Abstract

Abstract
As competitive and cooperative dynamics gain prominence in games, they present unique opportunities to study player behavior. This paper explores the orientations of different player types, as categorized by Bartles Taxonomy, through the lens of a Game With A Purpose (GWAP) called BartleZ. Bartle's Taxonomy identifies four distinct player types Achievers, Explorers, Socializers, and Killers. This study delves into how these different types approach competitive and cooperative gameplay, through structured dilemmas in BartleZ. Results with 45 participants, reveal that player orientations significantly influence engagement and decision-making. Achievers balanced both strategies; Explorers favored cooperation; Socializers consistently chose cooperation; and Killers preferred competition but adapted in some contexts. Overall, players leaned toward cooperation early on, with a shift toward competition as complexity increased. Our findings pinpoint the importance of tailoring GWAP mechanics with diverse player motivations, enhancing both engagement and problem-solving effectiveness.

Abstract

Abstract
ABSTRACT SMEs in developing economies operate under persistently volatile environments where economic instability, regulatory uncertainte and technological disruptions threaten their survival. Here, sustainability shifts from long-term environmental or socioeconomic performance to strategic resilience. In this study, we investigate how dynamic capabilities condition the effect of business environmental forces on SME sustainability in Nigeria. Grounded in contingency and dynamic capability theory, this study adopts a quantitative, cross-sectional survey design using data from 285 Nigerian SMEs. It examines the direct effects of economic, legal and technological environmental forces, as well as the moderating roles of sensing and seizing, and learning and reconfiguration capabilities, on SME strategic resilience using PLS-SEM. The results show that economic, legal and technological turbulence significantly affect SME strategic resilience, with legal turbulence emerging as the strongest constraint. Findings further reveal that dynamic capabilities–sensing and seizing, learning and reconfiguration–significantly moderate the effect of environmental turbulence on SME strategic resilience and strengthen SME capacity in absorbing shocks, reconfiguring resources and sustaining operations under disruptions. This study contributes by reframing SME sustainability as strategic resilience amid environmental turbulence, differentiating external pressures into economic, legal and technological dimensions, and showing how dynamic capability bundles condition SME strategic resilience in a highly volatile developing-economy context. This study offers insights relevant to other emerging economies characterised by institutional instability, policy unpredictability and uneven technological development. It also broadens understanding of contingency and dynamic capability theory in developing economies and positions dynamic capabilities as vital for resilience-building, not just competitive advantage.

Abstract
Counterfactual explanations (CFs) help users understand and act on black-box machine learning decisions by suggesting minimal changes to achieve a desired outcome. However, existing methods often ignore individual feasibility, leading to unrealistic or unactionable recommendations. We propose a personalized CF generation method based on cluster-specific fine-tuning of Generative Adversarial Networks (GANs). By grouping users with similar behavior and constraints, we adapt immutable features and cost weights per cluster, allowing GANs to generate more actionable and user-aligned counterfactuals. Experiments on the German Credit dataset show that our approach achieves a 6x improvement in prediction gain and a 30% reduction in sparsity compared to a baseline CounterGAN, while maintaining plausibility and acceptable latency for online use.

Abstract
Effective warehouse management plays a pivotal role in optimizing supply chain performance, particularly in high-demand, time-sensitive environments. This study introduces a simulation-based decision support system designed to improve the management of Picking-By-Line (PBL) operations in cross-docking distribution centres. Developed in FlexSim and calibrated with empirical data from an industrial case study, the model replicates real-world warehouse conditions and is validated against observed operational performance. The tool supports warehouse managers in evaluating and comparing operational strategies, such as dynamic storage allocation policies and picker routing constraints, with the goal of reducing operator travel distances, mitigating congestion, and enhancing overall efficiency. A key contribution of this work is the integration of congestion-sensitive performance indicators that allow for a detailed analysis of the trade-offs between travel efficiency and localized congestion-an aspect often overlooked in traditional optimization methods. This study demonstrates the value of simulation as a scalable and realistic decision-support tool for optimizing PBL operations in complex and variable environments where human movement is a major cost and performance driver. The proposed tool bridges the gap between theoretical modelling and practical implementation, offering actionable insights for warehouse layout, space utilization, and resource allocation.