Search results

This study seeks to understand how accountant stereotypes have been constructed and reconstructed at the macro-national and the structural level in Chinese society.

This qualitative investigation into China's social construction of accountant stereotypes employs Becker's (1963) labelling theory. Viewing stereotyping as a socially constructed practice, this study draws on a post-positivistic, reflexive epistemology in conducting 28 semi-structured interviews with accountants and related actors.

Chinese accountant stereotypes are constructed and reconstructed according to the rules created and enforced in different cultural-political periods. The accountant stereotypes constructed during the ancient Confucian period (500 BC – 1948) were replaced during 1949 and 2012 when the political focus shifted towards propagating socialism and later promoting economic growth. They also show how Confucian stereotypes of accountants resurfaced in 2013 but were reconstructed by the central government's cultural confidence policy of propagating Confucianism.

Empirically, prior literature has focused on what the accountant stereotype is and how accountants respond to such stereotypes, but it has neglected the ways in which these accountant stereotypes are politically and culturally constructed, diffused and legitimated. This paper fills in the gap by understanding the social practice of accountant stereotyping in a previously unexplored political-cultural context, namely Chinese society. In theoretical terms, by offering the first use of Becker's (1963) labelling theory in the accounting literature, it furthermore enhances our understanding of how accountants' identities and social standing are shaped by social rules.

This study evaluates the knowledge structure of building information modeling (BIM) in green buildings. Buildings are one of the main contributors to carbon emissions, and implementing BIM in green buildings is seen as an indispensable approach to mitigate environmental and climate change issues.

Through a bibliometric analysis, 297 publications retrieved from the Web of Science (WoS) were analyzed to explore their intellectual structure.

Bibliographic coupling analysis produced four clusters on current and emerging trends, while co-word analysis produced four clusters on future BIM and green building trends. Current and emerging trends revolve around BIM adoption in green and existing buildings, life cycle analysis (LCA) and sustainable rating tools. Future trends related to BIM and performance analysis and optimization, the BIM framework for green building design and construction, overcoming barriers and maximizing benefits in BIM adoption.

The implications of this study are relevant to all BIM and green building stakeholders, including developers, engineers, architects, occupants, tenants and the whole community.

This study examines the crucial integration of BIM and green building within the more extensive construction and building field scope.

This study investigates the impact of human capital (managers' capital, employees' capital) and orientation (market orientation, entrepreneurial orientation) for accelerating the digitalization process and improving the firm performance. It also studies the role of supply chains as both direct and indirect mediators of the correlation between digitalization and business performance.

This article aims to develop an empirical study using a random sampling technique and survey data collected from 368 managers and owners of different food enterprises in Vietnam. The study adopted a methodological approach quantitatively. Analysis of the relationships and confirmatory factors was performed using structural equation modeling (SEM), a technique to evaluate the proposed relationships.

In line with expectations, the findings emphasize the impact of human capital (managers' capital, employees' capital) and orientation (market orientation, entrepreneurial orientation) for accelerating the digitalization process and the role of supply chains as both direct and indirect mediators of the correlation between digitalization and improving the firm performance, in the context of emerging markets.

This is an important investigation, according to the authors' knowledge, regarding the role of developing human capital (managers' capital, employees' capital) and orientation (market orientation, entrepreneurial orientation) as a key strategy for accelerating the digitalization process and improving the firm performance. Further, the study's novelty reinforces the role of supply chains as both direct and indirect mediators of the correlation between digitalization and business performance in the Vietnamese food companies, where a market economy is emerging.

In this study, the researchers explored the roles played by digital technologies and digital innovation (DI) in Pakistan's Information, Communication and Technology (ICT) companies' firm performance (FP).

The researchers used a quantitative study to gather cross-sectional data from employees working in Pakistan's ICT companies. The authors based this study's findings on 396 valid samples.

The structural equation modeling (SEM) findings underline that digital capability (DC), digital orientation (DO) and digital transformation (DT) have positive and significant effects on DI and FP. Moreover, there is a positive and significant relationship between DI and FP. Finally, DI mediates DC's, DO's and DT's associations with FP.

By committing to embracing new digital technologies and updating existing DCs to become innovation leaders and to improve FP, the findings will help sectors to take advantage of developing digital technologies and the trend toward digitalization. The results are also valuable for policymakers when considering if SMEs should be provided with more money for the digital up-skilling of their employees. Finally, this study's findings enrich the depth of literature about companies' use of digital technologies.

This study's empirical findings confirm the roles played by DC, DO and DT in improving DI and FP in a developing country such as Pakistan.

Drawing on organizational design theory and organizational learning theory, this paper aims to examine component technology (CT) and the interaction between CT and experiential learning (EL) effects on the degree of integration (DI) of cross-border technological acquisitions.

Using a sample of 267 firms consisting of 229 acquirer firms who started cross-border technological acquisitions from developed economies and 38 acquirer firms who initiated cross-border technological acquisitions from emerging economies over the period of 1993–2016, this study adopts a value chain framework to measure the acquirers’ acquisition integration degree for the investigation of the effects of CT and the interaction between CT and EL.

First, this paper finds CT in cross-border technological acquisitions exerting a positive influence on the acquirer firm’s likelihood of the DI implementation, in line with the organizational design theory. Second, in view of organizational learning theory, this study finds EL and the combined effect of CT and EL to have an inverse influence on the DI.

The results imply that the moderating role of EL significantly optimizes decision choices for an acquirer firm for integration implementation strategies in the form of DI, such as full integration (structural integration), partial integration and no integration (structural separation), which appears to be crucial for cross-border technological acquisitions.

This study contributed to international business strategies by shedding light on the importance of the DI for an acquirer firm that undertakes a cross-border technological acquisition with a CT target firm. This study explains why structural integration might be necessary in cross-border technological acquisitions regardless of the costs of disruption it imposes, as well as the contexts in which it becomes less important or unnecessary. The study disclosed that the increase in the likelihood of DI because of CT depends on the EL of the acquisition company in the host country environment and fluctuates with the prior acquisition knowledge and EL of the host country. Combining two cross-border technological acquisition’s literature streams, such as CT and EL, this study enlightens the importance of organizational learning theory and theory of organization design strategic direction making on acquisition integration implementation strategies.

This study aims to examine the effect of corporate social responsibility (CSR) on banks’ capital, value and risk by investigating its impact on capital inflows and asset quality. The authors aim to investigate the value-protective characteristics of socially responsible performance.

This study uses a two-stage least squares approach with instrumental variables, with bank and year fixed effects to address concerns regarding endogeneity, specifically reverse causality and unobservable factors.

The results confirm a positive association of CSR with capital adequacy, including higher quality Tier 1 Capital. The authors find strong evidence that banks with higher CSR scores are associated with greater bank value and lower risk. The extended analyses find that the improvement in capital is from annual growth in capital and lower risky assets.

The research advances the field by providing new empirical evidence of a positive association between CSR and capital, including high-quality Tier 1 Capital. This study complements the prior research by simultaneously examining the dynamic links between CSR and capital, bank risk and bank value. The findings are consistent with the view that there is a dynamic link in which CSR affects the operations of banks.

Entrepreneurial ventures aspiring to disrupt existing market incumbents often use business-model innovation to increase the attractiveness of their offerings. A value proposition is the central element of a business model, and is critical for this purpose. However, how entrepreneurial ventures modify their value propositions to increase the attractiveness of their comparatively inferior offerings is not well understood. The purpose of this paper is to analyze the value proposition innovation (VPI) of aspiring disruptors.

The authors used a flexible pattern matching approach to ground the inductive findings in extant theory. The authors conducted 21 semi-structured interviews with managers from startups in the global electric vehicle industry.

The authors developed a framework, showing two factors, determinants and tactics, that play a key role in VPI connected by a continuous feedback loop. Directed by the determinants of cognitive antecedents, development drivers and realization capabilities, aspiring disruptors determine the scope, focus and priorities of various configuration and support tactics to enable and secure the success of their value proposition.

The authors contribute to theory by showing how cognitive antecedents, development drivers and capabilities determine VPI tactics to disrupt existing market incumbents, furthering the understanding of configuration tactics. The results have important implications for disruptive innovation theory, and entrepreneurship research and practice, as they offer an explanatory framework to analyze strategies of aspiring disruptors who increase the attractiveness of sustainable technologies, thereby accelerating their diffusion.

The purpose of this paper is to investigate whether banks’ environmental, social and governance (ESG) initiatives increase or decrease banks’ efficiency.

The sample used includes all listed banks in Saudi Arabia over the years 2016–2021. The authors performed different methods, including data envelopment analysis (DEA), ordinary least squares (OLS) and quantile regressions.

The OLS regression results show a negative linkage between ESG and banks’ efficiency. Further, the quantile regression analysis indicates that the ESG effect on banks' efficiency is negative across different quantiles. However, the DEA method shows that the DEA-generated scores for Banks’ efficiency are higher for ESG-adjusted scores in comparison to efficiency scores without incorporating ESG. Further, the comparison of the DEA-generated efficiency scores, over the sample period, of adjusted ESG banks still suffers from decreasing in their efficiency over the years. Concerning existing theory, the results are consistent with the stakeholders and the resource-based theories postulating that banks' ESG practices are ethical commitments and enable firms to gain competitive advantage and increase their reputation among stakeholders.

The findings of this study offer important implications for regulators and bankers. Policymakers and bank regulators should make collective efforts to encourage financial institutions to adopt green finance initiatives to create an efficient financial system capable of counteracting risks from the external environment and stimulating economic growth. Banks’ managers should be aware that ESG initiatives serve society and the environment and offer a positive influence on banks’ efficiency.

To the best of the authors’ knowledge, this is the first study to explore the influence of ESG activities on banks' efficiency using DEA for banks in Saudi Arabia.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

This study aims to analyze the moderating roles of familiarity, generation and gender on the impacts of coronavirus fear-uncertainty on the destination image and visiting intentions post-COVID-19.

This paper seeks to provide evidence for a research conclusion by conducting a survey of 431 potential travelers of various nationalities who visited Egypt. The data were examined using structural equation modeling with a multigroup analysis and PROCESS MACRO.

The findings indicated that the links between coronavirus fear-uncertainty, the destination image and intention to visit were significantly different from one gender and generation group to another. Moreover, it showed that destination familiarity moderates the negative effects of coronavirus fear-uncertainty on the destination image and intention to visit.

To the best of the authors’ knowledge, this paper is the first to explore the moderating role of destination familiarity, generation and gender in the effects post-COVID-19 of coronavirus fear-uncertainty on the destination image and travelers’ intention to visit using generational cohort theory and gender schema theory.