Systems, Volume 10, Issue 2 (April 2022) – 30 articles

In order to model the emergency facility location-allocation problem with uncertain parameters, an uncertain multi-objective model is developed within the framework of uncertainty theory. The proposed model minimizes time penalty cost, distribution cost and carbon dioxide emissions. The equivalents of the model are discussed via operational laws of uncertainty distribution. By employing the goal attainment technique, a series of Pareto-optimal solutions are generated that can be used for decision-making. Finally, several numerical experiments are presented to verify the validity of the proposed model and to illustrate decision-making strategy. Full article

Vaccines are one of the safest medical interventions in history and can protect against infectious diseases and ensure important health benefits. Despite these advantages, health professionals and policymakers face significant challenges in terms of vaccine rollout, as vaccine hesitancy is a global challenge, and varies greatly with context, i.e., place, time, and vaccines. The internet has rapidly become a widely used information source for health-related issues, and a medium where misinformation in relation to vaccines on social media can spread rapidly and influence many. This research models the impact of vaccine confidence on the transmission of infectious diseases. This involves two interacting contagion models, one for the disease itself, and the other for the public’s views on vaccination. Sensitivity analysis and loop impact analysis are used to explore the effects of misinformation and vaccine confidence on the spread of infectious diseases. The analysis indicates that high vaccine confidence has a reinforcing effect on vaccination levels and helps to reduce the spread of an infectious disease. The results show that higher vaccine confidence can mitigate against the impact of misinformation, and by doing so can contribute to the enhanced control of an infectious disease outbreak. Full article

Cyber-security incidents show how difficult it is to make optimal strategic decisions in such a complex environment. Given that it is hard for researchers to observe organisations’ decision-making processes driving cyber-security strategy, we developed a board game that mimics this real-life environment and shows the challenges of decision-making. We observed cyber-security experts participating in the game. The results showed that decision-makers who performed poorly tended to employ heuristics, leading to fallacious decision approaches (overreaction strategies in place of proactive ones), and were not always aware of their poor performances. We advocate the need for decision support tools that capture this complex dynamic nature. Full article

Systemic racism, which exists when minorities experience harmful outcomes from implicit or explicit bias, has recently been a much-discussed phenomenon. Systemic racism may exist, even though explicit bias is mostly illegal, because of structures of policy or behavior that generate deleterious outcomes. Bank financing for housing purchase or improvement is one such structure. An overtly discriminatory policy facilitated by an agency of the United States government, “redlining” on “residential security maps” depicted supposedly high-risk lending areas in red. These historical maps have led to low housing values today in formerly redlined areas. Even though the practice has been illegal for decades, traditional lenders nowadays decline loans in those areas because they are too small to be profitable. A system dynamics model shows the systemic structure of this situation. The model simulates various policies for its solution. Robust (but expensive) policies involve subsidies to lenders or lending from governments or nonprofits. Less robust but potentially cheaper policy would require lenders to make small loans anyway. Any of these policies would help break the adverse reinforcing loop of declining housing, inability to borrow to improve the housing, and further housing decline. Full article

Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect. Full article

At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion makes it very easy for actual topic views to go to extremes. Significantly, negative information seriously affects the healthy development of the social opinion ecology. Therefore, it is beneficial to maintain national credibility, social peace, and stability by exploring the communication structure of online public opinions, analyzing the logical model of extreme public attitudes, and guiding the communication of public opinions in a timely and reasonable manner. Starting from the J–A model and BA network, this paper explores the specific attributes of individuals and opinion network nodes. By incorporating parameters such as individual conformity and the strength of individual online relationships, we established a model of online group attitude polarization, then conducted simulation experiments on the phenomenon of online opinion polarization. Through simulations, we found that individual conformity and the difference in environmental attitude greatly influence the direction of opinion polarization events. In addition, crowd mentality makes individuals spontaneously choose the side of a particular, extreme view, which makes it easier for polarization to form and reach its peak. Full article

Exploring passengers’ consumption motivation can provide the basis for arranging commercial activities in high-speed rail (HSR) stations to generate more revenue for operations. This study uses a mixed multiple-attribute decision-making model for exploring the consumption motivation at HSR stations and complex influential relationships from the passengers’ perspective. The passenger traffic at five major HSR stations in Taiwan were evaluated. Based on the results of decision-making trial and evaluation laboratory (DEMATEL) and DEMATEL-based on the analytical network process methods, it is shown that station attributes and consumption environment attributes are key factors that impact product attributes. Moreover, store location, commercial activities offered, product diversity, time pressure, and service convenience have a “cause” characteristic and, therefore, should be focused on when deploying commercial services at HSR stations. The findings from the modified VlseKriterjumska Optimizacija I Kom-promisno Resenje method reveal that time pressure has the largest gap to aspiration level at almost all the stations. Finally, corresponding management implications to HSR stations are proposed. Full article

Part-whole Systems (S) structure is foundational to a diverse array of phenomena such as belonging and containment, networks, statistics, reductionism, holism, etc. and is extremely similar if not synonymous with sets, sorts, groups, combinations and combinatorics, clusters, etc. In Cabrera (1998), part-whole Systems (S) or “S-rule” is established as one of four universals for the organization of information and thus is foundational to systems and systems thinking as well as the consilience of knowledge. In this paper, seven empirical studies are presented in which (unless otherwise noted) subjects completed a task. Ranging from n = 407 to n = 34,398, the sample sizes vary for each study but are generalizeable to a normal distribution of the US population. With high statistical significance, the results of these studies support the predictions made by DSRP Theory regarding part-whole Systems (a.k.a., “S-rule”) including: the universality of S-rule as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); the internal structures and dynamics of S-rule; S-rule’s mutual dependencies on other universals of DSRP (Distinctions, Systems, Relationships, and Perspectives (i.e., Distinctions, Relationships, and Perspectives); the role S-rule plays in making structural predictions; and, S-rule’s efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of part-whole Systems (S). Full article

The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance. Full article

Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed. Full article

The transdisciplinary importance of distinctions is well-established as foundational to such diverse phenomena as recognition, identification, individual and social identity, marginalization, externalities, boundaries, concept formation, etc., and synonymous general ideas, such as thingness, concepts, nodes, objects, etc. Cabrera provides a formal description of and predictions for identity–other distinctions (D) or “D-rule” as one of four universals for the organization of information that is foundational to systems and systems thinking, as well as the consilience of knowledge. This paper presents seven empirical studies in which (unless otherwise noted) software was used to create an experiment for subjects to complete a task and/or answer a question. The samples varied for each study (ranging from N = 407 to N = 34,398) and were generalizable to a normal distribution of the US population. These studies support—with high statistical significance—the predictions made by DSRP theory regarding identity–other distinctions including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., relationships, systems, and perspectives); role in structural predictions; and efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of identity–other distinctions (D). Full article

As an indispensable component of today’s world economy and an increasing success factor in production and other processes, as well as products, software needs to handle a growing number of specific requirements and influencing factors that are driven by globalization. Two common success factors in the domain of Software Systems Engineering are standardized software development processes and process-supported toolchains. Development processes should be formally integrated with toolchains. The sequence and the results of toolchains must also be validated with the specifications of the development process on several levels. The outcome of a conceptual deductive analysis is that there is neither a formal general mapping nor a generally accepted validation mechanism for the challenges that such an integrated concept faces. To close this research gap, this paper focuses on the core issue of the integration of development processes and toolchains in order to create benefits for modeling and automatization in the domain of systems engineering. Therefore, it describes a self-developed integration approach related to the recently introduced prototypical technical implementation TOPWATER. A unified metamodel specifies how processes and toolchains are linked by a general mapping mechanism that considers test options for the structural, content, and semantic levels. Full article

Identity (ID) management systems have evolved based on traditional data modelling and authentication protocols that are facing security, privacy, and trust challenges with the growth of Internet of Things (IoT). Research surveys reveal that blockchain technology offers special features of self-sovereign identity and cryptography that can be leveraged to address the issues of security breach and privacy leaks prevalent in existing ID management systems. Although research studies are recently exploring the suitability of blockchain based support to existing infrastructure, there is a lack of focus on IoT ecosystem in the secured ID management with data provenance of digital assets in businesses. In this paper, we propose a blockchain based ID management system for computing assets in an IoT ecosystem comprising of devices, software, users, and data operations. We design and develop a proof-of-concept prototype using a federated and distributed blockchain platform with smart contracts to support highly trusted data storage and secure authentication of IoT resources and operations within a business case scenario. Full article

Finding sustainable measures for expressway financing has long been a significant issue owing to high demand for funds of expressway construction and maintenance of existing facilities. In the Chinese context of expressway financing it has become imperative to shift from indirect bank financing to Real Estate Investment Trusts (REITs). This research investigates the impact of various factors on the financing risk of expressway REITs and estimates the weight of the impact of various aspects and the link between the factors. We used literature review, keyword co-occurrence analysis and keyword cluster analysis methods to identify 19 risk factors that affect the financing of expressway REITs, then we classified factors into six dimensions: credit risk, underlying asset risk, operational risk, market risk, liquidity risk, and other risk. In addition, a multi-level hierarchical structure model was established by the Integrated Decision-Making and Trial Evaluation Laboratory (DEMATEL) and an interpretative structural model (ISM). The research finds that the project’s future cash flow under-expected risk, price risk, and counterparty limited risk are direct factors, the bankruptcy isolation of the underlying assets risk is a deep factor affecting the financing of expressway REITs, and other factors are indirect factors. This study fills the gap in financing risk of expressway REITs in the context of China and contributes to exploring and establishing the financing risks identification approach and risk factors in expressway REITs based on Chinese contexts. This research presents a theoretical foundation and methodologies for reducing the financing risk of expressway REITs projects and improving financing safety. Full article

Many believe good government to be essential for a nation’s progress, but, in fact, governance is a multidimensional concept with uncertain implications for economic development and global sustainability. The World Bank has tracked six country-level Worldwide Governance Indicators since 1996. Statistical regression analysis across 150 countries identified two of these indicators, Government Effectiveness and Regulatory Quality, that consistently help to explain changes in economic growth and CO₂ emissions. The regression results provided the evidence needed to incorporate the effects of governance in an existing climate-population simulation model. Policy testing of the revised model led to findings about what improved governance can and cannot do. The testing suggested that the best combination of such improvements could boost progress on emissions reduction without hindering economic development—but not enough to strongly mitigate climate change. Achieving the double goal of economic development and strong climate change mitigation would thus require some kind of extra effort that does not fall under the usual definitions of good national governance. Full article

To deal with environmental uncertainty, organizations need resilience to respond to disruptions, such as changing market conditions or variations in demand or supply, while avoiding large scale adjustments. The concept of resilience is ambiguous, often explained as the capability of an organization or a supply chain to recover its original state, within an appropriate time frame, after being disrupted. Resilient supply chains have event handling capabilities, can provide efficient responses, and can return to their normal operating performance, after the disruptive event. To increase their resilience, companies often make changes or adjustments to their internal IT infrastructure, which may temporarily disrupt their smooth operation. As a result, contemporary IT infrastructures are mixed and include varied systems or technologies. Although new technologies, including blockchain, IoT and cloud-based solutions, may facilitate the handling of changes by providing secure, low cost and scalable solutions, more traditional systems may hinder such changes. Therefore, the relationship between IT and supply chain resilience is still unclear. The paper intends to examine the above issues by adopting a socio-technical approach to explain the concept of supply chain resilience and investigate the role of IT. More specifically, based on previous literature and on the appreciative systems thinking theoretical perspective, the paper develops a theoretical framework to analyse the organisational and/or supply chain resilience. It then uses this framework to examine and explain the impact of IT, by identifying important characteristics of an IT infrastructure and examining whether they may support or hinder business resilience. Full article

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots and trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised. Full article

The current COVID-19 pandemic, which is alarming for another global economic recession, puts the sustainable development of the tourism system under high consideration. The tourism industry is a key generator of foreign exchange across the region. However, tourism is one of the sectors most affected by the global pandemic. Through a case study in Taiwan, the objective of this study is to show how an Evergreen hotel fixed itself on existing and recovering in the hospitality business during the COVID-19 pandemic in 2020 using the combination of the problem hierarchy analysis (PHA) and the Teoriya Resheniya Izobreatatelskih Zadatch (TRIZ) or the so-called theory of inventive problem-solving technique. Following PHA technique and extensive investigation, the management team determined that the most recent problems at the Evergreen hotel are in marketing and human resources. The 39 parameters and 40 principles of TRIZ were used to determine the improvement solution and create a solution strategy that simultaneously simplified critical control-point (CCP) processes and improved the correctness of tasks, increasing CCP efficiency and supporting and satisfying customer demands in the COVID-19 pandemic in the world in general and in Taiwan in particular. The results revealed that customer bookings grew over the four quarters of 2020 due to adjusting the cancellation policy, discounting, and segmenting the market from international to domestic, increasing the CCP efficiency percentage and customer rating score from 19% to 40% and 8.3 to 8.5 score, respectively. Aside from that, changing the hotel structure with a partnership with the Taixie company assisted Evergreen in reducing various cost pressures to manage the business and recover after a difficult period. This paper can be a useful reference for managers, investors, governments, and policymakers to improve the sustainability performance in the tourism industry. Full article

Corporate investment strategies and decision-making processes are crucial for understanding the operation and evolution of socioeconomic systems. Mergers and acquisitions (M&As) have been the main mode of corporate investment, growth, and upgrading, deeply affecting corporate reorganization, regional industrial restructuring, and economic globalization. By building a database including 5543 M&A partnerings and 1.89 million M&A non-partnerings, this study aims to uncover the systematic dynamics of M&A partnering in regional China during different phases since the mid-1990s, with particular attention given to the effects of firm heterogeneities and multi-dimensional proximities. Although geographical, cognitive, organizational, and institutional proximity dimensions are significantly influential for M&A partnering, we find that the effects of multi-dimensional proximities differ across M&A types and involving firms. Specifically, organizational proximity matters more for large- and medium-sized acquirers, while institutional proximity plays a more vital role in the acquisition target selection of private-owned and small-sized acquirers. Cognitive proximity measured by industrial and technical relatedness is more crucial for horizontal, vertical, and conglomerate M&As that are tightly associated with the corporate product, technical, and functional upgrading. The results indicate that the benefits of cognitive proximity may offset the risks and costs resulting from long-distance M&As, demonstrating the interactive dynamics between proximity dimensions. Our findings suggest that firm heterogeneities, proximity dynamics, and contextual factors should be focused on when explaining the investment decision-making processes of individual corporations in emerging and transitional economies such as China. Full article

With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library. Full article

This paper presents the results of a systematic literature review on power distribution and power dynamics in multiparty systems. Multiparty systems are underorganized social structures in which power dynamics unfold and impact collaboration effectiveness. We use a theory-driven approach to integrate the empirical literature that explored power differences and dynamics in multiparty systems and we have a two-fold contribution to literature. First, we explore the way power is conceptualized in multiparty systems. Second, we investigate which predictions and propositions of the Social Distance Theory of Power and the Approach Inhibition Model of Power can be used to integrate research on power distribution and dynamics in multiparty systems. We extend the predominantly experimental empirical support of these two theories with insights from the multiparty systems literature. With respect to the way in which power is conceptualized in the multiparty systems literature, our study shows a shift from a possession over resources to a relational perspective on power in the last decades. Moreover, based on the insights of the two psychological theories of power, the study reflects upon the benefits and drawbacks of high versus low power for collaboration effectiveness among stakeholders, pointing towards ways in which facilitators can work with power differences in multiparty systems. Finally, the study points toward directions for future research concerning power dynamics in multiparty systems. Full article

In the field of systems thinking, there are far too many opinioned frameworks and far too few empirical studies. This could be described as a “gap” in the research but it is more like a dearth in the research. More theory and empirical validation of theory are needed if the field and the phenomenon of systems thinking holds promise and not just popularity. This validation comes in the form of both basic (existential) and applied (efficacy) research studies. This article presents efficacy data for a set of empirical studies of DSRP Theory. According to Cabrera, Cabrera, and Midgley, DSRP Theory has equal or more empirical evidence supporting it than any existing systems theories (including frameworks, which are not theories). Four separate studies show highly statistically relevant findings for the effect of a short (less than one minute) treatment of D, S, R, and P. Subjects’ cognitive complexity and the systemic nature of their thinking increased in all four studies. These findings indicate that even a short treatment in DSRP is effective in increasing systems thinking skills. Based on these results, a longer, more in-depth treatment—such as a one hour or semester long training, such is the norm—would therefore likely garner transformative results and efficacy. Full article

The transportation sector is recognized as one of the largest contributors to the problems of global warming and environmental pollution, and is responsible for a great deal of global energy consumption, which is heavily dependent upon scarce crude oil reserves. Different countries have adopted promotional policies to replace conventional internal combustion engine vehicles with electric vehicles as a means of mitigating global warming. Nevertheless, the current market share of eco-friendly vehicles remains stagnant in many parts of the world. This study aims to investigate the impact and relative importance of financial, technical, and political measures on the market penetration of electric vehicles using an agent-based simulation. More specifically, a series of agent-based simulation experiments is carried out following the statistical experimental design scheme to systematically assess the diffusion of electric vehicles. Affected by various factors and measures, the choice behavior of individual agents is modeled with a multinomial logit utility function of experimental factors. The simulated data are analyzed using different analysis methods, including full factorial analysis, response surface methodology, and support vector machine, in order to scrutinize the effects of different measures. It is advocated that factors affecting the choice of vehicle by individuals, including two-way interactions among various measures as well as policy measures such as purchase subsidies and tax breaks, have more significant effects on the widespread adoption of electric vehicles than do technical improvements in terms of battery charging times and driving mileage. This implies that the adoption of such measures needs to be carefully designed in order to account for potential interactions among individual measures as well as their main effects on the diffusion of electric vehicles. Full article

In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast setup process occurs when the server’s states are changed from sleep to work. The servers in Group 1 are faster and cheaper than those of Group 2 so that Group 1 has a higher service priority. Putting each server in Group 2 to sleep can reduce system costs and energy consumption, but it must bear setup costs and transfer costs. For such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, both of which determine the switch rule between the work and sleep states for each server in Group 2. To find the optimal asynchronous dynamic policy, we apply the sensitivity-based optimization to establish a block-structured policy-based Markov process and use a block-structured policy-based Poisson equation to compute the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that a bang–bang control is always optimal for this optimization problem. We hope that the methodology and results developed in this paper can shed light on the study of more general energy-efficient data centers. Full article

DSRP Theory is now over 25 years old with more empirical evidence supporting it than any other systems thinking framework. Yet, it is often misunderstood and described in ways that are inaccurate. DSRP Theory describes four patterns and their underlying elements—identity (i) and other (o) for Distinctions (D), part (p) and whole (w) for Systems (S), action (a) and reaction (r) for Relationships (R), and point ($\rho$) and view (v) for Perspectives (P)—that are universal in both cognitive complexity (mind) and material complexity (nature). DSRP Theory provides a basis for systems thinking or cognitive complexity as well as material complexity (systems science). This paper, as a relatively short primer on the theory, provides clarity to those wanting to understand DSRP and its implications. Full article

At present, the accuracy of diffusion rate forecasting, at a macro-level, in the research literature, is nonexistent. This research reveals underlying macro-level trends of diffusion rate assessment using historical technological innovation diffusion data to explore the statistical characteristics of diffusion rate percent-error of the Bass and logistic model time stepped through its lifecycle. A quantitative exploratory data analysis (EDA) based approach was employed to uncover underlying macro-perspective patterns and insights on a technological innovation’s forecasted diffusion rate percent-error using the data of 42 matured U.S. consumer technological innovations. An objective of this effort is to determine the statistical characteristics (mean, median, variance, standard deviation, skewness, and kurtosis) of diffusion rate assessment using the Bass and logistic model at various points in a technological innovation’s lifecycle to reveal underlying directional and associative insights. Specifically, this effort explores the development of macro-perspective knowledge on quantifying the forecasting accuracy of a technological innovation’s diffusion rate using partial diffusion data. Developing such insights and a framework for accessing in situ (real-time) a technological innovation’s diffusion rate percent-error would benefit an organization’s decision makers in maximizing gains and minimizing losses. These insights include identifying whether the Bass and logistic models are more likely to overestimate or underestimate a technological innovation’s diffusion rate when assessed at various points in its diffusion lifecycle. Practitioners can use such information to set resource investment strategies and policies based on risk tolerance and the utility of the weighted outcomes via decision theory tools. Full article

Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions. Full article

Open innovation has drawn significant attention over the years, and there is a growing body of literature that highlights the importance of considering this phenomenon at the national level. Less appreciated, however, is the radiative capability of national innovation systems (NIS) and the linking inbound and outbound processes. We provide a measurement of the dual innovation capability (DIC) of NISs based on process-oriented concepts by using a multi-indicator approach, which provides a more comprehensive picture of sectoral NISs compared to currently used metrics. To assess the DIC of NISs, a composite weighting method was used to obtain the score of our selection of 65 countries. The results show the spatio-temporal evolution of DIC from 2010 to 2018 and explore the interactions among sub-elements within the framework. The 65 countries were grouped into 4 categories based on the sub-dimension scores, and we provided 3 possible paths that can be chosen to improve DIC. The index provides a powerful tool to enrich research on innovation systems, guide national positioning, and optimize policies. Full article

Digital twin (DT) is a technology that promises great benefits for the manufacturing industry. Nevertheless, DT implementation presents many challenges. This article looks to understand and study the problems associated with the implementation of DT models in a manufacturing domain. It applies systems thinking techniques to analyze and refine these problems. Systems thinking presents several methods and tools that help in studying a problem space and a solution space. The conceptagon framework describes the DT model as a system with several attributes and analyzes it in detail. A systemigram shows the relationship of manufacturing systems and the DT model. It maps the processes and components for DT implementation. The TRIZ method analyzes, and forecasts problems related to DT development and provides solutions based on patterns of invention. The CATWOE analysis allows identification of stakeholders and the study of the DT model from their perspectives. It provides a root definition of the DT model to refine a problem and the problem’s contradiction. The 9 windows tool helps to delimit the DT implementation problem, based on time and space. It gives eight more perspectives to solve the DT problem. Finally, the ideal final result (IFR) method provides the ideal DT model concept for manufacturing systems. Full article