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To encourage top management to recognize the need for adding new value to their organizations and commit to the creation of new internal capabilities for growth via the exploration of their company's strategic frontier.

Explains how the CEO can select a team and initiate a project to identify strategy frontier options.

The authors’ experience suggests that the team should first explore all areas of future growth potential in and adjacent to their industry, creating a long list of potential options. Identifying a breadth of strategic frontier options is more important than a depth of information on any one option.

More case studies of strategy frontier projects in action, with quantitative results, would be valuable.

The goal of this frontier team is to identify a portfolio of innovative new business opportunities that exist on the strategic frontier. It will be the responsibility of another, more qualified group with quantitative skills (strategic planners, business development) to develop a detailed business design and determine its profitability and attractiveness to the company.

The article offers top management an innovative how‐to approach to finding truly new growth opportunities.

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.

Research on performance management, as it applies to public sector organizations, has been addressed most often from a static perspective. A process-oriented view on performance is undertaken through use of perceived obstacles, garnered through two large surveys of U.S. government managers, to infer the adaptive paths federal agencies have followed. By applying a learning-based model founded on March’s framework of exploration and exploitation, the ideal-typical ways that public organizations adapt to a performance initiative can be distinguished, opening a window into the processes such learning entails. Structural equation modeling provides the statistical capacity to interpret exploration and exploitation as cohesive paths. Exploratory adaptation appears to have been largely counterproductive, as the obstacles associated with this dynamic indicated a roadblock rather than a path forward. But exploitation had the opposite effect, as its associated obstacles corresponded with greater use of performance measures for management activities and enhanced results orientation.

The purpose of this paper is to understand how digital technologies can help healthcare organisations and improve the exploration-exploitation paradox over time. The authors explore inputs, processes and outcomes of implementing digital transformation programs and advance four testable propositions.

The authors conducted multiple case studies with embedded units of analysis: digital transformation processes; hospitals; and regional healthcare systems. Primary sources come from 107 semi-structured interviews with key informants within 14 Italian hospitals between 2009 through 2011.

Three complementary paths emerge as fundamental to balance exploratory and exploitatory efforts in healthcare: assets digitalisation within hospitals; digitally based process integration; and disruptive decision-making through analytics. Intra- and inter-path characteristics are discussed to show how digital transformation can both move hospital within the exploration-exploitation space.

By its very nature, this study is exploratory. Notwithstanding the number of cases and interviews, its generalisability is limited.

Digital transformation programs are fundamental to resolve the tensions raised by the exploration-exploitation paradox. Their implementation leads to better performance (cost reductions, quality improvements). A framework is provided for practitioners to make better decisions.

This study sheds new light on how digital technologies are actually adopted and adapted in healthcare contexts. It does it by entailing a longitudinal perspective.

This study aims to build an integrated model for information technology (IT)/information system (IS) team exploration and exploitation innovation in the business-to-business (B2B) enterprise context by empirically investigating the mediating role of tacit-explicit knowledge co-creation and exploring the behavior approach of servant leaders for IT/IS team exploration-exploitation innovation.

The authors' analysis was supported by 182 enterprise-IT/IS teams (403 participants) in Taiwan. The authors used a questionnaire and Structural Equation Model (SEM)-SmartPLS to validate the development model. This study examines IT/IS exploration-exploitation innovation using a combination of quantitative survey research and qualitative case studies.

The specific roles of direct and mediating effects for two innovations of IT/IS team exploration and exploitation were investigated. The findings show a direct effect of knowledge creation (tacit and explicit) on IT/IS team exploration-exploitation innovation. Servant leader behavior positively influences tacit-explicit knowledge co-creation practices, IT/IS team exploration and exploitation. Moreover, knowledge creation (tacit and explicit) successfully mediates the correlation between servant leaders and IT/IS team innovations (for exploration and exploitation).

Managers, IT/IS consultants and enterprises at the executive level are suggested to encourage knowledge co-creation practices, both tacit and explicit to support their IT/IS team innovation. The greater the degree of explicit knowledge (i.e. socialization and internalization) and tacit knowledge creation (i.e. externalization and combination), the greater will be the opportunities for meeting the enterprise-IT/IS team exploration and exploitation innovation goals. The project manager may follow servant leadership behavior to promote effective knowledge co-creation process on the IT/IS team.

This effort contributes to greater and new understanding of how ambidexterity capability, tacit-explicit knowledge co-creation (mediators) and servant leaders for IT/IS team exploration-exploitation innovation in the B2B enterprise context and new foundations for future studies on a cross-enterprise IT/IS team. This research is also the first empirical effort to understand how a servant perspective leadership contributes through the knowledge co-creation process for IT/IS exploration-exploitation innovation.

This chapter addresses ambidexterity at the individual level. Ambidexterity is defined as a company's ability to guarantee both short- and long-term successes by simultaneously exploring new market or new technological paths and improving existing products. We demonstrate that this ability can result from the evolution of social networks linking individuals involved in idea development. We used a longitudinal approach that combined case study and social network structure analysis of the R&D center of a semiconductor company. Six cases have been selected according to the level of disruption of the first idea generated and the end result in terms of exploration and exploitation. For these six cases, data have been gathered from monthly project reviews, press articles and listings of patents. Seventy-four interviews with key actors in the idea-development process have also been conducted.We mapped the relationships between actors who have contributed to the development of the idea through creative thinking and/or helped it to be accepted both internally and externally over three-year windows. Consequently, two network pictures are drawn for each case, and network structure indicators are computed for these two representations. We created a description of network evolution and the consequences of this process on the level of disruption of the ideas involved. This research demonstrated that different network structures and types of connections are relied upon depending on the explorative or exploitative objectives of teams of individuals.

This paper explores how organizational capabilities and path dependence affect the implementation of supply chain (SC) sustainability initiatives. Through the lenses of contingency and evolutionary theory, the paper addresses the underexplored supply chain dynamics that enhance or inhibit sustainability trajectories.

Using in-depth multi-case studies for theory elaboration, five supply chains were studied through open-ended interviews with SC members, secondary data collection and site visit observation. The design consists of a combination of deductive and inductive approaches to elaborate theory on supply chain dynamics and enhanced sustainability trajectories.

The empirical study shows that learning is a fundamental condition for supply chains as they implement sustainability initiatives, and that exploitation capabilities are more frequently used than exploration capabilities. Path dependence plays a role in the outcomes of supply chain sustainability initiatives, which are influenced by both path dependence and contingencies of the contexts in which these systems operate.

This paper puts forward five propositions that emerge from the literature and from the field study results. Although this is an exploratory research bounded by geographical limitations and the limited number of SC cases, the goal of elaborating theory may open up several promising avenues for future large-scale and longitudinal research studies.

By enhancing our understanding of the dynamics of supply chain sustainability trajectories, decision-makers, scholars and policy-makers can better understand how supply chains learn, how they employ SC member capabilities and how they deal with stakeholder resistance.

This paper extends supply chain sustainability theory by addressing the knowledge gap that exists with regard to understanding the dynamics of evolving supply chain sustainability trajectories. This paper sheds additional light on this important topic and contributes in multiple ways to the sustainable supply chain management literature.

Robots equipped with LiDAR sensors can continuously perform efficient actions for mapping tasks to gradually build maps. However, with the complexity and scale of the environment increasing, the computation cost is extremely steep. This study aims to propose a hybrid autonomous exploration method that makes full use of LiDAR data, shortens the computation time in the decision-making process and improves efficiency. The experiment proves that this method is feasible.

This study improves the mapping update module and proposes a full-mapping approach that fully exploits the LiDAR data. Under the same hardware configuration conditions, the scope of the mapping is expanded, and the information obtained is increased. In addition, a decision-making module based on reinforcement learning method is proposed, which can select the optimal or near-optimal perceptual action by the learned policy. The decision-making module can shorten the computation time of the decision-making process and improve the efficiency of decision-making.

The result shows that the hybrid autonomous exploration method offers good performance, which combines the learn-based policy with traditional frontier-based policy.

This study proposes a hybrid autonomous exploration method, which combines the learn-based policy with traditional frontier-based policy. Extensive experiment including real robots is conducted to evaluate the performance of the approach and proves that this method is feasible.

Exploration and exploitation comprise one of the most well-known constructs in management and organization studies. However, there are three gaps in the extant literature on this topic. First, these studies focus mainly on large organizations and neglect small and medium-sized enterprises (SMEs) and new ventures. Second, when adopting a longitudinal perspective, the research typically consists of cross-sectional studies that fail to capture evolution. Third, the research focuses more on the role of antecedents and mediators of strategies that pursue exploration and exploitation than on the practices that embody such goals. In this chapter, we address these three gaps and complement the previous literature with a study of the growth of an SME from start-up to sale over a 19-year period (1993–2011). We depict the evolution of exploration and exploitation over time through an analysis of management system practices that employs a longitudinal perspective. We analyze the different roles that management systems have played in various stages of the growth paths of the organization. We show that the role of management systems in shaping exploration and exploitation only loosely depends on the design of these systems. The same management systems can fulfill an explorative function in one stage and an exploitative function in another, depending on how such systems are used. Conversely, across stages, the role of management systems typically changes from exploration to exploitation.

The purpose of this paper is to examine repatriate career exploration as a continuing growth‐oriented process and introduce repatriate hope as its crucial driver.

Through a review of relevant literature, the framework of hope theory is introduced to argue for a more “agentic” view of the repatriate that can act as an independent career agent in the increasingly boundaryless career environment.

The paper extends current knowledge of the repatriation process by describing ways in which repatriate hope drives career exploration toward valued outcomes of career growth and career success. It is also described how this repatriate career success will depend on the repatriate expectations and the social and organizational support received by the repatriate.

The main contribution of this paper is a new view of the repatriation process through the lens of the hope theory that emphasizes positive psychological perspective indicating career growth/success as a valued outcome of repatriate career exploration process. Thus, repatriate is viewed as a proactive individual managing his or her career success and using exploration as a means of coping with and adjusting to a shifting set of challenges presented by the dramatic role change.