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The purpose of this study is to ascertain the drivers of digital disruption and its consequences from a managerial viewpoint. Understanding the drivers and consequences of digital disruption can help business managers modify and align their organizational structures and strategies with digital disruptors to promote survival in the marketplace.

This study used survey data from US managers. Of 1,000 managers, 272 provided responses eligible for use in the analysis. The study used EQS 6.2 software to analyze the data.

Eight hypothesized relationships were tested in this study. The results of this study indicate that convergence of intelligence, convergence of technology, support from C-level executives, organizational cultures of innovation and managerial skills act as drivers of digital disruption. The results also show that digital disruption improves both user experience and firms’ digital disruptive performance.

This study builds upon the disruptive innovation theory. This study demonstrates that both technology- and organization-induced drivers serve as predictors of digital disruption. Digital disruption affects user experience and firms’ digital disruptive performance. In addition, user experience influences firms’ digital disruptive performance. Overall, this study improves our understanding of the role of technology- and organization-induced drivers of businesses’ responses to digital disruption and provides contributions to theory and practice.

This study aims to contribute to the comprehension of managerial implications in all the new convergences fields: bioinformation technology (BIT), nanoinformation technology (NIT), nanobiotechnology (NBT) and bioinformation–nanotechnology (BINT). The phenomenon of convergence that significantly impacted economies and societies in the information technology era has continued to be combined with new technologies such as nanotechnology and biotechnology. Many countries have tried to seize the initiative to strengthen the future competitiveness, and it become increasingly important for roles of companies to expand applications of the new convergence.

From a company-based perspective, the study quantifies leading companies’ degree of influence in each convergence field.

However, the result shows that companies’ influences are not mature enough to transfer the initiatives from both the NBT and BINT convergence fields. In addition, this study presents a perceivable framework for companies/institutions by identifying which technological fields the companies/institutions are influential in and what degrees of technological impact are shaped among them. As a result, this allows companies/institutions to recognize their environments squarely.

This study applied the replacement process that can reinforce the investigation of the extent to which a firm has a significant ability to influence others directly and indirectly. From the methodology perspective, such replacement can intensely display the structure and the extent of competition among firms. Therefore, the results provide an explicit information to understand the overall business situation.

Research paper

Strategic decision-making is a complex process and encompasses an exhaustive knowledge base, collective guidance, contemporary foresight, analytical capabilities, paradigmatic congruence, and risk assessment and optimization within mission space. Employing advanced sciences convergence and analytical methodologies, the aim of this report is to provide a set of plausible solution trajectories to complex scenarios.

Three methodologies are reported here which provide policymakers with plausible solution pathways and alternatives. The methodologies, namely: TechFARM, ADAMS, and NESTTS, involve convergence of scientific disciplines, cutting edge technologies, social dynamics, astute extraction, and principles of foresight to support the process of informed decision-making, as comprehensive tools to develop a plausible solution space and future trends.

The methodologies provided in this report provide scientific basis to trends analysis and foresight. Few selected examples are reported here indicating its practical implications. The methodologies are currently applied to and likely to be used for many applications in trends analysis for government, industry, and even academics. These applications are particularly relevant to policy-making due to their capacity for identification of emerging trends.

Being highly adaptable, these methodologies were initially generated for defense applications, but have since been applied to clean water, cyber-security, the medical sector, and environmental health and safety (EHS) and evaluating eco-toxicity of nanomaterials, to strategically address a variety of global challenges. Additionally, these methodologies support investment recommendations and implementation of policies that promise significant benefit to the public at large.

This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs.

This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result.

This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested.

The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI.

This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies.

This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.

This paper aims to explore the digital economy in the fields of the most promising new technologies: information and communication technologies, biotechnology and nanotechnology. It highlights the convergence of nano-, bio-, info- and cognitive (NBIC) technologies by developing a model for the accurate evaluation of different types of options in the development process of convergent technologies.

The empirical research is based on theoretical analysis and case studies. The authors conducted six in-depth interviews. The study covered different research projects led by centres of excellence, competence centres and institutions that support the transfer of innovations in the economic and business environment.

The research findings prove that there is a convergence of NBIC technologies that can be observed and modelled. The created evolutionary model of NBIC convergence, also based on the theory of real options, allows a proper evaluation of the entire convergence process.

For enterprises and scientific research institutions, the NBIC model could represent the starting point for developing further concepts of investment evaluation. The model also considers the indicators of the innovation system, which, in addition to the marketing area, include regulatory challenges of companies (competition, copyrights, patents, taxation, etc.).

This paper enhances the understanding of new technologies in a digital economy. The purpose of this study is to clarify the principal factors for the effective observation and measurement of the convergence phenomenon. It also offers suggestions for improvement of the research and innovation system in the new economy.

With the development of information technology (IT), governments around the globe are using state-of-the-art IT interfaces to implement the so-called 3E’s in public service delivery, that is, economy, efficiency and effectiveness. Two of these IT interfaces relate to Artificial Intelligence (AI) and Internet of Things (IoT). While AI focuses on providing a “human” garb for computing devices, thereby making them “intelligent” devices, IoT relies on interfaces between sensors and the environment to make “intelligent” decisions. Recently, the convergence of AI and IoT – also referred to as Artificial Intelligence of Things (AIoT) – is seen as a real opportunity to refurbish the public service delivery formats. However, there is limited understanding as to how AIoT could contribute to the improvisation of public service delivery. This study aims to create a modular framework for AIoT in addition to highlighting the drivers and barriers for its integration in the public sector.

This descriptive-explanatory study takes a qualitative approach. It entails a thorough examination of the drivers and barriers of integrating AI and IoT in the public sector. A review of literature has led to the development of a conceptual framework outlining the various factors that contribute to creating public value.

Value creation occurs when AI and IoT coalesce in the public service delivery mechanisms.

AIoT is a cutting-edge technology revolutionizing health care, agriculture, infrastructure and all other industrial domains. This study adds to the growing body of knowledge on the public sector's use of AI and IoT. Understanding these disruptive technologies is critical to formulating policies and regulations that can maximize the potential benefits for the public-sector organizations.

Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification.

Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam.

Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency.

Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

Fusion of new technologies and convergence has led to the emergence of new industries. This paper aims to present experiences of the concept of convergence from a patent citation analysis viewpoint. The main objective of the paper is to develop a tool for anticipating the emerging new industry segments.

The patent analysis methods have been used to get an idea of the stage of new industry segment in the intersection of the paper and electronics industries. The authors distinguish self‐citations from external citations, and further divide citations into two groups: within the industry and beyond the industry. Backward citations are used to evaluate the spill‐overs between industries and forward citations are used to evaluate the significance of technology‐based industry transformation.

The recognized trends of the trajectory changes and growing overlaps of technological fields show indications for possible convergence between industries. Differentiating between external and self‐citations within and beyond industry citations helps to provide more comprehensive prospects of a future technology competitive environment.

This study evaluated technology development in converging environments. It would be a scientifically significant contribution if the novel patent analysis methods could be used effectively for creating understanding in advance about the technology development and industry convergences.

The presented patent citation methodology provides new insights into the analysis of industry evolution, technological innovations and business development related to converging industries and technologies.