Search results

The purpose of this study is to explore the knowledge, attitude and practices (KAP) of Industry 4.0 between the academicians and industry players in construction engineering, further suggesting a mechanism to narrow the gap between the distinct parties.

This study was conducted through structured online and face-to-face interviews, using KAP survey, and semi-structured interviews. This constructive research was conducted among Malaysian construction industry players and academicians from the construction engineering department in public universities.

The findings exhibit the similarities and differences of KAP between academics and industry on Industry 4.0 in construction engineering. In general, both categories of respondents have displayed more similarities than differences in all aspects, except for knowledge. The better knowledge profile of Industry 4.0 among the academicians reflects the nature of the academic works that constantly seek new knowledge, thus suggesting the establishment of an industry-academic (I-A) knowledge equilibrium framework to leverage the knowledge profile between both parties.

This exploratory study that showcases the perspective of the academia and industry practitioners on Industry 4.0 acts as a cornerstone for bridging the gap between the two distinct sectors within the same field.

The gap between the academic and industry was highlighted, further establishing the I-A knowledge equilibrium framework that could also be applied to other fields of study.

The originality of this paper was the profiling of the KAP of Industry 4.0 for the academicians and industry players in construction engineering, further distinguishing the gap between both parties.

Industry 4.0 technologies are promising to increase manufacturing companies' performance through the new knowledge that such digital technologies allow to create and manage within the firm boundaries and through customer interactions. Despite the great attention on the Industry 4.0 adoption paths, little is known about the relationships with previous waves of digital technologies, namely, information and communication technologies (ICTs), and how different groups of both types of technologies link to knowledge and its related performances.

The study employed a quantitative research design using a survey method. Submitting the questionnaire to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, 206 respondents stated that their firm has adopted at least one of the seven Industry 4.0 technologies investigated.

The findings of the study highlight the positive relationship between ICT and Industry 4.0 technologies in terms of both intensity and groups of technologies (Web-based, Management and Manufacturing ICT; Operation, Customization and Data-processing 4.0), and how technologies affect knowledge-related performances in terms of products and processes, job-learning, product-related services and customer involvement.

This study is one of the first attempts to link groups of ICT to groups of Industry 4.0 technologies and to explore the effects in terms of knowledge-related performances as a measure of technology use. The study shows strong path dependency among ICT, Industry 4.0 and knowledge performance, enriching the literature on technological innovation and knowledge management.

The purpose of the study is to investigate the impact of technological pillars of Industry 4.0 based on knowledge to adopt the supply chain performance of Thai small and medium-sized enterprises (SMEs) 4.0. In addition, to increase knowledge and understanding of how to apply knowledge in technology 4.0 to improve the efficiency of supply chains and organizations.

An integrated model was developed from applying knowledge in five technological pillars of Industry 4.0 such as Internet of things (IoTs), cloud computing, big data and analytics, additive manufacturing and cyber-security. The bibliometric analysis was used to find the relationship between the technological pillars of Industry 4.0 and the literature review. The survey questionnaires were sent to Thai SME 4.0 (manufacturing aspect). Of these, 240 useable responses were received, resulting in a response rate of 65.84%, after then, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and validity were used to evaluate the model through IBM SPSS 21 and AMOS 22.

EFA showed the four groups of the technological pillars of Industry 4.0, such as support human, automation, real-time and security. These groups positively impact supply chain performance (increase delivery reliability, increase resource efficiency, decrease costs in the supply chain and reduce delivery time). Another important finding is that supply chain performance positively impacts organizational performance in profitability, return on investment (ROI) and sale growth.

This study is a model development to support the supply chain performance and increase understanding related to applying knowledge in technology 4.0 that remains unclear for SME 4.0.

Global value chains (GVC) incorporate internationally fragmented sources of knowledge so as to increase global competitiveness and performance. This paper sheds light on the role of Industry 4.0 technological capabilities in facilitating knowledge access from international linkages and improving firm productivity.

Drawing on organizational learning research, the present study argues that the relationship between GVC breadth, analyzed in respect to the geographical fragmentation of production facilities and productivity follows an inverted U-shaped pattern that can be explained by the interplay between external knowledge access and the coordination costs associated with GVC breadth. We test our predictions using a purpose-built survey that was carried out among a sample of 426 Spanish manufacturing firms.

Our results indicate that organizations adhering to a traditional manufacturing system are able to benefit from fewer transnational relationships (concretely 11 foreign facilities) in the search for productivity improvements. This can be largely attributed to the marginal value of the knowledge accessed and the costs of coordinating international counterparts' production and knowledge transfer. However, our study reveals that the adoption of Industry 4.0 technologies has the potential to broaden optimal GVC breadth, in terms of the number of linkages to interrelate with (concretely 131 foreign facilities) so as to obtain productivity gains while mitigating the complexities associated with coordination.

The study unveils that Industry 4.0 technologies enable management of broader GVC breadth, facilitating knowledge access and counteracting coordination costs from international counterparts.

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.

The main purpose of this study is to investigate the role of Industry 4.0 capability (I4.0 C) in creating a supportive environment for business model innovation by focussing on the mediating role of knowledge management capability (KMC) and the moderating effect of market uncertainty.

This paper uses interviews with managers and academics. Data were collected from 379 managers; CFA and finally regression analysis were performed, and the program “PROCESS” software was used to validate the data and examine the hypothesized relationships.

KMC mediates the relationship between Industry 4.0 capability and business model innovation. The effect of Industry 4.0 capability on KMC is stronger for firms with increased market uncertainty. In contrast, the impact of KMC on business model innovation is weaker for firms operating in highly-uncertain markets.

Data were collected at only one point in time from one country, Greece. This might pose limitations on the generalizability of our results. Future research should test the relationships examined in this study in other international contexts.

Managers should invest in more advanced technology in order to obtain knowledge and capability. In addition, they need to pay more attention to how their firms' knowledge derived from Industry 4.0 contributes to business model innovation.

The current state of knowledge of both theory and practise for critical organizational factors such as Industry 4.0 capability, KMC, business model innovation and market uncertainty will be extended.

The adoption of knowledge management (KM) to steer new skills and capabilities among people provides evidence that KM not only offers competitive advantages but also provides a means for organisational survival, by improvising core capabilities or generate new ones that can drive people in the Fourth Industrial Revolution (4IR) era. This paperaim to identify critical new skills and capabilities among people within an organisation to stay competitive, innovative and relevant.

The paper presents the findings on new skills assessment for Fourth Industrial Revolution. The study was carried out through an interview with a focus group discussion technique to gather data on the role of KM in creating new set of skills or capabilities in Fourth Industrial Revolution’s landscape. The study also reports a bibliographic study of critical skills based on more than a decade of related academic and industry publications to portray research trends and future directions.

There is a demand in “must-have” skills related to Industry 4.0 such as capability for complex decision-making, complex problem-solving, collaborative innovation, project management, creativity and critical thinking, social skill and social responsibility. While these skills are critical enablers to aiding individuals in the scenarios of plausible 4IR futures, several important new research trends that emerge have also not been adequately explored including KM and Industry 4.0 skill gap, skill evolution, machine knowledge, intuitive decision-making, rational decision-making, technostress, digital fluency, collaborative innovation, industrial policies, human–machine interaction and societal systems.

This research provides a roadmap for the next research trends and topics in the area of Fourth Industrial Revolution and new skills requirements. The study discusses some of the essential issues and challenges with upskilling required for Industry 4.0. It also focuses on how upskilling learning initiatives influence new knowledge creation. This primarily contributes to the educational field in deciding how and when to adopt appropriate strategies and identify which initiatives to best meet the needs of its community.

KM enables individuals to utilise their existing core capabilities or generate new ones for immediate investment in upskilling to meet current and future skills needs required by an organisation. Simply put, KM will improve the organisation’s talent-driven learning strategy and increase individuals’ ability to learn faster and attain sustainable competitive advantage in a fast-paced ever changing environment.

This paper is useful to academics, practitioners and policymakers in the fields of KM. The research provides initial insight into new skills mapping in the context of Fourth Industrial Revolution and the needs for researchers to understand the recent research trends in KM.

This study attempts to find out the impact of the human behavioral factors (HBFs) including emotion, factors of deals with processes within and between groups as well as with the impact of these processes on individuals’ attitudes and moods, personality, beliefs and values, perception and motivation on the knowledge management system–cycles (KMS-Cs) which comprises sharing; it considers findings from social psychology and discusses their applicability in knowledge management (KM) research and practice; social psychological concepts that strongly influence knowledge processes in organizations are first introduced. It is creating, storing and transferring of academic staff while analyzing the certificates on the acquired behaviors and knowledge which were involved in each of the communications, decision-making, creating new ideas, providing new knowledge, idea diversity, progressing, enhancing and improving the organization, using up-to-date technology and proactivity between the independent and dependent variables. In order to test the study hypotheses, data of 219 respondents working at the University of Sulaimani were collected. The results of the study revealed the academic staff psychology effect on KMS-Cs with a substantial relationship between the HBFs and cycles of KM during academic and administrative work. Also, it surged their academic staff efficiency through a conceptual model called KM behavior (KMB); knowledge management systems (KMSs) are applications of the organization's communication and information systems (CISs) designed to support the various KM processes. They are generally not technologically distinct from the CISs but rely on databases, such as those designed to put organizational participants in contact with recognized experts in a variety of topic areas (Yakan, 2008; Al Hayani, 2020). Information technology (IT) used in KM is known as KMS. In general, KMSs are computer systems that enable organizations to manage knowledge that is efficient and cost-effective. KMS is a class of information systems applied to the management of organizational knowledge. KMS is a system that increases organizational performance by enabling employees to make better decisions when applying their knowledge as part of their daily business activities.

Research hypotheses Ho: HBFs and KMS-Cs are not correlated. H1: HBFs have no impact on KMS-Cs. H2: certificates have no effect on HBFs and KMS-Cs. Data collection and sample demographics: in this study, the relevant information for assessing the HBFs and their impact on the KMS-Cs was gathered through a questionnaire survey. The HBF was measured using the following items: emotions, attitudes and moods, personality, beliefs and values, perception and motivation. The knowledge management cycle (KMC) was measured using the following items: knowledge sharing, knowledge creation, knowledge storing and knowledge transfer. The total number of employees at the University of Sulaimani, Sulaimaniya, at the time of data collection (May, 2019) was 117. Since the information available on the number of academic staff at the University of Sulaimani is according to the departments, this study employed a proportionate stratified random sampling method to select the number of academic staff from colleges and departments at the University of Sulaimani. The total number of academic staff at the University of Sulaimaniis is 1,740. Therefore, the appropriate sample size for this study is at least 5% of the population (i.e. 90 respondents) (Langham, 1999). The questionnaire was administered personally through Google Form where questionnaires were collected from the respondents. Examination of the response rate shows that the response rate for this study is excellent. The research instrument consists of two main sections. The first section incorporates a nominal scale to identify respondents' demographic information. The second section uses the five-point Likert-type scale from fully disagree (1) to fully agree (5). All of the measurement items went through backward translation (translated from English into Korean and back into English) to ensure consistency and to resolve discrepancies between the two versions of the instrument (Mullen, 1995; Aldiabat et al., 2018). The participants were almost equal in terms of gender, 59 were males and 58 were females. The certificate for each one of the PhD, MSc and BSc was 39 participants. The number of participants whose age was between 23 and 32 years was 26, between 33 and 42 years was 50, between 43 and 52 years was 29, between 53 and 62 years was 10 and above 62 years was 2. Validity and reliability: in addition to the steps mentioned earlier to assess the validity and reliability of the study tools, a further test was executed. The reliability to measure many inner variables in regularity, Cronbach’s alpha is generally utilized in order to evaluate it and the value should exceed 0.70 for each variable (Alharbi, and Drew, 2014) (Table 1). Cronbach's alpha regards to the test of reliability of a skill for each of the HBF and KMC.

The study is considered the organizations relationship between HBFs and KMS-Cs and the influence of the factors on the cycles. So, the new ideas emerge to create knowledge about product development among employees. The group experience works as an essential element (Grimsdottir and Edvardsson, 2018). Knowledge resides in human minds and, as a result, employee behavior and explanatory skills are the key drivers of KM (Prieto and Revilla, 2005). First, knowledge creation, sharing and storing is increased when the organization has motivated the employees. Second, knowledge is shared rapidly when the employees have owned a strong personality, new idea, impression and perception. Third, both the beliefs and values lead to creating new knowledge when the employees obtained it inside the organization. Then, the emotion factors illustrated the weak relation with knowledge sharing, knowledge creation, knowledge storing and knowledge transfer.

Knowledge is considered as a great factor in achieving organizational goals (Hammami and Alkhaldi, 2017). Therefore, this study has explained that knowledge is an essential element for employees and organizations. Furthermore, it progresses the skills and capabilities during the job. Nevertheless, this knowledge is impacted through human behaviors because the behavior evolves crucial factors that help the academic staff to create, share, store and transfer the knowledge through motivation, perception, personality, attitudes, moods, beliefs and values. Knowledge sharing is a culture of social interaction involving the exchange of knowledge, experiences and skills of employees across the organization (Zugang et al., 2018). Organizations need to pay particular attention to the method of communication used where knowledge becomes useless if employees are not encouraged to study and use it in their daily activities (Boatca et al., 2018). Knowledge sharing can be achieved by taking into account technical standards (KMS), social standards (environment) and personality (motivation) (Özlen, 2017).

High‐tech industry has been the principal economic source for Taiwan in recent years. The characteristics of high‐tech industries in Taiwan are changeable product markets, short product life cycles and high company attrition rate. In the globalization trend, the high‐tech industry has gradually increased corporate competitiveness and reached the goal of sustainable operations through knowledge management, knowledge sharing and new product research and development. Firms have aggressively strengthened and integrated their internal and external resources and enhanced knowledge sharing to increase industry operational performance. Effectively strengthening the knowledge management operation and performance evaluation of knowledge sharing in Taiwan’s high‐tech industry has become a critical issue. In the selection of knowledge sharing Key Performance Indicators (KPI), this research divided the knowledge sharing indicators into representative strategic indicators such as organizational knowledge learning, organizational knowledge resources, organizational information capacity and organizational knowledge performance through screening using Factor Analysis. The characteristics of the constructs were interdependent. This research calculated and adjusted the correlation among the key performance knowledge sharing indicators using ANP and determined the relative weight of knowledge sharing.

This paper aims to provide a contribution on the diffusion of Industry 4 (I4.0)-related knowledge in industrial districts (IDs). The main goal is to examine the dissemination of I4.0 knowledge, exploring the main mechanisms for its spreading and highlighting the main factors shaping such processes. Focus is on dissemination processes in IDs active in traditional industries, which could represent the “periphery” of I4.0 application context.

The methodology is qualitative. Notably, this paper presents a case study of the Pesaro ID specialized in furniture/woodworking machinery sector. A total of 18 in-depth one-to-one interviews have been conducted with relevant informants from a variety of organizations within the cluster: companies, institutions and universities.

The complexity of I4.0 requires a combination of traditional mechanisms with innovative ones within IDs characterized by the emergence of new players, activities and resources. These changes led to three main evolving patterns: the horizon of I4.0 upgrading shows blurred boundaries in terms of sectors and geographic location, the I4.0 diffusion appears fragmented in terms of initiatives and projects by both firms and institutions and the dissemination of I4.0 knowledge pushes ID firms and institutions to pursue deliberate initiatives leading to innovative forms of “collective” cooperation.

This paper contributes to both theory and practice. From the theoretical point of view, this paper contributes to the literature on innovation in IDs and clusters on two interrelated grounds. First, it provides further research on I4.0 and IDs and clusters. Second, it contributes to the stream of research on knowledge creation and diffusion in IDs and clusters, providing empirically based insights over emerging local learning processes in IDs. Moreover, relevant managerial and policy implications stem from the analysis.