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The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.

Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.

This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.

The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.

This study aims to empirically analyze the impact of technological innovation on the quantity and quality of employment in the hospitality industry.

Using the data of 30 provinces in China from 2010 to 2020, this paper makes an empirical analysis through the fixed effect model.

The results show that process innovation has a significant positive impact on employment quantity, while product innovation has a significant negative impact on employment quantity. The creative effect of process innovation and the substitution effect of product innovation offset each other, so in the long run, the impact of technological innovation on employment quantity is not significant. However, technological innovation has significantly improved the employment quality of the hospitality industry.

Because technological innovation has replaced part of the labor force, hospitality could guide the labor force in a positive direction. To promote innovation and retain talents, hotels should train employees’ digital thinking and attract high-skilled talents.

This research is unique in using process innovation and product innovation as the main measurement indicators of technological innovation, unlike previous studies that often relied on technological progress to conclude.

This study used an integrated framework that incorporates the technology acceptance model (TAM) (Davis, 1989), the theory of planned behavior (TPB) (Ajzen, 1991) and trust to examine factors that mainly influence consumers' intention towards the use of smart technologies in tourism and hospitality (T&H) industry. The Internet of things (IoT), artificial intelligence (AI), virtual reality systems, augmented reality systems, etc. are the Smart 4.0 technologies generally used in T&H industry these days.

Convenience sampling approach was employed in this study. Data were collected over the Internet using a survey instrument by posting the questionnaire link on social network web pages of travel agencies from November 10, 2021, to December 30, 2021. In the opening statement of the questionnaire, we have explained about the Smart 4.0 technologies so that every respondent could understand what we mean by Smart 4.0 technologies.

The findings show that conjoining the TAM and the TPB with trust resulted in a robust model for explaining customers' intention toward using smart technologies in the T&H industry.

Smart technologies have become one of the most profitable e-commerce applications. This study examines and integrates the various advantages of smart technologies for the consumers in T&H industry, as well as providing insight into the intentions of Indian consumers. Hence, this study gives significant information to IT companies, online travel agencies, tour operators, travel agents, T&H planners and other stakeholders on Indian consumers' behavioral intentions (BIs).

This study tested the utility of the extended model in predicting consumers' intention towards the use of smart technologies in T&H industry. As far as the authors' knowledge is concerned, this is the first study that predicted intention of Indian consumers towards the use of smart technologies in T&H industry by integrating TAM, TPB and trust.

The paper aims to provide an overview of the area of digital pattern developing for customized apparel.

The paper outlines several methods of digital pattern developing for customized apparel, and discusses the principles, characters and applications. Digital pattern developing process has two paths. One path develops apparel according to traditional 2D pattern‐making technology. There are three methods: parametric design, traditional grading technique, and pattern generating based on artificial intelligence (AI). Another path develops pattern through surface flattening directly from individual 3D apparel model.

For parametric method, it can improve greatly the efficiency of pattern design or pattern alteration. However, the development and application of parametric Computer‐Aided‐Design (CAD) systems in apparel industry are difficult, because apparel pattern has fewer laws in graphical structure. For grading technique, it is the most practical method because of its simple theory, with which pattern masters are familiar. But these methods require users with higher experience. Creating expert pattern system based on AI can reduce the experience requirements. Meanwhile, a great deal of experiments should be conducted for each garment with different style to create their knowledge databases. For 3D CAD technology, two methods of surface flattening have been outlined, namely geometry flattening and physical flattening. But many improvements should be done if the 3D CAD systems are applied in apparel mass customization.

The paper provides information of value to the future research on developing a practical made‐to‐measure apparel pattern system.

Every geometric model corresponding to a unique feature whose errors of parameters uncorrelated, so the linearization technique can be successfully applied. The solution of a linear least square problem can be applied straightforwardly. This method has advantages especially in calibrate the redundant robot because it’s relatively small. The parameters of kinematics are unique and determined by this algorithm.

In this paper, a geometric identification method has been studied to estimate the parameters in the Denavit–Hartenberg (DH) model of the robot. Through studying the robot’s geometric features, specific trajectories are designed for calibrating the DH parameters. On the basis of these geometric features, several fitting methods have been deduced so that the important geometric parameters of robots, such as the actual rotation centers and rotate axes, can be found.

By measuring the corresponding motion trajectory at the end-effector, the trajectory feature can be identified by using curve fitting methods, and the trajectory feature will reflect back to the actual value of the DH parameters.

This method is especially suitable for rigid serial-link robots especially for redundant robots because of its specific calibration trajectory and geometric features. Besides, this method uses geometric features to calibrate the robot which is relatively small especially for the redundant robot comparing to the numerical algorithm.

The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High‐Mix, Low‐Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots.

A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and Multiple‐Objective Optimization (MOOP) method related Cost‐effectiveness analysis is adopted to select the optimal ones.

It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results.

The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

Recently, disruptive technologies (DTs) have proposed several innovative applications in managing logistics and promise to transform the entire logistics sector drastically. Often, this transformation is not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships amongst them.

Initially, 12 critical barriers were identified through an extensive literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was built with the inputs from logistics experts working in the various departments of warehouses, inventory control, transportation, freight management and customer service management. ISM approach was then used to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers' driving and dependence power.

Results from the ISM-based technique reveal that the lack of top management support (B6) was a critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC analysis shows that the legal and regulatory framework and lack of top management support have the highest driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high dependence powers.

The authors' study has several implications in the light of DT substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor's framework (2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two parameters as follows: emergence challenge of new technology and extension opportunity of old technology. Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study proposes the recommended paths that DTs might want to follow to be adopted quickly.

The authors' study has several managerial implications in light of the adoption of DTs. First, the authors' study identified no autonomous barriers to adopting DTs. Second, other barriers belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.

The contributions of this study are four-fold. First, the study identifies the different DTs in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in logistics management and provides a foundation for future research to eliminate those barriers.

The purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.

The interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.

SMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.

Considering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.