The Universidad Anáhuac México (Anahuac University) is a private educational institution of higher learning in Mexico, located on two campuses: North Campus, in Huixquilucan de Degollado South Campus, in Mexico CityThese two campuses were independent institutions until August, 11, 2016, when they merged to form the Universidad Anáhuac México. The university belongs to the Catholic religious congregation of the Legionaries of Christ. Anáhuac means "near the water" in Nahuatl, the ancient Aztec language spoken in Tenochtitlan, which used to be the biggest and most crowded place in Central America, what is now Mexico City, the home of the university.
This study proposes and validates an integrated model explaining how perceptual experiences - cognitive, emotional, functional, and hedonic - drive business tourists' symbolic experience in Santiago de Chile and Mexico City. Using qualitative interviews and a sample of 1801 executives, factor analysis and structural equation modelling were applied. Results demonstrate that cognition strongly influences both functional and hedonic experiences; emotion enhances them; functional and hedonic experiences explain the symbolic experience; and perceived quality, loyalty, and gender moderate the main pathways. This research holds significant relevance for urban tourism and tourist cities, as it offers public and private stakeholders concrete tools to design segmented marketing strategies, optimise infrastructure and services, and create multi-sensory experiences that generate an emotional and symbolic bond. This, in turn, fosters long-term loyalty and economic growth, solidifying these cities as important business destinations. In essence, the study enables tourist cities to evolve from mere transit points into destinations with deep and lasting meaning for their visitors. This study makes an original and significant contribution to urban tourism by proposing and validating an integrated model that unravels the causal mechanisms between perceptual experiences (cognitive, emotional, functional, hedonic) and the construction of symbolic value for business tourists. Its novelty lies in specifying how cognition and emotion shape these experiences, and how perceived quality and loyalty act as key moderators. Furthermore, it uncovers gender-based moderation and suggests underlying psychological mechanisms, thereby providing a profound understanding for urban destinations to evolve from mere transit points into places with enduring meaning for their visitors.
Purpose This study aims to examine how leadership development priorities systematically change across career stages and how these shifts can be translated into evidence-based, level-differentiated training design. Rather than treating leadership competencies as static skill requirements, the study conceptualizes competency importance ratings as signals of developmental readiness, informing when, for whom and in what proportions leadership competencies should be developed. Design/methodology/approach Using survey data from 615 managers across five business functions and three organizational levels (lower, middle and top management), the authors analyzed importance ratings for 73 global leadership competencies. Analysis of variance, multivariate analysis of variance and profile analyses were used to identify level-specific competency patterns and derive actionable training allocation principles. Findings Results reveal clear, systematic progressions in competency across career stages. Lower level managers prioritize operational and self-management competencies (e.g. stress management, problem-solving); middle managers exhibit a hybrid profile emphasizing people development and strategic alignment (e.g. coaching, empowering others); and top executives prioritize strategic leadership competencies (e.g. visioning, trust-building). Significant level effects were confirmed [F(2,612) = 47.83, p < 0.001, eta & sup2; = 0.16]. Across functions, 68.5% of competencies showed no functional variation, supporting integrated cross-functional program design with targeted specialization. Practical implications The findings translate into level-differentiated training architecture. Approximately 70% of leadership development content can be delivered in cross-functional formats, while 30% should be allocated to function-specific modules, with proportions adjusted by the management level. A three-tiered framework specifies the content focus, learning methods and delivery formats for each career stage, providing concrete guidance for human resource development (HRD) professionals and training designers. Originality/value This study advances leadership development theory by showing that competency salience serves as a developmental readiness signal rather than a simple skills gap. It introduces the concept of developmental competency re-weighting, the systematic shift in the relative importance of leadership competencies across career stages, and empirically grounds a 70-30 cross-functional training model, providing a practical bridge between competency research and evidence-based training system design.
Purpose The purpose of this paper is to propose a novel model, to forecast demand for a third-party service by using the Grey Systems Theory (GST) and Markov Chains, where its forecast error performance is evaluated through mean percentage error, mean absolute percentage error, where it exceed other models' performance accuracy such as autoregressive integrated moving average. Design/methodology/approach The model performs data characterization to qualify the GM (1,1) model and then applies a Markov Chain transition probability matrix and the GM (1,1) to forecast a time series with high degree of vagueness and imprecision and by providing a forecast kernel range ⊗Aˆ. Findings The MCGM (1,1) model integrates the GST GM (1,1) and Markov Chains in a novel hybrid model, that reduces the mathematical calculation complexity while provides practical forecast performance that exceed or it is equally good as other traditional methods. Research limitations/implications The model outperforms other non-stationary models but does not incorporate multiple variables and requires additional mathematical treatment or combined methods, where its data is stationary, seasonal or negative. Practical implications This model can provide an accurate forecast projection of supply chain demand, for instance the space required in a third-party logistics services provider in Tijuana Mexico, it can be used to forecast complex supply chain systems with minimum, incomplete or poor data, to solve several practical application problems to forecast demand and resources. Social implications The novel MCGM (1,1) hybrid forecasting model combines multiple predictive approaches, allowing for greater accuracy and adaptability. Its implementation enhances decision-making in key sectors such as health, energy and manufacturing, optimizing resources and reducing costs. This drives economic growth, increases sustainability and improves the quality of life in society. Originality/value There are no MCGM (1,1) works applied in supply chain and current works have not established the model characterization criteria. The result of this investigation represents a novel proposal to solve uncertain models with poor information and small amounts of data (>4 records), with higher forecast accuracy.
Background. Smart warehousing increasingly relies on digital twin technologies to enhance operational efficiency, real-time visibility, and decision-making in logistics systems. However, existing research primarily focuses on technological capabilities while paying limited attention to the organizational practices that shape successful implementation. Methods. This study aims to identify and prioritize the critical success factors (CSFs) for integrating digital twins into smart warehousing using the Practice-Based View (PBV) as the theoretical lens. Based on insights from prior research and expert validation, nine CSFs were identified and evaluated using the Best-Worst Method (BWM). Empirical input was obtained from six industry experts with experience in digital transformation, warehousing, and supply chain management. Results. The results indicate that collaborative learning, contextual training, and gamification elements emerge as the most influential critical success factors, highlighting the importance of organizational practices in supporting digital twin adoption in smart warehousing. Conclusions. By linking technological capabilities with organizational routines, the proposed framework provides both theoretical insights and practical guidance for implementing digital twins in smart warehouse environments.
