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This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This paper aims to investigate the influence of four motivational elements (i.e. utilitarian, hedonic, social and escapism motivations) on the propensity of customers to utilize a metaverse hotel, as well as whether age, gender and mobility disability play substantial moderating roles in these relationships.

Data was gathered from 843 US residents who had experienced a hotel stay within the past two years. We tested the hypotheses using structural equation modeling and multigroup analysis.

The findings indicated that, in both age and gender groups, hedonic, social and escapism motivations had significant effects on intentions to use a metaverse hotel, whereas utilitarian motivation did not. The influence of escapism motivation on customers’ usage intentions was significantly more pronounced for males than females, suggesting the moderating role of gender in this relationship. Hedonic and social motivations exerted significant effects on usage intentions in both mobility disability and non-disability groups. The relationship between escapism motivation and intentions to use was significant for the non-disability group only, suggesting the moderating role of disability in this association.

This research provides recommendations for hotel managers and technology providers aiming to enhance the adoption of metaverse hotels by customers and to augment the worth of this technology.

This research fills the voids in the current literature by formulating and empirically evaluating a research framework to gain deeper insights into the motivations that drive the acceptance of a metaverse hotel.

Professional social networking sites (SNS) are widely employed by business individuals to build formal relationships, career opportunities, and professional development. While the characteristics of professional SNS are generally different from other SNS, there is limited understanding of the determinants of users’ continued usage on this platform. The study addresses this research gap by developing a conceptual framework that relates perceived values perspective (utilitarian value, hedonic value) and sociability dimensions (social presence, social benefit, social support, and self-presentation) to continuance intention to use professional SNS. Data were gathered from a questionnaire distributed on LinkedIn and analyzed using PLS-SEM. The findings contribute to the emerging literature on the IS continuance domain, particularly in the area of professional SNS. Furthermore, the study can help professional SNS providers properly manage to retain existing users for sustainable business performance.

Public scepticism is becoming more prevalent surrounding organizations' corporate social responsibility (CSR) implementation and communication. Management research has tended to over-fixate on the strategic paybacks of CSR, and less attention has been devoted to examining why CSR is under siege due to the pervasive issue of scepticism coming from a plethora of stakeholders. This study provides a scoping review to appraise the status quo of CSR vis-à-vis scepticism scholarship and examine how the two concepts have been contextualized in relation to one another in the extant literature in question. The findings illustrate that the process nature of CSR scepticism is complex, given that CSR can yield both a buffering and boomerang effect on CSR practitioners. The qualitative content analysis performed on 58 studies, published from 2007 to 2022, elaborates on CSR scepticism as a multidimensional construct with three distinctive typologies of research approaches identified, explicating how the CSR and scepticism topicalities have been cross-examined in relation to one another: “Typology 1: Dispositional Scepticism and the Buffering Effect of CSR on Scepticism”; “Typology 2: Situational Scepticism and the Boomerang Effect of CSR on Scepticism”; “Typology 3: Centrality of CSR Scepticism and CSR Scepticism Mitigation.” This study offers a conceptual insight into the prevalent issue of scepticism in the CSR context, while also informing marketing, communication, and public relations professionals about the necessity of mitigating CSR scepticism, which poses a barrier to effective CSR implementation and communication processes.

The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

This research examines the impact of the National Rural E-commerce Comprehensive Demonstration Project (NRECDP) on poverty reduction and income growth in rural China.

The study develops a theoretical framework, which considers the role of geographical, technological, institutional and cultural factors for the e-commerce poverty alleviation (e-CPA) model. Empirically, this study applies the difference-in-differences (DID) model and the event study approach to evaluate the effectiveness of NRECDP on the basis of large-scale county-level and household-level panel data spanning 2010 to 2020.

The study found that the NRECDP, as a government-led, information and communication technology (ICT)-enabled, market-based program, has led to a significant increase in per capita output of primary industry employees, as well as in the disposable income of rural residents, especially those in national-level poverty-stricken (NP) counties. The interventions of the NRECDP achieved these positive outcomes through transportation and Internet infrastructure improvement, ICT adoption and human capital accumulation in impoverished towns and villages in remote rural areas. These effects are larger in the eastern region of China, followed by the central region, whereas the weakest effects were found in the western region. However, we found little evidence of the NRECDP increasing household developmental expenditure.

The study findings have important practical and policy implications for rural e-commerce development and self-sustained poverty alleviation solutions. The research revealed the significance of government NRECDP interventions for increasing rural income, reducing living costs, and empowering the rural population in its multiple social roles, namely, as consumers, producers, employees and microentrepreneurs. The local cultural context may also play a role in ICT adoption and entrepreneurship cultivation with a downstream effect on the effectiveness of e-CPA practices. Policymakers would need to ensure a supportive entrepreneur-friendly environment for rural e-commerce development and continue implementing progressive policies for poverty alleviation.

This study explores poverty alleviation issues in China by developing for the first time a multi-faceted framework that is subsequently tested by both county-level and household-level large-scale observations. Also, it is the first study to provide nationwide empirical evidence on the effectiveness of e-CPA in narrowing down the spatial and digital divides in China. In addition to the impact of geography, technology and governmental support, this study also sheds light on the role of culture in the adoption and diffusion of digital technologies and as a source of local entrepreneurial opportunities.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

This study investigates the effect of auditing industry specialization (AIS) on the relative derivatives use for earnings management.

The sample chosen in this study comprises 30,599 firm-year observations of Chinese public companies from 2005 to 2018. The sample is divided into two time periods (2005–2013 and 2014–2018) according to the year when IFRS 9 was implemented (IFRS 9, first discussed by the International Accounting Standards Board in March 2008, is based on an expected credit loss model for determining new and existing expected credit losses on financial assets. The definition was completed in July 2014 and implemented in 2018). AIS was gauged with respect to audit firms and individual auditors, and measured by market share in number and scale of clients. Linear regression is adopted to test hypotheses. Moreover, two-stage least square model (2SLS) is used to eliminate the concern of possible endogeneity.

When gauged with respect to client scale, the scale-based AIS constrained the level of derivatives use for earnings management in the first period (2005–2013) while increased the level in the second period (2014–2018). The findings sustain for the analysis of audit firms and that of individual auditors, and for different definitions of AIS.

The positive AIS-IN relation after the adoption of IFRS 9 implies the sacrifice audit independence. This could be indebted to the government policy that favors local audit firms to be comparable to international Big 4 audit firms, and therefore results in competition among local auditors/audit firms in securing number rather than quality of clients.

The data of AIS in China are collected using a Python web crawler.

The purpose of this study is to review the overall trends in diversity, equity and inclusion (DEI) research in the management field.

We performed a scoping review to comprehensively understand how DEI research has been conducted and to inform future research and practices in the management field. We reviewed 725 articles published from 1976 to 2022 in SSCI-indexed management journals. We examined publication profiles (number of articles and research focus), publication tendency (journals and years), chronological publication trends, work environments (traditional vs remote/virtual work settings) and dimensions of DEI, including age, gender, race and culture.

We identified six common DEI research themes: (1) DEI management and practice, (2) perspectives on DEI, (3) team/group diversity, (4) DEI conceptualization, (5) leadership for DEI and (6) DEI climate. Finally, discussion, implications and recommendations for future research are presented.

Our research provides a comprehensive outline of the DEI research and suggests future directions to contribute to and advance knowledge on DEI in the management field.

The persistence and virality of conspiracy theories online have raised significant concerns. This study revisits Rogers’ Diffusion of Innovations theory to examine the spread of conspiracy theories on social media, specifically focusing on how factors influencing their diffusion evolve over time.

The study analyzes over 1.18 million COVID-19-related tweets using a combination of natural language processing, social network analysis and machine learning techniques. It explores the dynamic roles of novelty, content negativity, influencers, echo chamber members and social bots in the diffusion of conspiracy theories.

The results indicate that novelty, influencers, echo chamber members and social bots are positively associated with the spread of conspiracy theories. The initial dissemination of conspiracy theories is primarily driven by content novelty and influencer involvement. Over time, the perpetuation of these theories becomes increasingly influenced by content negativity and the involvement of echo chamber members and social bots. Social bots serve as important connectors within echo chambers and their removal significantly reduces network cohesion.

The findings provide practical guidance for social media platforms and policymakers in monitoring diffusion patterns and applying targeted interventions.

This study introduces a time-sensitive approach to understanding the spread of conspiracy theories on social media. By identifying the key drivers at different stages of the diffusion process, this study offers valuable insights for developing effective strategies to counteract the proliferation of conspiracy theories at various points in their lifecycle.