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Team social capital (TSC) has been attracting increasing research attention aiming to explore team effectiveness through within- and cross-team resource conduits. This study bridges two disconnected theories – TSC and evolutionary theory – to examine gaming clans and analyzes mechanisms of the clans' TSC building from an evolutionary perspective.

This research draws longitudinal data from a sample of gaming teams (N = 1,267) from anonymized player data from the game World of Tanks spanning 32 months. The authors explored teams' evolutionary patterns using hidden Markov models and applied longitudinal multilevel modeling to test hypotheses.

The results showed that teams of different sizes and levels of evolutionary fitness vary in team closure and bridging social capital. The authors also found that larger teams are more effective than smaller ones. The positive association between team-bridging social capital and effectiveness is more substantial for smaller teams.

This research advances the theoretical development of TSC by including the constructs of teams' evolutionary status when analyzing strategic social capital building. Adding to existing literature studying the outcome of TSC, this research also found a moderating effect of team size between TSC and effectiveness. Finally, this study also contributes to a longitudinal view of TSC and found significant evolutionary patterns of teams' membership, TSC, and effectiveness.

This paper aims to analyze the organizational capabilities involved in the adoption of environmental management tools in eight large French firms. The analysis also examines the antecedents that contributed to the emergence of those capabilities and the consequences of their involvement in terms of environmental management.

To analyze the organizational capabilities deployed when environmental management tools are adopted, this paper takes an exploratory approach based on a qualitative study of eight large French firms.

The findings show how organizational capabilities, dynamic and ordinary, are operationalized in the adoption of environmental management tools. This operationalization is made possible by internal and external antecedents and simple and complex routines. The findings also identify two possible configurations of organizational capabilities, each one leading to a specific form of environmental management. The first configuration leads to stand-alone environmental management systems, while the second succeeds in engendering integrated management systems. This study shows that this difference is explained by heterogeneous endowments in terms of antecedents across firms.

The study provides useful information for managers about the conditions that favor and facilitate adoption of environmental management tools and the ways these conditions operate.

The study illustrates the impact of society on large firms’ adoption of certain environmental management practices. It shows that external visibility – which has created strong societal pressure – is one of the external antecedents that led eight large French firms to develop specific organizational capabilities.

In analyzing the antecedents, routines and capabilities involved in the adoption of environmental management tools, the study adds some original, innovative contributions to current knowledge on the conditions for adoption of such tools.

The purpose of this paper is to examine how the mediating effect of strategic management impacts the relationship between dynamic capabilities and firm performance concerning environmental munificence in 3rd party logistics (3PL) firms operating in Turkey.

Variance-based structural equation modeling algorithm and correlation analysis were applied to survey data obtained from (n = 482) a top manager from 3PL companies.

Results revealed that dynamic capabilities were a strong predictor for organizational performance, environmental munificence also emerges as a key predictor for dynamic capabilities and strategic management, and strategic management fully mediates the link between dynamic capabilities and organizational performance, suggesting that they function as substitutes in affecting performance outcomes.

This paper provides empirical evidence of the relationship between the dynamic capability adaptation, strategic management, environment and performance of 3PL firms. As a limitation, the results are based on survey research with a limited sample size.

Organizations should manage not only dynamism but also the scarcity of environmental resources found to be significant on both dynamic capabilities and strategic management. Additionally, in the logistics sector, managers should focus on the big picture while they empower and lead capable followers to transform this strategic view into operational-level changes.

Depending on the relationships between constructs, studying environmental munificence is a different topic than the dynamic environment concept in the effectiveness of dynamic capabilities of 3PL firms. As well as dynamic capabilities at the level of individual and strategic management relationship on organization performance are confirmed.

In evolutionary robotics (ER), robotic control systems are subject to a developmental process inspired by natural evolution. The purpose of this paper is to utilize a control system representation based on finite state machines (FSMs) to build a decentralized online‐evolutionary framework for swarms of mobile robots.

A new recombination operator for multi‐parental generation of offspring is presented and a known mutation operator is extended to harden parts of genotypes involved in good behavior, thus narrowing down the dimensions of the search space. A storage called memory genome for archiving the best genomes of every robot introduces a decentralized elitist strategy. These operators are studied in a factorial set of experiments by evolving two different benchmark behaviors such as collision avoidance and gate passing on a simulated swarm of robots. A comparison with a related approach is provided.

The framework is capable of robustly evolving the benchmark behaviors. The memory genome and the number of parents for reproduction highly influence the quality of the results; the recombination operator leads to an improvement in certain parameter combinations only.

Future studies should focus on further improving mutation and recombination. Generality statements should be made by studying more behaviors and there is a need for experimental studies with real robots.

The design of decentralized ER frameworks is improved.

The framework is robust and has the advantage that the resulting controllers are easier to analyze than in approaches based on artificial neural networks. The findings suggest improvements in the general design of decentralized ER frameworks.

This study proposed the integrative model of dynamic dominant logic (DL) with exploitation (EP) and exploration (ER) as a pattern of actions in endeavoring firm performance (FP). This study also intended to explain the multiple patterns of DL in creating technical and evolutionary fitness simultaneously.

This study used a cross-sectional quantitative analysis of the Indonesian commercial banking population facing digital transformation and was analyzed using covariance-based structural equation modeling through parceling.

The model confirmed that DL positively affects EP and ER. It also revealed that DL indirectly impacts FP through EP, indicating changes in the traditional banking business through the strong acceptance of “new realities” in adapting to the rapid growth of technology. Hence, this study discovered that during the recent banking digital transformation, the beneficial inertia of the technical pattern of action might lose effectiveness in creating superior performance.

DL is vital in locking short-term performance while maintaining long-term performance opportunities through EP and ER to promote digital transformation. Accordingly, it induced banks to adopt new technology for value creation and fortifying competitive advantage.

This study provided a theory about how DL links the firm's decision-making process by promoting multiple patterns of action in achieving technical and evolutionary fitness. It highlighted the DL as a resource conceptualization that promotes resource development through EP and ER as microfoundation of dynamic capabilities during the tension of institutionalization and digital transformation.

The paper aims to propose an analytical framework for social influence and mathematical formulation for its main components: conformity and peer-pressure. The framework is conceived to explain why certain behaviours and beliefs propagate in a society and some others disappear. It can also be used to study the emergence and the evolution of the status of the norms in terms of their adoption by the population.

The paper is theoretical, making use of economic quantitative methods. The author proposes a new formulation for the evolutionary dynamics, increasingly borrowed by social scientists. Then, mathematically treating the equation, the author draws general conclusions in form of lemmas, which are proved.

The author's main contribution is to show that even behavioural rules and beliefs that emerge in a minority subset of the population, do not procure any benefit for the agents adopting them can under certain conditions, evolve into the consensus of a society, become a norm.

More general conclusion (theorems and lemmas) could be stated and proved. But given that the main contribution of the paper is to the fields of social and behavioural economics, along a number of disciplines less mathematical than economics, the author kept the analysis that required fairy advance mathematics for later.

The paper contributes to the evolutionary game theory, evolution of preferences, and evolution of beliefs and social norms. More precisely, the equation proposed in the paper can be used in the contexts the patterns of heterogeneity in a population are affected or caused by social influence. Or in the contexts, the social institutions are susceptible to affect an agent's sense of identity (e.g. voting, fashion industry, marketing).

In this paper, for the first time, a mathematical formulation is proposed for the social influence and its main psychological components (conformity and status seeking). Using the above, the author proposed a new parametric fitness function for the evolutionary dynamics. The author believes the paper matters to a multidisciplinary public. It answers a question that challenged and puzzled the economists (as well other social scientists): the reasons behind the emergence and the prevalence of social norms do not positively contribute to the utility or payoff of the agents adopting them (and at times they are costly).

Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon.

To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms.

The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark.

This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

So far, the simplicity of heuristics has been mostly studied at the rule level. However, actors' bounded rationality implies that small bundles of rules drive behavior. This study thus conducts a conceptual elaboration around such bundling. This leads to reflections on the various processes of heuristic emergence and to qualifications of the respective characteristics of basic heuristic classes.

Determining which rules – out of many possible ones – to select in one's small bundle constitutes a difficult combinatorial problem. Fortunately, past research has demonstrated that solutions can be found in evolutionary mechanisms. Those converge toward bundles that are somewhat imperfect yet cannot be easily improved, a.k.a., locally optimal bundles. This paper therefore identifies that heuristic bundles can efficiently emerge by social evolutionary mechanisms whereby actors recursively exchange, adopt and perform bundles of rules constitute processes of heuristic emergence.

Such evolutionary emergence of socially calculated small bundles of heuristics differs from the agentic process by which some simple rule heuristics emerge or from the biological calculation process by which some behavioral biology heuristics emerge. The paper subsequently proceeds by classifying heuristics depending on their emergence process, distinguishing, on the one hand, agentic vs evolutionary mechanisms and, on the other hand, social vs biological encodings. The differences in the emergence processes of heuristics suggest the possibility of comparing them on three key characteristics – timescale, reflectivity and local optimality – which imply different forms of fitness.

The study proceeds as a conceptual elaboration; hence, it does not provide empirics. At a microlevel, it enables classification and comparison of the largest possible range of heuristics. At a macrolevel, it advocates for further exploration of managerial bundles of rules, regarding both their dynamics and their substantive nature.

In the field, practitioners are often observed to socially construct their theory of action, which emerges as a bundle of heuristics. This study demonstrates that such social calculations provide solutions that have comparatively good qualities as compared to heuristics emerging through other processes, such as agentic simple rules or instinctive – i.e. behavioral biology – heuristics. It should motivate further research on bundles of heuristics in management practice. Such an effort would improve the ability to produce knowledge fitting the absorptive capacity of practitioners and enhance the construction of normative managerial theories and pedagogy.

Bundles of rules may also play a crucial role in the emergence of collective action. This study contributes to a performativity perspective whereby theories can become reality. It demonstrates how the construction of a managerial belief system may amount to the launching of a social movement and vice versa.

Overall, many benefits accrue from integrating the bundles of rules expressed and exchanged by practitioners under the heuristic umbrella. So far, in management scholarship, such emergent objects have sometimes been interpreted as naïve or as indicative of institutional pressures. By contrast, this study shows that socially calculated bundles may efficiently combine the advantages of individuals' reflective cognitive processes with those provided by massive evolutionary exchanges. In conclusion, the social calculations of small heuristic bundles may constitute a crucial mechanism for the elaboration of pragmatic theories of action.

A novel sexual adaptive genetic algorithm (AGA) based on Baldwin effect for global optimization is proposed to overcome the shortcomings of traditional GAs, such as premature convergence, stochastic roaming, and poor capabilities in local exploring. This paper seeks to discuss the issues.

The proposed algorithm simulates sexual reproduction and adopts an effective gender determination method to divide the population into two subgroups of different genders. Based on the competition, cooperation, and innate differences between two gender subgroups, the proposed algorithm adjusts adaptively sexual genetic operators. Furthermore, inspired by the acquired reinforcement learning theory based on Baldwin effect, the proposed algorithm guides individuals to forward or reverse learning and enables the transmission of fitness information between parents and offspring to adapt individuals' acquired fitness.

Global convergence of the proposed algorithm is proved in detail. Numerical simulations are conducted for a set of benchmark functions with different dimensional decision variables. The performance of the proposed algorithm is compared with that of the other evolutionary algorithms published recently. The results indicate that the proposed algorithm can find optimal or closer‐to‐optimal solutions, and is more competitive than the compared algorithms.

The proposed algorithm introduces, integrates and simulates correctly and adequately, for the first time, the mechanisms of sexual reproduction, Baldwin effect and adaptation to GAs by referring to the latest research results of modern biology and evolution theory.

The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.

First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.

The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.

This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.