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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 study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

The study aims is to explore the cointegration level among major Asian stock indices from pre- COVID-19 to post COVID-19 times.

Johansen cointegration test is employed to know the long run relationship among the stock market indices of Hong Kong, Indonesia, Malaysia, Korea, India, Japan, China, Taiwan, Israel and South Korea. The empirical testing was done to analyze whether any significant change has been induced by the COVID-19 pandemic on the cointegrating relationship of the selected markets or not. Through statistics of trace test and maximum eigen value, total number of cointegrating equations present among all the indices during different study periods were analyzed.

The presence of cointegration was found during all the sample periods and the findings suggests that the selected stock markets are associated with each other in general. During COVID-19 crisis period the cointegration level was reduced and again it regained its original level in the next year and again reduced in the subsequent next year. So, the cointegrating relationship among selected stock market indices remains dynamic and no evidence of impact of COVID-19 on this dynamism was found.

The study has explored the level of cointegration among the major stock indices of Asian nations in the pre, during, post-crisis and the most recent periods. The interconnectedness of the stock markets during the COVID-19 times has been compared with similar periods in different years immediately preceding and succeeding the COVID-19 times which has not been done in any of the existing study.

The purpose of this systematic literature review (SLR) is to examine the theoretical landscape of knowledge hiding (KH) research, identifies prevailing theories, the different ways KH is understood within these theories and the underlying assumptions that shape these views. Based on this, ideas for further research are derived to advance the theoretical basis of KH studies.

Using a theory-based SLR, the authors analysed 170 scientific papers from Scopus and Web of Science. This involved thematic analysis to categorise theories frequently applied in KH research and a detailed examination to link core assumptions to these theoretical perspectives.

The analysis revealed a reliance on 86 distinct theories, with a notable emphasis on social exchange theory and conservation of resources theory. KH is predominantly conceptualised as a negative, objective, reactive and relational behaviour rooted in social reciprocity and resource conservation. The review uncovers the multifaceted nature of KH, challenging the field to incorporate broader theoretical views that encompass positive aspects, subjective experiences, strategic intentions and non-relational determinants of KH.

To the best of the authors’ knowledge, this is the first study to systematically map and analyse the theoretical underpinnings of KH research. It offers a unique contribution by categorising the diverse theories applied in KH studies and explicitly linking these theories to their inherent assumptions about KH. This approach provides a comprehensive overview that not only identifies gaps in the current research landscape but also proposes alternative theoretical perspectives for exploring KH, thereby setting a new direction for future studies in this field.

This review analyzes data from research articles published from 2010 to 2022 related to workplace ostracism which include theoretical or empirical practical implications. The primary motive of this review is to identify main themes of practical implications relevant to workplace ostracism.

In total, 86 research articles published in 56 journals were retrieved from six well-known management science databases, namely, Science Direct, Emerald Online, Springer Link, Taylor and Francis, Wiley and Sage. The affinity diagram was utilized to organize the practical implications of the studies into meaningful themes. Moreover, in order to prioritize the main themes, the Pareto diagram was utilized.

Eleven themes have been used to categorize the practical implications of the reviewed articles, demonstrating various human resource (HR) interventions for avoiding or limiting the feelings of ostracism at work. Specifically, they focus on training and development, culture, formal and informal meetings, interpersonal relationships, task interdependence, monitoring, trust and transparency, proper channel, job autonomy and individual characteristics.

While many systematic and traditional literature reviews have been undertaken in an attempt to thoroughly organize extant literature on various aspects of workplace ostracism, no study has addressed the main themes of practical implications vis-à-vis employees experiencing workplace ostracism. Moreover, the majority of them are apparently out of date (prior to 2019), and there is just one study undertaken up to 2020.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

The purpose of this study is to assess the spatiotemporal patterns of future meteorological drought in the Yellow River Basin under different representative concentration pathway (RCP) scenarios.

Delta method is used to process the future climate data of the global climate models, then analyzed the spatiotemporal variation trend of drought in the Yellow River Basin based on standardized precipitation evaporation index (SPEI) under four RCP scenarios.

This research was funded by the National Natural Science Foundation of China (41901239), Soft Science Research Project of Henan Province (212400410077, 192400410085), the National Key Research and Development Program of China (2016YFA0602703), China Postdoctoral Science Foundation (2018M640670) and the special fund of top talents in Henan Agricultural University (30501031).

This study can provide support for future meteorological drought management and prevention in the Yellow River Basin and provide a theoretical basis for water resources management.

This study aims to investigate the “digitalization paradox” in manufacturing digital transformation, where significant investments in digital technology may not necessarily lead to increased returns. Specifically, it explores the intricate relationship between digital technology convergence, financial performance, productivity and technological innovation in listed Chinese manufacturing firms, drawing upon theories of digital innovation and knowledge networks.

Using a large panel data from 747 listed firms in China’s manufacturing sector and their 428,927 patents spanning from 2013 to 2022, this research first quantifies manufacturing firm-level digital technology convergence through patent network analysis. Furthermore, this study employs hierarchical regression analysis and the instrumental variable method to investigate the curvilinear relationship between digital technology convergence and financial performance. Furthermore, the moderating role of firms’ productivity and technological innovation is tested.

Three types of firm-level digital technology convergence (DTC) are delineated and quantified: local authority in digital convergence (DegreeDTC), convergence with heterogeneous digital knowledge (BetweenessDTC) and shortest-path convergence with digital technologies (ClosenessDTC, where a higher value signifies a more conservative and shorter path in adopting digital technologies). Network visualization shows that manufacturing firms' DTC has consistently increased over time. Contrary to traditional assumptions, our research reveals a U-shaped relationship between DTC (specifically, DegreeDTC and BetweenessDTC) and financial performance. This relationship is characterized by a negative correlation at lower levels and a positive one at higher levels. The joint effect of firms’ productivity and technological innovation significantly strengthens this relationship. These findings are robust across a series of robustness checks.

Our findings offer practical insights for both managers and policymakers. We recommend a balanced approach to digital innovation management within the technology convergence paradigm. Manufacturing firms can generate economic value by strategically choosing to either shrink or expand their digital technology application areas, thereby reducing uncertainties related to emerging convergent businesses. Additionally, the study underscores the synergistic strategy of combining innovation with productivity. Within the DTC business context, integrating productivity with technological innovation not only enhances cost flexibility but also improves problem-solution matching, ultimately amplifying synergistic benefits.

To the best of our knowledge, this is the first study to apply a digital technology co-occurrence network to unveil nuanced relationships in “DTC – finance performance” within the manufacturing sector. It challenges conventional thinking regarding the common positive effect of digital innovation and technological convergence. This study provides a comprehensive analysis of DTC, financial performance, productivity and technological innovation dynamics, as well as offers managerial implications for managers and policymakers.

• (1)

  We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

• (2)

  A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

• (3)

  The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

• (4)

  We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

Switching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.

It was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.

The simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.

This study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.