Search results

The purpose of this paper is to provide graduate students, researchers, governmental and independent agencies with an overview of technological disasters.

Technological disasters are subjects of concern to researchers, academicians, governmental and independent agencies. Disasters are classified into natural and man‐made disasters. For an incident to be classified as a disaster, the disaster criteria should be met. Several disaster criteria have been proposed defining the disasters in terms of casualties, economic loss and environmental impact. The disasters which involve major hazard installations (MHIs) are known as technological disasters. The technological disaster definition, stages, types, criteria, factors, models have been reviewed. This paper presents an overview of technological disaster definition, criteria, stages, models, factors, and prevention.

Although the technological disasters may occur at non‐MHIs, it has been noted that most of the technological disasters involved MHIs and that their impact is not limited to the plants but can extend to neighboring surroundings. The technological disaster consists of three stages: before, during, and after disaster. There are many factors contributing to the technological disasters, some of which are observed clearly while others are partially hidden. The main technological disaster factors were identified as human, organizational and technological errors. Few models have been drawn describing the sequence of development of the technological disaster.

This paper presents an overview on the technological disaster definition, criteria, types, stages, models, factors, and prevention and combines the scattered information on technological disaster into one record.

The purpose of this paper is to focus on the evaluation model of the enterprises' technological innovation system, based on the theory of complex adaptive system.

Combined with the status quo and recent studies of Chinese enterprises' technological innovation, the paper discusses the complex‐system features of the technological innovation. The stimulus‐response model is used to establish the two‐level framework for enterprises' technological innovation system. By means of the adaptive fitness function, the economic and social utility of enterprises' technological innovation is measured from two dimensions. Finally, the fuzzy catastrophe model is introduced to evaluate the enterprises' technological innovation.

The enterprises' technological innovation system has attributions of the subject aggregation, the systematic openness, nonlinearity and diversity. Thus, the macro‐micro based technological innovation system from the perspective of complex adaptive system is proposed. The system utility is considered based on the system subjects and system structure, and the calculation framework of the adaptive fitness for the whole system is obtained by considering the emergent property describing the system scale effect and structure effect. In fact, the fuzzy theory can well reflect the influential situation that the interactions between different factors may cause the mutation of the higher level and the interactions between enterprises can lead to the shifts of the system.

The paper proposes the complex adaptive system for the enterprises' technological innovation based on the special macro environment in China. A new framework for the research of technological innovation is provided by analyzing the system inner model. Fuzzy catastrophe model can reduce the evaluation irrationality due to the subjective index weights.

Complex technology not only provides potential economic benefits but also increases the difficulty of application. Whether and how upstream technological complexity affects downstream manufacturers' innovation through vertical separation structure is worth discussing, but it has not been effectively discussed.

Through theoretical analysis and empirical testing, this article discusses the cost effect and market competition effect caused by upstream technological complexity on downstream manufacturers and further elucidates the impact of upstream technological complexity on downstream manufacturers' innovation.

Research has found that the impact of upstream technological complexity on the downstream manufacturers' innovation depends on the cost effect and market competition effect. The cost effect caused by the complexity of upstream technology inhibits the innovation of downstream manufacturers. In contrast, the market competition effect promotes the innovation of downstream manufacturers. There are differences in the cost effect and market competition effect of upstream technological complexity on different types of downstream manufacturers, so there is also significant heterogeneity in the impact of upstream technological complexity on innovation of different types of downstream manufacturers.

The conclusions of this article improve the understanding of the relationship between upstream technological complexity and downstream innovation and provide helpful implications for industrial chain innovation.

Technical knowledge is a key factor in firm innovation. This study aims to construct a theoretical framework of technological boundary-spanning search, exploratory innovation and exploitative innovation to help firms adjust their search strategies and improve the effect of external resources on internal innovation.

The study uses questionnaires to collect data and conducts empirical analysis using SPSS25 and AMOS24.

Technological boundary-spanning search is positively correlated with ambidextrous innovation. Additionally, knowledge base positively moderates the effect of technological boundary-spanning search on ambidextrous innovation and knowledge distance negatively moderates the effect of technological boundary-spanning search on ambidextrous innovation. When a firm’s knowledge base is robust, its ambidextrous innovation can benefit more from technological boundary-spanning search. Additionally, when the knowledge distance is less, a firm’s ambidextrous innovation can benefit more from technological boundary-spanning search.

Considering organizational ambidexterity, this study divides firm innovation into exploratory innovation and exploitative innovation and presents a theoretical framework for the effect of technological boundary-spanning search on ambidextrous innovation. Additionally, it provides a comprehensive understanding of the crucial roles of knowledge base and knowledge distance in the relationship between technological boundary-spanning search and exploratory and exploitative innovation.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.

Despite the important role that exploitation plays in innovation and new product development (NPD), research on the relative impact of internal organisational stocks of existing knowledge on subsequent exploitation is largely absent. In particular, there is lack of clarity within the extant literature regarding the associations between organisational exploitation and, respectively, the distal-proximal technological experience and radical-incremental innovative experience generated by multiproduct firms. Thus, this study seeks to further enhance researchers’ theoretical understanding on the relationship between organisational exploitation and internal knowledge stocks categorised along two dimensions of organisational experience accumulated by multiproduct firms that have not previously been considered jointly.

This paper pursues a focussed literature review approach and applies the underlying theory of exploitation to develop a theory explaining the possible relationships between organisational exploitation and internal knowledge stocks.

Based on the theory of exploitation, this paper proposes a new direction in studying the various internal knowledge stocks and their respective impact on subsequent organisational exploitation.

The proposed research direction suggests an emerging framework of possible relationships between exploitative new radical products development in firms, and respectively, proximal and distal technological experience, and radical and incremental innovative experience, accumulated in multiproduct firms. This novel framework can guide further research on this topic.

To fill a research gap regarding the possible relationships between subsequent exploitative endeavours and two dimensions of organisational experience that have been traditionally associated with the exploration-exploitation construct, this paper proposes and develops a novel typology of knowledge stocks categorised along two dimensions of organisational experience accumulated by multiproduct firms that have not previously been considered jointly in the literature.

In the context of exponential technological advancements in the economy and the role of entrepreneurial ventures in significantly contributing towards social and economic growth, this study aims to examine the influence of learning motivation and personal innovativeness (PI) on students’ entrepreneurship intention. The study gains relevance in the context of students in emerging economies like India, where the digital divide is still on the higher side. The study also seeks to understand the extent to which varying levels of technological self-efficacy moderate the interaction effects of human capital in the aforementioned relationship.

The sample for this study consisted of 330 students from various disciplines, including science, technology and management, who were selected randomly from different universities in India. A self-administered and structured questionnaire was used to obtain responses from the participants. The data were analysed using confirmatory factor analysis (IBM SPSS 23.0), and path analysis was performed using Hayes PROCESS Macro.

The study's results indicated that learning motivation and PI had a significant indirect effect on students’ entrepreneurship intention, mediated through human capital. However, interestingly, the relationships were varyingly influenced by technological self-efficacy as the moderator.

The contemporary student cohort consists of individuals known as “digital natives,” who possess a strong inclination towards digital learning and demonstrate innovative behaviour. The insights gained from this study underscore the crucial role of technological self-efficacy in shaping entrepreneurial intentions, indicating the necessity of incorporating technological training and skill development within international business education curricula. Also, the results of the study emphasize the need for integrating PI into business education programs, as it positively influences the entrepreneurial mindset and potential.

This study is pioneering in conceptualizing and testing a model that provides a more comprehensive understanding of student attitudes and outcomes. This understanding can be used to customize entrepreneurial programs to better meet the needs of students. Entrepreneurship fosters technological innovation and creates employment opportunities, making student venture creations a crucial factor in economic development and sustainability. For educators and university administrators, it is essential to comprehend how to enhance entrepreneurial intentions among students to design a learning environment that facilitates the creation of new ventures as a result of higher education.

This study aims to build a theoretical model including intermediate-level outside-in marketing capabilities (ILOIMC), radical and incremental technological innovations and management innovation.

This research used 272 pairs of survey questionnaires from Chinese firms’ managers to examine the hypotheses.

The results indicate that ILOIMC enhance management innovation by stimulating radical technological innovation. Furthermore, the mediating effect of incremental technological innovation depends on technological turbulence.

This study may have several limitations which future research could try to overcome: cross-sectional data, Chinese samples, exclusive focus on ILOIMC, sociotechnical approach to innovation typology and measuring ILOIMC as a first-order variable.

ILOIMC can significantly improve innovations in technology and management systems by using customer value and market information.

This study proposes a new taxonomy to classify marketing capabilities into lower-level inside-out marketing capabilities, ILOIMC and higher-level outside-in marketing capabilities. It also provides an explicit discussion and examination of the influence of ILOIMC on technological and management innovations and the contingency effect of technological turbulence. Thus, it responds to Musarra and Morgan’s (2020) call for more research into the mechanism that explains when (the conditions under which) and how (the process by which) outside-in marketing capabilities could contribute to firm innovation.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.