quantum approach to time and organizational change … … · a quantum approach to time and...

A QUANTUM APPROACH TO TIME ANDORGANIZATIONAL CHANGE

ROBERT G. LORDJESSICA E. DINH

Durham University

ERNEST L. HOFFMANUniversity of Akron

Prevailing perspectives on time and change often emphasize the forward movementof time and the relative stability of attributes, an emphasis that fosters theories oforganizational evolution as a linear progression of a past that moves to the presentthat moves to the future. While useful in many respects, this perspective obscures theuncertainty of emerging organizational phenomena, and it offers little insight into therare and unpredictable events that change the course of history. To address theseconcerns, we draw on quantum mechanics and quantum probability theories topresent a quantum approach to time and change as a framework for understandingorganizational complexity and the common decision-making errors that lead to orga-nizational failures within uncertain environments. This perspective also explains howorganizations (or societies) can experience unforeseen potentialities that radicallychange their development by conceptualizing the future as existing in a state ofpotentiality that collapses to form the present based on the dynamics of systemconstraints. Our theory has broad implications for organizational theory and research,as well as management practice.

You can never plan the future by the past(Edmund Burke).

Learn from the past, set vivid, detailed goals forthe future, and live in the only moment of timeover which you have any control: now (DenisWaitley).

In the last two decades organizational schol-ars have recognized the importance of time inunderstanding organizational processes (An-cona, Goodman, Lawrence, & Tushman, 2001). Infact, the ability to control various aspects oftime, such as the structure of time, the subjectiveexperience of time, how one thinks about time,and the entrainment of events through time(Bluedorn & Jaussi, 2008; Sonnentag, 2012), isthought to underlie effective leadership, group,and organizational dynamics. However, per-haps the most important issue for individualsand organizations is to devise ways to controltheir future, and this is related to how we con-ceptualize and use time.

Time can be measured objectively, but it is thesubjective and intuitive aspects of time that maybe most critical in understanding how time relatesto organizational processes. Subjective time iscentral to individual sensemaking (Hernes & Mait-lis, 2010) and cultural sensemaking (Zerubavel,2003), which use both cognitive and emotionalschema to connect (or separate) even the distantpast to (from) the present and create anticipatedtrajectories leading to the future. Intuitive pro-cesses provide a basis for understanding the flowof time as a feed-forward process, where the pastflows into the present and then into the future.This conceptualization of time is a generalizationfrom how one physically moves through one’sphysical environments, such as moving forwardfrom one’s present location to another location. Italso reflects the structure of human memory sys-tems, which use remembered experiences (i.e., ep-isodic memory) as a basis for projecting into thefuture (Kaplan & Orlikowski, 2013; Suddendorf &Corballis, 2007).

Because we understand change and adapta-tion in terms of a subjective flow from the past tothe present, we naturally conceptualize the fu-ture as an extension of ongoing longitudinal

We thank Jerome Busemeyer, Mark Hall, Paul Hanges,Gerald Hodgkinson, Julian Marewski, Aron Polos, and threeanonymous reviewers for helpful comments on previousdrafts of this manuscript.

� Academy of Management Review2015, Vol. 40, No. 2, 263–290.http://dx.doi.org/10.5465/amr.2013.0273

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trajectories, guided by an evolution from thepast to the present and extending to the future.This view supports individual and collectivesensemaking (Ibarra & Barbulescu, 2010; Weick,1995), and when the future departs substantiallyfrom this trajectory, organizational scholars, aswell as managers, are motivated to explain itand build relevant theory. However, organiza-tional scholars have increasingly recognizedthat discontinuous change cannot be antici-pated easily from a narrow focus on the eventsthat occurred in the present or the past (MacKay& Chia, 2013; Plowman, Baker, Kulkarni, Solan-sky, & Travis, 2007). In fact, the world oftenchanges in ways that are unforeseeable and noteasily anticipated (Taleb, 2010), since it involvesthe emergence of new states (e.g., movementfrom despair to optimism, from munificence toscarcity, from failure to success), new capacities(e.g., skills, creativity, adaptability), and newresources (e.g., social capital) that previouslydid not exist or had no comparable analogue.Such unanticipated change tends to be ex-plained in terms of the functioning of complexsystems (Uhl-Bien & Marion, 2009) or, alterna-tively, as a consequence of turbulent environ-ments characterized by “relentless chance, en-vironmental circumstance, and unintendedconsequences” (MacKay & Chia, 2013: 221).

In this article we develop a different perspec-tive on time and the nature of individual andorganizational processes, which, we argue, hasgreat relevance to the issue of how futures un-fold and can be influenced. We propose that thechallenges of understanding organizational fu-tures and the dynamism of unfolding eventsover time can be addressed, in part, by undoingthe dominant tendency to view a forward flow oftime based on a past that leads to the future (i.e.,past ¡ future). Instead, we advocate efforts tomentally reverse the arrow of time to emphasizea future that flows into the present (i.e., future ¡

present), which is a view that has not receivedsufficient attention (e.g., Kaplan & Orlikowski,2013). Perceptually, this shift is analogous to be-ing in a train looking out the window and seeingthe distant horizon flow toward one’s presentlocation, even though one knows it is the trainthat is moving toward the horizon.1 We maintain

that this mental reversal of a basic assumptionguiding organizational science and intuitivereasoning can help us overcome limitationsfrom commonsense beliefs about developmentand causality (Sherover, 2003) and narrativesthat connect the past to the present (Taleb, 2010).The future offers many potentialities, which wedefine as alternative states and possible out-comes that could occur but have not yet occurredbecause, to be actualized, they require the en-actment of individual, social, and environmen-tal events that are often serendipitous.2 As such,many sets of unrealized potentialities (also re-ferred to as a superpotentiality state in quantumtheory; Greene, 2004) cannot be easily envi-sioned or pursued from a perspective that be-gins by considering the present or past. Rather,they can be better appreciated by starting withan unconstrained future with many possibleoutcomes and realizing that its flow into thepresent can be influenced by many individual,collective, and environmental factors that guideunfolding, organizing processes.

We maintain that this perspective has a nat-ural affinity to quantum probability theories de-rived from quantum mechanics (Greene, 2004,2011), which also begin with an undefined state,and it offers an innovative approach for under-standing the unfolding of complex organiza-tional phenomena. Although this approach tounderstanding process can be seen as a far cryfrom those advocated in the social sciences, wenote that the application of quantum theories tosocial phenomena is not entirely foreign, havingbeen applied to early philosophical teachingson general existence and the state of being (seeBakken & Hernes, 2006, and Epperson, 2004, for asummary of Alfred North Whitehead’s processphilosophy). However, because the focus andcontribution of our article is not to elaborate onthese existential or philosophical consider-ations, we direct readers interested in these is-sues to other sources (see Bakken & Hernes,2006; Epperson, 2004; Whitehead, 1978).

1 We thank an anonymous reviewer for suggesting thisperceptual analogue of our reasoning.

2 We note that potentialities are similar to affordances,which Gibson (1986) defined as possibilities that exist inone’s environment and that become available upon action.However, potentialities refer to a more general state contain-ing a set of undefined alternative outcomes that exist simul-taneously and that become defined only upon enactment ina specific context.

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In developing our perspective, we extend the-ories that were developed over the course of acentury in the physical sciences but have not yetbeen considered sufficiently by organizationalscience. We maintain that by integrating quan-tum theory with a view of the future as flowinginto the present, we can revolutionize our con-ception of the processes linking time to the de-velopment of events, offering the potential forgreater understanding and perhaps greater con-trol of the future, as well as an enhanced under-standing of change processes.

This combined approach, which we label aquantum approach to time and change (QATC),has at least three principal advantages. First, aQATC provides a novel perspective for theorybuilding in organizational science by emphasiz-ing that the future is often qualitatively differentfrom the present or past. As such, it provides anunderstanding of dynamic organizational phe-nomena, considering how organizational life,and life in general, can change by developing inmany different ways as individuals and collec-tives interact with their environment. Quantumtheory can represent multiple interacting pathsthrough time and, thus, can represent the com-plexity of change in ways that more conven-tional models cannot (e.g., Markov models; seeBusemeyer & Bruza, 2012).

Second, a QATC is grounded in quantumphysics, which offers a diverse set of researchtools and concepts that could supplement exist-ing tools for studying dynamic change in orga-nizations. It has the potential to build on in-sights provided by qualitative, process-orienteddescriptions of organizational change that em-phasize the role of context in guiding organizingprocesses (see Langley, Smallman, Tsoukas, &Van de Ven, 2013), while simultaneously devel-oping a precise conceptual and mathematicalrepresentation for incorporating context intopredictions of outcomes.

Third, many of the ideas that we address chal-lenge fundamental assumptions regarding thenature of change that have guided organiza-tional science. Such assumptions operate at thelevel that Alvesson and Sandberg (2011) havecharacterized as field assumptions becausethey undergird entire fields of study. These in-clude the assumptions that (1) time should beconceptualized as flowing from the past to thefuture, (2) future states can be reached frompaths grounded in the past and in existing prac-

tices, and (3) phenomena of interest (e.g., person-ality, attitudes, values, cultures, strategies) existindependent of our attempts to measure them.We did not set out to challenge such assump-tions but, rather, gradually realized that ques-tioning such taken-for-granted assumptionshelped us understand organizational and psy-chological processes in new and useful ways.Our hope is that explicitly acknowledging thisissue at the outset will help readers in a similarmanner.

In the following sections we begin by explain-ing our natural understanding of time and com-pare this with a very different understanding ofthe unfolding of actual experiences and eventsbrought forth by quantum theories. Finally, weaddress the theoretical, methodological, andpractical implications of a QATC for organiza-tional science.

TIME, ORGANIZATIONAL SYSTEMS,AND UNCERTAINTY

Commonsense Approaches for Understandingthe Flow of Time

Unique to humans is the striking ability totravel through time, psychologically departingfrom the present to consider events in the future(Buckner & Carroll, 2007; Kaplan & Orlikowski,2013; Sherover, 2003; Suddendorf & Corballis,2007). Individuals frequently traverse the tempo-ral boundaries of the present to consider theglimmering hopes and dangers hidden in thefuture (Dane & George, 2014). The advantage ofthese mental activities is that they allow one toforecast what the future may be based on one’sprior experiences and intrinsic desires (Dane &George, 2014; Seligman, Railton, Baumeister, &Sripada, 2013). It can also assist in forming so-cial inferences, anticipating the intentions andbeliefs of others (Buckner & Carroll, 2007), andformulating one’s own expectations and goal-directed behavior (Seligman et al., 2013). The abil-ity to accurately forecast the future is so criticalthat humans have developed extensive neuralnetworks dedicated to the storage, access, andintegration of existing memory in order to simu-late the possibilities that may arise tomorrow (Gil-bert & Wilson, 2007). Indeed, it may be humans’natural ability to forecast that has created astrong cognitive tendency to formulate decisionsand behaviors based on a forward-moving per-

2015 265Lord, Dinh, and Hoffman

spective of the future. This tendency also has beenreinforced by social-cultural development andlearning (i.e., the future as moving forward;Boroditsky & Ramscar, 2002), and it is a trend thatis central to many of today’s organizational theo-ries and research paradigms.

However, there are many cognitive fallaciesthat accompany prospective assessments of thefuture (Dane & George, 2014; Seligman et al., 2013).They occur as humans form erroneous assump-tions based on what has occurred while neglect-ing what may have occurred. In such instancesprospective errors can manifest as forecasting bi-ases that unduly influence decision making andbehavior in ways that fail to account for the un-certainty and nonlinearity of realistic organiza-tional events (Arrfelt, Wiseman, & Hult, 2013; Mac-Kay & Chia, 2013). Several examples demonstratethis. For example, the bankruptcy of several Amer-ican automobile companies (e.g., General Motors,Chrysler) in 2009 illustrates the culmination ofglobal market conditions and the failure of orga-nizational decision makers to forecast changingconsumer preferences for more fuel-efficient vehi-cles. Forecasting biases also manifest in everydaydecision-making errors, such as in the tendency tomake optimistic self-predictions with respect tosaving money or future performance (Helzer &Dunning, 2012; Koehler, White, & John, 2012), andin hindsight biases where accurate projectionsare inhibited because of limitations in one’s abil-ity to objectively remember prior information.

In brief, a wide body of research suggests thatalthough there is a biosocially grounded ten-dency for humans to infer the future by extend-ing the past, prospective cognitions are suscep-tible to processing failures because they areoften based on salient nonrepresentative mem-ories and experiences and are made in abstrac-tion without reference to context (Dane &George, 2014; Gilbert & Wilson, 2007). As de-scribed in the following section, there are manyadvantages in considering an alternative viewof the flow of time.

An Alternative Conceptualization of Time andOrganizational Uncertainty

Organizational events and events occurringwithin individuals manifest very differently atdifferent points in time (Hoffman & Lord, 2013),even though there is a tendency for humans andsocieties to subjectively represent time in ways

that create a sense of consistency (Hernes &Maitlis, 2010; Zerubavel, 2003). For example, in-dividual cognitive, emotional, and physicalstates all have inherent rhythms and dynamicsthat vary across time and context (e.g., hunger,fatigue, boredom, lust, circadian rhythms), andthese interacting systems create different con-texts that allow different thoughts and individ-ual behaviors to emerge (Dionysiou & Tsoukas,2013; Read et al., 2010). Similarly, complexitytheories have been used to describe the behav-iors of groups and organizations (Uhl-Bien &Marion, 2009), which are understood to fluctuaterapidly and often unpredictably (e.g., Crawford& LePine, 2013; Klarner & Raisch, 2013; Langleyet al., 2013) as contexts change and as organiza-tional processes take on different features orforms. Yet variations across time within individ-ual and organizational systems are typically ig-nored because of theoretical or methodologicaloversights (Kozlowski, Chao, Grand, Braun, &Kuljanin, 2013; Vancouver & Weinhardt, 2012),obscuring the richness of organizational phe-nomena. Variations are also ignored becausethey are seen as errors, rather than resultingfrom phenomena that should be explainedscientifically.

However, qualitative research has demon-strated the complexity inherent in organiza-tional systems and the need for more adequateexplanatory systems. For example, Plowman etal.’s notable study (2007) described how a culmi-nation of seemingly small events and existingpreconditions at a dying institution, MissionChurch, helped radically shift the organization’sstructure and image. Specifically, they de-scribed how a small event (i.e., a meeting of fiveto six relatively inactive churchgoers to orga-nize a charity event) initiated a chain of eventsthat disrupted existing patterns of behaviorwithin the organization and the community,thereby encouraging innovation and the rein-vention of the church. Importantly, high uncer-tainty, defined as the inability to accuratelypredict outcomes resulting from the lack ofinformation (Milliken, 1987), accurately char-acterizes Mission Church’s change, as well asthe change trajectories of many of today’s or-ganizations (see MacKay & Chia, 2013). In fact,for many organizations the strategic allocationof resources to nurture high-potential businesssubsidiaries is challenging simply because or-ganizational decision makers cannot foresee the


market conditions, economic trends, and soci-etal factors that may aggregate to impact thesurvival of one subsidiary over another (Arrfeltet al., 2013). Such observations reinforce our ar-guments that events unfold probabilistically inthe future in ways that forward-based prospec-tion cannot explain.

Meanwhile, for almost a century the field ofquantum physics and quantum mechanics hasbeen able to model the complexity and uncer-tainty in the movement of various entities, in-cluding subatomic particles and the universe,by using an intensively scrutinized mathemati-cal formalism (Greene, 2004, 2011). We believethat this way of thinking and representing pro-cesses can be extended to understanding howhuman systems construct the future. In the fol-lowing sections we describe our QATC perspec-tive, which implies that real-life organizationalprocesses follow a different logic in the flow oftime, a different set of probability laws, and amathematical formalism based on quantum me-chanics that can better account for complexitiesendemic to human and organizational systems.

A QATC

Quantum Physics and ProbabilityWave Functions

Quantum physicists describe a fundamentalproperty of matter and energy, which is that they

seem to have both particle and wave properties(e.g., light and magnetism; Feynman, Leighton,& Sands, 2010/1965; Rae, 2005). For example, elec-trons appear to travel across time and space notas specific particles with precise trajectories butas waves that occupy multiple locations. This isbecause the precise location of an electron isgenerally unknown, and it can appear in aninfinite number of places in space upon mea-surement (Greene, 2004). As such, the movementof an electron through space is better repre-sented by a “smeared” trajectory, rather than asingle line that would indicate a predictabletrajectory as used in classical probability theory(see Figure 1).

More precisely, the variable movement of anelectron through space can be depicted mathe-matically by abstract, algebraically derivedprobability wave functions (i.e., mathematicalrepresentations of likely possible outcomes),shown graphically as waves that indicatewhere the electron should be at a particularpoint in time. Such a probability wave is shownin Figure 2. In this figure the probability of find-ing an electron at a particular position in-creases with the height of a particular hill com-pared to the surrounding plane. Central to thisfigure is that probability waves are character-ized by a high degree of uncertainty in knowingwhere an electron will be when it movesthrough space. However, when a physicist mea-

FIGURE 1Comparison Between (a) Classical Probability Theory and (b) Quantum Probability Theory for

Representing the Predicted Outcomes of Physical and Psychological Phenomena As They TravelAcross Time


sures an electron’s position, constraints im-posed by the measurement process cause itsprobability wave to collapse, allowing the phys-icist to depict the electron at a specific, singlelocation. This phenomenon may also be ob-served when organizational scholars measureconstructs at one or multiple discrete points intime, where measurements are subject to con-straints inherent in the measurement tools theyuse (Feldman & Lynch, 1988; Harrison &McLaughlin, 1996). This explanation accountsfor why there is higher certainty in knowingexactly where an electron is, or what organiza-tional constructs are, upon measurement thanprior to measurement (Feldman & Lynch, 1988;Greene, 2004; Harrison & McLaughlin, 1996).

The principles of quantum theory have broadapplications beyond the domain of subatomicparticles. Recently, they have been applied tounderstanding the influence of context on deci-sion making and the creation of cognitive con-cepts (Busemeyer & Bruza, 2012; Busemeyer,Pothos, Franco, & Trueblood, 2011; Gabora,Rosch, & Aerts, 2008), to understanding the cre-ation of consciousness and how individual cog-nitive processes change over time (Hameroff &Penrose, 2014), and to theorizing regarding theexistence of alternate realities that parallel ourown in the universe (see Greene, 2004, 2011).These applications not only demonstrate the ro-bustness of quantum mathematics for modelingdiverse phenomena (Greene, 2004; Pothos &Busemeyer, 2013) but also suggest that futurepsychological and physical phenomena do notemerge from a single past. Rather, they are se-lected from an infinite set of potential realitiesthat are best represented by probability waves.In the following sections we describe the key

arguments of quantum theories and developtheir theoretical application to organizationalscience. We also describe the mathematical for-malism of quantum theories in the Appendix.

Probability Waves and Social Systems

Using probability waves as a metaphor fororganizational processes, we propose that theamount of certainty in knowing where a partic-ular particle is located at a point in time can begeneralized to understanding how social sys-tems create a specific, experienced reality at aparticular point in time when they, too, areguided by processes that are functionally repre-sented by probability waves. This reasoningsuggests that there are many possible outcomesthat can be represented and enacted since thefuture exists in a state with many potentialities.Quantum physics defines such a state as a su-perpotentiality (or superposition) state, in whichmany possibilities are in an indefinite state buthave the potential to occur when influenced by aspecific context. However, when conjoined witha particular context, this superpotentiality statewill collapse because of experienced con-straints to create a specific experienced reality,much like an electron appearing in a definedposition in space upon measurement.

This perspective also suggests that the pres-ent (and our soon to be experienced past) wasselected by the confluence of multiple eventsand processes that occurred across many levelsin relevant individual, group, and organizationsystems. For instance, the interaction of a spe-cific social unit (e.g., individuals), a particularcontext (i.e., setting), and a particular technol-ogy (i.e., form of interaction) could be understoodas working simultaneously together to guide theway people construct the present through theiractions. In this way alternative pasts could haveeasily happened had any of these elementsbeen different.

To account for these observations, our QATCperspective proposes that the future flows to-ward the present and eventual past as a wave ofinteracting potentialities—most of which are notdirectly experienced and are only realizedthrough careful retrospection, such as counter-factual thinking. Counterfactual thinking in-volves reconsidering the past and examininghow situational factors or one’s behavior couldhave been different, leading to different out-

FIGURE 2Probability Wave Function of an Electron

Traveling Through Space

Note: The probability of an electron being at any positionis proportional to the height of the wave at that point.


comes. For example, missing an airplane by fiveminutes might lead one to mentally examine themany possible ways one could have arrived atthe gate five minutes earlier and, ideally, tobuild some of these ways into one’s routine toavoid missing a future flight. Thus, a crucialissue for individuals, groups, organizations, andsocieties is to use processes like counterfactualthinking to understand how a present is se-lected from many different potential alterna-tives that once existed in the future, rather thanhow the past leads to one future state. Like driv-ing through a city with many streets, there areinfinitely many routes and destinations, but weonly experience the route taken. Alternative po-tentialities could be illustrated by a good roadmap, which would enable the driver to retro-spectively consider the other routes that couldhave been taken. However, there is no map fornavigating organizational futures since the met-aphorical roads that carry organizational pro-cesses forward have not yet been constructed byhuman actions. A QATC addresses this issue byadopting a process-oriented perspective that at-tempts to understand how different presents areactively created. This perspective can, therefore,help unleash creativity and optimism for under-standing how potentialities are realized byhighlighting the role of proactive change andemphasizing that many potentialities are al-ways available, which is less evident when weadopt an entity-guided view of the past.

Creating the Present from Multiple Futures

The creation of novel futures from a QATC canbe illustrated by a historical example. The de-velopment of microprocessors at Intel in theearly 1970s occurred as a response to serendip-itous events, not as a planned product develop-ment. Microprocessors emerged from a creativeand elegant engineering solution to a custom-er’s request at Intel in 1969 to produce a differenttype of memory chip. This new design createdan emergent bottom-up strategy that interactedwith other events so that in less than a decadeIntel changed from a memory chip producer to amicroprocessor powerhouse, and it led Intel toexit the memory chip market in 1985 (Hazy, 2008).Microprocessors, in turn, allowed the develop-ment of PCs, laptops, smartphones, the internet,and social media so that people, markets, andsocial institutions could be closely connected all

over the globe. Although this chain of develop-ments seems like a logical progression whenviewed retrospectively, most people would nothave foreseen these changes in the 1970s, andthey reveal only some of the many potentialitiesthat were available in the coming future. From aQATC perspective, the challenge for both orga-nizational leaders and organizational science isto recognize that these potentialities exist and tounderstand how alternative potentialities canbe created.

A crucial issue in understanding change ishow and why some potentialities develop as thefuture approaches the present, whereas othersfail to emerge. As explained in the followingsection, we maintain that the selection of onepossible alternative occurs as constraints in hi-erarchically organized systems attenuate somepotentialities while enhancing others. Theseconstraints interact with each other and withinputs from organizational processes to createattractors (i.e., points of stability created by re-inforcing systems that channel processes in def-inite and consistent ways) for interpretation andbehavior. However, we believe that thesespaces are hard to find because (1) they tend toemerge between multiple layers of hierarchi-cally nested systems as superpotentiality statescollapse to create new realities; (2) they are cre-ated by factors that organize processes and,thus, are one step removed from events and be-haviors; and (3) looking backward leads to adescription of behaviors and events in entityterms, a tendency that is exaggerated by lan-guage that better suits entity than process ex-planations (Hernes & Maitlis, 2010).

Returning to the previous Intel example canillustrate these three points. First, the collapseof spaces to create new realities such as micro-processors reflected a process involving sets ofavailable resources (e.g., knowledge, financialresources, and human capital) found at the con-junction of specific customers and engineeringgroups at Intel, and, later on, it involved theentire organization and the market systems sur-rounding Intel. Thus, the creation process in-volved actions at boundaries of multiple sys-tems. Over time, these resources interacted inunforeseeable ways to produce new productsand resources that eventually created a newidentity for Intel. Further, although the historicalprogression of Intel’s development could be de-scribed easily as a sequence of events, it is more


difficult to describe the underlying processesthat were involved in creating these dynamicchanges. This is because a description of pro-cesses required the development of new explan-atory principles and the use of an abstract sys-tem to model their flow and interaction (Hazy,2008). Hence, consistent with our second point,process descriptions were one step removedfrom events and behaviors. Third, language nat-urally focuses on the description of stable enti-ties (nouns), rather than describing the pro-cesses (verbs) used to create these entities(Hernes & Maitlis, 2010; Purser & Petranker,2005). This is problematic because processesevolve continuously and may be understand-able only once they have become defined andare available for retrospection. In other words,the underlying processes and outcomes of orga-nizational change may remain unclear at anypoint existing prior to the present as the futuremoves forward to continually shape the present.

There is also an important sociological factorassociated with looking backward that makesfuture potentialities harder to see. As Eaton, Vis-ser, Krosnick, and Anad (2009) document, controlover others and institutions tends to peak inone’s mid-forties, but futures are often createdby younger individuals. Thus, looking backwardemphasizes the skills and preferences of a gen-eration that will have less influence in the fu-ture. For example, Schmidt (2014) notes thatleadership in virtual teams is different than it isin face-to-face teams, but he also emphasizesthat younger individuals, who are more familiarwith virtual relations via experiences withFacebook, Google Hangouts, or Twitter, aremore adept at virtual teams. He predicts that asthese individuals become more numerous in theworkforce, how work is done will change, newpotentialities will be created, and the nature ofvirtual leadership processes will change.

Because it emphasizes potentialities in thefuture, a QATC perspective implies that radicalnonlinear change should be a common, un-avoidable occurrence, as illustrated by our pre-vious descriptions of sociological (Schmidt, 2014)and technology-related (Hazy, 2008) change.However, we maintain that organizational andinstitutional systems also resist change, whichhelps explain why many potentialities are neverpursued. In effect, organizational systems mayebb and flow stochastically around changingset points (attractors), as described by the notion

of dynamic homeostasis in open systems theory(Katz & Kahn, 1978). In the following section weprovide a more detailed examination of howorganizational system dynamics and stabilityare both created and maintained within a QATCframework by paying attention to the nature ofconstraints using a multilevel theory perspec-tive. We also address how processes can com-bine serendipitously and interactively to realizesome of the alternative potentialities that thefuture can offer.

QATC AND DYNAMICORGANIZATIONAL SYSTEMS

A general limitation of many organizationaltheories is that they examine processes at onelevel independently from the next level and atonly one time period, rather than explaininghow multilevel organizational phenomena dy-namically unfold over time. However, social sys-tems exist as a conglomerate of many smallersubsystems that function semi-autonomouslywithin a grander scale (Contractor, Wasserman,& Faust, 2006; Simon, 1981; Sytch & Tatarynow-icz, 2014; Weick, 1976). A QATC can be applied atany of these levels, which will change the cali-bration of time from milliseconds at lower levelsto months or years at higher levels. But it isbetween and at the junctures of these differentsystems that the processes fostering or retard-ing the development of new potentialities aremost critical. This is because it is at these con-nections and junctures that the emerging out-puts of processes at one level are transformedinto an input that is meaningful at another level.

To demonstrate, it is well known that com-plexity in systems depends on their hierarchicalnature (Aime, Humphrey, DeRue, & Paul, 2014;Kozlowski & Klein, 2000; Simon, 1981), where thelinkages between system levels are often repre-sented as constraints (Dinh, Lord, & Hoffman,2014; Freeman & Ambady, 2011; Read et al., 2010).Some constraints are momentary, such as emo-tions, goals, or cognitions. They are imple-mented by fast processes, and they may dissi-pate rapidly from an environment after beingcreated. Other more enduring constraints, suchas knowledge, relational ties, and even the mor-phology of one’s body, can influence the emer-gence of events over longer periods of time. Con-sequently, the evolution of psychological andorganizational phenomena can occur over dif-


ferent levels and time periods, involving pro-cesses that require milliseconds, minutes, days,weeks, and/or years to be completed.

Whether these phenomena are best conceptu-alized as ongoing processes or consequences ofstable entities also depends on the durability ofkey constraints and the level at which theory isfocused. Because higher-level systems gener-ally change more slowly than lower-level sys-tems, higher-level constraints generally chan-nel emergent lower-level processes in onedirection or another, thereby allowing some po-tentialities to develop while others never mate-rialize. However, lower-level systems must ag-gregate their outputs over multiple cycles toalign with the slower cycling rhythm of higher-level systems, and this aggregation can some-times move the entire system to new states. Forexample, individual selective attention pro-cesses can be constrained by higher-level men-tal schemata, such as goals or social perceptioncategories, but higher-level mental schematacan also be changed by the interaction of bot-tom-up processing systems like motivations andemotions. Such dynamics affect one’s assess-ment of potentialiaties because, as Johnson-Laird (1983) maintained, we know reality fromthe mental models we construct.

Figure 3 provides an illustration of the typesof hard and soft constraints that organizationalresearchers postulate operate at individual, dy-adic, group, and organizational levels. Addi-tional levels of analysis (e.g., societal, environ-mental) could also be considered, but here wefocus on these four to provide a simpler basis fortheorizing. The influence of top-down multilevelprocesses is demonstrated in research thatshows how such internal constraints as the eth-ical culture created at the company level for U.S.soldiers in Iraq cascaded downward to createan external constraint that affected the ethicalculture at the platoon and then the squad level(Schaubroeck et al., 2012). Similarly, internalconstraints created by dynamic processing sys-tems related to cognition, embodiment, andemotions can collectively mold the interpreta-tion of encountered stimuli, creating meaningthat is aggregated across time or people to pro-duce higher-level outcomes, such as interper-sonal cognitions, feelings of trust, or group re-sources (Dinh et al., 2014; Lord, Hannah, &Jennings, 2011; Smith-Jentsch, Kraiger, Cannon-Bowers, & Salas, 2009; Sy, Côté, & Saavedra,

2005). In a general sense, some constraints maybe subject to explicit description, such as iden-tity or social norms (Schultz & Hernes, 2013),while others operate automatically, such as howan active mental schema affects access to re-lated information (Bargh, Chen, & Burrows,1996). Also, as one moves downward in this fig-ure, processes operate on faster time scales.

FIGURE 3Internal and External Transitory and Enduring

Constraints Operating at Individual, Dyad,Group, and Organizational Levels of Analysis


It is important to note the role of the connec-tions in Figure 3, which provides a medium forcross-level integrative processes. These connec-tions can be manifested as the physical technol-ogies (e.g., internet, social media), materials(e.g., infrastructure, transportation), and networkcharacteristics (e.g., network structure, density)that affect how organizational systems and or-ganizational members interact. They also referto less tangible connections that exist intraper-sonally and interpersonally, such as an individ-ual’s mental structure and his or her interper-sonal relational networks. These connectionsnot only permit the transmission and diffusionof information but also provide a mechanismallowing recursive feedback loops to modify thestructural characteristics and the functioning ofparticipating units and individuals within thenetwork (Dourish & Mazmanian, 2013). Althoughwe have drawn these connections as being hi-erarchical and seemingly stable to maintainparsimony in Figure 3, it is important to notethat each connection may have multiple endpoints that can change over time as new and oldconnections are created or dissolved.

Our application of quantum theory maintainsthat it is the operation of external constraintsfrom higher-level systems and internal (horizon-tal) constraints from systems at the same level,along with the connections among these levels,that constrain encountered potentialities to cre-ate attractors and, if these constraints are suffi-cient, to cause potentialities to collapse as thefuture moves toward the present. For example,an encounter with coworkers around the coffeemachine may have no potential to activate men-tal schema with respect to leadership, so lead-ership may be in an inactive state. However, ina different context, such as an organizationalmeeting to address an impending crisis, there isthe potential for leadership to emerge as accessto an individual’s leadership (or followership)self-views influences the granting or claiming ofleadership for oneself or others (DeRue & Ash-ford, 2010). In this latter situation the aggregatedeffects of contextual constraints, individual con-straints, and horizontal constraints from otheractors can affect whether the interpretation ofan individual’s actions will coalesce around acommon social meaning, such as being per-ceived as a leader. This is just one of many waysthat constraints identified in Figure 3 could af-

fect the translation of future potentialities intoan experienced reality.

When interpreted using a QATC, this figuresuggests that the actualization of constraintsreflects the entanglement (i.e., interdependen-cies among entities and context) of a state andcontext such that emergent events that have noanalogue in the past can form—a principle re-ferred to as the principle of noncompositionality(described further in the Appendix). This princi-ple suggests that novel outcomes can emergewhen future potentialities collapse to create asingular event at a particular level. However,radical change isn’t expected to be continuallycreated because attractors channel processes ina consistent way since past information isloosely maintained within attractors. Thus, at-tractors can gradually evolve over time as newphenomena are experienced, and they can alsoserve to organize actions or guide the recon-struction of past memories (Hernes, 2014; Mc-Clelland, McNaughton, & O’Reilly, 1995).

The influence of attractors is shown in severalnotable examples. For instance, goal orienta-tions, which function as emergent attractorsformed from a “massively interconnected” set ofhigher- and lower-level goal constructs (DeShon& Gillespie, 2005), channel thoughts and actionsin terms of goal relevance (Johnson, Chang, &Lord, 2006). Similarly, enduring organizationalprocesses have been described by Lok and DeRond (2013), who show how highly institutional-ized processes were maintained over long peri-ods of time because deviations were containedthrough maintenance work by organizationalmembers. It is important to note, however, thatthe recursive dynamics among multilevel pro-cesses can cause the potentialities of local andglobal states to collapse in unforeseen ways,which shifts the positioning of attractors. In thisway the contoured surface of probability wavesas seen in Figure 2 continually changes overtime, thereby affecting the potentialities avail-able to an organization.

In short, Figure 3 illustrates how events at anychosen level are a function of the internal con-straints within that level and the external con-straints from higher- or lower-level contexts.Further, each juncture of systems is a nexus forunderstanding how the future flows into thepresent. When these multilevel considerationsare combined with a QATC perspective, the re-sulting framework provides insight for under-


standing how internal and external constraintsacting at multiple levels set limits on the wayprocesses emerge at a particular level. In gen-eral, weak constraints foster gradual, incremen-tal change, whereas strong constraints promotestability and periodic discontinuous change.This framework also addresses how lower-leveloutputs cumulate to affect higher-levels systemsin a way that is sensitive to quantum theory’sunderstanding of the flow of time.

TIME, PROBABILITY WAVES, ANDORGANIZATIONAL UNCERTAINTY

It is easiest to grasp the nature of time-relatedchange by looking at snapshots of differentprobability waves organized across time ratherthan a continually changing probability wave.Such a representation is provided in Figure 4,which shows probabilities at each time in termsof attractor regions (hills) that are defined bysets of internal and external constraints. Thisfigure shows five time slices from a local orglobal system, where time is also a dimension.It suggests that as time moves forward from thedistant future toward the present, some con-straints become solidified as different contextsare experienced, which causes the surface of theprobability wave to evolve continuously, becom-ing more definite based on the presence of spe-cific contexts and the constraints in these con-texts. Thus, one could think of Figure 4 asreflecting hypothetical time slices showingwhat the probability wave would look like if itwere measured at a particular time.3 Thischange is reflected in terms of fewer but higherhills as one moves from the left to the right ofthis figure and as potentialities become re-stricted to more likely states. What Figure 4 rep-resents, then, is a guide for theorizing about thefuture and how it can become the present asconstraints change. In terms of clock time, Fig-ure 4 could involve months or years if our focus

was on change at an organizational level, but itcould reflect only a few seconds if our focus wason the emergence of individual-level mentalconstructs or emotions.

Considered in the context of Figure 4, a QATChighlights several critical issues for social sci-ence. From an ontological perspective, under-standing how the present came about may re-quire insight into how it may have materializedfrom many different alternative possibilities, aprocess that is difficult to achieve, as we previ-ously described. In addition, Figure 4 representsthis uncertainty in a way that is not as clearwithout the tools of quantum theory, which sug-gest a transition from a superpotentiality stateto a definite state and a reconceptualization ofthe flow of time as a future that moves into thepresent (F ¡ P). Also, the representation in Fig-ure 4 is tied to the notion that phenomena indynamic systems emerge subject to internal andexternal constraints, and this conceptualizationhelps us understand how actions and events inthe present can project at least some distanceinto the future (P ¡ F) through their effects onprocess constraints.

In other words, stability comes not from endur-ing attributes but from the way that those attri-butes act upon constraints, thereby channelingemerging processes. Habit, for example, is notan entity but, rather, a pattern of behavior thatreoccurs in a certain context because the pro-cesses generating behavior operate similarly inthat context (Barsalou, Niedenthal, Barbey, &Ruppert, 2003; MacDonald, 2008). Change, then,depends on altering those channeling pro-cesses. For example, chronic eating habits canbe disrupted by factors as simple as eating witha nondominant hand, because they shift the un-derlying process generating behavior from anautomatic to a more conscious process (Neal,Wood, Wu, & Kurlander, 2011).

IMPLICATIONS FOR THEORY, RESEARCHMETHODS, AND PRACTICE

On a general level, a QATC framework andfeed-forward models (FFMs) both share an ap-preciation for understanding process, which isevident in qualitative-based research (e.g., Mac-Kay & Chia, 2013; Plowman et al., 2007). How-ever, as a conceptual framework, a QATCframework provides a counterpoint to the typi-cal FFMs of organizational change and individ-

3 One advantage of a quantum theory perspective is thatprobability waves can evolve over time while still in anindefinite state (Busemeyer & Bruza, 2012), whereas modelsof evolution based on classical probability theory (e.g.,Markov models) explain evolution in terms of moving fromone definite state to another. Thus, quantum theory betterenables the continuous interactions among various con-straints as the future approaches the present to changepotentialities that have never been realized.


ual development because it emphasizes the in-fluence of unrealized potentialities, which maybe useful in guiding theory generation. Exhibit 1provides a comparison of these two models,highlighting the many differences between thetwo perspectives. In the following sections weuse Exhibit 1 as a structure for understandingthe theoretical and methodological implicationsof a QATC framework compared to tradi-tional FFMs.

Theoretical Implications of a QATC Framework

Direction of time and the nature of concepts. AQATC framework offers several new groundingassumptions for organizational research. First,it is useful to conceptualize the future as flowinginto the present, which can be represented bythe evolution of probability wave functions asvarious types of constraints are encounteredand interact. As does Hernes (2014), a QATCperspective emphasizes the temporality of pro-cesses. By reversing the arrow of time, it impliesthat the present is continually created by theconjoining of various constructs where the na-ture of concepts—including attitudes, emotions,beliefs, and group and organizational struc-tures—all reflect the translation of a superpo-tentiality state into a defined state at a particu-lar time and context.

One advantage of beginning with an unde-fined future for understanding how systemschange is that we do not start with the assump-tion that organizational processes are predict-

able or consistent. Instead, beginning with anundefined future recognizes the significance ofuncertainty and the existence of future alterna-tive potentialities. This difference can be seeneasily by comparing the first and last panels ofFigure 4. Here the flow of time from a future to apresent acknowledges that constraints them-selves may be dynamic, shaping concepts suchas meaning and mental categories “on-the-fly”(Barsalou, 1983). Thus, quantum theory’s empha-sis on context and evolving processes can beparticularly helpful, especially when a processlike sensemaking is understood as an enacted,ongoing endeavor where the meaning attachedto the environment is discovered by and depen-dent on a person’s iterative interactions with hisor her environments (Gabora et al., 2008; Gibson,1986; Hernes & Maitlis, 2010; Weick, 1995).

Further, a transition from an undefined futureto an experienced present naturally emphasizesthe process of creation by individuals and col-lectives, whereas a past and present to futureperspective starts with unit attributes (e.g., or-ganizational climate or culture) and examinestheir effects on processes. Process views andtheir emphasis on patterns and dynamics are,therefore, more fitting as a theoretical frame-work for understanding change than is a focuson enduring attributes of individuals or systems(Hernes, 2014; Langley et al., 2013). This is not tosay that focusing on the enduring attributes ofindividual and organizational entities should beabandoned, particularly when they exhibit verylittle variation as ascertained from longitudinal

FIGURE 4Changes in Probability Waves Through Time Beginning in an Indeterminate Superpotentiality

State to a Definite State As the Probability Wave Is Acted Upon by Internal and ExternalConstraints


studies (Dinh et al., 2014). However, the recogni-tion that unforeseeable processes can createnew entities, as well as remain stable over time,is an advantage of a QATC framework. Thus, aQATC framework can direct attention to emer-gent phenomena like creativity, social capital,and the factors that can catalyze these phenom-ena, such as leadership (Uhl-Bien & Marion,2009), variability in emotional experience (Ble-dow, Rosing, & Frese, 2013), and heterogeneityamong unit members (Page, 2007). Understand-ing and managing these processes, to the extentthat managing emergent processes is possible,

is a critical strategic issue for organizationalleadership. In contrast, an FFM emphasizes en-tities that describe the past and are expected toendure into the future.

Although beyond the scope of this article, onetechnique, provided by Scharmer and Kaufer(2013), facilitates understanding and influencingprocesses. Consistent with our perspective,these scholars argue that one needs to focus onleading from an emerging future to successfullyaddress many contemporary problems. Theirframework also emphasizes developing one’slistening capacities to foster a more encompass-

EXHIBIT 1Theoretical, Methodological, and Practical Implications of a QATC and FFM

Theoretical Implications1. Direction of time and the nature of concepts

A QATC conceptualizes the future as flowing into the present into the past (i.e., F ¡ P ¡ P), whereas an FFMperspective conceptualizes the past as leading to the present to the future (i.e., P ¡ P ¡ F).

2. Representation of event probabilitiesA QATC is process focused and recognizes the potentialities available to interacting organizational units, whereas

an FFM perspective emphasizes stable entities and thereby limits potentialities that are considered.3. Relation among micro and macro multilevel systems

A QATC suggests that the interconnections among multilevel systems are critical to the dynamics of organizationalphenomena. These interconnections also challenge efforts to achieve organizational ambidexterity. An FFMperspective more often attends to local processes occurring independently in one or another level.

Methodological Implications1. Alternative possibilities

A QATC implies that methodological approaches based on FFM perspectives that include meta-analyses and thosedependent on a single sampling distribution are limited in predicting future behaviors or adding insight ondynamic individual, group, and organizational processes.

2. Quantum probability theoriesA QATC recognizes that new probability axioms and mathematical formalisms may be needed to better represent the

creative combination of constructs through noncompositional processes involving the entanglement of states andcontexts.

3. Dynamics of organizational processesA QATC stresses the continual involvement of multilevel interactive processes originating from individual, group,

and organizational levels of analysis in influencing organizational phenomena. Quantitative methodologiesincluding quantum probability theories and computational modeling are available, consistent with QATC.

4. Emergence of conceptsA QATC emphasizes that organizational and cognitive concepts may exist in a superpotentiality state that is

influenced by measurement procedures. An FFM perspective suggests that organizational and cognitive conceptsexist in a predefined state not likely to change with measurement procedures.

Practical Implications1. Decision making and forecasting biases

A QATC suggests that techniques such as prospection and counterfactual thinking, when they enable rather thanlimit the consideration of alternative potentialities, can offset or prevent decision-making biases created by feed-forward tendencies when the past is generalized to predict the future.

2. Creation of new futuresA QATC offers insights on the creation of new futures as multiple potentialities are explored and the innovations

they uncover diffuse through social systems. In contrast, an FFM would represent futures as branching from thepresent, thereby suggesting fewer potentialities.

3. Conflict between intuitive beliefs and abstract representationsCompetition between a QATC and FFM perspective may result in conflicting strategies for organizational problems.

Since each influences the efficiency of decision making, individual differences in the dependency of intuitiveversus abstract thinking should be considered.


ing self; increasing collaborative, idea-broaden-ing efforts; and employing radically differenteconomic and social logics instead of just per-petuating the ways of thinking that generatedpressing problems.

Representation of event probabilities. Second,a focus on the past naturally emphasizes thecontinuity of entities that are, in turn, used todescribe and explain past and future events. Forexample, one might explain an individual’s pastbehavior in terms of underlying personalitytraits, which are then used to predict future be-havior. The five-factor model of personality andthe use of personality measurement scales topredict behavior would characterize such an ap-proach. This entity view of personality leads to apoint estimate for behavior consistent with theindividual’s pattern of personality traits and aprobability distribution around that estimate. AQATC, in contrast, recognizes the many potenti-alities that may occur in the future, leading anindividual to behave quite differently. Thus,there are multiple points and multiple probabil-ity distributions around those points, as can beseen in a probability wave distribution.

Because events or behaviors emerge from theinteraction of context and an entity (or, as wehave proposed, higher-level contextual con-straints operating on processes that emerge at alower hierarchical level), how this combinationaffects the probability of a specific outcome is acritical concern. Here quantum theories can rep-resent probabilities in ways that traditionalprobability theories cannot, thereby offeringbetter insight for understanding how processesoperate and unfold. For example, a QATC per-spective can represent how the interactionamong entities can create entirely new entitiesor events, which is not easily addressed by FFMperspectives.

Relation among micro and macro systems. Al-though we recognize that scenario-based fore-casting (MacKay & McKiernan, 2004) and am-bidextrous leadership (O’Reilly & Tushman,2013; Rosing, Frese, & Bausch, 2011) have someof the elements that are described by a QATC,our perspective goes beyond these ap-proaches by suggesting that individuals arenested within much larger systems, which con-strain processes, eliminate potentialities, andcause select outcomes to emerge. We have arguedthat it is at the nexus of these nested systems thatthe present is created from the dynamic interplay

of higher- and lower-level systems across manydifferent temporal cycles. Thus, multilevel model-ing approaches are critical for understanding howentities and processes are continually created, butthe nature of relevant constructs and the span oftime in which things happen change with thelevel of analysis. This perspective differs fromthose offered by FFM that promote the modeling ofisolated, independent systems.

Within organizational settings, a QATC canbe applied to improving organizational innova-tion and ambidexterity. As this research shows,firms have difficulty managing both explorationand exploitation (March, 1991; O’Reilly & Tush-man, 2013) since different organizational unitshave different needs and goals at differenttimes, making intersystem synchronization dif-ficult (Davison, Hollenbeck, Barnes, Sleesman, &Ilgen, 2012; Zaccaro, Marks, & DeChurch, 2012).Our framework adds two insights to this issue.First, because many future potentialities mayexist in an indefinite state until they are con-joined with a context, they cannot be knownuntil they are tried out. Thus, exploration is afundamental part of creating entirely new anddifferent futures. Second, creating some futuresmay require the enactment of sets of constraintsdifferent from those being used in the present. Achallenge in initiating change, therefore, is torecognize that using techniques that solidify ex-tant constraints at one or another level to exploitpresent strategies may make it more difficult fororganizations to explore alternatives and inno-vate. For example, vertical integration that al-lows increased control of an organization’s re-sources may also reduce its capacity to explorealternative strategies through actions such asoutsourcing or adopting new technologies (Aimeet al., 2014).

Leaders can encourage exploration when theyencourage others to do things differently, to ex-periment, to think independently, and to deviatefrom normal routines (Rosing et al., 2011). Suchactions reduce current constraints, which facili-tates the discovery of new potentialities. In con-trast, Rosing et al. note that exploitation in-volves closing actions by leaders that reducevariability and solidify constraints by activitiessuch as developing guidelines and monitoringactivities. In brief, a QATC framework suggeststhat interventions that aim to increase (versusrestrict) organizational innovation should con-sider how configurations (versus independent


variables) of microlevel to macrolevel processesand entities can interact over time to catalyze orobstruct the attainment of desired organiza-tional outcomes.

Methodological Implications of aQATC Perspective

Alternative possibilities. First, changing thedirection of time and using probability waves torepresent time-related processes highlight thefact that the present represents just one poten-tial state. Thus, the present or the past is morepredictable than the future, and expectationsbased on the past may not generalize to a futurethat can be quite different. This idea has pro-found methodological implications since mostmethodology in organizational science is retro-spectively oriented. For example, meta-analysisfocuses on integrating past research based onthe notion of point estimates and a single sam-pling distribution. Although meta-analyticaltechniques can provide a general overview ofthe past relationship among constructs, the sta-tistical technique’s approach toward aggregat-ing across many different contexts interfereswith its ability to consider the effects of contextand time on the evolution of organizationalphenomena.

Because of these limitations, we believe thatmeta-analytical approaches and those based ona single sampling distribution would have lim-ited value in predicting future behaviors or inexplaining the moment-to-moment variabilityobserved in individual-level (Dinh & Lord, 2012;Fleeson, 2001; Read et al., 2010), group-level(Crawford & LePine, 2013; Smith-Jentsch et al.,2009), and organizational-level (Feldman & Pent-land, 2003; MacKay & Chia, 2013) phenomena,unless there were sufficient constraints to createstability in organizational and individual pro-cesses. In addition, models that are overly fit tothe certain past may not accurately generalizeto the future, which has many potentialities andmay be characterized by entities that are in dif-ferent states. When the future changes dramat-ically from the past, less restrictive models mayhave better predictive validities. For example,the decision-making and forecasting literatureshows that unit-weighted combinations of in-puts equal or outperform multiple regression-based combinations in cross-validation studies(Bobko, Roth, & Buster, 2007). Such results are

often explained in terms of sampling error cre-ating shrinkage for multiple regression-basedpredictions. Our interpretation, though, is that itis not sampling error so much as the fact thatphenomena change, making models built on thepast somewhat outdated when applied in thefuture.

A related issue involves the inability to in-clude emergent phenomena and constructs inmodels because they did not exist in the past.This problem may necessitate the use of com-putational modeling methodologies, ratherthan static retrospective questionnaires (Koz-lowski et al., 2013). These methodological tech-niques have become increasingly available toscholars and include agent-based modeling(e.g., Dionne & Dionne, 2008), nonlinear sto-chastic modeling (e.g., Guastello, 2001), ge-netic algorithms (e.g., Fernandez, Cotta, & Ce-ballos, 2008), and neural computationalmodeling (e.g., Eliasmith, 2013; for an overviewof the uses and applications of computationalmodeling, see Kozlowski et al., 2013, and Van-couver & Weinhardt, 2012).

Traditional quantitative approaches, whichfocus on quantifying individual and organiza-tional constructs, are also valuable, particularlywhen complemented with qualitative naturalis-tic methods (e.g., observation, interviews, expe-rience sampling) that offer scholars an under-standing of how and why certain constructs varyin different contexts or circumstances. In thisway the capacity to provide a descriptive anddetailed catalogue of organizational phenom-ena can complement quantitative approachesby triangulating findings and providing abroader description of these phenomena (Pope &Mays, 1995). Although such methods can excel atdescribing complexity in organizations and thecontextual factors that created the experiencedreality, focusing on what has happened andhow events occurred may overemphasize thecertainty in the past, and it may underempha-size the alternative realities that may have beenlikely in the past, as well as the new potential-ities that are offered by the future.

Quantum probability theories. Second, quan-tum theorists would argue that under uncer-tainty the evolution of physical and psycholog-ical phenomena more closely obeys themathematical formalisms of quantum theories(i.e., von Neumann axioms, which are defined interms of events projected onto spatial represen-


tations, as shown in the Appendix), rather thanclassical probability theories (i.e., Kolmogorovaxioms, which are defined on sets) that are foun-dational to the statistical models used in psy-chological research (Busemeyer et al., 2011;Wang, Busemeyer, Atmanspacher, & Pothos,2013). Because the mathematical axioms thatguide quantum probability theories can accountfor the interactive influence of simultaneouslyoccurring contexts and states (Busemeyer &Bruza, 2012; Hughes, 1989; Wang et al., 2013),applications of quantum probability theories tothe organizational sciences may not only allowscholars to better model the organizational con-text but also help overcome the limitations as-sociated with employing artificial paradigmsthat subtract uncertainty from realistic phenom-ena in psychological research (Pothos & Buse-meyer, 2013).

Dynamics of organizational processes. Third,we proposed that the multilevel dynamics inher-ent in the evolution of organizational phenom-ena require a process view that examines howindividual and group behaviors are affected byboth external constraints from higher-level sys-tems like groups and organizations and lower-level internal constraints from cognitions, em-bodiment, and affect. By focusing on howprocesses unfold over time at the nexus of sys-tems, a QATC framework provides both quanti-tative and qualitative analytical approachesthat can be applied at multiple levels andacross multiple time frames. Although beyondthe scope of this article, quantum probabilitytheories and quantum mechanics have beenused to understand how alternative possibili-ties can be created or destroyed by differentinteracting contexts, actors, and events overvarying expanses of time. These possibilitiesmay be revealed best through the use of newerinformation processing architectures that relyon vector-based representations (see Buse-meyer & Bruza, 2012, and Eliasmith, 2013). Sim-ilar to neural network modeling and symbolicarchitectures, such systems can learn rules asthey create solutions, mimicking the type ofcreative processes that, we argue, occur innatural systems.

Emergence of concepts. Fourth, a profound im-plication of a QATC approach is that, for muchof the time, many aspects of organizational phe-nomena may exist in a form that is unknown toobservers because these constructs are in a su-

perpotentiality state. Like a computer monitorthat is turned off, features are not activatedwhen a concept, attitude, emotion, trait, groupclimate, or organizational process is divorcedfrom context. Not only are features or propertiesrevealed solely through specific measurementprocedures, which can create contextual effectsof their own (Busemeyer & Bruza, 2012; Hughes,1989), but these individual or organizationalproperties may not exist outside of a physicalcontext or the mental framework used by per-ceivers to create meaning (Hernes & Maitlis,2010; Langley & Tsoukas, 2010; Weick, 2010).They may also change as contexts change,reflecting state-like, rather than trait-like,properties.

This perspective seems most appropriate forphenomena like emotions, which align with al-ternative states of an individual (Barsalou et al.,2003), but it has broader application than onemight realize, applying to traits and constructssuch as leadership and trust, which may mani-fest only in certain situations. Indeed, some con-structs may be better assessed from a processperspective since they are continually evolving(DeShon & Gillespie, 2005; Gabora et al., 2008;Hernes, 2014; Hernes & Maitlis, 2010), rather thanfrom a perspective that views constructs as sta-ble entities that can be assessed with retrospec-tive measures. This is particularly true when theconjoining of a context and construct createsnew features. Here the quantum representationof concepts allows the states of concepts tochange continually as new contexts are encoun-tered (Gabora & Aerts, 2002).

These possibilities are rarely considered, inpart because we assume that most constructs insocial and organizational science are stable en-tities (Feldman & Lynch, 1988). This view mayreflect the exaggerated stability created by anorientation toward the past, whereas a view ofthe future collapsing to create the present em-phasizes many potentialities and directs atten-tion to the processes that allow some of thesepotentialities to emerge while others remain un-realized. It also raises such questions as howalternative futures could be imagined, created,or redirected in ways that change emerging ex-periences. As eloquently stated by Weick, man-agerial work may be akin to a poetic process—“the imaginative process of creating forms out of‘airy nothing’” (2010: 102). The challenge for or-ganizational science is to capture this creative


process, which we maintain could be facilitatedusing the formalisms provided by quantumtheory.

Implications for Practice

Decision-making and forecasting biases. AQATC perspective maintains that there are al-ways multiple paths to the future, and some areassociated with pasts that did not occur but maybetter characterize future situations than thepast that did occur. This suggests that failing toconsider alternative possibilities may lead tosevere decision-making fallacies. For example,research has shown that hindsight (Arkes,Faust, Gulimette, & Hart, 1988) and foresight bi-ases (MacKay & McKiernan, 2004), which involveusing past experiences to predict the future, arethe cause of many organizational failures andexecutive decision-making errors because theyinterfere with the ability to successfully antici-pate or plan for alternative future events (e.g.,Arrfelt et al., 2013; Brown & Eisenhardt, 1999;Dane & George, 2014). This sense of certaintypromotes efforts that exploit only one future pos-sibility, when there are many equally probablepossibilities. These types of biases are evidentwhen corporate decision makers make seem-ingly well-placed investments to “ensure” thefuture survival of their organization. As Arrfeltet al. (2013) demonstrate, however, these plansmay interfere with the firm’s ability to grow andinnovate, especially when investments fail toproduce intended benefits while also consum-ing valuable resources. When reinforced overtime, prior interpretations may become “lockedin” to create a rigid action pattern, labeled pathdependence, that constrains the ability to craftadaptive solutions and strategies (Sydow,Schreyögg, & Koch, 2009).

However, decision-making biases can beavoided when organizational decision makersare able to recognize that alternative outcomesare possible when planning for future events.This may involve the use of forecasting orprospection to simulate possible events that canhappen in the future. As Seligman et al. (2013)argue, there is a natural proclivity for humans tomentally travel into the future, and this is en-abled as individuals combine (and recombine)memories of their past experiences to simulatethe likely occurrence of both familiar and novelexperiences. Prospection and the broadening of

cognitive perspective can also occur with thecycling of negative and positive affect over time(Bledow et al., 2013; George & Zhou, 2007), orwhen members are engaged in cohesive collabor-ative teams, since these interpersonal work con-figurations enable members to flexibly entertainmultiple perspectives and work strategies. The le-veraging of interpersonal relationships to acquirenew perspectives and potentialities can be espe-cially advantageous because there are manytypes of precarious situations that confront orga-nizational decision makers—each requiring itsown unique solution (Milliken, 1987; Wiltbank,Dew, Read, & Sarasvathy, 2006). Group complex-ity, which is dynamically constructed (Hannah,Lord, & Pearce, 2011; Page, 2007), may help revealthese potentialities. The effectiveness of prospec-tion can be limited, however, when mental simu-lations of the future omit essential features, suchas when they rely on general heuristics, are basedon nonrepresentative past information like relyingon a single salient memory, or are decontextual-ized by not accounting for environments (Gilbert &Wilson, 2007).

Creating new futures. A QATC perspectivecan help one see that the future is more mal-leable than one typically realizes. Taleb (2010)describes “black swans” as unexpected, im-pactful events that change the course of his-tory, and he explains that they are only under-standable retrospectively, when new mentalschemata become widely accepted because ofblack swan events. Our QATC perspective of-fers a different view, a view that helps explainhow black swan events can occur when onedoesn’t accept the past as given but, instead,considers alternative potentialities and theirimplications for the future. As this processgeneralizes across individuals, which it doeswhen individuals are part of socially con-nected networks, reality can eventually shiftin dramatic ways, producing discontinuousrather than incremental change.

Conflict between intuitive beliefs and ab-stract representations. Although there is evi-dence to suggest that forecasting the futurebased on the past can yield effective strategiz-ing when the recent past is generalized to ananticipated proximal future (Arrfelt et al., 2013;Gavetti & Levinthal, 2000), organizational deci-sion makers are often pressed to formulate dis-tal or long-term interventions and strategies tosatisfy requirements that are different from


those experienced in the present. In these in-stances a QATC perspective may be especiallyvaluable since it is an abstract, unrestrictedmethod for thinking about how the future willbecome the present.

However, conflicts may arise when the use ofone perspective is favored over another, partic-ularly when intuitive, experience-based per-spectives are contrasted with more analytic ap-proaches, as is often the case with views onclimate change. In order to use a QATC perspec-tive to guide decision making, therefore, onemust be able to manage the greater complexityand uncertainty associated with this abstractframework. Although an area for future re-search, this approach may be more acceptableto individuals with a future rather than a pasttime orientation (Shipp, Edwards, & Lambert,2009), individuals with greater intrapersonalcomplexity (Lord et al., 2011), or individuals whocan tolerate greater risk and anxiety (Hirsh,Marr, & Peterson, 2012), such as those who areembedded within diverse, richly connected so-cial-relational networks (Lee, Bachrach, &Lewis, 2014).

An Application to Leadership Emergence

Heretofore our discussion of a QATC frame-work has been abstract and general. In this sec-tion we emphasize a specific content domain—leadership—to illustrate how principlesdeveloped in quantum physics can be insight-fully applied to organizational and societalissues.

A QATC approach to the problem of modelingleadership emergence. An individual is per-ceived to be a leader when his or her attributesand behaviors activate a leadership-definingcognitive structure or schema in perceivers. Thiscognitive structure has been shown to be re-created each time it is used, subject to a varietyof constraints both external (e.g., leader attri-butes, context) and internal (e.g., momentaryemotions, use of leadership versus followerschemas) to the perceiver (Hanges, Lord, &Dixon, 2000; Lord, Brown, Harvey, & Hall, 2001).Thus, the schema influencing leadership per-ception is itself dynamic and can be viewed asan attractor that organizes and amplifies stim-ulus interpretations (Hanges, Lord, Godfrey, &Raver, 2002). Consequently, different perceivers,

or the same perceiver in different contexts, candefine leadership differently.

To see how this process can create problemsfor scholars attempting to model such a dy-namic phenomenon, it is useful to compare per-ceptions of males and females. The potentialleader being evaluated can be a powerful con-straint on the leadership schemas perceiversuse to integrate behavioral input over time(Brown, Marchioro, Tan, & Lord, 1998; Rosette,Leonardelli, & Phillips, 2008; Sy et al., 2010). Acritical question, then, is whether the probabil-ity of leadership emergence differs for malesand females when they demonstrate the samepattern of behavior. To address this issue, weneed a representational system that can includetypes of behavior (i.e., agentic, communal, pas-sive) and different perceptual schemas formales and females, and that also permits oper-ations that will estimate the relevant probabili-ties for emergent leaders.

The issue of jointly representing different cat-egorical systems is “new and unique to quan-tum theory, and it is [sic] never been raisedwithin classic theory” (Busemeyer & Bruza, 2012:32). This issue can also be thought of as an issueof compatibility of the male- and female-oriented leadership schema, where the termcompatibility has a special meaning. Whenevents and constructs share a common represen-tational structure (or basis in quantum theory;Busemeyer & Bruza, 2012), they are deemed com-patible, whereas incompatible events and con-structs require a different representational struc-ture (or basis) to be evaluated. Bias in socialperceptions such as leadership may then be as-sociated with perceivers having different repre-sentational systems.

The uncertainty principle developed byHeisenberger in the field of quantum physicsalso can be applied to incompatible representa-tion systems (Busemeyer & Bruza, 2012). Apply-ing this principle suggests that when a male-oriented representational structure is used,male leaders can be evaluated with certainty,but the perceiver will be uncertain with respectto the probability that agentic, communal, orpassive females will emerge as leaders; simi-larly, when a female-oriented representationalsystem is used, the perceiver can evaluate fe-male targets with certainty, but the probabilitythat agentic, communal, or passive males willemerge as leaders will be uncertain. This


schema incompatibility problem and its relationto uncertainty may help account for the well-documented difficulty females have in obtain-ing leadership roles in many of today’s organi-zations. Specifically, the “glass ceiling”experienced by females may, in part, reflect theuncertainty that perceivers experience whenevaluating a female’s leadership potential us-ing their dominant male-oriented leadershipschema.

Using the principles and formalisms of quan-tum theory, we briefly show in Table 1 how theprocessing of leadership emergence can be rep-resented, and in the following section we pro-vide a detailed description of how these formal-isms can be implemented. Readers notinterested in this quantum formalism should fo-cus on the concepts emphasized in the left andcenter columns of Table 1, whereas readers in-terested in a more precise representation mayalso want to reexamine Table 1 after reading theAppendix.

Detailed application of quantum theory toleadership emergence. In developing a repre-sentational model of leadership emergence, webegin by using an N-dimensional vector space(also known as a Hilbert space) that represents asuperpotentiality state. The vectors encompass-ing this state, labeled basis vectors, correspondto specific events, such as a male (or female)exhibiting a specific type of behavior emergingas a leader. For example, the emergence of amale leader exhibiting agentic, passive, or com-munal behavioral patterns can be representedas the orthogonal vectors AX, AU, and AV,whereas the emergence of a female leader ex-hibiting similar behavioral patterns can be rep-resented by a different set of orthogonal vectors,BX, BU, and BV. In addition, a normalized statevector, S, which represents a general state ofleadership, is also contained within the largerN-dimensional space, and this state vector canbe projected onto each basis vector to indirectlydetermine the probability that a male (or a fe-male) leader exhibiting a specific behavioralpattern will emerge as a leader. This projection,then, represents the collapse of a superpotenti-ality state as it is conjoined with a specific con-text. For example, when S is projected onto AU,this collapse yields an amplitude that reflectsthe probability an agentic male leader will

emerge.4 Quantitatively speaking, the coordi-nates of these vectors can be obtained from ob-served data using various questionnaire andexperimental research methodologies (e.g.,Wang, et al., 2013).

We have described the effects of one externalconstraint, the gender of a target person, on theperceptual structure (which is analogous to setsof basis vectors) used to understand leadershipemergence. This same approach can be ex-tended to reflect how other types of externalconstraints could change the way leadersemerge. For example, Lord, Foti, and De Vader(1984) showed that context (i.e., military, busi-ness, education, etc.) changed the nature ofleadership prototypes used to define catego-ries. More recently, Sy et al. (2010) demon-strated that the ethnicity of a target has thesame effect, and Rosette et al. (2008) showedthat the race of a leader changes leadershipprototypes. These different perceptual basescould all be represented in an abstract N-di-mensional vector space in a manner analo-gous to the representation of gender. Internalconstraints may also be included in this geo-metric space, such as the effects of a perceiv-er’s current emotions or goal states. In short,what this application of a QATC shows is howa wide variety of multilevel constraints can berepresented in a geometric space. Operationsin this space can then model the way a poten-tial leader’s behaviors are interpreted in agroup context to create an emerging reality.

Finally, we should stress that although thisapplication emphasizes the social-cognitive lit-erature on leadership perceptions, our frame-work is more general and could be applied to

4 In this example the male and female spaces are incom-patible because no common subspace can represent analo-gous events for male and female targets. Further, it can beshown (see Busemeyer & Bruza, 2012: 40–41) that the pointscorresponding to the emergence of an agentic, communal, orpassive male leader, which involve a single vector in themale space, require a combination of all three vectors in thefemale space. Thus, when there is certainty with respect tothe male behavioral vector space, since each vector repre-sents a different collapse of the superpotentiality state, thereis uncertainty in using the behavioral space to evaluatefemales because all three basic vectors are involved. Simi-larly, using the female behavioral space, the point corre-sponding to the probability that an agentic, communal, orpassive male will emerge involves a combination of allthree vectors in the male behavioral space. Hence, whencertainty exists for a female, uncertainty exists for the male.


TABL

E1

Qua

ntum

Form

alis

mof

Lead

ersh

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erge

nce

for

Men

and

Wom

enEx

hibi

ting

Age

ntic

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sive

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dC

omm

unal

Lead

ersh

ipTy

peBe

havi

ors

Org

aniz

atio

nal

Enti

tyQ

uant

umTe

rmin

olog

yQ

uant

umFo

rmul

izat

ion

Rep

rese

nta

tion

ofor

ga

niz

ati

ona

len

titi

esa

nd

ph

enom

ena

Ast

ate

inw

hic

ha

nin

div

idu

al

ha

sn

otye

tb

een

des

ign

ate

da

sa

lea

der

Gen

era

lst

ate

ofle

ad

ersh

ipor

asu

per

pot

enti

ali

tyst

ate

Rep

rese

nte

da

sa

sta

teve

ctor

Sth

at

isco

nta

ined

wit

hin

N-d

imen

sion

al

spa

ce;t

his

init

ial

gen

era

lst

ate

isa

ssu

med

toh

ave

au

nit

len

gth

equ

al

to1

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ale

ora

fem

ale

targ

etex

hib

itin

ga

na

gen

tic,

pa

ssiv

e,or

com

mu

na

lb

eha

vior

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tter

n

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sis

vect

orA

one-

dim

ensi

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lb

asi

sve

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rep

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nti

ng

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ceof

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asp

ecif

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al

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n,o

rB

X,

BU

,or

BV

rep

rese

nti

ng

the

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gen

ceof

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ma

leex

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ga

na

na

log

ous

beh

avi

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lp

att

ern

Rep

rese

nta

tion

ofth

ep

roce

ssin

gof

lea

der

ship

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ng

am

ale

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dle

ad

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hem

as

inte

rfer

esw

ith

the

ab

ilit

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acc

ess

afe

ma

le-o

rien

ted

lea

der

ship

sch

ema

s

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mp

ati

bil

ity

ofm

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an

dfe

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hem

as

Th

eb

asi

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ctor

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pre

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tin

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ma

lein

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sw

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pro

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ma

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al

pa

tter

ns,

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orB

X,B

U,B

V

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nta

tion

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iver

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as

for

ma

les

an

dfe

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are

hig

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dis

sim

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tive

sch

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sa

rein

com

pa

tib

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he

am

pli

tud

eof

pro

ject

ion

son

toa

na

log

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ba

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ors

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oteq

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nd

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sP

erce

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’sle

ad

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ipsc

hem

as

for

ma

les

an

dfe

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are

hig

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na

tive

sch

ema

sa

reco

mp

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ble

Th

ea

mp

litu

de

ofp

roje

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onto

an

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les

many issues or hierarchical levels. Broadly, ex-tending these ideas to a QATC framework sug-gests that new perspectives cannot be evaluatedwith certainty using perceptual schemagrounded in the past; they can only be evaluatedwith certainty after one has shifted to a newperceptual system. This principle may help ex-plain why people and organizations have somuch difficulty assessing new potentialitiesthat exist in the future.

LIMITATIONS

No theory is without limitations, and despitethe numerous theoretical and methodologicalbenefits that a QATC perspective affords, thereare several limitations and areas that merit fu-ture consideration. First, research on quantummechanics and quantum probability theoryhave only recently been applied to understand-ing social and psychological phenomena, suchas decision making (Busemeyer & Bruza, 2012;Wang et al., 2013), consciousness (Hameroff &Penrose, 2014), and cognitive constructivism(Gabora et al., 2008). In addition, only a handfulof scholars have begun to apply quantum for-malism and its mathematical representations tounderstanding individual and organizationalphenomena (e.g., Eliasmith, 2013). Although itmay be many years until a meaningful integra-tion of quantum theory with organizational re-search is realized, we maintain that a QATCperspective can advance and enrich organiza-tional research in many ways, and we encour-age its entanglement with the organizationalsciences.

Second, we applied our QATC perspective tounderstand organizational phenomena, ratherthan psychological phenomena as they relate totime travel. As our reviewers pointed out, nu-merous scholars and philosophers over the lastcentury have debated how human beings haveunderstood the concept of time (Mead, 1932;Sherover, 2003; Suddaby, Foster, & Trank, 2010;Weick, 1995), which may also vary across di-verse cultural perspectives. Although our per-spective borrows the concept of mentally revers-ing the arrow of time to consider a future thatcollapses into the present to understand thepresent and the past (Sherover, 2003), our appli-cation addressed the probabilistic nature of un-certainty in real-life phenomena, rather than thephilosophical question of the meaning of time

and its relevance to human existentialism. How-ever, the application of quantum theory to thedevelopment and resolution of our subjectiveknowledge of time may yield interesting find-ings and may be pursued in future endeavors.

Last, we also acknowledge that for most indi-viduals there is a substantial learning curve inunderstanding vector-based mathematics inquantum theory. The upside, though, is that wehave come a long way in terms of developing amathematical or energy-based understandingof changes, which can serve as helpful guide-lines for organizational science.

CONCLUSION

We proposed a quantum approach to concep-tualizing travel through time as a metastructurefor addressing process-related issues associ-ated with both stability and change. By revers-ing the arrow of time and thinking of the futureas flowing into the present, we can conceptual-ize the present as being created from a super-potentiality state as it is conjoined with a par-ticular context to create an entangled present.This approach also illustrates that the futureholds multiple potentialities that may each de-fine the present. We believe that this QATC ap-proach provides a useful framework that canhelp advance organizational theory and re-search by offering a novel perspective challeng-ing many long-standing assumptions on cer-tainty, time, and processes of realizing changefor individuals, groups, and organizations. Fur-ther, it suggests that there is greater flexibilityin how the future will unfold than many peoplerealize, in part because they have difficultyevaluating future potentialities using perspec-tives grounded in the past. Finally, as our lead-ership example showed, QATC also offers newavenues for theoretical development, and itraises profound quantitative and methodologi-cal questions.

APPENDIX: REPRESENTATION OFQUANTUM PROCESSES

Vectors and Hilbert spaces. In quantum theorya physical or psychological entity is representedas a system within a multidimensional, mathe-matical structure known as a Hilbert space. Inthis space vectors (lines created by a point con-nected to an origin with a pointed arrow) form


subspaces (a plane spanned by vectors), whichdefine the collection of elementary outcomesthat can be generated by a system. Vectorspaces allow for rich representations of phe-nomena within a high dimensional space, andthey can represent the compositional qualitiesof natural language, as well as the meaning ofmore complex compositions that conjoin con-structs with unique contexts (Eliasmith, 2013:297–299).

A graphic representation of a Hilbert spacethat contains a few sample vectors is shown in

Figure 5a. In this figure there are two generaltypes of vectors, which are represented by thecombined symbols |X�, called a “ket.” Mathe-matically, basis vectors (e.g., |p1�, |p2�, |p3�, . . .|pn�) represent the alternate elemental eventsand outcomes that can be obtained by a systemrepresenting a physical entity or a psychologi-cal state. Also, a state vector (|P�) is a generalstate of a system (e.g., one’s mental or emotionalstate), which is normalized to have a length of 1.When projected onto basis vectors, it yields anamplitude that, when squared, is the probability

FIGURE 5Hilbert Space and Probability Wave Representations of a Superpotentiality State and a

Collapsed State

Note: (a) A Hilbert space containing a sample set of orthogonal basis vectors (|p1›, |p2›, . . . |p5›) and a state vector (|P›) in asuperpotentiality state. The linear aggregation of sets of basis vectors, which represent the alternative possibilities of aholistic state of a physical or psychological phenomenon that may be at an individual, dyadic, group, and organizationallevel, forms the Hilbert space. (b) A cross-sectional view of the Hilbert space when a state vector, |P›, is projected onto its basisvectors as it becomes entangled with a specific context. The probability of |p1› occurring is higher than |p2› since the squaredamplitude of |p1› is greater than the squared amplitude of |p2›. (c) A probability wave representation of a state vector, |P›, ina superpotentiality state. (d) A collapsed Hilbert space when state vector, |P›, becomes entangled with a specific context.


of occurrence for the specific event representedby the basis vector. To illustrate, a set of basisvectors can refer to the possible range of spe-cific, observable outcomes (e.g., specific feltemotions like happy and sad) that can be de-rived from a more descriptive, nondeterminedstate (e.g., emotionality). Using these mathemat-ical notations, as shown in Figure 5b, the prob-ability that a particular elemental event or out-come will occur is determined by the projectionamplitude (perpendicular distance) of the statevector (|P�) onto each elemental event con-tained within the subspace (|p1�, |p2�,. . . |pn�).These probabilities are often based on empiri-cal data, or they may result from mathematicaloperations in Hilbert spaces. When a specificoutcome does occur, it reflects a change in thestate of the system, where the indefinite state(i.e., emotionality, |P�), collapses onto a definitestate (e.g., happy, |p1�), which, in turn, acquiresspecific features and qualities that can be mea-sured and observed.

When a collapse occurs, probabilities becomemore certain. This process is shown in Figure 5busing a vector representation, and in Figure 5das a probability wave, in which a contouredsurface is drawn over the projection amplitudesextending from a state vector onto the manybasis vectors composing a subspace. These fig-ures show that compared to a system in a super-potentiality state (Figures 5a and c), collapsingsystems have more definite outcomes as cer-tain events have higher projection amplitudes,which are represented as peaks in the proba-bility wave in Figure 5d. In contrast, Figure 5cshows that the probability a state vector willcollapse onto a particular event is equallylikely across all events (i.e., the amplitudesare equal in the flat surface), and, therefore,retains high potentiality.

In this illustration we provided a rudimentaryexample of how a single state (i.e., emotionality)has the potential to be actualized in many dif-ferent ways as it becomes conjoined with a spe-cific context. However, multiple states (emo-tions, values, attitudes, mental schemas) and/orphysical entities (e.g., people) can be simultane-ously experienced by larger systems, such asgroups and organizations. Notably, these ele-ments of organizational systems could also berepresented in a Hilbert space, given quantumtheory’s ability to represent interacting sub-spaces (referred to as tensor products). Con-

cretely, complex interactive processes could berepresented when our example is extended toinclude multiple interacting Hilbert spaces thateach represent different individual, group, andorganizational states and entities. When multi-ple, interacting Hilbert spaces are consideredtogether, systems of constraints can convolvesimultaneously to affect the global state of asystem (as an individual, group, organization)as it travels through time. The mathematics be-hind computing tensor products for interactingsubspaces is beyond the scope of this article(see Hughes, 1989).

Noncompositionality and the nexus of hierar-chical levels. In terms of semantic spaces, non-compositionality occurs when the combinationof two concepts produces a qualitatively differ-ent concept. To say that an automobile is alemon implies attributes not possessed by mostcars or most lemons, and it also drops somefeatures of each (Hampton, 2013), in that we arenot implying that the car is yellow or reliable.Compositional combination, in contrast, simplyincludes all the attributes of both concepts, as in“black cat.” The issue of how to conceptualizeand represent noncompositional concepts andtheir underlying semantic space is complex andhas a rich history (see Busemeyer & Bruza, 2012:Chapters 5–7). It is of importance to a QATCbecause we argue that the future collapses intothe present at the nexus of hierarchical levels,and this process involves the construction ofmeaning, either mentally or through organizingactions, by combining external and internalconstraints.

How meaning is created at the nexus of sys-tems is a particularly challenging area for un-derstanding combinations, because a commonmeaning system is often absent as one crosseshierarchical levels and time frames. For exam-ple, one’s self-identity creates a rich internalframework for reacting emotionally, interpretingevents, and translating this interpretation intoactions. However, when we cross the boundaryto a social system such as a dyadic relation,even though each member has a self-system, thecollective social system does not have the samesemantic space as each individual. Understand-ing the relational meaning of the combinedidentity, then, is a compositionality issue, whichcan explain dyadic (or group) identities. It isimportant to understanding issues such as thecreation of complexity in organizations because


complexity is in part created from the interac-tion of dissimilar individuals (Page, 2007) as itproduces novel outcomes.

The advantage of thinking of compositionalityissues in terms of a quantum framework is thatit can go beyond intersective semantics (e.g.,black cats) and use multidimensional Hilbertspaces to represent concepts. For example, eachindividual’s self-relevant meaning for an event,such as the change in state of a system, could berepresented by different basis vectors in a Hil-bert space. These different basis vectors, in turn,might yield different probabilities for a specificemotional reaction. What is critical in this rep-resentation is that in predicting each person’semotional or behavioral reaction, the other indi-vidual can be viewed as a context or a given, asin Bayesian mathematics, but the combinationscan be represented more richly in terms of ten-sor products (see Busemeyer & Bruza, 2012: 156).Returning to our hierarchical system example, itis therefore possible to represent the set of ex-ternal constraints at the nexus of systems as acontext that interacts with the internal con-straints at a lower level in producing anoutcome.

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Robert G. Lord ([email protected]) is a professor of leadership at DurhamBusiness School, Durham University. He earned his Ph.D in organizational-socialpsychology at Carnegie Mellon University. His research focuses on leadership andsocial perceptions, organizational complexity, motivation, emotions, and informationprocessing.


Jessica E. Dinh ([email protected]) is a postdoctoral researcher at DurhamBusiness School, Durham University. She earned her Ph.D. in industrial-organiza-tional psychology from the University of Akron. Her research focuses on leadership,organizational complexity, and the sociocognitive processes that influence dynamicorganizational behavior.

Ernest L. Hoffman ([email protected]) is a doctoral candidate studying industrial-organizational psychology at the University of Akron. He currently works as a man-agement consultant for Personnel Research and Development Corporation (PRADCO).His research interests include leader identity, event-level performance management,and organizational emergence.


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