Kybernetes最新文献

Purpose

Breakthrough technological innovation is of vital significance for firms to acquire and maintain sustainable competitive advantages. The construction of an innovation ecosystem and the interaction with heterogeneous participants have emerged as a new dominant model for driving sustained breakthrough technological innovation in firms. This study aims to explore the effects of collaborative modes within the innovation ecosystem on firms’ breakthrough technological innovation and the ecological legitimacy mechanisms involved.

Design/methodology/approach

The research employs data from 212 innovative firms and conducts empirical research using a two-stage structural equation modeling (SEM) and artificial neural network (ANN) analysis.

Findings

The results indicate that firm-firm collaboration (FF), firm-user collaboration (FU), firm-government collaboration (FG), firm-university-institute collaboration (FUI) and firm-intermediary collaboration (FI) all have significant positive effects on breakthrough technological innovation (BTI), with FU being particularly crucial. Furthermore, the results confirm the positive moderating effects of ecological legitimacy (EL) on the relationships between FF and BTI, as well as between FU and BTI. Conversely, EL has a negative moderating effect on the relationship between FUI and BTI, as well as between FI and breakthrough technological innovation. Additionally, EL does not have a significant influence on the relationship between FG and BTI.

Originality/value

Through resource dependence theory (RDT), this study unveils the black box of how collaboration modes within innovation ecosystems impact breakthrough technological innovation. By introducing ecological legitimacy as a contextual factor, a new research perspective is provided for collaboration innovation within innovation ecosystems. The study employs a combination of SEM and ANN for modeling, complementing nonlinear relationships and obtaining robust results in complex mechanisms.

Purpose

The purpose of this paper is to bring into the public domain converging ways of thinking about reality and human systems, exploring parallels between the theory of Physical Vacuum and the concept of Qi in Medical Qigong science.

Design/methodology/approach

The approach adopted in this paper includes: review of the relevant literature; dialogues between the first two authors over an eight-month period; review of the findings and discussion of interpretations by all.

Findings

There is evidence for the existence of an ideal information field. This field is a real space-time torsion structure. Qi is a torsion field. It spreads with superluminal velocity and connects the whole Universe. Any entity is in a constant dynamic connection with everything else in the Universe.

Research limitations/implications

This paper offers limited discussion of the wider area of scientific discoveries.

Social implications

The findings may impact future interdisciplinary research, health/well-being practices and public policy.

Originality/value

There is no known to us publication interpreting the parallels between the theory of the Physical Vacuum and the concept of Qi.

Purpose

We examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.

Design/methodology/approach

The DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.

Findings

Using daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.

Originality/value

We first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

Purpose

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Design/methodology/approach

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

Findings

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

Originality/value

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

Purpose

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

Design/methodology/approach

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Findings

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

Research limitations/implications

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

Social implications

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

Originality/value

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

Purpose

Drones will become part of daily lives similar to smartphones becoming a staple of modern living. Nonetheless, only several past studies investigated the intention to utilise drones for parcel delivery however, the intention to use drones among online shoppers was not fully explored. The study attempts to investigate the factors influencing the intention to use drones for last-mile delivery.

Design/methodology/approach

A total of 292 data were gathered via an online survey among online shoppers applying a snowball sampling method. Since the study operationalised the measures as composites, a combination of reflective and formative measurement, and the study focusses on predictive purposes, partial least squares structural equation modelling with SmartPLS 4 was applied to test the model developed based on the stimulus-organism-response model.

Findings

The analysis found that all the direct hypotheses were found supported. Moreover, Green support, green desire and pro-environmental behaviour positively and sequentially mediated future orientation and intention, whereas technology anxiety and perceived safety moderated the relationship between pro-environmental behaviour and intention.

Research limitations/implications

The respondents only limit to the online shoppers in Malaysia which based on purposive sampling method, thus the findings cannot be generalized to another countries.

Practical implications

Besides enriching the literature on drone studies, the findings provided practical insights to online platforms and drone operators to develop an effective strategy to encourage online shoppers to shift from conventional delivery to drone delivery.

Originality/value

The study developed a new model for drone delivery studies using the S-O-R model in introducing orientation towards the future and green support as the stimulus, green desire as an organism and pro-environmental behaviour and usage intention as a response. The study introduced multiple sequential mediators, also contributing to the S-O-R model to predict online shoppers' behaviour towards drones as a tool for last-mile delivery. Another important contribution, technology anxiety and perceived safety were confirmed to have a moderation effect for the relationship between pro-environmental behaviour and intention to use drones for last-mile delivery.

Purpose

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

Design/methodology/approach

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

Findings

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

Originality/value

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

Purpose

The maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.

Design/methodology/approach

In this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.

Findings

The usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.

Research limitations/implications

Based on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.

Originality/value

Previous studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

Purpose

The purpose of this study is to understand how the level of readiness for artificial intelligence (AI) affects the overall performance of companies, determine the role of organizational flexibility in adapting to new technologies and business models and assess the importance of lean sustainability and value creation for technology-focused companies.

Design/methodology/approach

Technology companies working in technoparks in Istanbul were determined, and a questionnaire was applied to senior employees such as experts, engineers and managers working in these companies. The results were processed with a sample of 456 units. SmartPLS program was used for analysis.

Findings

As a result of the analyzes, it is supported by hypotheses that AI readiness and organizational flexibility have positive effects on lean sustainability and value creation.

Research limitations/implications

When evaluated in terms of the limitations of the research, it would not be correct to evaluate the results of the analysis in general, since the data were collected from technology-focused companies in technoparks in Istanbul.

Practical implications

Examining the variables that make up the research model in technology-oriented companies helps to understand the critical factors for the future success of companies. At the same time, this research is important for companies to make more informed decisions in their strategic planning, technological transformation processes and value creation strategies.

Originality/value

This research topic offers a unique approach in terms of bringing together topics such as AI readiness, organizational flexibility, sustainability and value creation. These issues play an important role in the strategic planning of technology-focused companies, and when considered together, they are important in terms of examining the critical factors that affect the future success of companies.