What is a theory that proposes that every organizational system is made up of four main tasks structure and technology with an interaction among the components?

In identifying the key concepts of GST, Kast and Rosenzweig [10] (p. 450) defined a system as being “composed of interrelated parts or elements.” von Bertalanffy [11] (p. 416) defined a system as a “model of general nature, that is, a conceptual analog of certain rather universal traits of observed entities.” Perhaps the most related definition of a system is that it represents a whole consisting of several parts/members [12]. This definition hits on the distinction between a system and systems: whereas the former represents the whole (the system), the latter makes up the whole (components, systems, and subsystems). A system’s components are also dependent on other components [12].

When dealing with systems, researchers need to clearly identify which level they are analyzing. Kast and Rosenzweig [10] (p. 455) stressed identifying both “the boundaries of the system under consideration and the level of … analysis [systems].” These boundaries vary and are typically set by the researcher or the theoretical system. Once the boundary and appropriate level of analysis (system, systems, subsystems) have been identified, the system’s structure can be modeled, providing what von Bertalanffy [11] termed an explanation in principle. This explanation in principle provides both a level of explanation and prediction, as well as leads to the formation of systems thinking.

Systems theory has been portrayed in the HR literature as transforming inputs into outputs [13] and as a method of identifying inputs, processes, outputs, and feedback components of a system to discuss and research systems more intelligently [14]. Here, systems theory has been defined as “a theoretical framework by which elements that act in concert to produce some result are studied” [9] (p. 56). In other literature, systems theory, or general systems theory (GST), has been defined as “an openness of social systems, but also implies system boundaries and stable patterns of relationships within boundaries” [15] (p. 352). By identifying boundaries between systems and their environment, researchers are better able to study interactions between the systems and their environment [16]. Koopmans [17] (p. 21) highlighted von Bertalanffy’s [11] definition (one originator of GST) as viewing “the behavior of a system in terms of that of its constituent components and the interrelationships between those components [subsystems]”, with the unit of analysis as the system or its subsystems. Although von Bertalanffy initially used system in the singular, he saw the systems view as the foundation of a scientific paradigm for science and humankind; following, the common reference to GST is general systems theory, with systems as a plural [18].

Understanding that systems have common features, von Bertalanffy [11] and others derived GST. Under GST, it is understood that “there are general aspects, correspondences, and isomorphisms common to ‘systems’” [11] (p. 415). As GST evolved, it became more of an interdisciplinary field of study where different concepts, models, and principles were applied to systems [11], beginning the derivative to GST, systems theory. Other theoretical systems approaches beyond GST and systems theory, include “cybernetics, theory of automata, control theory, information theory, set, graph and network theory, relational mathematics, game and decision theory, computerization and simulation, and so forth” [11] (p. 416). However, for many social science disciplines, GST and systems theory have been the fields’ standard. All of these theoretical systems approaches, as highlighted by von Bertalanffy [11], relate to systems problems.

Comparing different systems approaches (e.g., cybernetics, information theory), von Bertalanffy [11] concluded that systems also include interrelations within the systems. Here, GST utilizes the system holistic principle, borrowed from Aristotle and the Gestalt movement: the whole is greater than the sum of its parts. Thus, GST approaches systems problems within stated boundaries. Identifying these boundaries, however, becomes more challenging when dealing with social systems: “It is hard to define such a boundary to an organization” [16] (p. 397). Applying systems theory to social systems, such as organizations, can be difficult [16]. As one of its main disadvantages, when dealing with social systems, GST has become too mechanistic: “systems theory is indeed the ultimate step toward the mechanization and devaluation of man and toward technocratic society” [11] (pp. 423–424). Also, one additional concern is that social systems are composed of humans, who typically operate with an understanding of having free will [10]. Social systems, according to Kast and Rosenzweig [10], have purpose and may evolve beyond the boundaries of the systems, thus making the systems unpredictable. Some would also go as far to say that “it is futile to try to solve problems in the human sciences [human resources] with tools appropriate to the natural sciences” [12] (p. 520).

In viewing changes in a system or subsystems over time, dynamic systems theory differentiates between three system states: asymptotically stable, neutrally stable, and unstable [11]. Stability refers to how a system responds to perturbations. A system is identified as being asymptotically stable if it returns to is original state after a disturbance and unstable if it changes states [11]. It is ideal to have an asymptotically stable system when viewed by GST. When a system has been identified as unstable, the system is controlled back to its asymptotically stable state: “a system which is not asymptotically stable is made so by incorporating a controller” [11] (p. 418). Having a system that is asymptotically stable or having the ability to place controls on a system to maintain stability, adds to a system’s predictability. These controls can also act as buffers to external perturbations [19], further manipulating the system, or at least a subsystem, to maintain a desired state. These controls counter, in many cases, the self-organizing processes within the system.

Systems theory has been instrumental to the social sciences and has served as a foundation for HR theory. However, systems theory has come under attack in recent years due to its inability to address complexity and non-linear systems [9,20] and its mechanistic nature when viewing human systems. To further develop the potential for expanding the use of complexity theory in HR and social science contexts, the current article focuses on describing complexity theory and its components to outline the differences between and overlaps of systems theory and complexity theory.

One key feature of GST is the concept of reversible and irreversible processes. For systems to be sustainable, they must have reversible processes—those that are capable of changing state and returning back to their initial state. One example of a reversible process is water and steam. Under the right conditions, water is capable of changing states to steam and is reversible in that the steam can return back to water. The problem within systems is when processes become irreversible; they tend to be unsustainable, uncontrollable, and can potentially destroy some of their own components. This follows the second law of thermodynamics as highlighted by von Bertalanffy [11] (p. 409): “The second law of thermodynamics prescribes that ordered systems in which irreversible processes take place tend toward most probable states and, hence, toward destruction of existing order and ultimate decay.”

Interactions drive a system toward new emerging states as it coevolves within its environment [53]. Complexity theory, synonymous with complexity science in the literature, is best described in the following quote:

Complexity science targets a sub-set of all systems; a sub-set which is abundant and is the basis of all novelty; a sub-set which is evidenced in biology, chemistry, physics, social, technical and economic domains; a sub-set which coevolves with its environment; a sub-set from which structure emerges. That is, self-organization occurs through the dynamics, interactions and feedbacks of heterogeneous components …. This sub-set of all systems is known as complex systems.

Having the potential of being able to provide insight into organizational change dynamics, which have been insufficiently modeled in the social sciences [32], complexity theory is more frequently found in the literature today and is showing new promise for disciplines studying complex systems. Table 3 highlights some of these areas.

The following propositions relate to complexity theory: “Simple systems give rise to complex behavior. Complex systems give rise to simple behavior. And most importantly, the laws of complexity hold universally, caring not at all for the details of a system’s constituent atoms” [60] (p. 304). Complexity theory differs in how it perceives the principle of system holism. Rather than viewing the whole as the sum of its parts, complexity theory asserts the following: “The whole is different from the sum of its parts and their interactions” [61] (p. 77). Through emergence, the whole cannot be reduced to the original parts, the whole is considered a new entity or unit. The whole is “qualitatively different from their parts …. They cannot be meaningfully compared–they are different!” [61] (p. System holism).

Table 4 provides a list of definitions and descriptions found in the literature relating to complexity theory/science.

Complexity science has developed three schools of thought: reductionistic complexity science, complexity thinking, and soft complexity science, also known as the metaphorical school [65,66]. The reductionistic school reduces elements into lower-level components and develops rules of interaction between the lower-level elements and the higher-level elements [65] as a means of explaining new emergent properties; here is where physics addresses the theory of everything. Complexity thinking focuses on what cannot be explained [65]. The epistemology is that knowledge concerning our environment is always incomplete; complexity thinking focuses on our limits of this incomplete knowledge [65]. The last school, the metaphorical school, believes that the “social world is intrinsically different from the natural world” [66] (p. 20) and challenges the Newtonian worldview [65] by viewing complexity through a connectionist perspective where causal connections cannot be identified, hence, are too simplistic compared to the whole system when analyzed. Rather than viewing the world as a mechanistic entity, the metaphorical school views the world as an organic entity [65]. In the current article, the authors take the perspective provided by the metaphorical school in which concepts such as “connectivity, edge-of-chaos, far-from-equilibrium, dissipative structures, emergence, epi-static coupling, coevolving landscapes, etc.,” [66] (p. 20) have been identified in explaining metaphorically, complex systems.

The basic tenets of complexity theory are non-linear dynamics, chaos theory, and adaptation/evolution [15]; others include emergence, self-organization, feedback, and chaos [21]. Complexity theory views systems as being non-linear, thus future states are unpredictable. As a system transitions from simple to complex, the predictive mechanisms become less reliable. Chaos is deterministic and linear, with mathematical meaning [63], and has sensitivity to its initial conditions [67]. Complexity theory applies mathematical modeling of linear and predictable states when viewing chaos, whereas it employs CAS to view unpredictable, non-linear systems. Using mathematical modeling, chaos identifies the global patterns from the components’ interactions in self-organizing systems [21], while CAS identify the interactions from these components. A key element of CAS involves emergence. Emergence occurs when the interactions from the system components tend to lead to new states, contributing to the system’s unpredictability. The conditions of feedback, evolution, and adaptation all refer to a system’s ability to learn and can be found in both chaos and CAS.

Complexity theory addresses open systems compared to closed systems. This is a major distinction of complexity theory compared to other theoretical systems approaches. This distinction goes against the second law of thermodynamics and is where complexity theory operates. Nearly all of the systems of interest in complexity theory are open systems [61] that include the components of self-organization and emergence. Related to the second law of thermodynamics, rather than irreversible processes causing the system to become self-destructive [11], the processes, self-organization and emergence, evolve new system states that are sustainable.

In its most basic form, complexity theory involves the primary concepts of chaos and CAS, along with the tenets of path dependence, system history, non-linearity, emergence, irreducibility, adaptiveness, operating between order and chaos, and self-organization, as portrayed in Figure 2. Chaos is supported by self-organization, adaptation/evolution, feedback, and deterministic systems, and CAS are supported through self-organization, emergence, adaptation/evolution, feedback/history, and nondeterministic systems. For the current article, the authors identify emergence to be primarily associated with CAS and identify complexity theory as being composed of two concepts, chaos and CAS. The authors realize, and represent (see Figure 2), that there is some overlap between the two systems and that emergence could be included in some chaotic states.

In relation to the purpose of the current research project, CAS are the model that is recommended for addressing today’s complexity in the social sciences. By implementing CAS, complexity theory could operate in parallel with systems theory. While GST can operate under the principle of system holism from a reductionistic perspective, complexity theory could expand the social sciences by providing a perspective counter to the principle of system holism that incorporates a connectionist approach rather than reductionism. The following section discusses further the differences between GST and complexity theory, including CAS.

There are multiple differences between GST and complexity theory. Presented in the following section are further distinctions between the two, including the following: the principle of system holism, open and closed systems, linear and non-linear systems, and the application of the concept of irreducibility.

Complexity theory has been described as a new science in which “nobody knows quite how to define it, or even where its boundaries lie” [72] (p. 9). Complexity theory has also been portrayed as a “paradigm shift from previous science” and as a “new paradigm” [15] (p. 353).

Complexity theory has been portrayed in two ways. The first places GST as the overarching theory for complexity theory and CAS. For example, Yawson [9] presented complexity theory as a subset of systems theory, positioning GST as the grand theory and complexity theory, CAS, and systems theory within its umbrella. However, later in the same research study, Yawson [9] identified three interrelating elements to complexity theory that could not be accounted for by GST: non-linear dynamics, chaos theory, and adaptation, providing some separation between GST and complexity theory. Other literature has described complexity theory as a new science, indicating that complexity theory was separate from other theories. This second portrayal identified GST and complexity theory as two separate theories, with systems theory under GST and CAS residing under complexity theory. Developed from scientific and mathematical fields, complexity science was influenced by the “original systems sciences of cybernetics, information theory, and General Systems Theory” [73] (p. 7), while remaining a new science, separate from GST. For the current article, the authors adopted Goldstein et al.’s [73] position that complexity theory is separate from GST with CAS and chaos theory residing underneath complexity theory. The authors recognize that there is some overlap between GST and CAS, as shown in Figure 3, however, still contend that complexity theory is separate from GST and other theoretical system approaches (e.g., systems theory, cybernetics, and theory of automata). For the social sciences, it is argued that future research should include examinations of the CAS tenets (path dependency, history, non-linearity, emergence, irreducibility, adaptability, balance between order and chaos, and self-organizing). The interactions within organizations are complex and can be explained better through the lens of complexity theory and CAS than by the other theoretical system approaches.

Problems are typically categorized as being simple, complex, or wicked [74]. Simple problems have an identifiable problem and solution, whereas complex problems typically have an agreed upon problem but differing potential solutions. For the third type of problem, wicked problems, there is no agreed upon problem or solution. Churchman defined wicked problems as “that class of problems which are ill-formulated, where the information is confusing, where there are many decision makers and clients with conflicting values, and where the ramifications in the whole system are confusing” (as cited in [75] (p. 200)).

Wicked problems are made up of the following properties: indeterminacy in problem formulation, non-definitiveness in problem solution, non-solubility, irreversible consequentiality, and individual uniqueness [76]. Wicked problems have no definitive problem statement and hence have no definitive solution, most often having no solution at all; wicked problems consist of irreversible characteristics due to complexity, which adds to the complications caused by constantly changing variables, and each wicked problem is each unique in its own context and structure.

Wickedness has been accepted in most disciplines today in such a manner that it has largely become the norm, relating to human issues such as “global climate change, sustainability, stem cell research and usage, resource management, terrorism, and urbanization” [76] (p. 2). Attempting to address wicked problems using traditional linear methods leads to partial analysis, at best, and deception that the problem has been solved [76]. These linear approaches have generated dissatisfaction with decision making, planning, and implementation [77]. Such attempts have also been described as trying to tame the untamable, in which the “inadequacy lies in the intellectual roots of these traditional approaches and skill sets” [76] (p. 1). The problem with trying to address wicked problems using the traditional linear methods that are applicable when using systems theory is that these particular problems are resistant to linear protocols [76]. Instead, researchers have called for awareness, acceptance, and new innovative strategies for addressing wicked problems [76]. One such method, in opposition to the linear systems approach, is the adaptive, participatory, and transdisciplinary method [76]. Other methods incorporate collaborative strategies that involve social learning to address wicked problems. However, these collaborative strategies or interventions must be structured using a complexity framework as opposed to a systems framework: “Social learning is more likely to be successful if it remains a self-organizing, complex adaptive systems that coevolves as stakeholders meet, interact, and inform one another’s actions” [78] (p. 16).

As a discipline, the social sciences would be better poised to address wicked problems using complexity theory and complexity thinking methodologies in lieu of systems theory or systems thinking. Systems theory has been identified as being incapable of addressing wicked problems: “…addressing wicked problems calls … to forge new ways of thinking, leading, managing, and organizing that recognize the complexity of the issues and processes” [77] (p. 722). The importance of complexity theory to scholars and scholar-practitioners will increase as wicked problems become more prevalent in their fields. This point is best illustrated by Roberts [78] (p. 16) when she stated the following: “Wicked problems will be with us for some time.”

To better make sense of complexity in organizations, Kurtz and Snowden [79] developed what they called the Cynefin framework. This framework originated in the knowledge management discipline but later expanded into multiple fields (e.g., training, culture change, and leadership) and has been used for multiple purposes (e.g., decisions, perspectives, and conflict) [79]. The original framework was presented in a two-by-two matrix involving chaos, complex, knowable, and known categories for each matrix (clockwise from the bottom left). In the middle of all four categories, Kurtz and Snowden [79] identified the state of disorder that could be occupied by any of the four states as they interact or transition between states [37]. Other representative models present the four categories as being chaotic, complex, complicated, and simple (clockwise from the bottom left). This representation is shown in Figure 4. Here, chaotic is best represented by unknowables, complex with unknown unknowns, complicated with known unknowns, and simple with known knowns. This construct of known represents things that are known to the collective, the entity of interest [79].

Chaotic states have been associated with having no cause and effect relationships, while complex states were identified with CAS in which cause and effect relationships “are only coherent in retrospect and do not repeat” [79] (p. 468). The left half of this framework is best associated with CAS (see Figure 4). The state of complicated has been associated with having cause and effect relationships and with systems thinking, while the simple state has been related to predictable cause and effect relationships and best practices [79]. The right half of this framework is best associated with systems theory. As shown in Figure 4, the overarching theory is complexity theory with CAS associated with complex and chaotic states and systems theory associated with complicated and simple states. The disorder represents the fuzzy border between the states, meaning that some complicated states could have a touch of complexity up to a point. Beyond that point, the state experiences a phase transition. This point in which a phase transition occurs differs with each system dependent upon the initial laws of complexity and unique composition. Once a state experiences a phase transition, the new state is irreducible and cannot be reduced to its initial composition.

Potential uses for this sensemaking framework (Cynefin framework) include contextualization and alternative history exercises (see [79] for additional examples). Contextualization is a brainstorming exercise in which the framework is used to draw multiple perspectives around a specific issue or event [79]. This exercise offers different solutions to specific issues by discussing how solutions would be portrayed in each of the four states (i.e., chaotic, complex, complicated, and simple). A second example includes an alternative history exercise, which involves discussing the history of a specific entity or event, identifying when and why within the framework critical turning points occurred [79].

This sensemaking framework provides a visual representation of how different states can occupy independent and interdependent states, depending on the context. In applying this sensemaking framework to contextualize problems of social systems, a shift from its current state within the complicated domain (systems theory) to the complex domain (complexity theory and CAS) to better prepare scholars and scholar-practitioners to address complexity and wicked problems will be necessary.

Reductionistic methods have been identified as methods of reducing “complex phenomena into elementary parts and processes” [11] (p. 409). Reductionistic methods work well as long as the observed agents can be isolated to causality, identifying the relationship between a few variables at one point in time) [11]. Reductionistic methods involve linear models that come with specific assumptions as identified by Jayanti:

Such assumptions include the premise that closed models are adequate for modeling processes occurring in open systems, that models can be universally applied and do not need to specify where and when they should be used, that a system is equal to the sum of its parts, that time is reversible, that causality is linear, that future outcomes–like the future itself–can be predicted, and that environments are relatively static and tend toward equilibrium.

However, even with the successes that have been achieved in the fields of physics and biology by using reductionistic methods, questions still remain [11]. Scientists have begun to understand that the interactions within and among systems must also be observed and understood, resulting in too many associations to be studied using simple causality methods. This problem was identified by von Bertalanffy [11] (p. 411) as the problem of organized complexity: “There loomed the problem of ‘organized complexity,’ that is, of interrelations between many but not infinitely many components”.

Given today’s complexity, globalization, and interconnectedness, von Bertalanffy [11] highlighted that reduction to simple particles using laws of physics is not practical. Rather, von Bertalanffy [11] (p. 423) supplanted reductionism with “new categories of interaction, transaction, organization, teleology, and so forth”, which also included developing newer techniques.

Network analysis has grown in recent years due to advances in technology and new methods of analysis. More specifically, social network analyses identify the “contacts, ties, and connections, the group attachments and meetings, which relate one agent to another and so cannot be reduced to the properties of the individual agents themselves” [81] (p. 3). In this description by Scott [81], social network analysis can be used as a method to address complexity using connectionist methods as opposed to traditional correlational techniques. Social network analysis is capable of identifying patterns, clusters, and strong ties (interactions) from large sets of data linked to human behavior. When viewing social activity through the lens of complexity theory, it is the patterns, clusters, and interactions that we wish to examine further once they have been identified. Equated by some to relational sociology, social network analysis encompasses a theory of the social world [81].

To examine the random nature of networks, Richardson [61] constructed an experiment that simulated 100,000 networks each with 10 nodes, two inputs, and the same random rules of association. In viewing these networks all irrelevant nodes (frozen or leaf nodes) were removed from each network. This resulted in each network having roughly 60% of their nodes being relevant or connected nodes [61]. Although this 60% estimate is not universal, it does, however, make the point that relevant nodes within a network can be scrutinized for further analysis. Identifying relevant nodes, patterns, or clusters begins the process of new discovery in social systems that were not possible previously.

When attempting to optimize a system the rules of optimization need to be acknowledged. One of these rules states the following: “Optimization of a system’s parts does not (necessarily) lead to an optimal system, and vice versa” [82] (p. This short analysis). Given this rule, it would be essential to identify which set of relevant nodes could be optimized in order to better optimize the whole system. This supports the redundancy of potential command principle: “To control a complex system we must first have a sufficiently good representation of it” [82] (p. Redundancy). Although the prediction of a complex and open system is not entirely possible, in part, due to unknown conditions (e.g., environment, emergent properties), near prediction is dependent upon extracting meaningful relationships [82]. Utilizing social network analysis provides scholars and scholar-practitioners with the tools to not only analyze social systems, but also to optimize these systems.

With an “apparent shift toward complexity-based inquiry in sustainability research” [83] (p. Abstract), newer methods are being called to support this complexity-based inquiry. Porter and Reischer [83] highlighted that, in the context of sustainability, reductionistic methodologies were least effective. They called for researchers to move beyond the “standard boxes-and-arrows thinking” [83] (p. Conceptual framework) when addressing complexity and wicked problems. In this context, complex problems cannot be answered using simple linear epistemology [80].

In response to their own call, Porter and Reischer [83] situated sustainability as a CAS, defined as being multilevel and ever-evolving. Much in the same manner as Porter and Reischer, [83] identified sustainability as being a CAS, so too can many constructs currently being researched in the social sciences (e.g., leadership, diversity, teams, workplace engagement) be categorized. Also, new multilevel, networked, and complexity-situated theories need to be developed within the literature in an effort to expand the theory and knowledge base of the social sciences, as well as to increase the fields’ utility. Aside from this conceptualization, complexity-related theories need to be operationalized prior to being tested [8,12], providing further support for a theory’s utility to the field.

Additional calls have been found in the literature. For example, Yawson [9] highlighted the need to re-conceptualize complexity theory for one’s discipline and noted that a conversation needs to occur within each discipline (see also [84]). Theorizing within the social sciences to examine social systems’ need to branch out to complexity thinking as a way of incorporating different epistemologies or thought-universes, Jayanti [80] (p. 110) stated the following: “it may be necessary to create entirely new models to more fully over-come the limits of Newtonian assumptions.” Similar calls identified chaos and complexity theory as providing a means of developing new perspectives for the social sciences to examine social systems, resulting in a better understanding of today’s complex issues [8].