data. The first phase included transcribing all of the thirteen interviews verbatim. Any incomprehensible utterances were noted as a question mark along with the interview recording time stamp. Sentences with incomprehensible utterances were listened to several times in order to be transcribed correctly if possible. After all of the interviews were transcribed, the student noted some initial ideas for coding. To illustrate, one early idea regarded the allocation of tasks and the collaboration between the participants and the IT department or vendor.
Phase 2: generate initial codes. In this phase, segments of data were initially coded based on meaningful patterns in the data set. This was done in Nvivo12, which is a widely used software tool for analyzing qualitative data. There were many changes in the codes during this process, and some codes were split up to create two or several different codes, while others were merged together to give a clearer view of the main category. For example, one code that concerned all the expressed experiences with the chatbot, was split up to
differentiate between the statements that regarded the experiences of HR, and the experiences
that regarded the organization. The codes were subsequently reviewed several times. Most of the data contained meaningful statements that were coded.
Phase 3: search for themes. Guided by the research questions, the codes were
systematized according to how they seemed to reflect central insights. For example, in terms of how the chatbot affects HR work (research question), several codes were created that reflected tasks associated with the chatbot and categorized under the theme ‘Work content HR’. Furthermore, similar codes were clustered together. For example, ‘concrete and
personal questions’ and ‘situational and seasonal questions’ were among the codes that make up the theme ‘types of inquiries’ (i.e., what the chatbot is being used for).
Phase 4: review themes. The clustered codes were subsequently made into themes by continuously reviewing the code against the higher-order category. Since themes can be created in many different ways and since the research questions in this study are open and exploratory, many different codes and themes could be interpreted from the data for each research question. This phase mainly consisted of renaming the initial themes and sorting the codes to reflect the different perspectives mentioned in the interviews. For example, ‘tailoring information and communication to match organizational needs’ could have been interpreted as a positive experience for the employees, but is here defined as a way that the chatbot supports the HR function through enabling HR to provide better service. The student also reread the entire dataset several times while reviewing and made several overviews to see whether the themes and their relation to each other reflected the meanings and patterns in the data.
Phase 5: define and name themes. The thematic analysis was conducted without a theoretical framework, and hence the names and definitions of the themes were mostly created based on the context provided by the research questions. For example, the themes under ‘experiences of positive impact of the HR chatbot’ could also be named ‘chatbot characteristics’ as it reflects several similar findings reported in the chatbot literature.
However, since the participants reported on how they experienced the chatbot to impact the HR function and the organization, the themes were phrased in this manner. This may also contribute to longer theme names, as the themes seek to be descriptive, distinctive and inductive.
Phase 6: produce the report. Lastly, a rich description of the themes was written, and illustrative quotations were translated to English and added to the report. Final reviews regarding the overall story and findings were undertaken, and the report was structured according to the overall research questions.
Quality and reflexivity in qualitative research
There are several ways to ensure quality in qualitative analysis and research. In thematic analysis, Braun & Clarke (2006; Braun & Clarke, 2020) emphasize the importance of transparency and the acknowledgment of the researcher as an active part of the meaning making process. In qualitative research, credibility, transferability and reflexivity are often considered to be central evaluation criteria (Willig, 2013, p. 170-171).
Credibility is seen to encompass several aspects related to ensuring trustworthy findings grounded in the data, as well as a clear link between the study objective and the research design. During the analysis, codes and themes were continuously discussed with the supervisors to increase confirmability and transparency. Several extractions of the data are presented in the findings, along with rich descriptions that show how the student has interpreted the meaning of the data.
Most importantly, the study included participants and companies that were relevant for the relationship and research questions under investigation. As previously described, the organizations recruited for this study have all implemented chatbot for internal purposes, and the chatbot is closely related to the HR function in all of the included companies.
Furthermore, qualitative research gives room for participants to challenge, question and correct the researcher’s assumptions involved in the investigation of the subject matter (Willig, 2013, p. 24), which is considered to contribute to the validity in this study. For example, the master student initially encouraged the participants to ask for clarification of questions if this was needed, which also the participants sometimes did. Similarly, the master student repeated or summarized what had been said by the participant to check whether the meaning was understood correctly.
The reflexivity throughout the whole research process ensures awareness and clarity regarding the researcher’s role and other aspects that might have impacted the research. The master student’s prior understandings of the phenomena were limited to courses and readings in work-and organizational psychology, personnel psychology and human factors.
Knowledge about chatbots and related technology came from personal interactions with such platforms, and the master student did not know about the use of chatbots for internal purposes in organizations. Understandings of the phenomena were for a large part developed through data collection and subsequent reading of relevant literature.
Furthermore, the student documented several reflections regarding experiences during the interviews. These reflections concerned balancing the structure of the interview guide with the conversational flow of the interview, prompting thoughts while staying naive and
curious, as well as reflections regarding how to establish the rapport between the interviewer and the interviewee over video call.
Reflections concerning the epistemological viewpoint are also made explicit along with the decisions made regarding the analysis.
It is worth noting that the study was conducted during special circumstances (Covid- 19), which had several implications regarding the planning and execution of the research process (e.g., the early recruiting process and how the interviews were conducted). Several of the participants mentioned the pandemic as a factor affecting their latest work with the chatbot. As such, time pressure and stressful circumstances may be aspects of the study context that made the shaping of the findings different from what it would have been in the times prior to Covid-19.
Transferability generally refers to describing contextual features and situating the sample so that meaningful characteristics of the study are made clear. This is also seen to be closely linked to the general notion of transparency in terms of how the study has been conducted. Therefore, a rich and detailed account of the study context has been provided.
Findings
In the following, the results from the thematic analysis are presented. The chapter is divided into three parts. First, findings on how the chatbot supports the HR function are presented. Second, findings regarding how the implementation of the chatbot technology affects HR tasks and roles are described, along with experienced challenges and limitations associated with chatbot work. Lastly, a presentation of how the chatbot is used in the wider organizational context is given, as well as perceived challenges concerning the interaction between the internal users and the chatbot.
Before this, however, a brief initial overview of the organization's general motivations for taking up an HR chatbot is provided. The motivations for taking up an HR chatbot in the organization were reported to concern both strategic and operational aspects. Key strategic motivations concerned the internal users and their experience when approaching HR information services. Many of the participants noted that the user perspective was central when considering how the HR function supports the organization and the employees, and that information regarding employment conditions and relationships should be easily accessible and available. Furthermore, many of the participants expressed that the implementation of a chatbot can contribute to HR being perceived as technologically advanced, which ultimately may serve to establish an experience of the HR function as a modern and user-oriented part of the organization. Many of the participants also noted that by reducing the amount of routine
and administrative tasks for HR personnel, they may contribute more to developmental activities and other strategic initiatives. Key operational motivations concerned
administrative efficiency and automation to free up time for more value-adding tasks, both for HR personnel and employees and leaders. Many of the participants reported that the chatbot was perceived to be a tool to relieve the HR function of high volumes of inquiries, and increase the self-service in the organization.
To provide an indication of the prevalence of the different themes within the participant sample, the following phrases are used when reporting the findings: a few
(reported by 2-3), several/some (reported by 4-5), many (reported by 6-8), most (reported by 9-12).
How the chatbot supports the HR function
Throughout the interviews, participants reported on how the chatbot supports the HR function in their daily tasks and routines, by relieving HR workload and enabling HR to provide better service to the organization. These forms of support are detailed in the following, along with chatbot improvements that participants perceived to contribute to increased support.
Relieve the HR function
All participants reported relieving HR workload to be a key function of the chatbot.
Such relief may manifest in reduced volume of inquiries to the HR function, automation of responses to frequently asked questions from the employees, and freeing up time for HR to focus on more complex, human matters.
Reduce the volume of inquiries. Lessening the total workload for HR through reducing the volume of inquiries, was reported as the most important way in which the chatbot supports the HR function. The participants often referred to these inquiries as routine questions, administrative matters, or inquiries in the form of frequently asked questions (FAQs). These “simple” questions have a standard and often rule-based answer that applies to every employee, and often concerns information that is already available through company websites or platforms. As such, the chatbot is seen to relieve HR by handling requests that are considered to be easy for leaders and employees to solve themselves:
“[...] answer questions [...] that have a concrete answer. For example vacation, salary, so things that are regulated. That’s a big area that he can answer. And then those routine questions, right. Overtime and flexible working time and home office, for example”
(P10).
Most of the participants noted that the chatbot has a visible effect on the volume of common requests that the HR personnel receive. To illustrate, the participants pointed out that all questions and queries answered by the chatbot represent a phone call, email or knock on the door that the HR function did not have to engage with or get interrupted by:
“[...] the most common question that we usually get, we’ve actually not received ever since he was launched [07:02?]. And that question is approximately fifty percent of all of our inquiries. [...] it has gone way down. And that was a big wish” (P11).
It should be noted that many of the participants expressed this main benefit of the chatbot in a future-oriented manner, and explained that, at the time of the interview, it was too early to have a clear overview over the actual effects of the chatbot and how many inquiries it manages on average.
Freeing up time and resources. Most of the participants noted that by reducing the amount of “routine” inquiries, the chatbot may free up time and resources for the HR function:
“[...] I also feel that it saves a lot of time for us in HR, because we get a lot of inquiries. That we instead of spending a lot of time on formulating an answer, we can just refer to the chatbot. Or you can find the answer there” (P9).
Some of the participants described how the chatbot serves to save time for HR personnel when helping employees obtain the information that they need. For example, participants noted that the chatbot allows the HR function to formulate a good answer to a question one time, instead of answering the same question over and over. Furthermore, the chatbot enables HR personnel to spend less time guiding employees to the right information:
“[...] if you start to analyze how much time is spent on just helping people with getting relevant information, then it has been markedly improved, through the use of the chatbot” (P2).
Also for this theme, the participants varied in the degree to which they expressed that the chatbot currently frees up time and resources for HR personnel or whether this was a
desired future outcome. Many of the participants expressed that the chatbot has a big
potential to free up resources, but this is considered to be dependent on different factors (e.g., how long the chatbot has been in the company, the quality and coverage of the chatbot content). Additionally, a few participants pointed out that it is challenging to estimate how much time and resources the chatbot frees up.
Support tool for HR. Some of the participants reported that the chatbot is also useful for HR personnel. These participants described that they frequently ask the chatbot to filter and more quickly find needed information:
“Yes, I use chatbot a lot myself. If I want some simple information that I’ve forgotten, whether it’s a post adress or organization number [...]. These standard things that you don’t have written down anywhere” (P7).
Two of the participants also noted how the chatbot’s assistance during the early days of a new job in HR was helpful to learn about the organization, as well as gaining knowledge about the relevant regulations within the field of HR.
How the chatbot enables HR to provide better service
Although much of the emphasis was put on how the chatbot may relieve HR work, it was also evident that the chatbot supports the HR function by making it possible to offer better service to employees. This was mainly expressed in terms of tailoring information to match organizational needs, and by focusing on value-adding services.
Tailoring information and communication to match organizational needs.
Participants expressed that the chatbot may enable HR to provide better information services by providing HR with descriptive statistics. Specifically, several of the participants reported that the feedback from employees that is available through the chatbot log, offers insights into employees’ actual needs for information and support:
“And we go in and analyze what people actually ask about. Because we thought that everybody asked very generally. But people ask very concretely. [...] Ask about different things than we thought that they ask about” (P6).
As such, the chatbot may contribute to added value by first and foremost providing HR personnel with access to a statistical overview of the most common questions or HR inquiries, and secondly by enabling HR to adjust and improve communication and support
based on these insights. To illustrate, some of the participants noted that by getting information through the chatbot, they may learn more about the organization and use this knowledge to prioritize differently:
“[...] it is important information. Because if we see, wow, there are many people who ask about pension. We haven’t thought about that. That is a need, then maybe we should put it as a high priority and this needs to be fixed [...] Get to see the trends like this” (P1).
A few participants reported on how the chatbot can be used in a proactive manner. For example, based on insights from previous seasonal questions, HR can prepare answers in advance. One participant also explained how the chatbot eventually can be a useful tool in strengthening the connection between HR and the company’s geographically distributed offices. This way, HR can contribute to these offices feeling more included, and also adapt the content of the communication to local needs.
Focusing on central activities and value-adding service. Most of the participants reported that a key benefit of the chatbot is that it allows HR to focus and spend more time on what they consider to be tasks of greater value and complexity, rather than spending time on guiding employees and leaders to the right place for information or answering basic
questions. To illustrate, participants expressed that the chatbot makes it possible to do more of the core HR work, including employee follow-up and increased attention to those in need of more in-depth assistance:
“[...] we can deliver better quality on the services to those who really need our help.
Because those who really just wondered about something simple, they can get help from the chatbot” (P10).
This was sometimes referred to as a reason why a chatbot can never replace HR, as professional guidance and support is considered to be too complex to be sufficiently
performed by chatbot technology. Furthermore, although HR performs both operational and strategic work in the organizations, several participants emphasized how the chatbot plays a role in shifting HR’s focus to more value-adding services and support on the business level:
“[...] HR [...] should kinda be the more strategic value-creating partner of the company, to discuss matters like competence up against resources, the strategic resource
