**1. Introduction**

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In EM, addressing these questions through a critical discussion of extant findings is relevant if we consider two, intertwined aspects.

First, in general, the adoption of approaches based on CT has become, in the 21st century, increasingly popular and highly supported. Concerning sustainability related issues, in particular, this is seemingly evident, especially when research grants, funding opportunities, and/or public tenders are released on themes regarding, for example, technology management, open innovation, circular economy, green procurement, or, more generally, sustainable ecosystems [1].

Second, as also highlighted by our analysis in this article, in the 21st century, the use of complexity approaches recurs in decision-making problems, regarding how to improve the effectiveness and efficiency of new product development (NPD), project management (PM), and supply chain management (SCM), or team organization. We know that these aforementioned problems have always been considered as key themes in EM. At the same time, we are confident that, to date, they also represent key challenges towards more sustainable business models [2].

As an example, in addressing a central issue for technology management research, i.e., understanding the nature of the industry environments in which firms play, Ndofor et al. [3] argue that "if the microfoundations of industry environments are indeed strongly impacted by nonlinear relationships, then the industry environment would evolve with chaotic dynamics, as opposed to equilibrium systems" (p. 200). Relatedly, as maintained by McCarthy et al. ([4], p. 437), "early research on NPD has produced descriptive frameworks and models that view the process as a linear system with sequential and discrete stages. More recently, recursive and chaotic frameworks of NPD have been developed, both of which acknowledge that NPD progresses through a series of stages, but with overlaps, feedback loops, and resulting behaviors that resist reductionism and linear analysis."

In the same vein, as stated by Amaral and Uzzi ([5], p. 1034), "a design engineer may know about the reliability of individual parts but find it difficult to estimate how failures in one part of system are tied together or how errors might cascade through the system when apparently separate components have a low probability of failure." Likewise, as posited by Baumann and Siggelkow ([6], p. 116), "should a product design team always consider all components simultaneously, searching for designs that have high overall performance? Or should it first experiment with a subset of components and expand this set gradually in the course of the design process?"

On this premise, starting in the 1960s, several contributions to CT have arisen from various science disciplines, such as biology, mathematics, physics, chemistry, and information technology [7,8]. This is why CT is growing as a cross-disciplinary scientific perspective, offering new approaches and answers, where reductionism demonstrates limits [9,10]. In particular, according to complexity science, the assumption of Newtonian thinking, where everything can be broken down into single pieces, studied separately, and then reassembled to form the initial totality, appears too simplistic when applied to understanding situations characterized by uncertainty and unpredictability [11].

Due to the body of knowledge and continuous, massive expansion of CT, most complexity theorists currently agree on some core characteristics of complexity, and a number of intertwined definitions have been developed over time [12]. Maguire and McKelvey, for example, seminally identify a complex system as "a system (whole) comprised of numerous interacting entities (parts), each of which is behaving in its local context according to some rule(s), law(s) or force(s). In responding to their own particular local contexts, these individual parts can, despite acting in parallel without explicit inter-part coordination nor communication, cause the system as a whole to display emergent patterns—orderly phenomena and properties—at the global or collective level" ([13], p. 4). Likewise, Mitchell conjectures a complex system as a "system in which large networks of components with no central control and simple rules of operation give rise to a complex collective behavior, sophisticated information processing, and adaptation via learning or evolution" ([14], p. 13). Moreover, since complex systems show a tendency to adapt, they are often referred to as complex adaptive systems (CAS); hence, we will use the latter term in this article.

Considering the foregoing, it seems that a conceptual article that critically discusses the current status of complexity research in EM is missing. Thus, the main contribution of our research is that we conceive it as a theoretical start intended to fill this gap. To do so, in Section 2, we first provide readers with the core concepts regarding CT. In Section 3, which constitutes the core of our research, we chose the 21st century to investigate the diffusion of complexity-based accounts in EM. In this regard, we use *IEEE Transactions on Engineering Management (TEM)*, because it is considered as the leading journal in EM [15], and as a reliable, heuristic proxy to start our focus. From this journal, we analyzed 38 representative publications on the topic published since 2000, and went through a rigorous keyword-based article search. Specifically, we provide the pillars of our contribution in terms of key thematic areas investigated and authorship coverage, together with the main research methodologies and core complexity features adopted. Therefore, in Section 4, we discuss some potential (and hopefully valuable) implications of our analysis for sustainability research and practices in this EM field. Section 5 concludes our contribution and presents its limitations.

As a piece of core evidence, our analysis shows that many key features of CAS seem to be clearly observable in the dataset, with modeling and optimizing DM under uncertainty as the dominant theme. Perhaps surprisingly, however, only a limited number of studies still seem to formally adhere to CT, to explain the different EM issues under investigation. This is also why, among the various avenues presented, we suggest that more all-inclusive complexity-based research frameworks would be needed. Accordingly, formally embedding fine-tuned co-evolutionary logics in these frameworks could also add value.
