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The advent of robotics and automation technologies was augmenting firm initiatives to attain competitive advantage. From a resource-based view perspective, human-led capabilities were important to operate with technology resource base of an organisation. This was evident for both manufacturing as well as services firms. However, employees as an individual confronted technology anxiety (TA) when they were working with new technologies like robotics and automation technologies. Thus, the purpose of this paper was to examine the factors causing TA.

Given the novelty of this research study context a qualitative exploratory method was designed. For this research study, the data collected was through in-depth interviews conducted through open-ended semi-structured questionnaire. The data was collected from 62 frontline employees who were working with robotics and automation-based technologies in manufacturing firms. The authors applied thematic content analysis on collected data for analysis.

Technology anxieties ranged from fear of complete inability to learn new technologies, failure to learn new technologies properly, incapability to implement the learned skills and job loss to younger technology savvy employees. Finally, there was anxiety over job loss as automation and robotic technologies over the years was expected to erode the employment of human workforce altogether.

The author undertook the research study based upon the TA perspective advocated by Meuter et al. (2003) and Yang and Forney (2013). Furthermore, this research study in the context of robotics and automation-based technologies in the manufacturing sector applied the mental accounting theory (Thaler, 1999) and technology self-efficacy perspective (Huffman et al., 2013).

Managers involved in the implementation of robotics and automation-based technologies were required to address TA of employees. Fear of job loss had to be addressed specially in a country like India. Anxiety regarding the ability to learn to work with robotics and automation technologies also was needed to be addressed by managers through adequate training and time for preparation. The benefits of robotics and automation-based technologies for employees as well as organisations have to be ascertained through open communication between the management and the frontline employees.

To the best of the authors’ knowledge, this was one of the first empirical research studies which deliberated regarding TA in the context of frontline workers working with robotics and automation-based technologies in the manufacturing sector. This research study was based upon a combination of varied perspectives ranging from micro foundations theory, TA, mental accounting theory and technology self-efficacy perspective.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

This study aims to investigate the contribution of blockchain technology to supply chain risk management and its impact on performance among Indian manufacturing companies.

Drawing on a resource-based view, dynamic capability and system of systems theory, this study examines the direct relationships between blockchain, supply chain risk management and supply chain performance. The authors validate the mediating effects of three supply chain risk management components, namely supply risk management, demand risk management and cyber security management, on financial transaction reliability and information reliability. Data were collected from 204 Indian manufacturing companies that have adopted blockchain technology.

The results demonstrate that companies adopting blockchain technology have experienced positive outcomes in managing supply chain-related risks, financial transaction reliability and information reliability. These findings provide valuable guidance to managers, highlighting blockchain as a competitive advantage for supply chain management.

To the best of the authors’ knowledge, no previous research on blockchain-based risk management capabilities has been conducted.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

This study aims to explore how the shift from traditional to digital banking transforms the nature of trust between banks and their younger clients (aged 18–35) from the perspective of bank employees.

Qualitative semi-structured interviews with representatives of Ukrainian classical banks and neobanks were conducted. The interviews were analysed using the theoretical approach of institution-based and social network-based trust to identify the key distinctions between the nature of trust in traditional and digital banking.

The employees of the banks reported that digitalization processes have helped to mitigate trust issues; as a result, their banks have not experienced any difficulties in this regard among young people. Furthermore, social networks, particularly social approval, were found to be significant factors for establishing trust in digital banking among young people.

The results of this study could assist bank managers in adapting their strategies for cultivating trust among younger clients and aiding international law regulators and government institutions in preventing unintended circumstances in financial services. These contributions were shaped by the study’s limitations, including its focus on only two concepts of trust building: institution-based and social network-based, as well as its specific Ukrainian context.

This study highlights social approval as a valuable constituent of the trust-building process that influences trust in institutions. Furthermore, while gaining social approval – particularly through digital platforms – can promote trust-building among young people, this “easy way” may have negative societal consequences by endorsing unscrupulous institutions.

The non-fungible token (NFT) market has been multiplying in recent years. NFTs are tokens stored on a blockchain network based on smart contract technology that can be used to represent ownership of digital assets and cannot be changed like-for-like. With NFTs, all recorded digital properties can be freely traded and stored with values, making them possible to increase content transactions' privacy and security. In addition, NFTs engender new ways to organize, consume, share and store digital content. Despite the rapid growth of the NFT market, related consumer behaviors have yet to be well-known and relevant academic research results are very scarce. This study aims to explain how NFT fits with blockchain and cryptocurrency and how consumers accept it. This paper also develops a structured causal model with multiple paths to explain the antecedents and attitude variables for NFT acceptance.

The data collection was conducted from 542 young consumers in Korea via an online survey. The structural equation modeling method was used to analyze the hypotheses.

Attitudes toward technology and assets positively affect NFT purchase behavioral intentions. Additionally, symbolic driver affects behavioral intention directly.

The results expanded the understanding of the NFT market and consumers, which are still in their early stages. They also provide valuable insights for establishing future market strategies for NFT.

This paper aims to review and summarize the findings of research dedicated to studying the process of building sustainable business models (BM) triggered by development of COVID-19 pandemic.

Bibliometric analysis is performed to identify the papers most relevant to the topic. The authors review the findings of more than 50 papers from Scopus database published between 2020 and 2022 dedicated to studying BM during COVID-19 pandemic, as well as papers dedicated to sustainability phenomenon and most cited BM research.

The paper identifies the gap in defining BM sustainability and contributes to better understanding of this phenomenon by demarcating it from traditional environment-based United Nations agenda. It also describes why network-based approach to BM helps to better address sustainability aspects. The paper demonstrates how representation of a networked BM by three levels of analysis (namely, structure of a BM, interaction mechanism between BM actors and results of their interaction) is organically connected to the key milestones of the value creation process (value definition, value creation, value distribution and value capture) and shows how these three levels can be used to analyze and structure the practical changes proposed in COVID-19-oriented BM. Finally, the paper summarizes key findings of the studies dedicated to BM during the pandemic and structures key insights in relation to building sustainable BM.

The results of the paper contribute to developing theory around BM sustainability as well as provide insights for business practitioners on how to adjust BM during the crisis. At the same time, many insights shown in the paper are industry specific, which limits their generalizability, as well as consequences of the pandemic are still not fully clear. Therefore, the authors argue that future research should be primarily focused on developing generalizable measurement frameworks to evaluate the antecedents, process and results of BM adaptation.

The paper strengthens theoretical foundations for the research focused on BM sustainability and helps businesses to better manage the adaptation in the fast-changing environment.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Sleep arousal detection is an important factor to monitor the sleep disorder.

Thus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.

The proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.

No other researchers have suggested U-Net-based detection of sleep arousal.

From the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.

Sleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.

It will help in improving mental health by monitoring a person's sleep.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.