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© 2022 American Psychological Association
WORKPLACE BULLYING AS AN ORGANIZATIONAL PROBLEM: SPOTLIGHT ON PEOPLE MANAGEMENT
PRACTICES
Michelle R. Tuckey , Yiqiong Li, Annabelle M. Neall, Peter Y. Chen, Maureen F.
Dollard, Sarven S. McLinton, Alex Rogers, Joshua Mattiske
Author Affiliations
Michelle R. Tuckey
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
Yiqiong Li
* The University of Queensland Business School, The University of Queensland
Annabelle M. Neall
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
* School of Psychology, The University of Queensland
Peter Y. Chen
* Department of Psychology, Auburn University
Maureen F. Dollard
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
Sarven S. McLinton
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
Alex Rogers
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
Joshua Mattiske
* Centre for Workplace Excellence, UniSA Justice & Society, University of South
Australia
Tuckey, M. R., Li, Y., Neall, A. M., Chen, P. Y., Dollard, M. F., McLinton, S.
S., Rogers, A., & Mattiske, J. (2022). Workplace bullying as an organizational
problem: Spotlight on people management practices.Journal of Occupational Health
Psychology, 27(6), 544–565. https://doi.org/10.1037/ocp0000335
ABSTRACT
Though workplace bullying is conceptualized as an organizational problem, there
remains a gap in understanding the contexts in which bullying
manifests—knowledge vital for addressing bullying in practice. In three studies,
we leverage the rich content contained within workplace bullying complaint
records to explore this issue then, based on our discoveries, investigate people
management practices linked to bullying. First, through content analysis of 342
official complaints lodged with a state health and safety regulator (over 5,500
pages), we discovered that the risk of bullying primarily arises from
ineffective people management in 11 different contexts (e.g., managing
underperformance, coordinating working hours, and entitlements). Next, we
developed a behaviorally anchored rating scale to measure people management
practices within a refined set of nine risk contexts. Effective and ineffective
behavioral indicators were identified through content analysis of the complaints
data and data from 44 critical incident interviews with subject matter experts;
indicators were then sorted and rated by two independent samples to form a risk
audit tool. Finally, data from a multilevel multisource study of 145 clinical
healthcare staff nested in 25 hospital wards showed that the effectiveness of
people management practices predicts concurrent exposure to workplace bullying
at individual level beyond established organizational antecedents, and at the
team level beyond leading indicator psychosocial safety climate. Overall, our
findings highlight where the greatest risk of bullying lies within
organizational systems and identifies effective ways of managing people within
those contexts to reduce the risk, opening new avenues for bullying intervention
research and practice.
KEYWORDS:
people management practices, workplace bullying, risk contexts, risk audit tool,
work environment hypothesis
Workplace bullying is a form of systematic mistreatment that occurs repeatedly
and regularly over time, whereby the target has difficulty defending themselves
due to the power imbalance between the parties involved (Einarsen et al., 2011).
The persistent and frequent nature of bullying, together with power imbalance as
a sustaining factor, helps to distinguish it from other mistreatment concepts
such as incivility, abusive supervision, and social undermining (Hershcovis,
2011). Bullying undermines the healthy functioning of employees and
organizations alike. It has an erosive effect on targets, triggering a resource
loss process (Naseer & Raja, 2021; Tuckey & Neall, 2014) that results in diverse
deleterious effects such as psychological health problems, symptoms of
posttraumatic stress, emotional exhaustion, elevated intention to leave, and
reduced job satisfaction and organizational commitment (Boudrias et al., 2021;
Nielsen & Einarsen, 2012). There is also growing evidence that bullying at work
is related to poorer cardiovascular health (e.g., Kivimäki et al., 2003; Xu et
al., 2019), suicidal ideation (Leach et al., 2017), and sleep problems (Nielsen
et al., 2020). Once escalated, it is difficult to effectively resolve bullying
situations (Zapf & Gross, 2001), particularly in unsupportive work environments
(Kwan et al., 2016; Törnroos et al., 2020). There is thus a strong impetus for
evidence-based prevention and intervention to circumvent cycles of damaging
interpersonal interactions.
Though it manifests in the form of negative acts within dyads or small groups,
bullying at work has long been recognized as an organizational problem (see
Leymann, 1996). Research under the work environment hypothesis, which positions
“characteristics of the psychosocial work environment as precursors of bullying”
(Skogstad et al., 2011, p. 476), has emphasized job characteristics, leadership
styles, and facets of organizational climate as risk factors. Less discussed is
how, in daily working life, these risk factors operate and intersect within
organizational contexts in which tasks and roles are coordinated, job
performance is managed, and relationships are nurtured in the pursuit of
organizational objectives. For example, managers display certain leadership
styles not in a vacuum, but in the context of clarifying tasks, allocating
workloads, appraising performance, and so on, in order to steer employees toward
organizational goals. This steering process is, in turn, likely to affect
perceived job characteristics typically associated with bullying, such as the
level of job autonomy, supervisor support, or role ambiguity. The actions taken
by managers in these contexts also determine how climate is transmitted from
organizational to work group level, with potential flow-on effects for bullying
exposure. Paying explicit attention to the organizational contexts in which
bullying manifests is thus likely to be valuable for enriching knowledge of the
antecedents beyond existing studies, and for understanding how those antecedents
are connected to the development of bullying—important considerations for
effective prevention.
Recognizing the critical role of organizational contexts in affecting the
behavioral phenomena within (cf. Porter, 1996), we set out to leverage the rich
content contained within official workplace bullying complaint records to
uncover the contexts in which bullying occurs within organizations; these “risk
contexts” (cf. Lazzerini & Pistolesi, 2013) are indicative of “systemic errors
in the way the organization functions” (Akerboom & Maes, 2006, p. 23) that, in
this case, foster bullying. We discover inductively in Study 1 that bullying
manifests in organizational contexts related to people management (see Purcell &
Hutchinson, 2007). Here, we refer to micro-organizational conditions wherein
supervisors implement human resource (HR) policies and procedures to meet
organizational goals by managing and organizing people and tasks within time and
resource constraints. After documenting the (people management) contexts in
which bullying arises in Study 1, we develop a behaviorally anchored rating
scale (BARS)—called a risk audit tool—comprising indicators of effective and
ineffective people management practices (Study 2) and validate the risk audit
tool as a predictor of bullying exposure (Study 3).
Theoretically, our research contributes to understanding bullying as an
organizational problem by identifying where the greatest risk of bullying lies
in day-to-day organizational life and providing guidance on how to intervene to
mitigate bullying risk. Specifically, we demonstrate that exposure to bullying
is associated with ineffective people management in particular risk contexts;
conversely, cultivating more effective people management within those contexts
offers a targeted opportunity for proactive bullying prevention. Practically,
our discoveries open the door to the possibility of “designing out” bullying
from organizational systems—enhancing people management practices in general,
and particularly within the contexts identified here, offers concrete focal
points for prevention and intervention. Methodologically, our research develops
and validates a measurement tool for assessing people management practices
linked with workplace bullying that can be used to inform and evaluate workplace
bullying interventions in both research and practice.
THEORETICAL BACKGROUND
The extensive body of research on workplace bullying antecedents illustrates
that bullying is largely influenced by work environment factors such as job
characteristics (e.g., job demands, job resources), leadership styles (e.g.,
transformational leadership, laisse-faire leadership), and organizational
climate (e.g., psychosocial safety climate, mistreatment climate; see Feijó et
al., 2019). To prevent bullying, the consensus from this literature is to ensure
job demands are reasonable and job resources are sufficient; positive leadership
styles are selected for and/or developed; and healthy organizational climates
are cultivated. So far, however, few studies have examined how these factors
shape the development of bullying (see Baillien et al., 2009, for an exception)
or how they come together to influence bullying risk (as recently explored by
Plimmer et al., 2021). Moreover, there remains a lack of research effort toward
understanding how these factors are situated within and connect to the broader
organizational context and how this context influences bullying.
By way of illustration, we suspect that one reason for inconsistent findings
regarding the impact of job characteristics on bullying (e.g., Gardner et al.,
2016; Notelaers et al., 2010; Van den Broeck et al., 2011) is a lack of
knowledge about how such characteristics emerge and in what contexts they take
effect to shape bullying exposure. For example, if task autonomy does not offer
protection against bullying exposure (e.g., Notelaers et al., 2010),
contradicting prevailing findings regarding job control, could context matter
here? A context wherein supervisors drive subordinate staff to attain particular
goals, and grant wide-ranging discretion around the methods by which the goals
are attained, may be ripe for bullying through competition for essential
resources (cf. Tuckey et al., 2012); in this kind of context, task autonomy may
be associated with higher levels of bullying. Similarly, the leadership
literature focuses on the general traits and behavioral styles of leaders and
how these are associated with bullying exposure, where there have also been
unexplained findings (e.g., Gardner et al., 2016; Yun & Kang, 2018). More
attention needs to be paid to the contingencies concerning how and in what
circumstances those traits and styles are manifested in the service of
organizational objectives to result in bullying. For example, transformational
leaders motivate followers to aim higher. In a context where supervisors do not
manage workloads with appropriate resourcing, such expectations may exacerbate
the impact of work stress, and counteract the supportive role of
transformational leadership in mitigating bullying. Further, organizational
climate reflects the shared sense held by employees regarding the formal
policies, procedures, and practices within an organization (Schneider &
Reichers, 1983). Neglected in this picture, however, is the important role
played by line managers in their implementation, which heavily influences
employees’ shared perceptions of the climate and flow-on outcomes such as
bullying (e.g., Plimmer et al., 2021).
Exploring what happens at the interface between supervisors and subordinates is
thus likely to be useful for advancing our understanding of bullying at work in
a way that builds on the knowledge generated through the major streams of
inquiry. Informed by our inductive exploration of the contexts in which bullying
manifests in organizations, we examine the supervisor–subordinate interface
through the lens of people management practices.
THE ROLE OF SUPERVISORS IN PEOPLE MANAGEMENT
Organizations utilize HR policies and practices to encourage employee behavior
that contributes to desirable operational and financial objectives (Jiang et
al., 2012). There is a growing devolution of HR responsibilities wherein HR
managers are increasingly tasked with designing policies to ensure integration
of HR issues with strategic decision marking while the implementation of people
management practices is progressively shifting to line managers (Perry & Kulik,
2008). Line managers have thus begun to play a more critical role in
coordinating, appraising, and motivating employees to work toward organizational
goals.
Although widely recognized, the role of line managers in enacting people
management is underresearched (Knies et al., 2020). Existing research has
focused on formally established HR policies developed by HR managers (the
so-called intended HR; Wright & Nishii, 2006; e.g., Gong et al., 2010) or on
perceived HR practices in the eyes of employees (e.g., Alfes et al., 2012). Yet,
line managers build the bridge between intended HR policies and perceived HR
practices by enacting them through their day-to-day interactions with employees.
The effectiveness with which they do this is considered the major cause of the
gap between intended and perceived HR (Purcell & Hutchinson, 2007). Negligence
of this aspect of contemporary line manager roles is detrimental to
understanding the mechanisms through which formal organizational policies
contribute to organizational performance and employee health and well-being
outcomes (Boxall et al., 2011).
Key to this diminished line of research is the dearth of appropriate measurement
instruments for people management. There have been some important attempts to
operationalize people management by line managers, such as Gilbert et al.’s
(2011) measure of line manager enactment of HR practices and Knies et al.’s
(2020) people management scale. Both instruments distinguish management and
leadership components within the role of line managers, though these dimensions
are conceptualized as being mutually reinforcing. Therefore, as reflected in the
measures, items from the management component appear to tap content similar to
the leadership component, making it difficult to distinguish between these two
dimensions. By contrast, the risk audit tool we develop in Study 2 incorporates
management and leadership components reflecting how line managers use leadership
behaviors to perform people management. Moreover, the risk audit tool uses an
extended range of effective and ineffective behaviors to capture how line
managers implement people management practices in each risk context. In
comparison, the existing measures assess fewer people management practices,
utilize just one or two items per practice, and focus on general perceptions of
the existence of the practices rather than on the effectiveness through which
they are enacted.
The risk audit tool we develop here is also distinct from measures of
leadership. Many measures of leadership behavior “are not related to line
managers’ implementation of HR practices” (Knies et al., 2020 p. 709).
Leadership measures tend to be abstract, reflecting how supervisors manage
individual employees’ personal feelings, interests, and needs, without being
connected to how supervisors perform people management. That is, leadership
tends to be measured without being grounded in the contexts in which people
management practices are implemented to influence employees’ attitudes and
behaviors. For example, transformational leaders value individualized
consideration (Bass & Avolio, 1989), which may manifest in how supervisors
assign tasks in accordance with employee competencies, how they accommodate
reasonable leave and break requests, and/or how they tailor training and
development opportunities—all aspects of people management. In this way,
measures of leader behavior do not explicitly indicate to line managers how they
can provide better leadership while implementing people management practices as
a supervisor. Our risk audit tool bridges this gap and provides instrumental
guidance regarding where and how supervisors are performing well in terms of
people management and where and how they can improve their performance.
THE PRESENT RESEARCH
Here, we begin with an inquiry into the risk contexts in which bullying arises
at work, with the premise that these contexts serve as indicators of where
systemic errors in organizational functioning increase the likelihood of
bullying. In Study 1, we carry out a content analysis of 342 official case
records of workplace bullying complaints lodged with a state work health and
safety regulatory body and discover that the risk contexts for bullying relate
to people management. In Study 2, through a sequence of stages, we develop a
BARS to measure the people management practices linked to bullying, called a
risk audit tool, and establish its psychometric properties. Finally, in Study 3,
we use the risk audit tool as part of a multilevel multisource study with 25
hospital teams to establish criterion-related validity. We demonstrate that the
perceived effectiveness of people management practices, assessed by the risk
audit tool, predicts self-reported workplace bullying exposure at individual
level for subordinates beyond established bullying antecedents, and at the team
level beyond organizational climate for both supervisors and subordinates. The
samples and analyses used in each study are summarized in Table 1.
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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Overall, our findings highlight which aspects of people management are
associated with the greatest risk of bullying and pinpoint targets for effective
ways of managing people to mitigate bullying risk, providing a foundation for
organizational interventions in research and practice.
STUDY 1
To explore the organizational risk contexts for bullying, we examined the
official case records of 342 workplace bullying complaints lodged by South
Australian workers with the state government work health and safety body. The
official case records provide access to rich textual information on what
complainants perceive as fundamental to their experiences of bullying at work,
which can shed light on the complexities of how work environment factors
manifest in bullying situations in a way that quantitative research designs
cannot, and in a form that offers potential for maximum variation in experiences
rather than being narrowly constrained by existing thinking on the role of the
work environment in bullying (cf. Guerin, 2016). Further, noting that few
studies have investigated bullying at work and related phenomena using methods
other than self-report surveys and, more recently, diary entries (see the review
by Neall & Tuckey, 2014), we follow several other scholars in recognizing the
value of official documents and records as valuable sources of information for
learning about personnel issues beyond those methods traditionally used in the
organizational sciences (e.g., Barrett & Kernan, 1987; Miller et al., 1990;
Russell, 1984; Werner & Bolino, 1997).
METHOD
Ethics approval was obtained from the University of South Australia Human
Research Ethics Committee before commencing the research. The following sections
outline the sourcing, transcribing, cleaning, and coding of the official case
files analyzed in Study 1.
DATA ACQUISITION
Between 2004 and 2013 (March), 1,205 requests for an investigation into alleged
workplace bullying were lodged with SafeWork SA. Close inspection of the case
files revealed that the quality and volume of the material varied considerably.
Some cases had more than 480 pages of information; others contained as few as 10
pages. In addition, the breadth and thoroughness of the SafeWork SA
investigation process for psychosocial hazards have evolved since 2010 (in line
with an expanding legal scope), with a corresponding increase in the quality and
volume of case-file data. As a result, the ability to identify key contextual
factors from cases prior to 2010 was severely limited and, based on their
comprehensiveness, we utilized case files from 2010 onwards. A total of 524
cases were opened between January 2010 and March 2013. Of these, 55 were still
under investigation at the time of transcription, and a further 124 were not
available on site. In addition, two cases were removed from the analysis as the
complaints were outside of the jurisdiction of the SafeWork SA investigation
process, and one case was removed due to insufficient information provided
regarding employment. Thus, a final data set of 342 usable cases was available
for analysis (referred to as Sample 1 in Table 1).
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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Information from the 342 case files was de-identified and transcribed into
Microsoft Word files onsite at SafeWork SA by an independent agency. Key
materials and documents transcribed for analysis included the lodged complaint
form for the case (the main element of which was a summary of the complaint)
plus a range of accompanying materials, such as written evidence provided by the
complainant (e.g., the initial complaint to their employer organization); email
communications (i.e., between the complainant, SafeWork SA, and the
organization); transcripts of mediated meetings; diary entries detailing the
dates, times, places, and events involved; evidence of impact on health and
safety (e.g., statements from medical professionals); and records, results, and
outcome correspondence for the internal and external (SafeWork SA) investigation
processes. Demographic characteristics were also recorded (e.g., gender of the
complainant, industry, and whether the complainant had left the organization).
This diverse array of documentary evidence enabled the research team to
“triangulate” data from different sources (cf. Eisenhardt, 1989). The complete
data set consisted of over 5,500 pages of single-spaced case-related
information.
CASE SUMMARY INFORMATION
Table 2
TABLES AND FIGURES
Table 2. Study 1: Summary of Workplace Bullying Complaint Characteristics
(Sample 1)
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shows the gender, industry, and work status of complainants within the Study 1
sample. Responsible for 59.7% of the complaints, female employees were
overrepresented in the sample compared with male employees with respect to the
composition of the South Australian labor force (wherein women comprise
approximately 45.7% of the workforce; see http://stat.abs.gov.au). The three
industries with the highest number of complaints were health and community
services, property and business services, and retail trade, which together were
represented in half (49.4%) of the complaints. Most employees remained employed
in the organization at the time of the complaint, although a sizeable proportion
(38.3%) had left the organization at the time of or soon after lodging the
complaint.
DATA ANALYSIS
The transcribed documents were imported into the qualitative data-analysis
software, NVivo Version 10.0 (QSR International, 2010). The analytic method used
for coding the transcripts was guided by an interpretivist inductive methodology
(Eisenhardt et al., 2016; Schilling, 2006), which reflects a coding paradigm
that emphasizes subjectivity, interpretation, and reflexivity, and rejects the
possibility of an objective “truth” (often adopted in positivist approaches)
that researchers can assess reliability through a well-articulated coding
protocol (Syed & Nelson, 2015, p. 3). Specifically, we were guided by the
inductive analysis process specified by Schilling (2006) in our systematic,
stepped framework for undertaking quality content analysis. Key features and
phenomena in the data were analyzed following Levels 2–5 of Schilling’s content
analysis spiral.
First, an initial review of 50 randomly selected cases was conducted by four
members of the author team, combined with information gathered from 1-hr
interviews with SafeWork SA inspectors, to identify the dimensions in focus
within the data set. This review also allowed researchers to describe the data
set (i.e., where key information lies within case files), and define meaningful
units of analysis (in this instance, short paragraphs; see Locke, 2002).
In the next step, the textual data from all 342 cases were submitted to
“structuring content analysis” (Schilling, 2006, p. 32), whereby an exhaustive
qualitative coding process was conducted by two authors (the primary coders)
working together to create a preliminary category system that represented
meaningful elements of the data by asking the question “what organizational
conditions and factors are connected to the perceptions of bullying within the
case?” The preliminary category system was represented by five core subjects:
misuse of human resource management procedures (67.4% of cases), communication
(64.1%), supervision process (58.9%), role clarification (52.2%), and
performance management (34.8%).
Third, the primary coders then revisited and refined each of the five subjects
to identify specific risk contexts associated with bullying perceptions and
complaints (marking a jump from the preliminary category system to coded
protocols; Schilling, 2006). For example, a category for the risk context
managing underperformance was created because a review of the performance
management subject node revealed that many employees felt mistreated when they
were subject to performance management processes (i.e., poor representation,
failure to provide guidance on how to improve performance). Overall, this
process resulted in the identification of 11 risk contexts associated with
perceptions of bullying in the sample of complaint records (see Table 3),
TABLES AND FIGURES
Table 3. Study 1: Risk Contexts for Workplace Bullying Evident in the Complaints
(Sample 1)
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reflecting different aspects of people management. Segments of raw text were
allocated to each risk context, with an open discussion between the primary
coders and, where necessary, the other authors to resolve ambiguous text or
create new categories. These risk contexts were modified in an iterative process
according to the researchers’ evolving understanding (cf. Tracy & Hinrichs,
2017) of the contexts in which perceptions of bullying arise.
Fourth, categories for the risk contexts were further broken down into practice
subcategories by two additional authors (secondary coders). Using two different
pairs of coders ensured rigor and trustworthiness of the coding scheme (as per
Lincoln & Guba, 1985; Tracy & Hinrichs, 2017). The secondary coders revisited
the raw text associated with each risk context to identify information about the
practices involved by asking the question “what practices are being performed
within the risk contexts when the perception of bullying occurs?” In the
two-level data structure generated in this step, each category represents a risk
context (e.g., rostering, scheduling, arranging, and compensating working
hours), and contains a number of subcategories which each refers to a specific
people management practice within the risk context (e.g., assigns employees to
work on days they have indicated as being unavailable or provides insufficient
consultation about rostering changes). The secondary coders worked closely
together and discussed the refinement of categories and subcategories with other
authors in an iterative process of resolving differences through consensus.
After the data were exhaustively sorted into categories and subcategories, the
authors systematically explored the similarities and differences in categories,
links among categories, and connections to relevant literature to develop a
higher order conceptual interpretation of the coded protocols (as per Schilling,
2006). In this step, the research team repeatedly consulted the literature on
management competencies to aid interpretation of the findings, and iteratively
revisited the literature, higher order dimensions, categories, subcategories,
and raw data. This step produced a coherent data structure with three levels: 92
people management practice subcategories categorized in 11 risk context
categories, organized into three conceptual dimensions.
Finally, to ensure trustworthiness and rigor the authors conducted “member
checks” (see Tracy & Hinrichs, 2017) by presenting the data structure to
representatives from SafeWork SA who were familiar with the complaint files;
several academic scholars in the field; and key contacts in employee unions,
employer organizations, regulators, and industry association bodies. The member
checks revealed widespread endorsement of the three-tiered framework, which they
described as reflecting their perspectives of how bullying arises within dynamic
organizational systems. Through these discussions minor adjustments were made to
the data structure, mainly reflecting phrasing issues.
RESULTS AND DISCUSSION
RISK CONTEXTS FOR WORKPLACE BULLYING
Our analysis of the 342 case records revealed that perceptions of workplace
bullying arise in three broad dimensions of people management: (a) coordinating
and administrating working hours, focused on day-to-day administrative duties
relating to work arrangements and schedules; (b) managing work performance,
focused on the quality of subordinate job performance; and (c) shaping
relationships and the work environment, focused on healthy and effective
relationships with and among subordinates, and workplace safety. As shown in
Table 3,
TABLES AND FIGURES
Table 3. Study 1: Risk Contexts for Workplace Bullying Evident in the Complaints
(Sample 1)
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just under half (46.8%) of the complaints involved matters relating to how
working hours are coordinated and administrated (the first category). A majority
(82.5%) of the 342 complaints involved some aspects of managing work performance
(the second category). Finally, in two-thirds of cases (65.2%), there were
issues regarding the way that relationships were managed (with individuals, or
when leading the work unit more broadly) and ensuring a safe working environment
(the third category).
More detailed information is presented in Tables 4–6 [table 4,
TABLES AND FIGURES
Table 4. Study 1: Risks Related to Coordinating and Administrating Working Hours
(Sample 1)
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table 5,
TABLES AND FIGURES
Table 5. Study 1: Risks Related to Managing Work Performance (Sample 1)
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table 6]
TABLES AND FIGURES
Table 6. Study 1: Risks Related to Shaping Relationships and the Work
Environment (Sample 1)
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regarding the people management practices associated with the perception of
bullying in each of the risk contexts. In the first dimension (Table 4),
TABLES AND FIGURES
Table 4. Study 1: Risks Related to Coordinating and Administrating Working Hours
(Sample 1)
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analysis of the complaints showed that when supervisors are involved in
rostering, scheduling, arranging, and compensating working hours, perceptions of
bullying were linked to ineffective people management practices such as
underrostering, underpayment, and inadequate input into and control over work
schedules. In terms of administering leave and entitlements, the complaints
revealed ineffective people management practices such as unequal and inefficient
access to leave, breaks, and other entitlements.
As shown in Table 5,
TABLES AND FIGURES
Table 5. Study 1: Risks Related to Managing Work Performance (Sample 1)
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the second dimension is comprised of various risk contexts regarding how work
performance is managed: (a) clarifying, defining, and assigning job roles,
involving ineffective people management practices such as changing details of
the role description without consultation; (b) guiding, directing, and
motivating employees, including practices such as undermining the work of
subordinates and misusing the position of authority; (c) providing training,
development, and personal growth, incorporating practices such as providing
insufficient training to undertake the role and blocking further development by
denying training requests; (d) managing tasks and workload, for instance
enforcing unmanageable workloads and unreasonable deadlines; (e) appraising and
rewarding job performance, including excessive monitoring of work, and
neglecting to provide appropriate reward or recognition for performance; and,
finally, (f) managing underperformance (represented in around half of the cases
in this dimension) encompassing practices such as using the formal performance
management process to intimidate subordinates, and not conducting formal
performance management in a prompt, clear, and legitimate manner.
Finally, in the third dimension, we found that a major concern relates to
respecting, valuing, and involving individual employees, conveyed in nearly
three-quarters (72.6% in) of the cases (refer to Table 3).
TABLES AND FIGURES
Table 3. Study 1: Risk Contexts for Workplace Bullying Evident in the Complaints
(Sample 1)
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The ineffective people management practices associated with perceptions of
bullying (see Table 6)
TABLES AND FIGURES
Table 6. Study 1: Risks Related to Shaping Relationships and the Work
Environment (Sample 1)
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include not responding to communication or making (false) accusations about
inappropriate behavior. Also evident was perceived mistreatment in the way that
relationships with employees within the work unit were handled collectively when
leading the work unit, such as excluding or isolating employees and using
different rules, and concerns related to maintaining a safe environment, for
instance by ignoring safety concerns and safety-related complaints.
STUDY 2
In Study 1, we developed a set of 11 contexts in which workplace bullying
manifests, all reflecting the use of people management practices. Said another
way, when people management practices are used ineffectively or unreasonably in
these 11 contexts, there is a risk that employees will feel bullied. In
contrast, the implementation of effective people management practices in these
contexts should lower the risk of bullying at work.
The risk contexts identified in Study 1 shed new light on where and how the risk
of bullying arises from a work environment perspective. Our second study sought
to apply the BARS technique to construct a behaviorally based measure of
effective and ineffective people management practices in the risk contexts and
then investigate (in Study 3) whether the effectiveness of those people
management practices is associated with self-reported exposure to bullying. The
BARS technique facilitates information processing and can reduce subjectivity
and errors in ratings (Campbell et al., 1973). Construction of the BARS in Study
2 first involved the identification of specific behavioral examples of people
management practices in each risk context, at varying levels of effectiveness.
We utilized the set of people management practices coded in Study 1, described
by employees as part of their experiences of (perceived) bullying when making a
bullying complaint, together with additional interview data (collected in Study
2) to translate the risk contexts into concrete, specific behavioral indicators
of effective and ineffective people management practices. Once a comprehensive
set of indicators was identified, following the approach used by Landy et al.
(1991), two validation techniques were employed to convert the indicators into
the BARS: retranslating the indicators into the corresponding risk context and
rating each indicator according to effectiveness in order to scale them.
Archival data (such as we analyzed in Study 1) are useful for avoiding
inadvertent biases that can be introduced by researchers when studying a
sensitive topic like workplace bullying. However, sampling only cases of alleged
bullying (in the complaints) gives rise to the possibility of a different kind
of bias. Employees who feel mistreated may form a negatively skewed view of how
the organization operates, raising questions about the validity of the work
environment factors that emerge as salient in their accounts. Further, even if
all targets of bullying report similar organizational risk factors, it must
still be established whether those factors are also present in cases wherein
bullying does not occur. To overcome the potential for this type of bias and
create a valid measurement instrument, the focus on alleged bullying associated
with ineffective people management practices was countered in Study 2 by
conducting critical incident interviews with diverse stakeholders to collect
examples of both effective and ineffective behavior while executing people
management in each risk context.
METHOD AND RESULTS
Research ethics approval was obtained from the Human Research Ethics Committee
at the University of South Australia prior to commencing the research.
BEHAVIORAL ANCHOR DEVELOPMENT
As described in Study 1, 92 people management practices were identified in the
workplace bullying complaint files, reflecting ineffective people management in
the 11 risk contexts for workplace bullying. To identify behavioral indicators
of effective people management, 44 critical incident interviews with workers (n
= 22), managers (n = 19), and work health and safety representatives (n = 3;
referred to collectively as Sample 2; see Table 1)
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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from a range of industries in Australia were conducted. Participants were
recruited by sending an email invitation through the professional networks of
the research team, including representatives from regular industry partners
(e.g., health and safety regulators, industrial unions), to ensure that people
experienced in the risks areas had an opportunity to participate.
During the interviews, which lasted 45 min on average, interviewees were asked
to identify “critical” experiences, describing what occurred and why it was
significant (Hughes, 2008). Specifically, participants were asked to recount
detailed examples of their experiences of effective and ineffective people
management in each of the 11 risk contexts. Interviews were audio-recorded with
permission, transcribed by a professional transcription service, and imported
into NVivo Version 10.0 for analysis.
The interview data were analyzed following the same systematic approach to
content analysis outlined in Study 1 (see Schilling, 2006), guided by the
question “what practices are being executed by the supervisor within the risk
contexts when the perception of workplace bullying arises?” The two secondary
coders from Study 1 completed the analysis independently and, in the event of
disagreement, reached consensus through discussion with another two co-authors.
This coding process resulted in the identification of 138 behavioral indicators
of people management practices, 72 of which overlapped with those identified in
the bullying complaint files and 66 of which were unique. The unique behaviors
predominantly represented those at the effective end of the spectrum. For
example, for the risk context related to providing training, development, and
personal growth, the people management practice denying request for training
without justification was identified in Study 1, while the practice providing
sufficient training for role and duties was derived from the interview data.
Additional examples of effective practices arising from the interviews are
provided in Table 7.
TABLES AND FIGURES
Table 7. Study 2: Examples of Effective Behavioral Indicators Generated in the
Critical Incident Interviews for Each of the Risk Contexts (Sample 2)
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In total, the content analysis. of the interviews, together with the analysis of
the 342 workplace bullying complaints reported in Study 1, generated a total of
159 behavioral indicators of people management practices nested in the original
11 risk contexts.
BEHAVIORAL ANCHOR RETRANSLATION
Following Landy et al. (1991), the 159 indicators were presented to an
independent sample of 132 participants (Sample 3) to see if each could be
accurately retranslated into the originally coded risk context. Participants
were recruited via email through the professional networks of the research team
with support from industry partners in advertising the study to their employees
and/or member organizations. Sample 3 consisted of 28 males and 100 females
(four participants did not provide gender information), who were employed in 11
different industries (10 participants did not specify industry) in Australia.
The average age of participants was 28.08 (SD = 13.19). Most participants (n =
102, 77.27%) had been employed in their current position for more than 2 years.
Data were collected using the online survey platform Qualtrics. Upon commencing
the survey, each participant was allocated a random selection of 40 behavioral
indicators from the pool of 159. Below each indicator was a drop-down list of
the 11 risk contexts. Participants were asked to sort each indicator into the
single-risk context that they felt best represented the people management
practice in question, in a forced-choice scenario. Each indicator was rated at
least 25 times across the sample (M = 27.36, SD = 2.91), with a total of 4,350
ratings.
Next, each of the 4,350 responses was classified as representing a correct or
incorrect retranslation into the original risk context category. A threshold of
60% correct retranslation for an indicator across the sample was then applied to
retain behavioral indicators for the BARS. In typical BARS studies, percentage
criteria for retranslation range from 60% to90% (Hauenstein & Foti, 1989),
representing clear agreement about the dimension to which the indicator belongs
(Burke & Dunlap, 2002). Altogether, 83 (52%) of the 159 behavioral indicators
met the threshold. During this process, due to a lack of agreement across
participants in retranslating the relevant indicators, two original risk
contexts—guiding, directing, and motivating employees and leading the work
unit—were deleted, and some of the corresponding indicators were included in the
remaining nine risk contexts.
BEHAVIORAL ANCHOR SCALING
In the scaling phase, each of the 83 indicators retained in the retranslation
process was rated by an independent sample of participants in terms of
effectiveness (Sample 4). These participants were recruited through the
professional networks of the research team, intranet advertisements, and emails
administered by regular industry partners. The sample was made up of 74
participants (18 males, 56 females) employed in 12 different industries (6
participants did not specify industry) in Australia. The average age of
participants was 25.59 (SD = 13.08). About three-quarters of participants (n =
56, 75.68%) had worked for more than 2 years in their current position at the
time of the study.
Again using the Qualtrics platform, participants were randomly allocated five
risk contexts and asked to rate each indicator from within that context on a
scale of 1 = least effective to 10 = most effective for carrying out people
management within the particular context. For example, for the risk context of
appraising and rewarding job performance, participants rated indicators such as
neglects to provide appropriate reward or recognition and delivers performance
feedback privately and respectfully in terms of their level of effectiveness. A
score of 1 meant that the indicator represented a very ineffective people
management practice, while a score of 10 signified that it was considered a very
effective practice. By design, each survey contained both effective and
ineffective indicators in order to generate a spectrum of indicators for each
risk context. Each indicator was rated by at least 38 participants (M = 43.72,
SD = 2.86), with a total of 3,629 ratings.
The means and standard deviations for the ratings of each behavioral indicator
were calculated. This process revealed that, within each risk context, the
indicators consistently fell into two distinct groups rather than being spread
evenly over the 10-point scale—one group of indicators at the higher end of the
scale (representing more effective indicators) and one group at the lower end of
the scale (representing less effective indicators). Accordingly, indicators from
the upper grouping were retained if the mean rating for each was outside of the
95% confidence interval of the mean ratings of all indicators within the lower
grouping, and vice versa for items from the lower grouping. In this way, the
ratings for each group of items did not significantly overlap. A total of 75
items met this threshold and were utilized in the BARS.
Thus, the final risk audit tool consisted of 75 behavioral indicators of people
management practices across nine risk contexts. The set of nine final risk
contexts is as follows: clarifying and defining job roles, providing training,
development and personal growth, appraising and rewarding job performance,
managing tasks and workload, managing underperformance, managing interpersonal
and team relationships, maintaining a safe work environment, administering leave
and entitlements, and working hours and rostering and scheduling. The behavioral
indicators were placed as anchors onto a graphical rating scale—one graphical
scale for each risk context—according to their mean effectiveness ratings.
STUDY 3
The creation of a behaviorally anchored risk audit tool in Study 2 enabled us to
test, in our third study, whether perceptions of ineffective people management
practices within the risk contexts are associated with increased exposure to
workplace bullying. We collected multisource and multilevel data from 25
hospital teams (Samples 5 and 6) using the newly constructed risk audit tool and
linked these data at the group level with existing data collected independently
from the same hospital teams (Sample 7). Using this linked data set, we examined
whether people management practices within the risk contexts (operationalized by
the risk audit tool score from Sample 6): (a) at individual level, predicted
concurrent exposure to workplace bullying in Sample 6 beyond established
organizational antecedents of bullying (role clarity, role conflict, role
overload, and job autonomy; as per the meta-analysis of Bowling & Beehr, 2006)
while taking account of the nonindependence of the observations; (b) at group
level, predicted concurrent exposure to workplace bullying measured using
multisource reports from Samples 5, 6, and 7, beyond organizational psychosocial
safety climate reported by Sample 7 (a facet-specific component of
organizational climate relating to senior management commitment, support,
organizational communication, and participation in relation to psychological
health and safety, established as a leading indicator of bullying; Law et al.,
2011). To establish the validity for aggregating scores on the risk audit tool
to the group level, we also explored: (a) the extent to which scores differ
systematically between work units in Sample 6; and (b) the level of agreement on
tool scores across staff working in the unit in Sample 6.
METHOD
Prior to commencing the research, approval was granted from the University of
South Australia Human Research Ethics Committee and the Southern Adelaide
Clinical Human Research Ethics Committee.
SURVEY PARTICIPANTS AND PROCEDURE
The research team approached 32 work units (teams, primarily clinical wards)
across three hospital sites located within metropolitan South Australia, from a
sample of 63 teams that had recently finished participating in a 3-year research
project on physical and psychosocial safety climate. Four teams did not respond
to multiple requests for a meeting, and a further two teams were in the process
of decommissioning at the time of the study, leaving 26 teams. Following an
initial meeting with participating teams to describe the nature of the research
project, hard-copy surveys were distributed to staff members and team
supervisors for completion. All surveys were marked with a two-digit numerical
code, to enable researchers to match responses within each team while
maintaining the confidentiality of individual participant information. Completed
surveys were placed in a sealed box for collection by the researchers. At that
stage, data from one team were excluded from analysis because only one
participant had responded.
Responses were received from 62 supervisors and managers (Sample 5) and 145
healthcare staff (Sample 6) nested within 25 teams, primarily clinical wards. As
described below, data analysis was primarily performed using Sample 6 data. For
this sample, 121 respondents identified as female (84.6%), 22 identified as male
(15.4%), and two respondents did not indicate gender. The average team size was
6.02 (SD = 4.05). Half of the respondents were employed on a full-time basis (n
= 66) and about 48.3% worked part-time (n = 70). The majority were aged between
21 and 59 (n = 134, 93.7%). About 78.5% (n = 113) had worked in the hospital for
at least 2 years. For information, the demographic characteristics of Sample 5
are overviewed briefly in Table 1.
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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SURVEY MEASURES
Each team member in Sample 6 completed the risk audit tool (the BARS created in
Study 2); responded to measures of four organizational antecedents of workplace
bullying established within the scholarly evidence base (see the meta-analysis
by Bowling & Beehr, 2006)—role clarity, role conflict, role overload, and job
autonomy; and rated their exposure to workplace bullying from supervisors and
coworkers. Team supervisors (Sample 5) completed the same survey measures
including the risk audit tool, except that they rated the frequency and duration
of bullying they experienced from subordinates. Unless noted, the following
measures were assessed on a 7-point rating scale for which we specified 1 = very
false, 3 = neither true nor false, and 7 = very true. The reliability
(coefficient α) for each reflective scale is reported in Table 8.
TABLES AND FIGURES
Table 8. Study 3: Means, Standard Deviations, and Intercorrelations of the Study
Variables at Individual Level (Sample 6)
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The risk audit tool contains nine graphical scales reflecting people management
practices in the refined set of nine risk contexts (refer to the Appendix, for
an illustrative graphical scale and example worked response). For each graphical
scale, a definition was provided regarding the risk context in question.
Participants were then instructed to place a tick next to the practices (the
behavioral indicators) typically performed in the risk context1Footnote 1
While the indicator data were not used in Study 3, going through the rating
process focuses participants on the indicators to inform the overall rating.
Separately, the behavioral indicators may have utility in an applied context to
inform understanding of the challenges and guide potential interventions.
hide footnote and, using the indicators as a guide, place a cross at the
position on the vertical arrow that most accurately represents, overall, how
effectively or ineffectively activities in the risk context are managed in their
work unit. The position of the cross is associated with a score ranging from 0
to 10, with 10 representing the highest level of effectiveness. Although
numerals are not shown on the graphical scale, the bottom of the arrow
represents 0; the top of the arrow represents 10; and there are nine makers in
between spaced 2-cm apart (giving a total scale length of 20 cm). The overall
score was calculated by measuring the distance along the axis at which the cross
was placed, noting that 2 cm on the scale is equivalent to 1 scoring unit. A
measurement of 14.2 cm is thus equivalent to a score of 7.1. Scores were rounded
to one decimal place and ranged from 0.0 to 10.0 across the nine risk contexts.
We conducted an exploratory factor analysis (EFA) via principal axis factoring
with oblimin rotation using the supervisor ratings (Sample 5). The
subject-to-variable ratio was 7:1, above the generally accepted minimum ratio of
5:1 for reaching a stable factor structure (Ferguson & Cox, 1993). The EFA
resulted in a one-factor structure that accounted for 53.23% of the variance.
Both Kaiser 1 and parallel analysis confirmed the one-factor structure. The
average of the scores across the nine risk contexts was calculated, with
Cronbach’s α of .93 for this composite scale.
We then ran confirmatory factor analysis (CFA) using the team member ratings
(Sample 6) via Mplus with “Type = Complex” selected to take into account the
nonindependence of observations and correct the standard errors for clustering.
We ran a three-factor model reflecting the three conceptual dimensions
identified in Study 1, which fit the data well: χ2(24) = 37.40; p < .01,
comparative fit index (CFI) = .98, Tucker–Lewis index (TLI) = .97, standardized
root-mean-square residual (SRMR) = .03, and root-mean-square error of
approximation (RMSEA) = .07. The three factors were, however, highly correlated
(r = .68–.79, p < .01). Following Chen et al.’s (2006) recommendation, we thus
assessed a second-order factor model wherein the factors were loaded onto a
higher order factor as “an alternative approach for representing general
constructs comprised of several highly related domains” (p. 189). Because the
number of estimated endogenous relationships and the degrees of freedom are the
same for the second-order factor model and the three-factor model, the fit
statistics of the second-order factor model indicate the same good fit with the
data.
The second-order factor model was compared to two alternative models: (a) a
single-factor model with all nine indicators loaded onto the same factor and (b)
a three-factor model with no correlations among the three factors. As shown in
Table 9,
TABLES AND FIGURES
Table 9. Study 3: Confirmatory Factor Analysis Results (Sample 6)
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the second-order model showed superior fit relative to both alternative models,
based on the χ2 difference test for the nested model and ΔCFI cut-off values of
.002 (Meade et al., 2008). Finally, following Credé and Harms’ (2015)
recommendation, the average variance extracted (AVE; Fornell & Larcker, 1981)
was computed to assess the ability of the second-order factor to explain
variation in the first-order factors. The AVE value of .96 was above the .70
threshold recommended by Johnson et al. (2011). The second-order factor was also
found to explain an average of 62% of the variance in the nine risk contexts,
well above the percentage reported in previous studies that supported a higher
order factor structure (e.g., 22% in Hoffman et al., 2010). These results
together suggest that the three conceptual dimensions identified in Study 1 are
indicative of a broader concept that reflects the overall effectiveness of
people management practices. To account for this second-order structure, we used
the mean score across the three dimensions for data analysis (rather than across
the nine risk contexts).
EXPOSURE TO WORKPLACE BULLYING
Participants were first provided with a definition of bullying (from Lindström
et al., 2000) used in national monitoring of workplace bullying rates within
Australia (Potter et al., 2016):
> Bullying is a problem at some workplaces and for some workers. To label
> something as bullying, the offensive behaviour has to occur repeatedly over a
> period of time, and the person confronted has to experience difficulties
> defending him or herself. The behaviour is not bullying if two parties of
> approximate equal ‘strength’ are in conflict or the incident is an isolated
> event.
Participants were then asked to rate the frequency to which they had been
subjected to workplace bullying from their supervisors or coworkers respectively
over the past 6 months (i.e., never, now and then, monthly, weekly, daily) and
the duration they had been subjected to workplace bullying overall (i.e., less
than 1 month, 1–6 months, 7–12 months, 1–2 years, 2+ years). To mirror the
existing measure of workplace bullying exposure from the independent linked data
(Sample 7, see below), bullying severity indexes were computed by multiplying
the frequency and duration scores for exposure to bullying from supervisors or
coworkers.
ROLE CLARITY AND ROLE CONFLICT
Using the scales from scale Rizzo et al. (1970), participants were asked to rate
how clear and certain they were of their job role as well as the extent of
incongruence or incompatibility in the requirements of their job role, via six
and eight statements respectively. Example items are “I have clear, planned
goals and objectives for my job” for role clarity and “I receive incompatible
requests from two or more people” for role conflict.
ROLE OVERLOAD
Participants were asked to rate three items from Bolino and Turnley (2005)
regarding the extent to which they feel too many responsibilities or activities
are expected of them in light of the time available, their abilities, and other
constraints. An example item is “I never seem to have enough time to get
everything done at work.”
JOB AUTONOMY
Following Courtright et al. (2016), participants were asked to rate the amount
of control they hold over their work in response to three statements, such as “I
decide on my own how to go about doing my work.”
LINKAGE OF INDEPENDENT DATA
We linked the survey data collected in Study 3 (Samples 5 and 6) at the group
level with data from an earlier survey completed by 193 clinical healthcare
staff (Sample 7) working in the same teams (see Table 1,
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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for an overview of Sample 7). Data from this independent survey included
measures of psychosocial safety climate and workplace bullying severity, among a
range of other psychosocial work environment factors. Psychosocial safety
climate assessed the extent to which employees collectively perceive that
policies, practices, and procedures that protect workers psychological health
and safety are prioritized in the work unit, using the 12-item scale (Hall et
al., 2010). An example item is “In my team, management clearly considers the
psychological health of employees to be of great importance.” Workplace bullying
severity was rated in response to the same definition of bullying described
above, assessed as the interaction of self-reported frequency (i.e., never,
rarely, at least once per month, at least once per week, daily) and duration
(i.e., less than 1 year, 2–4 years, 5–7 years, 8–10 years and more than 10
years) of exposure to bullying from supervisors or coworkers, aggregated at the
group level.
ANALYSIS
We analyzed the risk audit tool data in four ways. First, a multilevel
regression analysis was performed to assess the relationship between people
management practices (rated on the risk audit tool) and bullying exposure at the
individual level, controlling for the four organizational antecedents of
bullying collected by the same survey (Sample 6). We performed the analysis
using multilevel random coefficient modeling specifying the intercept as random
(i.e., allowing teams to differ in their mean level on the dependent variable)
to take account of the nonindependence of the data (i.e., nesting of
participants in teams). The analysis was performed using Mplus 8.7 with robust
maximum-likelihood estimation (Muthén & Muthén, 2017) because it is robust to
nonnormality and nonindependence of observations. In Model 1, the four
organizational antecedents of bullying were entered as predictors; in Model 2,
the risk audit tool score was added as a predictor. All predictors were grand
mean centered (cf. González-Romá & Hernández, 2022). The outcome variables were
severity of workplace bullying from supervisors and coworkers, as collected in
the survey. The intraclass correlation coefficient (ICC) for bullying severity
was .07, indicating that 7% of the variance could be explained by team
membership, and also supporting the use of multilevel modeling.
Next, the nested nature of our data contains means that respondents from the
same team are likely to share similar perceptions of people management
practices, while respondents from different teams are more likely to vary in
their scores. Thus, a second analysis, in which both between-group variability
and within-group interrater agreement were evaluated, was conducted to establish
whether or not the risk audit tool scores can be aggregated and used as a
team-level construct using Sample 6.
Third, at the team level, a regression analysis was performed in SPSS with
shared perceptions of psychosocial safety climate and shared perceptions of the
risk audit tool entered as predictors, and three bullying severity measures as
outcome variables. As explained previously, Study 3 collected employee-reported
risk audit tool scores, employee-reported bullying severity from coworkers and
supervisors (Sample 6), as well as supervisor-reported bullying severity from
subordinates (Sample 5). Psychosocial safety climate and bullying severity
(self-reported bullying from managers, supervisors, or colleagues) data were
collected in an independent study (Sample 7).
RESULTS
PREDICTIVE CAPACITY OF THE RISK AUDIT TOOL AT INDIVIDUAL LEVEL
Table 8
TABLES AND FIGURES
Table 8. Study 3: Means, Standard Deviations, and Intercorrelations of the Study
Variables at Individual Level (Sample 6)
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presents the means, standard deviations, and correlations among the variables of
interest. The majority of respondents had never been exposed to bullying
behaviors from supervisors (76.7%) nor coworkers (67.6%) in the last 6 months.
However, up to one-third of participants had been exposed to bullying, with a
frequency ranging from “now and then” to “daily.” As shown in Table 10,
TABLES AND FIGURES
Table 10. Study 3: Model Statistics for Work Bullying Predicted by Psychosocial
Antecedents and the Individual Score for the Risk Audit Tool (Sample 6)
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at the individual-level scores on the risk audit tool significantly negatively
predicted the severity of workplace bullying from supervisors and coworkers,
after controlling for four established antecedents of bullying. The tool
explained an additional 4.1% variance in bullying severity beyond these factors,
with a total of 20.2% of variance explained by all predictors. Following Liu et
al. (2014), we calculated the Pratt index to examine the relative importance of
the predictor variables. The Pratt index is calculated as the product of the
standardized regression coefficient and the Pearson correlation, divided by the
total R2 at either the within or between level. It indicates how much each
predictor accounts for the explained variance in the outcome variable at a given
level in a multilevel structure. The Pratt index showed that the risk audit tool
explained 28.4% of the explained variance of bullying severity at the individual
level, only lower than role conflict (69.0%). This pattern of findings indicates
that the risk audit tool (which measures the effectiveness of people management
practices) is a valid predictor of concurrent exposure to workplace bullying
when controlling for known antecedents.
JUSTIFICATION FOR THE RISK AUDIT TOOL AS A GROUP-LEVEL CONSTRUCT
To support the aggregation of team members’ ratings on the risk audit tool as a
team-level construct, average interrater agreement indices rwg(j) and ICC were
calculated using Sample 6. Median rwg(j) was .82 and ICC was .26, well above the
recommended levels in prior research for aggregation of measures (James, 1982;
Ostroff & Schmitt, 1993), thereby justifying the aggregation of individual
ratings on the risk audit tool into a team score.
PREDICTIVE CAPACITY OF THE RISK AUDIT TOOL AT TEAM LEVEL
We first examined if scores on the risk audit tool could predict workplace
bullying beyond psychosocial safety climate. Table 11
TABLES AND FIGURES
Table 11. Study 3: Means, Standard Deviations, and Intercorrelations of the
Study Variables at Team Level (Samples 5, 6, and 7)
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presents the means, standard deviations, and correlations among the variables of
interest. Team-level scores on the risk audit tool (Sample 6) were significantly
negatively related to three bullying severity measures aggregated at the team
level (Samples 5, 6, and 7; r = −.53, p < .01; r = −.76, p < .01 and r = −.51, p
< .05), while psychosocial safety climate was only negatively related to
bullying severity in the same sample (Sample 7; r = −.45, p < .05). As shown in
Table 12,
TABLES AND FIGURES
Table 12. Study 3: Model Statistics for Work Bullying Predicted by Psychosocial
Safety Climate and the Risk Audit Tool at the Team Level (Samples 5, 6, and 7)
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at the team level, the risk audit tool significantly negatively predicted all
three workplace bullying severity measures, while controlling for psychosocial
safety climate, with an increase in R2 ranging from .16 to .47. In contrast,
psychosocial safety climate was not a significant predictor of bullying. We
performed relative weight analysis (RWA; Johnson, 2000; Tonidandel & LeBreton,
2011) using RWA Web (Tonidandel & LeBreton, 2015) to determine the unique
variance contribed by each predictor to the explained variance. Confidence
intervals [95% CIs] for the relative weights of each predictor and the
significance tests were based on bootstrapping with 10,000 replications.
According to the rescaled relative weights reported in Table 12,
TABLES AND FIGURES
Table 12. Study 3: Model Statistics for Work Bullying Predicted by Psychosocial
Safety Climate and the Risk Audit Tool at the Team Level (Samples 5, 6, and 7)
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the risk audit tool accounted for a major portion (60.7%–90.7%) of the explained
variance of bullying severity across three measures. This indicates that the
risk audit tool adds unique variance to the prediction of bullying exposure at
the group level, over and above psychosocial safety climate.
GENERAL DISCUSSION
In this research, we aimed to uncover the organizational contexts in which
perceptions of bullying arise, recognizing the powerful influence of such
contexts on behavioral phenomena within organizations. Through analyzing a
series of 342 real-life alleged cases of workplace bullying, we discovered 11
risk contexts for bullying at work that reflect different aspects of people
management (Study 1). The description of people management practices used in the
risk contexts was enriched through reanalysis of the complaints together with 44
critical incident interviews (Study 2), then translated using sorting and rating
methods into a behaviorally based measurement tool comprising effective and
ineffective people management practices implemented by supervisors in a set of
nine refined contexts (Study 2). The risk audit tool was used in the final study
(Study 3) to predict individual-level workplace bullying exposure beyond
established organizational antecedents, and team-level workplace bullying
exposure beyond leading indicator, psychosocial safety climate.
Overall, our findings support the conclusion that ineffective people management
practices used by supervisors in nine risk contexts (such as managing
underperformance, clarifying and defining job roles, and managing interpersonal
and team relationships) represent areas of organizational functioning ripe for
the development of workplace bullying. Demonstrating the bullying risk in
particular contexts of people management has important implications for the
understanding of this form of workplace mistreatment conceptually and, together
with the risk audit tool created in our research, for preventing it in practice.
THEORETICAL CONTRIBUTIONS
Our research set out to explore the contexts of organizational functioning that
hold increased risk of bullying. Our first discovery is that the risk contexts
for bullying are directly connected to people management. Rather than
emphasizing the spectrum of people management within organizations, our research
addresses people management practices implemented by supervisors within nine
risk contexts for bullying, reflecting the role of supervisors in coordinating
working hours and entitlements, managing work performance, and cultivating
workplace relationships and safe working conditions. Surprisingly, in the
literature to date, bullying antecedents (chiefly, job characteristics,
leadership styles, and organizational climate) have not been integrated with the
practices used to guide and direct employees to act in ways that benefit the
financial and operational objectives of the organization. In contrast, the
central finding of our series of studies is that the risk of employees feeling
bullied increases when frontline supervisors implement people management
practices in ineffective ways, repeatedly and regularly, in the pursuit of
organizational goals.
From a theoretical perspective, these results enrich knolwedge of workplace
bullying as an organizational problem. The work environment hypothesis proposes
that a stressful psychosocial work environment is the underlying precursor for
workplace bullying (Skogstad et al., 2011). Our research leads the way in
examining how supervisors implement people management as part of the work
environment picture of bullying. Based on our findings, the conceptual lens for
understanding workplace bullying as an organizational issue needs to be expanded
to take into account these pressure points for bullying at work. Our findings
shine a spotlight on supervisor–subordinate interactions that take place
throughout the process of people management and can inform effective bullying
prevention strategies.
Examining the implementation of people management practices by supervisors
offers a deeper layer of understanding regarding how the work environment
hypothesis might function to enable bullying—an understanding that is more
closely aligned with the conceptualization of bullying as a product of
organizational systems. For instance, in addition to demonstrating that role
stressors are associated with greater bullying exposure (as illustrated in the
line of research on psychosocial job characteristic antecedents), our research
sheds light on how and when such stressors may enable bullying; according to our
results, this might occur through people management practices used for
clarifying and defining job roles, appraising and rewarding job performance,
and/or managing tasks and workload. Likewise, our findings highlight some of the
ways in which leadership may offer protective effects against bullying (as per
the line of research on leadership antecedents); for example, specific people
management practices used by leaders to appraise and reward job performance,
manage tasks and workload, and/or manage interpersonal and team relationships.
Additionally, our research addresses key limitations inherent to current
conceptualizations of the organizational climate—bullying relationship. While
organizational climate is often oriented toward senior managers, the risk
contexts represent how broader policies are translated and enacted in practice
via daily interactions between subordinates and supervisors. Repositioning the
lens to shared perceptions of practices at the unit level explicitly aligns with
robust findings in the bullying literature identifying supervisors are the most
common (perceived) perpetrators of bullying behavior (Hauge et al., 2009).
Additionally, the risk audit tool assesses ineffective and effective people
management practices in the eyes of staff, rather than policies, procedures, and
practices to deter hostile behaviors or protect psychological health and safety
directly, as captured by psychosocial safety climate. Indeed, Study 3
demonstrated that the risk contexts explain variance in bullying exposure beyond
shared perceptions of psychosocial safety climate. It would be valuable in
future research to examine connections between the people management practices
discovered here and other organizational antecedents to enhance the robustness
of the work environment hypothesis as a conceptual frame for bullying.
It is important to keep in mind that supervisors involved in (alleged) bullying
may also be working in a challenging environment. A study involving interviews
with 24 managers accused of bullying gives a rare insight into their experiences
(Jenkins et al., 2012). The findings highlighted that although all managers
agreed they had used unreasonable behaviors at work, they saw those behaviors as
tightly connected to their managerial role. The managers also described how the
difficult psychosocial working conditions they faced (e.g., work pressure, role
overload, role conflict, role ambiguity), together with a lack of personal
coping resources, contributed to the alleged bullying behavior. They called out
the potential for reasonable managerial action to be interpreted as bullying and
reported being held accountable for organizational practices for which they were
not personally responsible. Finally, the managers described working in an
environment in which numerous staff used inappropriate workplace behaviors.
These findings reinforce the value of understanding bullying as an
organizational issue, revealing antecedents very similar to those experienced by
targets but from a different organizational level. Drawing on these findings, it
would be useful in future research to examine the extent to which the people
management practices linked to bullying uncovered here are shaped by the
workings of the organizational system, and to seek out both supervisor and
subordinate perspectives on the interplay of these factors.
Importantly, our findings linking people management practices within the risk
contexts to workplace bullying exposure are valid at individual and group
levels. A recent systematic review (Gupta et al., 2020) indicated that although
work environment factors have been frequently investigated as bullying
antecedents, most studies have fallen short of examining organizational (as
compared with individual) effects. In other words, group- and
organizational-level antecedents and consequences have received scant attention
as compared with individual-level perceptions. Through group-level analyses in
our research, we explicitly looked at the effects of shared perceptions of
people management practices on bullying exposure aggregated at group level. We
observed that the effectiveness of people management practices within a work
unit affects individuals and collectively impacts teams. Our regression analyses
were performed with bullying exposure data reported by both supervisors and
employees, indicating widespread effects of poor people management practices in
fostering bullying at multiple levels. Overall, paying attention to how people
management practices within work units are typically performed, where a work
unit could be a team, department, branch, or a whole organization, positions the
risk audit tool to identify healthy or unhealthy work units as group-level
antecedents of bullying and capture how the group-level context shapes
individual and team experiences at work.
Finally, our research makes a significant contribution to the field through the
development and validation of a behaviorally anchored measurement tool for
assessing the people management practices linked to bullying at work. The tool
was grounded in data from real-life workplace bullying complaints and critical
incident interviews, refined using the BARS method, and validated at individual
and group levels using multilevel multisource data. Creating a valid measurement
instrument opens a pathway to continue building the core knowledge base by
exploring a range of research questions regarding the role of people management
practices in the genesis of workplace bullying, solely and together with other
antecedents at organization, team, and individual levels. In addition to
validating the tool prospectively in a wider range of occupational settings,
investigating the mechanisms through which people management practices interact
with leadership styles, job characteristics, and organizational climate to
enable bullying would be a valuable next step.
STRENGTHS, LIMITATIONS, AND FUTURE RESEARCH DIRECTIONS
The official case records of bullying complaints analyzed in our first study
represent a unique data source in the field of workplace bullying research; a
data source enriched by the vivid lived experiences of many people working in
diverse industries, unconstrained by any theoretical lens. Information in the
records was, however, confined by the purpose and format of the regulatory
framework and documents, restricted to the level of detail provided by
complainants, and unable to be verified. Potential biases in the data were
offset in our second and third studies by focusing on effective, as well as
ineffective, behavioral indicators in the interviews; sampling teams in the
validation survey; and integrating multiple data sources within and across
studies. Nonetheless, it is possible that some relevant people management
practices may be missing from the risk audit tool.
Another important limitation to consider is the cross-sectional validation data.
Based on our findings across the three studies, we concluded that ineffective
people management practices in certain contexts contribute to bullying risk. It
is also possible, however, that workers who feel victimized may evaluate the
work environment and, specifically, supervisory behavior more negatively than
workers who do not feel bullied. In other words, the relationship between people
management and bullying flows in the other direction. Examining this issue more
directly, Agervold and Mikkelsen (2004) found that when bullied employees were
removed from the analyses, departments with high, medium, and low levels of
bullying could be differentiated only based on levels of job demands and
management style. This finding corroborates the conclusion that people
management practices play an antecedent role in the bullying process.
Longitudinal studies are, however, needed to establish support for the causal
direction of the relationship. Longitudinal studies would also be useful on a
practical front; while our results support the concurrent and discriminant
validity of the risk audit tool, it will be important to establish predictive
validity over time to increase confidence in using the tool to isolate focal
points for prevention and intervention.
Further, though we were able to link data at the group level in Study 3 to
provide a comprehensive assessment of validity, we acknowledge the relatively
small sample size at this level (N = 25 teams). A larger number of teams would
enhance statistical power for the multilevel analysis. For example, a sample
size of 30/5 (teams/individuals) has been recommended by Arend and Schäfer
(2019). Although we recruited a relatively small number of teams, we collected
data from more than the required number of individuals per team (N = 5.8
employees per team), which may counterbalance concerns about the small number of
teams to some degree. Moreover, published multilevel research (e.g., Hirst et
al., 2009; Huang et al., 2014) has reported significant relationships in studies
with a similar sample size (N = 25). Finally, future research with larger sample
sizes could use multilevel CFA to establish the factor structure of the risk
audit tool at both individual and team levels.
PRACTICAL IMPLICATIONS
The findings of our study underpin a powerful bullying prevention opportunity.
Recommendations within the existing body of literature on the work environment
causes of bullying have emphasized the selection or training of supervisors for
certain leadership styles (e.g., Laschinger et al., 2012), redesigning
psychosocial job characteristics to reduce demands and increase resources (e.g.,
Li et al., 2019), supporting employees to change their experience of their jobs
(e.g., Baillien et al., 2011), or improving the overall work environment (e.g.,
Hauge et al., 2011). Despite these recommendations, a major barrier to
effectively addressing bullying at work is that common approaches treat bullying
as an behavioral problem between staff members rather than as an organizational
issue (Salin et al., 2020; Tuckey et al., 2019). Widely used strategies such as
antibullying policies, bullying awareness training, incident reporting, and
investigating complaints (see Caponecchia et al., 2019) focus directly on the
behavior that takes place between individuals, overlooking the root causes of
the behavior in the organizational system.
Our research stemmed from an interest in uncovering contexts in which systematic
errors in organizational functioning increase the risk of bullying. We
discovered that these contexts relate to people management, which offers a new
avenue for bridging the gap between what we know about the causes of workplace
bullying as an organizational phenomenon and what can be done to prevent it.
Specifically, people management practices represent a concrete focal point for
the proactive risk management of bullying as an occupational health hazard.
Organizations, work health and safety regulators, and other stakeholders (e.g.,
unions, professional associations) can use the risk audit tool designed and
validated in our studies to identify which contexts of people management should
be the focus of workplace bullying risk mitigation efforts. The tool can be used
to guide interventions in a targeted, intelligence-led way, including by
highlighting effective ways of managing people in the core risk areas for
bullying.
Based on our findings, prevention-focused interventions for workplace bullying
should involve shared participation from supervisors and team members in
improving the implementation of people management practices within work teams,
as indicated by best practice principles for organizational interventions (e.g.,
Nielsen & Christensen, 2021; Nielsen et al., 2010). Though supervisors must play
a critical role, interventions should illuminate effective people management
from the view of team members as well and bring together staff at all levels to
enhance how people management is carried out. When designing and implementing
interventions, team members, supervisors, and senior managers could be supported
to collaborate on solutions that shape what supervisors and team members do in
the pursuit of organizational goals, and surface new ideas for driving changes
in formal (higher level) organizational policies related to people management.
Finally, when seeking to prevent bullying through cultivating more effective
people management practices, it will be important to pay attention to how
different sets of practices operate together and in relation to other functions
within the organization. For instance, to consider how changes in one risk
context might impact the others, and how processes such as recruitment,
selection, and training might need to be changed to support more effective
people management in the risk contexts identified here.
CONCLUSION
In this study, we highlight the crucial role of people management practices in
bullying at work. Research on the work environment origins of bullying has
primarily focused on psychosocial job characteristics, leadership styles, and
organizational climate. Our contribution expands the conceptual understanding of
bullying as an organizational phenomenon by identifying the risk arising from
day-to-day practices used to manage people in organizations. When these
practices are not carried out effectively, there is an increased risk of
bullying exposure at individual and team levels. To combat workplace bullying
and its negative effects, prevention efforts should focus on optimizing the ways
in which supervisors and team members manage and coordinate working hours and
entitlements, work performance, workplace relationships, and issues regarding
physical and psychological safety. Toward this end, the risk audit tool
generated and validated in our studies can be used to guide intelligence-led
changes in people management practices, targeting the risk contexts for bullying
for specific teams, departments, branches, or organizations.
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APPENDIX
EXAMPLE GRAPHICAL RATING SCALE AND ILLUSTRATIVE RESPONSE ON THE RISK AUDIT TOOL
Note. See the online article for the color version of this figure.
FOOTNOTES
1
While the indicator data were not used in Study 3, going through the rating
process focuses participants on the indicators to inform the overall rating.
Separately, the behavioral indicators may have utility in an applied context to
inform understanding of the challenges and guide potential interventions.
ACKNOWLEDGEMENTS
CORRESPONDING AUTHOR
This research was funded by SafeWork SA with the support of the Australian
Nursing and Midwifery Federation (SA Branch).Correspondence concerning this
article should be addressed to Michelle R. Tuckey, Centre for Workplace
Excellence, UniSA Justice & Society, University of South Australia, GPO Box
2471, Adelaide, SA 5001, Australia
Email: michelle.tuckey@unisa.edu.au
PUBLICATION HISTORY
Received July 29, 2021
Revision received May 31, 2022
Accepted June 16, 2022
First published online August 11, 2022
TABLES & FIGURES
TABLES AND FIGURES
Table 1. Summary of Analyses and Samples Used in Each Study
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TABLES AND FIGURES
Table 2. Study 1: Summary of Workplace Bullying Complaint Characteristics
(Sample 1)
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TABLES AND FIGURES
Table 3. Study 1: Risk Contexts for Workplace Bullying Evident in the Complaints
(Sample 1)
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TABLES AND FIGURES
Table 4. Study 1: Risks Related to Coordinating and Administrating Working Hours
(Sample 1)
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TABLES AND FIGURES
Table 5. Study 1: Risks Related to Managing Work Performance (Sample 1)
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TABLES AND FIGURES
Table 6. Study 1: Risks Related to Shaping Relationships and the Work
Environment (Sample 1)
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TABLES AND FIGURES
Table 7. Study 2: Examples of Effective Behavioral Indicators Generated in the
Critical Incident Interviews for Each of the Risk Contexts (Sample 2)
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TABLES AND FIGURES
Table 8. Study 3: Means, Standard Deviations, and Intercorrelations of the Study
Variables at Individual Level (Sample 6)
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TABLES AND FIGURES
Table 9. Study 3: Confirmatory Factor Analysis Results (Sample 6)
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TABLES AND FIGURES
Table 10. Study 3: Model Statistics for Work Bullying Predicted by Psychosocial
Antecedents and the Individual Score for the Risk Audit Tool (Sample 6)
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TABLES AND FIGURES
Table 11. Study 3: Means, Standard Deviations, and Intercorrelations of the
Study Variables at Team Level (Samples 5, 6, and 7)
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TABLES AND FIGURES
Table 12. Study 3: Model Statistics for Work Bullying Predicted by Psychosocial
Safety Climate and the Risk Audit Tool at the Team Level (Samples 5, 6, and 7)
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Journal of Occupational Health Psychology
Editor: Sharon Clarke, PhD
2022 Volume 27, Issue 6 (Dec)
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