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

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

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

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

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

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

(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

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.

β P β p

H1: Pjf -> Lifesat

0,645 *** 0,241 **

H2a: Support -> Pjf

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

model R-squared for Lifesat = .073

model R-squared for Pjf = .503

Table 6: Results of the multi-group analysis.

Multi-group comparison

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

Reference is the freely estimated base model

=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

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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