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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.
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European Journal of Operational Research 280 (2020) 1171–1187
Contents lists available at ScienceDirect
European Journal of Operational Research
journal homepage:
Interfaces with Other Disciplines
Effects of proactive decision making on life satisfaction
Johannes Ulrich Siebert
a , b , , Reinhard E. Kunz
, Philipp Rolf
Management Center Innsbruck, Department Business and Management, 6020 Innsbruck, Austria
Faculty of Law, Business and Economics, University of Bayreuth, 95440 Bayreuth, Germany
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.
( )
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: ,
(J.U. Siebert), (R.E. Kunz),
(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
0377-2217/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. ( )
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
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
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
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-
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
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-
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–