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This study aims to undertake a comprehensive analysis of innovation models, tracing their evolution from Innovation 1.0 to Innovation 4.0 and introducing the concept of Innovation 5.0. It explores the intersection between innovation models and the principles of sustainability, resilience and human-centeredness, providing insights into their implications for Industry 5.0, and their potential to foster a resilient ecosystem amidst challenges and multiple crisis.

To achieve this objective, the authors used a systematic literature review approach, considering academic articles on Innovation 4.0, Industry 5.0 (specifically in the context of innovation) and helix models of innovation. The authors conducted thematic analysis and content analysis, followed by keyword co-occurrence analysis, enabling us to systematically synthesize and interpret the relevant literature.

The results conclude that Innovation 5.0 is a new paradigm for innovation that fosters broader societal engagement, and emphasizes sustainability, resilience and human-centeredness. Innovation 5.0 is evolving, but it has the potential to transform the way we produce, consume and live. Using insights from the sextuple helix model, this research leverages media and ICT as sixth helix vital role of knowledge sharing, digital transformation, innovation ecosystem and next industrial revolution in this process.

This study contributes to the ongoing discourse on exploring Innovation 5.0 through the sextuple helix model, offering a fresh perspective on innovation models and their collaborative potential. Its contribution lies in providing practical insights into the transition to Innovation 5.0, emphasizing the need for sustainability, regulatory support and awareness while also offering clear recommendations for future research.

This study investigates to what extent the values perceived by tourists throughout cultural tours impact their overall satisfaction levels and behavioral intentions related to the destination. This study further examines the moderating role of tour guide competency in the relationship patterns concerned.

Empirical data were collected via a survey from 420 foreign tourists who visited Cappadocia and participated in guided cultural tours. Partial least squares-structural equation modeling was used for data analysis.

Study results reveal that the effects of quality, emotional, monetary and social value perceptions of tourists gained through cultural tour experiences on their overall satisfaction levels and the effects of overall satisfaction on recommendation and revisit intention are positive and significant. Furthermore, the moderating role of tour guide competency is significant and positive in the relationships between quality value and satisfaction and between satisfaction and revisit intention.

This study offers a critical analysis of discoveries concerning the pivotal role of tour guide competency within the cultural tour experience.

Considering the important development that research on debt financing decisions of family firms (FFs) has undergone in recent years, we aim to assess the current state of the literature with the latest advances in this field.

We undertake a systematic review of 42 journal articles published on this topic in recent years.

As a result of our work, new directions for the advancement of this research field are established, such as the consideration of different methodologies and sources of heterogeneity of FFs, the need for an integration of the supply and demand side of funds or the importance of evaluating a diversity of firm-specific and contextual factors affecting the debt financial behaviour of FFs.

Considering the notable development of the field of debt financing decisions of FFs in recent years, we find it opportune and valuable to revise the advances and trends published in the most recent papers. Thus, by connecting previous and current knowledge, we provide an updated integrative model of the state of the art and posit key research questions to solve in the future.

The purpose of this study is to examine the drivers and barriers of travel behavior associated with tourist behavior during/post-COVID-19 pandemic to provide a knowledge base as well as an agenda for future research.

The authors utilized the Systematic Literature Review (SLR) technique for searching the articles published in Web of Science (WOS) and Scopus, to identify the main drivers and barriers affecting the tourist behavior during/post-COVID-19 pandemic. Eventually, 47 articles were chosen for the final analysis.

The findings are reported in three sections, (1) quantitative research profile, (2) qualitative synthesis and (3) future agenda. This study addresses the nuanced questions regarding the significant change in tourist travel behavior, emotional dynamics and a detailed understanding of mechanisms, such as which drivers and barriers affect tourist behavior in a particular destination. Drivers and barriers to tourist travel behavior were characterized in personal-related, destination-related, and health-related factors. Moreover, this study provides thought-provoking ideas in theory, policy and practice in the field of tourism and hospitality.

This study has three limitations, as follows. First, the authors searched only two databases, Scopus and Web of Science, due to which the authors might be missing some related studies existing on the other databases. Although these databases provide an extensive range of academic literature, further studies could extend the data collection from the other databases (e.g. via Taylor & Francis). However, our systematic literature review (SLR) coverage is quite extensive, since journals are listed on these three main databases. Second, the authors followed a main study search protocol based on the synonyms and related keywords, however, some of the studies that may be related to the tourist behavior towards the destination are missing on account of the lack of our keywords in there, title, author, keyword and abstract. Furthermore, future research could endeavor to add other keywords to expand the results of studies. Third, although the accurate analysis was conducted to reduce subjectivity in identifying themes for drivers and barriers of tourist behavior, future studies on categorization could work to ensure that other sub-themes categorize.

The recent study has some key practical implications. First, this study is valuable for all the stakeholders in a unique way, including destination managers, academicians and policymakers, because it provides insight into barriers and drivers that influence the development of tourist behavior towards the destination. Second, the current study also offers practical implications for people involved in tourism service industries including governments and private businesses. Policymakers and other leaders are increasingly interested in harnessing the economic potential of tourism. Therefore, identifying the barrier which is inhibiting the tourist traffic towards the destination is beneficial to understand and effectively develop strategies to minimize the effect of such factors. Moreover, drivers and barriers of tourist behavior towards the destination in the COVID-19 pandemic situation towards the destination may help to create a framework for the development of destinations according to the current vulnerable situation. Third, current findings suggest that tourism marketers understand the drivers and barrier constructs found in this study and tailor their marketing strategies for attracting existing and new tourists. For instance, marketers should understand the drivers and barriers of tourist behavior for effective strategy development to increase the positive effect of drivers and to reduce the negative effect of barriers.

This is the first systematic literature review on the impact of drivers and barriers of tourist travel behavior. This paper analyses the methods and approaches that have been used in the previous literature to examine the drivers and barriers of tourist travel behavior. The paper ends with the research implication and limitations of the studies.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.

Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.

In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.

The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.

Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

Entrepreneurship education is widely regarded as a fundamental means of fostering individuals' entrepreneurial intentions. In this paper, we delve into a distinctive empirical context: the integration of entrepreneurship education within Indonesia’s nationwide higher education system since 2010. Our goal is to investigate how prior participation in mandatory entrepreneurship training by company employees influences their inclination to leave their current employment and initiate a new business.

We employed structural equation modeling to analyze empirical data (n = 337) collected from full-time Indonesian employees in established companies.

Our findings indicate that previous engagement in entrepreneurship education predicts employees' entrepreneurial intentions, leading to reduced commitment to their current careers and an increased likelihood of having intentions to pursue new ventures. By comparing two groups of participants, our results suggest that government-mandated entrepreneurship education in Indonesia has a more substantial impact on employees' entrepreneurial intentions and turnover intentions.

This study, based on a unique sample from Indonesia, explores the entrepreneurial entry of organizational employees and the long-term effects of entrepreneurship education.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Considering the significance of the human resource management (HRM) and corporate social responsibility (CSR) relationship, the aim of this research is twofold: first is to measure the cultural differences between HRM, CSR and sustainable performance relationship (study 1) and second is to identify the how HRM instigates CSR and sustainable performance (study 2) in the hospitality industry of UK and Pakistan.

A mixed-method approach was used to collect the qualitative and quantitative data from upscale hotels. In Study 1, a multi-respondent and time-lagged strategy was employed to collect the data from 162 Pakistani and 290 UK upscale hotels. In Study 2, in-depth semi-structured interviews were conducted to understand the HRM–CSR–performance nexus.

The results of Study 1 highlight the significant cultural differences in the relationships of HRM–CSR–performance, while Study 2 explains that ethical culture, shared objectives, transparency, training and development, and economic incentives are the factors that push the employees to take part in CSR-related activities and attaining higher sustainable performance.

This study addresses the debate on the difference between cross-cultural studies related to implementing Western theories in shaping, developing and implementing business strategies, including CSR, HRM and sustainable performance in an Asian context.