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The purpose of this study is to investigate managers’ decision-making processes when evaluating suggestions provided by human collaborators or artificial intelligence (AI) systems. We employed the framework of Social Comparison Theory (SCT) in the business context to examine the influence of varying social comparison orientation levels on managers’ willingness to accept advice in their organization.

A survey was conducted on a sample of 192 US managers, in which we carried out an experiment manipulating the source type (human vs AI) and assessing the potential moderating role of social comparison orientation. Results were analyzed using a moderation model by Hayes (2013).

Despite the growing consideration gained by AI systems, results showed a discernible preference for human-generated advice over those originating from Artificial Intelligence (AI) sources. Moreover, the moderation analysis indicated how low levels of social comparison orientation may lead managers to be more willing to accept advice from AI.

This study contributes to the current understanding of the interplay between social comparison orientation and managerial decision-making. Based on the results of this preliminary study that used a scenario-based experiment, future research could try to expand these findings by examining managerial behavior in a natural context using field experiments, or multiple case studies.

This is among the first studies that examine AI adoption in the organizational context, showing how AI may be used by managers to evade comparison among peers or other experts, thereby illuminating the role of individual factors in affecting managers’ decision-making.

This paper aims to identify the success factors (SFs) for total quality management (TQM) implementation in automotive spare parts companies to improve their business performance. It also intends to rank these factors in a hierarchical structure in descending order of their criticality.

In this study, a significant number of automotive spare parts companies were extensively surveyed to ascertain the contributions made by various factors toward the successful deployment of TQM practices. The collective and individual evaluation and ranking of the SFs were determined using the analytical hierarchy process (AHP) approach to develop the framework based on the prioritisation of the identified SFs.

The findings of the study show that five success factors, namely, internal environment, top management involvement, process management, supplier management and external environment, were ranked as critical factors with a total weight of 49.2%. Nine success factors, namely, employee training, teamwork, customer satisfaction, continuous improvement, communications, using new technologies, zero-defect processes, employee empowerment and benchmarking, were ranked as important with a total weight of 39.1%. The last five success factors, namely, strategic planning, quality policy, employee satisfaction, self-assessment and cost of quality, were ranked as minor factors with a total weight of 11.7%.

The current study adds to the existing body of knowledge for scholars and practitioners of TQM by specifically focusing on identifying and categorising the critical SFs for TQM implementation. The 19 categorised critical SFs have been used to construct a framework for TQM implementation in the Palestinian automotive spare parts companies. Such a framework would offer a comprehensive overview of the SFs, their categories, significance and priorities within a TQM environment in the automotive spare parts companies.

The current study heeds prior calls to test the hypothesis that perceptions of inclusion mediate the relationship between inclusive practices and employee outcomes.

Using responses from 3,367 employees drawn from three time-separated surveys administered between 2020 and 2022 within a large retail fashion chain, structural equation modeling (SEM) was conducted to examine the mediating role of perceptions of inclusion connecting inclusion’s antecedents and outcomes. Exploratory multigroup SEM and relative weights analysis was conducted to examine the relative contribution of each antecedent of perceptions of inclusion across racial/ethnic groups.

We identified manager support, career support, organizational support, transparency, and employee recognition as antecedents of perceptions of inclusion, as well as work engagement, organizational commitment, and intent to stay as outcomes of perceptions of inclusion. Recognition indirectly relates to employee outcomes via perceptions of inclusion, but we did not find evidence of mediation for the other antecedents. Exploratory analyses suggest that career support and employee recognition are the most predictive antecedents of perceptions of inclusion overall. However, there are racial/ethnic group differences regarding which inclusive practices most contribute to perceptions of inclusion.

Results uncover several directions for future research and suggest that to truly make employees feel both included and unique at work, organizations should focus on supporting employees' career development goals and recognizing their valuable contributions.

The objective of this study is to investigate the concept of “earthquake resistance” in high-rise buildings and assess the current level of structural strength in the areas where these structures are situated. The study aims to identify and implement necessary measures to enhance resilience in these areas. A comprehensive literature review was conducted to develop a conceptual framework focusing on earthquake resistance's meanings, stages and physical elements to achieve these goals.

This study focuses on Istanbul, a city known for its high earthquake risk, specifically targeting the Atasehir district. The research utilizes the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to evaluate urban resilience parameters. Additionally, the Fuzzy TOPSIS (Preference Ranking Technique by Similarity) method is employed to analyze the location of five buildings in Atasehir, using criterion weights derived from this methodology.

The findings indicate that resilience varies depending on the distance of the buffers. Moreover, the amount and quality of urban equipment in the study areas have a significant impact on the earthquake resistance level of the surrounding areas where high-rise buildings are situated. Building upon this analysis, the study suggests the implementation of measures aimed at augmenting the quantity and quality of urban facilities in the study areas, consequently enhancing urban resilience.

The originality and value of this study lie in its examination of seismic resilience within the context of high-rise buildings and the identification of necessary measures to increase resilience in areas where these structures are prevalent. By focusing on Istanbul, a city with a high earthquake risk, and specifically selecting the Atasehir district as the study area, this research provides a comprehensive conceptual framework for understanding urban resilience and its physical components. Moreover, the study offers a fresh perspective on urban resilience by highlighting the influence of tall buildings on the surrounding areas. Ultimately, it provides practical recommendations for architects, urban planners and other stakeholders to improve regional earthquake resilience.

The purpose of this study is to compare measurement scales of sports fans’ motivations applied to women’s football.

A survey research approach was used to collect 574 valid responses from participants in Brazil and the USA. Three prominent scales – Sport Interest Inventory (SII), Sport Fan Motivation Scale (SFMS) and Motivation Scale for Sport Consumption (MSSC) are were compared using the structural equation modeling technique.

The results indicate that the SII scale demonstrates superior predictive power for variables such as “purchase intention,” “electronic word of mouth,” “identification as a fan” and “interest in women’s football” compared to the SFMS and MSSC scales. The primary motivation among followers and spectators of women’s football in the study was “supporting women’s opportunities” in sport.

While the study is grounded in the most relevant scales pertinent to the theme, the limited academic production on the subject hinders direct comparisons with prior research.

Leveraging the insights from the SII scale, football team managers can refine their marketing strategies by understanding the primary motivations driving women’s football consumption. This knowledge can inform targeted efforts to enhance women’s football consumption, subsequently expanding opportunities for women in the sport.

This study provides valuable information that can inform initiatives aimed at boosting women’s soccer consumption, thereby contributing to increased opportunities for women in the sport.

To the best of the authors’ knowledge, this study represents the first attempt to compare scales in the specific context of women’s soccer, contributing with a unique perspective to the development of women’s sports.

Low-income consumers’ perceptions of access and inclusion in financial services, remain underresearched. To fill this gap, the purpose of this study, is to investigate elements of low-income consumers’ informal and formal financial service experiences, from their personal experience.

Mixed methods using data collected from low-income consumers in Latin America, reveal a spectrum of consumer perceptions making up access, inclusion and social dependence within financial service experiences. Scales, grounded in the consumer experience, are developed, validated and used to test a model of consumers’ service inclusivity perceptions.

Service costs, information and documentation difficulty, convenience and social dynamics influence low-income consumers’ perceptions of financial service inclusivity.

Analysis reveals differentiation in the impact of aspects of low-income consumers’ experiences between formal and informal financial services. Working directly with this unique population exposes the nuance of their financial service experiences.

This research provides a more holistic perspective on low-income consumers’ financial service experience and provides contextually relevant scales with robust psychometric properties. Services marketers can use this research to inform design and evaluation of financial service offerings for low-income consumers.

This research contributes to study of the wellbeing of low-income consumers by providing understanding of their financial service experiences from their point-of-view and providing contextually-relevant, empirically validated tools for future inquiry.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

This paper aims to propose an instrument for measuring social commerce among entrepreneurs from the USA, Mexico and Colombia.

The methodological design was quantitative, explanatory, observational and transversal, where a sample of 1,644 entrepreneurs from the USA (n = 525), Mexico (n = 608) and Colombia (n = 510) was obtained. For the validation and reliability of the instruments, a structural equation model (SEM) was developed for the three studies carried out. Regarding the goodness and adjustment indices of the SEM in the three countries, they turned out to be acceptable.

Through the results obtained in the three studies carried out, it has been verified that the instrument of social commerce in its two factors: Marketing and Market Participation, has sufficient validity and statistical reliability. Likewise, it has been verified that both the manifest variables and the latent variables of the construct show a significant relationship between the three studies carried out in different populations.

The findings obtained in the presented study contribute to entrepreneurs, leaders and managers of the business sector to improve the entrepreneurial project through marketing and market participation of the product and service, as well as the business unit that seeks to position itself in the market. Likewise, it helps entrepreneurs to understand analytically and systematically the constructs that social commerce is made up of, which will help entrepreneurial leaders reduce or control their risk when considering social commerce in their entrepreneurship, achieving the success of the entrepreneurial project through its positioning in the market considering marketing and market participation as main factors of social commerce.

The findings are relevant and of great value to the literature because at present there is not enough research that is focused on the variables analysed related to social commerce in the contexts of the USA, Mexico and Colombia. The relevance of the present scale in comparison to others proposed by the literature is that the proposed scale is focused on entrepreneurs who seek to be more successful through the positioning of their business unit, product or service in the market through the market participation. It is achieved through marketing strategies. Another contribution provided by the present study lies in the methodological robustness of the scale and its analysis, comparing it with studies in three leading entrepreneurship countries in Latin America, comparing the validity and reliability as well as the goodness of fit indices of the proposed model in each of the studies. They were significant and very similar, so the proposed scale is of great value and usefulness in the literature.

The purpose of this research is to establish a robust numerical framework for the calibration of macroscopic constitutive parameters, based on the analysis of polycrystalline RVEs with computational homogenisation.

This framework is composed of four building-blocks: (1) the multi-scale model, consisting of polycrystalline RVEs, where the grains are modelled with anisotropic crystal plasticity, and computational homogenisation to link the scales, (2) a set of loading cases to generate the reference responses, (3) the von Mises elasto-plastic model to be calibrated, and (4) the optimisation algorithms to solve the inverse identification problem. Several optimisation algorithms are assessed through a reference identification problem. Thereafter, different calibration strategies are tested. The accuracy of the calibrated models is evaluated by comparing their results against an FE2 model and experimental data.

In the initial tests, the LIPO optimiser performs the best. Good results accuracy is obtained with the calibrated constitutive models. The computing time needed by the FE2 simulations is 5 orders of magnitude larger, compared to the standard macroscopic simulations, demonstrating how this framework is suitable to obtain efficient micro-mechanics-informed constitutive models.

This contribution proposes a numerical framework, based on FE2 and macro-scale single element simulations, where the calibration of constitutive laws is informed by multi-scale analysis. The most efficient combination of optimisation algorithm and definition of the objective function is studied, and the robustness of the proposed approach is demonstrated by validation with both numerical and experimental data.

The main goal of this research is to analyze and assess the barriers to Digital Transformation (DT) of the healthcare Supply Chain (SC) in developing countries and evaluate strategies to overcome such barriers. The DT has been related not only to the development of SC performance but also to the expansion of its resilience capabilities in a healthcare setting to overcome the results of unavoidable risk events and return to its previous or new state, which has at least the same or better conditions, after the occurrence of an unpredicted event.

Assessment of the barriers and determination of the importance or effectiveness of proposed strategies to handle these barriers are conducted through a hybrid Multi-Criteria-Decision-Making method consisting of an Analytical Hierarchy Process and multi-attributive border approximation area comparison (MABAC) under picture fuzzy (PiF) environment.

Based on the outcomes of the research, the most important barriers for DT in healthcare SC functions were the organizational and technological issues, including Low support from top managers (O1), Lack of digital culture (O2), and Info/data safety and privacy (T4). To handle these barriers, the prior strategy was selected as “Making breakthroughs and incentives of top managers for the DT in the industry (ST3)”.

The study may be extended to include developed countries and or experts from developed countries. Through this, the study will be generalized to a global level. The most common qualitative criteria, which include subjectivity, are considered. The research may be enhanced by including quantitative criteria in the decision-making process. The barriers related to DT for healthcare SC were considered. Thus, the study may be detailed by looking at the problem in terms of operational failure and determining the digital technology that can overcome such operational failures. The precedence or importance of any barrier may fluctuate with the upgradation of technological tools and improvements.

In terms of practical contribution, this research provides real-world suggestions to facilitate digital transformation in their SCs. Specifically, this study increases the awareness of healthcare and other industries' managers about obstacles that prevents digital transformation in SC and provides managerial path to relieve the effects of barriers.

To the authors' best knowledge, this is the first study to examine barriers of DT required for the promotion of resilient healthcare SCs in this concept. In addition, no previous research applied PiFSs-based AHP and MABAC integration to assess barriers and develop strategies for the DT of healthcare SC. The findings may be references for governmental institutions, policymakers, decision-makers, and stakeholders to develop proper strategies for a successful DT in healthcare SC resilience and the healthcare industry.