Dynamic resource allocation for exploitation and exploration with ambidexterity: Logical mechanism and simulations

Strategy and organizational theorists have emphasized the importance of balancing exploitation and exploration for organizations’ sustainable success in regards to organizational learning and adaptation. However, few researchers have addressed the mechanisms or criteria in regards to an organization’s resource allocation for exploitation and exploration with an ambidextrous balance, although previous researchers agree that exploitation and exploration are important for organizational success and that a balance between the two should be achieved in order to obtain sustainable competitiveness. The main purpose of this research is to make a logical argument on how team creativity evolves from the creativity revelation processes through knowledge creation by balancing exploitation and exploration. Specifically, this research presents a new logical mechanism to allocate a team’s limited resources to exploitation and exploration, keeping a balance between the two activities. We prove the validity of the proposed logical mechanism by conducting longitudinal simulations.     

A Multi‐Level Model for Organizational Ambidexterity in the Search Phase of the Innovation Process

Innovation is one of the most critical means in supporting and improving the competitive position of the firm, in particular, and a firm's survival and growth depend greatly on its ability to balance the exploitation of existing knowledge with the exploration of new possibilities, by building ambidexterity capability. While different alternatives to realize the simultaneous reconciliation of exploration and exploitation at an operational level have been proposed, how organizations build ambidexterity capability is not fully understood. The aim of this paper is thus to explore how exploration and exploitation balancing can be achieved in practice. We decided to focus on the early phase of the process where firms search for new ideas with which to renew themselves. To this end, we analysed the search phase of a highly innovative technology‐based company by investigating structural design choices combined with the presence of specific roles and searching practices. The results show how the exploration and exploitation balancing act can actually be achieved and maintained through a multi‐level approach that integrates both the operational and the strategic levels. Our findings thus contribute to the organizational ambidexterity literature, by proposing a first interpretative model for dealing with ambidexterity in the search phase of the innovation process.     

Task difficulty and team diversity on team creativity: Multi-agent simulation approach

Organizational structures are complex and vary according to sector, field, and type of business or service. In order to be effective, an organization needs to tailor its activities to the environment in which is it located. Based on contingency perspectives, this study is focused on investigating effective ways to design team diversity and maximize team creativity according to task difficulty levels. Considering the organizational team member as an agent, the study employed a multi-agent simulation method to understand the progress of creative manifestation, by observing the exploration and exploitation activity of team members over certain periods of time. The results first reveal that the level of team diversity influences the amount of creativity manifested by team members’ activities, such as exploration and exploitation. Second, managers have to properly facilitate either exploration or exploitation depending on task difficulty by striking a balance between them.     

IT‐mediated social interactions and knowledge sharing: Role of competence‐based trust and background heterogeneity

In the knowledge‐based economy, organizational success is dependent on how effectively organizational employees share information. Many studies have investigated how different types of communication activities and communications media influence knowledge sharing. We contribute to this literature by examining increasingly prevalent yet understudied IT‐mediated social interactions and their effects on knowledge sharing among employees in comparison to face‐to‐face social connections. By integrating the literature on knowledge sharing, social networks, and information systems, we theorize the ability of IT‐mediated social interaction to (1) afford interactions between individuals with heterogeneous backgrounds and (2) facilitate frequent IT‐mediated social interactions that are high in competence‐based trust—both supporting effective sharing of knowledge. Through a social network analysis of the employees in a high‐tech organization, this study finds that IT‐mediated frequent social interactions are the most effective in promoting knowledge sharing.     

Exploration and Exploitation within Firms: The Impact of CEOs' Cognitive Style on Incremental and Radical Innovation Performance

Previous studies have provided valuable insights into how environmental and organizational factors may influence levels of explorative and exploitative innovation in firms. At the same time, scholars suggest that individual characteristics, such as cognitive and behavioural inclinations of top executives, might also have significant impact on the ability of a firm to engage in explorative and exploitative activities. The importance of the CEO is of interest, especially in medium‐sized companies, where the CEO appears to be most influential. Very few studies, however, have quantitatively examined the relationship between individual characteristics of top managers and firm‐level exploration and exploitation. Most of the existing research focuses on observable managerial characteristics and the composition of top management teams. Therefore, some important psychological issues may have been bypassed. This study complements prior research in two fundamental ways. First, whereas previous studies focus on extrinsic organizational factors that influence individual exploration and exploitation, we rely on insights from cognitive psychology to hypothesize a relationship between intrinsic factors (i.e., cognitive style) and individuals' tendency for exploration versus exploitation. Second, whereas existing research remains silent on the implications of individual CEO characteristics for firm performance, we hypothesize a relationship between CEOs' tendency for exploration or exploitation and firm‐level innovation performance.     

On‐the‐job Innovation: The Impact of Job Design and Human Resource Management through Production Ownership

A growing number of practitioners and academics endorse that the ability of organizations to foster, develop and use the innovative potential of their employees contributes to organizational success. Yet empirical investigation of individual innovation processes is lacking. In this research we address the question of whether both more flexibility in an employees’ job design and commitment‐oriented HRM activities promote individual innovative work behaviour. Findings suggest that a multifunctional job design and the perceived HRM system promote employee involvement in innovative activities through increased feelings of ownership for work‐related issues and problems.     

An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

Teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self-feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability.     

Dynamic small world particle swarm optimizer for function optimization

Performance of particle swarm optimization technique is highly influenced by the population topology. It determines the way in which particles communicate and share information within a swarm. If path length is too small, it implies that a particle communicates with other particles in its close proximity leading to exploitation. On the contrary, if path length is large then the particle interacts with other remote particles leading to exploration. There needs to be a balance between exploration and exploitation and Small world network fits to this need of ours. In this paper, dynamic small world network has been proposed with the objective to have a balanced trade-off between exploration and exploitation. In order to make learning process dynamic linearly decreasing inertia weight has been employed. Experimental study is performed on a set of 23 test functions using different performance evaluation measures. Results obtained are compared with other state of the art techniques demonstrating the effectiveness of the proposed approach.     

The Role of Intuition and Deliberation for Exploration and Exploitation Success

Advances in social and cognitive psychology and related fields have drawn attention to the role of intuition in organizational decision making. In this study we link intuitive and deliberate decision‐making styles to the success of exploration and exploitation activities, which are understood as two qualitatively opposing strategies that organizations can adopt. We provide empirical evidence that the two opposing strategies are linked to two opposing styles of decision making – intuitive and deliberate. In doing so, we draw on data which we received from 140 entrepreneurs and managing partners of Austrian companies and show that exploration is strongly related to intuitive decision making whereas exploitation draws on both intuitive and deliberate decision making. Based on our findings, we stress the complementarity of the two decision‐making styles, and point out that particularly in the light of the fast‐changing premises in which organizations have to manoeuvre today, decision makers are well advised to use both decision‐making styles to their best benefit.     

基于Q学习的多任务多目标粒子群优化算法

多任务粒子群优化算法(multi-task particle swarm ptimization, MTPSO)通过知识迁移学习,具有快速收敛能力,广泛应用于求解多任务多目标优化问题.然而, MTPSO难以根据种群进化状态自适应调整优化过程,容易陷入局部最优,收敛性能较差.针对此问题,利用强化学习的自我进化与预测能力,提出一种基于Q学习的多任务多目标粒子群优化算法(QM2PSO).首先,设计粒子群参数动态更新方法,利用Q学习方法在线更新粒子群算法的惯性权重和加速度参数,提高当前粒子收敛到Pareto前沿的能力;其次,提出基于柯西分布的突变搜索策略,通过全局和局部交替搜索多任务最优解,避免算法陷入局部最优;最后,设计基于正向迁移准则的知识迁移方法,采用Q学习方法更新知识迁移率,改善知识负迁移现象.与已有经典算法的对比实验结果表明所提出的QM2PSO算法具有更优越的收敛性.     

Facilitating Project Team Learning and Contributions to Organizational Knowledge

Innovative products, services, and processes are consequences of knowledge integration. Often the integration is of newly generated knowledge with other knowledge that the firm already has available. Especially in firms where long run performance depends on innovation, it behooves managers to think about how newly generated knowledge can be transferred quickly, effectively, and reliably, and thereby can be integrated with the firm’s current knowledge for early exploitation or captured as organizational knowledge in the form of practices, procedures, or files. New knowledge frequently originates in the context and activity of project teams – e.g., R&D teams, design teams, and re-engineering teams. In order to carry out their tasks, such teams frequently need to learn things already known to other organizational units, i.e., they need to acquire and assimilate organizational knowledge. Theoretically then, project teams both draw on the firm’s knowledge and contribute to the firm’s knowledge. The more effectively they carry out these actions, the more effective they are and the more effective their parent firms will be. This article identifies project team and organizational design practices that facilitate project team learning and contributions to organizational knowledge.     

Achieving novelty and efficiency in business model design: Striking a balance between IT exploration and exploitation

Digitalization is encouraging an increasing number of firms to design their business models based on information technology (IT) for exploring business opportunities. This study examines the effect of the balance (imbalance) between IT exploration and exploitation on novelty-centered business model design (NBMD) and efficiency-centered business model design (EBMD). Using matched data from the IT-related executive and chief executive officer of 183 firms, this study finds that IT exploration is positively related to NBMD and EBMD and that IT exploitation is positively related to only EBMD. NBMD and EBMD are significantly related to firm performance. Polynomial regression and response surface analysis reveal that NBMD and EBMD first decline and then rise with the increase in the level of balance between IT exploration and exploitation. NBMD declines but EBMD rises with the increase in the level of imbalance between IT exploration and exploitation. This study contributes to the understanding on leveraging IT capability to influence the business model design and firm performance.     

Intra‐organizational user‐centred design practices: The impact of design research departments at design consultancies

The user is a critical factor in design and innovation. Firms experiment with different approaches to involving the user in design processes, which results in new forms of intra‐ and extra‐organizational collaboration. The establishment of in‐house design research units within design consultancies is one such intra‐organizational user‐centred design practice that targets designer‐researcher collaboration. This paper addresses this issue and reports on the findings from multiple case study research exploring the impact of in‐house design research teams on designers' user knowledge construction. We utilized constructivist learning theory to assess major aspects of these intra‐organizational user‐centred design practices. Ethnographically informed field studies were conducted at six design consultancies representing three design fields (i.e., architecture, industrial design and interaction design) in the Northwestern United States. Three of the consultancies have design research departments and three do not. The findings indicate that in‐house design research units play a role in designers' user knowledge construction via their results, processes and human resources. Among these, the active participation of designers in the research process was observed to have the largest impact because of its contribution to designers' contextual and collaborative learning about users.     

Enhancing Discontinuous Innovation through Knowledge Combination: The Case of an Exploratory Unit within an Established Automotive Firm

The literature on innovation management underlines the necessity to separate the exploratory unit that builds new businesses on the basis of radical innovation from the exploitation unit that emphasizes continuous improvement. However, little research focuses on the exploratory unit in itself: the very nature of its activity, its composition, etc. The aim of this article is to analyse the exploratory unit in mobilizing results highlighted by research on organizational creativity. It is argued that in order to enhance discontinuous innovation, knowledge combination should occur and be facilitated in the exploratory unit. Hence, the research question is what organizational design at a fine‐grained level and creativity processes are likely to enhance knowledge combination and thus discontinuous innovation? Based on an in‐depth study of an exploratory unit created in an established multidivisional firm pursuing the development of discontinuous innovation and which generated several actual breakthroughs, we highlighted four key factors that enhanced knowledge combination: (i) the definition of the scope of the unit, (ii) the composition of the unit and the dual roles of its members, (iii) the boundary objects that supported the interactions between these members during the creativity process, and (iv) the arenas where new knowledge was further created.     

An adaptive balance optimization algorithm and its engineering application

The policy of balance between exploration capability and exploitation capability directly affects the solution performance of the meta-heuristic algorithm in a limited time. In order to better balance the exploration and exploitation capabilities of the algorithm and meet the solution requirements of complex real-world problems, the adaptive balance optimization algorithm (ABOA) is proposed in this paper. The algorithm consists of a global search phase (GSP) and a local search phase (LSP) and is controlled by a fixed parameter. ABOA not only considers the balance of exploration and exploitation capabilities of the algorithm throughout the whole iterative process but also focuses on the balance of exploration and exploitation in both GSP and LSP. The search in both phases is focused around the respective search centers from outside to inside. ABOA balances the exploration and exploitation capabilities of the algorithm throughout the search process by two adaptive policies: changing the search area and changing the search center. Fifty-two unconstrained benchmark test functions were employed to evaluate the performance of ABOA. The results of ABOA were compared with nine excellent optimization algorithms available in the literature. The statistical results and Friedman test showed that ABOA was significantly competitive. Finally, the results of the examined engineering design problems showed that ABOA can solve the constrained optimization problem better compared to other methods.     

A new Q-learning algorithm based on the metropolis criterion

The balance between exploration and exploitation is one of the key problems of action selection in Q-learning. Pure exploitation causes the agent to reach the locally optimal policies quickly, whereas excessive exploration degrades the performance of the Q-learning algorithm even if it may accelerate the learning process and allow avoiding the locally optimal policies. In this paper, finding the optimum policy in Q-learning is described as search for the optimum solution in combinatorial optimization. The Metropolis criterion of simulated annealing algorithm is introduced in order to balance exploration and exploitation of Q-learning, and the modified Q-learning algorithm based on this criterion, SA-Q-learning, is presented. Experiments show that SA-Q-learning converges more quickly than Q-learning or Boltzmann exploration, and that the search does not suffer of performance degradation due to excessive exploration.     

一种高斯过程的带参近似策略迭代算法

在大规模状态空间或者连续状态空间中,将函数近似与强化学习相结合是当前机器学习领域的一个研究热点;同时,在学习过程中如何平衡探索和利用的问题更是强化学习领域的一个研究难点.针对大规模状态空间或者连续状态空间、确定环境问题中的探索和利用的平衡问题,提出了一种基于高斯过程的近似策略迭代算法.该算法利用高斯过程对带参值函数进行建模,结合生成模型,根据贝叶斯推理,求解值函数的后验分布.在学习过程中,根据值函数的概率分布,求解动作的信息价值增益,结合值函数的期望值,选择相应的动作.在一定程度上,该算法可以解决探索和利用的平衡问题,加快算法收敛.将该算法用于经典的Mountain Car 问题,实验结果表明,该算法收敛速度较快,收敛精度较好.     

The Antecedents of Exploitation‐Exploration and Their Relationship with Innovation: A Study of Managers' Cognitive Maps

Based on the resource‐based view (RBV) and the dynamic capabilities (DC) approach, in this work we carry out a thorough literature review in order to identify which organizational actions and resources act as the main antecedents of exploration and exploitation. Also, by using the cognitive maps technique, we analyse to what extent these antecedents are present in managers' cognitive maps when they develop exploration or exploitation strategies, as well as when they try to develop radical and incremental innovations. Our results show that, firstly, managers develop cognitive maps that match our theoretical proposals about the organizational resources and capabilities that can lead to exploitation and exploration. Secondly, regarding innovation, although the antecedents of exploitation are used by managers as the way towards incremental innovation, some antecedents of exploration are expected to facilitate not only radical but also incremental innovations. These results provide interesting points for reflection on the topics addressed and lead us to conclude that the antecedents of exploration and exploitation should not be seen as separate ways towards different types of innovation, but as sets of resources on which to build configurations that facilitate radical or incremental innovation.     

Supporting learning in a shared design environment

Design is a complex problem solving and knowledge refinement process. Learning is a part of this process that can improve computer based design support systems by using the knowledge representing the experience and expertise of designers. Learning from past design examples, and acquiring new knowledge during the process of design are closely related activities that must be supported by future computer supported design systems.This paper analyses the relation between design and learning activity to propose a learning model of design first. Then it presents the software architecture of a design agent with an inductive learning mechanism. Subsequently it introduces the knowledge representation and learning algorithms in a multi-agent design system. Finally, the paper examines the possible ways in which the proposed learning model and the software architecture can be placed in an industrial and engineering design context for developing application tools for design support.     

基于共轭增强策略的差分进化算法

为了平衡差分进化算法的全局探测能力和局部搜索能力,提出基于共轭增强策略的差分进化算法.首先,根据个体适应度信息设计基于轮盘赌的个体选择策略,选取适应值较差的个体组建子种群;然后,基于个体的时间和空间知识设计共轭增强方向,在不丧失全局探测能力的前提下实现子种群的局部增强,以提高算法的局部搜索能力;最后,18个标准测试函数的实验结果表明,所提算法在计算代价、可靠性及收敛速度方面均优于所介绍的主流改进差分进化算法和非差分进化算法.