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Drawing from knowledge-based view and balanced scorecard approach, this study seeks to develop an integrative model to examine the influence of two knowledge management (KM) strategies, codification KM strategy and personalization KM strategy, on the multi-stage KM evolution (KM adoption, implementation and institutionalization stages), which in turn affects balanced scorecard outcomes (financial performance, internal process performance, customer performance, and growth and learning performance).

Survey data from 244 managers (currently and directly in charge of KM activities) in large Taiwanese firms were collected and used to test the research model using the structural equation modeling (SEM) approach.

The results have revealed that both the codification KM strategy and personalization KM strategy are positive factors for stage-based KM evolution, but their relative importance differs across the three subsamples. Additionally, the results showed that the internal process and customer perspectives play a critical role in measuring performance during the earlier stages of KM evolution, while the financial and learning and growth perspectives emphasize the performance achievements from the latter stages.

Since KM implementation is an evolutionary process, using both financial and non-financial measures to assess organizational performance through KM efforts, such as the four balanced scorecard perspectives, can take full advantage of stage-based KM evolution. The results indicate that the time-lag effect is critical to distinguishing different forms of organizational performance.

Theoretically, this study aims to provide a research model that is capable of understanding the antecedents and consequences of staged-based KM evolution. From a managerial perspective, the findings of this study provide valuable guidelines to policy-makers and practitioners in accelerating KM evolution and achieving organizational performance.

Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

The thought and rationale of sustainable competitive advantage in strategy are significantly influenced by the Schumpeterian models of dynamic competition in IO and evolutionary economics. Yet, most analytical accounts of sustainable competitive advantage fail to explain how firms' investment choices influence, and are simultaneously influenced by, the co‐evolution of “external” industry competition and “internal” firm competences. This paper aims to contribute to the development of a theory of endogenous market structure in strategy.

Two alternative assumptions are developed – concerning temporally heterogeneous firm investment strategy – that lie central to a proposed behavioral theory of endogenous market structure. Additionally, a theoretical description is provided of the endogeneity of the demand‐side determinants of firm investment strategy and industrial market structure. Finally, guidelines are provided for empirical application of [incorporating] the alternative assumptions and theoretical arguments.

It is expected that the theoretical arguments in the paper will influence strategy scholars to develop dynamic models of firm performance that render themselves amenable to sound empirical analyses.

The paper contributes towards developing a theory of endogenous market structure in strategy.

The extant “supply‐side” frameworks of industry evolution fail to predict the evolutionary patterns in industries based on systemic technologies. This paper aims to describe the complex demand environment in industries based on systemic technologies and to explain how the continuously evolving demand structure influences the choice and level of firm investments in the above context.

The paper identifies a conceptual gap in the “technology‐centric” literature on industry evolution by conducting a detailed interpretive survey of the literature that focuses on the demand‐side determinants of firm‐ and industry‐level technological processes underlying industry evolution, and co‐evolution of the technological system underlying an industry and the consumer applications based on the same.

The paper provides a set of empirically verifiable mechanisms to explain competing firms' choice and level of investment under conditions of technological and demand uncertainty in industries based on systemic technologies. On one hand, firms' investments influence the evolution of both the technological system(s) and their constituent components that underlie such industries and, on the other, firms' investments influence the consumption of the array of consumer applications that are generated in these industries.

The theoretical explanation provided herein not only enhances the understanding of the role of demand‐side factors as determinants of rate and direction of technological advances but also lies central to the understanding of the evolution of industries based on systemic technologies. More specifically, the paper explains how the interaction between continuously evolving demand structure in the downstream market(s) for consumer applications and the technological components comprising the technological system influences competing firms' choice and level of investments.

The objective of this paper is to investigate the efficiency of hybrid solution methods when incorporated into large‐scale optimization problems solved by evolution strategies (ESs) and to demonstrate their influence on the overall performance of these optimization algorithms. ESs imitate biological evolution and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified multi‐membered evolution strategies with discrete variables are adopted. Two solution methods are implemented based on the preconditioned conjugate gradient (PCG) algorithm. The first method is a PCG algorithm with a preconditioner resulted from a complete Cholesky factorization, and the second is a PCG algorithm in which a truncated Neumann series expansion is used as a preconditioner. The numerical tests presented demonstrate the computational advantages of the proposed methods, which become more pronounced in large‐scale optimization problems and in a parallel computing environment.

This paper presents the application of Differential Evolution (DE), an evolutionary computation technique for the optimal design of gas transmission network. As a gas transmission system includes source of gas, delivery sites with pipeline segments and compressors, the design of efficient and economical network involves lot of parameters. In addition, there are many equality and inequality constraints to be satisfied making the problem highly non‐linear. Hence an efficient strategy is needed in searching for the global optimum. In this study, DE has been successfully applied for optimal design of gas transmission network. The results obtained are compared with those of nonlinear programming technique and branch and bound algorithm. DE is able to find an optimal solution with a cost that is less than reported in the earlier literature. The proposed strategy takes less computational time to converge when compared to the existing techniques without compromising with the accuracy of the parameter estimates.

The study focuses on drug safety regulation capture, reveals the inner mechanism and evolutionary characteristics of drug safety regulation capture and provides suggestions for effective regulation by pharmacovigilance.

The article introduces prospect theory into the game strategy analysis of drug safety events, constructs a benefit perception matrix based on psychological perception and analyzes the risk selection strategies and constraints on stable outcomes for both drug companies and drug regulatory authorities. Moreover, simulation was used to analyze the choice of results of different parameters on the game strategy.

The results found that the system does not have a stable equilibrium strategy under the role of cognitive psychology. The risk transfer coefficient, penalty cost, risk loss, regulatory benefit, regulatory success probability and risk discount coefficient directly acted in the direction of system evolution toward the system stable strategy. There is a critical effect on the behavioral strategies of drug manufacturers and drug supervisors, which exceeds a certain intensity before the behavioral strategies in repeated games tend to stabilize.

In this article, the authors constructed the perceived benefit matrix through the prospect value function to analyze the behavioral evolution game strategies of drug companies and FDA in the regulatory process, and to evaluate the evolution law of each factor.

Evolution has long been a biological process “borrowed” by management sciences to define structural and procedural development in organizations. The theory of Darwinian Evolution in biology has existed for a long time and still (with modification) remains the main theory in life sciences. However in biotechnology new concepts have risen. In parallel, organization sciences have been evolving the concept of evolution on different levels of the organization, discussing the evolution of organization during their life cycle, the evolution of populations of organizations, sectors, etc. Directed evolution in biology creates new organisms that can produce molecules with attributes better fitting industrial use, from naturally occurring organisms, allowing new organisms to function in non‐biological environments and perform processes they never needed to perform in a natural environment. We will show that by translating the concept from biology into organization sciences, we can develop the techniques for the evolution of new organizational structures and fitting routines, that would fit new emerging environments, where we seek the best adapted routines and structures for performance. We will adopt the concept of directly evolving a structure fitting for pre‐designed purposes by using bio‐technology methods, and will try and bridge the gap in organization sciences between the current development of the evolutionary theory and the advance made in biology. At the end discusses opportunities for research (the European Framework Program, national programs), together with a proposed general plan of action. The theory and the techniques descried can lead to further research and active experimentation.

Reports on a study of the use of flexible manufacturing systems in three strategic business units over a period of six years. Describes each SBU′s manufacturing technology strategy and contrasts it with tactical measures of actual operations to gain insight into the role of manufacturing technology in the SBU′s business strategy and its evolution. Presents study results in ten propositions which question our knowledge of how strategy truly evolves in organizations.

The purpose of this paper is to provide a review of key research contributions on the topic of service strategies in manufacturing by focusing on descriptions of the phenomenon and theoretical explanations of its evolution and financial consequences.

A summary analysis of the extant literature is provided. Valuable contributions and fundamental methodological issues are identified and discussed. Challenges, limitations and directions for future research avenues are also highlighted.

As a result of the analysis and discussion presented, the concept of service‐driven manufacturing is integrated through the provision, evolution and impact of services in industrial settings.

The paper contains guidelines for manufacturing managers interested in the evolution from products to services in different industries.

The paper is expected to be used as a relevant source of ideas and guidance for all those interested in doing research in services strategies in manufacturing.