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The purpose of this paper is to clarify the conceptual confusion in the extant literature about organizational agility and explore its role in different relationships in the context of digital transformation.

An integrative review of the relevant literature on agility was conducted. The literature on organizational agility and other variables in recent quantitative research was also examined to explore its role in different relationships.

Organizational agility is the ability to quickly respond and proactively embrace unanticipated changes in dynamic environments through effective resource reconfiguration and rapid decision-making. The role of organizational agility in achieving digital transformation has not been addressed from a holistic conceptual perspective. This paper addresses that gap and proposes that organizational agility is the underlying mechanism for an organization to fully use and engage its workforce, operation and network in the process of digital transformation.

This research is an integrative review of the existing literature on the concept of agility and its relationships. The next phase of research needed for theory building will be the operationalization of constructs.

Organizations should strive to strategically develop both the reactivity and proactivity sides of organizational agility in achieving digital transformation that involves fundamental changes at different levels of the organization.

This paper explores the role of organizational agility in digital transformation through an integrative review of the relevant literature.

This study aims to explore the key attributes of robot chef restaurants and their influencing factors from the perspective of customers and analyzes how these key attributes affect customer perceived value.

A mixed-methods research design was used in this study. Using 473 online reviews and ratings (Study 1), the research summarized customers’ evaluations on three types of attributes (environment, service and food) and identified the key attributes along with their influencing factors. Subsequently, through field questionnaires (Study 2) involving 269 actual customers, structural equation modeling was used to analyze how the identified key attributes and their influencing factors impact customer perceived value.

This study reveals that customers in robot chef restaurants prioritize food attributes, particularly valuing food authenticity alongside food quality. In contrast to traditional restaurants, customers’ evaluations of food attributes in robot chef restaurants are significantly influenced by the competence of robot chefs. Notably, customers’ negative attitudes toward robots diminish the positive effects on both food quality and food authenticity.

To enhance customer perceived value, robot chef restaurants should concentrate on food attributes. They can achieve this by fostering a high-quality, authentic food experience through the elevation of robot chefs’ competence and by providing customer education.

This study expands research on the customer experience in robotic restaurants by proposing an integrated model determining factors that affect the perceived customer value.

Online games are widely adopted electronic applications that facilitate flow experiences, which is a highly enjoyable experience for players, thus motivating further engagement in online gameplay. During gameplay, players set gaming goals, and they must make cognitive efforts to achieve these goals. However, we do not know how goal-setting and cognitive gaming elements (game complexity and game familiarity) create flow, indicating a research gap. To fill this gap, we use the cognitive gaming elements in the literature and the theoretical elements of goal-setting theory to build a model.

Conducting a large-scale online survey, we collect 3,491 responses from online game players and use structural equation modeling for data analysis.

We find that challenging goals, game complexity, game familiarity and telepresence are positively linked to player-perceived flow, explaining 45% of the variance. The new finding is that challenging goals can strengthen the link between game complexity and flow. We also find that telepresence can strengthen the link between game familiarity and flow.

Our study provides the novel insight that gaming goals and cognitive gaming elements can generate player-perceived flow. This insight can help game makers design gaming elements to accommodate players' cognitive efforts to achieve in-game goals, thus creating flow and effectively increasing players' game engagement.

Coordination of digital transformation within globally dispersed factories belonging to international manufacturing networks (IMNs) is essential for competitiveness. This paper explores how digital transformation necessitates changes in the coordination of IMNs.

A case study is conducted with three Swedish manufacturing companies, thus adding to the limited empirical research covering the examined research field. Data analysis uses the technology-organization-environment (TOE) framework.

The results highlight 15 digital transformation attributes linked by intermediate themes to 13 changes in the coordination of IMNs and provide concrete industry examples. Four major themes emerged as significant in the coordination of IMNs: increased speed of technology development and rollout, amplified emphasis on a global mindset, increased need to adapt the organizational structures to enable collaboration and a higher degree of uncertainty.

Although coordination of IMNs is acknowledged as directly related to competitive advantage, the ways digital transformation necessitates changes in the coordination of IMNs have been missing in contemporary research. This research decreases this omission.

Leakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions.

Seal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated.

The results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence.

This work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.

Using the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.

It is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.

This study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.

The purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms.

This research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings.

According to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency.

The study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance.

This study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers.

Given the sudden disruption caused by COVID-19, knowledge sharing between organizations has become a meaningful way to improve supply chain resilience. However, there is still a lack of in-depth research on how to reduce the threat to knowledge sharing caused by increased levels of relational risk. With the emergence of new digital technologies, whether blockchain governance can control relational risk and replace traditional relational governance remains to be demonstrated.

This study uses a cross-sectional survey approach in which quantitative data are collected from 300 participants from Chinese manufacturing enterprises to test the hypotheses.

The results show that relational and blockchain governance can significantly and complementarily reduce the level of relational risk in knowledge sharing. When the relational risk is at a low, medium or high level, the best matches of relational and blockchain governance are low-level relational governance–low-level blockchain governance, high-level relational governance–low-level blockchain governance and high-level relational governance–high-level blockchain governance, respectively.

The findings of this study have important practical implications for manufacturing enterprises in terms of how to choose reasonable governance modes to manage relational risk behaviour according to different relational risk levels to better understand the positive role of knowledge sharing in supply chain resilience.

The antecedent variables of knowledge sharing in previous studies are based on transaction cost theory or relational theory and have not moved beyond the original theoretical framework. This paper addresses this limitation, puts knowledge sharing in the academic context of digital technology, considers blockchain governance into the process of relational risk-knowledge sharing and defines blockchain governance, which is a novel approach in the supply chain resilience management literature.

Purpose: Industry 5.0 is characterized by a revolution in the industrial field where humans collaborate with machines. This study aims to highlight the role of the concept of the “Digital Twin” (DT) within Industry 5.0, aiming to predict the effects of natural disaster scenarios in advance and to take preventive measures more effectively.

Need for the study: The innovations brought by Industry 5.0 demonstrate the possibility of creating DTs of cities to predict and minimize the effects of natural disasters. This is of great importance in terms of preparation for future natural disasters and risk management.

Methodology: This study was conducted by analyzing the fundamental principles of Industry 5.0 and the concept of DTs. Scientific literature and industry reports were examined to explore how DTs can be used in the field of risk management related to natural disasters.

Findings: The use of DTs has significant potential in simulating natural disaster scenarios in advance and predicting potential damages. For example, through DTs of cities, the effects of disaster scenarios such as earthquakes, tsunamis, and floods can be analyzed in advance, and necessary measures can be taken accordingly.

Practical implications: These findings offer important practical implications for decision-makers working in areas such as urban planning and infrastructure management. The use of DTs can assist in the development of preparation and risk management strategies for natural disasters, thereby minimizing the impact of disasters and ensuring the safety of individuals.

This paper aims to address the limitations of current deep learning algorithms for sound source localization (SSL), which focus on a single feature and frequency scale, neglecting the integration of multi-scale information. The method developed in this study enhances localization accuracy by effectively using the spatial information and spectral diversity provided by microphone arrays.

The method is based on a multi-scale cross-short-time Fourier transform (STFT) complex-valued convolutional neural network (CCNN). It uses cross-STFT spectra at different scales to capture detailed acoustic information across various frequencies. The effectiveness of the algorithm was validated through both simulations and experimental studies.

Experimental results demonstrate that the proposed multi-scale cross-STFT CCNN not only outperforms the single-scale cross-STFT model but also delivers superior localization performance compared to other advanced methods, achieving consistently higher accuracy. The method shows excellent robustness across various signal-to-noise ratio (SNR) conditions and performs well even on imbalanced datasets, confirming its strong generalization capabilities.

This paper introduces a novel approach to SSL that integrates multi-scale information, addressing a key limitation of existing methods. The findings offer significant value to researchers and practitioners in the field of acoustic signal processing, particularly those focused on deep learning-based localization techniques.