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This paper aims to determine new-normal uncertainty considerations stemming from the COVID-19 pandemic to consider within transaction-cost analysis for pharmaceuticals. It also aims to propose new-normal market entry strategies to address the uncertainty as a result of COVID-19's implications and provide for lack of knowledge and information in an uncertain business environment by way of Internet of Things (IoT) ecosystem for pharmaceutical market entry.

In this paper, we focus on the uncertainty facet within transaction-cost analysis consideration and utilise a descriptive three-case study approach taking in Johnson and Johnson (J&J), GlaxoSmithKline (GSK) and Novartis to present an ADO (Antecedent-Decisions-Outcomes) understanding of their usual market entry approach, the approach undertaken during the pandemic and the outcomes thereafter facilitating new-normal uncertainty considerations to factor in. Further with this insight, we develop a conceptual framework addressing the transaction-cost analysis implications of uncertainties toward lack of knowledge and information for a new-normal market entry approach and operating strategy for pharmaceuticals applicable due to IoT (Internet of Things).

Uncertainty (external and internal) is different now in the new-normal business environment for pharmaceuticals and boils down to acute shortage of knowledge and information impact to make an appropriately informed decision. Therefore, considering the changed factors to consider, pharmaceuticals need to be able to undertake market entry with vaccines and medicines by way of IoT thereby enabling, the filling of the gap via real-time data access and sharing, including enhancing predictive analysis for sustenance.

The paper's findings have many theoretical implications highlighted in the manuscript.

The paper's findings have many practical implications highlighted in the manuscript.

This is the first study to our knowledge that throws light on transaction-cost analysis theory's uncertainty facet for pharmaceuticals. It is also the first study that provides a new-normal market entry strategy for pharmaceutical companies built on interoperability of real-time IoT.

This paper explores the role of Internet of things (IoT) enabling factors in adopting digital supply chain.

Analytical hierarchy process (AHP) was used to rank performance measures and prioritise the enabling factors. Semi-structured interviews were conducted to validate and support key research findings from the AHP analysis.

The results show that level of customer demand is the most important indicator in adopting IoT while the level of product/process flexibility is the least important. System integration and IoT infrastructure are the top two enabling factors in increasing the level of process stability, supply chain connectivity, and product/process flexibility, respectively. Furthermore, the study suggests that the enabling factors for IoT adoption are directly connected with organisational resources/technological capabilities that support the resource-based view theory. This research identified interdependencies between IoT enabling factors and key performance measures for IoT adoption success in managing the digital supply chain.

Supply chain managers can use the empirical findings of this study to prioritise IoT adoption, based on the relative importance of enabling factors and performance measures. The research findings are focused on broader supply chain practices of large companies rather than a specific industry and SMEs. Hence, any industry-specific adoption factors and SMEs were not evident from this study.

This research study empirically established priorities of enabling factors for IoT adoption, along with inter-dependencies among enabling factors as a basis for developing guidelines for IoT adoption.

The purpose of this paper is to highlight the drivers, barriers, benefits and risks affecting the integration of Internet of Things (IoT) into the e-government and to provide a future research agenda.

Existing literature examining the relationships between e-government and IoT is scanned and evaluated by conceptualizing the IoT concept in the e-government perspective.

The study shows that there are drivers to integrate IoT in e-government, such as ensuring the economy, efficiency and effectiveness of government operations, which would largely establish a relationship between the government and the citizens. Furthermore, there are barriers to such integration, given the lack of political will, the appropriate information technology infrastructure, the training of the stakeholders with a focus on the employee and the like.

The integration of IoT in e-government is a novel and weakly explored concept, particularly in the light of new advances such as blockchain in the e-government, which requires further exploration and conceptualization, thereby achieving a shared/common vision and body of knowledge for its further successful and sustainable adoption – to the best of the authors’ knowledge, the current study is one of these initial attempts.

This study aims to explore the mediating role of digital technologies-based supply chain integrating (SCI) strategies on the agri-supply chain performance (SCP) and firm performance (FP). This research has introduced recently emerged digital technologies such as Internet of Things (IoT). Further, based on theoretical support and an extensive literature review, this research has proposed some hypotheses, which have been quantitatively validated for their significance.

A conceptual model was formulated based on an extensive literature review. Data for this research were gathered from a survey completed by 119 respondents from different departments of agri-firms. Further, partial least square (PLS)-based structured equation modelling (SEM) was used to test the proposed hypothetical model.

The results confirm that IoT-based digital technologies and supply chain processes (organization integration [OI], information sharing and customer integration [CI]) have a significant positive correlation. Furthermore, supply chain practices are positively associated with SCP. Finally, it has been found that FP is positively impacted by SCP.

This research is used to analyse the mediating impacts of digital supply chain processes as a linking strategy for SCP and FP. For practical purposes, this research provides investment decisions for implementing digital technologies in SC strategies. The findings have proposed implications for managers and practitioners in agri-firms based on existing theories: contingency theory (CT) and relational view theory. Also, this study suggests the deployment of smarter electronically based tags and readers, which improve the data analytics capabilities based on auto-captured data. Thus, the availability of quality information improves the data-driven decisional capabilities of managers at company level.

This is a unique and original study exploring the relationship between digitalization, resilient agri-food supply chain (AFSC) management practices and firm performance. This research may be extended to other industries in view of the results from SCP and impact of digitalization.

The public sector use of the internet of Things (IoT) technology is an emerging field with a potential for a variety of institutional applications in the fields of information gathering and policy implementation to establish resilience for modern society. In this context, we aim to investigate the success factors which can positively influence the continuous usage intention of IoT service with the mediation effect of perceived public value.

A conceptual model based on the strong literature and theoretical background is tailored to provide factors for IoT services success in the public sector. A quantitative survey was conducted through 381 citizens using IoT services to validate the proposed conceptual model using structural equation modeling approach in order to examine the influence of identified constructs on the continuous usage intention.

The results highlighted that the proposed model successfully accounted for about 59% perceived variance in public value creation and 63% perceived variance in citizens' continuous usage intention. IoT services success is best dignified by the perceptions of citizens who use the services, therefore, this study identifies the positive role of digital society affinity in context of social sustainability and it climaxes that the real value of IoT in public sector comes from establishment of services on top of the service delivery and that's where the public value is going to be created.

The use of IoT technology in public services will fetch much benefit to the citizens as well as to the government. The study findings are significant in identifying good practices for generating public value in digital society affinity.

The integrated conceptual model explores the driving factors of citizens' inclination towards IoT technology in a social context. Through this study, we can analyze the role of perceived public value in enhancing the citizens' engagement with IoT services. The government policymakers and ICT managers take help from this study to designing IoT public services with making the government more responsive to citizens.

The Internet of Things has made the shift to the digital era possible, even though the Architecture, Engineering and Construction (AEC) sector has not embraced nor integrated it within the core functions compared to other sectors. The need to enhance sustainable construction with the adoption of Internet of Things in this sector cannot be overemphasized. However, the real-world applications of Internet of Things in smart buildings remain relatively unexplored in the AEC sector due to several issues related to deployment and energy-saving potentials. Given these challenges, this paper proposes to identify the present state of development and research in Internet of Things and smart buildings, identify Internet of Things clusters and applications in smart buildings.

Bibliometric analyses of papers from 2010 to 2023 using the Scopus database and scientometric evaluations using the VosViewer software were undertaken. The proper search keyword was identified by using the phrases “ Internet of Things” and “Smart Building”. A total of 1158 documents in all, written by 3540 different writers, representing 2285 different institutions from 97 different countries were looked at. A metasynthesis was conducted and a system of Internet of Things applications in a smart building is illustrated.

The development of IoT and Smart Buildings is done in two phases: initiation (2010–2012) and development phase (2013–2023). The IoT clusters comprised Internet of things, energy efficiency, intelligent buildings, smart buildings and automation; while the most commonly used applications were analysed and established. The study also determined the productive journals, documents, authors and countries.

Documents published in the Scopus database from 2010 to 2023 were considered for the bibliometric analysis. Journal articles, conference papers, reviews, books and book chapters written in English language represent the inclusion criteria, while articles in press, conference reviews, letters, editorials, undefined sources and all medical and health publications were excluded.

The results of this study will be used by construction stakeholders and policymakers to identify key themes and applications in IoT-enabled smart buildings and to guide future research in the policymaking process of asset management.

The study utilised bibliometric analysis, scientometrics and metasynthesis to investigate Internet of things applications in smart buildings. The study identified Internet of things clusters and applications for smart building design and construction.

There is an increasing interest in the supply chain’s digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain’s digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.

This study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and abstracts and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.

The results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.

While the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.

This study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.

The digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

The purpose of this study is to verify that if applications categories are segmented and resources are allocated based on their specific category, how effective scheduling can be done?.

This paper proposes a scheduling framework for IoT application jobs, based upon the Quality of Service (QoS) parameters, which works at coarse grained level to select a fog environment and at fine grained level to select a fog node. Fog environment is chosen considering availability, physical distance, latency and throughput. At fine grained (node selection) level, a probability triad (C, M, G) is anticipated using Naïve Bayes algorithm which provides probability of newly submitted application job to fall in either of the categories Compute (C) intensive, Memory (M) intensive and GPU (G) intensive.

Experiment results showed that the proposed framework performed better than traditional cloud and fog computing paradigms.

The proposed framework combines types of applications and computation capabilities of Fog computing environment, which is not carried out to the best of knowledge of authors.