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Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

This paper takes the manufacturing cluster supply chain as the research object and explores the evaluation and enhancement strategy of manufacturing cluster supply chain synergy. The purpose of this study was to (1) analyze the mechanism of manufacturing cluster supply chain synergy; (2) construct manufacturing cluster supply chain synergy evaluation model; (3) algorithm realization of manufacturing cluster supply chain synergy evaluation and (4) propose manufacturing cluster-based supply chain synergy enhancement strategy.

Breaking through the limitations of traditional manufacturing cluster supply chain synergy evaluation, we take horizontal synergy and vertical synergy as coupled synergy subsystems, use the complex system synergy model to explore the horizontal synergy between core enterprises and cluster enterprises and the vertical synergy of supply chain enterprises and use the coupling coordination model to construct the coupled synergy evaluation model of manufacturing cluster supply chain, which is an innovation of the evaluation perspective of previous cluster supply chain synergy and also an enrichment and supplementation of the evaluation methodology. This is not only the innovation of the evaluation perspective but also the enrichment and supplementation of the evaluation method.

Using Python software to conduct empirical analysis on the evaluation model, the research shows that the horizontal and vertical synergies of the manufacturing cluster supply chain interact with each other and jointly affect the coupling synergy. On this basis, targeted strategies are proposed to enhance the synergy of the manufacturing cluster supply chain.

This study takes manufacturers, suppliers and sellers in the three-level supply chain as the research object and does not consider the synergistic evaluation between distributors and consumers in the supply chain, which can be further explored in this direction in the future.

Advanced manufacturing clusters, as the main force of manufacturing development, and the synergistic development of supply chain are one of the important driving forces for the high-quality development of China’s manufacturing industry. As a new type of network organization coupling industrial clusters and supply chains, cluster supply chain is conducive not only to improving the competitiveness of cluster supply chains but also to upgrading cluster supply chains through horizontal synergy within the cluster and vertical synergy in the supply chain.

Research can help accelerate the transformation and upgrading of clustered supply chains in the manufacturing industry, promote high-quality development of the manufacturing industry and accelerate the rise of the global value chain position of the manufacturing industry.

(1) Innovation of research perspective. Starting from two perspectives of horizontal synergy and vertical synergy, we take a core enterprise in the cluster supply chain as the starting point, horizontally explore the main enterprises of the cluster as the research object of horizontal synergy, vertically explore the upstream and downstream enterprises of the supply chain as the research object of vertical synergy and explore the coupling synergy of cluster supply chain as two subsystems, which provides new perspectives of evaluation of the degree of synergy and synergy evaluation. (2) Innovation of research content. Nine manufacturing clusters are selected as research samples, and through data collection and model analysis, it is verified that the evaluation model and implementation algorithm designed in this paper have strong practicability, which not only provides methodological reference for the evaluation of manufacturing cluster-type supply chain synergy but also reduces the loss caused by the instability of clusters and supply chains and then provides a theoretical basis for improving the overall performance of cluster-type supply chains.

Product color names related to a consumption setting are commonly used in advertising to persuade. This study aims to use consumption imagery fluency as an underlying mechanism for assessing how such a naming tactic impacts product evaluation.

Three between-subjects experiments examine how product evaluation, in response to the use of color names containing consumption situation information, varies as a function of their accessibility (Study 1), and also test the role of a naming explanation (Study 2). How readily a consumer takes in consumption imagery is evaluated as a mediator. The studies further check if color attribute serves as a moderator of such color naming effect and that the naming factor contributes to consumption imagery fluency directly or indirectly alters such through their impact on comprehension fluency (Study 3).

Marketing products with color names related to the consumption setting is more effective than using generic names. Consumption imagery fluency mediates the results. This positive outcome is reduced when color names are less accessible. Fortunately, including an explanation to facilitate reasoning for product color names is helpful to reverse this disadvantage. The same patterns are not evident for highly accessible names. In addition, the effectiveness of consumption situation-related color names is restricted to the circumstance of color attribute as secondary, as opposed to primary. Furthermore, naming factors influence the ease of consumption of imagery whether or not facilitated by comprehension fluency.

This research provides evidence of consumers’ responses to product color naming that involves consumption situations and identifies consumption imagery fluency as a potential means for mediating the studied effect.

Naming a product color in consumption situation-related terms triggers consumption imagery, driving evaluation when color is the secondary attribute of a product.

This research contributes to understanding the influence of naming a product’s color in promotional communication and correlates to productive tactics for advertising messages.

This study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.

The evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.

The study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.

This research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.

The purpose of this paper is to investigate perceptions of primary education (PE) teachers with respect to evaluation within the framework of Total Quality Management (TQM) and their readiness to engage in the process of quality improvement. Within this context, a relevant measurement instrument is developed and empirically validated.

The instrument developed herein included 30 items distributed across seven dimensions. It was disseminated among public PE units in Greece, yielding a substantial 2,088 responses. Several tests were performed, including principal component analysis, confirmatory factor analysis and structural equation modelling.

The findings supported the validity and reliability of the instrument, confirming that the proposed measures encapsulate actual dimensions that are most suitable for exploring evaluation perception and readiness in the context of TQM in PE. The study explored several correlations between the validated dimensions and independent variables such as gender, level of education, years of teaching experience and age. These analyses yielded additional valuable insights, enriching the depth of understanding provided by the present research.

The fields of TQM and PE lack empirical evidence. However, this study offers valuable insights into teachers’ perceptions of evaluation and broader quality improvement efforts. This can help school managers, policymakers and practitioners refine their implementation strategies for educational excellence.

To the best of the authors’ knowledge, this study is the first to focus on the examination of evaluation as a key aspect of TQM within the context of PE. Through the development and validation of a real-time measurement instrument, it bridges a significant research gap, providing practitioners and researchers with a vital tool to understand and improve educators’ evaluation perceptions, enabling targeted interventions for enhanced performance.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

In today’s digital economy, it is very important to cultivate digital professionals with advanced interdisciplinary skills. The purpose of this paper is that universities play a vital role in this effort, and research teams need to use the synergistic effect of various educational methods to improve the quality and efficiency of personnel training. For these teams, a powerful evaluation mechanism is very important to improve their innovation ability and the overall level of talents they cultivate. The policy of “selecting the best through public bidding” not only meets the multi-dimensional evaluation needs of contemporary research, but also conforms to the current atmosphere of evaluating scientific and technological talents.

Nonetheless, since its adoption, several challenges have emerged, including flawed project management systems, a mismatch between listed needs and actual core technological needs and a low rate of conversion of scientific achievements into practical outcomes. These issues are often traced back to overly simplistic evaluation methods for research teams. This paper reviews the literature on the “Open Bidding for Selecting the Best Candidates” policy and related evaluation mechanisms for research teams, identifying methodological shortcomings, a gap in exploring team collaboration and an oversight in team selection criteria.

It proposes a theoretical framework for the evaluation and selection mechanisms of research teams under the “Open Bidding for Selecting the Best Candidates” model, offering a solid foundation for further in-depth studies in this area.

Research progress on the Evaluation Mechanism of Scientific Research Teams in the Digital Economy Era from the Perspective of “Open Bidding for Selecting the Best Candidates.”

This study examined the relationship between pedagogical self-efficacy and student course evaluations among an international sample of management education faculty. We also investigated gender’s moderating role in this relationship and its impact on the development of pedagogical self-efficacy.

We conducted semi-structured interviews with 20 professors at an international business school, identifying three subdomains of pedagogical self-efficacy: course design, classroom management, and feedback provision. We designed a 25-question faculty survey to measure pedagogical self-efficacy, administered it to 84 faculty members, and analyzed the data alongside 20,000 student course evaluations.

All three pedagogical self-efficacy domains significantly predicted student course evaluations. The self-efficacy of female faculty had a positive relationship with course evaluations across all subdomains. In contrast, the self-efficacy of male faculty had a negative relationship with course evaluations on the course design subdomain. Student evaluations of courses taught by women were 10% lower than those taught by males and male faculty had significantly higher self-efficacy ratings than their female counterparts.

The results suggest that interventions designed to boost pedagogical self-efficacy can enhance student learning, irrespective of faculty gender. However, given biases in how students perceive female faculty, it is likely that female and male faculty members develop self-efficacy differently.

This study is the first to examine how pedagogical self-efficacy affects course evaluations, focusing on gender as a potential moderator. We also added an international higher education perspective to self-efficacy theories.

This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.