Internet of Things and Big Data as enablers for business digitalization strategies

Study on the impact of digital transformation on green competitive advantage: The role of green innovation and government regulation

Digital transformation enables small and medium enterprises (SMEs) to reduce or overcome their reliance on resources and energy, thereby minimizing their environmental impact and providing them with sustainable green competitive advantages. However, the reasons for this phenomenon are not yet clear. To further investigate this issue, we selected 391 Chinese SMEs to examine the relationships among green transformation, green innovation, government regulation, and green competitive advantages. Green innovation includes green product innovation and green process innovation, while government regulation includes incentive regulation, constraint regulation, and guidance regulation. The empirical results show that digital transformation can enhance SMEs' green competitive advantages. Additionally, the hypothesized mediating effect of green product innovation and green process innovation between digital transformation and green competitive advantages is supported, while the moderating effect of incentive regulation, constraint regulation, and guidance regulation on the relationship between digital transformation and green product innovation and green process innovation is also confirmed. The findings of this study may contribute to more effective management of digital transformation and green innovation in SMEs, thereby promoting their development.

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.

Inverting social innovation to transform health system responses to climate change adaptation and mitigation in the global south

Systems thinking is aimed at understanding and solving complex problems that cut across sectors, an approach that requires accurate, timely, and multisectoral data. Citizen-driven big data can advance systems thinking, considering the widespread use of digital devices. Using digital platforms, data from these devices can transform health systems to predict and prevent global health crises and respond rapidly to emerging crises by providing citizens with real-time support. For example, citizens can obtain real-time support to help with public health risks via a digital app, which can predict evolving risks. These big data can be aggregated and visualized on digital dashboards, which can provide decision-makers with advanced data analytics to facilitate jurisdiction-level rapid responses to evolving climate change impacts (e.g., direct public health crisis communication). In the context of climate change, digital platforms can strengthen rapid responses by integrating information across systems (e.g., food, health, and social services) via citizen big data. More importantly, these big data can be used for rapid decision-making,a paradigm-changing approach that can invert social innovation, which we define as co-conceptualizing societal solutions with vulnerable communities to improve economic development with a focus on community wellbeing. However, to foster equitable and inclusive digital partnerships that invert social innovation, it is critical to avoid top-down approaches that sometimes result when researchers in the Global North and South collaborate. Equitable Global South-North partnerships can be built by combining digital citizen science and community-based participatory research to ethically leverage citizen-driven big data for rapid responses across international jurisdictions.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.

Digital threads in turbulent times: unraveling technostress and cleaner production in the food industry

Introduction: In the current landscape marked by swift digital transformations and global disruptions, comprehending the intersection of digitalization and sustainable business practices is imperative. This study focuses on the food industries of China and Pakistan, aiming to explore the influence of digitalization on cleaner production. Methods: Employing a cross-sectional design, data were gathered through online surveys involving a diverse group of employees. Special attention was given to the emergent phenomenon of technostress and its subsequent implications for individuals in the workplace. Results: The findings of the study demonstrate a significant impact of digitalization on both resource mobilization and interaction quality within the surveyed food industries. Notably, technostress emerged as a mediating factor, shedding light on the psychological challenges associated with digital transitions. The study further reveals the moderating role of the COVID-19 pandemic, altering the dynamics among the variables under investigation. Discussion: From a theoretical perspective, this research contributes to the cleaner production literature by bridging it with the human-centric nuances of technological adaptation. On a practical level, the study emphasizes the importance of aligning digital strategies with resource mobilization to achieve sustainable outcomes. For the food industry and potentially beyond, the research offers a roadmap for integrating digital tools into operations, ensuring efficiency, and promoting cleaner production.

Analysing the current status, hotspots, and future trends of technology management: Using the WoS and scopus database

This study presents a comprehensive overview of the development process and the latest trends in technology management (TM), laying a robust foundation for further advancements in this domain. To achieve this, we analysed 1944 TM articles from the Web of Science database and 2642 articles from Scopus, spanning the last 20 years. Employing methodologies that involve scientific knowledge graphs and bibliometrics, we analysed diverse aspects such as changes in the annual publication of articles; geographical distribution among countries, institutions, disciplines, and authors; keyword co-occurrence and clusters; and timezone view. Our findings reveal a significant surge in TM's growth in recent years, showcasing its highly promising potential. The USA is the frontrunner in contributing to TM research, followed by China and the UK. TM research is relatively concentrated in the UK, while it appears more dispersed in China. The University of Cambridge had the highest volume of research, and the disciplines of Business, Management, Engineering, and Computer Science occupied the top spots. As TM evolves, a possible challenge could be the emergence of new authoritative authors. Second, TM's vibrant landscape is characterised by hotspots such as innovation, technology strategy, technology acquisition, technology application, technology standards, and sustainable development. Among these, information and medical technologies stand out as the most frequently referenced technologies. Third, the trends in TM are as follows: innovation is subdivided into technological innovation and open innovation, bibliometric analysis and patent analysis have become pivotal methods for knowledge management, the scope of TM has expanded from internal organisational processes to encompass external aspects, and TM is gradually evolving into a mature science, with its focus transitioning from macro to micro and becoming more profound and detailed. Last, Industry 4.0, artificial intelligence, big data, and the IoT represent the latest frontier technologies in the realm of TM.

Overview of Food Preservation and Traceability Technology in the Smart Cold Chain System

According to estimates by the Food and Agriculture Organization of the United Nations (FAO), about a third of all food produced for human consumption in the world is lost or wasted-approximately 1.3 billion tons. Among this, the amount lost during the storage stage is about 15-20% for vegetables and 10-15% for fruits. It is 5-10% for vegetables and fruits during the distribution stage, resulting in a large amount of resource waste and economic losses. At the same time, the global population affected by hunger has reached 828 million, exceeding one-tenth of the total global population. The improvement of the cold chain system will effectively reduce the amount of waste and loss of food during the storage and transportation stages. Firstly, this paper summarizes the concept and development status of traditional preservation technology; environmental parameter sensor components related to fruit and vegetable spoilage in the intelligent cold chain system; the data transmission and processing technology of the intelligent cold chain system, including wireless network communication technology (WI-FI) and cellular mobile communication; short-range communication technology, and the low-power, wide-area network (LPWAN). The smart cold chain system is regulated and optimized through the Internet of Things, blockchain, and digital twin technology to achieve the sustainable development of smart agriculture. The deep integration of artificial intelligence and traditional preservation technology provides new ideas and solutions for the problem of food waste in the world. However, the lack of general standards and the high cost of the intelligent cold chain system are obstacles to the development of the intelligent cold chain system. Governments and researchers at all levels should strive to highly integrate cold chain systems with artificial intelligence technology, establish relevant regulations and standards for cold chain technology, and actively promote development toward intelligence, standardization, and technology.

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

BACKGROUND

The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making.

OBJECTIVE

The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care.

METHODS

The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory's community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard's interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data.

RESULTS

The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security.

CONCLUSIONS

Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/46810.

Digital transformation and marketing: a systematic and thematic literature review

This article provides a systematic review of the extensive and fragmented literature focused on Digital Transformation (DT) and marketing by identifying the main themes and perspectives (i.e., employees, customers, and business processes) studied by previous research. By mapping the DT literature in the area of marketing, 117 articles, published between 2014 and 2020, have been identified. Through the adoption of a content analysis process, a multi-dimensional framework synthesizing the DT and marketing binomial has been provided. Results identify two thematical patterns: the macro-themes, related to the main digital technologies adopted within the marketing function, and the micro-themes, related to the effect/impact of these technologies on marketing processes and activities. Concerning the micro-themes, findings show how they have mainly studied from the customer and business processes’ perspectives, thus identifying an interesting research gap related to the analysis of the DT-marketing phenomenon from the employees’ standpoint. Based on these results, the paper derives a research agenda by also providing theoretical and managerial implications. Theoretically, it is the first systematic and thematic review focused on DT and marketing. In particular, it analyses this binomial from a broad and comprehensive perspective, thus offering a synergistic framework of the existing literature, which allows an inclusive vision and understanding about the phenomenon. At the managerial level, the paper could help organizations to enhance their awareness about marketing areas and processes that could better benefit from digitalization, thus driving the overall transition of firms towards DT.

Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry

Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory.

User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice.

Voice assistants in private households: a conceptual framework for future research in an interdisciplinary field

The present study identifies, organizes, and structures the available scientific knowledge on the recent use and the prospects of Voice Assistants (VA) in private households. The systematic review of the 207 articles from the Computer, Social, and Business and Management research domains combines bibliometric with qualitative content analysis. The study contributes to earlier research by consolidating the as yet dispersed insights from scholarly research, and by conceptualizing linkages between research domains around common themes. We find that, despite advances in the technological development of VA, research largely lacks cross-fertilization between findings from the Social and Business and Management Sciences. This is needed for developing and monetizing meaningful VA use cases and solutions that match the needs of private households. Few articles show that future research is well-advised to make interdisciplinary efforts to create a common understanding from complementary findings-e.g., what necessary social, legal, functional, and technological extensions could integrate social, behavioral, and business aspects with technological development. We identify future VA-based business opportunities and propose integrated future research avenues for aligning the different disciplines' scholarly efforts.

Flexible supplier selection and order allocation in the big data era with various quantity discounts

This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of the modeling and solution difficulty. All suppliers offering the same discount are far from reality, especially when the number of suppliers is large. The proposed model is a variant of the NP-hard knapsack problem. The greedy algorithm, which solves the fractional knapsack problem optimally, is applied to cope with the challenge. Three greedy algorithms are developed using a problem property and two sorted lists. Simulations show the average optimality gaps are 0.1026%, 0.0547%, and 0.0234% and the model can be solved in centiseconds, densiseconds, and seconds for supplier numbers 1000, 10000, and 100000. This allows the full use of data in the big data era.

European IoT Use in Homes: Opportunity or Threat to Households?

The residential sector is characterized by new digital challenges. The Internet of Things (IoT) is a key-driver of innovation and operations management. This study aims to measure and assess IoT devices at the level of individuals, which are households, in European countries. For this scope, through the multi-criteria decision analysis (MCDA), we analyse data from Eurostat providing a mix of indicators allowing information to be aggregated at the level of individual Europeans and disaggregated by age group. The results highlight that only four countries (Netherlands, Denmark, Sweden and Malta) are classified as a high cluster in the examined scenarios. The 16-24 age group is the most involved in the uses of IoT devices, but the previous three northern European countries also show very high values for the 35-44 age group. IoT devices serve as a springboard for achieving a powerful propulsion toward technological innovation in the new business models, identifying opportunities and being a way to make many routine tasks more agile. Training programs and awareness campaigns are policy suggestions for the development of IoT devices favouring a cultural change on their use. However, there is an emerging need for studies that monitor environmental health impacts to prevent possible threats.

Design of Flute Music Remote Teaching System Based on Multi-Pass Scheduling Optimization

With the gradual development of the Internet industry, every aspect of people's life has been affected by the Internet, playing increasingly irreplaceable functions in people's entertainment, office, and other aspects. Judging from the current development situation, the old Internet digital teaching system has many problems, such as low artificial intelligence, weak information processing ability, and lack of effective learning ability. This paper designs the flute music remote teaching system, which can realize remote music teaching and provide help in providing real-time music teaching. The music learning system includes the user's access records, the user's operation and the completion of the test data, the discussion and communication of online participation, the user's interests, specialties and operation methods, learning progress and scoring, and so on. In addition, it explores and explains all the key steps required by the current distance education model and invents a sample of the distance education model. On this basis, Internet algorithm programs will be used for all key processing functions of the system. The use of Internet algorithm programs is interactive and automated, which greatly enhances the role of the education system. This article first discusses the unique teaching and automated teaching mode of the system, which lays the cornerstone for further reforms in this field in the future.

Big data from customers and non-customers through crowdsourcing, citizen science and crowdfunding

Purpose

The unprecedented growth in the volume, variety and velocity with which data is generated and collected over the last decade has led to the spread of big data phenomenon. Organizations have become increasingly involved in the collection and analysis of big data to improve their performance. Whereas the focus thus far has mainly been on big data collected from customers, the topic of how to collect data also from those who are not yet customers has been overlooked. A growing means of interacting with non-customers is through crowd-based phenomena, which are therefore examined in this study as a way to further collect big data. Therefore, this study aims to demonstrate the importance of jointly considering these phenomena under the proposed framework.

Design/methodology/approach

This study seeks to demonstrate that organizations can collect big data from a crowd of customers and non-customers through crowd-based phenomena such as crowdsourcing, citizen science and crowdfunding. The conceptual analysis conducted in this study produced an integrated framework through which companies can improve their performance.

Findings

Grounded in the resource-based view, this paper argues that non-customers can constitute a valuable resource insofar as they can be an additional source of big data when participating in crowd-based phenomena. Companies can, in this way, further improve their performance.

Originality/value

This study advances scientific knowledge of big data and crowd-based phenomena by providing an overview of how they can be jointly applied to further benefit organizations. Moreover, the framework posited in this study is an endeavour to stimulate further analyses of these topics and provide initial suggestions on how organizations can jointly leverage crowd-based phenomena and big data.

THE PLATFORMISATION OF MANUFACTURING: TOWARDS A HOLISTIC PERSPECTIVE FOR SYSTEMATISING DIGITAL MANUFACTURING PLATFORMS

Digital Platforms have profoundly changed many sectors and shaped the concept of the platform economy. While they have been a part of the consumer goods sector for several decades, their development and rise in the manufacturing industry seems to be just beginning. A number of different digital platforms now exist specifically for product manufacturers, such as digital marketplaces for industrial goods, manufacturing as a service, platforms for product development, or platforms addressing digital production and digital product-service offers via the Industrial Internet of Things. Due to this high diversity, this paper aims to deliver a holistic concept that systematises digital platforms in manufacturing using a morphological box. We distinguish and subsequently embed three different main types in an overall development. The results provide insights into the platformisation trend of manufacturing, which not only holds implications for future research, but also serve as a basis for strategic decisions in manufacturing firms.

Milk-Run Collection Monitoring System Using the Internet of Things Based on Swarm Intelligence

In our country Morocco, several dairy factories are placed in rural regions with a bad road network, which means that milk collection has a significant impact on profit, affecting milk transport costs. Actually, the milk run logistics process has been transformed from a traditional farm to the new cheese factory, so it’s needed efficient methods and models to improve the process of production and collection of milk from those units. For that, we will apply new technologies such as the internet of things (IoT) and big data to collect and analyze this information to optimize the milk delivery process. The main goal of this work is to design a new smart decision method using the internet of things and big data to optimize the milk run logistics, reduce the cost of transportation and improve collection density. This method will be based on the swarm artificial intelligence concept to find and calculate the shortest path between units to optimize the collection of milk.

What Do We Know and What Do We Need to Know about COVID-19’s Implications on Business Economics? From Bibliometric Analysis to a Conceptual Framework

This paper presents a bibliometric analysis of COVID-19-related research in business economics. The current status of research on economic management in COVID-19 is shown through descriptive statistics. The corresponding knowledge maps are obtained based on keyword clustering analysis, and research topics of interest to Chinese and foreign readers are identified. This paper finds that the impact of COVID-19 on business economics is mainly manifested in six major themes, namely COVID-19 and crisis management, COVID-19 and supply chain, COVID-19 and digitalization, COVID-19 and economic development, COVID-19 and organizational management, and COVID-19 and sustainable development. Based on these research foundations, this paper proposes a research framework for economic management under the influence of COVID-19. It describes the current research status, research directions, and future topics of six key research themes from macro, meso, and micro perspectives, to provide a knowledge base for research and practice in the field of economic management in the post-pandemic era.

An ISM-MICMAC approach for analyzing dependencies among barriers of supply chain digitalization

Purpose

Supply chain digitalization (SCD) facilitates a competitive advantage and is equally challenging. A literature analysis reveals that research related to SCD is available, but analysis of key barriers and their interrelations for effective SCD is limited. This paper aims to identify and find out interrelationships among the key barriers affecting SCD in the electronic supply chain (SC).

Design/methodology/approach

Interpretive structural modeling (ISM) is used to identify the contextual relationship among the SCD barriers. The model is validated using MICMAC analysis.

Findings

The findings of this study have implications at both theoretical and practical levels. The proposed ISM model can help organizations analyze and interpret interrelationships among barriers to SCD. It can lead to better insights and understanding for minimizing the dominant barriers to effective SCD.

Originality/value

This study tries to bridge the gap in identifying and finding the interrelationships among the key barriers to effective SCD in an electronic SC. The result assists practicing managers and academicians in effective decision-making on the SCD process.

Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece

Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing.

Towards the Achievement of Frugal Innovation: Exploring Major Antecedents among SMEs

The study in hand investigates the impact of the Internet of things (IoT) and digital platforms on frugal innovation. Furthermore, we also tested the intervening role of digital platforms and moderating role of organizational readiness. Data were collected from SMEs and the collected data were analyzed with various statistical techniques. Findings shown that the IoT positively affects digital platforms and frugal innovation. Results proved that digital platforms mediate the association between the IoT and frugal innovation. Furthermore, organizational readiness also moderates on the association between digital platforms and frugal innovation. The findings revealed that only those businesses that update their business operations according to the needs of their customers can achieve success. Frugal innovation is an emerging concept, and its achievement is a very tricky and complex task. This study shows that only those businesses that update their business operations using the IoT and digital platforms can achieve success. Moreover, modern SMEs should be able to adapt to the required changes in their business activities. Hence, SMEs also require some strategic changes in their operations in order to cope with changing business circumstances.

Tools for Correlation and Regression Analyses in Estimating a Functional Relationship of Digitalization Factors

Digitalization processes affect all levels and spheres of human activities, from personal communications to public events. The widespread implementation of digital technologies has an ambiguous effect on the personal, social, and economic paths of modern society’s development. At the moment, there is no single approach to the estimation of digitalization’s impact, particularly on the financial and economic properties of a company. The objective of this study was to create multiple models for the assessment of digitalization’s impact on company performance. To accomplish this objective, the following steps were performed: conducting a literature survey on the experience with digital technologies’ implementation; selecting the most appropriate mathematical tools for correlation and regression analyses; determining a functional relationship between the factors and consequences of digitalization in terms of companies’ performance; identifying digitalization factors, risks, and their impact on the financial sustainability of companies; and creating a multiple regression model of the functional relationship between digitalization factors and the financial sustainability of companies. In the course of the study, a correlation analysis of the dependence of companies’ financial sustainability on a number of digitalization factors has been conducted, and different ways of using company performance data as effective features and predictive factors are offered. The article includes sampling data on the parameterization and quality evaluation of multiple regression models. The validity of the multiple models suggested was tested with actual statistical data obtained from 16 Russian companies. The application of the multiple regression model was devised to estimate digitalization’s impact on companies’ performance, and their financial sustainability can be seen as the most important practical implication of this study.

The influence of technological innovations on international business strategy before and during COVID-19 pandemic

In the last two years, the world has gone through an unprecedented change in the most diverse dimensions (social, economic, and even political), leading that society had to adapt very quickly to the contingencies imposed by COVID-19. All organizations (independent of their area of activity) had to adjust their processes to respond, efficiently and effectively, to these constraints. In this context, companies with concerns in internationalization (those that are already internationalized and those in an internationalization process) have had to resort to technologies to support the change in their modus operandi. The digital transformation (until now had an essential role in the transformation of organizations, but which was in a relatively slow implementation process) started to perform, in an accelerated way, the base of work for the heads of the organizations to be able to respond to these challenges. In this context, the transformation of the business model, supported by digital technology, has been documented as one of the strategies used to respond to disruptive environmental changes, particularly technologies that help companies identify new business practices. This study aims to find evidence of the importance of integrating and influencing technological innovations in the practice of international business strategy before and during COVID-19 pandemic. The results show the influence of the digitalization on the business strategies.

The Importance of Digital Transformation in International Business

This study was developed under the scope of a Portuguese project focused on the entrepreneur’s perspective and perception on the internationalization process of his company: more specifically, about the factors that enhanced the company entry into foreign markets as well as the constraints found in this process. This work focuses on the importance of using digital transformation to integrate technological tools in international business practice and strategy and the obstacles encountered with introducing these new technologies. This study aims to determine the relationships between technology categories and obstacles. The final goal is to assess the impact of these characteristics of the companies by the sector of economic activity, size, and percentage of profits resulting from international expansion. A questionnaire was designed and sent by email to 8183 companies from the AICEP database, distributed by three main activity sectors. A total of 310 valid answers were gathered from the Portuguese internationalized companies. The research limitations are related to the reduced number of interviews. These interviews showed that managers were not aware of the concept of digital transformation and misunderstood the use of digital technologies in the internationalization process of the business. This limitation can add some bias to the qualitative results. In addition to these limitations, the number of responses per sector was also not homogeneous. The practical implications of this study are that managers and top-level executives can use that to better understand how companies could use digital tools and what obstacles they should avoid when they want to internationalize their business. This paper is one of the first research contributions to analyze the impact of digital transformation in the internalization of Portuguese companies.

Awareness of industry 4.0 and its tools across the V4 countries, Serbia and Bulgaria

The researched countries, which include the V4 countries, Serbia and Bulgaria, are working to exploit the potential that digitization offers under the Industry 4.0 concept. The rise of Industry 4.0 has resulted in tools such as machine learning, big data, automation, and robotics. In the current environment, we observe it in almost every business entity and across the V4 countries, Serbia and Bulgaria. Awareness of Industry 4.0 continues to grow in the current environment, precisely as a result of the expansion of Industry 4.0. The countries studied are taking Industry 4.0 tools to a new level. To obtain an up-to-date picture of the researched issue, our own questionnaire survey was used, which was used to examine and analyze the current level of Industry 4.0, and the results included in this paper can be compared with the results of the current level of Industry 4.0 in other countries. In order to obtain objective results from the questionnaire survey, five research areas were determined using five hypotheses. The main benefit of the paper lies in the interpretation of the results of the authors' scientific research, which point to the current level and rate of use of Industry 4.0 tools in the defined areas.

Research trends in digital transformation in the service sector: a review based on network text analysis

Digital transformation has influenced value chain operations of both manufacturing and service firms. This study examined digital transformation in the service sector through network text analysis of 330 related articles published during the past 10 years. The selected papers’ keyword link relations were analyzed to create network maps of research topics, ranging from traditional to emerging ideas of researchers. Dominant research topics and their clusters were identified using centrality and community analyses, and research trends were identified. The results of this study will help researchers and practitioners in the relevant fields capture the overall picture of the field.

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19

Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable 'digital wildfires' that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one's attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of 'echo chambers' at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice.

Information Technology Adoption on Digital Marketing: A Literature Review

Data generation is currently expanding at an astonishing pace, and the function of marketing is becoming increasingly sophisticated and customized. Companies seek to understand their internal corporate environment and externalities and to exponentially enhance their marketing power. This study aims to understand the influence of Big data analysis on digital marketing. The methodologies used to approach this issue were: (a) a systematic literature review based on articles dated between 2014 and 2020; and (b) a bibliometric analysis of articles dated between 2000 and 2020 using the software VOSviewer. The literature review allowed us to conclude that in the next decades, the business world in general, and marketing in particular, will define more oriented strategies based on a more profound knowledge of consumer behavior. Artificial intelligence agents driven by machine learning methods, technology, and Big data will be a conditioning factor in defining these strategies.

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as ‘machine learning’, blockchain, ‘Internet of Things or IoT’, and keywords conjoined with ‘healthcare’ and ‘health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

Internet of Things adoption barriers in the Indian healthcare supply chain: An ISM-fuzzy MICMAC approach

Internet of things (IoT) plays a significant role in offering a wide range of benefits to the healthcare supply chain. It improves the quality of patient care, reduces the cost of medical procedures, maintaining flawless operations and supports clinical decisions. Despite strong policy interest and vast potential benefits of IoT in the healthcare supply chain, the actual adoption rate is relatively low and it's in the nascent stage in developing countries like India. The objective of this paper is to identify and analyze the potential barriers which would restrain the healthcare industry from adopting IoT. Based on the review of the literature and brainstorming with experts from industry and academia, 14 barriers were identified for IoT adoption. The contextual relationship between the identified barriers was developed using interpretive structural modeling and the obtained results were used as input to fuzzy MICMAC analysis to determine the driving and dependence power of the IoT adoption barriers. From the result, Legal and regulatory standards, Lack of IT Infrastructure are major barriers influencing the adoption of IoT in the healthcare supply chain. The findings will support the decision-makers to understand the contextual relationship between the IoT adoption barriers in the Indian healthcare supply chain.

Assessing the level of digitalization and robotization in the enterprises of the European Union Member States

One of the main reasons for the dynamic global economic development observed in recent years is the process of digitalization, referred to as Industry 4.0. The significance of digitalization for this development is appreciated by the EU-27. In order for these actions to be effective, it is necessary to diagnose the current level of digitalization in the EU-27countries. The article presents the results of the assessment of the level of digitalization of enterprises in the EU-27 countries. An empirical analysis was conducted using 16 determinants which describe the digitalization in a sample of 27 EU countries. Based on the adopted criteria and the Technique for Order Preference by Similarity to an Ideal Solution method, these countries were divided into four classes in terms of the level of digitalization. The analysis looked at the size of enterprises and was performed independently for small, medium and large enterprises. The adopted indicators allowed for the analysis of similarity between the EU-27 countries in terms of digitalization, using the Kohonen’s networks. The result of this research was the division of the EU-27 countries into groups, also taking into account the size of studied enterprises. Due to the immensely diverse EU-27 economy, such a huge undertaking as the digital transformation process requires building logical internal "digital coalitions". The designated assessment and similarity between countries creates such opportunities, also in terms of building an effective policy to support these processes by the EU. This increases the chances of success of joint ventures and building a sustainable European community based on the latest technologies.

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.