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Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

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.

Marketers increasingly use social media influencers to appeal to young consumers. This study aims to investigate the impact of the influencers’ country of origin (COO) on young customers' online brand advocacy (OBA). It also tests the mediating effects of trustworthiness and perceived homophily on these relationships.

Data were obtained from 197 Generation Z (Gen-Z) consumers of skin care products living in Trinidad and Tobago, using a quasi-experimental study and online self-administered questionnaires. The data were analyzed using confirmatory factor analysis, analysis of covariance and multiple regression analysis.

The findings support the role of an influencer's COO on young consumers' OBA and the mediating effects of influencers' trustworthiness and perceived homophily. The findings show that local influencers have a more substantial effect on OBA for Gen-Z customers. In addition, results show that both variables of trustworthiness and perceived homophily mediate the influencer’s COO and OBA relationship. The findings also show that local influencers benefit from higher levels of trustworthiness and greater perceived homophily than foreign ones.

The study fills the gap in the marketing literature by understanding how an influencer’s extrinsic characteristics, such as country of origin, can affect the marketing outcome of OBA among Gen-Z consumers in a small developing country. It also demonstrates the importance of perceived homophily and trustworthiness between influencers and audiences for marketing success.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Foreign subsidiaries of multinational enterprises (MNEs) operate in complex and competitive international environments, implement market and non-market strategies, manage resources and value-added activities and contribute to the overall performance of their parent firms. Thus, the research question on the determinants of MNE foreign subsidiaries’ performance is of interest to managers and academic researchers. The empirical literature has flourished over the recent decades; however, the domains are fragmented, and the findings are inclusive. The purpose of this study is to systematically review, analyse and synthesize the empirical articles in this area, identify research gaps and suggest a future research agenda.

This study uses the qualitative content analysis method in reviewing and analysing 150 articles published in 24 scholarly journals during the period 2000–2023.

The literature uses a variety of theoretical perspectives to examine the key determinants of subsidiary performance which can be grouped into six major domains, namely, home- and host country-level factors; distance between home and host countries; the characteristics of parent firms and of subsidiaries; and governance mechanisms (the establishment modes and ownership strategy, subsidiary autonomy and the use of home country expatriates for transferring knowledge from the headquarters and controlling foreign subsidiaries). A range of objective and subjective indicators are used to measure subsidiary performance. Yet, the research shows a lack of broader integration of theories and presents inconsistent theoretical predictions, inconclusive empirical findings and estimation bias, which hinder our understanding of how the determinants independently and jointly shape the performance of foreign subsidiaries.

This study provides a comprehensive, nuanced and systematic review that synthesizes and clarifies the determinants of subsidiary performance, offers deeper insights from both theoretical, methodological and empirical aspects and proposes some promising avenues for future research directions.

The purpose of this study is to analyze the relationship between academic writing and generative artificial intelligence (AI).

This paper is characterized as exploratory and descriptive, with a qualitative approach. Two approaches were used: the first, a narrative review of the literature with a systematic search from which a data collection stage was carried out using asynchronous interviews by means of an online questionnaire.

The results indicate that generative AI should be seen as a complementary tool for creative and critical academic writing. The data collected also highlighted issues related to academic dishonesty and the new type of plagiarism – plagiarism made possible by technologies – as well as issues of authorship and legitimacy of work carried out with AI and the loss of reflective and critical thinking and creativity.

The considerable increase in resources using AI in all dimensions of human life.

The impact that the use of generative AIs can have on the creation of knowledge and the dissemination of scientific research.

The impact that the use of generative AIs can have on the creation of knowledge and on the dissemination of scientific research.

The need for academia to anticipate the use of AI in academic writing and to incorporate its benefits into this process, especially considering researchers in training.

El objetivo de este artículo es analizar la relación entre la escritura académica y la inteligencia artificial generativa.

Este artículo se caracteriza por ser exploratorio y descriptivo, con un enfoque cualitativo. Se utilizaron dos enfoques: el primero, una revisión narrativa de la literatura con una búsqueda sistemática, a partir de la cual se llevó a cabo una etapa de recogida de datos mediante entrevistas asincrónicas a través de un cuestionario online.

Los resultados indican que la IA generativa debe considerarse una herramienta complementaria para la escritura académica creativa y crítica. Los datos recogidos también pusieron de manifiesto cuestiones relacionadas con la deshonestidad académica y el nuevo tipo de plagio, el plagio posibilitado por las tecnologías, así como cuestiones de autoría y legitimidad del trabajo realizado con Inteligencia Artificial, la pérdida de pensamiento reflexivo y crítico y la creatividad.

La necesidad de que el mundo académico se anticipe al uso de la IA en la escritura académica e incorpore sus ventajas a este proceso, considerando principalmente a los investigadores en formación.

El considerable aumento de los recursos que utilizan la IA en todas las dimensiones de la vida humana.

El impacto que puede tener el uso de las IA generativas en la creación de conocimiento y la difusión de la investigación científica.

El impacto que puede tener el uso de las IA generativas en la creación de conocimiento y la difusión de la investigación científica.

O objetivo deste artigo é analisar a relação entre a redação acadêmica e a inteligência artificial generativa.

Este artigo é caracterizado como exploratório e descritivo, com uma abordagem qualitativa. Foram usadas duas abordagens: a primeira, uma revisão narrativa da literatura com uma busca sistemática, a partir da qual foi realizada uma etapa de coleta de dados usando entrevistas assíncronas por meio de um questionário on-line.

Os resultados indicam que a IA generativa deve ser vista como uma ferramenta complementar para a redação acadêmica criativa e crítica. Os dados coletados também destacaram questões relacionadas à desonestidade acadêmica e ao novo tipo de plágio - o plágio possibilitado pelas tecnologias, bem como questões de autoria e legitimidade do trabalho realizado com a Inteligência Artificial, a perda do pensamento reflexivo e crítico e da criatividade.

A necessidade de a academia antecipar o uso da IA na redação acadêmica e incorporar seus benefícios nesse processo, considerando principalmente pesquisadores em formação.

O aumento considerável de recursos usando IA em todas as dimensões da vida humana.

O impacto que o uso de IAs generativas pode ter sobre a criação de conhecimento e a disseminação de pesquisas científicas.

O impacto que o uso de IAs geradoras pode ter na criação de conhecimento e na disseminação de pesquisas científicas.

This research examines the extent overall supervisor injustice reduces self-control resources while simultaneously enhancing anticipatory injustice beliefs. Minimized self-control resources, in turn, are expected to alter the anticipatory supervisor injustice beliefs’ impact on subsequent unjust encounters. Self-control resources therefore act as boundary conditions in the continued receipt of unjust treatment, potentially highlighting Pygmalion effects (self-fulfilling prophecies) connected with subordinates’ overall injustice judgments.

Using a two-survey, time-separated design, we test our hypothesized model in structural equation modeling (SEM) in MPlus with a sample of 163 US-employed adults recruited through online panel services. Main, interactive, and conditional indirect effects were used to examine our proposed relationships.

Empirical results showed that lower self-control resources and higher ASI beliefs resulted from subordinates holding high overall supervisor injustice judgments. Further, ASI beliefs were found to only explain the relationships of overall supervisor injustice with interpersonal injustice encounters, not informational justice encounters. This effect emerged when the subordinate’s self-control resources were low, not high.

This paper integrates fairness heuristics and ego depletion theories to highlight a previously understudied phenomenon–Pygmalion effects (e.g. expectations or anticipations becoming reality) pertaining to subordinates who hold high overall supervisor injustice judgments. The theoretical contribution and results offer a tantalizing lens regarding how anticipation may adversely affect future supervisor-subordinate interactions.

This research examines the long-held belief in the adaption-related literature that a firm’s ability to adapt organizational structure to changing environments is related to superior performance. We create and test a construct that reflects an organization’s ability to change structure, which we call Supply Chain Structural Adaptability (SCSA), rather than relying on proxies (e.g. structural form or organizational modularity) used in prior studies.

Quantitative data was collected from 218 firms to test our conceptual model.

We find that SCSA has a mixed effect on profitable growth under various environmental conditions.

We find evidence that refutes two widely held assumptions in organization research, namely, that structural form serves as a reasonable proxy for structural adaptability and that the benefits of adaptive capabilities always increase as environmental dynamism increases.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The COVID-19 pandemic has caused havoc on a global scale for supply chains, which put forward higher demand for organizations to reassess their global supply chain strategy and improve supply chain sustainability. The purpose of this paper is to understand how leader's paradoxical cognition affect supply chain sustainability.

This study conceptualizes a research model grounded in upper echelons theory and propose a chain-mediating model under the moderating effect of big data analytics. Using PLS-SEM method, we test the hypotheses using survey data collected from supply chain managers or leaders of the supply chain team from 193 firms.

The results indicate that supply chain ambidexterity and organizational learning play a mediating role in the relationship between leaders' paradoxical cognition on supply chain sustainability, respectively, and these two variables have a chain-mediating role in the relationship above. In addition, the big data analytics negatively moderates the relationship between leader's paradoxical cognition and organizational learning, and further moderates our chain mediating model.

This research initiatively focuses on the micro-foundations of supply chain sustainability from managerial cognition and firstly provides empirical evidence about the impact of leader's paradoxical cognition on supply chain sustainability.