Search results

The purpose of this paper is to advance our understanding of international crisis mediation by introducing and examining the nested insider-partial mediator (NIPM) concept, a nuanced perspective on IPM behavior. This study challenges the traditional view of effective mediators as external, unbiased entities by delving into the behavior and contribution of mediators who are deeply embedded in the conflict environment, such as South Korea’s unique position in navigating the US–DPRK crisis in 2017–2018. By analyzing South Korea’s dual role as mediator and negotiator and its employment of both nondirective and directive mediation strategies, the paper demonstrates the potential effectiveness of NIPMs in managing complex biases and contributing to de-escalation in intense crisis scenarios.

This paper uses a focused single-case study approach to analyze South Korea’s role as an NIPM. Using a process-tracing methodology, it examines how contextual factors such as relationships, interests and inherent biases influenced South Korea’s mediation strategies in this complex geopolitical scenario. Empirical evidence was retrieved from public sources, including official statements and press interviews, providing an empirical foundation for understanding NIPM behavior. This approach facilitates a detailed study of South Korea’s unique mediation role within the intricate dynamics of the Korean Peninsula conflict.

The study’s findings illustrate the pivotal role NIPMs can play in complex international conflicts, underlining the significant potential of NIPMs in crisis prevention. The findings highlight South Korea’s adept navigation through intricate geopolitical dynamics, leveraging its unique insider position and established relationships with both the USA and North Korea. This behavior was instrumental in mitigating a potentially explosive situation, steering the crisis toward negotiation and de-escalation. The research underscores the effectiveness of the NIPM framework in understanding the nuanced behavior of mediators who are deeply integrated into multi-level conflicts, influenced by their connections, interests and inherent biases.

This research not only broadens the theoretical framework of insider-partial mediation by introducing the concept of NIPM, but also has practical implications for policymakers and practitioners in leveraging regional mediation strategies for international crisis mitigation. The study underscores the importance of mediators’ deep-rooted connections, biases and vested interests in influencing their mediation tactics, thus offering a comprehensive understanding of the multifaceted nature of international mediation in complex geopolitical conflicts.

This study aims to address customer focus as an important component of total quality management (TQM) and explore the key drivers of member satisfaction in tennis clubs via a novel theory-based member satisfaction index (MSI) model with high explanatory and predictive power. Furthermore, the study aims to investigate the relationship between satisfaction and behavioral intentions (willingness to stay; WTS) with consideration of the mediating effect of identification with the club.

This study uses variance-based partial least squares structural equation modeling (PLS-SEM) to estimate the MSI model, which was tested in a leading tennis club in Germany (n = 185).

The results reveal that club atmosphere, club facilities and the price/quality ratio of the membership fee are the most important drivers of member satisfaction in tennis clubs. Member satisfaction has a large influence on the WTS of tennis club members. Identification with the club, when included as a mediator in the model, increases the variance explained in WTS considerably.

The small sample limits the generalizability of findings, and further research is recommended.

The MSI model is a useful benchmark tool for club managers who want to quantify the satisfaction and WTS of their club members. In addition, because of the integrated formative measurement models, the PLS-SEM results show which indicators can be used to positively impact satisfaction with each of the service quality dimensions, overall member satisfaction and WTS. The most important of these results are discussed in an importance-performance map analysis.

The MSI model is a multi-attribute index model through which members' evaluations of various dimensions of service and value are derived through multivariable linear function with each dimension weighted according to its importance in one holistic model. The model shows the strong impact of satisfaction on WTS of sports club members and reveals that findings of previous research on the relationship between fan and spectator identification and loyalty are transferable to sports club members. The MSI represents a new contribution to the literature; it was applied here to tennis clubs but is also suitable for application to other sports clubs.

Technology is sometimes used to support assessments of academic research in the form of automatically generated bibliometrics for reviewers to consult during their evaluations or by replacing some or all human judgements. With artificial intelligence (AI), there is increasing scope to use technology to assist research assessment processes in new ways. Since transparency and fairness are widely considered important for research assessment and AI introduces new issues, this review investigates their implications.

This article reviews and briefly summarises transparency and fairness concerns in general terms and through the issues that they raise for various types of Technology Assisted Research Assessment (TARA).

Whilst TARA can have varying levels of problems with both transparency and bias, in most contexts it is unclear whether it worsens the transparency and bias problems that are inherent in peer review.

This is the first analysis that focuses on algorithmic bias and transparency issues for technology assisted research assessment.

This study aims to explore the adoption of enterprise risk management (ERM) in developing and developed countries. Is there a similarity or difference between the two contrasting institutional markets and the reasons behind them?

The adoption of ERM is analyzed on the basis of the institutional framework. The author draws empirical evidence by comparing the cases of a British and an Indian insurance company using evidence from multiple sources. This paper focuses on extra-organizational pressures exerted by economic, social and political situations across two countries that influenced the adoption decision of ERM.

The findings of this research revealed that early adopters of ERM in different institutional markets face coercive and normative pressure but not mimetic pressure. The adoption of ERM in India and the UK is dissimilar. Companies in the British insurance market encounter higher institutional forces than those in the Indian market because of higher coercive and normative pressure. The aspirations to adopt ERM in the Indian and UK markets included improved strategic decision-making to maintain stakeholder expectations and higher standards of corporate governance. In the UK, ERM was adopted to reduce surprises and fluctuations under flexible regulations but with stricter adoption and to improve credit ratings.

Previous literature has discussed ERM adoption in similar markets or within one market with similar institutional pressure. In contrast, this research is a comparative study that explains the analysis of institutional theory in two different institutional environments in the adoption of ERM.

This paper aims to provide a living tribute to the leading autoethnographer, Alec Grant.

Alec provided Jerome with a list of names of people he might approach to write a tribute on his behalf.

The accounts describe the influence that Alec has had both as an educator and as a trusted colleague for the people approached.

While this is a living tribute, it is about one man and could, therefore, be described as a case study. Some people wonder what can be learned from a single case study. Read on and find out.

Alec has carved out a path for himself. In many senses, he chose “The Road Less Travelled”. He has never shied away from challenging “The System” and defending the rights of the marginalized and socially excluded. It is not a road for the faint-hearted.

For systems to change, radical thinkers need to show the way. “Change keeps us safe” (Stuart Bell).

Alec was a well-known and highly respected cognitive behavioural academic practitioner and the author of key textbooks in the field. He then decided to reinvent himself as an autoethnographer. This has brought him into contact with a much more diverse group of people. It has also brought him home to himself.

To promote the success of peer-to-peer accommodation, this study examines the effects of online host–guest interaction as well as the interaction's boundary conditions of listing price and reputation on listing popularity.

Using 330,686 data collected from Airbnb in the United States of America, the authors provide empirical evidence to answer whether social-oriented self-presentation and response rate influence listing popularity from the perspective of social exchange theory (SET). In addition, the authors investigate how these two kinds of online host–guest interactions work with listing price and reputation to influence listing popularity.

The results reveal the positive association between online host–guest interaction and listing popularity. Notably, the authors find that listing price strengthens but listing reputation weakens the positive effects of online host–guest interactions on listing popularity in peer-to-peer accommodation.

This study is the first attempt to adopt SET to explain the importance of online host–guest interactions in influencing listing popularity as well as examine the moderating role of listing price and reputation on the above relationship.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

The purpose of this study was to perform sentiment analysis and analyze the growth and popularity of Drupal, Joomla and WordPress on YouTube over a four-year period. This included identifying the most liked and commented videos for each content management system (CMS), ranking the CMSs based on the number of positive comments they received, and using natural language processing techniques to identify the top ten most frequently appearing words in videos about the CMSs.

The data for assessing the features of the videos of Drupal, WordPress and Joomla was extracted using Webometric Analyst version 4.4. with the help of the YouTube application programming interface key for videos on the selected CMSs uploaded from 2019 to 2022. The extraction of comments and sentiment analysis for the relevant videos was done using Mozdeh.

This study scrutinized 371, 234 and 313 videos of WordPress, Joomla and Drupal on YouTube. The findings reveal that there is a chronological growth of videos of the three CMSs in four years and till the present time, WordPress has the highest number of videos followed by Drupal and then Joomla. Regarding the ranking of highly liked videos, WordPress again wins the list with the highest number of likes in its videos followed by Drupal and then Joomla. For analyzing sentiments of the total comments extracted 123,409 for WordPress, 1,790 for Joomla and 1,783 for Drupal, respectively, WordPress receives the highest average positive comments followed by Drupal then Joomla. In top word frequency, the word “thank” highly occurs and viewers are asking for more tutorial videos.

To the best of the authors’ knowledge, this study is the first attempt for analyzing the sentiments of WordPress, Drupal and Joomla using Mozdeh software within the concerning period.

In a complex, ever-changing, and turbulent business world, encouraging employees to express their improvement-oriented novel ideas through voice behavior is crucial for organizations to survive and thrive. Understanding how to foster employee promotive voice at work is a significant issue for both researchers and managers. This study explores how to foster employee promotive voice through specific HRM practices and positive employee attitudes. It also examines the effect of employee promotive voice on perceived organizational performance.

This study employed a time-lagged multisource survey design. Data were collected from 215 executives, 790 supervisors, and 1,004 employees in 113 firms, and analyzed utilizing a multilevel moderated serial mediation model.

The findings of this study revealed that promotive voice was significantly related to perceived organizational performance. Innovation-enhancing HRM was positively associated with employee promotive voice. The HRM-voice relationship was partially mediated by employee job satisfaction. Power distance orientation was found to significantly moderate the relationship between innovation-enhancing HRM and employee job satisfaction at the firm level. Our findings showed that innovation-enhancing HRM policies may fail to foster promotive voice if they do not enhance employee job satisfaction.

This study challenges some taken-for-granted assumptions in the literature such as any high performance HRM bundles (e.g. HPWS) can foster employee promotive voice, and the effects of HRM are direct and even unconditional on organizational outcomes. It emphasizes the need to avoid potential unintended effects of HRM on employee voice and the importance of contextualizing voice research.

This study aims to examine the effect of Industry 4.0 technology on resilience in established cross-border supply chain(s) (SC).

A literature review provides insight into the resilience capabilities of cross-border SC. The research uses a case study of operational international SC: the producers, importers, logistics companies and UK Government (UKG) departments. Semi-structured interviews determine the resilience capabilities and approaches of participants within cross-border SC and how implementing an Industry 4.0 Internet of Things (IoT) and capitals Distributed Ledger (blockchain) based technology platform changes SC resilience capabilities and approaches.

A blockchain-based platform introduces common assured data, reducing data duplication. When combined with IoT technology, the platform improves end-to-end SC visibility and information sharing. Industry 4.0 technology builds collaboration, trust, improved agility, adaptability and integration. It enables common resilience capabilities and approaches that reduce the de-coupling between government agencies and participants of cross-border SC.

The case study presents challenges specific to UKG’s customs border operations; research needs to be repeated in different contexts to confirm findings are generalisable.

Operational SC and UKG customs and excise departments must align their resilience strategies to gain full advantage of Industry 4.0 technologies.

Case study research shows how Industry 4.0 technology reduces the de-coupling between the SC and UKG, enhancing common resilience capabilities within established cross-border operations. Improved information sharing and SC visibility provided by IoT and blockchain technologies support the development of resilience in established cross-border SC and enhance interactions with UKG at the customs border.