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The purpose of the paper is to explore how the effects of the COVID-19 pandemic will influence the development of revenue management practice in the visitor attractions sector.

This viewpoint paper builds on the argument that tracking previous patterns of behaviour and trends can be used to predict future actions and developments.

The paper identifies how historically the development of revenue management practice has been driven by major external trigger points often linked to sudden increases in competitive pressures, such as the deregulation of the airline industry, and expands on this to argue that the pandemic is one such trigger point that has fundamentally changed the approach to revenue management through a refocusing on key principles to manage demand and that this could potentially accelerate its development within the visitor attraction sector.

Pre-COVID, the practice of revenue management in the visitor attraction sector was underdeveloped, and the opportunities to develop revenue management had not been discussed in the academic literature. This paper suggests that the challenges of the pandemic that forced visitor attractions to focus on visitor demand management can now begin to be extended to incorporate the management of revenue and will consequently be of value for academics and practitioners.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The purpose of this study is to comprehensively explore the impact of digitalization on healthcare supply chains (HcSCs). It seeks to understand how digital technologies enhance efficiency, transparency and responsiveness within these complex logistical systems. The study aims to provide a holistic view of the transformative potential of digitalization in the healthcare sector, with a particular focus on improving patient care and streamlining operational processes.

This research employs a systematic review methodology, carefully curating a selection of 45 relevant articles from 66 articles rigorously screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to provide a holistic view. It follows established systematic review protocols, incorporating a meticulous search strategy and precise keyword selection. The chosen research design enables a comprehensive examination of the existing body of knowledge concerning digital platforms, real-time tracking technologies, transparency and responsiveness in the context of HcSCs.

The findings of this study emphasize the pivotal role of digital technologies in reshaping HcSCs. Digital platforms, real-time tracking systems and technological integrations substantially enhance efficiency, transparency and responsiveness. Data-driven decision-making, improved communication and agile responses to dynamic demands are key aspects. These findings underscore the transformative impact of digitalization on healthcare logistics, emphasizing the potential for streamlined operations, enhanced patient care and more efficient resource allocation.

Despite the systematic methodology, this study is subject to certain limitations. It relies on existing literature, which may not cover the most recent developments in the rapidly evolving field of digital HcSCs. Furthermore, the study may be influenced by publication bias. The implications suggest the need for continued research to explore emerging digital technologies and their effects on healthcare logistics, ensuring that supply chains remain agile and responsive.

The practical implications of this research are significant for HcSC managers with insights into digital technologies to enhance transparency and collaboration and improve resource visibility. The integration of data analytics can lead to more effective inventory management and demand forecasting. Blockchain (BC) technology can ensure transparent and secure transactions, fostering trust among stakeholders. For practitioners, this research offers actionable guidance for navigating the digital age, promoting operational efficiency and ensuring a consistent supply of essential medical products. Researchers can use these insights as a foundation for further exploration into the potential of digitalization in HcSCs.

The social implications of digitalization in HcSCs are far-reaching. They encompass improved patient care, as digital technologies enhance the efficiency, transparency and responsiveness of supply chains. This translates to better access to critical medical supplies, potentially reducing healthcare disparities and benefiting underserved populations. Enhanced patient safety is a significant social outcome, as transparent and secure transactions enabled by technologies like BC mitigate the risks associated with counterfeit medications. Furthermore, digitalization builds trust among stakeholders, promotes accountability and fosters resilient healthcare systems, which are capable of responding effectively to crises. It also has the potential to make healthcare more affordable, contributing to increased healthcare access and transparency in decision-making.

The originality and value of this study lie in its comprehensive synthesis of diverse findings related to digitalization in HcSCs. While prior studies have examined isolated facets of digital technology adoption, this research provides a comprehensive overview. It contributes to a deeper understanding of the transformative potential of digitalization within the healthcare sector, offering practical approaches to enhance patient care and streamline operations.

The purpose of the study was to assess the current state and demand of the project management education. Project management has gained increasing importance as a critical discipline for achieving successful project outcomes in various industries and sectors. In the Kingdom of Saudi Arabia (KSA), the demand for skilled project management professionals has increased due to different developmental projects and ambitious initiatives like NEOM.

The study adopted a qualitative multi-method approach to comprehend the objective in depth. In the first round, a systematic literature review (SRL) was applied to explore the state of project management education (PME) in KSA. In the second, structured interviews with professionals were arranged to explore the phenomenon of interest in more detail. Moreover, three case studies were also included to support, comprehend and generalize the findings.

The study highlights the strengths and gaps in PME. The study also provides an insight to enhance the quality and effectiveness of the PME in KSA. Besides these, the research recommends exploring different avenues for the promotion of PME in the country.

The study offers a pioneering exploration of PME in KSA. It uses a multi-method approach to unveil unique insights into the country's educational landscape and its alignment with global industry standards.

This study aims to analyse the resilience strategy utilized by small and medium-sized enterprises (SMEs), enabling these businesses to effectively adapt their operations in response to varying conditions by providing them with essential resources. SMEs operate in marketplaces that are both dynamic and frequently tumultuous. These markets provide SMEs with a variety of obstacles, including economic ups and downs, advances in technology, evolving customer tastes and new regulatory requirements. SMEs need to create a strategic strategy to survive and grow in such situations. This strategy ought to help strengthen their resiliency and make it possible for them to make the most of emerging opportunities while simultaneously lowering the dangers.

The questionnaires adopted and adapted from previous research served as the basis for gathering the data. The manufacturing industry was polled through the use of questionnaires. To test the hypothesis, the data were analysed using Smart PLS. Through the use of closed-ended questions directed to the proprietors, managers or senior executives of SMEs, data were collected from each and every institution in the sample. Following the examination of the data by means of descriptive analysis and the presentation of several scenarios using information relating to SMEs, the findings were presented.

The ambidextrous strategies that are used by SMEs have a propensity to offer a constructive contribution to SMEs. In this study, it was discovered that ambidexterity, which is defined as the capacity to both seek and capitalise on possibilities, has a significant bearing on the organisational effectiveness of SMEs. The results showed that ambidextrous strategies have a propensity to work as mediators in interactions involving proactive resilience tactics and performance.

The research expands our understanding of how SMEs in the manufacturing sector may improve their performance by concentrating on growing their ambidextrous strategies.

This study provides a plausible explanation of two crucial management mechanisms for enhancing the sustainability of organisational effectiveness. The relationships between ambidextrous capabilities and firm effectiveness are malleable, and this study suggests that nurturing formal and informal relationships may be the key to SMEs' long-term sustainable performance. Improving the knowledge and performance of supply chain systems for SMEs in the manufacturing sector and boosting their competitiveness in domestic and international markets are the practical contributions of this study.

Our comprehension of monitoring, cooperation and innovation within social management was deepened as a result of these facts. In addition, the study conducted in the sector uncovered four essential connections that outline how managers should actively work towards lowering social risks, developing new possibilities and increasing business performance. These capacities and links, when taken as a whole, provide the foundation upon which an integrated framework and five research propositions are built.

This research offers a convincing explanation of fundamental management processes for enhancing the sustainability of organisational effectiveness. This research implies that developing formal and informal interactions may be the key to the sustainable performance of SMEs over the long run. The relationships between ambidextrous capabilities, methods and organisational effectiveness are flexible, and this study also suggests that these relationships may be shaped. The practical contributions made by this research include boosting the understanding and performance of supply chain systems for SMEs as well as the competitive power of these businesses in both local and international markets.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

The purpose of this study is to examine the effect of job, organizational and personal factors on exhaustion (EXH) in early career construction professionals in India.

A survey questionnaire was developed and circulated among early career construction professionals in India. The responses on job, organizational and personal demands (role ambiguity, stakeholder management and work-family conflict respectively) and job, organizational and personal resources (job autonomy (JA), perceived organizational support and emotional intelligence respectively) were sought using standard measures. The valid data (N = 187) thus collected, was analysed using structural equation modelling (SEM).

The study finds that in terms of demands, stakeholder management and work family conflict have a significant positive effect on EXH. In other words, EXH level increases with the increase in these two. With respect to resources, JA has a negative effect on EXH. This implies that as JA increases, the EXH level goes down.

This study is the first to examine factors causing EXH in early career construction professionals in India. This study is significant on two counts. First, it proposes a comprehensive theoretical model based in job demands-resources (JD-R) theory customized for construction sector. Second, the empirical examination is situated in the Indian context, which remains underexplored despite its economic and demographic significance.

The coronavirus disease 2019 (COVID-19) pandemic represents one of the most significant disruptions to supply chains (SCs), stimulating both practitioners and scholars to seek ways to enhance supply chain performance (SCP). Recent advancements in technology, particularly supply chain analytics (SCA) technologies, offer promising avenues for mitigating risks associated with SC disruptions like those posed by the COVID-19 pandemic. However, the existing literature lacks a comprehensive analysis of the connection between SCA and healthcare SC (HSC) performance. To address this research gap, we employed the dynamic capability perspective to investigate the mediating roles of supply chain innovation (SCI), resilience (SCR) and flexibility (SCF) in the relationship between SCA and HSC performance. The study further examined the moderating role of a data-driven culture (DDC).

The proposed model was tested using survey data from 374 managers of healthcare facilities in Ghana. SPSS and Amos were used to analyze the data gathered.

The results showed that while SCA may drive HSC performance, the presence of SCI, SCR and SCF may serve as channels to drive enhanced HSC performance. Additionally, we also found that different levels of a DDC induce varying effects of SCA on SCI, SCR and SCF.

The study’s results have theoretical and practical implications, offering valuable insights for the advancement of SCA in healthcare literature. They also deepen SC managers’ comprehension of how and when SCA can boost HSC performance. However, as the study was limited to healthcare facilities in Ghana, its findings may not be universally applicable.

This study contributes to the literature by demonstrating that SCI, SCR, SCF and a DDC could serve as transformative mechanisms to reap superior HSC outcomes. This study also offers contemporary guidance to managers regarding SCA investment decisions.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.