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This study aims to outline the role of causal attributions in consumer responses to irresponsible corporate behaviour. Specifically, this paper presents a moderated mediation model that explains how four types of perceived motives behind an irresponsible action shape corporate blame and word-of-mouth recommendations.

To test the hypotheses, the study uses data from a large survey assessing consumer reactions to a real case of corporate socially irresponsible behaviour in the banking industry.

The findings show that market-, unethicality- and rogue employee-driven attributions increase corporate blame and subsequently make people more likely to spread negative comments regarding the culprit. The difficult situation of a bank, as a perceived reason for wrongdoing, does not reduce the blame attributed to the irresponsible organisation.

The literature offers little information on the attributions people make following egregious corporate behaviour; however, such cognitions can play an important role in stakeholders’ reactions to wrongdoing. This study therefore extends the understanding of how irresponsibility attributions affect consumers’ responses to misbehaviour. Given the empirical context, the findings might be particularly important for communication and bank managers.

The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time.

The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer.

The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme.

The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.

This study aims to explore the effects of long-term orientation (LTO) and strategy formation mode on corporate social responsibility. While many researchers have investigated how large businesses address corporate social responsibility (CSR), there is little empirical evidence on how small- and medium-sized businesses implement CSR or what individual drivers shape this process.

The paper surveyed 282 small and medium-sized managers from the United Kingdom. The respondents were recruited using platform Prolific Academic.

The findings reveal that LTO is a prerequisite for developing CSR and shapes strategy formation mode. The findings also suggested that deliberate strategies are positively related to CSR. The results are consistent across different components of LTO (futurity, continuity and perseverance) and CSR types (internal and external).

The results show that all aspects of LTO are relevant for CSR in SMEs. Besides LTO, deliberate strategy formation model is an important factor contributing to CSR. The paper presents as first an empirical contribution to the strategy literature by examining positive relationship between LTO and deliberate strategy formation mode.

Literature on entrepreneurial finance has long overcome the view of an investor as a sole provider of financial capital. Entrepreneurs need to consider more aspects when deciding on an investor. Especially the depiction of corporate venture capital (CVC) investors has long highlighted advantages and disadvantages compared to independent VC (IVC) investors. The authors investigate what drives entrepreneurs' preferences for CVC relative to IVC and thereby focus on two key issues in the entrepreneur's consideration – the role of resource requirements and exit strategies.

The data were collected in an online survey that gathered information on several characteristics of entrepreneurs and their ventures. The resulting data set of 105 German entrepreneurs was analyzed using logistic regression and revealed important drivers for entrepreneurs' investor preferences.

The study’s findings confirm that the venture's resource needs, specifically the need for marketing resources and access to the corporate network, which play a significant role in the decision on whether a CVC or IVC investor is preferred. Moreover, the analysis debunks the hypothesis that entrepreneurs view a CVC investment as the first step toward acquisition. However, those entrepreneurs striving for an IPO are less likely to prefer CVC.

The study expands the literature on CVC attractiveness and specifically considers the entrepreneurs' intentions and needs. The results confirm but also debunk some widespread perceptions about why entrepreneurs choose to pursue financing from a CVC investor.

This study aims to examine the multidimensional nature of institutional distance as a driver of acquisition decisions in emerging markets. Then, this study aims to offer a nuanced perspective on the role of its various formal and informal dimensions by taking into account the potential contingency role played by a firm’s context experience.

Building on institutional economics and organizational institutionalism, this study explores the heterogeneity of institutional distance and its effects on the decision to enter emerging versus advanced markets through cross-border acquisitions. Thus, institutional distance is disentangled into its formal and informal dimensions, the former being captured by regulatory efficiency, country governance and financial development. Furthermore, our framework examines the moderating effect of an acquiring firm’s experience in institutionally similar environments, defined as context experience. The hypotheses are analyzed on a sample of 496 cross-border acquisitions by Italian companies in 41 countries from 2008 to 2018.

Findings indicate that at an increasing distance in terms of regulatory efficiency and financial development, acquiring firms are less likely to enter emerging markets, while informal institutional distance is positively associated with such acquisitions. Context experience mitigates the negative effect of formal distance and enhances the positive effect of informal distance.

This study contributes to institutional distance literature in multiple ways. First, by bridging institutional economics and organizational institutionalism and second, by examining the heterogeneity of formal and informal dimensions of distance, this study offers a finer-grained perspective on how institutional distance affects acquisition decisions. Finally, it offers a contingency perspective on the role of context experience.

Purpose: Urban centres have been argued to be crucial in the battle for sustainability. With more than half of the global population presently living in cities, the sustainability challenges of global warming, environmental degradation, social inequality, and economic recessions have continued to thrive. To this end, there have been efforts to revive and improve the existing physical and social structure of cities in a process known as urban regeneration. The aim of this paper is to explore the role of sustainability assessment frameworks in urban regeneration.

Approach: Aligning with the positivism philosophical position, and using document analysis as a data collection method, the study discusses the state of the art of urban regeneration and its application in recent times. The study also reviewed selected neighbourhood sustainability assessment frameworks as a tool for decision-making towards sustainability to know the extent in which they capture the goals of urban regeneration.

Findings: Findings showed that the uptake of the sustainability assessment frameworks could play a role in enhancing integration of local context, social wellbeing and economic prosperity, environmental quality, and stakeholder engagement at the neighbourhood level which are the main aspects of urban regeneration.

Theoretical and practical implications: In theory, this paper establishes the assumption that with some revisions, sustainability assessment frameworks could serve as a tool for decision-making in urban regeneration process. Practise-wise, urban regeneration at the neighbourhood level can now be measured against sustainability benchmarks and indicators.

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.