STRATEGIC ENTREPRENEURSHIP: EXPLORATION, EXPLOITATION, AND AMBIDEXTERITY PRACTICES IN CRAFT BREWERIES FROM UBERLÂNDIA, MG

ABSTRACT Purpose: This paper aims to understand the configuration of strategic entrepreneurship practices of exploration, exploitation, and ambidexterity in craft breweries from Uberlândia, MG. Originality/value: Strategic entrepreneurship enables the comprehension of entrepreneurial phenomena from an organizational perspective. Furthermore, this research is conducted in an emergent industry in Brazil, with few studies in the field of management that consider the idiosyncrasies of craft breweries. Design/methodology/approach: We conducted a qualitative multicase study with three craft breweries from Uberlândia, MG. Twelve interviews were performed, and the set of empirical data collected were analyzed through narrative analysis technique. Findings: We identified several strategic entrepreneurship practices in the craft breweing sector. First, exploration practices were found in some radical innovations, such as the pioneering nature in the production of craft beers in Uberlândia, the creation of new products, and in the setting of new business models. Second, exploitation practices were also identified in incremental innovations that enabled business development. Hence, ambidexterity relied on the balance of exploration and exploitation practices, as innovative endeavors enabled the creation and development of new products and access to new markets. Thus, strategic entrepreneurship practices reflect the initiatives of entrepreneurial agents that seek to promote organizational innovations in terms of quality improvements, new production and marketing strategies, and the adoption of new technologies. Therefore, strategic entrepreneurship reflects and contributes to innovation possibilities, strategic renewals, and the competitiveness of craft breweries, revealing the analytical power of this theoretical approach for the study of entrepreneurial phenomena.

E xploration E xploitation Problem in Policy Based Deep Reinforcement Learning for Episodic and Continuous Environments

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.

Resource allocation procedures for unknown sales response functions with additive disturbances

We develop a modified exploration–exploitation algorithm which allocates a fixed resource (e.g., a fixed budget) to several units with the objective to attain maximum sales. This algorithm does not require knowledge of the form and the parameters of sales response functions and is able to cope with additive random disturbances. Note that additive random disturbances, as a rule, are a component of sales response functions estimated by econometric methods. We compare the developed algorithm to three rules of thumb which in practice are often used to solve this allocation problem. The comparison is based on a Monte Carlo simulation for 384 experimental constellations, which are obtained from four function types, four procedures (including our algorithm), similar/varied elasticities, similar/varied saturations, high/low budgets, and three disturbance levels. A statistical analysis of the simulation results shows that across a multi-period planning horizon the algorithm performs better than the rules of thumb considered with respect to two sales-related criteria.

Naïve individuals promote collective exploration in homing pigeons

Group-living animals that rely on stable foraging or migratory routes can develop behavioural traditions to pass route information down to inexperienced individuals. Striking a balance between exploitation of social information and exploration for better alternatives is essential to prevent the spread of maladaptive traditions. We investigated this balance during cumulative route development in the homing pigeon Columba livia. We quantified information transfer within pairs of birds in a transmission-chain experiment and determined how birds with different levels of experience contributed to the exploration–exploitation trade-off. Newly introduced naïve individuals were initially more likely to initiate exploration than experienced birds, but the pair soon settled into a pattern of alternating leadership with both birds contributing equally. Experimental pairs showed an oscillating pattern of exploration over generations that might facilitate the discovery of more efficient routes. Our results introduce a new perspective on the roles of leadership and information pooling in the context of collective learning.

Probabilistic Evaluation of the Exploration–Exploitation Balance during the Search, Using the Swap Operator, for Nonlinear Bijective S-Boxes, Resistant to Power Attacks

During the search for S-boxes resistant to Power Attacks, the S-box space has recently been divided into Hamming Weight classes, according to its theoretical resistance to these attacks using the metric variance of the confusion coefficient. This partition allows for reducing the size of the search space. The swap operator is frequently used when searching with a random selection of items to be exchanged. In this work, the theoretical probability of changing Hamming Weight class of the S-box is calculated when the swap operator is applied randomly in a permutation. The precision of these probabilities is confirmed experimentally. Its limit and a recursive formula are theoretically proved. It is shown that this operator changes classes with high probability, which favors the exploration of the Hamming Weight class of S-boxes space but dramatically reduces the exploitation within classes. These results are generalized, showing that the probability of moving within the same class is substantially reduced by applying two swaps. Based on these results, it is proposed to modify/improve the use of the swap operator, replacing its random application with the appropriate selection of the elements to be exchanged, which allows taking control of the balance between exploration and exploitation. The calculated probabilities show that the random application of the swap operator is inappropriate during the search for nonlinear S-boxes resistant to Power Attacks since the exploration may be inappropriate when the class is resistant to Differential Power Attack. It would be more convenient to search for nonlinear S-boxes within the class. This result provides new knowledge about the influence of this operator in the balance exploration–exploitation. It constitutes a valuable tool to improve the design of future algorithms for searching S-boxes with good cryptography properties. In a probabilistic way, our main theoretical result characterizes the influence of the swap operator in the exploration–exploitation balance during the search for S-boxes resistant to Power Attacks in the Hamming Weight class space. The main practical contribution consists of proposing modifications to the swap operator to control this balance better.

Embracing Paradox: TMT Paradoxical Processes as a Steppingstone between TMT Reflexivity and Organizational Ambidexterity

Top management teams (TMTs) are crucial in managing the ambidexterity paradox. This endeavour, however, generates cognitive conflicts. Surprisingly, this particular topic has received little attention within the ambidexterity literature. We aim to address this lacuna, and in doing so, extend the paradox literature and the emerging socio-cognitive perspective on ambidexterity. In our hypothesized mediation model, TMTs embrace the exploration-exploitation paradox through reflexivity, then overcome this paradox through paradoxical cognitive processing -the capacity to cognitively differentiate and integrate exploration and exploitation- which finally fosters ambidexterity. We test and find support for our hypotheses using a sample of 335 Dutch and German SMEs. We end with a discussion on how socio-cognitive factors influence the management of the ambidexterity paradox. In doing so, we refine scholarly understanding of motivating and enabling factors that allow TMTs to deal with the paradoxical tensions surrounding ambidexterity.