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With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Hyperautomation is a technological concept whose popularity has been growing continuously since the German manufacturing industry “initiated” the Fourth Industrial Revolution (Industry 4.0), whereas, on the basis of theory, hyperautomation is a term still new and little recognized. This applies equally to scientific studies (articles, conference reports) and empirical studies (quantitative, qualitative). Therefore, this article attempts to fill definition gap that exists in the literature on management and quality sciences on the term hyperautomation.

The authors use literature review approach to identify the gaps in the existing literature on hyperautomation. They present a nominal definition of hyperautomation, discuss related issues and provide a comparative perspective between hyperautomation and automation.

The article’s findings include a precise definition of hyperautomation and the problems it raises. The authors point out that the term “hyperautomation” is still relatively new and underutilized in the management and quality sciences literature. It also compares hyperautomation to automation from several angles and emphasizes how it affects businesses, industries and other economic sectors.

Authors emphasize that in order to deploy hyperautomation successfully, enterprises must take a distributed and integrated approach.

This article addresses a gap in the management and quality sciences literature about the definition of hyperautomation. Authors give a thorough explanation of hyperautomation, along with relevant problems, useful implications and a comparison between hyperautomation versus automation.

This paper aims to examine the mediating role of occupational stress in addressing the missing gap between organisational intelligence (OI) traits and digital government service quality.

By employing multistage cluster sampling, a total of 394 responses from the Malaysian service providers at federal government agencies were obtained. For data analysis, the partial least square structural equation modelling (PLS-SEM) approach with a disjoint two-stage approach was employed to assess the proposed higher-order model. The analysis was carried out to examine how occupational stress mediates the relationship between OI traits at each component level and digital service quality.

Occupational stress mediates the relationship between OI traits at the third-order component level and digital service quality. At the second-order component level, only the employee-oriented OI traits exhibit a significant indirect effect on the digital government service quality. Narrowing down to the first order component level, two OI traits, namely “Alignment and Congruence”, and “Heart” demonstrate significant indirect effects in the mediation analysis.

By incorporating the organisational model of stress (OMS) with public service-dominant logic (PSDL), this paper takes an approach to revitalise the stressors and individual-level performance used in a traditional work setting. Precisely, it examines how digital service quality is influenced by today's high-performing public organisation stressors (OI traits) along with non-technical element (occupational stress). More importantly, digital government service quality was examined from a less emphasised perspective, namely the supply side or service providers’ standpoint in sustaining the digital government service performance.

The purpose of this study is to investigate the mediating role of intellectual capital (IC) on the association between corporate governance mechanism (CGM) and the financial efficiency of Islamic banks (Z-score, net investment income and loan to deposit) and verify it through standard mediation in the panel based on interaction.

The data of this study draws from 125 full-fledged Islamic banks and windows from 26 Organization of Islamic Cooperation (OIC) over the period of 2009 to 2019. A two-step system generalize method of moment estimation is used to test the hypotheses.

The results underwrite that the inclusion of IC as a mediating variable has influenced positively the corporate governance and financial efficiency of IBs. Besides, only CEO power and Shariah supervisory board positively affect the financial efficiency of IBs. While structural capital and relational capital positively affect the financial efficiency of IBs. Apart from that, results show that the CGM has a significant relationship with the IC value of IBs.

These findings are valuable for policymakers and regulators to set policies to improve CG structure and effective use of IC resources to improve banking efficiency. Additionally, findings might be helpful for the bankers to proficiently use the IC as a premise to plan new strategies to get an upper hand in financial performance.

This study extends and contributes to the current literature by analysing the role of IC along with CG to boost the financial efficiency of banks in OIC countries.

This paper aims to explore the problem of power imbalance within decentralized autonomous organizations (DAOs) and propose potential solutions that could contribute to enhancing the democratic nature of DAOs.

In this paper, the authors apply a qualitative methodology. Using a thematic coding analysis, the authors process data collected from interviews with 11 experts.

Multiple factors contribute to the perceived lack of democracy within DAOs, such as token concentration and effective stakeholder communication. Next, quadratic voting has the potential to enhance democracy within DAOs, but this mechanism must be implemented mindfully. Finally, the results were nuanced when it comes to the effectiveness of liquid democracy in DAOs to enhance voter participation and representation.

To the best of the authors’ knowledge, this paper is one of the first research contributions to propose recommendations to address the power imbalance within DAOs and to contribute to the advancement of decentralized decision-making structures.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.

This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.

The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.

To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.

This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

Despite being a seminal explanation of the workforce emotional experiences, capable of mapping the path from the antecedents to consequences, affective events theory (AET) only offers a “macrostructure” of a working environment. To date, little is known about the universal features of the work environment that may guide the understanding of imperative work aspects triggering employees’ emotions at work. Hence, the study proposes and validates that Stafford Beer’s viable system model (VSM) can provide a holistic view of the organizational work environment, enabling a comprehensive understanding of work events or factors triggering workforce emotions.

First, the VSM structural layout is used to fill in the “macrostructure” of the “working environment” in AET to diagnose the functional and relational aspects of the work and the related work events occurring within. Using a deductive approach, 31 work events were adopted to determine the impact of VSM-based work environment events on the employees’ emotional experiences and subsequent work attitudes (job satisfaction) and behaviors (citizenship behavior). To field test the proposed nexus of VSM and AET, the survey was conducted on two hundred and fifteen employees from 39 different organizations. PLS-SEM tested the explanatory power of the suggested VSM’s systemic approach for understanding the affective work environment in totality.

The findings confirmed that the VSM metalanguage provides a holistic view of the organizational functioning and social connectivity disposing of affective work events, helpful in assessing their aggregate influence on employees’ emotions and work-related outcomes.

The findings identify how employees' emotions can be triggered by everyday work operations and social relations at work, which can affect their extra-role behaviors and necessary work-related attitudes.

The study utilized Beer’s VSM framework based on the systemic principle of “holistic view” for ascertaining the affective work environment and its related features holistically, which filled in well the macrostructure of “work environment features” with micro-structures of organizational inter-related aspects which are yet to be known in AET – a seminal explanation for managing workforce emotions.