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This study investigates how the maximum cultural intelligence (Max CQ) within a team – defined as the highest cultural intelligence level of an individual member – affects intra-team communication, conflict dynamics and, ultimately, team satisfaction and performance in global virtual teams (GVTs).

Utilizing quantitative research methods, this investigation draws on a dataset comprising 3,385 participants, which forms a total of 686 GVTs.

The study reveals that MaxCQ significantly enhances team communication, which in turn mitigates conflict, increases satisfaction and improves performance. It is noteworthy that the influence of MaxCQ on GVT success is more significant than the average cultural intelligence (CQ) of team members, providing critical insights for effective GVT management strategies.

The findings suggest that managers may optimize team dynamics not by uniformly increasing each member’s CQ but by concentrating on maximizing the CQ of one individual who can act as an influencer within the team. Strategically placing individuals with high CQ in GVTs can enhance overall team function.

While existing literature primarily examines the individual effects of CQ on communication and conflict management, this study sheds light on the collective interplay between MaxCQ, communication and conflict. It highlights the importance of MaxCQ, along with the frequency of team communication and conflict, in influencing team satisfaction and performance in GVTs.

The chapter provides a comprehensive outline of how technology can be harnessed to promote inclusivity within higher education. The article begins by defining inclusive education and explains how cross-cultural collaboration can be facilitated by technological intervention. The article focuses on how the amalgamation of different theories based on behaviorism, cognitivism, and constructivism is crucial for the successful implementation of inclusive education. It highlights the importance of emerging technologies in promoting inclusive pedagogy. It provides a strategic framework that uses artificial intelligence (AI) and different digital tools to cater the needs and aspirations of diverse and global higher educational institutions. Several case studies are also presented to help in understanding the barriers and corresponding strategies to enhance inclusivity. The impact of the strategy surpasses immediate learning enhancements which contribute to a broader vision for a more equitable, accessible, and interconnected educational ecosystem. The chapter concludes with a discussion on the challenges and ethical considerations for successful implementation of technologies to bring inclusive internationalization to higher education.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.

The Internet has introduced new ways of conducting business. Online auction of nonperishable experience goods (NPEG) items, which are generally rare, expensive and durable and need to be experienced to be valued, is one area that is undergoing significant change. Online auction platforms are encouraging participants to interact publicly in a social media type comment format. This paper investigates how such public interactions impact the auction outcomes of NPEG.

Auction records of vintage automobiles sold between 2015 and 2023 on one of the biggest online auction platforms in the USA were collected. The dataset contains multiple variables per record such as winning bid price, number of comments by sellers and browsers and various details of the automobile, for example brand, vintage and mileage. Ordinary least squares was utilized to analyze close to 42,000 records in the dataset.

The paper found that comments in general are positively correlated with the winning bid; it also found that online buyers value browser comments higher than seller comments. Furthermore, the relationship between vintage and origin country and winning bid is enhanced by browser comments.

This research furthers theory on two fronts. Firstly, we analyze how auction outcomes are impacted when buyers, casual observers and sellers interact with each other publicly when the auction is ongoing. To the best of the author's knowledge, this is one of the first studies that delve into participant interactions in online ongoing auctions. Secondly, we investigate how potential buyers utilize participant interaction information to guide their decisions about the value of NPEG.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

This study aims to investigate earnings management practice of classification shifting of revenues in Chinese-listed firms.

The study employs a dataset of 2,920 A-listed firms from Chinese stock exchanges of Shanghai and Shenzhen for the period of 2003–2019. We apply both univariate and panel regression analysis by using fixed effect estimation with robust standard errors.

Our findings reveal that firms misclassify revenues by taking advantage of the flexibility provided by applicable financial reporting standards. The empirical evidence obtained through regression analysis suggest that managers reclassify non-operating revenues as operating revenue to alter the economic reality while seeking the advantage of financial reports users’ vulnerability for valuing the upper half of income statement items more as compared to lower part. The results further indicate that international financial reporting standards adoption inhibits the earnings management practices using classification shifting of revenues. It is also concluded that firms, which are suffering losses or having low growth, are more persistently involved in misclassification of revenues.

The study is unique from the point of view that it investigates earnings management from the prospective of revenue’s classification in an emerging market characterized by various market imperfections such as lower investor protection and higher information asymmetry.

The organizational decision-making perspective (ODM) has a legacy regarding its concern for budgeting as an essential organizational routine in decision-making. Budgeting has also become a direct concern to organizational institutional theory (OIT) because of its prominent role in institution building, where budgeting can build trust in inter-organizational relationships. This paper builds on these two perspectives to explore organizational budget processes' formation, disruption, and re-creation over time.

We conducted a comprehensive review and critical analysis of the ODM and OIT perspectives, focusing on a fundamental paradox between ODM's emphasis on stability through organizational routines and OIT's focus on organizational legitimacy through the decoupled expression of organizational values. We then expanded on these paradoxical concerns in the context of budgeting, formalizing them into specific research propositions for future studies.

Tensions around the stability, decay, and re-creation of budgets as organizational routines emerge as a pressing issue requiring further empirical investigation from the ODM perspective. A critical issue in the OIT perspective is the potential for organizational budgets to provide an opportunity to decouple from practice through routinized expressions of rationality and to facilitate loose coupling in practice. These findings offer a fresh perspective and open up new avenues for future research in this area.

This paper contributes to the accounting and organizational research literature by shedding light on how organizations respond to the potential decay of budget routines and the manifestation of organizational values in decoupling processes by further re-creating and elaborating budget processes.