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This study aims to explain the effects of different types of innovations on organizational performance in terms of firms’ external effectiveness and internal efficiency. The study examines the interrelationship of technical and nontechnical innovations in complex services and the mediating effect of customer participation on the relationship between innovation type and organizational performance.

The study draws on a neo-Schumpeterian model for innovation to examine the complex service setting of healthcare provision. Data from Statistics Sweden, containing 38 hospitals and 242 primary care units in Sweden, provided the study's results.

The findings show the importance of combining different types of innovations in complex services, demonstrating a mediating effect of nontechnical innovation on both the relationship between technical innovations and external effectiveness and internal efficiency. Moreover, the results show that customer participation has a positive mediating effect for technical innovation and nontechnical innovation on external effectiveness. However, there is no such significant effect on internal efficiency.

The findings are based on self-assessment data, which has inherent limitations. The innovation data used were cross-sectional, which may lack reliability (although self-assessed data counter this risk to some extent).

Managers should pursue both technical and nontechnical innovations for gains in external effectiveness and internal efficiency. However, complex services call for technical innovations to be accompanied by nontechnical innovations to support positive effects. The results cause a dilemma for managing customer participation in complex services. As the results show customer participation resulting in external effectiveness, they also fail to establish an effect on internal efficiency.

The primary contribution is to add to the knowledge of different types of innovation in complex services by demonstrating their interdependent effects on both external effectiveness and internal efficiency. Furthermore, the study tests and advances the mediating effect of customer participation in complex services on organizational performance.

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

Among the proposed radio access strategies for improving system execution in 5G networks, the non-orthogonal multiple access (NOMA) scheme is the prominent one.

Among the most fundamental NOMA methods, power-domain NOMA is the one where at the transmitter, superposition coding is used, and at the receiver, successive interference cancellation (SIC) is used. The importance of power allocation (PA) in achieving appreciable SIC and high system throughput cannot be overstated.

This research focuses on an outage probability analysis of NOMA downlink system under various channel conditions like Rayleigh, Rician and Nakagami-m fading channel. The system design's objectives, techniques and constraints for NOMA-based 5G networks' PA strategies are comprehensively studied.

From the results of this study, it is found that the outage probability performance of downlink ordered NOMA under Rayleigh, Rician and Nakagami-m fading channel was good.

Outage probability analysis of downlink ordered NOMA under various channel conditions like Rayleigh, Rician and Nakagami-m fading channels were employed. Though the performance of Nakagami-m fading channel is lesser compared to Rayleigh channel, the performance for user 1 and user 2 are good.

The performance of Wireless Sensor Networks (WSNs) applications is bounded by the limited resources of battery-enabled Sensor Nodes (SNs), which include energy and computational power; the combination of which existing research seldom focuses on. Although bio-inspired algorithms provide a way to control energy usage by finding optimal routing paths, those which converge slower require even more computational power, which altogether degrades the overall lifetime of SNs.

Hence, two novel routing protocols are proposed using the Red-Deer Algorithm (RDA) in a WSN scenario, namely Horizontal PEG-RDA Equal Clustering and Horizontal PEG-RDA Unequal Clustering, to address the limited computational power of SNs. Clustering, data aggregation and multi-hop transmission are also integrated to improve energy usage. Unequal clustering is applied in the second protocol to mitigate the hotspot problem in Horizontal PEG-RDA Equal Clustering.

Comparisons with the well-founded Ant Colony Optimisation (ACO) algorithm reveal that RDA converges faster by 85 and 80% on average when the network size and node density are varied, respectively. Furthermore, 33% fewer packets are lost using the unequal clustering approach which also makes the network resilient to node failures. Improvements in terms of residual energy and overall network lifetime are also observed.

Proposal of a bio-inspired algorithm, namely the RDA to find optimal routing paths in WSN and to enhance convergence rate and execution time against the well-established ACO algorithm. Creation of a novel chain cluster-based routing protocol using RDA, named Horizontal PEG-RDA Equal Clustering. Design of an unequal clustering equivalent of the proposed Horizontal PEG-RDA Equal Clustering protocol to tackle the hotspot problem, which enhances residual energy and overall network lifetime, as well as minimises packet loss.

The evolving visitors' expectations and the unfolding digital transformation compel rethinking on the service offering of museums and cultural institutions. Although digitalization and people-centeredness are widely exploited to enhance the visiting experience, there is limited evidence of their implications on organizational attractiveness. The article investigates this issue, examining the service attributes that entice visitors.

The study collected secondary data from the latest census study by the Italian Institute of Statistics on museums and cultural institutions. Two hierarchical regression models have been run on a sample of large publicly owned organizations (n = 312) to identify the service factors that were most effective in attracting Italian and foreign visitors.

Museums and cultural institutions undergoing a digital transformation were more effective in attracting visitors. The delivery of virtual tours and online events captivated the Italian audience. Foreigners appreciated the opportunity to use applications augmenting the on-site visit.

Digitalization and people-centeredness improve the attractiveness of museums and cultural institutions. Using digital channels to engage visitors fosters their desire to interact with cultural heritage. Furthermore, digitalization enriches the on-site visit, expanding conventional services with virtuality. However, the adverse effects on cultural heritage should be carefully handled.

This study highlights the service attributes that add to the attractiveness of museums and cultural institutions, enabling them to engage visitors and improve the visiting experience.

The pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.

This paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.

The study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.

This study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.

The previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.