Sociotechnical Determinants’ Effects on Person-job Fit and Life
Satisfaction in Two Different Knowledge Work Contexts
Ilona Toth, Sanna Heinänen, Anna-Maija Nisula and Aino Kianto
LUT University, Lappeenranta/Lahti, Finland
Keywords: Knowledge Workers, Digital Platforms, Organization Engagement, Technology Ease of Use, Person-job Fit,
Life Satisfaction.
Abstract: The purpose of this paper is to study the role of person-job fit as a mediator between perceived
organizational support, organization engagement, as well as technology ease of use and general life
satisfaction. New forms of organizing knowledge work may challenge conventional employee well-being
theories; therefore, it is important to investigate aspects that affect knowledge workers’ satisfaction with
life. We built a theoretical model and used SEM with LISREL to test our hypotheses with a dataset (N =
332) composed of traditional knowledge workers (n = 190) and digital work platform experts (n = 142). Our
results show that the relationship between person-job fit and life satisfaction is stronger for traditional
knowledge workers, and that organization engagement is more important to traditional knowledge workers,
while technology ease of use is more important for digital work platform experts. Our findings indicate that
there are differences in the antecedents of person-job fit depending on the knowledge work context.
1 INTRODUCTION
The recent advent of positive psychology and
sustainability discussions has marked the growth of
interest in aspects of work that support individuals’
life satisfaction. While life satisfaction is naturally
of the utmost importance for humanistic reasons, it
has also been demonstrated to yield positive impacts
on many facets of work-life success (Lyubomirsky
et al., 2005), such as job performance (Erdogan et
al., 2012). Therefore, understanding the sources of
life satisfaction is important from a managerial
perspective.
It can be argued that for knowledge workers, the
linkage between the work they conduct, and their
general life satisfaction is especially prominent. For
a knowledge worker, work is not only a source of
income but also of personal meaning (Parker, 2002).
The quality of work life (Hyde et al., 2003; Lee et
al., 2013; Ryff, 2013), work-life balance (Van den
Born & Van Witteloostuijn, 2013), possibility to
develop one’s expertise (Horwitz et al., 2003), and
ownership of one’s career (Arthur et al., 2017) tend
to be especially important issues for knowledge
workers, as they represent the degree to which an
individual’s universal psychological needs and life
aspirations are satisfied (Deci & Ryan, 2008). When
work is more than a job that helps to pay the bills,
individuals’ personal satisfaction with it becomes
crucial (Hall & Chandler, 2005).
In this paper, we address aspects of work life
that may have a role in knowledge workers’ life
satisfaction. Specifically, we examine how person-
job fit (i.e., the match between an employee and
their job) impacts life satisfaction and how issues
related to social and technological work
determinants impact that perception of fit.
As knowledge work has become more common,
with some estimating the number of such workers to
exceed 1 billion today, knowledge workers are
diverse in characteristics. In fact, knowledge
workers should be seen as a heterogeneous group,
given that not all knowledge-intensive tasks take
place among people who are employed by an
organization with permanent office locations
(“traditional knowledge workers”). In the current
age of the platform economy, an important novel
category of knowledge workers has emerged that
comprises freelancers, various contract-based project
experts, and self-employed independent knowledge
workers. We argue that for these “new” knowledge
workers, the associations between work and life
satisfaction may be different from that of more
traditional knowledge workers (Van den Born &
Van Witteloostuijn, 2013).
Toth, I., Heinänen, S., Nisula, A. and Kianto, A.
Sociotechnical Determinants’ Effects on Person-job Fit and Life Satisfaction in Two Different Knowledge Work Contexts.
DOI: 10.5220/0010016300510062
In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS, pages 51-62
ISBN: 978-989-758-474-9
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
51
To examine these issues, the paper at hand
applies a survey research strategy and analyzes
survey data collected from 142 new and 190
traditional knowledge workers (N = 332). The
results demonstrate not only that person-job fit
mediates the impact of work determinants on life
satisfaction but also that the type of knowledge work
conducted moderates these relationships.
This paper is structured as follows. We begin by
defining traditional and new knowledge work in
chapter 2.1. We continue by introducing our
research model and hypotheses development in
chapter 2.2. The remaining sections of chapter 2
introduce our research concepts and their
connections. In chapter 3 we describe our data and
research methods. Chapter 4 contains the results of
our statistical analysis, and chapter 5 draws
conclusions based on these findings.
2 THEORETICAL
BACKGROUND
2.1 Traditional and New Knowledge
Work
Peter Drucker (1959, 2001), who coined the term
knowledge worker, had already predicted decades
ago that knowledge would be the key resource of our
future work society and that the amount of
knowledge workers would increase dramatically.
While it might be difficult to clearly define
knowledge-intensive work and workers, we
understand knowledge workers as employees who
continuously orchestrate and generate knowledge
(Davenport & Cantrell, 2002) in their day-to-day
work for better production and performance (Dul et
al., 2011). Knowledge workers can be classified as
possessing “a combination of subject-specific skills
and knowledge, generic intellectual skills, generic
process skills, competencies and personal attributes”
(Atkins, 1999, p. 277). Knowledge work is about
creating, searching, sharing, and applying
knowledge (Davenport & Cantrell, 2002) for
performance improvement. The growth of
autonomy, demand for flexibility in work
arrangements, and a desire for independent work
challenges (Kelloway & Barling, 2000) differentiate
knowledge work from conventional organizational
employee work. Knowledge workers are changing
the context of work in various ways. They master
independent focused work, engage in co-operation
and teamwork, employ working methods including
learning and teaching others, and are interested in
socialization, that is, creating and forming work-
related relationships (Kubátová, 2014).
Knowledge work is currently undergoing several
significant changes. The emergence of the platform
economy (Caballer et al., 2005), an increasing
amount of freelancing in highly complex expert
work (Turner & Pennington, 2015), and various
forms of temporary organizing (Aguinis & Lawal,
2013; Spinuzzi, 2012) are leading to a shift from
steady traditional work relationships to increased
heterogeneity in work relationships and related tasks
(Sullivan et al., 2007). One of the new business
trends enabled by digitalization is the platform
economy—benefiting from a set of online digital
arrangements in organizing and structuring
economic and social activities. The platform
economy necessitates radically changing the way we
work, socialize, and create value (Kenney &
Zysman, 2016). Platforms can mediate work in
many ways, including transforming traditional work
into tasks that can be performed by contractors or
freelancers. The platform economy also enables
different types of arrangements for modes of
working, such as interdependent co-creation or
autonomous distance work at opposite ends of the
spectrum (Kenney & Zysman, 2016). Johnson et al.
(2009) foresaw that computer-mediated technology
would revolutionize the way in which employees
would interact with each other in the future,
introducing reliance on virtual teamwork. Today,
many organizations are moving toward collaborative
virtual platform work (Johns & Gratton, 2013).
Independent contractors, or freelancers, are a
novel group of employees among knowledge
workers. They cannot be considered entrepreneurs in
the true sense of the word, as they often have a
permanent relationship with an organization that
employs them regularly (Van den Born & Van
Witteloostuijn, 2013). Other independent contractors
sell their expertise on a case-by-case basis or for a
certain duration to an organization. This highly
specialized group of knowledge work experts is
clearly distinguishable from traditional, temporary,
or seasonal workers whose efforts are in demand
during high seasons or other special occasions, as
highly specialized knowledge workers’ preference
for short-term contracting is often voluntary and
they have continuously reported positive outcomes
about job and career satisfaction (Van den Born &
Van Witteloostuijn, 2013). They choose self-
employment for various reasons, of which the most
commonly mentioned are issues related to
autonomy, flexibility, and work-life balance (Van
KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems
52
den Born & Van Witteloostuijn, 2013). Despite
increased freedom and flexibility, freelancers seek
social support in their work to reduce stress caused
by work demands (García-Herrero, Mariscal,
Gutiérrez, & Ritzel, 2013).
However, theoretical contributions concerning
the effects of these platforms on work carried out by
knowledge workers are still scarce (Kenney &
Zysman, 2016). While few previous studies exist
that address the various types of knowledge work,
we argue that understanding the similarities and
differences between the traditional and novel types
of knowledge work would expand understanding of
knowledge workers and significantly benefit
management.
2.2 Research Model and Hypotheses
Life satisfaction, i.e., a person’s quality of life based
on chosen criteria, has many beneficial impacts on
work life success. It has been demonstrated to be
linked to a wide variety of issues, such as job
performance (Erdogan et al., 2012), organizational
citizenship behaviors (Crede et al., 2007), customer
satisfaction (George, 1995), and creativity (Staw et
al., 1994). Thus, we aim to examine the overall
research question: What are the determinants of new
and traditional knowledge workers’ life satisfaction?
One of the most influential conceptual
paradigms for understanding cognition and behavior
in organizations is the interactionist theory (Rice et
al., 1985). Its origins can be traced back to the 1930s
in psychology, social psychology, and sociology.
According to interactionist theory, to understand and
predict behavior, both individual- and situation-
related factors, as well as their interactions, should
be considered (Chatman, 1989). The interactionist
theory further emphasizes the importance of the fit
between environmental demands and opportunities
on the one hand and the relevant abilities and needs
of a person on the other (Rice et al., 1985).
To build a research model describing how social
and technological determinants in knowledge work
lead to the experience of person-job fit and to
general life satisfaction, we first lean on the
interactionist theory (Figure 1). We specifically
utilized the theory of Rice et al. (1985), which
asserts that quality of life is influenced by
organizational work (in addition to non-work-related
issues). The influence of work environment and
activities on overall quality of life is, however,
mediated by personal perceptions concerning work
life quality.
Second, drawing on self-determination theory
(Deci & Ryan, 2008), we connect person-job fit with
knowledge workers’ life satisfaction. Based on self-
determination theory, we propose that the conditions
of work life, supporting versus thwarting, refer to
the degree to which knowledge workers’ needs and
life aspirations are addressed (Deci & Ryan, 2008).
The experienced person-job fit correlates with a high
degree of satisfied needs and life aspirations and is
likely to predict higher life satisfaction.
Consequently, in this paper, we address aspects of
work life that may play a role in knowledge
workers’ life satisfaction. Specifically, we examine
how person-job fit (i.e., the match between an
employee and their job) impacts life satisfaction and
how issues related to social and technological work
determinants impact that perception of fit.
Figure 1: Research model.
In summary, we propose the following
hypotheses.
1. Knowledge workers’ life satisfaction is
positively influenced by person-job fit.
Social determinants are assumed to have a
positive influence on knowledge workers’ life
satisfaction through person-job fit. Thus, we
hypothesize that:
2a. Perceived organizational support has a
positive effect on person-job fit; and
2b. Organization engagement has a positive
effect on person-job fit.
The technological determinants are also assumed to
influence knowledge workers’ life satisfaction
through person-job fit. Therefore, we propose that:
3. Technology ease of use positively affects
person-job fit.
The final hypotheses aim to explore the
differences that occur when knowledge work is
discussed in two different work contexts. We
purport that:
Sociotechnical Determinants’ Effects on Person-job Fit and Life Satisfaction in Two Different Knowledge Work Contexts
53
4a. The relationships of social and technical
determinants with person-job fit are different based
on the nature of knowledge work; and
4b. The relationships between person-job fit
and life satisfaction are different based on the nature
of knowledge work.
In the following sections, we outline the main
components in our research model and discuss their
associations.
2.3 Person-job Fit and Knowledge
Workers’ Life Satisfaction
Work impacts the overall perception of quality of
life through multiple pathways (Rice et al., 1985).
We argue that for knowledge workers, person-job fit
is an especially important antecedent of life
satisfaction. This is because in knowledge work,
self-determination plays an important role, that is,
knowledge workers are motivated to satisfy their
psychological needs (autonomy, competence, and
relatedness) and personal life aspirations (Deci &
Ryan, 2008) through their work roles. Knowledge
workers’ connection between their personal needs
and life aspirations as well as work conditions
indicates that personal needs are being addressed,
and this is likely to lead to life satisfaction.
Life satisfaction is defined as “a global
assessment of a person’s quality of life according to
his chosen criteria” (Shin & Johnson, 1978, p. 478),
and it consists of hope and optimism (Bailey et al.,
2007). The Satisfaction with Life Scale (SWLS) has
been widely used around the world for measuring
well-being and overall happiness in life (Diener et
al., 1999). Despite the wide use of this scale, the
management research domain has neglected to a
great extent to account for the effect the work role
has on the level of life satisfaction (Erdogan et al.,
2012), while much research has been conducted on
measuring job satisfaction and its consequences. In
earlier research, engagement was shown to generate
life satisfaction (Saks, 2019), and higher life
satisfaction has been shown to facilitate positive
work-related outcomes, such as job performance
(Erdogan et al., 2012).
Person-job fit describes a match between
personal abilities and job demands (Cable & Judge,
1996; Lauver & Kristof-Brown, 2001). Resick et al.
(2007) defined job fit as the degree to which a
person feels that their personality aligns with their
current job’s values. In addition to the fit between
personal abilities and job demands, person-job fit
also deals with employees’ needs and preferences in
the work tasks that they perform (Resick et al.,
2007). Furthermore, person-job fit is part of a larger
framework of person-environment fit relationships.
A meta-analysis conducted by Kristof-Brown et al.
(2005) identified four types of person-environment
fit relationships in work contexts: person-job fit,
person-organization fit, person-group fit, and
person-supervisor fit. Where person-organization fit
emphasizes the compatibility between an employee
and the organization, person-job fit focuses on the
match between an employee and the attributes of
their job. Research in social sciences and
management has largely concentrated on person-job
fit (Sekiguchi, 2004), and researchers who have
studied it have suggested that it provides
possibilities for individually meaningful work
(Shuck et al., 2011) as well as trust and value
congruence (Siebert et al., 2016). As person-job fit
stems from the extent to which one’s wants and
capabilities are met by the job, it should have an
important bearing on how satisfied one generally is
with life conditions.
According to past research, person-job fit leads
to job satisfaction in traditional work contexts
(Latham & Pinder, 2005) and is also related to
beneficial organizational results (Edwards, 2008;
Tims & Bakker, 2010). Tims et al. (2016) recently
connected person-job fit to meaningful work in a
diary study. It can be argued that person-job fit
becomes even more significant in the context of new
work, in which knowledge workers operate between
increasingly blurred organizational boundaries, and
the focus on the work task’s suitability to the
individual is intensified.
2.4 Sociotechnical Determinants of
Person-job Fit in the Knowledge
Work Context
2.4.1 Perceived Organizational Support
Perceived organizational support (POS) refers to
employees’ experienced psychological safety.
Psychological safety at work assumes that an
employee is not afraid of negative consequences
from expressing their true self at work (Kahn, 1990).
Kahn (1990) argued that a certain amount of care
and supportive management are needed for
employees’ psychological safety. In organizations,
psychological safety is manifested through
organizational and supervisor support. Kahn (1990)
and May et al. (2004) found that a supportive
supervisor relationship is positively associated with
psychological safety. An important aspect of
psychological safety is created by the care and
KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems
54
support employees perceive in the relationships
between them and their superiors (May et al., 2004).
A lack of support from superiors has been shown to
ultimately result in employee burnout (Maslach et
al., 2001).
Organizational support theory (Eisenberger et
al., 1986) is a perspective that investigates how
employees’ performance can be enhanced through
shared values and perceived support (Fee & Gray,
2020). POS “represents employees’ beliefs about
whether and how the organization is willing and able
to provide them with the help they need to perform
their work and manage stressful situations” (Fee &
Gray, 2020, p. 3). POS advances in three stages.
First, a reciprocity norm of felt obligation develops
between employees and representatives of an
organization. This is followed by a socioemotional
need, which leads to an experience of worthiness
and the creation of a social identity in the
organization. In the end, employees become
convinced that the organization recognizes and
rewards accomplishments, which leads to increased
performance (Rhoades & Eisenberger, 2002).
There are several structural features associated
with POS, including role clarity, autonomy, right
degree of challenges, and sufficient time and
resources to accomplish allocated tasks. These need
to be supported by other managerial HR processes,
such as access to mentors and/or development
opportunities (Allen & Rhoades Shanock, 2013).
Additionally, POS has been connected to several
positive outcomes, such as work engagement (Rich
et al., 2010; Saks, 2006, 2019), organizational
commitment, job involvement, and job satisfaction
(Riggle et al., 2009). Fee and Gray (2020) argue that
POS may also play an important role in temporary
work relationships, which might extend to virtual
platform work as well. Based on self-determination
theory (Deci & Ryan, 2008), we propose that for
knowledge workers, POS indicates the perceived
degree to which knowledge workers’ needs
(autonomy, competence, and relatedness) and life
aspirations are supported versus thwarted.
2.4.2 Organization Engagement
Organization engagement was originally seen as
personal engagement with an organization. Saks
(2006) made the distinction between organizational
engagement and work engagement, with the first
being an emotional and intellectual commitment to
the organization and the latter the amount of
discretionary effort exhibited by employees in their
jobs. Many researchers use the term employee
engagement when they talk about engagement
directed towards the organization. Schaufeli and
Salanova (2007) also included the relationship with
the employee’s professional or occupational role
with his or her organization in their definition of
employee engagement. Organization engagement is
“the extent to which employees identify with their
organization: its people, values, purpose, and
culture. It is about the level of emotional connection
employees feel toward their organization, the
passion and enthusiasm they feel, and their
motivation towards supporting the company’s goals”
(Hicks et al., 2014, p. 12). Strong organizational
identification is a crucial element of this type of
engagement, which makes employees interested in
organizational well-being and more willing to strive
towards common organizational benefit (Dutton et
al., 1994). In fact, Farndale et al. (2014) found that
organization engagement was a stronger predictor of
affective commitment and job satisfaction than work
engagement.
A distinguishing factor between organizational
commitment and organization engagement is that
organizational commitment is principally concerned
with employees’ relationships with their
organizations and not with the actual work (Hicks et
al., 2014), which is the first prerequisite for
organization engagement to develop. Furthermore,
organizational commitment seems to be more
dependent on extrinsic motivational circumstances,
while organization engagement is more inclined
toward intrinsic motivation (Hallberg & Schaufeli,
2006). Additionally, organizational commitment
reflects a need and an obligation to maintain
membership in an organization (Meyer & Allen,
1991), thus referring to a person’s attitude and
attachment toward their organization instead of
being directed to the employee’s role as a member of
an organization. It has also been argued that
organizational commitment is a passive attitude,
while engagement requires the active presence of
employees (Yalabik et al., 2015). According to
Shuck et al. (2012), there is clear evidence that at the
structural and fundamental levels, organization
engagement is empirically separable from
organizational commitment, job satisfaction, and job
involvement. Furthermore, Hallberg and Schaufeli
(2006) have empirically shown that engagement, job
involvement, and organizational commitment are
distinct constructs. Based on self-determination
theory (Deci & Ryan, 2008), we propose that the
positive connection that knowledge workers
experience through their role as members in an
Sociotechnical Determinants’ Effects on Person-job Fit and Life Satisfaction in Two Different Knowledge Work Contexts
55
organization or on a digital work platform acts as an
important antecedent to person-job fit.
2.4.3 Importance of Technology Ease of Use
in Knowledge Work
If knowledge work, especially applied to digital
platforms, is considered in terms of shared economy,
technology has the possibility to offer flexibility,
matchmaking, extended reach, managed
transactions, trust building, and facilitating
collectivity (Sutherland & Jarrahi, 2018).
Knowledge work is closely attached to the
applicability of various information and
communication tools in the digital environment.
Research has indicated that digitalization can have
either a positive or a negative impact on the
performance of the knowledge work actions (Vuori
et al., 2019). Technology acceptance has long been
seen as a necessity for technology to be completely
utilized with all its potential benefits (Davis et al.,
1989; Lee et al., 2003; Wixom & Todd, 2005; Yi et
al., 2006). Technology acceptance within various
models has its roots in the technology acceptance
model developed by Davis (1985). The model
proposes that the basic background determinants are
technology usefulness and ease of use. Both of these
are perceived concepts of potential technology users
and have been found to influence technology
attitudes, behavioral intentions, and the actual usage
of technology (see extensive meta-analysis by
Yousafzai et al. [2007a, 2007b]). By definition
(Davis, 1985), technology usefulness refers to the
degree to which an individual believes that using a
particular system would enhance their job
performance, and perceived ease of use is the degree
to which an individual believes that using a
particular system would be free of physical and
mental effort. As in the current field of knowledge
work, digitalization can be seen as a necessity with
various applications; thus, technology is present, and
instead of its usefulness, the interest here is the
extent to which workers perceive it to be easy to use.
If the technology does not require much mental
effort and is, in other words, a natural part of the
work, it is assumed to influence the working habits
and lead to better person-job fit.
3 SAMPLE AND DATA
COLLECTION
The data were collected from September 2017 to
March 2018 through an online questionnaire sent to
experts listed at two digital work platforms whose
headquarters are in Finland. The first platform
organization is based on the idea of co-creation,
where complex problem-solving tasks are assigned
to temporary project teams brought together from
members of a large expert community. The survey
questionnaire was sent to all listed experts (N =
1,830), regardless of their activity with the platform,
and the response rate was 12.0% (n = 219). The
second platform organization offers autonomous
expert services, where clients submit task requests
online, and the organization assigns suitable
freelancers from their community. The survey
questionnaire was sent to all active experts on the
platform (N = 342), and the response rate was 43.0%
(n = 147). Comparison data were collected from
September to October 2017 from members of a
Finnish academic trade union (N = 3,000), and the
response rate was 9.6% (n = 289).
3.1 Measures
We used valid measurement scales for measuring
our concepts: perceived organizational support was
measured with Saks’ (2006) scale, consisting of
eight items; organization engagement was measured
with Saks’ (2006) scale, consisting of six items;
technology ease of use was measured with
Venkatesh and Davis' (2000) scale, consisting of
three items; person-job fit was measured with
Kristof-Brown et al.'s (2005) scale, consisting of five
items; and finally, life satisfaction was measured
with Diener et al.'s (1985) scale, consisting of five
items. A list of measurement items included in the
analyses can be found in the Appendix.
3.2 Procedure
Structural equation modeling (SEM) was applied to
analyze both the measurement model, i.e., the
confirmatory factor analysis, and the structural
model that tested the proposed hypotheses. The
empirical analysis began with the analysis of the
measurement model. To analyze the research model
presented in Figure 1, the measurement model must
be verified as equal across the groups. This is called
measurement invariance and includes three steps—
(1) configural invariance (the same items reflecting
the latent concepts in both groups), (2) metric
invariance (factor loadings fixed to be equal across
the groups), and (3) factor variance invariance
(Atienza et al., 2003; Steenkamp & Baumgartner,
1998)—that are required for testing the structural
invariance (see Milfont & Fischer [2011]). LISREL
KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems
56
(version 8.80) was utilized for data analysis with
maximum likelihood estimation.
4 RESULTS
4.1 Descriptive Statistics
Data concerning the experts contracted with the
digital work platforms included 366 respondents, out
of which 57.8% were male, and 42.2% were female.
More than half the respondents (58.6%) were
between 25 and 44 years old, 37.6% were Finnish,
and 62.4% were of mixed nationalities. The
comparison data from members of a Finnish
academic trade union included 276 respondents who
had a steady work relationship in traditional
organizational settings (instead of classifying
themselves as entrepreneurs or freelancers). Out of
these 276, 70.5% were male, and 29.5% were
female. Most of the respondents (61.0%) were
between 25 and 44 years old. The comparison data
consisted of Finnish citizens, apart from 10
participants who were of mixed nationalities. Due to
missing values in the responses, the effective sample
size was 332 responses: 142 digital work platform
experts and 190 traditional knowledge workers
(Table 1).
Table 1: Descriptive statistics for the respondents.
Trad KW New KW
N of respondents 190 142
Gender (%)
Male 67.7% 56.3%
Female 32.3% 43.7%
Age (%)
Under 25 0% 1.4%
25–34 15.3% 31.7%
35–44 30.0% 27.5%
45–54 31.1% 17.6%
55–64 23.2% 19.0%
Over 64 0.5% 2.8%
Education (%)
Bachelor’s or equivalent 1.6% 28.2%
Master’s or equivalent 84.7% 52.1%
Doctoral or equivalent 11.1% 15.5%
Other, please specify 2.6% 1.4%
Vocational 0.0% 2.8%
4.2 Confirmatory Factor Analysis and
Measurement Invariance
The measurement model was analyzed
simultaneously for both groups. Configural
invariance was the first step of the analysis, and
during this process, seven items were removed either
because of low loading in one group or in both or
based on the high correlation of error variances. The
level of invariance and model fit values are shown in
Table 2. After achieving configural invariance, the
invariance on factor loadings was analyzed. In
practice, this means that the model was no longer
estimated freely for both groups; it was made to
assume that the item loadings were equal across the
groups. The Chi-square change was used as the
major indicator of model fit and deterioration. The
results indicated that metric invariance was not
achieved. By taking a closer look at the indicators, it
was observed that three items caused non-
invariance. One was part of the perceived
organizational support construct, the second item
reflected person-job fit, and the final one was an
indicator of life satisfaction. With these three
indicators being freely estimated for both groups, the
measurement model reached the level of partial
metric invariance. The last step for verifying proper
measurement concerned latent factor variances. The
model was then estimated so that the factor
variances were fixed equal across the groups, and
the model estimation proposed that the measurement
model achieved factor variance invariance and
therefore met the necessary criteria for further
progression toward the analysis of structural
invariance.
Table 2: Measurement model fit values and invariance
analysis.
Level of invariance χ2 dχ2 (ddf) RMSEA
Configural invariance 151.32 - 0.028
Metric invariance 223.15 71.83 (9)* 0.058
Partial metric
invariance 158.39 7.07 (6)** 0.028
Factor variance
invariance 161.89 3.50 (5) 0.027
* significant model deterioration
** model compared to level of configural invariance
In addition to the invariance discussion, the
measurement was also analyzed in terms of
measurement reliability. Reliability was assessed
with the help of construct reliability and the average
variance extracted. These coefficients for both
groups are presented in Table 3.
To conclude the measurement analysis, it can be
said that the measurement model met the required
level of reliability and validity for analyzing the
research model with the groupwise average scores
and standard deviations. Table 4 summarizes the
descriptive information of the key constructs of the
research model.
Sociotechnical Determinants’ Effects on Person-job Fit and Life Satisfaction in Two Different Knowledge Work Contexts
57
Table 3: Reliability indicators.
Construct reliability (average
variance extracted)
Latent construct Trad KW New KW
Perceived
organizational support 0.895 (0.745) 0.921 (0.795)
Organization
engagement 0.831 (0.710) 0.784 (0.645)
Technology ease of use 0.889 (0.728) 0.914 (0.781)
Person-job fit 0.868 (0.702) 0.888 (0.727)
Life satisfaction 0.889 (0.728) 0.992 (0.801)
Table 4: Scale descriptive statistics.
Average
(Standard deviation)
Latent construct Trad KW New KW
Perceived organizational
support 3.94 (1.47) 4.13 (1.64)
Organization engagement 4.74 (1.39) 4.32 (1.44)
Technology ease of use 4.89 (1.33) 5.42 (1.30)
Person-job fit 5.33 (1.16) 5.54 (1.27)
Life satisfaction 4.99 (1.22) 5.32 (1.17)
4.3 Hypotheses Testing and
Multi-Group Comparison
The multi-group approach was also applied for
empirically testing the research model. In the
beginning, the model was freely estimated for both
groups. Table 5 represents the results of the
modelling in terms of the standardized path
coefficients (β) and their significance levels when all
structural paths were freely estimated. All paths
were significant, as hypothesized, except the effect
of POS on person-job fit. Organization engagement
and technology ease of use both have a positive
relationship with person-job fit. Furthermore, the
path from person-job fit to life satisfaction was
significant, indicating that the level of person-job fit
has a positive influence on life satisfaction.
Considering the overall model, the R-squared for
person-job fit as a dependent was high (.503), and
for life satisfaction, it was low (.073), as there was
only one explanatory variable.
A multi-group approach was applied for testing
the moderating effect of work context, testing
whether the path coefficients were equal across the
groups. In practice, this is performed by running the
model several times, where each time, one of the
paths is forced to be equal across the groups. The
Chi-square difference was used as an indicator of the
significance of the difference, and the reference was
always the baseline model in which the paths were
freely estimated for both categories. Table 6 presents
the results of the multi-group comparison.
Table 5: Results of the baseline model.
Trad.
KW
N
ew
KW
β P β p
H1: Pjf -> Lifesat
1)
0,645 *** 0,241 **
H2a: Support -> Pjf
2)
0,040 ns 0,131 ns
H2b: Oeng -> Pjf 0,595 *** 0,326 ***
H3: Tame -> Pjf 0,231 ** 0,478 ***
*** p < .001
** p <. 010
* p <.050
ns = not significant
1)
model R-squared for Lifesat = .073
2)
model R-squared for Pjf = .503
Table 6: Results of the multi-group analysis.
Multi-group comparison
1)
dχ2 (ddf=1) RMSEA
H4a: Support -> Pjf 3,21ns 0,035
H4a: Oeng -> Pjf 6,95* 0,037
H4a: Tame -> Pjf 5,56* 0,037
H4b: Pjf -> Lifesat 14,35* 0,041
* p <.050
ns = not significant
1)
Reference is the freely estimated base model
χ
2
=179,91 (df=151) RMSEA = 0.034
There were four paths analyzed this way, and
based on the results, three paths were found to differ
across the groups. No statistical difference was
found in the relationship between perceived
organizational support and person-job fit.
Organization engagement had a different influence
on person-job fit; for traditional knowledge workers,
the path coefficient was significantly higher than for
the individuals in the new type of knowledge work.
Technology ease of use had a higher influence on
person-job fit among the new type of knowledge
work. Finally, the path from person-job fit to life
satisfaction was also different between the groups as
the path coefficient was significantly higher for
traditional knowledge work.
5 CONCLUSIONS
In response to a growing interest in the changing
context of knowledge work and the increase of
alternative work arrangements (Spreitzer et al.,
2017), this paper examined the role of social and
technological determinants in the development of
both the experience of person-job fit and general life
satisfaction in contemporary knowledge work. The
paper contributes to the literature by deepening the
understanding of knowledge workers from an
KMIS 2020 - 12th International Conference on Knowledge Management and Information Systems
58
interactionist perspective. The research model
integrated personal perceptions of job fit with social
and technological work determinants to explain
knowledge workers’ life satisfaction. Further, the
paper differentiated between two distinct types of
knowledge workers: those who use knowledge for
higher productivity and performance (Davenport &
Cantrell, 2002; Dul et al., 2011) as independent
contractors and those who are employees in
organizations. Especially in digital work platforms,
dedicated and reliable contractors are the most
important asset when platform providers build their
competitive advantage; hence, understanding this
novel type of knowledge workers and what
motivates them is essential.
Our findings are novel for the following reasons.
First, previous studies have investigated the direct
relationship between person-job fit and job
satisfaction (Latham & Pinder, 2005) and other
positive organizational results (Tims & Bakker,
2010), but they have neglected to study employees’
overall life satisfaction as a resulting condition.
Second, while person-job fit has been studied as a
mediator between job crafting and job engagement
(Chen et al., 2014), Christian et al. (2011) have
suggested that engaged workers may develop a
stronger sense of fit in the job or in the environment.
Moreover, no previous studies have examined the
mediating role of person-job fit on general life
satisfaction. Our study bridged this gap by showing
the positive connection between organization
engagement and person-job fit, which extends
further to life satisfaction, especially in the
traditional knowledge work context.
As expected, the role of technology ease of use
had a stronger impact on person-job fit in the digital
work platform context than on traditional knowledge
workers. This sends a clear message to the digital
platform providers to pay attention to the usability of
their platform functions and to make sure that people
who are using their platform are given enough
support and guidance about how to use the platform.
As with all studies, this study had some
limitations. Christian et al. (2011) suggested the
possibility of reciprocal relations between
engagement and fit perceptions. However, our study
was cross-sectional in nature, so we could only show
the positive relationships between our constructs and
suggest that further longitudinal studies are needed
to judge their causality. We also need to consider the
possibility that our measurements, which were based
on self-evaluations, may have been affected by
common method bias (Chang et al., 2010). Further
longitudinal studies could decrease these concerns as
well.
ACKNOWLEDGEMENTS
The data collection for this article was supported by
the Finnish Work Environment Fund, grant no.
117147.
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APPENDIX
The final set of items included in the analysis:
Perceived Organizational Support
My organization really cares about my well-
being.
My organization strongly considers my goals
and values.
My organization cares about my opinions.
Organization Engagement
Being a member of my work organization is
very captivating.
I am highly engaged in my work organization.
Technology Ease of Use
My interaction with digital work tools is clear
and understandable.
Interacting with digital work tools does not
require a lot of my mental effort.
I find digital work tools to be easy to use.
Person-job Fit
To what extent - do your knowledge, skills and
abilities match the requirements of your work?
To what extent - does your work fit with your
expectations?
To what extent - does your work suit you?
Satisfaction with Life
In most ways my life is close to my ideal.
The conditions of my life are excellent.
I am satisfied with my life.
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