The profound ramifications stemming from a multitude of global events and the ongoing progression of the fourth and fifth industrial revolutions necessitate a broadening of skillsets beyond the mere acquisition of technical and digital proficiencies. The practical, intelligent, responsible, and sustainable utilisation of technologies relies exclusively on human agency. Such employment necessitates a type of cognitive processing that machines find arduous, emphasising the importance of aligning human reasoning with machine intelligence. The significance of digital skills is widely acknowledged within the framework of cultivating suitable sets of employee abilities. Nonetheless, it is imperative to underscore the importance of human skills as they remain irreplaceable by robots. Furthermore, the labour market acknowledges and rewards these skills owing to their capacity to confer flexibility and adaptability, thereby embodying the competing attributes of the future workforce. In light of the prevailing circumstances outlined in Industry 5.0-characterised by an amplified utilisation of technologies and diminished interpersonal interactions resulting from the pervasive impact of the Covid-19 pandemic-this study seeks to provide a theoretical description of the significance of soft skills and their categorisation, while investigating the practical demand for such skills. The dataset used in this study encompasses information pertaining to skill prerequisites extracted from job posts published on a job portal over five years, encompassing 19 000 distinct organisations. The findings of our study revealed that within technologically driven domains, there is a discernible demand for soft skills, such as critical and analytical thinking, problem-solving, communication skills, and creativity with flexibility. Furthermore, our results indicate that individuals must possess balanced proficiency in both soft and digital skills to thrive in a future characterised by technological advancements.

Although there are several articles that have carried out a systematic literature review of the analytical hierarchy process (AHP), many of them work with a limited number of analyzed documents. This article presents a computer-aided systematic literature review of articles related to AHP. The objectives are: (i) to identify AHP usage and research impact in different subject areas; (ii) to identify trends in the popularity of the AHP from the first introduction of the method in 1980 to the present; (iii) to identify the most common topics related to AHP and topic development over time. We process 35,430 documents related to AHP, published between 1980 and 2021, retrieved from the Scopus database. We provide detailed statistics about research interest, research impact in particular subject areas over the analyzed time period. We use Latent Dirichlet Allocation (LDA) using Gibbs sampling to perform topic modeling based on the corpus of abstracts. We identify nine topics related to AHP: Ecology & Ecosystems; Multi-criteria decision-making; Production and performance management; Sustainable development; Computer network, optimization and algorithms; Service quality; Fuzzy logic; Systematic evaluation; Risk assessment. We also present the individual topics trends over time and point out the possible future direction of AHP.

Purpose: This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.Design/methodology/approach: In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.Findings: The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.Originality/value: To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

Purpose: The purpose of this paper is to develop a theoretical model that explains the impact of big data analytics capabilities (BDAC) on company's supply chain innovation capabilities and sustainable supply chain performance. BDAC is represented through two dimensions of big data technological capabilities (BDTC) and big data personal capabilities (BDPC). Moreover, the relationships between BDTC and BDPC with sustainable supply chain performance through the mediation effect of supply chain innovation capabilities are examined.Design/methodology/approach: The study used a quantitative research approach. A survey of 400 Jordanian manufacturing companies was carried out to conduct this research. However, the responses of 207 managers were valid to be used in the analysis. In this study, the SmartPLS software was used to perform structural equation modeling using a partial least squares approach (PLS-SEM) and to examine the measurement and structural model's validity and reliability.Findings: According to the results of this study, BDPC has a significant positive impact on supply chain innovation capabilities. Furthermore, the findings indicate that supply chain innovation capabilities are the most influential predictor of sustainable supply chain performance and act as a positive significant mediator in the relationship between BDPC and firm sustainable performance. Surprisingly, the study found that BDTC had no significant effect on supply chain innovation capabilities. Besides that, no significant relationship exists between BDTC and firm sustainable performance via the mediation effect of supply chain innovation capabilities.Originality/value: This study provides an integrated research model that incorporates BDAC, supply chain innovation capabilities, and sustainable supply chain performance in order to analyze supply chain innovation and sustainable supply chain performance. This suggests that the scope of the study is broader in terms of predicting sustainable supply chain performance. As a result, the study intends to fill a gap in the literature by explaining how BDAC affects supply chain innovation capabilities and firms sustainable performance. In addition, the role of supply chain innovation capabilities as a mediator between BDAC and sustainable supply chain performance is investigated.

This study focuses on the Kano model which is used to categorise the requirements of customers. The study describes the three most frequently used modifications of the Kano model and then systematically analyses the key effects of these modifications. A new approach – Type IV – is then proposed. Using Type IV, it is possible to eliminate the disadvantages identified in the above-mentioned existing approaches – the level of accuracy for the producer and the difficulty of the method for the customer. The proposed approach is based on a modification of the process of requirement categorisation intended to minimise the discrepancy zone between the calculated and the real position of a particular requirement. The Type IV Kano model is then validated by using an example of categorisation of requirements for after-sales services in the car industry. Validation found that the approaches proposed in this study significantly improve the accuracy of requirement categorisation without increasing the difficulty for the customer.

The examination of implementing ISO standards can provide new insight as to their quality and environmental management benefits. These insights can be more impactful in manufacturing sectors and especially textiles as this sector is known for its environmental degradation and questionable supply chain practices. This exploratory study investigates the extent to which organizations that implement either ISO 9001 or 14001 standards impact sustainable supply chain management (SSCM). We also investigate the extent to which these two standards improve SSCM for the textile industry within a block of European countries consisting of Poland, Slovakia, and the Czech Republic. In this empirical study, we use several measures to help identify the extent of impacts of examined standardized management systems on improvements in different processes of SSCM. Our contributions to the field include finding that quality management systems support environmental processes, relationships with stakeholders, processes within the supply chain, and SSCM. Other findings include implementing environmental management system standards impact the rationalization of other processes. Finally, we find more significant supply chain impacts with the implementation of both standards than by either standard alone. The study concludes with suggestions for extension of this study and opportunities for further research.