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This study aims to investigate the causal relationships within an experimental culture of improvisation capability and firm performance, following the logic of “culture-capability-performance” and building on informal institution theory and dynamic capability theory.

Data was mainly collected via on-site questionnaires from firm managers, and 196 valid questionnaires were analyzed using structural equation modeling to test the relationship among experimental culture, improvisation capability and firms’ performance.

Trust and support had a positive impact on firm spontaneity, while the effect of action promotion and error tolerance was not significant. Action promotion, trust and support demonstrate substantial positive effects on the creativity of a firm. Both dimensions of improvisation capability positively and significantly influence a firm’s performance.

The research respondents were firm managers. Cross-sectional data were used to analyze the model, which may cause common method variance. The research context was limited to China, and the generalizability of the results needs to be considered.

Firms can cultivate a culture of trust and support to enhance their spontaneity and improvisation capability. They can encourage cross-departmental communication, empower employees with autonomy in decision-making, provide appropriate resource support for employees’ decisions and use praise and reward incentives to spur further innovation achievements.

This study addresses the gaps in a firm’s improvisation capability within a Chinese market context by theoretically and empirically examining the role of experimental culture and assessing the relationship among each of the dimensions of improvisation capability in relation to firm performance identified in this study.

The present study assesses the impact resistance of the shear thickening fluids-filled (STFs-filled) foam through drop-hammer impact tests.

The maximum residual impact load and specific impact energy absorption rate of STF-filled foam are studied with varying thickness (4–14 mm), densities (0.35–0.6 g/cm3) and hardness (40–50 Rockwell Hardness C Scale (HRC)) under different ambient temperatures (−20−20 °C) and impact energies (25–75 J).

The following conclusions are obtained from this study: (1) the higher the impact energy, the greater the maximum residual impact force and energy absorption efficiency of the material; (2) the impact resistance of STF-filled foam can be improved with the decrease of ambient temperature, achieving the highest energy absorption rate at −10?. (3) STF-filled foam substrate has the highest impact resistance, the lowest maximum residual impact force and the highest energy absorption coefficient when the density is 0.35  g/cm3, the hardness is 45HC and the thickness is 10 mm.

This is the first paper to analyze the impact of both environmental factors and material properties on the impact resistance of STF-filled foam. The results show that the decrease in temperature and the increase in hardness can enhance the impact resistance of STF-filled foam.

Financial flexibility, investment efficiency and effective corporate governance mechanisms have been issues of concern to stakeholders. Yet, little empirical evidence on the combined moderating effects investment efficiency and corporate governance mechanisms on the nexus between financial flexibility and firm performance. This study aims to address this gap and extend the extant literature by examining the moderating effects of corporate governance and investment efficiency on the nexus between financial flexibility and financial performance.

The empirical study is based on progression analysis using a sample of 13,865 US listed companies selected from BoardEx (WRDS) for the period (2010–2022) with 89,198 firm-year observations.

Findings of this study indicate that financial flexibility improves firm value as well as accounting performance. Furthermore, the results reveal that both investment efficiency and corporate governance moderate the effect of financial flexibility on firm performance. The authors complement and extend the literature on the optimal investment strategies domain by showing that the combined impact of corporate governance mechanisms and investment efficiency strengthens the nexus between financial flexibility and firm performance.

Key limitations of this study due to the characteristics of the sample selection: country-specific context and proxies used by this study.

Findings of this study have managerial and theoretical implications for firms’ boardrooms, institutional and individual investors, regulators, academics and other stakeholders regarding behavioural aspects of investment decision-making.

The authors’ novel contribution to the extant literature is articulated by the conceptual framework underlying this study and by the new evidence regarding exploring the combined effect of corporate governance mechanisms on nexus between financial flexibility and companies’ performance.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Financial flexibility and investment efficiency are of vital importance in strategic choices at boardrooms, particularly in post-crisis recovery strategies. This study examines the moderating effects of investment efficiency and investment scale on the relationship between financial flexibility and firm performance.

The authors use sample of 10,755 US-listed firms over the period 2010–2021 to examine the relationships between investment scale, investment efficiency, financial flexibility and firm performance. Particular attention is paid to overinvestment and underinvestment.

Findings of this study reveal that financial flexibility mitigates investment inefficiency through reducing overinvestment. Financial flexibility contributes to boost a firm’s accounting and market performance. Additionally, investment efficiency and investment scale have moderating effects on the relationship between financial flexibility and firm performance. However, the influence of investment efficiency is greater than the influence of investment scale. Finally, the authors find that the direct and indirect effects of financial flexibility are stronger on market performance than accounting performance, implying that market is more sensitive to corporate financial policies.

Findings of this study have implications for scholars, decision-makers policymakers, investors and other stakeholders.

This study has its own limitations due to the sample selection issues, country context and the research model adopted by this study.

The novel contribution to the extant literature is incorporating the influence of investment scale and investment efficiency into the relationship between financial flexibility and firm performance.

When encountering novel technology, customers often use the term “cool” to express their thoughts; therefore, coolness has become crucial for launching service robots. However, research on the impact mechanism of “coolness” is lacking. This study explored the relationship between delight and behavioral intention regarding the coolness of service robots in the food and beverage industry while discussing the mediating roles of utilitarian and hedonic values.

Questionnaires were distributed online with links to the survey posted on restaurant discussion boards on Facebook and online community platforms such as Dcard. In total, 540 responses were deemed valid. The hypotheses were tested using the partial least squares structural equation modeling method.

The results indicate that coolness positively impacted both utilitarian and hedonic values and that both perceived values positively impacted delight. Moreover, coolness does not directly impact delight but must be mediated by perceived value to be effective.

Increasing customer perceptions of the coolness of service robots is recommended. Moreover, regarding customer revisits, utilitarian value services can delight customers more effectively than hedonic value services.

The stimulus-organism-response model was used to identify the relationships among coolness, perceived value, delight and behavioral intention. Moreover, the authors investigated the impact of coolness on utilitarian and hedonic values. These findings are significant for the development of smart restaurants and provide a critical reference for exploring service robots.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

The paper aims to reveal the intellectual structure of studies on artificial intelligence (AI) in the fields of tourism and hospitality. Evaluations regarding the intellectual structure have been made based on co-author, co-word and citation.

The study is exploratory in nature. The study, using bibliometric analysis, provides a Web of Sciences (WOS) overview. The data has been obtained from the WOS database by coding as “artificial intelligence” and “tourism” and “hospitality.” VOSviewer program has been used to obtain and analyze the data.

The findings of the research show that studies on the use of AI in tourism and hospitality have become very popular, especially in the last 4 years. The authors of the study are working in the tourism and hospitality fields and have a high h-index. Generally, in current AI studies in tourism, topics such as robot, automation, ChatGPT, technology adoption and mechanical learning are studied. It has also been determined that topics related to the future of destinations and literature reviews are also discussed.

Although this paper examines all studies identified as a result of filtering, the analysis is limited to 195 studies. However, due to the widespread use of AI in tourism-related studies recently, bibliometric analysis has been made with extensive filtering. As studies on the subject become more widespread in the coming years, it would be useful to repeat similar studies by filtering with more specific quotas.

There are a few similar studies on the subject in the field. However, these studies need to be repeated at certain periods. This paper contributes to monitoring the literature of AI studies, which are new to use in tourism and hospitality, and to the formation of a theoretical framework on the subject.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature.

This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights.

The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature.

This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research.

To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.