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The purpose of this paper is to provide a theoretical account of how firms make choices between dynamic capability-based and ad hoc problem-solving approaches toward strategic change.

A model has been developed to answer the questions of how and under what conditions firms develop appropriate approaches to handle strategic change.

Drawing upon structural inertia theory (SIT) and the resource-based view (RBV), the model predicts that firms, regardless of their age and size, are more likely to adopt an ad hoc problem-solving approach to handle change in both highly dynamic and low-dynamic environments. However, in moderately dynamic environments, a dynamic capability-based approach may be more appropriate, depending on which theoretical logic (SIT or RBV) the decision is made.

The paper builds on the useful distinction made by Winter (2003) in terms of the ways to handle organizational change and extends the recent research on temporary vs sustainable competitive advantages to investigate how firms tackle strategic change within the contexts of both environmental dynamism and organizational attributes.

The purpose of this paper is to develop a user‐oriented decision‐supporting applicable tool for selection of a single supplier out of a group of potential suppliers in a dynamic business environment over a finite planning horizon.

A qualitative and quantitative description of the impact of a change in one or several business environment parameters on current and future supplier choice; the methodology is accompanied by a visual representation of those impacts for the decision maker. The paper presents extended simulation experiments to test the proposed methodology.

A strategy of replacing suppliers over a definite planning horizon based on a forecast of the business environment is significantly (2‐9 per cent) more efficient than a strategy of relying on a single leading supplier throughout the planning horizon. This efficiency gain is greater the more the business environment is dynamic.

The proposed methodology is applicable to a broad range of service and manufacturing organizations that operate in dynamic business environments and rely on complex purchasing systems. Thanks to its simplicity, it can be applied to very large systems with a broad range of selection and/or environmental parameters.

Although the supplier selection process has been extensively studied, the literature still lacks appropriate reference to the effects of a dynamic business environment on this process.

The purpose of this paper is to analyse the relationship between environmental dynamism and supply chain flexibility (SCF) and to evaluate if two dynamic capabilities, i.e. operational absorptive capacity (OAC) and organisational learning (OL), are necessary competences for firms to develop such a responsive supply chain strategy as flexibility.

Hypothesised relationships are tested with survey data from 302 Spanish manufacturing firms using structural equations modelling.

Environmental dynamism is positively associated with both OAC and OL, and both dynamic capabilities enable SCF. The authors also find that the relationship between environmental dynamism and SCF is partially mediated by both dynamic capabilities.

The study contributes to the literature, for example, by determining when it is advantageous to develop SCF, by specifying what capabilities a firm needs to develop to align SCF with the environment, and by opening the black box that is the relationship between the environment and SCF.

Managers should develop SCF via OAC and OL when they detect a high degree of environmental dynamism. Knowing when this is necessary relies on a good understanding of the environment. OAC is found to be a stronger enabler of SCF than OL. The findings provide managers with an insight into why some firms are able to develop more effective responses to dynamic environments than others.

This study is one of only a limited number of studies that adopt a dynamic capabilities approach to supply chain management. Prior literature has shown that dynamic capabilities can aid in developing strategic, structural, and operational flexibility. The authors extend this literature by showing that OAC and OL have an effect on SCF.

This study aims to examine the effect of exploratory innovation offshoring on the level of hierarchical control and how this effect is moderated by transnational and dynamic environments.

This study draws on a sample of 148 Taiwanese multinational enterprises to examine their governance decisions on foreign investments.

Findings show that the more innovation offshoring is exploratory, the higher the level of hierarchical control will be used by multinational enterprises (MNEs) and that transnational and dynamic environments have different moderation effects on the positive exploratory innovation offshoring-hierarchical control relationship.

This study has two theoretical implications. First, this study extends the concept of complexity from a transaction attribute level (problem) to an environmental level (transnational environment) and finds that exploratory innovation offshoring and transnational environments interactively impact governance choices. Second, this study distinguishes between two sources of technological uncertainty – uncertainty due to transaction-level attributes (exploratory innovation offshoring) and external environments (dynamic environments) and finds that exploratory innovation offshoring and dynamic environments interactively impact governance choices.

The practical implication of this study lies in the simultaneous consideration of exploratory innovation offshoring and transnational/dynamic environments, which will allow international decision-makers to adjust/select the governance forms most appropriate for speedy responding to and handling environmental changes.

This study employs the theoretical perspectives of transaction cost economics (TCE) and resource-based view (RBV) to analyze and discuss the impact of operational environments – transnational and dynamic environments – on MNEs’ decisions on the governance structure for a given innovation offshoring.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

Teams focus on a common and valued goal, and effective teams are able to alter their behaviors in pursuit of this goal. When teams are viewed in the context of a dynamic environment, they must adapt to challenges in the environment in order to maintain team effectiveness. In this light, we describe various sources of team variation and how they combine with individual-level, team-level, and dynamical mechanisms for maintaining team effectiveness in a dynamic environment. The combination of these elements produces a systems view of team effectiveness. Our goals are to begin to define, both in words and in operational terms, team effectiveness from this perspective and to evaluate this definition in the context of team training using intelligent tutoring systems (team ITS). In addressing these goals, we present an example of real-time analysis of team effectiveness and some challenges for team ITS training based on a dynamical systems view of team effectiveness.

This work aims to combine the cloud robotics technologies with deep reinforcement learning to build a distributed training architecture and accelerate the learning procedure of autonomous systems. Especially, a distributed training architecture for navigating unmanned aerial vehicles (UAVs) in complicated dynamic environments is proposed.

This study proposes a distributed training architecture named experience-sharing learner-worker (ESLW) for deep reinforcement learning to navigate UAVs in dynamic environments, which is inspired by cloud-based techniques. With the ESLW architecture, multiple worker nodes operating in different environments can generate training data in parallel, and then the learner node trains a policy through the training data collected by the worker nodes. Besides, this study proposes an extended experience replay (EER) strategy to ensure the method can be applied to experience sequences to improve training efficiency. To learn more about dynamic environments, convolutional long short-term memory (ConvLSTM) modules are adopted to extract spatiotemporal information from training sequences.

Experimental results demonstrate that the ESLW architecture and the EER strategy accelerate the convergence speed and the ConvLSTM modules specialize in extract sequential information when navigating UAVs in dynamic environments.

Inspired by the cloud robotics technologies, this study proposes a distributed ESLW architecture for navigating UAVs in dynamic environments. Besides, the EER strategy is proposed to speed up training processes of experience sequences, and the ConvLSTM modules are added to networks to make full use of the sequential experiences.

Small and mid‐sized enterprises (SMEs) are facing challenges in an increasingly fierce environment. This paper aims to explore the promise of information systems (IS) in enhancing the survival and competitiveness of SMEs in a dynamic environment. To address this issue, the paper draws upon the dynamic capability theory and develops a research model of IS‐enabled dynamic capabilities to examine the role of IS competence for enhancing SMEs dynamic capabilities in a competitive business environment.

An empirical study is conducted by using survey data from senior managers of 120 SMEs in China.

The analytic outcomes support the research model and confirm that IS competence significantly contributes to SMEs' dynamic capabilities for gaining competitive advantage.

This paper contributes to the literature on IS impact on dynamic capabilities of SMEs by incorporating IS competences into a research model of IS‐enabled dynamic capabilities and articulating the relationships between IS competences and dynamic capabilities of SMEs in a changing business environment. The research findings enrich dynamic capabilities theory by justifying IS as an enabling antecedent for organizational capability development. The findings may empirically convince SMEs owners and management to effectively invest in and deploy IS for enhancing SMEs' dynamic capabilities and performance.

A capability‐building perspective is used to examine how IS can leverage SMEs' capabilities to enhance their competitive advantage in a dynamic environment.

Research on entrepreneurial orientation (EO) has concluded a positive link between EO and firm performance and that relationship depends on several contingencies. The purpose of this paper is to derive insights from the absorptive capacity and contingency perspectives to introduce extra-organizational advice as a moderator of the relationship between EO and firm performance in a dynamic environment.

Using survey data from 340 small- and medium-sized enterprises (SMEs) in Ghana, the study examines the moderating influence of extra-organizational advice on the EO-firm performance relationship in dynamic environments.

The study’s empirical findings suggest that extra-organizational advice amplifies the EO-performance relationship in dynamic environments.

The cross-sectional design of the study does not permit causal inferences to be made regarding the variables examined. Future studies may use longitudinal design to examine the causal links of the variables. Limitations aside, the study helps to answer how extra-organizational advice translates EO into improved performance in an environment characterized by constant flux.

The results of this paper can assist entrepreneurs and policy makers in understanding the dynamics and processes involved in implementing a strategic orientation to achieve higher performance. For SME managers, firm performance is determined by high levels of EO and extra-organizational advice in dynamic environments. The understanding of this issue can promote the development and maintenance of entrepreneurial ventures.

The paper examines an important, but under-researched issue – the moderating effect of extra-organizational advice on the EO-performance relationship in dynamic environments. To the best of the authors’ knowledge, the present study pioneers research in this area.

Although information technology (IT) governance and IT capability have been extensively examined, the impact of IT governance mechanisms on IT-enabled dynamic capability (ITDC) with moderators has received less attention. This study investigates how the impact of IT governance mechanisms on firm performance is achieved through an ITDC through the moderating role of IT governance decentralization and a turbulent environment.

This study extends from the traditional view of IT capabilities and integrates dynamic capability theory to propose that IT governance is vital for the ITDC. Path analysis, hierarchical regression analysis and moderation analysis were performed using partial least squares (Smart PLS 3.0) as the data analysis methods. This study empirically tests the proposed mediated moderation model by using data collected from 254 firms in China to test the hypotheses.

Significant and impactful relationships are found in the model that includes turbulent environment moderating effects. Contrary to expectations, IT governance decentralization is also significant but not very strong.

This study’s findings have implications for investigating IT governance, IT-enabled capabilities and moderators. Accordingly, this study has implications for board and executive management to capitalize on dynamic IT capability, to keep pace with the challenges and turbulent conditions associated with business needs and for the productivity paradox in the context of Chinese firms.

This country-specific research study theoretically contributes to the IT governance, dynamic capabilities and turbulent environment in the information systems literature and proposes many practical guides to the board and executive management of companies in the Chinese context.