Effects of Proactive Decision Making on Life Satisfaction

Abstract

Proactive decision making, a concept recently introduced to behavioural operational research and decision analysis, addresses effective decision making during its phase of generating alternatives. It is measured on a scale comprising six dimensions grouped into two categories: proactive personality traits and proactive cognitive skills. Personality traits are grounded on such theoretical constructs as a proactive attitude and proactive behaviour; cognitive skills reflect value-focused thinking and decision quality. These traits and skills have been used to explain decision satisfaction, although their antecedents and other consequences have not yet been the subject of rigorous hypotheses and testing.
This paper embeds proactive decision making within a model of three possible consequences. We consider—and empirically test—decision satisfaction, general self-efficacy, and life satisfaction by conducting three studies with 1,300 participants. We then apply structural equation modelling to show that proactive decision making helps account for life satisfaction, an explanation mediated by general self-efficacy and decision satisfaction. Thus proactive decision making fosters greater belief in one’s abilities and increases satisfaction with one’s decisions and with life more generally. These results imply that it is worthwhile to help individuals enhance their decision-making proactivity.
Demonstrating the positive effects of proactive decision making at the individual level underscores how important is the phase of generating alternatives, and it also highlights the merit of employing “decision quality” principles and being proactive during that phase. Hence the findings presented here confirm the relevance of OR, and of decision-analytic principles, to the lives of ordinary people.

Full text

European Journal of Operational Research 280 (2020) 1171–1187
Contents lists available at ScienceDirect
European Journal of Operational Research
journal homepage: www.elsevier.com/locate/ejor
Interfaces with Other Disciplines
Effects of proactive decision making on life satisfaction
Johannes Ulrich Siebert
a , b , ∗, Reinhard E. Kunz
c
, Philipp Rolf
b
a
Management Center Innsbruck, Department Business and Management, 6020 Innsbruck, Austria
b
Faculty of Law, Business and Economics, University of Bayreuth, 95440 Bayreuth, Germany
c
Faculty of Management, Economics and Social Sciences, Department Media and Technology Management, University of Cologne, 50969 Cologne, Germany
a r t i c l e i n f o
Article history:
Received 23 February 2018
Accepted 6 August 2019
Available online 12 August 2019
Keywo rds:
Behavioral OR
Decision satisfaction
Life satisfaction
General self-efficacy
Proactive decision making
a b s t r a c t
Proactive decision making, a concept recently introduced to behavioral operational research and decision
analysis, addresses effective decision making during its phase of generating alternatives. It is measured on
a scale comprising six dimensions grouped into two categories: proactive personality traits and proactive
cognitive skills . Personality traits are grounded on theoretical constructs such as proactive attitude and
proactive behavior; cognitive skills reflect value-focused thinking and decision quality. These traits and
skills have been used to explain decision satisfaction, although their antecedents and other consequences
have not yet been the subject of rigorous hypotheses and testing.
This paper embeds proactive decision making within a model of three possible consequences. We
consider—and empirically test—decision satisfaction, general self-efficacy, and life satisfaction by con-
ducting three studies with 1300 participants. We then apply structural equation modeling to show that
proactive decision making helps to account for life satisfaction, an explanation mediated by general self-
efficacy and decision satisfaction. Thus proactive decision making fosters greater belief in one’s abilities
and increases satisfaction with one’s decisions and with life more generally. These results imply that it is
worthwhile to help individuals enhance their decision-making proactivity.
Demonstrating the positive effects of proactive decision making at the individual level underscores
how important the phase of generating alternatives is, and it also highlights the merit of employing
“decision quality” principles and being proactive during that phase. Hence the findings presented here
confirm the relevance of OR, and of decision-analytic principles, to the lives of ordinary people.
©2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Making good decisions is a crucial skill at every level.
—Peter F. Drucker (1909–2005)
1. Introduction
Individual and organizational decision making has long at-
tracted the attention of academics from various disciplines ( Bell,
Tversky & Raiffa, 1988 ). This interest is hardly surprising given that
it is only by making decisions that individuals and organizations
can purposefully affect the outcomes that are relevant to them
( Keeney, 2013 ). Hence understanding the mechanisms of decision
making, deriving suitable techniques to structure and solve deci-
sion problems, and then applying those methods appropriately is
∗Corresponding author at: Management Center Innsbruck, Department Business
and Management, 6020 Innsbruck, Austria.
E-mail addresses: johannes.siebert@mci.edu , johannes.siebert@uni-bayreuth.de
(J.U. Siebert), reinhard.kunz@uni-koeln.de (R.E. Kunz), philipp.rolf@uni-bayreuth.de
(P. Rolf).
widely viewed as the key to better decision making and hence to
better decisions ( Hämäläinen, Luoma & Saarinen, 2013; Roy, 2005 ).
The importance of this topic is clear when one considers that many
individuals overestimate their decision-making abilities ( Keeney,
1992 ).
The field of operational research has for some time focused
mainly on the development and evaluation of approaches—to
structuring decisions and solving problems—that facilitate sys-
tematic thinking and so enable decision makers to derive viable
solutions in complex settings ( Becker, 2016 ). Yet OR researchers
have begun to rethink their field’s predominantly choice-centric
and often normative orientation; in so doing, they have initiated a
return to the OR profession’s roots (e.g., Dutton & Walton, 1964 ) by
considering the individuals actually involved in decision-making
processes (cf. Hämäläinen et al., 2013 .) There is an increasing num-
ber of studies that account for personal differences among decision
makers and that focus on their actual decision making ( Franco &
Hämäläinen, 2016; White, 2016 ); such research does not assume
https://doi.org/10.1016/j.ejor.2019.08.011
0377-2217/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

1172 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 117 1– 11 87
any uniformity of decision makers beyond individual risk pref-
erences. These studies adopt an interdisciplinary perspective and
examine, for instance, psychological heuristics in OR ( Keller & Kat-
sikopoulos, 2016 ), behavior observed during problem-structuring
method interventions ( White, Burger & Year wor th, 2016 ), and
the effects of emotions and of information overload on decision
quality ( Korhonen et al., 2018 ).
Along similar lines, OR has become increasingly interested in is-
sues related to happiness and well-being—as in the context of sus-
tainability ( Barbosa-Póvoa, da Silva & Carvalho, 2018 ) or commu-
nity development ( Johnson, Midgley & Chichirau, 2018 ). So rather
than focusing only on those decision-making problems that af-
fect particular organizations or their functions, OR also examines
decision-making issues germane to the betterment of society in
general and, ultimately, to the betterment of individuals (e.g., Cook,
1973 ). For instance, Baucells and Sarin (2012, p. 4) develop a com-
prehensive framework for arguing that happiness can be engi-
neered; their key premise is that “the very essence of attaining
a happier life is choice”. In other words, individuals can improve
their outlook on life simply by deliberately choosing to follow that
framework’s “six laws of happiness” (cf. Baucells & Sarin, 2013 ).
Cordero, Salinas-Jiménez and Salinas-Jiménez (2017) similarly in-
tegrate the fields of OR and happiness economics ( Kahneman &
Krueger, 2006 ) by exploring factors, across countries, that affect in-
dividual levels of happiness. In terms of social well-being, recent
work has studied decision-making competence and its value for
building resilience in youth ( Taylo r, 2018 ) and has emphasized the
importance of OR for overcoming social problems such as human
trafficking ( Konrad, Trapp, Palmbach & Blom, 2017 ).
It is surprising that, despite the growing interest in these two
offshoots of OR, few scholars have sought to integrate them. Pre-
vious research has largely neglected to analyze how individual dif-
ferences in decision-making behavior contribute to higher levels of
happiness and subjective well-being. Moreover, even those stud-
ies that address this relationship (e.g., Geisler & Allwood, 2015 )
have not provided satisfactory answers about its nature. From a
decision-analytic perspective, the question remains of just how a
state of subjective well-being is influenced by effective decision-
making behavior (as defined e.g., by Howard, 1988; Keeney,
1992 ).
This paper contributes to both of these developments in OR by
focusing on individual differences in decision making and their ef-
fect on individual well-being. Thus we are inspired by the idea of
linking decisions and happiness ( Baucells & Sarin, 2012 ) and ask:
How does effective decision making contribute to life satisfaction? Al-
though the task of generating alternatives—unlike that of evaluat-
ing those alternatives—is commonly considered to be the most cru-
cial phase of decision making, the former is given short shrift in
most of the extant research on this topic (see Arbel & Tong, 1982;
Siebert & Keeney, 2015 ). Hence we are motivated to analyze how
individuals’ differences that become manifest during this phase are
related to the life satisfaction of those decision makers. Of course,
even a good choice (i.e., one based on the well-considered evalua-
tion of alternatives) cannot compensate for a set of “bad” alterna-
tives; in that case, the likely result will be inferior decision making
( Ackoff, 1978 ). We therefore posit that individuals’ differences in
this phase yield different decision-making outcomes and also, as a
consequence, varied self-perceptions of well-being.
In analyzing this relationship, we pursue three main research
objectives. First, we seek further insight into the nature of the re-
lationship between effective decision making and life satisfaction—
that is, beyond the platitude that choice is key to individual happi-
ness ( Schwartz et al., 2002 ). For this purpose, we propose a model
in which the mediators are self-efficacy and decision satisfaction.
Second, we aim to offer em pirical support for the widely (but
so far only theoretically) assumed importance of the “generating
alternatives” task in terms of subjectively positive decision out-
comes. Finally, we stress the utility of skilled behavior during the
phase of generating alternatives by establishing the existence of a
positive association between effective individual decision making
and increased life satisfaction.
In order to accomplish our research objectives, we build on two
strands of research. First, we rely on the descriptive research re-
lated to subjective well-being, life satisfaction, and relevant de-
terminants (e.g., Diener, 1984 ) and, more generally, on insights
gleaned from research in the areas of personality and cognitive
psychology (e.g., Bandura, 1986; Lent et al., 2005 ). Second, our
paper incorporates the prescriptive principles of decision analysis
and exploits the recently introduced concept of proactive decision
making ( Siebert & Kunz, 2016 ), which captures the skills and per-
sonality traits most strongly related to effective decision making
during its phase of generating alternatives. Thus, we answer re-
peated calls by OR scholars (e.g., Corbett & van Wassenhove, 1993;
Franco & Hämäläinen, 2016 ) to adopt an interdisciplinary research
approach.
This paper proceeds as follows. Section 2 presents our study’s
theoretical and conceptual background. We review the literature
devoted to decision making and its effect on life satisfaction in
Section 3 , where we also develop our formal research hypothe-
ses. Section 4 is dedicated to describing our research procedure,
the measures used, and our analytical strategy. The empirical re-
sults of our hypotheses testing are summarized in Section 5 , and in
Section 6 we discuss their implications. Section 7 outlines the
study’s limitations and suggests possible avenues for further re-
search. Finally, we conclude in Section 8 with an overall summary.
2. Theoretical and conceptual background
We aim to establish that, in a decision-making process,
individual-level differences arise during the phase of generating al-
ternatives ( Siebert & Kunz, 2016 ) and hence differentially affect lif e
satisfaction ( Diener, 1984 ). In order to substantiate this claim, we
start by introducing our study’s conceptual background.
2.1. The phase of generating alternatives and proactive decision
making
Most research in the field of decision science agrees that the
phase of generating alternatives is a critical determinant of the de-
cisions made by both individuals and organizations (e.g., Gettys,
Pliske, Manning & Casey, 1987; Siebert, 2016; Siebert & Keeney,
2015 ). This task is especially important for decisions that have far-
reaching consequences, which tend to affect (directly and/or indi-
rectly) future choices as well. From a decision-analytic perspective,
success in the choice phase of a decision depends in no small part
on the quality of alternatives from which the decision maker can
choose—in other words, regardless of any particular method em-
ployed to make that choice and solve the decision problem ( Siebert
& Kunz, 2016 ). Yet suppose there are better options that have been
excluded from the set of alternatives (cf. Montibeller & von Win-
terfeldt, 2015 ); then one can reasonably suppose that any choice
among the available (inferior) options, and their respective conse-
quences, will be suboptimal even if the evaluation of alternatives
itself was handled properly. To a great extent, then, effective de-
cision making depends on obtaining a good result in the phase of
generating alternatives.
Although the importance of that phase has been emphasized
by scholars (e.g., Howard, 1988 ), there are only a few studies
that either concern it specifically or examine individual differ-
ences in performing the task of generating alternatives ( Butler &
Scherer, 1997; Pitz, Sachs & Heerboth, 198 0 ). For example, Keeney

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 1173
(1992) observes that many decision makers devote most of their
decision-making effort s to solving the presented problem. Thus in-
dividuals often merely identify the most obvious alternatives, or
those that their experiences have already shown to be appropri-
ate. Yet this alternatives -focused, reactive approach cannot ensure
that the decision maker identifies the best possible alternatives.
Keeney therefore recommends a value -focused, proactive approach
whereby values guide effort s to solve the decision problem. Siebert
and Keeney (2015) show that the use of objectives stimulates the
process of generating alternatives and increases both their num-
ber and quality; however, Selart and Johansen (2011) report that
decision makers frequently have little or no experience with using
objectives to generate alternatives.
Siebert and Kunz (2016) adopt a more holistic perspective in
their discussion of generating alternatives by analyzing the traits
and decision-making skills of those who are actually engaged in
this phase. In particular, these authors seek to identify the traits
most associated with the successful performance of that task—
that is, in terms of “decision quality” principles ( Howard, 1988 ).
They propose, and validate empirically, a multi-dimensional model
of proactive decision making (PDM). In describing the real-world,
decision-related behavior (i.e., specific skills) and traits of proactive
decision makers, Siebert and Kunz draw on three distinct sources:
previously elaborated notions that the concept of proactivity ap-
plies to a dispositional personality trait as well as to actual be-
havior ( Grant & Ashford, 2008 ); related scholars’ insights into de-
cision analysis (e.g., Bell et al., 1988; Howard, 1988 ); and research
on value-focused thinking ( Keeney, 1992, 2020 ).
Siebert and Kunz (2016, p. 875) account for the two-
dimensional nature of proactivity in defining proactive decision
making as “the purposeful use of [proactive] cognitive skills and
certain foresighted personality traits of the decision maker”. They
also specify that PDM connotes the value-orientated and self-
initiated decision making of individuals who strive for improve-
ment and, toward that end, adopt these means: systematically
identifying objectives; generating a variety of suitable alternatives;
gathering information about opportunities and threats; and antici-
pating the outcomes that might follow from any chosen course of
actions.
More specifically, Siebert and Kunz (2016) elaborate two general
personality traits and four cognitive skills that distinguish—during
the phase of generating alternatives—proactive from reactive de-
cision making. Concerning the proactive personality traits , Siebert
and Kunz distinguish between “striving for improvement” and
“taking the initiative”, which they regard as distinct but comple-
mentary facets of one’s commitment to proactive behavior during
decision processes. Proactive decision makers are presumed to
be interested in effecting meaningful outcomes ( Grant & Ashford,
2008 ) and are expected to strive for improvement in decision
situations ( Parker, Bindl & Strauss, 2010 ). Siebert and Kunz assume
that—in the absence of this pursuit of improvement as exemplified
by humans’ proactive capacity for self-regulation ( Bandura, 1991 )—
there would be no reason or particular motivation for individuals
to behave proactively and to apply their PDM skills accordingly.
Note also that decision makers are viewed as proactive only if they
actually apply those skills; it is not enough merely to be given
that opportunity. Hence, according to Siebert and Kunz, proactive
decision makers take the initiative in decision situations ( Frese &
Fay, 2001 ) and wish to actively shape their environment ( Grant &
Ashford, 2008 ).
In terms of proactive cognitive skills , which reflect the no-
tion that analytical thinking entails deliberate reasoning processes
( Novak & Hoffman, 2009; Smith & DeCoster, 20 0 0 ), Siebert and
Kunz (2016) identify four complementary skills: “systematic iden-
tification of objectives”, “systematic identification of alternatives”,
“systematic search for information”, and “using a decision radar”.
Unlike other aspects of decision making, such as the evaluation
of alternatives, these skills are not employed by reactive decision
makers. Rather, they are behavioral requirements for proactive de-
cision making during the phase of generating alternatives.
The first skill, systematic identification of objectives , is based on
the idea that proactive individuals are value-driven, are often “vi-
sionary”, and clearly perceive their future ( Keeney, 1992 ). Hence
Siebert and Kunz (2016) reason that PDM requires an awareness
of the objectives derived from one’s vision, which ultimately gives
purpose to life ( Emmons, 2004 ) while both encouraging and di-
recting behavior toward the pursuit of those objectives ( Locke &
Latham, 2002 ). With respect to decision making, clarity concerning
goals is crucial for systematically creating alternatives and gath-
ering information and for anticipating future decisions ( Siebert &
Keeney, 2015 ).
According to social cognitive theory ( Bandura, 1986 ), proac-
tive decision makers differ from their reactive counterparts in that
the former refuse to accept unconditionally the alternatives al-
ready given in a specific context—and especially if those options
are poorly matched to their own objectives. Siebert and Kunz
(2016) therefore argue that proactive individuals engage in the sys-
tematic identification of alternatives and so task themselves with
creating more and better alternatives (see also Keeney, 1992 ). Con-
sidering their own objectives is a critical aspect of this activity for
two reasons. First, recall that there is empirical support for the hy-
pothesis that using objectives when identifying alternatives results
in more and also better alternatives ( Siebert & Keeney, 2015 ). Sec-
ond, the use of objectives-oriented alternatives has been shown
to increase the likelihood that individuals will actually achieve
their objectives ( Gollwitzer & Brandstätter, 1997; Grant & Ashford,
2008 ).
Siebert and Kunz (2016) suppose further that proactive deci-
sion makers will undertake a systematic search for information —a
process that facilitates the evaluation of how well each identified
alternative matches their objectives. The implication, per Keeney
(1992) , is that proactive decision makers do not rely solely on ap-
parent or easily accessible information; instead, they pursue a pol-
icy of informed decision making ( Becker, 2016 ).
Finally, PDM is rooted in the tendency of proactive individu-
als to be future oriented ( Frese & Fay, 20 01; Greenglass, 20 02 )
and is therefore assumed to involve what amounts to a continu-
ous search for future decision contexts; this skill is captured by
the phrase using a decision radar . Taking into account the two di-
mensions of proactive (coping) activity distinguished by Parker,
Williams and Turner (2006) , Siebert and Kunz (2016) expect that
this search involves the anticipation and prevention of potential
problems ( Aspinwall, 2005 ) as well as the self-determined cre-
ation of decision opportunities ( Frese & Fay, 2001; Keeney, 1992 ).
In short, proactive decision makers are presumed to be actively
engaged in a continuous process of decision making. Hence such
individuals should be able to plan their decisions in a relatively
broad context, which is conducive to ensuring that they “sort out”
problems and make correct decisions ( Howard, 1988 ).
In tests of its nomological validity, research has documented
that proactive decision making has a significant effect on individu-
als’ satisfaction with their decisions. So in addition to its relevance
to explaining decision satisfaction, PDM should be considered as a
means to account for other constructs and variables—and to pre-
dict their effects—in the context of behavioral OR ( Siebert & Kunz,
2016 ). As mentioned previously, the relation between PDM and its
potential consequences has yet to be established.
2.2. Subjective well-being and life satisfaction
Along with affective balance, life satisfaction (LSA) is a key
dimension of subjective well-being (SWB). Unlike moods or

1174 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7
emotions, LSA is not considered to be an ongoing affective
self-evaluation of or response to events that occur in a person’s
life. Life satisfaction is instead viewed as a cognitive process
involving global judgments about an individual’s overall quality of
life ( Diener, Suh, Lucas & Smith, 1999 ). The causal logic underlying
such judgments can be described from either a top-down or a
bottom-up perspective ( Diener, 198 4 ), approaches that are the
subject of a vibrant discourse in extant literature (e.g., Mallard,
Lance & Michalos, 2017 ).
The top-down causal perspective views LSA in static, trait-like
terms ( Lent & Brown, 2008 ) and supposes that LSA leads to cer-
tain outcomes, such as satisfaction with a particular life domain
( Headey, Veenhoven & Wearing, 1991 ). In other words, persons
are (say) satisfied with their job because they are mainly satis-
fied with life—and not vice versa. From the bottom-up perspec-
tive, in contrast, certain variables cause LSA; thus individuals are
satisfied overall because of their aggregate satisfaction with vari-
ous aspects or domains in their life ( Lance, Lautenschlager, Sloan
& Varca, 1989 ). Examples of such aspects include job satisfaction
( Judge & Watanabe, 1993 ), family satisfaction ( Schimmack & Oishi,
2005 ), and health satisfaction ( Dolan, Peasgood & White, 2008 ).
However, there is another perspective that questions this di-
chotomous assessment and favors a more complex, bi-directional
or reciprocal relationship between LSA and satisfaction with life
domains ( Rojas, 2006 ). Thus it proposes that, even when individu-
als are satisfied with their job because they are satisfied with life,
it is also the case that their LSA is influenced by domain-specific
satisfaction(s) ( Diener, 1984 ). Although they use different sets of
data, Lance et al. (1989) and Scherpenzeel and Saris (1996) both
show empirically that neither of the two traditional models can it-
self fully explain variations in the best solutions that follow from
assuming a one-directional causal path between domain satisfac-
tion and satisfaction with life in general.
In line with recent research ( Mallard et al., 2017; Steel, Schmidt,
Bosco & Uggerslev, 2019 ), the theoretical position adopted in our
study is one allowing for logic that transcends beyond the top-
down perspective. Implicit in this position is the assumption that
LSA is not entirely static ( Lent & Brown, 2008 ). Recalling the
premise stated in our paper’s introductory paragraph (viz., that
one’s life can be purposefully influenced only by making deci-
sions), we assume that differences in the effectiveness of deci-
sion making—especially during the phase when alternatives are
generated—therefore result in different levels of life satisfaction.
Also, we show that this postulated connection is most likely medi-
ated by two other factors: general self-efficacy and decision satis-
faction.
3. Literature review and hypotheses development
3.1. Life satisfaction and its correlates
In recent decades, researchers from different disciplines have
identified numerous correlates of LSA beyond domain-specific sat-
isfaction ( Rojas, 2006 ) and demographics ( Dolan et al., 2008 ).
Among these additional correlates, those that have arguably re-
ceived the most attention are personality traits and motivational
processes (e.g., Emmons & Diener, 1985; Proctor, Linley & Maltby,
2009 ) as well as socio-economic and socio-cultural factors (e.g.,
Cordero et al., 2017; Diener & Suh, 20 0 0; Dolan et al., 2008 ).
Although there is broad agreement that personality plays a sig-
nificant role in LSA (e.g., Schimmack, Oishi, Furr & Funder, 2004 ;
for a review, see Steel, Schmidt & Shultz, 2008 ), other relevant psy-
chological determinants include cognition and beliefs (e.g., Lent et
al., 2005 ). In particular, examining LSA from a dynamic perspective
reveals that self-efficacy is a construct of even greater relevance
( Lent & Brown, 2008 ). Self-efficacy, as defined by Bandura (1977) ,
is one of several cognitive processes that many view as essential
to human self-regulation and motivation. Thus self-efficacy, a fo-
cus primarily of scholars who advocate social cognitive theory (e.g.,
Bandura, 1986; Lent et al., 2005 ), is described as a comprehensive,
reciprocal mechanism of the individual’s cognitive drivers of be-
havior ( Gist & Mitchell, 1992 ). This notion captures a “person’s self-
constructed judgment about his or her ability to execute certain
behaviors or [to] reach certain goals” ( Ormrod, 2008 , p. 356). Sev-
eral studies have documented that those who are confident about
achieving their aims—in other words, who self-report higher levels
of self-efficacy—experience significantly higher degrees of LSA (see
e.g. Luszczynska, Gutiérrez-Doña & Schwarzer, 2005 ).
The socio-economic and socio-cultural factors most frequently
studied in relation to LSA are education level, employment sta-
tus, health, income, and social relationships ( Diener & Suh, 20 0 0;
Dolan et al., 2008 ). It is interesting that, as suggested by the re-
ported results, among these factors there are no relationships that
persist—when other effects are controlled for—except for those in-
volving employment status ( Frey & Stutzer, 20 0 0 ) or health status
( Dolan et al., 2008 ). Effects on LSA of the other listed factors are
comparatively ambiguous. For example, LSA is seldom increased in
a linear way when income rises to particular levels ( Dolan et al.,
2008 ); there may exist (often unobserved) factors that alter the
general trend, such as one’s perception of goal attainment ( Lent et
al., 2005 ). In other words, we can assume that life satisfaction’s re-
lation to socio-economic and socio-cultural factors depends at least
in part on how individuals evaluate these factors vis-à-vis their
goals.
Despite the prevailing agreement that goals are a determinant
of LSA (e.g., Oishi, 20 0 0 ), there is a surprising paucity of research
that directly analyses the relationship between actual behavior as
a means to achieve goals and life satisfaction. Some studies do ad-
dress factors that might indicate a relationship between certain ac-
tivities and LSA ( Dolan et al., 2008 ), but the particular behaviors
that characterize those activities are rarely considered. This gap
in the literature is puzzling given that, within the interactionist
paradigm ( Bandura, 1977 ; Terborg, 1981 ), humans are not consid-
ered to be mere passive and reactive respondents to their person-
ality, context, and externally defined goals ( Crant, 20 0 0 ). Rather,
humans are viewed as taking an active role in shaping their sit-
uation (e.g., health status) for the purpose of facilitating such de-
sired outcomes as increased satisfaction ( Grant & Ashford, 2008 ). It
follows that goal-directed behavior, when guided by effective deci-
sion making, should also help determine LSA ( Lent & Brown, 2008;
Locke, 2002 ).
3.2. Effective decision making and life satisfaction
Most scholars consider decision-making competence ( Parker &
Fischhoff, 2005 ) and making decisions in accordance with the
principles of decision quality ( Howard, 1988 ) to be indicators
of effective decision making. However, only a few studies link
these two skills to life satisfaction. One such work ( Deniz, 2006 )
finds low correlations among LSA, decision self-esteem, and the
decision-making styles described by Mann, Burnett, Radford and
Ford (1997) . His results suggest that individuals with higher de-
cision self-esteem and/or a more effective, or “vigilant”, decision-
making style are more satisfied with their lives. Another exam-
ple is the study of Cenkseven-Önder and Çolakkadıo
˘
glu (2013) ,
who present similar results regarding the positive correlation be-
tween LSA and the vigilant decision-making style. Yet their step-
wise multiple regression analysis indicates that—unlike problem-
solving skills ( Heppner & Petersen, 1982 ) and decision self-esteem,
which explain 7% of the total variance—the vigilant decision-
making style is not a statistically significant predictor of life sat-
isfaction. The authors offer no explanation for this finding, but our

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 117 5
consideration of the examined constructs leads us to suppose that
it probably reflects the similarity (in terms of item content) be-
tween the problem-solving and vigilance scales. For this reason, we
question the informational value of the Cenkseven-Önder and Ço-
lakkadıo
˘
glu’s results.
Geisler and Allwood (2015) look for a direct relationship be-
tween decision-making competence and life satisfaction. They em-
ploy a cognitively oriented definition of competence, the Adult
Decision-Making Competence (ADMC) scale of Bruine de Bruin,
Parker and Fischhoff (2007) , to measure decision-making compe-
tence. Their surprising result is that decision-making competence
accounts for only a non-significant percentage (7%) of the variance
in life satisfaction. We believe that this finding indicates that re-
searchers should either expand the definition of decision-making
competence—for example, by following the claim of Del Missier,
Mäntylä and Bruine de Bruin (2012) about its multifaceted nature
and considering abilities or traits relevant to decision making other
than those that constitute the ADMC scale (cf. Dewberry, Juanchich
& Narendran, 2013 )—or revise the theoretical model of how LSA is
affected by decision-making competence and thus effective deci-
sion making.
We remark that nearly all previous studies link decision-making
competence and skills to antecedent upstream constructs: decision-
making styles ( Bavol’ár & Orosová, 2015; Galotti et al., 2006 ;
Parker, Bruine de Bruin & Fischhoff, 2007 ), general cognitive abil-
ities ( Bruine de Bruin et al., 2007; Del Missier et al., 2012 ; Parker
& Fischhoff, 2005; Stanovich & West, 2008 ), or personality traits
( Davis, Patte, Tweed & Curtis, 2007; Dewberry et al., 2013 ). Rarely
examined are downstream constructs—that is, direct and indirect
consequences of effective decision making such as decision satis-
faction ( Anderson, 1992 ) and objective life outcomes ( Bruine de
Bruin et al., 2007 ; Parker et al., 2007 ).
3.3. Hypotheses and research model
The consensus that emerges from research in decision analysis
is that a sound decision process, or a choice based on decision-
analytic guidelines is more likely to be a good one and so increases
the odds of achieving the desired outcome ( Hammond, Keeney &
Raiffa, 2007; Keren & Bruine de Bruin, 2005; Larrick, 2011 ). It is
therefore safe to assume that effective decision makers are more
satisfied with their recently made choices ( Anderson, 1992 ), and
with the “life domains” affected by their decisions, than are less
competent decision makers.
In all likelihood, proactive individuals are effective decision
makers who generate more and better alternatives to choose from
as well as a greater number of decision opportunities ( Keeney,
1992 ). Selecting among better alternatives increases the odds that
a decision will achieve an individual’s objectives than if one
approached decisions with a reactive mindset. In turn, achiev-
ing one’s objectives is naturally expected to enhance satisfaction
more generally ( Sheldon & Elliot, 1999 )—provided those objectives
are self-concordant ( Judge, Bono, Erez & Locke, 2005; Sheldon &
Kasser, 199 8 ). We therefore posit that proactive decision making is
positively related to life satisfaction.
Although the direct effect of PDM on LSA might be only mod-
erate or even low, the total effect—when one considers also their
indirect relationships—is presumed to be strong and significant.
Given this presumption, we suppose that other constructs mediate
the relationship between PDM and LSA; that is, we hypothesize the
existence of additional antecedents of LSA that are closely related
enough to help account for life satisfaction (see Fig. 1 , to follow).
In the decision-making context, decision satisfaction (DSA) could
well be one such antecedent of LSA. Decision satisfaction is a
domain-specific form of subjective decision success that conforms
to “success” as defined in other disciplines (e.g . , Seibert, Kraimer
& Crant, 2001 ). Similarly to LSA ( Diener, Emmons, Larsen & Grif-
fin, 1985 ), DSA does not connote a repeated affective evaluation of
and response to one’s own decision making. Instead, DSA is a cog-
nitive process involving global judgments about the overall quality
of one’s decision making.
In comparison with reactive decision making, PDM is a more
systematic and structured approach: it requires active engagement,
deliberate thinking, and enhanced cognitive effort. There are sev-
eral reasons why an awareness of these aspects should increase
the individual’s perceived satisfaction with one’s decision making
(cf. Anderson, 1992 ). First, we argue that proactive decision makers
can more easily achieve their objectives and therefore experience
better decision outcomes; those positive outcomes likely yield,
in retrospect, a satisfying decision-making experience ( Sainfort &
Booske, 20 0 0 ). Second, individuals who undertake the additional
cognitive effort necessary for PDM are also more likely to experi-
ence positive self-belief in terms of their decisions. In other words,
DSA can serve to affirm one’s adoption of PDM by reinforcing the
advantages of exerting the cognitive effort required by that ap-
proach. Third, we assume that proactive decision makers are more
confident about their decision making—that is, given their con-
scious choice to employ a structured and forward-looking deci-
sion strategy—and, as shown elsewhere, decision confidence can be
linked to DSA ( Heitmann, Lehmann & Herrmann, 2007 ). Finally, the
mainly information-driven nature of PDM is indicative of reduced
decision uncertainty , which can have only a positive effect on any
judgments about DSA ( Small & Venkatesh, 20 0 0 ).
Whereas LSA considers the satisfaction that could result from
all previous decisions and their outcomes, DSA is related more
closely to current decision making and so, in the short term, is
less dependent on the long-term consequences of decisions. Sup-
pose, for example, that individuals choose a reasonable alternative
that turns out—for reasons beyond their control—to yield poor
outcomes; under these circumstances, these decision makers may
nonetheless be (at least temporarily) satisfied with their choice
( Howard, 1988 ). Yet one can argue from the long-term perspective
that DSA, just like LSA, declines for individuals whose decisions
consistently result in poor outcomes. In that event, the decision
makers’ assessments of DSA will probably be affected by the
negative feedback they receive from their previous decision mak-
ing. Conversely, we have the intuitive result that decision makers
are seldom unsatisfied when their decisions result in positive
outcomes. So if the decision outcomes that drive DSA produce
accessible and persistently positive feedback to the LSA judgment
process ( Schimmack & Oishi, 2005 ), then the decision makers in
question will almost certainly be satisfied with their lives. These
considerations, which are supported by the findings of Greguras
and Diefendorff (2010) and Siebert and Kunz (2016) , motivate our
first hypothesis.
Hypothesis 1 . Proactive decision making is positively related to deci-
sion satisfaction, which positively mediates the relationship between
proactive decision making and life satisfaction.
Next we posit that also general self-efficacy (GSE), which is an
equally relevant contributor to LSA ( Sherer et al., 1982 ), can help
account for decision satisfaction. Individuals with high levels of
GSE believe in their abilities to cope with a wide range of novel
and demanding situations ( Schwarzer, Bassler, Kwiatek, Schröder
& Zhang, 1997 ), to complete the most challenging tasks, and ul-
timately to reach their goals ( Ormrod, 2008 ). With such a gen-
erally positive belief in one’s competence, which encourages in-
creased effort and persistence when faced with taxing situations,
such a decision maker should perform better than individuals char-
acterized by low self-efficacy ( Jiang, Hu, Wang & Jiang, 2017 ). With
regard to human thinking, the strong sense of competence epito-
mized by GSE facilitates cognitive processes and increases perfor-

1176 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7
Fig. 1. Research model.
mance ( Schwarzer et al., 1997 ). Also, high-GSE individuals are more
likely—than are their low-GSE counterparts—to acknowledge their
responsibility for failures; in turn, that realization fosters motiva-
tion to review their capabilities and thus to remedy and overcome
any weaknesses revealed by such failure ( Azizli, Atkinson, Baugh-
man & Giammarco, 2015 ).
For example, Stajkovic and Luthans (1998) provide empir-
ical support for these effects by documenting a significantly
positive association between GSE and work-related performance.
Luszczynska, Gutiérrez-Doña et al. (2005) similarly demonstrate a
positive relationship between GSE and performance in school. Be-
yond actual performance, research has also shown that GSE is pos-
itively and significantly correlated not only with LSA (e.g., Azizli et
al., 2015 ) but also with domain-specific satisfaction (e.g., Judge et
al., 2005 ), where the latter is a likely mediator of the GSE–LSA re-
lationship. In other words, GSE is linked to the positive emotions
and satisfaction experienced when performing well in a particular
situation or domain, which naturally contributes to LSA ( Jiang et
al., 2017; Lent et al., 2005 ). So in terms of decision making, and in
line with results reported by Schwarzer et al. (1997) , individuals of
high GSE—unlike those of low GSE—are expected to perform better
and to be more satisfied with their decisions.
Finally, we assume that PDM at least partly contributes to ex-
plaining GSE (and vice versa). Although GSE is commonly regarded
as a relatively stable factor ( Mikkelsen & Einarsen, 2002 ; Parker,
2007; Sherer et al., 198 2 ), an association can be shown between
PDM and one’s internal attributional analysis of previous positive
experiences; such analysis is a highly predictive antecedent of GSE
( Gist & Mitchell, 1992; Shelton, 1990 ). If we suppose that PDM
leads to better decision making and hence to more positive de-
cision outcomes, then the experience of those outcomes—namely,
in terms of increased DSA and/or LSA—can be attributed to the in-
dividual’s decision-making capability. This dynamic increases the
decision maker’s belief in the own competence and thereby in-
creases one’s level of general self-efficacy. It follows that GSE is it-
self a probable mediator of the PDM–DSA relation because it facili-
tates cognitive processes related to PDM, and thereby increases the
commitment of individuals to their own proactive decision mak-
ing (cf. Ozgen & Baron, 2007 ). In this regard, it seems that espe-
cially the PDM traits “striving for improvement” and “taking the
initiative” must be, in common with GSE, strongly future oriented
( Luszczynska, Gutiérrez-Doña et al., 2005 ). Hence we can formal-
ize our second hypothesis, which is (indirectly) supported by the
findings of Krueger and Dickson (1994) and Tumasjan and Braun
(2012) that suggest a positive relationship between higher levels
of self-efficacy and identified (decision) opportunities.
Hypothesis 2 . Proactive decision making is positively related to gen-
eral self-efficacy, which positively mediates the relationship between
proactive decision making and decision satisfaction.
In Fig. 1 we depict the model and illustrate the relation be-
tween Hypotheses 1 and 2.
4. Methodology
4.1. Participants and procedure
We employed a cross-sectional survey research strategy and
conducted our study electronically. In order to collect data, we
used Qualtrics (an “experience management” platform) for the de-
sign of an online questionnaire to which participants responded by
answering questions about themselves and their decision-making
behavior. At the beginning of each questionnaire, we informed par-
ticipants about the purpose of our study. Likewise, they were told
that participation was voluntary, that there were no right or wrong
answers, and that their privacy would be protected. The “intrin-
sic” nature of the phenomena we investigated dictated that all our
measures consist of respondent self-evaluations (cf. Chan, 2009;
Conway & Lance, 2010; Spector, 1994 ), which means that common
method bias could have been an issue ( Feldman & Lynch, 1988;
Lindell & Whitney, 2001; Podsakoff, MacKenzie, Lee & Podsakoff,
2003 ). We addressed this concern ex ante by following the recom-
mendations of Podsakoff, MacKenzie and Podsakoff (2012) . More
specifically, we separated predictor and criterion variables in differ-
ent blocks of the questionnaire, ensured the anonymity of respon-
dents, reduced ambiguity by devising applied measures of reason-
ably low complexity, and used different scale types to reduce the
number of common scale properties.
The online questionnaire, which participants could complete in
about 10 minutes, was administered in three independent surveys.
We used the first survey as a pre-study whose purpose was to re-
validate the PDM scale of Siebert and Kunz (2016) and to perform
some preliminary hypotheses testing. The second two surveys con-
stituted the main study; they used different data sets and were
meant to confirm the results of our initial hypotheses.
For the first two surveys—that is, for the pre-study and main
study 1—we recruited participants from Amazon’s Mechanical Turk
(MTurk). In order to ensure a high quality of participants and
results, we followed previous studies (e.g., Goodman, Cryder &
Cheema, 2013 ) in selecting only individuals who had previously (a)
completed at least 50 0 0 (pre-study) and 10 0 0 (main study 1) of
MTurk’s Human Intelligence Tasks and (b) garnered an approval
rate of no less than 98% across all tasks. Another restriction on
those who participated in these two surveys was that they cur-
rently reside in the United States. All participants that were re-
cruited via MTurk received a fair financial reward of $2 for their
participation (approximating or exceeding the average US hourly
minimum wage). Participants in the third survey (main study 2)
were attendees of an undergraduate course at a German university.
Data collection took place in (respectively) February 2015, July–

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 1177
August 2016, and October 2017. After removing incomplete data
sets, we were left with a total of exactly 130 0 study participants.
Our pre-study survey sample consisted of 420 participants (180
females and 240 males) aged between 20 and 76 years; the average
individual was 37.4 years old, for which the standard deviation was
11.9 years. Most participants were either employees (38.3%), self-
employed (16.2%), managers (13.3%), or contract laborers (10.0%);
the rest were students (6.2%), temporarily without work (5.5%),
homemakers (4.0%), or retirees (3.6%). Among this sample, 11. 7 %
had finished high school, 26% had completed some college courses,
51.5% possessed a bachelor’s degree, and 10.2% had earned a mas-
ter’s, doctoral, or professional degree.
Main study 1

s survey sample included 474 participants (226
females and 248 males) between the ages of 18 and 73; the av-
erage age was 37.1 years with a standard deviation of 11.3 years.
Most of these individuals were employees (43.8%), self-employed
(16.7%), contract laborers (9.7%), or managers (9.5%); the others
were homemakers (6.1%), temporarily without work (5.5%), stu-
dents (4.9%), or retirees (1.7%). In this group, 11.8 % had finished
high school, 25.5% had attended college, 50.8% had earned a col-
lege degree, and 11.2 % had a master’s, doctoral, or professional de-
gree.
The sample for main study 2

s survey consisted of 406 par-
ticipants (115 females and 291 males) who were between 17 and
46 years of age. On average, these participants were 21.08 years
old with a standard deviation of 3.04 years. Most (94.1%) of them
were citizens of Germany, although a few (2.21%) hailed from
other European countries and several more (3.69%) came from a
country outside Europe. All of the participants in this third survey
were students.
4.2. Measures
Our questionnaire’s measures had all been previously estab-
lished and shown to exhibit adequate levels of reliability and valid-
ity. We viewed the fulfillment of these criteria as a credible indi-
cator of the measures’ suitability for capturing the underlying the-
oretical constructs of interest ( Kelley, Clark, Brown & Sitzia, 2003 ).
Proactive decision making (PDM) was assessed using the Proac-
tive Decision-Making Scale ( Siebert & Kunz, 2016 ; cf. electronic
appendix for an updated version). It consists of six dimensions—
“systematic identification of objectives”, “systematic identifica-
tion of alternatives”, “systematic search for information”, “us-
ing a decision radar”, “taking the initiative”, and “striving for
improvement”—as represented by 21 items that participants rated
on a Likert scale from 1 (“disagree very strongly”) to 7 (agree very
strongly). The 7-point Likert scale is a reliable measure whose in-
ternal consistency, as measured by Cronbach’s alpha, ranges from
0.63 to 0.80 ( Siebert & Kunz, 2016 ).
Life satisfaction (LSA) was assessed using the Satisfaction with
Life Scale (SWLS; Diener et al., 1985 ). The SWLS is well established
and widely used ( Pavot & Diener, 1993 ); this scale is a single-factor
global measure of LSA that consists of five items rated on the same
7-point Likert scale used to evaluate PDM. Various studies have es-
tablished that the SWLS has favorable psychometric properties (see
e.g. Pavot, Diener, Colvin & Sandvik, 1991 ): Cronbach’s alpha values
show that its internal consistency typically lies between 0.77 and
0.89 ( Gouveia, Milfont, Nunes da Fonseca & de Miranda Coelho,
2009; Sachs, 2003 ).
Decision satisfaction (DSA) was assessed using a revised form
( Siebert & Kunz, 2016 ) of the Fitzsimons (20 0 0) Decision Satis-
faction Scale (DSAS). This adjusted form of the DSAS is a single-
factor global measure of DSA that asks respondents to indicate
their satisfaction with three different aspects of decisions—namely,
final sets of alternatives, decision processes, and choice—on a scale
ranging from 0 (“extremely unsatisfied”) to 10 (“extremely satis-
fied”). Both the original and adjusted form of this scale are re-
ported to be reliable, as their Cronbach’s alphas range from 0.82
to 0.84 ( Fitzsimons, 20 0 0; Siebert & Kunz, 2016 ).
General self-efficacy (GSE) was assessed using the Schwarzer–
Jerusalem General Self-Efficacy Scale (GSES; Schwarzer &
Jerusalem, 1995 ). The GSES is a frequently used single-factor
measure of GSE ( Cheung & Sun, 1999; Leganger, Kraft & Røysamb,
20 0 0 )—that is, of the global strength of an individual’s belief in
her own capacity to cope with novel or challenging situations.
This scale consists of 10 items rated on a 7-point Likert scale
in the first and third survey; in the second survey, however,
respondents rated each item on a 4-point scale ranging from 1
(“not at all true”) to 4 (“exactly true”). The GSES exhibits favorable
psychological properties across extensive cross-cultural research
(e.g., Luszczynska, Gutiérrez-Doña et al., 2005 ) in addition to
high Cronbach’s alphas, whose values are usually between 0.75
and 0.94 ( Luszczynska, Scholz & Schwarzer, 2005; Schwarzer &
Jerusalem, 1995; Schwarzer et al., 1997 ).
4.3. Analyses
Following the recommendations of Anderson and Gerbing
(1988) , we tested our hypotheses by way of a two-stage analytic
procedure. In the first stage, covariance-based confirmatory factor
analyses (CFAs) were conducted to confirm the psychometric prop-
erties and dimensionalities of the employed constructs (viz., PDM,
GSE, DSA, and LSA) and to fit a measurement model to the data.
In the second stage, covariance-based structural equation modeling
was applied to assess the strength and significance of the hypothe-
sized paths among the constructs, to test for mediation, and to de-
termine the model fit of the several posited structural models. We
employed the full-information maximum likelihood method to es-
timate parameters. All analyses were performed using IBM’s “SPSS
Amos 25” statistical software.
Our elimination of non-fitting items was based on low factor
loadings and indicated cross-loadings. We evaluated the psycho-
metric properties of our employed constructs by computing com-
posite reliability (CR ≥0.6; see Bagozzi & Yi, 1988 ), average vari-
ance extracted (AVE ≥0.5; Fornell & Larcker, 1981 ), and the Fornell
and Larcker (1981) criterion that compares AVE with maximum
shared variance (MSV). In order to determine how well the data
are fit by the measurement and structural models, we used the ra-
tio of chi square to degrees of freedom ( χ2
/df ≤3), the Root Mean
Square Error of Approximation (RMSEA ≤0.10), the Standardized
Root Mean Square Residual (SRMR ≤0.08), the Comparative Fit
Index (CFI ≥0.9), and the Tucker–Lewis Index (TLI ≥0.9). The
fit statistics were gauged according to the parenthetical thresh-
olds just given—that is, as recommended by Browne and Cudeck
(1992), Homburg and Baumgartner (1995) , and Hu and Bentler
(1999) .
All structural models were tested by assessing standardized
gamma values, the significance of relationships, and endoge-
nous constructs’ coefficients of determination (i.e., R
2
). We tested
mediation—and enabled calculation of confidence intervals for the
indirect effects—via a bootstrapping procedure, which is a non-
parametric approach to hypotheses testing whereby a model’s pa-
rameters (and their standard errors) are tested only through statis-
tical re-sampling of the available data ( Preacher & Hayes, 2008 ).
Following standard recommendations ( Mooney & Duval, 1993 ),
we used 10 0 0 bootstrap samples with 95% bias-corrected confi-
dence intervals. Finally, a model comparison approach consistent
with Kelloway (1998) was chosen to test whether the proposed
model or instead alternative models better fit the data. In this ap-
proach, each model included either (i) one or more distinct freed
(i.e., “direct”) paths or (ii) no fewer than one constrained (i.e.,
“zero”) paths. The unmediated models were not nested within the

1178 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 117 1– 1187
mediated models and contained the identical set of variables;
hence models were compared in terms of values computed for
the Akaike Information Criterion (AIC), the Consistent Akaike’s In-
formation Criterion (CAIC), and the Bayesian Information Crite-
rion (BIC). For each criterion, smaller values indicate a better fit
( Bozdogan, 1987; Rust, Lee & Valente, 1995 ).
We addressed the issue of common method bias analytically
by applying Harman’s single-factor test ( Podsakoff et al., 2003 ).
When we considered all items included in the final measurement
models, the one-factor solutions extracted via unrotated principal
component analyses explained 45.49% (pre-study), 39.11% (main
study 1), and 28.96% (main study 2) of the total variance. As a
consequence, we cannot rule out the possibility of an influential
common method bias inherent to the data. Yet after comparing
our results with the widely used “majority of variance” criterion
of Harman’s test ( Podsakoff & Organ, 1986 ), we concluded that the
level of possible shared variances was acceptable.
Finally, we tested all predictor variables in each study for
multicollinearity by computing variance inflation factors (VIFs)
based on those factors, which were obtained after identifying
the final measurement models (cf. Hair, Black, Babin & Ander-
son, 2010 ). Our rationale for adopting this approach was that se-
vere multicollinearity—as indicated by VIF scores greater than 10
( O’Brien, 2007 )—has been shown to yield, in causal models, both
inaccurate parameter estimates and an increase in the likelihood of
Type II errors ( Grewal, Cote & Baumgartner, 2004 ). Our calculated
VIF scores were all below that threshold: they ranged from 1.853
to 3.677 in the pre-study, from 1.719 to 3.179 in main study 1, and
from 1.4 9 5 to 4.056 in main study 2. We therefore concluded that
multicollinearity was not a major issue, even if the stricter thresh-
old of VIF < 5 (as used in Menard, 1995 ) was applied.
5. Results
5.1. Pre-study
Given measurement model 1, which matches the first model
proposed by Siebert and Kunz (2016) , we began by subjecting the
six hypothesized dimensions of PDM (Initiative, Improvement, Ob-
jectives, Information, Alternatives, Radar ) —as well as PDM’s three
presumed consequences—to a first-order confirmatory factor anal-
ysis. Thus all 39 items were constrained to load on their pre-
dicted factor. Scale items that did not represent their predicted fac-
tor reliably or validly were excluded from further analyses. During
this iterative process, five items (ALT_4, LSA_5, RAD_2, RAD_3, and
GSE_2) were deleted because either their factor loadings were low
or their cross-loadings with other factors were high.
Table 1 shows that the final, 34-item first-order factor mea-
surement model exhibited fit statistics that were well within com-
monly accepted thresholds. The CR values of all constructs were
clearly above the threshold of 0.60 ( Bagozzi & Yi, 1988 ). Adopting
Netemeyer, Bearden and Sharma (2003) criterion that the AVE of a
recently developed scale should exceed 0.45, we saw evidence for
the convergent validity of all examined factors. Except for Initiative
(0.494) , all other factors were above the acceptable threshold of
0.50 ( Fornell & Larcker, 1981 ). That said, there were discriminant
validity issues with the three factors related to the PDM dimen-
sions of Objectives, Information , and Radar.
We addressed the observed discriminant validity issues by
testing an alternative measurement model. So in line with mea-
surement model 2, which also was proposed by Siebert and
Kunz (2016) , we aggregated the four factors associated with the
proactive cognitive skills to the higher-order factor Skills. For those
four sub-dimensions of SKILLS , a CFA yielded standardized factor
loadings that ranged from 0.820 to 0.965—at the same time, the
loadings of items related to the other factors remained almost
constant. Overall, then, most loadings were within the desired
range. All CR values were also above the acceptable threshold (of
0.60), indicating that the factors were sufficiently reliable. The
AVE value of this alternative model’s second-order factor cleared
the 0.50 threshold, so the convergent validity of that factor was
sufficient. We observed no discriminant validity issues resulting
from reduced inter-factor correlations.
Given the good fit statistics of the pre-study’s second-order
factor measurement model, as summarized in Table 1 , we next
tested our structural model. Table 2 presents our findings for
the hypothesized direct and indirect paths between PDM and its
proposed consequences (Model 1). This table reveals that there
is, for the most part, solid statistical support for both hypotheses.
All direct and indirect paths (except for those originating from
Improvement ) were statistically significant; furthermore, they ex-
hibited our hypothesized algebraic signs and no standardized path
coefficient was less than 0.15. Altogether, this structural model
explained 60.1% of the variance in DSA, 51.0% in GSE , and 32.1% in
LSA.
As a further test for the possibility of full or partial media-
tion effects, we also tested several alternative structural models;
see Table 3 . A comparison of each alternative model’s AIC, CAIC,
and BIC values with those derived for the hypothesized structural
model showed that the existence of a freed direct path from GSE
to LSA ( Model 3 ) resulted in the greatest improvement in model
fit; this outcome is illustrated in Fig. 2 . Hence these results sug-
gest that PDM’s effect on LSA is fully mediated and that the effect
of GSE on LSA is partially mediated. The results also indicate that
GSE partially mediates the effect of PDM on DSA—that is, when
one considers that neither of the (more constrained) Models 5 and
6 exhibit a better fit. Note that the statistical values obtained for
Models 7–9 support our assumption that GSE is probably the de-
pendent rather than the independent variable in the PDM–GSE re-
lationship. In short: the fit statistics of the hypothesized and alter-
native structural models were, by large, within the recommended
thresholds.
5.2. Main studies
After iteratively removing the same items omitted from the pre-
study (viz., ALT_4, LSA_5, RAD_2, RAD_3 , and GSE_2 ) and also re-
moving GSE_1 and GSE_3 from main study 2 (because of low fac-
tor loadings high cross-loadings with other factors, as explained
in Section 5.1 ), we tested the measurement models correspond-
ing to the pre-study model. Thus we examined the two models—
labeled “Study 1” and “Study 2”—in which the four factors related
to proactive cognitive skills were modeled to load on the higher-
order factor SKILLS. Results are reported in Table 4 . Running a
CFA with these second-order measurement models yielded stan-
dardized factor loadings that were within the desired range (i.e.,
from 0.533 to 0.931 and from 0.607 to 0.916 for Studies 1 and
2, respectively). Furthermore, all CR values were above the 0.60
threshold, indicating sufficient reliability of the factors. The AVE
value of the second-order factor exceeded the acceptance thresh-
old of 0.50, so that the factor’s convergent validity was considered
to be sufficient. No discriminant validity issues were observed, and
each model’s fit statistics met all criteria for a good fit to the
data.
With the results from these second-order measurement models
in hand, we next tested our structural models—all of which like-
wise yielded good fit-statistics. Table 5 presents our findings for
the hypothesized direct and indirect paths between PDM and its
proposed consequences (Model 1). Just as we had observed in the
pre-study, both hypotheses were mostly supported. All direct and
indirect paths, except for those originating from IMPROVEMENT
and for the direct path between INITIATIVE and DSA ( Study 1 only),

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 117 9
Tabl e 1
Pre-study: confirmatory factor analysis.
Factors/constructs/
dimensions
Item n=420
First-order model (measurement model 1) Second-order model (measurement model 2)
Std. factor
loading
CR AVE MSV Std. factor
loading
CR AVE MSV
Initiative INI_1 0.841 0.734 0.494 0.362 0.845 0.734 0.495 0.361
INI_3 0.442 0.443
INI_5 0.762 0.758
Improvement IMP_1 0.754 0.878 0.707 0.537 0.754 0.878 0.708 0.486
IMP_2 0.900 0.900
IMP_3 0.862 0.863
Objectives OBJ_1 0.788 0.806 0.581 0.733
OBJ_2 0.794
OBJ_3 0.701
Information INF_2 0.730 0.790 0.558 0.704
INF_3 0.813
INF_4 0.692
Alternatives ALT_1 0.800 0.843 0.642 0.619
ALT_2 0.762
ALT_3 0.839
ALT_4
Decision radar RAD_1 0.745 0.826 0.613 0.733
RAD_2
RAD_3
RAD_4 0.769
RAD_5 0.833
Skills Objectives 0.965 0.947 0.819 0.486
Information 0.912
Alternatives 0.820
Radar 0.916
Decision satisfaction DSA_S 0.821 0.912 0.775 0.496 0.821 0.912 0.775 0.504
DSA_P 0.918 0.917
DSA_D 0.899 0.900
General self-efficacy GSE_1 0.789 0.949 0.672 0.619 0.778 0.947 0.665 0.504
GSE_2
GSE_3 0.757 0.762
GSE_4 0.876 0.871
GSE_5 0.866 0.860
GSE_6 0.802 0.796
GSE_7 0.795 0.797
GSE_8 0.792 0.778
GSE_9 0.852 0.844
GSE_10 0.843 0.846
Life satisfaction LSA_1 0.929 0.937 0.789 0.375 0.889 0.929 0.767 0.379
LSA_2 0.832 0.770
LSA_3 0.911 0.937
LSA_4 0.878 0.899
LSA_5
Overall model fit RMSEA = 0.066, SRMR = 050,
χ2
/df = 2.803,
TLI = 0.908, CFI = 0.920
RMSEA = 0.065, SRMR = 0.0747,
χ2
/df = 2.751,
TLI = 0.911, CFI = 0.920
Tabl e 2
Pre-study: path analysis (structural model 1).
Predictor Outcome
General self efficacy Decision satisfaction Life satisfaction
Direct Indirect Direct Indirect Direct Indirect
Improvement 0.021 –0.095 0.010 –0.048
(–0.152; 0.193) (–0.234; 0.045) (–0.069; 0.094) (–0.143; 0.054)
Initiative 0.326
∗∗∗ 0.164
∗0.150
∗∗∗ 0.178
∗∗
(0.219; 0.471) (0.026; 0.307) (0.090; 0.250) (0.096; 0.273)
Skills 0.488
∗∗ 0.349
∗∗ 0.225
∗∗∗ 0.325
∗∗∗
(0.317; 0.677) (0.186; 0.514) (0.138; 0.338) (0.234; 0.442)
General self-efficacy 0.461
∗∗ 0.261
∗∗∗
(0.324; 0.603) (0.175; 0.371)
Decision satisfaction 0.566
∗∗
(0.476; 0.653)
R ²0.510 0.601 0.321
Note: Values reported in parentheses are the lower level (first number) and upper level (second number) of 95% bias-corrected confidence intervals of 10 0 0 bootstrap
re-samples.
∗p < 0.05,
∗∗p < 0.01,
∗∗∗p < 0.001.

1180 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 –11 87
Tabl e 3
Pre-study: comparison of structural models.
Structural model χ²/df TLI CFI RMSEA BIC AIC CAIC
1. Hypothesized model 2.846 0.906 0.915 0.066 1974.52 1618.98 2062.52
Freed path(s)
2. PDM → LSA 2.838 0.906 0.916 0.066 1979.86 1612.19 2070.86
3. GSE → LSA 2.751 0.911 0.920 0.065 1929.36 1569.78 2018.36
4. PDM + GSE → LSA 2.751 0.911 0.920 0.065 1939.40 1567.70 2031.40
Constrained path(s)
5. PDM → DSA ( = 0) 2.909 0.903 0.912 0.067 1996.94 1653.52 2081.94
Constrained path(s) and freed path(s)
6. PDM → DSA ( = 0) and GSE → LSA 2.820 0.907 0.916 0.066 1954.90 1607.44 2040.90
Other alternative models
7. GSE ( = IV) → PDM ( = DV), GSE +
PDM → DSA, and DSA → LSA 3.088 0.894 0.903 0.071 2088.29 1744.87 2173.29
8. GSE ( = IV) → PDM ( = DV), GSE + PDM → DSA, and GSE + DSA → LSA 2.994 0.898 0.908 0.069 2043.32 1695.85 2129.32
9. GSE ( = IV) → PDM ( = DV), GSE + PDM → DSA, and PDM + GSE + DSA → LSA 2.994 0.898 0.908 0.069 2052.60 1693.02 2141.60
Note: IV = independent variable, DV = dependent variable.
Fig. 2. Best structural model (Model 3) in terms of data fit.
were statistically significant; they also had the hypothesized al-
gebraic signs, and no standardized path coefficient was less than
0.12. These structural models explained about 57% of the variance
in DSA, between 46.4% and 49% of the variance in GSE , and be-
tween 35.6% and 38.2% in LSA.
Once again, we checked for full or partial mediation effects
by testing several alternative structural models; see Table 6 for
the results. Comparing these models’ AIC, CAIC, and BIC values
with those of the hypothesized structural model revealed, as
before, that the best-fitting model was one that included a freed
direct path from GSE to LSA. These results tend to confirm our
findings obtained from the pre-study in that (i) the effect of PDM
on LSA (excepting the IMPROVEMENT factor ) is fully mediated
and (ii) the effect of GSE on LSA is partially mediated. They also
lend additional support to our previous finding that GSE partially
mediates the effect of PDM on DSA (again considering that neither
of the more constrained Models 5 and 6 fit the data any better;
see Table 6 ). Our assumption regarding the PDM–GSE relationship
likewise received further support.
6. Discussion
Despite OR’s increased interest in behavior and well-being,
hardly any research has sought either to integrate these topics
or to investigate the impact of effective decision making on life
satisfaction. This paper has systematically analyzed that impact
by examining the relationships among PDM, DSA, GSE, and LSA.
The hypothesized mediation effects of DSA on the relationship
between PDM and LSA (Hypothesis 1), and of GSE on the rela-
tionship between PDM and DSA (Hypothesis 2), are largely sup-
ported by the results not only of the pre-study but also of our
main studies. These results bear three implications as detailed
next.
First, our findings establish that PDM promotes generally pos-
itive personal outcomes in the form of enhanced life satisfaction.
On the one hand, this result supports our assumption that effective
decision making is a multifaceted task ( Del Missier et al., 2012 )
that requires more than the seven skills enumerated by Bruine
de Bruin et al. (2007) as being necessary for decision-making

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 118 1
Tabl e 4
Main studies: confirmatory factor analyses (measurement model 2).
Factors/constructs/
dimensions
Item Study 1 ( n = 474) Study 2 ( n = 406)
Std. factor
loading
CR AVE MSV Std. factor
loading
CR AVE MSV
Initiative INI_1 0.748 0.741 0.495 0.392 0.781 0.803 0.577 0.419
INI_3 0.533 0.801
INI_5 0.800 0.693
Improvement IMP_1 0.793 0.899 0.749 0.356 0.700 0.848 0.653 0.448
IMP_2 0.929 0.887
IMP_3 0.869 0.826
Skills Objectives 0.783 0.906 0.707 0.445 0.822 0.881 0.652 0.448
Information 0.824 0.654
AlternativeS 0.856 0.916
Radar 0.897 0.815
Decision satisfaction DSA_S 0.750 0.885 0.721 0.445 0.607 0.760 0.517 0.419
DSA_P 0.875 0.760
DSA_D 0.914 0.777
General self-efficacy GSE_1 0.770 0.931 0.600 0.423 0.884 0.522 0.398
GSE_2
GSE_3 0.681
GSE_4 0.812 0.719
GSE_5 0.836 0.751
GSE_6 0.726 0.641
GSE_7 0.750 0.658
GSE_8 0.751 0.708
GSE_9 0.807 0.784
GSE_10 0.823 0.781
Life satisfaction LSA_1 0.931 0.946 0.813 0.366 0.772 0.824 0.540 0.320
LSA_2 0.884 0.710
LSA_3 0.923 0.780
LSA_4 0.867 0.672
LSA_5
Overall model fit RMSEA = 0.057, SRMR = 0.058,
χ2
/df = 2.562,
TLI = 0.923, CFI = 0.930
RMSEA = 0.042, SRMR = 0.053,
χ2
/df = 1.716,
TLI = 0.931, CFI = 0.938
Tabl e 5
Main studies: path analysis (structural model 1).
Predictor Outcome
Study 1 ( n = 474) Study 2 ( n = 406)
General self-efficacy Decision satisfaction Life satisfaction General self-efficacy Decision satisfaction Life satisfaction
Direct Indirect Direct Indirect Direct Indirect Direct Indirect Direct Indirect Direct Indirect
Improvement 0.047 –0.113 0.018 –0.059 0.009 –0.102 0.002 –0.059
(–0.083;
0.169)
(–0.246;
0.013)
(–0.027;
0.072)
(–0.143;
0.025)
(–0.161;
0.172)
(–0.275;
0.072)
(–0.041;
0.055)
(–0.168;
0.055)
Initiative 0.426
∗∗ 0.109 0.158
∗∗∗ 0.165
∗∗ 0.240
∗∗ 0.457
∗∗∗ 0.059
∗∗ 0.308
∗∗∗
(0.295;
0.533)
(–0.015;
0.246)
(0.094;
0.254)
(0.084;
0.258)
(0.021;
0.387)
(0.329;
0.598)
(0.005;
0.138)
(0.216;
0.408)
Skills 0.342
∗∗ 0.463
∗∗ 0.127
∗∗∗ 0.365
∗∗∗ 0.531
∗∗ 0.277
∗0.132
∗∗ 0.244
∗∗
(0.193;
0.506)
(0.293;
0.613)
(0.067;
0.221)
(0.260;
0.481)
(0.342;
0.732)
(0.009;
0.487)
(0.045;
0.284)
(0.115;
0.360)
General self-efficacy 0.372
∗∗
(0.236;
0.511)
0.230
∗∗
(0.143;
0.328)
0.248
∗∗
(0.052;
0.447)
0.148
∗∗
(0.032;
0.281)
Decision satisfaction 0.618
∗∗∗
(0.533;
0.699)
0.597
∗∗
(0.475;
0.685)
R ²0.490 0.570 0.382 0.464 0.567 0.356
Note: Values reported in parentheses are the lower level (first number) and upper level (second number) of 95% bias-corrected confidence intervals of 10 0 0 bootstrap
re-samples.
∗p < 0.05,
∗∗p < 0.01,
∗∗∗p < 0.001.
competence. In light of Geisler and Allwood’s (2015) non-
significant LSA-related results based on the ADMC scale, our
own findings underscore the importance, in decision processes, of
the “generating alternatives” phase for effective decision making
( Siebert & Keeney, 2015 ). Our results also demonstrate how in-
dividual differences in that phase, as reflected in PDM, can have
a positive effect on LSA. By actively and systematically devel-
oping decision alternatives that are aligned with personal goals,
proactive decision makers increase their odds of achieving desired
outcomes; that increase contributes in turn to the positive self-
evaluations of their life, which naturally follow from the positive
experiences associated with those outcomes (e.g., Hammond et
al., 2007 ). On the other hand, the observed positive relationship
between PDM and LSA empirically substantiates—from a decision-
analytic perspective—the merit of considering antecedents to PDM
and of asking whether (and, if so, how) proactive decision making
might be taught.
Second, the results reported here indicate that PDM affects LSA
not directly but rather through the individual’s experience of DSA.
This finding is notable because it extends existing research that has

1182 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 –11 87
Tabl e 6
Main studies: comparison of structural models.
Structural model Study 1 ( n = 474) Study 2 ( n = 406)
χ²/df TLI CFI RMSEA BIC AIC CAIC χ²/df TLI CFI RMSEA BIC AIC CAIC
1. Hypothesized model 2.57 0.923 0.929 0.058 1825.9 1480.6 1908.9 1.85 0.916 0.924 0.046 1376.1 1051.6 1457.1
Freed path(s)
2. PDM → LSA 2.58 0.922 0.929 0.058 1841.4 1483.5 1927.4 1.86 0.915 0.923 0.046 1392.7 1056.2 1476.7
3. GSE → LSA 2.55 0.924 0.930 0.057 1819.2 1469.7 1903.2 1.84 0.918 0.925 0.045 1372.3 1043.8 1454.3
4. PDM + GSE → LSA 2.56 0.923 0.930 0.057 1837.5 1475.5 1924.5 1.85 0.917 0.925 0.046 1389.3 1048.7 1474.3
Constrained path(s)
5. PDM → DSA ( = 0) 2.70 0.916 0.923 0.060 1881.3 1548.4 1961.3 1.99 0.903 0.911 0.049 1427.9 1115.5 1505.9
Constrained path(s) and
freed path(s)
6. PDM → DSA ( = 0) and GSE → LSA 2.68 0.917 0.924 0.060 1874.3 1537.3 1955.3 1.97 0.905 0.913 0.049 1424.3 1107.8 1503.3
Other alternative models
7. GSE ( = IV) → PDM ( = DV), GSE + PDM → DSA, and DSA → LSA 2.74 0.914 0.921 0.061 1904.9 1572.0 1984.9 2.02 0.899 0.908 0.050 1445.8 1133.3 1523.8
8. GSE ( = IV) → PDM ( = DV), GSE + PDM → DSA, and GSE + DSA → LSA 2.72 0.915 0.922 0.060 1898.1 1561.1 1979.1 2.01 0.901 0.910 0.050 1442.3 1125.8 1521.3
9. GSE (
= IV) → PDM ( = DV), GSE + PDM → DSA, and PDM + GSE + DSA → LSA 2.74 0.914 0.922 0.061 1916.4 1566.9 2000.4 2.02 0.900 0.909 0.050 1458.9 1130.4 1540.9
Note: IV = independent variable, DV = dependent variable.
investigated the relationship between decision making and LSA yet
without generating useful insights or a deeper understanding of
the connection. Thus our study addresses the call of Geisler and
Allwood (2015) for answers to the question of how effective deci-
sion making is most likely to influence an individual’s subjective
well-being; we do so by positing and then establishing DSA as a
central determinant in that relationship.
Third, in line with previous research ( Siebert & Kunz, 2016 ),
our findings strongly suggest that proactive decision makers are
more able to achieve their objectives and therefore experience bet-
ter decision outcomes—outcomes that can be expected to posi-
tively shape perceptions of their own decision making ( Sainfort
& Booske, 20 0 0 ). These results similarly lead us to conclude that
the additional cognitive effort necessary for PDM might stimulate
DSA in this sense: satisfaction with one’s own decision making
serves as a self-affirmation to employ a more structured decision-
making approach as well as confirmation that exerting the ad-
ditional cognitive effort needed for PDM was worthwhile. That
positive decision-making experience, which yields easily accessi-
ble positive information ( Schimmack & Oishi, 2005 ), then has a
positive effect on LSA and thereby gives credence to the bottom-
up perspective on life satisfaction ( Lance et al., 1989 ). Moreover,
showing that PDM has a greater impact on a decision domain-
specific type of satisfaction (here, DSA) than on LSA itself supports
the PDM measure’s nomological validity and also argues (cf. Franco
& Hämäläinen, 2016 ) for the usefulness of PDM in behavioral OR.
Beyond its central role of DSA, the observed mediation effects
of GSE underpin the notion that GSE facilitates the cognitive
processes required for effective decision making. These mediation
effects point to the complex nature of the interaction between
PDM and its consequences—to the extent that it is hardly pos-
sible to consider decision behavior, personality, and (cognitive)
motivational processes in isolation from each other. Because they
are inclined to take the initiative in decision situations and to
approach the phase of generating alternatives more effectively,
proactive decision makers tend to be more convinced (than their
reactive counterparts) that their decision behaviors will result in
desired outcomes. Given this strong positive effect of PDM, one can
argue that self-efficacy is increased by following the principles of
decision quality. Previous studies (e.g., Stajkovic & Luthans, 1998 )
have identified significant positive effects of GSE on different per-
formance measures, from which it follows that effective decision
making in the phase of creating alternatives might well increase
other (objective) measures of performance; of course, that possibil-
ity needs to be tested empirically. Finally, we remark that the re-
sults presented here confirm the importance of self-efficacy for any
study of decision-making behavior in an OR context (as assumed,
e.g., by Arumugam, Antony & Linderman, 2016; Taylo r, 2018 ).
Notwithstanding the significantly positive overall effect of PDM,
only two of that construct’s dimensions (Initiative and Skills ) were
positively correlated with either LSA or DSA. When one considers
that the zero-order correlations between Improvement and each
satisfaction type were significantly positive and that there were
no collinearity issues these results speak to an intricate relation-
ship among PDM’s three dimensions—a common phenomenon in
multiple regression analysis ( Kennedy, 2005 ). The negative and
non-significant effect of Improvement in our proposed structural
model suggests that individuals express a desire for improvement
by behaving in accordance with the principles of proactive de-
cision making. So depending on their inclination (or aversion)
to improvement, individuals are likely to adjust their behavior
with regard to the identification of objectives, the search for in-
formation and future decision situations, and the development of
alternatives. That dynamic explains why any striving for improve-
ment in excess of the effort made during the phase of generating
alternatives enhances neither LSA nor DSA, a finding in at least

J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 – 118 7 118 3
partial concordance with prior research. Those studies have shown
that being disposed to maximize, or to strive continuously for im-
provement in particular decision situations regardless of whether
such effort s align with one’s fundament al objectives, actually has
a negative correlation with DSA ( Dar-Nimrod, Rawn, Lehman &
Schwartz, 2009 ) and also with LSA ( Schwartz et al., 2002 ). Perhaps
more importantly, this finding likewise implies that meaningful, ef-
fective decision making during the phase of generating alternatives
does require, to some extent, a match between skills and traits. In
other words, it is not enough to apply the four proactive cognitive
skills essential to the pursuit of improvement ( Siebert & Kunz,
2016 ); individuals must also engage in that pursuit by applying
those skills in a way that has a personally meaningful impact.
Finally, the effect sizes found for the Skills component
outcomes—namely, GSE, DSA, and LSA—were (on average) signifi-
cantly higher across the three studies than were those found for
traits (e.g., Initiative); thus our results highlight the importance
of proactive cognitive skills for proactive decision making. These
results provide further support for the argument that skilled de-
cision behavior in the phase of generating alternatives is the key
to effective decision making. We therefore conclude that, for such
effectiveness, the application of foresighted decision-making skills
counts for more than does possessing traits that might in them-
selves encourage proactive decision behavior at the outset. Given
the relatively less importance of traits for effective decision making
and the consensus view that traits are fairly stable factors ( McCrae
& Costa, 1997 ), our findings shed new light on the question of
whether proactive cognitive skills can be learned. Enhancing such
skills while considering their interdependence with “striving for
improvement” and “taking the initiative” could increase life sat-
isfaction; such enhancement might also boost other performance
indicators in response to the consequent increased levels of gen-
eral self-efficacy.
In addition to those results obtained from the structural analy-
ses, we have provided evidence that confirms Siebert and Kunz’s
(2016) finding that the PDM scale has good psychometric prop-
erties; in other words, as a measure it is both reliable and valid.
In terms of measurement models, our results suggest that PDM is
a three-dimensional construct consisting of two distinct traits and
four closely related cognitive skills that relate to a higher-order fac-
tor. Although by definition those skills apply to different decision-
making aspects and hence should be separable, our results clearly
indicate that they are barely noticeable in isolation. That is to say:
individuals typically apply all four skills to a similar extent dur-
ing the phase of generating alternatives—the phase that determines
the effectiveness of their decision making. The process of generat-
ing our conclusions about the nature of PDM and effective decision
making merits a more detailed examination by interested scholars.
In any event, the implications following from the co-occurrence of
these four skills seem to align with prior psychological research on
proactivity, such as Bandura’s (1991) self-regulation theory.
7. Limitations and future research
In common with nearly all research designs, our study is
characterized by several limitations. The first is that all data were
collected from single sources via self-reported measures. Although
most researchers agree on the usefulness and preferability of
self-evaluations when analyzing, as we did, phenomena of an in-
trinsic nature ( Chan, 2009; Conway & Lance, 2010; Spector, 2006 ),
such measures have some well-known shortcomings ( Podsakoff
& Organ, 1986 ). Among these downsides, the most relevant is
their possible threat to internal validity. Hence common method
bias could have been an issue. We account for this possibility by
following the recommendations given by Podsakoff et al. (2012) .
Other potential biases in this regard could be the confounding
effects of participants’ overconfidence or penchant for social de-
sirability. Hence future research should control for these potential
problems by taking peer evaluations and experimental decision
settings into consideration. However, both alternative research
approaches may cause further issues, since our purpose was to
study how individual decision makers approach the phase of gen-
erating alternatives in general and not to analyze decision-specific
behavior at a given moment in time.
This study’s second limitation concerns the generalizability of
our findings, which mainly reflects the composition of our samples.
In the three studies, most of the participants either lived in the
United States or were from Germany. It is therefore hardly possible
that our results could be generalized to different cultural contexts
(e.g., Asia or Africa). Hence examining the effect of cultural differ-
ences on PDM offers intriguing avenues for future research, which
could extend previous studies on national differences with regard
to happiness (e.g., Cordero et al., 2017 ). Scholars might explore,
for instance, how different types of cultural socialization affect the
observed positive relationship between proactive decision making
and life satisfaction. Answering such questions would increase our
understanding of individuals’ perceptions of their approach to the
phase of generating alternatives during decision processes, which
in turn might yield new insight into the intrinsic motivation to be-
have as a proactive decision maker. Finally, the high level of ed-
ucation among the three samples’ constituent members was like-
wise relatively homogeneous; also, being highly educated is itself
often viewed as an indicator of enhanced decision-making abilities
( Bruine de Bruin et al., 2007; Klein, 1999 ). It follows that future
studies should examine whether our findings hold also for samples
consisting of less educated individuals.
A third limitation involves causality, an issue that plagues
most non-experimental studies ( Anderson & Vastag, 2004 ). The
cross-sectional design of our studies, which examined PDM and
its proposed consequences as measured at a particular given
time, precluded any assessment of temporal priority. Therefore,
confirming those of our conclusions that involve PDM’s “conse-
quences” is advisable. Even though we can cite the results of
our three independent surveys to substantiate those conclusions
empirically—and despite the theoretical soundness of the relation-
ships we hypothesize—longitudinal research is needed to establish
the true causal ordering of the constructs examined here.
Apart from the research possibilities stemming from these
methodological limitations, there is also considerable potential for
work on related topics. For example, future research could scruti-
nize the relationships between PDM and other measures, such as
the Big Five personality traits ( John & Srivastava, 1999 ). Also, iden-
tifying and analyzing the possible antecedents of PDM would help
us better understand why some individuals are more effective than
others when applying proactive cognitive skills during the phase
of generating alternatives. Research along those lines would also
provide educators, decision analysts, and recruiters with valuable
information about the conditions under which decision training
is likely to help individuals—even those who have already been
taught how to make decisions in accordance with the principles of
decision quality—become more effective proactive decision makers.
8. Conclusion
In this paper we sought to integrate two current OR streams,
and to extend the literature at the interface of well-being and be-
havioral OR, by examining the relationship between life satisfac-
tion and effective decision making (i.e., decisions made according
to the principles of decision quality). Given the widely held opin-
ion in decision sciences that generating alternatives may be the
most critical phase of decision making, we focused the analysis

1184 J.U. Siebert, R.E. Kunz and P. Rolf / European Journal of Operational Research 280 (2020) 1171 –11 87
on whether—and, if so, in what way—behavioral differences during
that phase are related to an individual’s life satisfaction.
Analyzing empirical data obtained from three independent sur-
veys revealed the existence of a significant positive relationship
between PDM and LSA. From this finding we drew three conclu-
sions. First, our results further substantiate the baseline argument
that subjective well-being (as proxied by LSA) is, in part, a mat-
ter of choice. However, this choice reflects not only the “six laws
of happiness” proposed by Baucells and Sarin (2012, 2013 ) but
also one’s fundamental approach to decision making. In support of
the notion that humans do not merely respond passively to their
environment and personality, our results imply that individuals
can have a positive effect on their life satisfaction by deliberately
choosing to follow a more effective decision making approach.
We therefore conclude that also the goal-directed behavior driven
by effective decision making is a meaningful determinant of life
satisfaction.
Second, given that previous studies have not adequately ex-
plained the relation between effective decision making and sub-
jective well-being, our results highlight how important the phase
of generating alternatives is—for subjective decision outcomes and
also for effective decision making. For the latter, it is not sufficient
to make a good choice based on appropriate evaluations of alterna-
tives (i.e., even when suitable problem-solving methods are used);
being able to choose among good alternatives is also necessary.
This requirement implies that decision analysts and decision sci-
entists should expand their definitions of decision-making compe-
tence by explicitly considering the “generating alternatives” phase
and the skills related thereto. Such augmented definitions should
encourage further research on this understudied phase of decision
making.
Our third and most important conclusion is that these findings
underscore the advantage, in decision processes, of not only fol-
lowing decision quality principles but also being proactive in the
phase of generating alternatives. The result that applying proactive
decision-making skills leads to effective decision making in this
phase is meaningful at the individual level in terms of enhanced
LSA, DSA, and GSE; it also highlights the relevance of OR and deci-
sion sciences to individuals and their lives. From a social perspec-
tive, we thus have a good starting point from which to argue that
the topics of decision quality and effective decision-making should
be integrated into educational programs. In this way, OR and de-
cision analysis can make a positive contribution to community de-
velopment: providing methods and principles that enable individu-
als to avoid socially undesirable and other negative outcomes (e.g.,
a low level of subjective well-being) that result from their deci-
sion making. From a decision-analytic perspective, that claim raises
the questions of (i) how decision scientists and OR scholars can
help individuals become more effective in the phase of generat-
ing alternatives, (ii) how well such individual-level effectiveness
carries over to organizational and group decision-making contexts,
and (iii) how this effectiveness is related to other objective indica-
tors of performance.
Finally, our finding that the relationship between PDM and LSA
is not direct—and instead is mediated by DSA and GSE—establishes
how crucial it is for OR to become more interdisciplinary when
analyzing the applicability and usefulness of its proposed deci-
sion structuring and of its problem-solving models and techniques.
There are direct effects between one’s decision-making and ex-
pected outcomes, yet there also exist indirect effects that can in-
fluence that relation. Hence we argue that behavioral OR and deci-
sion sciences would benefit from further considering factors (e.g.,
job satisfaction) that affect well-being in a reciprocal way, since
doing so would expand our knowledge of how individual deci-
sion making is related to the decision maker. The strong relation-
ship we document between PDM and GSE backs the argument
that, in addition to other psychological and cognitive factors and
personality variables, especially personal incentive aspects should
be considered when analyzing the processes and consequences
of an individual’s decision making. In order to support effective
decision making, as suggested by our paper’s opening quotation,
it is critical for OR and decision analysis to understand it at all
levels.
Acknowledgements
The authors would like to thank the three anonymous review-
ers for their positive comments and constructive suggestions that
greatly contributed to improving the quality of the paper. They
would also like to thank Editor Robert Graham Dyson for his valu-
able comments and support during the review process. Further,
they like to thank the EURO Working Group on Behavioral OR and
its for valuable feedback and comments in early as well as late
stages of this research.
Supplementary materials
Supplementary data associated with this article can be found,
in the online version, at doi: 10.1016/j.ejor.2019.08.011 .
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