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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.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.

This paper identifies the development of and gaps in knowledge in various management disciplines, including international marketing in relation to political ties in emerging markets, based on a systematic review of the related literature. The paper develops a synthesized integrative framework and provides a research agenda and pathways for future research.

The study adopts the systematic literature review protocol to investigate the ways in which political ties have been examined in the management literature in various disciplines, such as international business, marketing, entrepreneurship, strategy, innovation, and organization. In total, 114 articles published in peer-reviewed journals from 2000 to 2022 were analyzed.

The authors believe that studying the impact of political ties on firm outcomes is timely and important as interest in this area of research is growing rapidly. The review reveals that the diverse conceptual and methodological approaches adopted in different management disciplines have resulted in inconclusive and mixed findings on the relationship between political ties and performance.

This is one of the few systematic literature reviews of political ties and firm performance in emerging markets. The authors clarify some of the ambiguities around the subject and offer a path forward for developing current understanding and insights. The study also highlights the major perspectives in management and clarify the similarities and differences in the conceptualization of political ties. In addition, the authors develop an integrative framework of the political ties–performance link in emerging markets.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This study explores the influence of cultural heritage tourism perception on China's tourism image. It analyzes the role of the spiritual bond established between overseas Chinese youth and the motherland during their visit to the cultural heritage sites in China. This study constructs a theoretical model with 350 overseas Chinese youth as samples based on the identity theory, Stimulus-Organism-Response (S-O-R) theory, and Howard-Sheth model. The results show that cultural heritage tourism perception directly and positively promotes cultural identity among overseas Chinese youth. It is also indirectly and positively associated with their cultural identity through enhancing the tourism image. Cultural intelligence plays a positive moderating role between cultural heritage tourism perception and cultural identity. The results provide significant implications for developing cultural heritage tourism and cultural communication.

This study aims to explore the effects of remote work on employee productivity and innovation and how these effects are moderated by knowledge sharing and digital business intensity.

The study draws on survey data from a random sample of 231 remote workers across the USA. The analysis and empirical validation of the research model used partial least square.

The results demonstrate a positive association between remote work and employee productivity. In addition, the findings present empirical support for hitherto anecdotal evidence regarding the impact of remote work on innovation. In particular, the study notes that knowledge sharing and digital business intensity amplified the positive relationship between remote work and employee productivity. The results further revealed that the positive link between remote work and innovation was stronger in the presence of knowledge sharing.

The study contributes to the ongoing inquiry into remote work by drawing on the knowledge-based view as an underlying lens to understand the consequence of remote work. Identifying knowledge sharing and digital business intensity as moderators of the linkage between remote work and employee productivity is an important contribution, especially when researchers and practitioners are trying to understand the business value of working remotely. Furthermore, to the best of the authors’ knowledge, this study is the first to identify knowledge sharing as a key mechanism that strengthens innovation outcomes in a remote work environment.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

This study aims to empirically examine the effect of employee commitment (EC) (affective, normative and continuance) and emotional intelligence (EI) on work engagement (WE) (vigor, dedication and absorption). Furthermore, the moderating effects of psychological capital (PsyCap) and EI on strengthening WE are investigated.

A carefully crafted survey instrument was developed, and data were collected from 511 employees working in the banking sector in southern India. First, the psychometric properties of the measures were checked. Second, the results were analyzed using Hayes’ PROCESS macros.

The results indicate EC and EI positively predict WE; EI moderates the relationship between EC and WE; and PsyCap (second moderator) moderates the relationship between EI (first moderator) and EC influencing WE.

This study’s limitations include the common method and social desirability biases inherent in any social science research based on surveys. However, the authors have taken adequate care to minimize these limitations. In addition, this study has several implications for practicing managers interested in enhancing WE.

The three-way interaction between EC, EI and PsyCap, which is investigated in this study, represents an original and unique contribution to this study. To the best of the authors’ knowledge, previous researchers have not done the moderated-moderation, especially in the context of a developing country such as India. This research advances the growing literature on EC and WE. Implications for theory and practice are discussed.